{"paper_id":"19e1e2fa-86ad-450a-a8f5-8d3c30538fef","body_text":"Leveraging lactate transporters for superior 3-hydroxypropionic (3-HP) acid production from methanol in Komagataella phaffii | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Leveraging lactate transporters for superior 3-hydroxypropionic (3-HP) acid production from methanol in Komagataella phaffii Sílvia Àvila-Cabré, Joan Albiol, Pau Ferrer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5386323/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Journal of Biological Engineering → Version 1 posted 13 You are reading this latest preprint version Abstract Background Bioconversion of methanol derived from CO 2 reduction into value-added chemicals is crucial for mitigating global warming and reducing fossil fuels dependence within a circular economy. Production of 3-hydroxypropionic (3-HP) acid, a key building block for the development of biobased products such as acrylates and 1,3-propanediol, has been successfully achieved using methanol as the sole carbon and energy source in the methylotrophic yeast Komagataella phaffii (syn. Pichia pastoris ). However, challenges remain in meeting commercially relevant concentrations, yields and productivities of 3-HP, prompting further strain optimization. In the present study, we have combined metabolic engineering strategies aiming at increasing metabolic precursors supply and redirecting carbon flux towards 3-HP production. Results A combinatorial metabolic engineering strategy targeted to increase precursor supply and 3-HP export was applied to the original 3-HP producing K. phaffii strain harboring the synthetic b-alanine pathway and a heterologous NADP-dependent formate dehydrogenase. To do so, several genes encoding for enzymes catalyzing reactions immediately upstream of the β-alanine pathway were overexpressed to enhance the pathway’s precursors supply. However, only the overexpression of the pyruvate carboxylase PYC2 gene significantly increased the 3-HP yield on biomass (Y P/X ) in small-scale cultivations. Co-overexpression of PYC2 and the lactate permeases ESBP6 and JEN1 genes led to a 55% improvement in titer (1.5 g l − 1 ) and product yield (0.13 g g − 1 ) compared to the reference strain, mostly due to Esbp6 activity, proving its effectiveness as a 3-HP transporter. Deletion of the native formate dehydrogenase gene FDH1 did not increase methanol flux entering the assimilatory pathway. Instead, knockout strains showed severe growth defects due to toxic intermediates accumulation. Co-expression of a gene encoding for a mutated NADP-dependent formate dehydrogenase in these strains failed to compensate for the loss of native FDH . The strain combining PYC2 , ESBP6 and JEN1 overexpression was further tested in fed-batch cultures at pH 5, achieving a final 3-HP concentration of 27.0 g l − 1 in 39.3 h, with a product yield of 0.19 g g − 1 and a volumetric productivity of 0.56 g l − 1 h − 1 . These results represent a 42% increase in final concentration and over 20% improvement in volumetric productivity compared to the original 3-HP producing strain. Furthermore, bioreactor-scale cultivations at pH 3.5 revealed increased robustness of the strains overproducing monocarboxylate transporters. Conclusions Our results point out the potential of lactate transporters to efficiently drive 3-HP export in K. phaffii , leading to higher titers, yields, and productivities, even at lower pH conditions. 3-hydroxypropionic acid Pichia pastoris Komagataella phaffii methanol β-alanine pathway metabolic engineering lactate transporters Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The valorization of industrial CO 2 effluents has the potential to play a crucial role in addressing climate crisis and advancing towards a more sustainable production cycle. CO 2 can be reduced with hydrogen to produce methanol, a sustainable carbon and energy source [1,2]. Bioconversion of this low-cost one-carbon (C1) feedstock into valuable chemicals not only has the potential to reduce reliance on fossil fuels but also to help mitigating climate change, representing a promising approach within the framework of a circular economy [3–5]. 3-Hydroxypropionic (3-HP) acid is a key building block that can be used as a precursor for the production of several valuable chemicals, including acrylates, 1,3-propanediol, propiolactone, malonic acid, and hydroxyamides. Moreover, as a homopolymer or when integrated into other biopolymers, 3-HP has the potential to replace petrochemistry-based polymers, offering a sustainable alternative for the synthesis of advanced materials [6,7]. In 2004, the US Department of Energy (DOE) ranked this C3 organic acid in a significant third position among the top twelve biobased value-added chemicals [8]. An updated version of this ranking was published six years later, where 3-HP was included again [9]. Despite its potential, chemical synthesis of 3-HP faces several challenges, including the high cost of raw materials, low yields, complex reaction steps, and the involvement of toxic, noxious, or carcinogenic compounds [10]. Several microorganisms naturally synthesize 3-HP, either as an intermediate or as an end product, through different metabolic pathways and diverse substrates such as glycerol, glucose, CO 2 or uracil. The most studied pathways include the coenzyme B12-dependent glycerol pathways (both CoA-dependent and CoA-independent) and the malonyl-CoA reductase pathway, which is part of the 3-hydroxypropionate/malyl-CoA and 3-hydroxypropionate/4-hydroxybutyrate autotrophic cycles [11,12]. However, significant byproduct formation and suboptimal yields and productivities still hinder the commercial viability of biological 3-HP production. To overcome these issues, metabolic engineering of both natural and non-natural producers has been investigated. Among bacterial hosts, Escherichia coli and Klebsiella pneumoniae have been the most widely used for 3-HP production via the glycerol pathway [13]. These bacteria have produced by far the highest reported 3-HP titers (76.2 and 102.6 g l − 1 , respectively) and productivities (1.89 and 1.07 g l − 1 h − 1 , respectively) [14,15]. Several yeast species have also been engineered to produce 3-HP, primarily through the introduction of the malonyl-CoA reductase pathway and the synthetic β-alanine pathway [16]. The bifunctional malonyl-CoA reductase from Chloroflexus aurantiacus (MCR Ca ) has been successfully expressed in Saccharomyces cerevisiae [17,18], Schizosaccharomyces pombe [19,20], and Komagataella phaffii (syn. Pichia pastoris ), achieving the highest volumetric productivity reported so far in yeast (0.712 g l − 1 h − 1 ) [21]. Additionally, the synthetic β-alanine pathway has been engineered into S. cerevisiae [22,23], and subsequent bioprocess optimization enabled a final concentration of 27 g l − 1 , with a product yield on glucose of 0.26 g g − 1 [24]. Only fungal hosts are able to withstand acidity as low as pH 3 [25,26], which is more than 1 unit below the pKa of 3-HP (4.51) [27]. In these circumstances, the undissociated form of 3-HP is the most abundant fraction in the solution, lowering costs of downstream processing [28]. Therefore, yeasts stand as promising cell factories for 3-HP production. Of particular interest is the methylotrophic yeast K. phaffii , which shows a distinct ability to grow on methanol as its sole carbon and energy source, making it an attractive cell factory for the production of high-value-added chemicals from this low-cost and renewable substrate [29]. Moreover, methanol is a highly reduced carbon source (γ = 6.0), whose oxidation provides more redox equivalents than most sugars, potentially becoming a competitive substrate for the synthesis of highly reduced compounds such as terpenoids [30,31] and fatty acids and their derivatives [32,33]. In addition, recent studies have demonstrated K. phaffii ’s ability to produce different organic acids from methanol as sole carbon source, including malic acid [34], D-lactic acid [35], itaconic acid [36], and 3-HP [37]. Recently, we have successfully engineered K. phaffii for 3-HP production from methanol by implementing the β-alanine pathway [38,39]. Specifically, we expressed the genes coding for a β-alanine-pyruvate aminotransferase from Bacillus cereus (BAPAT Bc ), a 3-hydroxypropionate dehydrogenase from E. coli (YDFG Ec ), and two copies of an aspartate-1-decarboxylase from Tribolium castaneum (PAND Tc ), obtaining a 3-HP-producing base strain (PpCβ21) that produced up to 21.4 g l − 1 of 3-HP in methanol fed-batch cultures, with a yield of 0.15 g g − 1 and a volumetric productivity of 0.48 g l − 1 h − 1 . Additional redox engineering of this strain to increase NADPH availability further improved both methanol consumption (q S ) and specific productivity (q P ) rates in strain PpCβ21-P. Despite our demonstration of the potential of K. phaffii for 3-HP production from renewable C1 feedstocks, the obtained yields and productivities fell short of the commercially feasible metrics. Typically, minimum values of 0.5 g g − 1 and 2.5 g l − 1 h − 1 , respectively, are required to develop an economically viable bioprocess for carboxylic acid production [8,40]. Besides increasing NADPH availability, other strategies have been investigated to improve 3-HP yield in engineered yeast harboring the b-alanine pathway. In particular, Borodina et al. [23] demonstrated that improvement of the metabolic precursors supply of the b-alanine pathway in S. cerevisiae had a significant positive impact on 3-HP production. This was achieved by overexpressing the AAT2 , PYC1 and PYC2 genes, encoding for the enzymes catalyzing the conversion of pyruvate into aspartate, the precursor of this synthetic pathway. Another engineering strategy commonly used in yeast/fungi to enhance organic acid production is the overexpression of genes encoding for membrane transporters to facilitate final product export [41,42]. To date, no 3-HP-specific transporters have been identified in yeasts. However, given that lactate and 3-HP are structural isomers, some studies have focused on existing lactate transporters as potential candidates for 3-HP export. In S. cerevisiae , two monocarboxylate permeases have been identified, Esbp6 [43] and Jen1 [44]. Esbp6, also known as Mch3, may play a role in acid-stress adaptation responses, as well as in functions related to cell wall integrity maintenance. Overexpression of ESBP6 in the plasma membrane of an engineered S. cerevisiae strain improved lactic acid production by 20% under non-neutralizing conditions, probably due to enhanced lactic acid tolerance [45]. Similarly, Qin et al. [46] demonstrated that deleting ESBP6 in a wild-type strain cultivated in the presence of 50 g l − 1 3-HP at pH 3.5 inhibited growth considerably, whereas ESBP6 overexpression significantly increased cell tolerance to 3-HP compared to the wild-type strain. The lactate-proton symporter Jen1 can mediate the electroneutral transport of lactic acid when it accumulates inside the cell [28]. In fact, overexpression of Jen1 has been shown to improve lactic acid production in recombinant S. cerevisiae strains [47–50]. This effect has also been observed in the non-conventional yeast K. phaffii by Lima et al. [51], who identified a JEN1 ortholog in the K. phaffii genome, showing 50.19% identity to the JEN1 gene in S. cerevisiae . Overexpression of the gene encoding for this putative lactate transporter in a recombinant K. phaffii strain producing L-lactic acid from glycerol resulted in a 46% increase in lactate yield compared to the control strain. In this study, we further optimized our previously developed 3-HP-producing strains [38,39] by implementing a range of metabolic engineering strategies. First, efforts were focused on a “push” strategy aiming at increasing the supply of β-alanine pathway direct precursors, i.e. oxaloacetate and aspartate, and to further channel flux through the heterologous NADP-dependent formate dehydrogenase by deleting the FDH1 gene coding for the endogenous NAD-dependent analogous enzyme. Second, using a “pull” strategy, we sought to promote 3-HP export by overexpressing the endogenous JEN1 gene and the ESBP6 gene from S. cerevisiae . Notably, the latter strategy resulted in a substantial increase in 3-HP productivities. Both strategies were tested individually and in a combined way. Moreover, we characterized the resulting strains at pH 3.5, an industrially relevant condition for carboxylic acid production. Materials and methods Plasmid and strain construction The parental strains K. phaffii PpCβ21 and PpCβ21-P [38,39] were used as platform chassis to construct a new generation of strains with optimized metabolic pathway towards 3-HP production and exportation. Homology-directed integrations of heterologous genes and knockout of the endogenous FDH1 coding sequence were performed using CRISPR/Cas9 technology. Plasmids and strains constructed in this study are listed in Table 1 . Further description of the molecular biology workflow followed in this study can be found in Additional file 1. Following the CRISPR/Cas9-mediated genome editing protocol in K. phaffii , two sgRNAs were individually designed and generated for each locus to target (Table 2 ) and inserted into BB3cN_p GAP _23*_pLAT1_Cas9 plasmid from the CRIS Pi kit [52]. The potential sgRNA candidates binding target regions within the selected loci were assessed with CHOPCHOP [53], a widely used web tool for CRISPR-based genome editing. Electrocompetent K. phaffii cells were prepared as described elsewhere [54]. Co-transformation of the donor DNA template and the BB3 plasmid harboring the sgRNA and a human codon optimized Cas9 for episomal expression in K. phaffii was performed as described in Gassler et al. [52]. The AAT2 and AspDH Spe expression cassettes were individually targeted into an intergenic region located 250-bp upstream of P ENO1 (PP7435_Chr3-1154). PYC2 expression unit was located 600-bp downstream of AOX1tt , disrupting the coding sequence of a putative protein of unknown function (PP7435_Chr4-0131). JEN1 and ESBP6 Sc heterologous cassettes were targeted right after GUT1tt , disrupting the sequence coding a hypothetical protein (PP7435_Chr4-0173). However, no positive transformants were found after several attempts. Homology regions (HR) for JEN1 and ESBP6 Sc expression cassettes were redesigned to individually target the ENO1 genomic locus, showing high editing efficiency. When co-expressing both monocarboxylate permeases, ESBP6 Sc was targeted upstream of P ENO1 , while JEN1 was located 1000-bp upstream of P OPT1−1 (PP7435_Chr4-1011). Finally, the FDH(V9) Pse expression cassette was targeted within 50-bp upstream of P AOX1 (PP7435_Chr4-0130) [38,39] (Table 2 ). Correct genomic insertions from three individual transformants from each strain were checked at both 5’ and 3’ ends of the integrated donor DNA by Sanger sequencing. The K. phaffii FDH1 gene (PP7435_Chr3-0238) was disrupted using insertions or deletions (InDel) mutations by expressing a BB3 vector containing both a sgRNA and Cas9. The resulting double-strand break (DSB) in the genomic DNA occurred 25–32 base pairs from the FDH1 start, depending on the specific sgRNA used (Table 2 ). The disrupted locus was verified by Sanger sequencing. The sequencing was performed by the Genomics and Bioinformatics Service of the Universitat Autònoma de Barcelona. Recombinant yeast strains were grown at 30°C in YPD agar plates (1% yeast extract, 2% peptone, 2% dextrose and 15 g l − 1 agar). The medium was supplemented with Nourseothricin (200 µg ml − 1 working concentration for K. phaffii ) from Dismed S.A. (Asturias, Spain). Table 1 List of plasmids and strains used in this study. Plasmids/Strains Modules/Genotype Reference Plasmids BB1_12_pDAS1 BB1_12_pDAS2 P DAS1 , KanR + P DAS2 , KanR + [55] BB1_12_SHB17 P SHB17 , KanR + BB1_23 Ø, KanR + BB1_34_RPS3tt RPS3 tt, KanR + BB1_34_ScCYC1tt ScCYC1 tt, KanR + BB2_BC Ø, AmpR + BB1_23_AAT2 AAT2 + , K. phaffii gene encoding for the cytosolic aspartate aminotransferase Aat2 This work BB1_23_SpeAspDH aspDH + , S. proteamaculans codon-optimized gene encoding for the aspartate dehydrogenase AspDH BB1_23_PYC2 PYC2 + , K. phaffii codon-optimized gene encoding for the pyruvate carboxylase Pyc2 BB1_23_JEN1 JEN1 + , K. phaffii gene encoding for the putative lactate transporter Jen1 BB1_23_ScESBP6 ESBP6 + , S. cerevisiae gene encoding for the monocarboxylate permease Esbp6 BB1_23_PseFDH(V9) fdh + , Pseudomonas sp. (strain 101) gene encoding for the FDH Pse (V9) (A199G/D222Q/S381V/C256A/H380K) [38,39] BB2_BC_AAT2 P DAS1 - AAT2 -RPS3tt This work BB2_BC_SpeAspDH P DAS1 -AspDH Spe -RPS3tt BB2_BC_PYC2 P SHB17 - PYC2 -RPS3tt BB2_BC_JEN1 P DAS2 - JEN1 -ScCYC1tt BB2_BC_ScESBP6 P DAS1 - ESBP6 Sc -ScCYC1tt BB2_BC_PseFDH(V9) P FDH1 -FDH(V9) Pse -TDH3tt [38,39] BB3nK_ext_AD Ø, KanR + [52] BB3nK_ext_AD_AAT2 5’-HR_P DAS1 - AAT2 -RPS3tt_3’-HR(p ENO1 UP ) This work BB3nK_ext_AD_SpeAspDH 5’-HR_P DAS1 -AspDH Spe -RPS3tt_3’-HR(p ENO1 UP ) BB3nK_ext_AD_PYC2 5’-HR_P SHB17 - PYC2 -RPS3tt_3’-HR( AOX1 tt DOWN ) BB3nK_ext_AD_JEN1 5’-HR_P DAS2 - JEN1 -ScCYC1tt_3’-HR( GUT1 tt DOWN ) 5’-HR_P DAS2 - JEN1 -ScCYC1tt_3’-HR(p OPT1-1 UP ) BB3nK_ext_AD_ScESBP6 5’-HR_P DAS1 - ESBP6 Sc -ScCYC1tt_3’-HR( GUT1 tt DOWN ) 5’-HR_P DAS1 - ESBP6 Sc -ScCYC1tt_3’-HR(p ENO1 UP ) BB3nK_ext_AD_PseFDH(V9) 5’-HR_P FDH1 -FDH(V9) Pse -TDH3tt_3’-HR(p AOX1 UP ) [38,39] BB3cN_pGAP_23*_pLAT1_Cas9 P GAP _ Ø_P LAT1 _Cas9, NrsR + [52] BB3cN_pGAP_gRNA1(pENO1 UP ) _pLAT1_Cas9 P GAP _ gRNA1(P ENO1 UP )_P LAT1 _Cas9, NrsR + This work BB3cN_pGAP_gRNA2(pENO1 UP ) _pLAT1_Cas9 P GAP _ gRNA2(P ENO1 UP )_P LAT1 _Cas9, NrsR + BB3cN_pGAP_gRNA1(AOX1tt DOWN ) _pLAT1_Cas9 P GAP _ gRNA1( AOX1tt DOWN )_P LAT1 _Cas9, NrsR + BB3cN_pGAP_gRNA1(GUT1tt DOWN ) _pLAT1_Cas9 P GAP _ gRNA1( GUT1tt DOWN )_P LAT1 _Cas9, NrsR + BB3cN_pGAP_gRNA2(GUT1tt DOWN ) _pLAT1_Cas9 P GAP _ gRNA2( GUT1tt DOWN )_P LAT1 _Cas9, NrsR + BB3cN_pGAP_gRNA1(pOPT1-1 UP ) _pLAT1_Cas9 P GAP _ gRNA1(P OPT1−1 UP )_P LAT1 _Cas9, NrsR + BB3cN_pGAP_gRNA2(ΔFDH1) _pLAT1_Cas9 P GAP _gRNA2(ΔFDH1)_P LAT1 _Cas9, NrsR + BB3cN_pGAP_gRNA1(pAOX1 UP ) _pLAT1_Cas9 P GAP _gRNA1(P AOX1 UP )_P LAT1 _Cas9, NrsR + [38,39] K. phaffii strains PpCβ21 PpCβ21-P 2x(P AOX1 _PAND Tc ) + P FDH1 _BAPAT Bc + P POR1 _YDFG Ec [38,39] PpCβ21 + P FDH1 _FDH(V9) Pse PpCβ21-A PpCβ21 + P DAS1 _ AAT2 This work PpCβ21-D PpCβ21 + P DAS1 _AspDH Spe PpCβ21-Y PpCβ21 + P SHB17 _ PYC2 PpCβ21-YA PpCβ21-Y + P DAS1 _ AAT2 PpCβ21-YAP PpCβ21-YA + P FDH1 _FDH(V9) Pse PpCβ21-PE PpCβ21-P + P DAS1 _ ESBP6 Sc PpCβ21-PJ PpCβ21-P + P DAS2 _ JEN1 PpCβ21-PEJ PpCβ21-PE + P DAS2 _ JEN1 PpCβ21-PEJY PpCβ21-PEJ + P SHB17 _ PYC2 PpCβ21-PEJΔfdh1 PpCβ21-PEJ + FDH1 − PpCβ21-PEJYΔfdh1 PpCβ21-PEJY + FDH1 − Table 2 Variable sgRNA targets custom-designed in this study. Target name Locus Sequence (5’ → 3’) Module inserted pENO1 UP _sgRNA1 PP7435_Chr3-1154 TTATTGAAGATACCGCCCGG P DAS1 - AAT2 -RPS3tt P DAS1− AspDH Spe -RPS3tt pENO1 UP _sgRNA2 CGTTTAAACTTACCTCCGGG P DAS2 - JEN1 - ScCYC1tt P DAS1 - ESBP6 Sc - ScCYC1tt AOX1tt DOWN _sgRNA1 PP7435_Chr4-0131 ACTATTGATCCAAGCCAGTG P SHB17 - PYC2 -RPS3tt AOX1tt DOWN _sgRNA2 GCTGATTTGGGGTTTAATAC GUT1tt DOWN _sgRNA1 PP7435_Chr4-0173 GGTAGATGAGTTATTAACTG P DAS2 - JEN1 -ScCYC1tt P DAS1 - ESBP6 Sc -ScCYC1tt GUT1tt DOWN _sgRNA2 AAACTGCGAATAAGAAAGCA pOPT1-1 UP _sgRNA1 PP7435_Chr4-1011 ACTAACCAGAGCAAACACTG P DAS2 - JEN1 -ScCYC1tt pAOX1 UP _sgRNA1 PP7435_Chr4-0130 ATTGTGAAATAGACGCAGAT P FDH1 -FDH(V9) Pse -TDH3tt pAOX1 UP _sgRNA2 GCAGTCGATCTCAAAAGCAA ΔFDH1_sgRNA1 PP7435_Chr3-0238 ATCGGCGGCGTGCTTACCAG - ΔFDH1_sgRNA2 GTTCTCGTTTTGTACTCCGC The sgRNA target used for each genomic insertion is highlighted in bold. Screening in deep-well plates Three clones of every K. phaffii strain were inoculated into 50-ml falcon tubes containing 5 ml of YPG medium (1% yeast extract, 2% peptone and 1% v/v glycerol) and grown overnight at 30 °C and 180 rpm in an incubator shaker Multitron Standard from Infors HT (Bottmingen, Switzerland) with a 2.5 cm orbit. Overnight cultures were inoculated in triplicate at a starting OD 600 of 0.1 into 24-deep well plates containing 2 ml of buffered minimal methanol medium (BMM; 100 mM potassium phosphate buffer pH 6, 1.34% yeast nitrogen base (YNB), 0.4 mg l − 1 biotin and 0.5% v/v pure methanol). Cultures were incubated at 25 °C and 220 rpm in the same incubator shaker. The deep well plates were placed on a platform with a slope of 20° to improve the aeration. Relative humidity (rh) in the incubation chamber was fixed to 80%. After 24 h, 1% v/v pure methanol pulse (7.9 g l − 1 ) was added to each well. Cultures were grown for 48 h to ensure the full consumption of the substrate. At the end of the culture, the endpoint OD 600 of each well was measured in duplicate with a 96-well microtiter plate using a Multiskan FC Microplate Photometer from Thermo Fisher Scientific (Waltham, MA, USA). Thereafter, culture samples were collected into 2-ml microcentrifuge tubes and centrifuged at 13,400 rpm for 5 min using a MiniSpin from Eppendorf (Hamburg, Germany). Supernatant was then filtered with a 0.2 µm pore size single-use syringe filter (SLLGX13NK) from Merck Millipore (Burlington, MA, USA). 3-HP and methanol were quantified from the filtered supernatant by HPLC analysis. Bioreactor cultivations For the bioreactor cultivations, the batch medium consisted of 40 g l − 1 glycerol, 1.8 g l − 1 citric acid, 0.02 g l − 1 CaCl 2 ·2H 2 O, 12.6 g l − 1 (NH 4 ) 2 HPO 4 , 0.5 g l − 1 MgSO 4 ·7H 2 O, 0.9 g l − 1 KCl, 50 µl antifoam Glanapon 2000 kz (Bussetti and Co GmbH, Vienna, Austria), 0.4 mg l − 1 biotin, 2 ml l − 1 of vitamin stock solution [56], and 5 ml l − 1 of PTM1 trace salts [57]. The pH was adjusted to 5 using 5 M HCl. The vitamins, the biotin and the trace salts were filter-sterilized and added to the bioreactors after cooling down. Fed-batch cultures were performed in duplicate using a DASGIP Parallel Bioreactor System from Eppendorf (Hamburg, Germany). Reactors were inoculated at a starting OD 600 equal to 1, and the initial volume was 500 ml. The pH was controlled at 5 throughout the culture using 15% ammonia. The temperature was set to 28°C during the batch phase. The inlet gas was fed into the reactors at an aeration rate of 1 vvm (0.5 l min − 1 ). The dissolved oxygen (DO) was set to 30%. The agitation gradually increased from 400 to 1,000 rpm to maintain DO set point. When agitation reached 1,000 rpm, pure oxygen was mixed with air in the inlet gas to maintain a pO 2 above 30%, while maintaining an aeration rate of 0.5 l min − 1 . For the fed-batch phase, the temperature was set to 25°C. Pure methanol (ρ = 792 g l − 1 ) and feeding salts were added separately to the culture to avoid precipitation. The feeding salts medium composition was 0.35 g l − 1 CaCl 2 ·2H 2 O, 10 g l − 1 KCl, 6.45 g l − 1 MgSO 4 ·7H 2 O, 200 µl antifoam Glanapon 2000 kz, 1.2 mg l − 1 biotin, 6 ml l − 1 of vitamin stock solution, and 15 ml l − 1 of PTM1 trace salts. This media was prepared at 2× concentration since pure methanol was used. The vitamins, the biotin and the trace salts were filter-sterilized and added to this final feeding solution. At the end of the glycerol batch phase, two consecutive pulses of pure methanol (1 and 2 g l − 1 , respectively) were added to the reactors. Once methanol was fully depleted, the cultures were fed with a constant methanol feed (F o ) to complete the transition phase. For cultivations at pH 3.5, the pH set point was adjusted from 5 to 3.5 at the start of the second stage of the transition phase (i.e. during the constant methanol feed), allowing the pH to gradually decrease over the following hours (see Additional file 2). After that, the feeding medium was added to the bioreactors using a pre-programmed exponential feeding strategy for controlled specific growth rate described in the following equation: $$\\:F\\left(t\\right)=\\:\\frac{{\\mu\\:}\\left[X\\left({t}_{o}\\right)V\\left({t}_{o}\\right)\\right]}{{Y}_{X/S}{S}_{o}}{e}^{\\left[{\\mu\\:}\\left(t-{t}_{o}\\right)\\right]}$$ where growth rate (µ) was set to 0.03 h − 1 , initial biomass concentration (X 0 ) was fixed at 22 g l − 1 , biomass yield (Y X/S ) was 0.273 g g − 1 , and initial substrate concentration (S o ) was 792 g l − 1 . The reactors were sampled during 39 h to measure OD 600 , biomass dry cell weight (DCW) and supernatant metabolites. For the biomass DCW determination, 10 ml of a 9 g l − 1 NaCl solution were used to wet the pre-weighted glass microfiber filters (APFF04700) from Merck Millipore (Burlington, MA, USA) before filtering 2 ml of culture for each triplicate. After that, the filters were washed using the same volume of the NaCl solution and dried for 24 h at 105°C. Filters containing the dry biomass were weighted to calculate the DCW. To quantify the metabolites, 2 ml of culture samples were centrifuged 5 min at 13,400 rpm using a MiniSpin (Eppendorf, Germany). The supernatant was then filtered with a 0.2 µm pore size single-use syringe filter (SLLGX13NK, Merck Millipore). The filtered supernatant was stored at -20°C until HPLC analysis for methanol and 3-HP quantification. Analytical methods and data processing Methanol and 3-HP were quantified using an HPLC Dionex Ultimate3000 from Dionex (Thermo Fischer Scientific, Waltham, MA, USA). The compounds were separated with an ionic exchange column ICSep ICE-COREGEL 87H3 from Transgenomic (Omaha, NE, USA) using 6 mM sulphuric acid as mobile phase at a flow rate of 0.6 ml min − 1 . Both metabolites were quantified from the Refractive Index (RI) spectrum. The OD 600 measurements were performed in triplicate using a Lange DR 3900 spectrophotometer from Hach (Loveland, CO, USA). Analytical replicates were averaged, and the standard deviation (SD) was calculated for each OD 600 determination. Biomass (dry cell weight, DCW) was determined in triplicate for three samples throughout the fed-batch. For the rest of the samples, the biomass DCW values were calculated from the measured OD 600 values, based on the calibration curve made for the given strain. Offline and online state variables —such as biomass (X), substrate (S), product (P), flow rate (F), and initial volume (V o )— were recorded during the fed-batch phase of the bioreactor-scale experiments (see Additional files 2 and 3). These variables were used to calculate derived metrics, including growth rate (µ), q-rates, and yields, following the methodology described elsewhere [38,39]. For mass balance verification, the elemental biomass composition CH 1.88 O 0.63 N 0.20 S 0.004 was assumed, based on cells growing on methanol at µ = 0.035 h − 1 [58]. Carbon and electron balances were satisfied with a deviation of less than 7%. A statistical χ2 consistency test, based on the h-index, was applied to the measured data balances [59,60], following the detailed method described by Ponte et al. [61]. The consistency tests passed with a confidence level of 95%, confirming no major measurement errors. Computational methods The genome-scale metabolic model iMT1026 v3.0 of K. phaffii [58] was employed to calculate the theoretical maximum 3-HP yield (Υ 3−HP max ) achievable on methanol via the β-alanine pathway under different growth conditions. This model was expanded to include 3-HP production and excretion reactions. Simulations were constrained using experimental values obtained for the key physiological macroscopic parameters µ and q S , derived from methanol fed-batch cultivations. Specifically, µ was set to 0.028 h − 1 and q S was 0.109 g MetOH g DCW −1 h − 1 (Table 3 ). Additionally, the Υ 3−HP max was calculated under non-growth conditions (µ = 0 h − 1 ). The non-growth associated maintenance energy (NGAME) parameter for methanol growth from Tomàs-Gamisans et al. [58] was also applied as a constraint. Flux Balance Analysis (FBA) was performed in COBRA Toolbox v3.0 [62] with Matlab R2021a (Mathworks, Inc., Natick, MA, USA) to maximize specific 3-HP productivity (q P ) as the objective function. Finally, q P values resulting from the FBA, together with the experimental q S , were used to determine the Υ 3−HP max . Results and discussion Rewiring the upstream module of the β-alanine pathway for enhanced precursors supply To enhance pyruvate flux into the β-alanine pathway, i.e. reactions leading from this key metabolic node to oxaloacetate (OAA) and L-aspartate, we individually overexpressed native genes encoding for the pyruvate carboxylase isoform 2 ( PYC2 ), which catalyzes the ATP-dependent carboxylation of pyruvate to form OAA, and for the cytosolic aspartate aminotransferase 2 ( AAT2 ), which enables the conversion of OAA to aspartate by transferring an amino group from glutamate (Fig. 1 ). These genes were overexpressed in the original strain PpCβ21 [38,39] by integrating a second copy of the corresponding coding sequence under the control of methanol-inducible promoters into the K. phaffii genome. The expression of AAT2 in strain PpCβ21-A was driven by the strong dihydroxyacetone synthase 1 promoter (P DAS1 ), while PYC2 in strain PpCβ21-Y was expressed under the control of the medium-strength sedoheptulose bisphosphatase promoter (P SHB17 ) to avoid additional disturbances in the central metabolic node of pyruvate. Additionally, a highly active and stable NAD- or NADP-dependent aspartate dehydrogenase from Serratia proteamaculans (SpeAspDH) [63], which directly aminates OAA using ammonium as the amino donor, was tested as an alternative to K. phaffii AAT2 under P DAS1 , resulting in strain PpCβ21-D strain (Fig. 1 ). Three independently isolated transformants from each strain were cultured in 24-deep well plates containing buffered minimal methanol (BMM) medium, alongside the reference strain PpCβ21. After 48 h of incubation, all strains reached a similar cell density (see Additional file 4). None of the individually overexpressed genes led to a significant improvement in final 3-HP concentration or yield on methanol (Y P/S ), except in the case of PYC2 overexpression (strain PpCβ21-Y), which resulted in a slight but statistically significant 5.4% increment ( p = 0.03) in the 3HP yield on biomass (Y P/X ), increasing from 0.556 to 0.586 g 3-HP l − 1 per OD 600 unit compared to the reference strain (see Additional file 4). We hypothesize that the modest increase in 3-HP yields could be due to an insufficient ATP supply, as the carboxylation of pyruvate by Pyc2 is ATP-dependent [64]. Additionally, bicarbonate, a co-substrate in this carboxylation reaction, might also become a rate-limiting factor in these strains [46,65]. Neither AAT2 nor the S. proteamaculans AspDH’s encoding gene overexpression did improve 3-HP production, suggesting that the conversion of OAA into aspartate may not be a rate-limiting step of the 3-HP pathway. These findings are consistent with those from a previous study involving a recombinant K. phaffii strain that converted methanol into β-alanine, in which overexpression of either mitochondrial AAT1 or cytosolic AAT2 did not significantly enhance β-alanine production [63]. Coherently, rewiring the entire upstream module of the β-alanine pathway, i.e., co-overexpression of an additional copy of both PYC2 and AAT2 , resulting in strain PpCβ21-YA, did not lead to significant improvements in Y P/S and Y P/X compared to the PpCβ21-Y strain ( p = 0.58 and 0.30, respectively) (see Additional file 4). These results highlight a potential bottleneck in the OAA biosynthesis enzymes, rather than in the supply of aspartate. In a previous study, we overexpressed a mutated NADP-dependent formate dehydrogenase from Pseudomonas sp. 101, PseFDH(V9), aiming at enhancing cofactor supply of the NADPH-dependent b-alanine synthetic pathway (Fig. 1 ). This led to increased q P and q S in methanol fed-batch cultures [38,39]. Building on this, we further modified the PpCβ21-YA strain by introducing the gene encoding for PseFDH(V9) under the native formate dehydrogenase promoter (P FDH1 ), creating strain PpCβ21-YAP. However, expression of PseFDH(V9) led to a significant decrease in final 3-HP titer of approximately 11% ( p = 0.0017) in small-scale (24-deep well plate) cultivations, resulting in lower Y P/S and Y P/X values compared to PpCβ21-YA, despite both strains reaching the same final cell concentration (see Additional file 4). These results were consistent with our previous findings [38,39], where the introduction of this ectopic NADPH-regenerating reaction also reduced 3-HP titers in small-scale cultivation experiments, probably due to a carbon flux redistribution through the methanol dissimilation pathway instead of biomass and 3-HP generation. Nevertheless, overexpression of PseFDH(V9) in strain PpCβ21-P resulted in a 14% and 10% increase in Y P/X and q P during methanol fed-batch cultivations, respectively, compared to the reference strain PpCβ21. As a result, strain PpCβ21-P was selected for further engineering efforts aimed at enhancing 3-HP export. Modulation of 3-HP export capacity through the overexpression of monocarboxylate permeases encoding genes leads to improved extracellular 3-HP concentration and product yields Efficient export of 3-HP from microbial cells is a cornerstone in industrial bioproduction. Owing to the near-neutral pH of the cytoplasm, 3-HP dissociates at the time of being produced, releasing protons and anions that poorly diffuse to extracellular space. To maintain intracellular pH (pHi) homeostasis, these (an)ions must be actively exported through different free energy-dependent processes such as: i) primary transport, via ATP-Binding Cassette (ABC) transporters and plasma membrane H + -ATPases, and ii) secondary transport, via transporters that use (electro-)chemical gradients as the driving force (e.g., Jen1 symporter) (Fig. 1 ). Nonetheless, specific ABC transporters for 3-HP export are not yet identified. In this study, the JEN1 and ESBP6 genes encoding putative monocarboxylate permeases were individually introduced into the PpCβ21-P strain [38,39], under the control of strong methanol-inducible promoters, resulting in strains PpCβ21-PJ and PpCβ21-PE, respectively. After 48 h of cultivation in BMM medium in 24-deep well plates, the highest concentration of 3-HP (1.40 ± 0.04 g l − 1 ) was obtained with strain PpCβ21-PE, resulting in a 44% increase in both titer and yields compared to the reference strain (Fig. 2 ). Notably, although PpCβ21-PE produced a higher amount of 3-HP than PpCβ21-P, the biomass values reached by both strains were the same (see Additional file 4). While the function of Esbp6 remains unknown [43], overexpression of its gene clearly enhanced 3-HP titer and yields in the recombinant strain PpCβ21-PE. These results are consistent with findings from other studies [45,46], where overexpression of ESBP6 improved yeast tolerance to acid-stressing conditions by reducing the intracellular 3-HP content, ultimately leading to a higher 3-HP production. Conversely, overexpression of JEN1 in strain PpCβ21-PJ led to modest yet statistically significant increase of 8% in Y P/S and 12% in Y P/X ( p = 0.0008 and 0.044, respectively) compared to PpCβ21-P (Fig. 2 ), suggesting that Jen1 may have a lower export efficiency or capacity for 3-HP compared to Esbp6. Further characterization of these transporters could provide insights into the distinct effects on 3-HP export observed in this study. Interestingly, disruption of the endogenous JEN1 gene in a S. cerevisiae 3-HP-producing strain did not result in significant differences in the final 3-HP concentration compared to the parental strain, indicating that 3-HP efflux in the Δ jen1 strain remained functional [46]. Similar results were observed in S. cerevisiae strains producing lactic acid, where Δ jen1 strains, and even isogenic yeast mutants Δ jen1 Δ ady2 , showed no differences in lactic acid production compared to the wild-type strain, suggesting the presence of other lactic acid exporters besides Jen1 [48,49]. However, Lima et al. [51] reported a 46% increase in lactate yield when JEN1 was overexpressed in a recombinant K. phaffii strain. Therefore, it is also plausible that the limited impact of JEN1 overexpression in strain PpCβ21-PJ might be due to the lower affinity of this putative lactate transporter for 3-HP anions compared to lactate. When JEN1 and ESBP6 were overexpressed simultaneously in strain PpCβ21-PEJ, the Y P/X was similar to that of ESBP6 overexpression alone, while the Y P/S showed a modest but significant increase of 6% ( p = 0.001), rising from 0.118 g g − 1 in strain PpCβ21-PE to 0.125 g g − 1 (Fig. 2 ). These findings corroborate that the monocarboxylate transporters, particularly Esbp6, effectively facilitated the export of 3-HP in K. phaffii . Finally, in an attempt to combine “push” and “pull” metabolic engineering strategies, a second copy of the K. phaffii PYC2 gene was integrated into the PpCβ21-PEJ genome, aiming to enhance metabolic precursors supply to the b-alanine pathway, resulting in strain PpCβ21-PEJY. However, this strain did not show significant changes in Y P/S and Y P/X compared to PpCβ21-PEJ when grown in 24-deep well plates, with p-values of 0.34 and 0.31, respectively (see Additional file 4). Overall, strains PpCβ21-PEJ and PpCβ21-PEJY showed the highest improvement in both Y P/S (53 and 55%, respectively) and Y P/X (44 and 48%) compared to the reference strain PpCβ21-P (Fig. 2 ). As a result, both strains were taken for further evaluation in bioreactors. Production of 3-HP in fed-batch cultivations Strains PpCβ21-PEJ and PpCβ21-PEJY were further characterized in bioreactor-scale fed-batch cultivations using a pre-programmed exponential methanol-limited feeding strategy at a growth rate of µ SP = 0.02 h − 1 . Both strains reached a final 3-HP concentration of 24.6 g l − 1 and nearly identical biomass levels (38.0 and 37.8 g l − 1 , respectively) after 39.3 h of methanol feeding. Approximately 70 g of methanol were added during the exponential feed, yielding an overall Y P/S of 0.20 g g − 1 for both strains (see Additional file 3). Residual methanol was not detected by HPLC analysis over the course of fermentation. Given that both PpCβ21-PEJ and PpCβ21-PEJY strains performed similarly, one of them, strain PpCβ21-PEJY, was further tested in a methanol-limited fed-batch culture at µ SP = 0.03 h − 1 , i.e. under the same conditions used for characterization of our original strains [38,39]. During the 39.3-h feeding phase, almost 125 g l − 1 of methanol were supplied to the bioreactors, leading to a final concentration of 27.0 ± 0.4 g 3-HP l − 1 . This represents a 42% increase in 3-HP titer compared to the parental strain PpCβ21-P under the same conditions (Fig. 3 ), which is consistent with results observed in small-scale experiments. These findings corroborate that lactate transporters, such as Esbp6 and Jen1, effectively facilitate 3-HP export. Interestingly, strain PpCβ21-PEJY significantly decreased the CO 2 yield on methanol (Y CO2/S ) from 0.81 (strain PpCβ21-P) to 0.68 g CO2 g MetOH −1 (p-value = 0.01), while both Y P/S and Y X/S were significantly increased by 27 and 19%, respectively (Table 3 ) (p-values = 0.04 and 0.01, see Additional file 3). A plausible explanation for this observation may be that the increased export of 3-HP caused by the overexpression of monocarboxylate permeases in the PpCβ21-PEJY strain, particularly Esbp6, seems to provoke a metabolic “pull” effect, draining a higher proportion of the incoming methanol flux towards the assimilatory pathway, thereby favoring 3-HP production and biomass synthesis, while reducing CO 2 generation through the dissimilatory methanol oxidation pathway. The 3-HP yield on methanol reported in this study is comparable to that of our top-performing K. phaffii strain engineered with the malonyl-CoA pathway on glycerol, both achieving 0.19 g g − 1 [21]. This finding is particularly significant considering that the Υ 3−HP max achievable on methanol is considerably lower than on C-sources such as glucose or glycerol, assuming all carbon is used solely for 3-HP synthesis (µ = 0 h − 1 ) [38,39]. The yield achieved here corresponds to 70% of the Υ 3−HP max under biomass-generating conditions (0.27 g 3 − HP g MetOH −1 ), i.e., reflecting the specific growth rate in our fed-batch experiments. Coherently, the Υ 3−HP max is considerably higher when all carbon is exclusively channeled toward 3-HP production, with no biomass generation (0.78 g 3 − HP g MetOH −1 ). Under these conditions, our experimental Y P/S represents 24.4% of this theoretical yield (see Materials and Methods section). Strain PpCβ21-PEJY also demonstrated a notably higher overall 3-HP volumetric productivity (Q P ) compared to the reference strain (Table 3 ), achieving nearly four times the Q P value reported for a K. phaffii strain producing 3-HP solely from methanol (0.15 g l − 1 h − 1 , calculated from Wu et al. [37]). Table 3 Averaged value of key process parameters obtained for the methanol fed-batch phase using a preprogrammed µ of 0.03 h -1 . Volumetric productivity (Q P ), biomass yield on methanol (Y X/S ), 3-HP yield on methanol (Y P/S ), 3-HP yield on biomass (Y P/X ), CO 2 yield on methanol (Y CO2/S ), specific substrate consumption rate (q S ), specific 3-HP production rate (q P ), specific carbon dioxide evolution rate (q CO2 ), and experimentally measured mean specific growth rate (µ). Cultivations were performed in duplicate and biomass concentration analyses were performed in triplicate. ± indicates SD of the biological replicates. PpCβ21-P PpCβ21-PEJY Q P (g 3 − HP l − 1 h − 1 ) 0.46 ± 0.01 0.56 ± 0.03 Y X/S (g DCW g MetOH −1 ) 0.21 ± 0.01 0.25 ± 0.01 Y P/S (g 3 − HP g MetOH −1 ) 0.15 ± 0.01 0.19 ± 0.01 Y P/X (g 3 − HP g DCW −1 ) 0.69 ± 0.03 0.74 ± 0.02 Y CO2/S (g CO2 g MetOH −1 ) 0.81 ± 0.03 0.68 ± 0.02 q S (g MetOH g DCW −1 h − 1 ) 0.124 ± 0.003 0.109 ± 0.001 q P (mmol 3 − HP g DCW −1 h − 1 ) 0.201 ± 0.008 0.227 ± 0.006 q CO2 (mmol CO2 g DCW −1 h − 1 ) 2.30 ± 0.08 1.69 ± 0.04 µ (h − 1 ) 0.026 ± 0.001 0.028 ± 0.001 Reference [38,39] This study Fermentation of K. phaffii strains at pH 3.5 Production of 3-HP at a low pH allows for a more economical downstream processing, while reducing the risk of bacterial contamination. For instance, acidification is not needed for product recovery, effectively reducing costs. Furthermore, the amounts of base titrant required for neutralization during fermentation are greatly decreased at low pH, especially at industrial scale [8,28]. Additionally, the pH of the medium has been shown to have a drastic impact on the effectiveness of 3-HP solvent extraction. For instance, Chemarin et al. [66] obtained a 3-HP extraction yield of only about 5% in a solution starting at pH 5 due to acid dissociation. However, the yield significantly increased to 74% after lowering the pH to 3.2. Thereby, production of 3-HP with strains PpCβ21-P and PpCβ21-PEJY was also evaluated at pH 3.5, following the same feeding strategy used for their initial characterization at pH 5. Both PpCβ21-P and PpCβ21-PEJY strains produced 3-HP at acidic pH, achieving final titers of 15.9 ± 0.1 g l − 1 and 24.2 ± 0.1 g l − 1 , respectively (Fig. 4 ). As expected, strain PpCβ21-PEJY outperformed PpCβ21-P also at lower pH values. It is known that the energy demands of product export can hinder overall 3-HP yield, particularly if the metabolic pathway involved has little to no net ATP yield [28]. Accordingly, the two tested strains produced lower 3-HP yields at pH 3.5 compared to pH 5. However, while strain PpCβ21-P produced 3-HP at a yield of 0.11 g g − 1 at pH 3.5, that is, a decrease of 26.7% compared to the yield at pH 5, strain PpCβ21-PEJY achieved a 3-HP yield of 0.17 g g − 1 at low pH, that is, just 10.5% decrease from that obtained at pH 5 (0.19 g g − 1 ), (see Additional file 3). These findings are coherent with earlier research on 3-HP synthesis by S. cerevisiae [23] and K. phaffii cultivated under pH 3.5 conditions [67]. Under such acidic conditions, the undissociated fraction of 3-HP is the predominant form in the solution. Particularly at high concentrations, uncharged 3-HP may cross the cell membrane through either simple diffusion or facilitated diffusion (via a channel or a permease). Once in the cytoplasm, neutral pH leads to dissociation of the 3-HP acid, releasing protons and anions (Fig. 1 ). The released protons may cause cytosolic acidification, which is harmful to the cell, whereas anions may trigger the generation of free radicals, resulting in severe oxidative stress [68,69]. Hence, to maintain pH homeostasis in the cytosol, active transport mechanisms, which require metabolic energy, allow for exporting these accumulated (an)ions from the cytosol. The lower 3-HP yields achieved at pH 3.5 compared to pH 5 may reflect this increased ATP demand, underscoring ATP as a limiting factor in 3-HP production, consistent with previous findings [67]. Nevertheless, overexpression of genes encoding for monocarboxylate transporters, especially ESBP6 , effectively facilitated the export of anionic 3-HP in strain PpCβ21-PEJY. This could explain why the lower pH had a reduced effect on the 3-HP yield of strain PpCβ21-PEJY compared to strain PpCβ21-P. Interestingly, both strains PpCβ21-P and PpCβ21-PEJY reached comparable levels of biomass concentration in cultures at the two pH values (Figs. 3 and 4 ), suggesting that acidic pH had no impact on biomass yields (see Additional file 3). However, studies investigating carboxylic acid production in S. cerevisiae [70] and K. phaffii [67] found contrasting results, linking reduced biomass yields to increased maintenance-energy requirements in such conditions. Partial blockage of methanol dissimilatory pathway to reduce carbon loss via CO 2 release In K. phaffii , methanol is oxidized to formaldehyde by alcohol oxidase ( AOX ) in the peroxisomes. Several studies have revealed that less than half of the formaldehyde is assimilated into dihydroxyacetone (DHA) and glyceraldehyde 3-phosphate (G3P) for biomass synthesis [71,72], while the majority enters the dissimilation pathway, where it is further oxidized to CO 2 , generating NADH and ATP (Fig. 1 ). Although this pathway is essential for formaldehyde detoxification and energy production, it leads to a huge loss of carbon atoms through CO 2 release [73]. The combination of different metabolic engineering strategies described so far resulted in a shift of carbon flux from the dissimilatory pathway towards 3-HP production. In particular, strain PpCβ21-PEJY, which overexpresses a mutated NADP-dependent formate dehydrogenase (PseFDH(V9)), two monocarboxylate transporters (Esbp6 and Jen1), and pyruvate carboxylase isoform 2 (Pyc2), showed a 16% reduction in CO 2 yield and a 27% increase in 3-HP yield in methanol fed-batch cultures compared to the parental strain (Table 3 ). To further decrease CO 2 formation, the K. phaffii FDH1 endogenous gene, encoding for a NAD-dependent formate dehydrogenase, was deleted in strains PpCβ21-PEJ and PpCβ21-PEJY, resulting in strains PpCβ21-PEJΔ fdh1 and PpCβ21-PEJYΔ fdh1 , respectively. This strategy aimed to channel formate oxidation to CO 2 exclusively through the NADP-dependent PseFDH(V9) (Fig. 1 ). Strains PpCβ21-PEJΔ fdh1 and PpCβ21-PEJYΔ fdh1 were cultivated in triplicate alongside their parental strains in 24-deep well plates containing BMM medium. Both 3-HP production and cell growth were drastically reduced by over 90% compared to their respective parental strains (see Additional file 4). 3-HP titers as low as 0.10 g l − 1 were observed in strains with FDH1 deletion, while strains PpCβ21-PEJ and PpCβ21-PEJY produced up to 1.50 g l − 1 3-HP. This suggests that NADH generated from dissimilated formaldehyde via FLD was not sufficient to make up for the loss of NADH (and consequently, ATP) typically produced by native FDH , leading to an energy imbalance and suboptimal growth on methanol. Notably, around 1 g l − 1 of formic acid (FA) accumulated, indicating that PseFDH(V9) expression alone was not enough to fully oxidize FA to CO 2 , creating a bottleneck in the dissimilatory pathway. The accumulation of toxic intermediates, such as FA (and formaldehyde), likely contributed to the impaired growth observed. In fact, it has been postulated that the main physiological role of FDH is detoxifying formate rather than enhancing energy production [74]. Moreover, partial blocking of the dissimilatory pathway also led to up to 4.3 g l − 1 of residual methanol (Additional file 4), suggesting additional metabolic bottlenecks downstream the methanol assimilatory pathway. Similar challenges were reported by Guo et al. [34] when the methanol dissimilation pathway was completely blocked through FDH deletion in a K. phaffii strain engineered to produce malic acid from methanol. In contrast, a recent study found no significant growth differences between a K. phaffii GS115 strain and an FDH -deficient GS115 strain grown on 1% YPM, suggesting context-dependent effects of FDH deletion on methanol metabolism. Moreover, comparative transcriptomic analysis revealed that the impact of FDH deletion was less pronounced than that caused by the deletion of other genes involved in the methanol dissimilatory pathway ( FLD , FGH ) [75]. Conclusions In this study, we enhanced the performance of our previously developed 3-HP-producing strains through several metabolic engineering strategies focusing on the improvement of yields and productivities. These strategies included: i) overexpressing the upstream module of the β-alanine pathway, ii) partially blocking the methanol dissimilation pathway, and iii) reducing intracellular 3-HP accumulation. To our knowledge, this is the first time the S. cerevisiae ’s gene encoding for the lactate transporter Esbp6 has been expressed in K. phaffii , proving its effectiveness in facilitating 3-HP export. Overall, co-overexpression of ESBP6 and JEN1 , encoding two lactate transporters, along with PYC2 to enhance oxaloacetate supply, led to a final 3-HP concentration of 27.0 g l − 1 in 39.3 h, with a product yield of 0.19 g g − 1 and a volumetric productivity of 0.56 g l − 1 h − 1 . However, deleting FDH1 in K. phaffii impaired growth, probably due to an energy imbalance. Notably, we further demonstrated 3-HP production under industrially relevant cultivation conditions, specifically at a low pH of 3.5, and highlighted the beneficial effects of overexpressing genes encoding lactate transporters, such as Esbp6 and Jen1, to support 3-HP production at pH 3.5. While this work highlights the potential of using efficient monocarboxylate transporters to achieve high productivities, yields, and titers of the target carboxylic acid, further improvements are needed to reach industrially relevant metrics. Furthermore, a deeper understanding of the export mechanisms, substrate specificity, and regulation of carboxylate transporters is crucial for the successful development of microbial cell factories for industrial carboxylic acid production, regardless of the pH conditions used in the fermentation processes. Declarations Supplementary information The online version contains supplementary material available at: Acknowledgements We thank Enrique Vázquez-Pereira and Jordi Reig for their assistance in designing specific engineered loci and for their contributions to the corresponding cloning procedures. Figure 1 was created by SAC in BioRender. BioRender.com/w53i486 Authors’ contributions SAC, JA, and PF conceived and designed the research project. SAC performed all experiments and analyzed the data. MPT performed the NMR analyses and metabolite identification. PF and JA contributed to data interpretation. SAC wrote the first draft of the manuscript and PF contributed to the manuscript final version. All authors read and approved the final manuscript. Funding This work was supported by project ‘innoVative bIo-based chains for CO 2 VALorisation as aDded-value organic acids’ – VIVALDI (ID: 101000441) from the Horizon 2020 Program of the European Commission; 2021-SGR-00143 from the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) of the Catalan Government. SAC was supported by a FI fellowship (2022FI_B1_00173) from AGAUR. Availability of data and materials The data that supports the findings of this study are included in this published article/Additional files. Further datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Not applicable. Competing interests The authors declare that they have no competing interests. Consent for publication Not applicable. References Olah GA. Beyond oil and gas: the methanol economy. Angew Chem Int Ed. 2005;44:2636–9. Sarwar A, Lee EY. Methanol-based biomanufacturing of fuels and chemicals using native and synthetic methylotrophs. Synth Syst Biotechnol. 2023;8:396–415. Bachleitner S, Ata Ö, Mattanovich D. The potential of CO 2 -based production cycles in biotechnology to fight the climate crisis. Nat Commun. 2023;14:6978. Vásquez Castro E, Memari G, Ata Ö, Mattanovich D. Carbon efficient production of chemicals with yeasts. Yeast. 2023;40:583–93. Lv X, Yu W, Zhang C, Ning P, Li J, Liu Y, Du G, Liu L. C1-based biomanufacturing: Advances, challenges and perspectives. Bioresour Technol. 2023;367:128259. Jiang X, Meng X, Xian M. Biosynthetic pathways for 3-hydroxypropionic acid production. Appl Microbiol Biotechnol. 2009;82:995–1003. Rathnasingh C, Raj SM, Jo JE, Park S. Development and evaluation of efficient recombinant Escherichia coli strains for the production of 3-hydroxypropionic acid from glycerol. Biotechnol Bioeng. 2009;104:729–39. Werpy T, Petersen G. Top value added chemicals from biomass: Volume I — Results of screening for potential candidates from sugars and synthesis gas. Springfield (VA): US Department of Energy; 2004. Report No. DOE/GO-102004-1992. Bozell JJ, Petersen GR. Technology development for the production of biobased products from biorefinery carbohydrates—the US Department of Energy’s “Top 10” revisited. Green Chem. 2010;12:539–55. Della Pina C, Falletta E, Rossi M. A green approach to chemical building blocks. The case of 3-hydroxypropanoic acid. Green Chem. 2011;13:1624–32. Kumar V, Ashok S, Park S. Recent advances in biological production of 3-hydroxypropionic acid. Biotechnol Adv. 2013;31:945–61. de Fouchécour F, Sánchez-Castañeda AK, Saulou-Bérion C, Spinnler HÉ. Process engineering for microbial production of 3-hydroxypropionic acid. Biotechnol Adv. 2018;36:1207–22. Jers C, Kalantari A, Garg A, Mijakovic I. Production of 3-hydroxypropanoic acid from glycerol by metabolically engineered bacteria. Front Bioeng Biotechnol. 2019;7:124. Kim JW, Ko YS, Chae TU, Lee SY. High-level production of 3-hydroxypropionic acid from glycerol as a sole carbon source using metabolically engineered Escherichia coli. Biotechnol Bioeng. 2020;117:2139–52. Zhao P, Ma C, Xu L, Tian P. Exploiting tandem repetitive promoters for high-level production of 3-hydroxypropionic acid. Appl Microbiol Biotechnol. 2019;103:4017–31. Ji RY, Ding Y, Shi TQ, Lin L, Huang H, Gao Z, Ji XJ. Metabolic engineering of yeast for the production of 3-hydroxypropionic acid. Front Microbiol. 2018;9:2185. Kildegaard KR, Jensen NB, Schneider K, Czarnotta E, Özdemir E, Klein T, Maury J, Ebert BE, Christensen HB, Chen Y, Kim IK, Herrgård MJ, Blank LM, Forster J, Nielsen J, Borodina I. Engineering and systems-level analysis of Saccharomyces cerevisiae for production of 3-hydroxypropionic acid via malonyl-CoA reductase-dependent pathway. Microb Cell Fact. 2016;15:53. Yu W, Cao X, Gao J, Zhou YJ. Overproduction of 3-hydroxypropionate in a super yeast chassis. Bioresour Technol. 2022;361:127690. Suyama A, Higuchi Y, Urushihara M, Maeda Y, Takegawa K. Production of 3-hydroxypropionic acid via the malonyl-CoA pathway using recombinant fission yeast strains. J Biosci Bioeng. 2017;124:392–9. Takayama S, Ozaki A, Konishi R, Otomo C, Kishida M, Hirata Y, Matsumoto T, Tanaka T, Kondo A. Enhancing 3-hydroxypropionic acid production in combination with sugar supply engineering by cell surface-display and metabolic engineering of Schizosaccharomyces pombe. Microb Cell Fact. 2018;17:176. Fina A, Heux S, Albiol J, Ferrer P. Combining metabolic engineering and multiplexed screening methods for 3-hydroxyprionic acid production in Pichia pastoris. Front Bioeng Biotechnol. 2022;10:942304. Kildegaard KR, Wang Z, Chen Y, Nielsen J, Borodina I. Production of 3-hydroxypropionic acid from glucose and xylose by metabolically engineered Saccharomyces cerevisiae. Metab Eng Commun. 2015;2:132–6. Borodina I, Kildegaard KR, Jensen NB, Blicher TH, Maury J, Sherstyk S, Schneider K, Lamosa P, Herrgård MJ, Rosenstand I, Öberg F, Forster J, Nielsen J. Establishing a synthetic pathway for high-level production of 3-hydroxypropionic acid in Saccharomyces cerevisiae via β-alanine. Metab Eng. 2015;27:57–64. Lis AV, Schneider K, Weber J, Keasling JD, Jensen MK, Klein T. Exploring small-scale chemostats to scale up microbial processes: 3-hydroxypropionic acid production in S. cerevisiae. Microb Cell Fact. 2019;18:50. Cregg JM, Vedvick TS, Raschke WC. Recent advances in the expression of foreign genes in Pichia pastoris. Biotechnology (N Y). 1993;11:905 − 10. Werten MWT, Van Den Bosch TJ, Wind RD, Mooibroek H, De Wolf FA. High-yield secretion of recombinant gelatins by Pichia pastoris. Yeast. 1999;15:1087–96. Budavari S. The Merck index: An encyclopedia of chemicals, drugs, and biologicals. 11th ed. Rahway: Merck; 1989. van Maris AJ, Konings WN, Dijken JPV, Pronk JT. Microbial export of lactic and 3-hydroxypropanoic acid: Implications for industrial fermentation processes. Metab Eng. 2004;6:245–55. Guo F, Qiao Y, Xin F, Zhang W, Jiang M. Bioconversion of C1 feedstocks for chemical production using Pichia pastoris. Trends Biotechnol. 2023;41:1066-79. Liu X Bin, Liu M, Tao XY, Zhang ZX, Wang FQ, Wei DZ. Metabolic engineering of Pichia pastoris for the production of dammarenediol-II. J Biotechnol. 2015;216:47–55. Gao J, Zuo Y, Xiao F, Wang Y, Li D, Xu J, Ye C, Feng L, Jiang L, Liu T, Gao D, Ma B, Huang L, Xu Z, Lian J. Biosynthesis of catharanthine in engineered Pichia pastoris. Nat Synth. 2023;2:231–42. Cai P, Li Y, Zhai X, Yao L, Ma X, Jia L, Zhou YJ. Microbial synthesis of long-chain α-alkenes from methanol by engineering Pichia pastoris. Bioresour Bioprocess. 2022;9:58. Cai P, Wu X, Deng J, Gao L, Shen Y, Yao L, Zhou YJ. Methanol biotransformation toward high-level production of fatty acid derivatives by engineering the industrial yeast Pichia pastoris. Proc Natl Acad Sci U S A. 2022;119:e2201711119. Guo F, Dai Z, Peng W, Zhang S, Zhou J, Ma J, Dong W, Xin F, Zhang W, Jiang M. Metabolic engineering of Pichia pastoris for malic acid production from methanol. Biotechnol Bioeng. 2021;118:357–71. Yamada R, Ogura K, Kimoto Y, Ogino H. Toward the construction of a technology platform for chemicals production from methanol: D-lactic acid production from methanol by an engineered yeast Pichia pastoris. World J Microbiol Biotechnol. 2019;35:37. Severinsen MM, Bachleitner S, Modenese V, Ata Ö, Mattanovich D. Efficient production of itaconic acid from the single-carbon substrate methanol with engineered Komagataella phaffii. Biotechnol Biofuels Bioprod. 2024;17:98. Wu X, Cai P, Gao L, Li Y, Yao L, Zhou YJ. Efficient bioproduction of 3-hydroxypropionic acid from methanol by a synthetic yeast cell factory. ACS Sustain Chem Eng. 2023;11:6445–53. Àvila-Cabré S, Pérez-Trujillo M, Albiol J, Ferrer P. Engineering the synthetic β-alanine pathway in Komagataella phaffii for conversion of methanol into 3-hydroxypropionic acid. Microb Cell Fact. 2023;22:237. Àvila-Cabré S, Pérez-Trujillo M, Albiol J, Ferrer P. Correction to: Engineering the synthetic β-alanine pathway in Komagataella phaffii for conversion of methanol into 3-hydroxypropionic acid. Microb Cell Fact. 2024;23:235. Mazzoli R. Current progress in production of building-block organic acids by consolidated bioprocessing of lignocellulose. Fermentation. 2021;7:248. Soares-Silva I, Ribas D, Sousa-Silva M, Azevedo-Silva J, Rendulić T, Casal M. Membrane transporters in the bioproduction of organic acids: State of the art and future perspectives for industrial applications. FEMS Microbiol Lett. 2020;367:fnaa118. Wu T, Li J, Tian C. Fungal carboxylate transporters: Recent manipulations and applications. Appl Microbiol Biotechnol. 2023;107:5909–22. Makuc J, Paiva S, Schauen M, Krämer R, André B, Casal M, Leão C, Boles E. The putative monocarboxylate permeases of the yeast Saccharomyces cerevisiae do not transport monocarboxylic acids across the plasma membrane. Yeast. 2001;18:1131–43. Casal M, Paiva S, Andrade RP, Gancedo C. The lactate-proton symport of Saccharomyces cerevisiae is encoded by JEN1. J Bacteriol. 1999;181:2620-3. Sugiyama M, Akase S pei, Nakanishi R, Kaneko Y, Harashima S. Overexpression of ESBP6 improves lactic acid resistance and production in Saccharomyces cerevisiae. J Biosci Bioeng. 2016;122:415–20. Qin N, Li L, Wan X, Ji X, Chen Y, Li C, Liu P, Zhang Y, Yang W, Jiang J, Xia J, Shi S, Tan T, Nielsen J, Chen Y, Liu Z. Increased CO 2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast. Nat Commun. 2024;15:1591. Porro D, Bianchi M, Ranzi B, Frontali L, Vai M, Winkler A, Alberghina L. Yeast strains for the production of lactic acid. Patent WO1999014335A1; 1999. Branduardi P, Sauer M, De Gioia L, Zampella G, Valli M, Mattanovich D, Porro D. Lactate production yield from engineered yeasts is dependent from the host background, the lactate dehydrogenase source and the lactate export. Microb Cell Fact. 2006;5:4. Pacheco A, Talaia G, Sá-Pessoa J, Bessa D, Gonçalves MJ, Moreira R, Paiva S, Casal M, Queirós O. Lactic acid production in Saccharomyces cerevisiae is modulated by expression of the monocarboxylate transporters Jen1 and Ady2. FEMS Yeast Res. 2012;12:375–81. Zhu P, Luo R, Li Y, Chen X. Metabolic engineering and adaptive evolution for efficient production of L-lactic acid in Saccharomyces cerevisiae. Microbiol Spectr. 2022;10:e0227722. Lima PBA, Mulder KCL, Melo NTM, Carvalho LS, Menino GS, Mulinari E, de Castro VH, Dos Reis TF, Goldman GH, Magalhães BS, Parachin NS. Novel homologous lactate transporter improves L-lactic acid production from glycerol in recombinant strains of Pichia pastoris. Microb Cell Fact. 2016;15:158. Gassler T, Heistinger L, Mattanovich D, Gasser B, Prielhofer R. CRISPR/Cas9-mediated homology-directed genome editing in Pichia pastoris. Methods Mol Biol. 2019;1923:211–25. Labun K, Montague TG, Gagnon JA, Thyme SB, Valen E. CHOPCHOP v2: A web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 2016;44:W272–6. Sears IB, O’connor J, Rossanese OW, Glick BS. A versatile set of vectors for constitutive and regulated gene expression in Pichia pastoris. Yeast. 1998;14:783–90. Prielhofer R, Barrero JJ, Steuer S, Gassler T, Zahrl R, Baumann K, Sauer M, Mattanovich D, Gasser B, Marx H. GoldenPiCS: A Golden Gate-derived modular cloning system for applied synthetic biology in the yeast Pichia pastoris. BMC Syst Biol. 2017;11:123. Jensen NB, Strucko T, Kildegaard KR, David F, Maury J, Mortensen UH, Forster J, Nielsen J, Borodina I. EasyClone: Method for iterative chromosomal integration of multiple genes in Saccharomyces cerevisiae. FEMS Yeast Res. 2014;14:238–48. Maurer M, Kühleitner M, Gasser B, Mattanovich D. Versatile modeling and optimization of fed batch processes for the production of secreted heterologous proteins with Pichia pastoris. Microb Cell Fact. 2006;5:37. Tomàs-Gamisans M, Ferrer P, Albiol J. Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources. Microb Biotechnol. 2018;11:224–37. Noorman HJ, Rornein B, Ch M Luyben KA, Heijnen JJ. Classification, error detection, and reconciliation of process information in complex biochemical systems. Biotechnol Bioeng. 1996;49:364–76. van der Heijden RT, Heijnen JJ, Hellinga C, Romein B, Luyben KC. Linear constraint relations in biochemical reaction systems: I. Classification of the calculability and the balanceability of conversion rates. Biotechnol Bioeng. 1994;43:3–10. Ponte X, Montesinos-Seguí JL, Valero F. Bioprocess efficiency in Rhizopus oryzae lipase production by Pichia pastoris under the control of PAOX1 is oxygen tension dependent. Process Biochem. 2016;51:1954–63. Becker SA, Feist AM, Mo ML, Hannum G, Palsson B, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox. Nat Protoc. 2007;2:727–38. Miao L, Li Y, Zhu T. Metabolic engineering of methylotrophic Pichia pastoris for the production of β-alanine. Bioresour Bioprocess. 2021;8:89. Tong T, Tao Z, Chen X, Gao C, Liu H, Wang X, Liu GQ, Liu L. A biosynthesis pathway for 3-hydroxypropionic acid production in genetically engineered Saccharomyces cerevisiae. Green Chem. 2021;23:4502–9. Malubhoy Z, Bahia FM, de Valk SC, de Hulster E, Rendulić T, Ortiz JPR, Xiberras J, Klein M, Mans R, Nevoigt E. Carbon dioxide fixation via production of succinic acid from glycerol in engineered Saccharomyces cerevisiae. Microb Cell Fact. 2022;21:102. Chemarin F, Athès V, Bedu M, Loty T, Allais F, Trelea IC, Moussa M. Towards an in situ product recovery of bio-based 3-hydroxypropionic acid: Influence of bioconversion broth components on membrane-assisted reactive extraction. J Chem Technol Biotechnol. 2019;94:964–72. Fina A, Millard P, Albiol J, Ferrer P, Heux S. High throughput 13 C-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux. Microb Cell Fact. 2023;22:117. Peetermans A, Foulquié-Moreno MR, Thevelein JM. Mechanisms underlying lactic acid tolerance and its influence on lactic acid production in Saccharomyces cerevisiae. Microb Cell. 2021;8:111–30. Piper P, Calderon CO, Hatzixanthis K, Mollapour M. Weak acid adaptation: The stress response that confers yeasts with resistance to organic acid food preservatives. Microbiology (Reading). 2001;147:2635-42. Hakkaart X, Liu Y, Hulst M, el Masoudi A, Peuscher E, Pronk J, et al. Physiological responses of Saccharomyces cerevisiae to industrially relevant conditions: Slow growth, low pH, and high CO 2 levels. Biotechnol Bioeng. 2020;117:721–35. Jordà J, Suarez C, Carnicer M, ten Pierick A, Heijnen JJ, van Gulik W, Ferrer P, Albiol J, Wahl A. Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary 13 C flux analysis. BMC Syst Biol. 2013;7:17. Jordà J, De Jesus SS, Peltier S, Ferrer P, Albiol J. Metabolic flux analysis of recombinant Pichia pastoris growing on different glycerol/methanol mixtures by iterative fitting of NMR-derived 13 C-labelling data from proteinogenic amino acids. N Biotechnol. 2014;31:120–32. Yurimoto H, Kato N, Sakai Y. Assimilation, dissimilation, and detoxification of formaldehyde, a central metabolic intermediate of methylotrophic metabolism. Chem Rec. 2005;5:367–75. Sakai Y, Murdanoto AP, Konishi T, Iwamatsu A, Kato N. Regulation of the formate dehydrogenase gene, FDH1, in the methylotrophic yeast Candida boidinii and growth characteristics of an FDH1-disrupted strain on methanol, methylamine, and choline. J Bacteriol. 1997;179:4480-5. Yu YF, Yang J, Zhao F, Lin Y, Han S. Comparative transcriptome and metabolome analyses reveal the methanol dissimilation pathway of Pichia pastoris. BMC Genomics. 2022;23:366. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file 1: Molecular cloning materials and methods. The file contains Table S1: List of primers used in this study. (DOCX 49KB) Additionalfile2.xlsx Additional file 2: Raw data of the online monitored standard process parameters. The file also contains two charts representing the evolution of key process parameters (DO, pH, T and outlet gas CO 2 ) throughout fed-batch cultivations of strain PpCβ21-PEJY at pH 5 and 3.5. (XLS 8.3MB) Additionalfile3.xlsx Additional file 3: Raw and processed data obtained from bioreactor-scale experiments. The file includes raw HPLC and biomass data, quantification of metabolites and biomass concentrations, averaged values for key physiological and production parameters from fed-batch cultures, and statistical analyses. (XLS 238KB) Additionalfile4.xlsx Additional file 4: Raw data from the 24-deep well plate cultivations. The file includes raw HPLC and OD 600 data, quantification of metabolites, product yields determination, and statistical analyses. (XLS 91KB) Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Journal of Biological Engineering → Version 1 posted Editorial decision: Revision requested 15 Dec, 2024 Reviews received at journal 14 Dec, 2024 Reviews received at journal 12 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 04 Dec, 2024 Reviewers agreed at journal 04 Dec, 2024 Reviewers agreed at journal 04 Dec, 2024 Reviews received at journal 04 Dec, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviewers invited by journal 19 Nov, 2024 Editor assigned by journal 05 Nov, 2024 Submission checks completed at journal 05 Nov, 2024 First submitted to journal 04 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5386323\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":374484360,\"identity\":\"f63c5971-5703-449f-b054-011cb9f72539\",\"order_by\":0,\"name\":\"Sílvia Àvila-Cabré\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Autonomous University of Barcelona\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sílvia\",\"middleName\":\"\",\"lastName\":\"Àvila-Cabré\",\"suffix\":\"\"},{\"id\":374484362,\"identity\":\"cd0253d4-8304-43fc-99d3-470340740d08\",\"order_by\":1,\"name\":\"Joan Albiol\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Autonomous University of Barcelona\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Joan\",\"middleName\":\"\",\"lastName\":\"Albiol\",\"suffix\":\"\"},{\"id\":374484364,\"identity\":\"11b7ae1c-3b09-42e6-8fd4-ff47a12074c8\",\"order_by\":2,\"name\":\"Pau Ferrer\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYLCCCjDJfICBsYFYLWfAJFsCRAsb8Vp4DIjTYs5+xoDhQMW9fH7pM98kfu5gkOefT8B1lj05QC1nii1n9uVuk+w9w2A44xgBWwwO5Bgwf2xLMDA4w7tNgreNIYGBoJbzbwwYDv5LMLA/w/NM8i9QizxBLTeADjvYALSFh4dNGmSLASEtljOeFRw4cCzBQOIMm7G17BkJw43HEvBrMedP3vjgQE2CAX8P88Obb3fYyMsdPkDAYUCMrESCgKugWkbBKBgFo2AU4AcAvIRBdIV1htUAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Autonomous University of Barcelona\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Pau\",\"middleName\":\"\",\"lastName\":\"Ferrer\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-11-04 08:39:06\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5386323/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5386323/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s13036-025-00488-x\",\"type\":\"published\",\"date\":\"2025-02-20T15:57:40+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":69262342,\"identity\":\"a2f3e72e-1df5-45a8-89a4-cc64f1d98984\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 13:55:01\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":98044,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic representation of methanol metabolism, 3-HP production via the β-alanine pathway, and carboxylic acids transport mechanisms reported in yeast. The pathway of 3-HP synthesis is shaded in green; and the methanol dissimilatory pathway is shaded in red. Heterologous enzymes are indicated with colored gears. While uncharged 3-HP (HOCH\\u003csub\\u003e2\\u003c/sub\\u003eCH\\u003csub\\u003e2\\u003c/sub\\u003eCOOH) can passively diffuse across the plasma membrane, its anionic form (HOCH\\u003csub\\u003e2\\u003c/sub\\u003eCH\\u003csub\\u003e2\\u003c/sub\\u003eCOO\\u003csup\\u003e-\\u003c/sup\\u003e) is hypothesized to require active transport via ABC transporters or permeases (i.e., Jen1 symporter). Dissociation of 3-HP in the cytosol releases protons (H\\u003csup\\u003e+\\u003c/sup\\u003e), which are mainly extruded by H\\u003csup\\u003e+\\u003c/sup\\u003e-ATPases, maintaining pH balance and electrochemical potential across the membrane (Z∆pH) . \\u003cem\\u003eFDH1\\u003c/em\\u003e, NAD-dependent formate dehydrogenase; PseFDH(V9), mutated NADP-dependent formate dehydrogenase; \\u003cem\\u003ePYC2\\u003c/em\\u003e, pyruvate carboxylase isoform 2; \\u003cem\\u003eAAT2\\u003c/em\\u003e, aspartate aminotransferase 2; SpeAspDH, NAD- or NADP-dependent aspartate dehydrogenase; PAND, aspartate-1-decarboxylase; BAPAT, β-alanine-pyruvate aminotransferase; YDFG, 3-hydroxypropionate dehydrogenase.\\u003c/p\\u003e\\n\\u003cp\\u003e[69]\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"OnlineFig.1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/6b4bc78750cb17472a8465ef.png\"},{\"id\":69262627,\"identity\":\"48deaabf-06f2-4822-aaf0-0f6fb51e6129\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 14:03:01\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":13519,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAverage global product yields calculated for the strains constructed in this study, Y\\u003csub\\u003eP/S\\u003c/sub\\u003e and Y\\u003csub\\u003eP/X\\u003c/sub\\u003e. The salmon-colored bars show the average 3-HP yield on methanol (g\\u003csub\\u003e3-HP\\u003c/sub\\u003e g\\u003csub\\u003eMeOH\\u003c/sub\\u003e\\u003csup\\u003e-1\\u003c/sup\\u003e), while light blue was used for bars representing the average 3-HP yield on biomass (g\\u003csub\\u003e3-HP\\u003c/sub\\u003e OD\\u003csub\\u003e600\\u003c/sub\\u003e unit\\u003csup\\u003e-1\\u003c/sup\\u003e). The error bars show the standard deviation. Significant differences between two groups are shown in the graph. Statistical analysis was conducted using a two-tailed unpaired Student’s \\u003cem\\u003et\\u003c/em\\u003e-test (*\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05, **\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01, ***\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.001).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"OnlineFig.2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/56508f92140cd5435860d04d.png\"},{\"id\":69262341,\"identity\":\"91707866-7f84-44c6-be35-400f428d8cf9\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 13:55:01\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":16920,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFed-batch phase profiles from the bioreactor-scale experiments with PpCβ21-P and PpCβ21-PEJY strains at pH 5. Concentration of dry cell weight and 3-HP are represented in the left-side y-axis. The total amount of methanol added, normalized by the actual volume of the reactor at every time, is represented using the right-side y-axis. The cultivation profile shown for each strain corresponds to the average result of two independent cultivations. The error bars denote the standard deviation for the duplicate.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"OnlineFig.3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/7a0ae03d83279256c4829bcd.png\"},{\"id\":69262343,\"identity\":\"ffda138a-5574-43b3-aa42-fd87a6fb8788\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 13:55:01\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":18836,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFed-batch phase profiles from the bioreactor-scale experiments with PpCβ21-P and PpCβ21-PEJY strains at pH 3.5. Concentration of dry cell weight, 3-HP, and residual methanol are represented in the left-side y-axis. The total amount of methanol added is represented using the right-side y-axis. Data represent a single cultivation replicate for each strain. The error bars denote the standard deviation from triplicate measurements of each sample.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"OnlineFig.4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/8c05dbd5153922e7b6d332c4.png\"},{\"id\":77058234,\"identity\":\"5f4478ed-e495-49f7-a8de-1228b72672ac\",\"added_by\":\"auto\",\"created_at\":\"2025-02-24 16:55:28\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1689234,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/9196473d-86b8-47dc-b7c6-329b7c464668.pdf\"},{\"id\":69262626,\"identity\":\"04bac573-0742-433b-afe4-7815e4507ccd\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 14:03:01\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":49532,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdditional file 1:\\u003c/strong\\u003e Molecular cloning materials and methods. The file contains \\u003cstrong\\u003eTable S1\\u003c/strong\\u003e: List of primers used in this study. (DOCX 49KB)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Additionalfile1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/05cc93743f6b06f9d7cdcbce.docx\"},{\"id\":69262347,\"identity\":\"4d776e0f-f74e-4d1e-b196-eb24b9695133\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 13:55:01\",\"extension\":\"xlsx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":8482844,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdditional file 2: \\u003c/strong\\u003eRaw data of the online monitored standard process parameters. The file also contains two charts representing the evolution of key process parameters (DO, pH, T and outlet gas CO\\u003csub\\u003e2\\u003c/sub\\u003e) throughout fed-batch cultivations of strain PpCβ21-PEJY at pH 5 and 3.5. (XLS 8.3MB)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Additionalfile2.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/aab234862d788a3fa7723072.xlsx\"},{\"id\":69262348,\"identity\":\"c16491f3-fca3-43ad-a2e7-cd112ecea6e6\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 13:55:02\",\"extension\":\"xlsx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":243432,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdditional file 3: \\u003c/strong\\u003eRaw and processed data obtained from bioreactor-scale experiments. The file includes raw HPLC and biomass data, quantification of metabolites and biomass concentrations, averaged values for key physiological and production parameters from fed-batch cultures, and statistical analyses. (XLS 238KB)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Additionalfile3.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/52925a0a889cabfa41e997f6.xlsx\"},{\"id\":69262346,\"identity\":\"a6c312ef-e60c-4abf-a738-8fb52238c5c1\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 13:55:01\",\"extension\":\"xlsx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":92219,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdditional file 4: \\u003c/strong\\u003eRaw data from the 24-deep well plate cultivations. The file includes raw HPLC and OD\\u003csub\\u003e600\\u003c/sub\\u003e data, quantification of metabolites, product yields determination, and statistical analyses. (XLS 91KB)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Additionalfile4.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5386323/v1/b4a8c415c0d787d867addfa7.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Leveraging lactate transporters for superior 3-hydroxypropionic (3-HP) acid production from methanol in Komagataella phaffii\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eThe valorization of industrial CO\\u003csub\\u003e2\\u003c/sub\\u003e effluents has the potential to play a crucial role in addressing climate crisis and advancing towards a more sustainable production cycle. CO\\u003csub\\u003e2\\u003c/sub\\u003e can be reduced with hydrogen to produce methanol, a sustainable carbon and energy source [1,2]. Bioconversion of this low-cost one-carbon (C1) feedstock into valuable chemicals not only has the potential to reduce reliance on fossil fuels but also to help mitigating climate change, representing a promising approach within the framework of a circular economy [3\\u0026ndash;5].\\u003c/p\\u003e \\u003cp\\u003e3-Hydroxypropionic (3-HP) acid is a key building block that can be used as a precursor for the production of several valuable chemicals, including acrylates, 1,3-propanediol, propiolactone, malonic acid, and hydroxyamides. Moreover, as a homopolymer or when integrated into other biopolymers, 3-HP has the potential to replace petrochemistry-based polymers, offering a sustainable alternative for the synthesis of advanced materials [6,7]. In 2004, the US Department of Energy (DOE) ranked this C3 organic acid in a significant third position among the top twelve biobased value-added chemicals [8]. An updated version of this ranking was published six years later, where 3-HP was included again [9]. Despite its potential, chemical synthesis of 3-HP faces several challenges, including the high cost of raw materials, low yields, complex reaction steps, and the involvement of toxic, noxious, or carcinogenic compounds [10]. Several microorganisms naturally synthesize 3-HP, either as an intermediate or as an end product, through different metabolic pathways and diverse substrates such as glycerol, glucose, CO\\u003csub\\u003e2\\u003c/sub\\u003e or uracil. The most studied pathways include the coenzyme B12-dependent glycerol pathways (both CoA-dependent and CoA-independent) and the malonyl-CoA reductase pathway, which is part of the 3-hydroxypropionate/malyl-CoA and 3-hydroxypropionate/4-hydroxybutyrate autotrophic cycles [11,12]. However, significant byproduct formation and suboptimal yields and productivities still hinder the commercial viability of biological 3-HP production. To overcome these issues, metabolic engineering of both natural and non-natural producers has been investigated.\\u003c/p\\u003e \\u003cp\\u003eAmong bacterial hosts, \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e and \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e have been the most widely used for 3-HP production via the glycerol pathway [13]. These bacteria have produced by far the highest reported 3-HP titers (76.2 and 102.6 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively) and productivities (1.89 and 1.07 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively) [14,15]. Several yeast species have also been engineered to produce 3-HP, primarily through the introduction of the malonyl-CoA reductase pathway and the synthetic β-alanine pathway [16]. The bifunctional malonyl-CoA reductase from \\u003cem\\u003eChloroflexus aurantiacus\\u003c/em\\u003e (MCR\\u003csub\\u003eCa\\u003c/sub\\u003e) has been successfully expressed in \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e [17,18], \\u003cem\\u003eSchizosaccharomyces pombe\\u003c/em\\u003e [19,20], and \\u003cem\\u003eKomagataella phaffii\\u003c/em\\u003e (syn. \\u003cem\\u003ePichia pastoris\\u003c/em\\u003e), achieving the highest volumetric productivity reported so far in yeast (0.712 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) [21]. Additionally, the synthetic β-alanine pathway has been engineered into \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e [22,23], and subsequent bioprocess optimization enabled a final concentration of 27 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, with a product yield on glucose of 0.26 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e [24]. Only fungal hosts are able to withstand acidity as low as pH 3 [25,26], which is more than 1 unit below the pKa of 3-HP (4.51) [27]. In these circumstances, the undissociated form of 3-HP is the most abundant fraction in the solution, lowering costs of downstream processing [28]. Therefore, yeasts stand as promising cell factories for 3-HP production.\\u003c/p\\u003e \\u003cp\\u003eOf particular interest is the methylotrophic yeast \\u003cem\\u003eK. phaffii\\u003c/em\\u003e, which shows a distinct ability to grow on methanol as its sole carbon and energy source, making it an attractive cell factory for the production of high-value-added chemicals from this low-cost and renewable substrate [29]. Moreover, methanol is a highly reduced carbon source (γ\\u0026thinsp;=\\u0026thinsp;6.0), whose oxidation provides more redox equivalents than most sugars, potentially becoming a competitive substrate for the synthesis of highly reduced compounds such as terpenoids [30,31] and fatty acids and their derivatives [32,33]. In addition, recent studies have demonstrated \\u003cem\\u003eK. phaffii\\u003c/em\\u003e\\u0026rsquo;s ability to produce different organic acids from methanol as sole carbon source, including malic acid [34], D-lactic acid [35], itaconic acid [36], and 3-HP [37]. Recently, we have successfully engineered \\u003cem\\u003eK. phaffii\\u003c/em\\u003e for 3-HP production from methanol by implementing the β-alanine pathway [38,39]. Specifically, we expressed the genes coding for a β-alanine-pyruvate aminotransferase from \\u003cem\\u003eBacillus cereus\\u003c/em\\u003e (BAPAT\\u003csub\\u003eBc\\u003c/sub\\u003e), a 3-hydroxypropionate dehydrogenase from \\u003cem\\u003eE. coli\\u003c/em\\u003e (YDFG\\u003csub\\u003eEc\\u003c/sub\\u003e), and two copies of an aspartate-1-decarboxylase from \\u003cem\\u003eTribolium castaneum\\u003c/em\\u003e (PAND\\u003csub\\u003eTc\\u003c/sub\\u003e), obtaining a 3-HP-producing base strain (PpCβ21) that produced up to 21.4 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of 3-HP in methanol fed-batch cultures, with a yield of 0.15 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and a volumetric productivity of 0.48 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. Additional redox engineering of this strain to increase NADPH availability further improved both methanol consumption (q\\u003csub\\u003eS\\u003c/sub\\u003e) and specific productivity (q\\u003csub\\u003eP\\u003c/sub\\u003e) rates in strain PpCβ21-P. Despite our demonstration of the potential of \\u003cem\\u003eK. phaffii\\u003c/em\\u003e for 3-HP production from renewable C1 feedstocks, the obtained yields and productivities fell short of the commercially feasible metrics. Typically, minimum values of 0.5 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 2.5 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively, are required to develop an economically viable bioprocess for carboxylic acid production [8,40].\\u003c/p\\u003e \\u003cp\\u003eBesides increasing NADPH availability, other strategies have been investigated to improve 3-HP yield in engineered yeast harboring the b-alanine pathway. In particular, Borodina et al. [23] demonstrated that improvement of the metabolic precursors supply of the b-alanine pathway in \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e had a significant positive impact on 3-HP production. This was achieved by overexpressing the \\u003cem\\u003eAAT2\\u003c/em\\u003e, \\u003cem\\u003ePYC1\\u003c/em\\u003e and \\u003cem\\u003ePYC2\\u003c/em\\u003e genes, encoding for the enzymes catalyzing the conversion of pyruvate into aspartate, the precursor of this synthetic pathway.\\u003c/p\\u003e \\u003cp\\u003eAnother engineering strategy commonly used in yeast/fungi to enhance organic acid production is the overexpression of genes encoding for membrane transporters to facilitate final product export [41,42]. To date, no 3-HP-specific transporters have been identified in yeasts. However, given that lactate and 3-HP are structural isomers, some studies have focused on existing lactate transporters as potential candidates for 3-HP export. In \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e, two monocarboxylate permeases have been identified, Esbp6 [43] and Jen1 [44]. Esbp6, also known as Mch3, may play a role in acid-stress adaptation responses, as well as in functions related to cell wall integrity maintenance. Overexpression of \\u003cem\\u003eESBP6\\u003c/em\\u003e in the plasma membrane of an engineered \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e strain improved lactic acid production by 20% under non-neutralizing conditions, probably due to enhanced lactic acid tolerance [45]. Similarly, Qin et al. [46] demonstrated that deleting \\u003cem\\u003eESBP6\\u003c/em\\u003e in a wild-type strain cultivated in the presence of 50 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e 3-HP at pH 3.5 inhibited growth considerably, whereas \\u003cem\\u003eESBP6\\u003c/em\\u003e overexpression significantly increased cell tolerance to 3-HP compared to the wild-type strain. The lactate-proton symporter Jen1 can mediate the electroneutral transport of lactic acid when it accumulates inside the cell [28]. In fact, overexpression of Jen1 has been shown to improve lactic acid production in recombinant \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e strains [47\\u0026ndash;50]. This effect has also been observed in the non-conventional yeast \\u003cem\\u003eK. phaffii\\u003c/em\\u003e by Lima et al. [51], who identified a \\u003cem\\u003eJEN1\\u003c/em\\u003e ortholog in the \\u003cem\\u003eK. phaffii\\u003c/em\\u003e genome, showing 50.19% identity to the \\u003cem\\u003eJEN1\\u003c/em\\u003e gene in \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e. Overexpression of the gene encoding for this putative lactate transporter in a recombinant \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain producing L-lactic acid from glycerol resulted in a 46% increase in lactate yield compared to the control strain.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we further optimized our previously developed 3-HP-producing strains [38,39] by implementing a range of metabolic engineering strategies. First, efforts were focused on a \\u0026ldquo;push\\u0026rdquo; strategy aiming at increasing the supply of β-alanine pathway direct precursors, i.e. oxaloacetate and aspartate, and to further channel flux through the heterologous NADP-dependent formate dehydrogenase by deleting the \\u003cem\\u003eFDH1\\u003c/em\\u003e gene coding for the endogenous NAD-dependent analogous enzyme. Second, using a \\u0026ldquo;pull\\u0026rdquo; strategy, we sought to promote 3-HP export by overexpressing the endogenous \\u003cem\\u003eJEN1\\u003c/em\\u003e gene and the \\u003cem\\u003eESBP6\\u003c/em\\u003e gene from \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e. Notably, the latter strategy resulted in a substantial increase in 3-HP productivities. Both strategies were tested individually and in a combined way. Moreover, we characterized the resulting strains at pH 3.5, an industrially relevant condition for carboxylic acid production.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePlasmid and strain construction\\u003c/h2\\u003e \\u003cp\\u003eThe parental strains \\u003cem\\u003eK. phaffii\\u003c/em\\u003e PpCβ21 and PpCβ21-P [38,39] were used as platform chassis to construct a new generation of strains with optimized metabolic pathway towards 3-HP production and exportation. Homology-directed integrations of heterologous genes and knockout of the endogenous \\u003cem\\u003eFDH1\\u003c/em\\u003e coding sequence were performed using CRISPR/Cas9 technology. Plasmids and strains constructed in this study are listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Further description of the molecular biology workflow followed in this study can be found in Additional file 1.\\u003c/p\\u003e \\u003cp\\u003eFollowing the CRISPR/Cas9-mediated genome editing protocol in \\u003cem\\u003eK. phaffii\\u003c/em\\u003e, two sgRNAs were individually designed and generated for each locus to target (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) and inserted into BB3cN_p\\u003cem\\u003eGAP\\u003c/em\\u003e_23*_pLAT1_Cas9 plasmid from the CRIS\\u003cem\\u003ePi\\u003c/em\\u003e kit [52]. The potential sgRNA candidates binding target regions within the selected loci were assessed with CHOPCHOP [53], a widely used web tool for CRISPR-based genome editing.\\u003c/p\\u003e \\u003cp\\u003eElectrocompetent \\u003cem\\u003eK. phaffii\\u003c/em\\u003e cells were prepared as described elsewhere [54]. Co-transformation of the donor DNA template and the BB3 plasmid harboring the sgRNA and a human codon optimized Cas9 for episomal expression in \\u003cem\\u003eK. phaffii\\u003c/em\\u003e was performed as described in Gassler et al. [52]. The \\u003cem\\u003eAAT2\\u003c/em\\u003e and AspDH\\u003csub\\u003eSpe\\u003c/sub\\u003e expression cassettes were individually targeted into an intergenic region located 250-bp upstream of P\\u003csub\\u003e\\u003cem\\u003eENO1\\u003c/em\\u003e\\u003c/sub\\u003e (PP7435_Chr3-1154). \\u003cem\\u003ePYC2\\u003c/em\\u003e expression unit was located 600-bp downstream of \\u003cem\\u003eAOX1tt\\u003c/em\\u003e, disrupting the coding sequence of a putative protein of unknown function (PP7435_Chr4-0131). \\u003cem\\u003eJEN1\\u003c/em\\u003e and \\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e heterologous cassettes were targeted right after \\u003cem\\u003eGUT1tt\\u003c/em\\u003e, disrupting the sequence coding a hypothetical protein (PP7435_Chr4-0173). However, no positive transformants were found after several attempts. Homology regions (HR) for \\u003cem\\u003eJEN1\\u003c/em\\u003e and \\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e expression cassettes were redesigned to individually target the \\u003cem\\u003eENO1\\u003c/em\\u003e genomic locus, showing high editing efficiency. When co-expressing both monocarboxylate permeases, \\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eSc\\u003c/em\\u003e\\u003c/sub\\u003e was targeted upstream of P\\u003csub\\u003e\\u003cem\\u003eENO1\\u003c/em\\u003e\\u003c/sub\\u003e, while \\u003cem\\u003eJEN1\\u003c/em\\u003e was located 1000-bp upstream of P\\u003csub\\u003e\\u003cem\\u003eOPT1\\u0026minus;1\\u003c/em\\u003e\\u003c/sub\\u003e (PP7435_Chr4-1011). Finally, the FDH(V9)\\u003csub\\u003ePse\\u003c/sub\\u003e expression cassette was targeted within 50-bp upstream of P\\u003csub\\u003e\\u003cem\\u003eAOX1\\u003c/em\\u003e\\u003c/sub\\u003e (PP7435_Chr4-0130) [38,39] (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Correct genomic insertions from three individual transformants from each strain were checked at both 5\\u0026rsquo; and 3\\u0026rsquo; ends of the integrated donor DNA by Sanger sequencing.\\u003c/p\\u003e \\u003cp\\u003eThe \\u003cem\\u003eK. phaffii FDH1\\u003c/em\\u003e gene (PP7435_Chr3-0238) was disrupted using insertions or deletions (InDel) mutations by expressing a BB3 vector containing both a sgRNA and Cas9. The resulting double-strand break (DSB) in the genomic DNA occurred 25\\u0026ndash;32 base pairs from the \\u003cem\\u003eFDH1\\u003c/em\\u003e start, depending on the specific sgRNA used (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The disrupted locus was verified by Sanger sequencing. The sequencing was performed by the Genomics and Bioinformatics Service of the Universitat Aut\\u0026ograve;noma de Barcelona.\\u003c/p\\u003e \\u003cp\\u003eRecombinant yeast strains were grown at 30\\u0026deg;C in YPD agar plates (1% yeast extract, 2% peptone, 2% dextrose and 15 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e agar). The medium was supplemented with Nourseothricin (200 \\u0026micro;g ml\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e working concentration for \\u003cem\\u003eK. phaffii\\u003c/em\\u003e) from Dismed S.A. (Asturias, Spain).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eList of plasmids and strains used in this study.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlasmids/Strains\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModules/Genotype\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlasmids\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_12_pDAS1\\u003c/p\\u003e \\u003cp\\u003eBB1_12_pDAS2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e, KanR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e, KanR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e[55]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_12_SHB17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eSHB17\\u003c/em\\u003e\\u003c/sub\\u003e, KanR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026Oslash;, KanR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_34_RPS3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003csub\\u003eRPS3\\u003c/sub\\u003ett, KanR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_34_ScCYC1tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003csub\\u003eScCYC1\\u003c/sub\\u003ett, KanR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB2_BC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026Oslash;, AmpR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_23_AAT2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAAT2\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e+\\u003c/em\\u003e\\u003c/sup\\u003e, \\u003cem\\u003eK. phaffii\\u003c/em\\u003e gene encoding for the cytosolic aspartate aminotransferase Aat2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eThis work\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_23_SpeAspDH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003easpDH\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e+\\u003c/em\\u003e\\u003c/sup\\u003e, \\u003cem\\u003eS. proteamaculans\\u003c/em\\u003e codon-optimized gene encoding for the aspartate dehydrogenase AspDH\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_23_PYC2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ePYC2\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e+\\u003c/em\\u003e\\u003c/sup\\u003e, \\u003cem\\u003eK. phaffii\\u003c/em\\u003e codon-optimized gene encoding for the pyruvate carboxylase Pyc2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_23_JEN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJEN1\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e+\\u003c/em\\u003e\\u003c/sup\\u003e, \\u003cem\\u003eK. phaffii\\u003c/em\\u003e gene encoding for the putative lactate transporter Jen1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_23_ScESBP6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e, \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e gene encoding for the monocarboxylate permease Esbp6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB1_23_PseFDH(V9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003efdh\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e+\\u003c/em\\u003e\\u003c/sup\\u003e, \\u003cem\\u003ePseudomonas\\u003c/em\\u003e sp. (strain 101) gene encoding for the FDH\\u003csub\\u003ePse\\u003c/sub\\u003e(V9)\\u003csub\\u003e(A199G/D222Q/S381V/C256A/H380K)\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e[38,39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB2_BC_AAT2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eAAT2\\u003c/em\\u003e-RPS3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eThis work\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB2_BC_SpeAspDH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-AspDH\\u003csub\\u003eSpe\\u003c/sub\\u003e-RPS3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB2_BC_PYC2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eSHB17\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003ePYC2\\u003c/em\\u003e-RPS3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB2_BC_JEN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eJEN1\\u003c/em\\u003e-ScCYC1tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB2_BC_ScESBP6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e-ScCYC1tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB2_BC_PseFDH(V9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003c/sub\\u003e-FDH(V9)\\u003csub\\u003ePse\\u003c/sub\\u003e-TDH3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e[38,39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3nK_ext_AD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026Oslash;, KanR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e[52]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3nK_ext_AD_AAT2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eAAT2\\u003c/em\\u003e-RPS3tt_3\\u0026rsquo;-HR(p\\u003cem\\u003eENO1\\u003c/em\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eThis work\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3nK_ext_AD_SpeAspDH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-AspDH\\u003csub\\u003eSpe\\u003c/sub\\u003e-RPS3tt_3\\u0026rsquo;-HR(p\\u003cem\\u003eENO1\\u003c/em\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3nK_ext_AD_PYC2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eSHB17\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003ePYC2\\u003c/em\\u003e-RPS3tt_3\\u0026rsquo;-HR(\\u003cem\\u003eAOX1\\u003c/em\\u003ett\\u003csub\\u003eDOWN\\u003c/sub\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3nK_ext_AD_JEN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eJEN1\\u003c/em\\u003e-ScCYC1tt_3\\u0026rsquo;-HR(\\u003cem\\u003eGUT1\\u003c/em\\u003ett\\u003csub\\u003eDOWN\\u003c/sub\\u003e)\\u003c/p\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eJEN1\\u003c/em\\u003e-ScCYC1tt_3\\u0026rsquo;-HR(p\\u003cem\\u003eOPT1-1\\u003c/em\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3nK_ext_AD_ScESBP6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e-ScCYC1tt_3\\u0026rsquo;-HR(\\u003cem\\u003eGUT1\\u003c/em\\u003ett\\u003csub\\u003eDOWN\\u003c/sub\\u003e)\\u003c/p\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e-ScCYC1tt_3\\u0026rsquo;-HR(p\\u003cem\\u003eENO1\\u003c/em\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3nK_ext_AD_PseFDH(V9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u0026rsquo;-HR_P\\u003csub\\u003e\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003c/sub\\u003e-FDH(V9)\\u003csub\\u003ePse\\u003c/sub\\u003e-TDH3tt_3\\u0026rsquo;-HR(p\\u003cem\\u003eAOX1\\u003c/em\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e[38,39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_23*_pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_ \\u0026Oslash;_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e[52]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA1(pENO1\\u003csup\\u003eUP\\u003c/sup\\u003e) _pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_ gRNA1(P\\u003csub\\u003e\\u003cem\\u003eENO1\\u003c/em\\u003e\\u003c/sub\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"6\\\" rowspan=\\\"7\\\"\\u003e \\u003cp\\u003eThis work\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA2(pENO1\\u003csup\\u003eUP\\u003c/sup\\u003e) _pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_ gRNA2(P\\u003csub\\u003e\\u003cem\\u003eENO1\\u003c/em\\u003e\\u003c/sub\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA1(AOX1tt\\u003csub\\u003eDOWN\\u003c/sub\\u003e) _pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_ gRNA1(\\u003cem\\u003eAOX1tt\\u003c/em\\u003e\\u003csub\\u003eDOWN\\u003c/sub\\u003e)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA1(GUT1tt\\u003csub\\u003eDOWN\\u003c/sub\\u003e) _pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_ gRNA1(\\u003cem\\u003eGUT1tt\\u003c/em\\u003e\\u003csub\\u003eDOWN\\u003c/sub\\u003e)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA2(GUT1tt\\u003csub\\u003eDOWN\\u003c/sub\\u003e) _pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_ gRNA2(\\u003cem\\u003eGUT1tt\\u003c/em\\u003e\\u003csub\\u003eDOWN\\u003c/sub\\u003e)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA1(pOPT1-1\\u003csup\\u003eUP\\u003c/sup\\u003e) _pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_ gRNA1(P\\u003csub\\u003e\\u003cem\\u003eOPT1\\u0026minus;1\\u003c/em\\u003e\\u003c/sub\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA2(ΔFDH1) _pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_gRNA2(ΔFDH1)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBB3cN_pGAP_gRNA1(pAOX1\\u003csup\\u003eUP\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003cp\\u003e_pLAT1_Cas9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eGAP\\u003c/em\\u003e\\u003c/sub\\u003e_gRNA1(P\\u003csub\\u003e\\u003cem\\u003eAOX1\\u003c/em\\u003e\\u003c/sub\\u003e\\u003csup\\u003eUP\\u003c/sup\\u003e)_P\\u003csub\\u003eLAT1\\u003c/sub\\u003e_Cas9, NrsR\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e[38,39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eK. phaffii strains\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePpCβ21\\u003c/p\\u003e \\u003cp\\u003ePpCβ21-P\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2x(P\\u003csub\\u003e\\u003cem\\u003eAOX1\\u003c/em\\u003e\\u003c/sub\\u003e_PAND\\u003csub\\u003eTc\\u003c/sub\\u003e)\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003c/sub\\u003e_BAPAT\\u003csub\\u003eBc\\u003c/sub\\u003e + P\\u003csub\\u003e\\u003cem\\u003ePOR1\\u003c/em\\u003e\\u003c/sub\\u003e_YDFG\\u003csub\\u003eEc\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e[38,39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003c/sub\\u003e_FDH(V9)\\u003csub\\u003ePse\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e_\\u003cem\\u003eAAT2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"8\\\" rowspan=\\\"9\\\"\\u003e \\u003cp\\u003eThis work\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-D\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e_AspDH\\u003csub\\u003eSpe\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-Y\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eSHB17\\u003c/em\\u003e\\u003c/sub\\u003e_\\u003cem\\u003ePYC2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-YA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-Y\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e_\\u003cem\\u003eAAT2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-YAP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-YA\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003c/sub\\u003e_FDH(V9)\\u003csub\\u003ePse\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-PE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-P\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e_\\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-PJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-P\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e_\\u003cem\\u003eJEN1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-PE\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e_\\u003cem\\u003eJEN1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJ\\u0026thinsp;+\\u0026thinsp;P\\u003csub\\u003e\\u003cem\\u003eSHB17\\u003c/em\\u003e\\u003c/sub\\u003e_\\u003cem\\u003ePYC2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJΔfdh1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJ\\u0026thinsp;+\\u0026thinsp;\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJYΔfdh1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJY\\u0026thinsp;+\\u0026thinsp;\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eVariable sgRNA targets custom-designed in this study.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTarget name\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLocus\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSequence (5\\u0026rsquo; \\u0026rarr; 3\\u0026rsquo;)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModule inserted\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003epENO1\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003eUP\\u003c/b\\u003e\\u003c/sup\\u003e\\u003cb\\u003e_sgRNA1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePP7435_Chr3-1154\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTTATTGAAGATACCGCCCGG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eAAT2\\u003c/em\\u003e-RPS3tt\\u003c/p\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u0026minus;\\u003c/em\\u003e\\u003c/sub\\u003eAspDH\\u003csub\\u003eSpe\\u003c/sub\\u003e-RPS3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003epENO1\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003eUP\\u003c/b\\u003e\\u003c/sup\\u003e\\u003cb\\u003e_sgRNA2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCGTTTAAACTTACCTCCGGG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eJEN1\\u003c/em\\u003e- ScCYC1tt\\u003c/p\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e- ScCYC1tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAOX1tt\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003eDOWN\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003e_sgRNA1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePP7435_Chr4-0131\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eACTATTGATCCAAGCCAGTG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eSHB17\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003ePYC2\\u003c/em\\u003e-RPS3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAOX1tt\\u003csub\\u003eDOWN\\u003c/sub\\u003e_sgRNA2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGCTGATTTGGGGTTTAATAC\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGUT1tt\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003eDOWN\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003e_sgRNA1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePP7435_Chr4-0173\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGGTAGATGAGTTATTAACTG\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eJEN1\\u003c/em\\u003e-ScCYC1tt\\u003c/p\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eESBP6\\u003c/em\\u003e\\u003csub\\u003eSc\\u003c/sub\\u003e-ScCYC1tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGUT1tt\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003eDOWN\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003e_sgRNA2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAAACTGCGAATAAGAAAGCA\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003epOPT1-1\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003eUP\\u003c/b\\u003e\\u003c/sup\\u003e\\u003cb\\u003e_sgRNA1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePP7435_Chr4-1011\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eACTAACCAGAGCAAACACTG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eDAS2\\u003c/em\\u003e\\u003c/sub\\u003e-\\u003cem\\u003eJEN1\\u003c/em\\u003e-ScCYC1tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003epAOX1\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003eUP\\u003c/b\\u003e\\u003c/sup\\u003e\\u003cb\\u003e_sgRNA1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePP7435_Chr4-0130\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eATTGTGAAATAGACGCAGAT\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eP\\u003csub\\u003e\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003c/sub\\u003e-FDH(V9)\\u003csub\\u003ePse\\u003c/sub\\u003e-TDH3tt\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003epAOX1\\u003csup\\u003eUP\\u003c/sup\\u003e_sgRNA2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGCAGTCGATCTCAAAAGCAA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔFDH1_sgRNA1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePP7435_Chr3-0238\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eATCGGCGGCGTGCTTACCAG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e-\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eΔFDH1_sgRNA2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGTTCTCGTTTTGTACTCCGC\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe sgRNA target used for each genomic insertion is highlighted in bold.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eScreening in deep-well plates\\u003c/h3\\u003e\\n\\u003cp\\u003eThree clones of every \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain were inoculated into 50-ml falcon tubes containing 5 ml of YPG medium (1% yeast extract, 2% peptone and 1% v/v glycerol) and grown overnight at 30 \\u0026deg;C and 180 rpm in an incubator shaker Multitron Standard from Infors HT (Bottmingen, Switzerland) with a 2.5 cm orbit. Overnight cultures were inoculated in triplicate at a starting OD\\u003csub\\u003e600\\u003c/sub\\u003e of 0.1 into 24-deep well plates containing 2 ml of buffered minimal methanol medium (BMM; 100 mM potassium phosphate buffer pH 6, 1.34% yeast nitrogen base (YNB), 0.4 mg l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e biotin and 0.5% v/v pure methanol). Cultures were incubated at 25 \\u0026deg;C and 220 rpm in the same incubator shaker. The deep well plates were placed on a platform with a slope of 20\\u0026deg; to improve the aeration. Relative humidity (rh) in the incubation chamber was fixed to 80%. After 24 h, 1% v/v pure methanol pulse (7.9 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) was added to each well. Cultures were grown for 48 h to ensure the full consumption of the substrate.\\u003c/p\\u003e \\u003cp\\u003eAt the end of the culture, the endpoint OD\\u003csub\\u003e600\\u003c/sub\\u003e of each well was measured in duplicate with a 96-well microtiter plate using a Multiskan FC Microplate Photometer from Thermo Fisher Scientific (Waltham, MA, USA). Thereafter, culture samples were collected into 2-ml microcentrifuge tubes and centrifuged at 13,400 rpm for 5 min using a MiniSpin from Eppendorf (Hamburg, Germany). Supernatant was then filtered with a 0.2 \\u0026micro;m pore size single-use syringe filter (SLLGX13NK) from Merck Millipore (Burlington, MA, USA). 3-HP and methanol were quantified from the filtered supernatant by HPLC analysis.\\u003c/p\\u003e\\n\\u003ch3\\u003eBioreactor cultivations\\u003c/h3\\u003e\\n\\u003cp\\u003eFor the bioreactor cultivations, the batch medium consisted of 40 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e glycerol, 1.8 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e citric acid, 0.02 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e CaCl\\u003csub\\u003e2\\u003c/sub\\u003e\\u0026middot;2H\\u003csub\\u003e2\\u003c/sub\\u003eO, 12.6 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (NH\\u003csub\\u003e4\\u003c/sub\\u003e)\\u003csub\\u003e2\\u003c/sub\\u003eHPO\\u003csub\\u003e4\\u003c/sub\\u003e, 0.5 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e MgSO\\u003csub\\u003e4\\u003c/sub\\u003e\\u0026middot;7H\\u003csub\\u003e2\\u003c/sub\\u003eO, 0.9 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e KCl, 50 \\u0026micro;l antifoam Glanapon 2000 kz (Bussetti and Co GmbH, Vienna, Austria), 0.4 mg l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e biotin, 2 ml l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of vitamin stock solution [56], and 5 ml l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of PTM1 trace salts [57]. The pH was adjusted to 5 using 5 M HCl. The vitamins, the biotin and the trace salts were filter-sterilized and added to the bioreactors after cooling down.\\u003c/p\\u003e \\u003cp\\u003eFed-batch cultures were performed in duplicate using a DASGIP Parallel Bioreactor System from Eppendorf (Hamburg, Germany). Reactors were inoculated at a starting OD\\u003csub\\u003e600\\u003c/sub\\u003e equal to 1, and the initial volume was 500 ml. The pH was controlled at 5 throughout the culture using 15% ammonia. The temperature was set to 28\\u0026deg;C during the batch phase. The inlet gas was fed into the reactors at an aeration rate of 1 vvm (0.5 l min\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). The dissolved oxygen (DO) was set to 30%. The agitation gradually increased from 400 to 1,000 rpm to maintain DO set point. When agitation reached 1,000 rpm, pure oxygen was mixed with air in the inlet gas to maintain a pO\\u003csub\\u003e2\\u003c/sub\\u003e above 30%, while maintaining an aeration rate of 0.5 l min\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. For the fed-batch phase, the temperature was set to 25\\u0026deg;C. Pure methanol (ρ\\u0026thinsp;=\\u0026thinsp;792 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and feeding salts were added separately to the culture to avoid precipitation. The feeding salts medium composition was 0.35 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e CaCl\\u003csub\\u003e2\\u003c/sub\\u003e\\u0026middot;2H\\u003csub\\u003e2\\u003c/sub\\u003eO, 10 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e KCl, 6.45 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e MgSO\\u003csub\\u003e4\\u003c/sub\\u003e\\u0026middot;7H\\u003csub\\u003e2\\u003c/sub\\u003eO, 200 \\u0026micro;l antifoam Glanapon 2000 kz, 1.2 mg l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e biotin, 6 ml l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of vitamin stock solution, and 15 ml l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of PTM1 trace salts. This media was prepared at 2\\u0026times; concentration since pure methanol was used. The vitamins, the biotin and the trace salts were filter-sterilized and added to this final feeding solution.\\u003c/p\\u003e \\u003cp\\u003eAt the end of the glycerol batch phase, two consecutive pulses of pure methanol (1 and 2 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively) were added to the reactors. Once methanol was fully depleted, the cultures were fed with a constant methanol feed (F\\u003csub\\u003eo\\u003c/sub\\u003e) to complete the transition phase. For cultivations at pH 3.5, the pH set point was adjusted from 5 to 3.5 at the start of the second stage of the transition phase (i.e. during the constant methanol feed), allowing the pH to gradually decrease over the following hours (see Additional file 2). After that, the feeding medium was added to the bioreactors using a pre-programmed exponential feeding strategy for controlled specific growth rate described in the following equation:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:F\\\\left(t\\\\right)=\\\\:\\\\frac{{\\\\mu\\\\:}\\\\left[X\\\\left({t}_{o}\\\\right)V\\\\left({t}_{o}\\\\right)\\\\right]}{{Y}_{X/S}{S}_{o}}{e}^{\\\\left[{\\\\mu\\\\:}\\\\left(t-{t}_{o}\\\\right)\\\\right]}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere growth rate (\\u0026micro;) was set to 0.03 h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, initial biomass concentration (X\\u003csub\\u003e0\\u003c/sub\\u003e) was fixed at 22 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, biomass yield (Y\\u003csub\\u003eX/S\\u003c/sub\\u003e) was 0.273 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, and initial substrate concentration (S\\u003csub\\u003eo\\u003c/sub\\u003e) was 792 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe reactors were sampled during 39 h to measure OD\\u003csub\\u003e600\\u003c/sub\\u003e, biomass dry cell weight (DCW) and supernatant metabolites. For the biomass DCW determination, 10 ml of a 9 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e NaCl solution were used to wet the pre-weighted glass microfiber filters (APFF04700) from Merck Millipore (Burlington, MA, USA) before filtering 2 ml of culture for each triplicate. After that, the filters were washed using the same volume of the NaCl solution and dried for 24 h at 105\\u0026deg;C. Filters containing the dry biomass were weighted to calculate the DCW. To quantify the metabolites, 2 ml of culture samples were centrifuged 5 min at 13,400 rpm using a MiniSpin (Eppendorf, Germany). The supernatant was then filtered with a 0.2 \\u0026micro;m pore size single-use syringe filter (SLLGX13NK, Merck Millipore). The filtered supernatant was stored at -20\\u0026deg;C until HPLC analysis for methanol and 3-HP quantification.\\u003c/p\\u003e\\n\\u003ch3\\u003eAnalytical methods and data processing\\u003c/h3\\u003e\\n\\u003cp\\u003eMethanol and 3-HP were quantified using an HPLC Dionex Ultimate3000 from Dionex (Thermo Fischer Scientific, Waltham, MA, USA). The compounds were separated with an ionic exchange column ICSep ICE-COREGEL 87H3 from Transgenomic (Omaha, NE, USA) using 6 mM sulphuric acid as mobile phase at a flow rate of 0.6 ml min\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. Both metabolites were quantified from the Refractive Index (RI) spectrum.\\u003c/p\\u003e \\u003cp\\u003eThe OD\\u003csub\\u003e600\\u003c/sub\\u003e measurements were performed in triplicate using a Lange DR 3900 spectrophotometer from Hach (Loveland, CO, USA). Analytical replicates were averaged, and the standard deviation (SD) was calculated for each OD\\u003csub\\u003e600\\u003c/sub\\u003e determination. Biomass (dry cell weight, DCW) was determined in triplicate for three samples throughout the fed-batch. For the rest of the samples, the biomass DCW values were calculated from the measured OD\\u003csub\\u003e600\\u003c/sub\\u003e values, based on the calibration curve made for the given strain.\\u003c/p\\u003e \\u003cp\\u003eOffline and online state variables \\u0026mdash;such as biomass (X), substrate (S), product (P), flow rate (F), and initial volume (V\\u003csub\\u003eo\\u003c/sub\\u003e)\\u0026mdash; were recorded during the fed-batch phase of the bioreactor-scale experiments (see Additional files 2 and 3). These variables were used to calculate derived metrics, including growth rate (\\u0026micro;), q-rates, and yields, following the methodology described elsewhere [38,39]. For mass balance verification, the elemental biomass composition CH\\u003csub\\u003e1.88\\u003c/sub\\u003eO\\u003csub\\u003e0.63\\u003c/sub\\u003eN\\u003csub\\u003e0.20\\u003c/sub\\u003eS\\u003csub\\u003e0.004\\u003c/sub\\u003e was assumed, based on cells growing on methanol at \\u0026micro;\\u0026thinsp;=\\u0026thinsp;0.035 h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e [58]. Carbon and electron balances were satisfied with a deviation of less than 7%. A statistical χ2 consistency test, based on the h-index, was applied to the measured data balances [59,60], following the detailed method described by Ponte et al. [61]. The consistency tests passed with a confidence level of 95%, confirming no major measurement errors.\\u003c/p\\u003e\\n\\u003ch3\\u003eComputational methods\\u003c/h3\\u003e\\n\\u003cp\\u003eThe genome-scale metabolic model iMT1026 v3.0 of \\u003cem\\u003eK. phaffii\\u003c/em\\u003e [58] was employed to calculate the theoretical maximum 3-HP yield (Υ\\u003csub\\u003e3\\u0026minus;HP max\\u003c/sub\\u003e) achievable on methanol via the β-alanine pathway under different growth conditions. This model was expanded to include 3-HP production and excretion reactions. Simulations were constrained using experimental values obtained for the key physiological macroscopic parameters \\u0026micro; and q\\u003csub\\u003eS\\u003c/sub\\u003e, derived from methanol fed-batch cultivations. Specifically, \\u0026micro; was set to 0.028 h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and q\\u003csub\\u003eS\\u003c/sub\\u003e was 0.109 g\\u003csub\\u003eMetOH\\u003c/sub\\u003e g\\u003csub\\u003eDCW\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Additionally, the Υ\\u003csub\\u003e3\\u0026minus;HP max\\u003c/sub\\u003e was calculated under non-growth conditions (\\u0026micro;\\u0026thinsp;=\\u0026thinsp;0 h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). The non-growth associated maintenance energy (NGAME) parameter for methanol growth from Tom\\u0026agrave;s-Gamisans et al. [58] was also applied as a constraint. Flux Balance Analysis (FBA) was performed in COBRA Toolbox v3.0 [62] with Matlab R2021a (Mathworks, Inc., Natick, MA, USA) to maximize specific 3-HP productivity (q\\u003csub\\u003eP\\u003c/sub\\u003e) as the objective function. Finally, q\\u003csub\\u003eP\\u003c/sub\\u003e values resulting from the FBA, together with the experimental q\\u003csub\\u003eS\\u003c/sub\\u003e, were used to determine the Υ\\u003csub\\u003e3\\u0026minus;HP max\\u003c/sub\\u003e.\\u003c/p\\u003e\"},{\"header\":\"Results and discussion\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRewiring the upstream module of the β-alanine pathway for enhanced precursors supply\\u003c/h2\\u003e \\u003cp\\u003eTo enhance pyruvate flux into the β-alanine pathway, i.e. reactions leading from this key metabolic node to oxaloacetate (OAA) and L-aspartate, we individually overexpressed native genes encoding for the pyruvate carboxylase isoform 2 (\\u003cem\\u003ePYC2\\u003c/em\\u003e), which catalyzes the ATP-dependent carboxylation of pyruvate to form OAA, and for the cytosolic aspartate aminotransferase 2 (\\u003cem\\u003eAAT2\\u003c/em\\u003e), which enables the conversion of OAA to aspartate by transferring an amino group from glutamate (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). These genes were overexpressed in the original strain PpCβ21 [38,39] by integrating a second copy of the corresponding coding sequence under the control of methanol-inducible promoters into the \\u003cem\\u003eK. phaffii\\u003c/em\\u003e genome. The expression of \\u003cem\\u003eAAT2\\u003c/em\\u003e in strain PpCβ21-A was driven by the strong dihydroxyacetone synthase 1 promoter (P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e), while \\u003cem\\u003ePYC2\\u003c/em\\u003e in strain PpCβ21-Y was expressed under the control of the medium-strength sedoheptulose bisphosphatase promoter (P\\u003csub\\u003e\\u003cem\\u003eSHB17\\u003c/em\\u003e\\u003c/sub\\u003e) to avoid additional disturbances in the central metabolic node of pyruvate. Additionally, a highly active and stable NAD- or NADP-dependent aspartate dehydrogenase from \\u003cem\\u003eSerratia proteamaculans\\u003c/em\\u003e (SpeAspDH) [63], which directly aminates OAA using ammonium as the amino donor, was tested as an alternative to \\u003cem\\u003eK. phaffii AAT2\\u003c/em\\u003e under P\\u003csub\\u003e\\u003cem\\u003eDAS1\\u003c/em\\u003e\\u003c/sub\\u003e, resulting in strain PpCβ21-D strain (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThree independently isolated transformants from each strain were cultured in 24-deep well plates containing buffered minimal methanol (BMM) medium, alongside the reference strain PpCβ21. After 48 h of incubation, all strains reached a similar cell density (see Additional file 4). None of the individually overexpressed genes led to a significant improvement in final 3-HP concentration or yield on methanol (Y\\u003csub\\u003eP/S\\u003c/sub\\u003e), except in the case of \\u003cem\\u003ePYC2\\u003c/em\\u003e overexpression (strain PpCβ21-Y), which resulted in a slight but statistically significant 5.4% increment (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.03) in the 3HP yield on biomass (Y\\u003csub\\u003eP/X\\u003c/sub\\u003e), increasing from 0.556 to 0.586 g 3-HP l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e per OD\\u003csub\\u003e600\\u003c/sub\\u003e unit compared to the reference strain (see Additional file 4). We hypothesize that the modest increase in 3-HP yields could be due to an insufficient ATP supply, as the carboxylation of pyruvate by Pyc2 is ATP-dependent [64]. Additionally, bicarbonate, a co-substrate in this carboxylation reaction, might also become a rate-limiting factor in these strains [46,65].\\u003c/p\\u003e \\u003cp\\u003eNeither \\u003cem\\u003eAAT2\\u003c/em\\u003e nor the \\u003cem\\u003eS. proteamaculans\\u003c/em\\u003e AspDH\\u0026rsquo;s encoding gene overexpression did improve 3-HP production, suggesting that the conversion of OAA into aspartate may not be a rate-limiting step of the 3-HP pathway. These findings are consistent with those from a previous study involving a recombinant \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain that converted methanol into β-alanine, in which overexpression of either mitochondrial \\u003cem\\u003eAAT1\\u003c/em\\u003e or cytosolic \\u003cem\\u003eAAT2\\u003c/em\\u003e did not significantly enhance β-alanine production [63]. Coherently, rewiring the entire upstream module of the β-alanine pathway, i.e., co-overexpression of an additional copy of both \\u003cem\\u003ePYC2\\u003c/em\\u003e and \\u003cem\\u003eAAT2\\u003c/em\\u003e, resulting in strain PpCβ21-YA, did not lead to significant improvements in Y\\u003csub\\u003eP/S\\u003c/sub\\u003e and Y\\u003csub\\u003eP/X\\u003c/sub\\u003e compared to the PpCβ21-Y strain (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.58 and 0.30, respectively) (see Additional file 4). These results highlight a potential bottleneck in the OAA biosynthesis enzymes, rather than in the supply of aspartate.\\u003c/p\\u003e \\u003cp\\u003eIn a previous study, we overexpressed a mutated NADP-dependent formate dehydrogenase from \\u003cem\\u003ePseudomonas\\u003c/em\\u003e sp. 101, PseFDH(V9), aiming at enhancing cofactor supply of the NADPH-dependent b-alanine synthetic pathway (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). This led to increased q\\u003csub\\u003eP\\u003c/sub\\u003e and q\\u003csub\\u003eS\\u003c/sub\\u003e in methanol fed-batch cultures [38,39]. Building on this, we further modified the PpCβ21-YA strain by introducing the gene encoding for PseFDH(V9) under the native formate dehydrogenase promoter (P\\u003csub\\u003e\\u003cem\\u003eFDH1\\u003c/em\\u003e\\u003c/sub\\u003e), creating strain PpCβ21-YAP. However, expression of PseFDH(V9) led to a significant decrease in final 3-HP titer of approximately 11% (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0017) in small-scale (24-deep well plate) cultivations, resulting in lower Y\\u003csub\\u003eP/S\\u003c/sub\\u003e and Y\\u003csub\\u003eP/X\\u003c/sub\\u003e values compared to PpCβ21-YA, despite both strains reaching the same final cell concentration (see Additional file 4). These results were consistent with our previous findings [38,39], where the introduction of this ectopic NADPH-regenerating reaction also reduced 3-HP titers in small-scale cultivation experiments, probably due to a carbon flux redistribution through the methanol dissimilation pathway instead of biomass and 3-HP generation. Nevertheless, overexpression of PseFDH(V9) in strain PpCβ21-P resulted in a 14% and 10% increase in Y\\u003csub\\u003eP/X\\u003c/sub\\u003e and q\\u003csub\\u003eP\\u003c/sub\\u003e during methanol fed-batch cultivations, respectively, compared to the reference strain PpCβ21. As a result, strain PpCβ21-P was selected for further engineering efforts aimed at enhancing 3-HP export.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eModulation of 3-HP export capacity through the overexpression of monocarboxylate permeases encoding genes leads to improved extracellular 3-HP concentration and product yields\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eEfficient export of 3-HP from microbial cells is a cornerstone in industrial bioproduction. Owing to the near-neutral pH of the cytoplasm, 3-HP dissociates at the time of being produced, releasing protons and anions that poorly diffuse to extracellular space. To maintain intracellular pH (pHi) homeostasis, these (an)ions must be actively exported through different free energy-dependent processes such as: i) primary transport, via ATP-Binding Cassette (ABC) transporters and plasma membrane H\\u003csup\\u003e+\\u003c/sup\\u003e-ATPases, and ii) secondary transport, via transporters that use (electro-)chemical gradients as the driving force (e.g., Jen1 symporter) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Nonetheless, specific ABC transporters for 3-HP export are not yet identified.\\u003c/p\\u003e \\u003cp\\u003eIn this study, the \\u003cem\\u003eJEN1\\u003c/em\\u003e and \\u003cem\\u003eESBP6\\u003c/em\\u003e genes encoding putative monocarboxylate permeases were individually introduced into the PpCβ21-P strain [38,39], under the control of strong methanol-inducible promoters, resulting in strains PpCβ21-PJ and PpCβ21-PE, respectively. After 48 h of cultivation in BMM medium in 24-deep well plates, the highest concentration of 3-HP (1.40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) was obtained with strain PpCβ21-PE, resulting in a 44% increase in both titer and yields compared to the reference strain (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Notably, although PpCβ21-PE produced a higher amount of 3-HP than PpCβ21-P, the biomass values reached by both strains were the same (see Additional file 4). While the function of Esbp6 remains unknown [43], overexpression of its gene clearly enhanced 3-HP titer and yields in the recombinant strain PpCβ21-PE. These results are consistent with findings from other studies [45,46], where overexpression of \\u003cem\\u003eESBP6\\u003c/em\\u003e improved yeast tolerance to acid-stressing conditions by reducing the intracellular 3-HP content, ultimately leading to a higher 3-HP production.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eConversely, overexpression of \\u003cem\\u003eJEN1\\u003c/em\\u003e in strain PpCβ21-PJ led to modest yet statistically significant increase of 8% in Y\\u003csub\\u003eP/S\\u003c/sub\\u003e and 12% in Y\\u003csub\\u003eP/X\\u003c/sub\\u003e (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0008 and 0.044, respectively) compared to PpCβ21-P (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), suggesting that Jen1 may have a lower export efficiency or capacity for 3-HP compared to Esbp6. Further characterization of these transporters could provide insights into the distinct effects on 3-HP export observed in this study.\\u003c/p\\u003e \\u003cp\\u003eInterestingly, disruption of the endogenous \\u003cem\\u003eJEN1\\u003c/em\\u003e gene in a \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e 3-HP-producing strain did not result in significant differences in the final 3-HP concentration compared to the parental strain, indicating that 3-HP efflux in the Δ\\u003cem\\u003ejen1\\u003c/em\\u003e strain remained functional [46]. Similar results were observed in \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e strains producing lactic acid, where Δ\\u003cem\\u003ejen1\\u003c/em\\u003e strains, and even isogenic yeast mutants Δ\\u003cem\\u003ejen1\\u003c/em\\u003eΔ\\u003cem\\u003eady2\\u003c/em\\u003e, showed no differences in lactic acid production compared to the wild-type strain, suggesting the presence of other lactic acid exporters besides Jen1 [48,49]. However, Lima et al. [51] reported a 46% increase in lactate yield when \\u003cem\\u003eJEN1\\u003c/em\\u003e was overexpressed in a recombinant \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain. Therefore, it is also plausible that the limited impact of \\u003cem\\u003eJEN1\\u003c/em\\u003e overexpression in strain PpCβ21-PJ might be due to the lower affinity of this putative lactate transporter for 3-HP anions compared to lactate.\\u003c/p\\u003e \\u003cp\\u003eWhen \\u003cem\\u003eJEN1\\u003c/em\\u003e and \\u003cem\\u003eESBP6\\u003c/em\\u003e were overexpressed simultaneously in strain PpCβ21-PEJ, the Y\\u003csub\\u003eP/X\\u003c/sub\\u003e was similar to that of \\u003cem\\u003eESBP6\\u003c/em\\u003e overexpression alone, while the Y\\u003csub\\u003eP/S\\u003c/sub\\u003e showed a modest but significant increase of 6% (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.001), rising from 0.118 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in strain PpCβ21-PE to 0.125 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). These findings corroborate that the monocarboxylate transporters, particularly Esbp6, effectively facilitated the export of 3-HP \\u003cem\\u003ein K. phaffii\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eFinally, in an attempt to combine \\u0026ldquo;push\\u0026rdquo; and \\u0026ldquo;pull\\u0026rdquo; metabolic engineering strategies, a second copy of the \\u003cem\\u003eK. phaffii PYC2\\u003c/em\\u003e gene was integrated into the PpCβ21-PEJ genome, aiming to enhance metabolic precursors supply to the b-alanine pathway, resulting in strain PpCβ21-PEJY. However, this strain did not show significant changes in Y\\u003csub\\u003eP/S\\u003c/sub\\u003e and Y\\u003csub\\u003eP/X\\u003c/sub\\u003e compared to PpCβ21-PEJ when grown in 24-deep well plates, with p-values of 0.34 and 0.31, respectively (see Additional file 4).\\u003c/p\\u003e \\u003cp\\u003eOverall, strains PpCβ21-PEJ and PpCβ21-PEJY showed the highest improvement in both Y\\u003csub\\u003eP/S\\u003c/sub\\u003e (53 and 55%, respectively) and Y\\u003csub\\u003eP/X\\u003c/sub\\u003e (44 and 48%) compared to the reference strain PpCβ21-P (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). As a result, both strains were taken for further evaluation in bioreactors.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eProduction of 3-HP in fed-batch cultivations\\u003c/h3\\u003e\\n\\u003cp\\u003eStrains PpCβ21-PEJ and PpCβ21-PEJY were further characterized in bioreactor-scale fed-batch cultivations using a pre-programmed exponential methanol-limited feeding strategy at a growth rate of \\u0026micro;\\u003csub\\u003eSP\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.02 h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. Both strains reached a final 3-HP concentration of 24.6 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and nearly identical biomass levels (38.0 and 37.8 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively) after 39.3 h of methanol feeding. Approximately 70 g of methanol were added during the exponential feed, yielding an overall Y\\u003csub\\u003eP/S\\u003c/sub\\u003e of 0.20 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for both strains (see Additional file 3). Residual methanol was not detected by HPLC analysis over the course of fermentation.\\u003c/p\\u003e \\u003cp\\u003eGiven that both PpCβ21-PEJ and PpCβ21-PEJY strains performed similarly, one of them, strain PpCβ21-PEJY, was further tested in a methanol-limited fed-batch culture at \\u0026micro;\\u003csub\\u003eSP\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;0.03 h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, i.e. under the same conditions used for characterization of our original strains [38,39]. During the 39.3-h feeding phase, almost 125 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003eof methanol were supplied to the bioreactors, leading to a final concentration of 27.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4 g 3-HP l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. This represents a 42% increase in 3-HP titer compared to the parental strain PpCβ21-P under the same conditions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), which is consistent with results observed in small-scale experiments. These findings corroborate that lactate transporters, such as Esbp6 and Jen1, effectively facilitate 3-HP export.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eInterestingly, strain PpCβ21-PEJY significantly decreased the CO\\u003csub\\u003e2\\u003c/sub\\u003e yield on methanol (Y\\u003csub\\u003eCO2/S\\u003c/sub\\u003e) from 0.81 (strain PpCβ21-P) to 0.68 g\\u003csub\\u003eCO2\\u003c/sub\\u003e g\\u003csub\\u003eMetOH\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e (p-value\\u0026thinsp;=\\u0026thinsp;0.01), while both Y\\u003csub\\u003eP/S\\u003c/sub\\u003e and Y\\u003csub\\u003eX/S\\u003c/sub\\u003e were significantly increased by 27 and 19%, respectively (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) (p-values\\u0026thinsp;=\\u0026thinsp;0.04 and 0.01, see Additional file 3). A plausible explanation for this observation may be that the increased export of 3-HP caused by the overexpression of monocarboxylate permeases in the PpCβ21-PEJY strain, particularly Esbp6, seems to provoke a metabolic \\u0026ldquo;pull\\u0026rdquo; effect, draining a higher proportion of the incoming methanol flux towards the assimilatory pathway, thereby favoring 3-HP production and biomass synthesis, while reducing CO\\u003csub\\u003e2\\u003c/sub\\u003e generation through the dissimilatory methanol oxidation pathway.\\u003c/p\\u003e \\u003cp\\u003eThe 3-HP yield on methanol reported in this study is comparable to that of our top-performing \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain engineered with the malonyl-CoA pathway on glycerol, both achieving 0.19 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e [21]. This finding is particularly significant considering that the Υ\\u003csub\\u003e3\\u0026minus;HP max\\u003c/sub\\u003e achievable on methanol is considerably lower than on C-sources such as glucose or glycerol, assuming all carbon is used solely for 3-HP synthesis (\\u0026micro;\\u0026thinsp;=\\u0026thinsp;0 h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) [38,39]. The yield achieved here corresponds to 70% of the Υ\\u003csub\\u003e3\\u0026minus;HP max\\u003c/sub\\u003e under biomass-generating conditions (0.27 g\\u003csub\\u003e3\\u0026thinsp;\\u0026minus;\\u0026thinsp;HP\\u003c/sub\\u003e g\\u003csub\\u003eMetOH\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e), i.e., reflecting the specific growth rate in our fed-batch experiments. Coherently, the Υ\\u003csub\\u003e3\\u0026minus;HP max\\u003c/sub\\u003e is considerably higher when all carbon is exclusively channeled toward 3-HP production, with no biomass generation (0.78 g\\u003csub\\u003e3\\u0026thinsp;\\u0026minus;\\u0026thinsp;HP\\u003c/sub\\u003e g\\u003csub\\u003eMetOH\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e). Under these conditions, our experimental Y\\u003csub\\u003eP/S\\u003c/sub\\u003e represents 24.4% of this theoretical yield (see \\u003cspan refid=\\\"Sec2\\\" class=\\\"InternalRef\\\"\\u003eMaterials and Methods\\u003c/span\\u003e section). Strain PpCβ21-PEJY also demonstrated a notably higher overall 3-HP volumetric productivity (Q\\u003csub\\u003eP\\u003c/sub\\u003e) compared to the reference strain (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), achieving nearly four times the Q\\u003csub\\u003eP\\u003c/sub\\u003e value reported for a \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain producing 3-HP solely from methanol (0.15 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, calculated from Wu et al. [37]).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAveraged value of key process parameters obtained for the methanol fed-batch phase using a preprogrammed \\u0026micro; of 0.03 h\\u003csup\\u003e-1\\u003c/sup\\u003e. Volumetric productivity (Q\\u003csub\\u003eP\\u003c/sub\\u003e), biomass yield on methanol (Y\\u003csub\\u003eX/S\\u003c/sub\\u003e), 3-HP yield on methanol (Y\\u003csub\\u003eP/S\\u003c/sub\\u003e), 3-HP yield on biomass (Y\\u003csub\\u003eP/X\\u003c/sub\\u003e), CO\\u003csub\\u003e2\\u003c/sub\\u003e yield on methanol (Y\\u003csub\\u003eCO2/S\\u003c/sub\\u003e), specific substrate consumption rate (q\\u003csub\\u003eS\\u003c/sub\\u003e), specific 3-HP production rate (q\\u003csub\\u003eP\\u003c/sub\\u003e), specific carbon dioxide evolution rate (q\\u003csub\\u003eCO2\\u003c/sub\\u003e), and experimentally measured mean specific growth rate (\\u0026micro;). Cultivations were performed in duplicate and biomass concentration analyses were performed in triplicate. \\u0026plusmn; indicates SD of the biological replicates.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePpCβ21-P\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePpCβ21-PEJY\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ\\u003csub\\u003eP\\u003c/sub\\u003e (g\\u003csub\\u003e3\\u0026thinsp;\\u0026minus;\\u0026thinsp;HP\\u003c/sub\\u003e l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eY\\u003csub\\u003eX/S\\u003c/sub\\u003e (g\\u003csub\\u003eDCW\\u003c/sub\\u003e g\\u003csub\\u003eMetOH\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eY\\u003csub\\u003eP/S\\u003c/sub\\u003e (g\\u003csub\\u003e3\\u0026thinsp;\\u0026minus;\\u0026thinsp;HP\\u003c/sub\\u003e g\\u003csub\\u003eMetOH\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eY\\u003csub\\u003eP/X\\u003c/sub\\u003e (g\\u003csub\\u003e3\\u0026thinsp;\\u0026minus;\\u0026thinsp;HP\\u003c/sub\\u003e g\\u003csub\\u003eDCW\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eY\\u003csub\\u003eCO2/S\\u003c/sub\\u003e (g\\u003csub\\u003eCO2\\u003c/sub\\u003e g\\u003csub\\u003eMetOH\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eq\\u003csub\\u003eS\\u003c/sub\\u003e (g\\u003csub\\u003eMetOH\\u003c/sub\\u003e g\\u003csub\\u003eDCW\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.124\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.109\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eq\\u003csub\\u003eP\\u003c/sub\\u003e (mmol\\u003csub\\u003e3\\u0026thinsp;\\u0026minus;\\u0026thinsp;HP\\u003c/sub\\u003e g\\u003csub\\u003eDCW\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.201\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.227\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eq\\u003csub\\u003eCO2\\u003c/sub\\u003e (mmol\\u003csub\\u003eCO2\\u003c/sub\\u003e g\\u003csub\\u003eDCW\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.30\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026micro; (h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.026\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.028\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e[38,39]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThis study\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eFermentation of\\u003c/b\\u003e \\u003cb\\u003eK. phaffii\\u003c/b\\u003e \\u003cb\\u003estrains at pH 3.5\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eProduction of 3-HP at a low pH allows for a more economical downstream processing, while reducing the risk of bacterial contamination. For instance, acidification is not needed for product recovery, effectively reducing costs. Furthermore, the amounts of base titrant required for neutralization during fermentation are greatly decreased at low pH, especially at industrial scale [8,28]. Additionally, the pH of the medium has been shown to have a drastic impact on the effectiveness of 3-HP solvent extraction. For instance, Chemarin et al. [66] obtained a 3-HP extraction yield of only about 5% in a solution starting at pH 5 due to acid dissociation. However, the yield significantly increased to 74% after lowering the pH to 3.2. Thereby, production of 3-HP with strains PpCβ21-P and PpCβ21-PEJY was also evaluated at pH 3.5, following the same feeding strategy used for their initial characterization at pH 5.\\u003c/p\\u003e \\u003cp\\u003eBoth PpCβ21-P and PpCβ21-PEJY strains produced 3-HP at acidic pH, achieving final titers of 15.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 24.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). As expected, strain PpCβ21-PEJY outperformed PpCβ21-P also at lower pH values. It is known that the energy demands of product export can hinder overall 3-HP yield, particularly if the metabolic pathway involved has little to no net ATP yield [28]. Accordingly, the two tested strains produced lower 3-HP yields at pH 3.5 compared to pH 5. However, while strain PpCβ21-P produced 3-HP at a yield of 0.11 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e at pH 3.5, that is, a decrease of 26.7% compared to the yield at pH 5, strain PpCβ21-PEJY achieved a 3-HP yield of 0.17 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e at low pH, that is, just 10.5% decrease from that obtained at pH 5 (0.19 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), (see Additional file 3). These findings are coherent with earlier research on 3-HP synthesis by \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e [23] and \\u003cem\\u003eK. phaffii\\u003c/em\\u003e cultivated under pH 3.5 conditions [67].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eUnder such acidic conditions, the undissociated fraction of 3-HP is the predominant form in the solution. Particularly at high concentrations, uncharged 3-HP may cross the cell membrane through either simple diffusion or facilitated diffusion (via a channel or a permease). Once in the cytoplasm, neutral pH leads to dissociation of the 3-HP acid, releasing protons and anions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The released protons may cause cytosolic acidification, which is harmful to the cell, whereas anions may trigger the generation of free radicals, resulting in severe oxidative stress [68,69]. Hence, to maintain pH homeostasis in the cytosol, active transport mechanisms, which require metabolic energy, allow for exporting these accumulated (an)ions from the cytosol. The lower 3-HP yields achieved at pH 3.5 compared to pH 5 may reflect this increased ATP demand, underscoring ATP as a limiting factor in 3-HP production, consistent with previous findings [67]. Nevertheless, overexpression of genes encoding for monocarboxylate transporters, especially \\u003cem\\u003eESBP6\\u003c/em\\u003e, effectively facilitated the export of anionic 3-HP in strain PpCβ21-PEJY. This could explain why the lower pH had a reduced effect on the 3-HP yield of strain PpCβ21-PEJY compared to strain PpCβ21-P.\\u003c/p\\u003e \\u003cp\\u003eInterestingly, both strains PpCβ21-P and PpCβ21-PEJY reached comparable levels of biomass concentration in cultures at the two pH values (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), suggesting that acidic pH had no impact on biomass yields (see Additional file 3). However, studies investigating carboxylic acid production in \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e [70] and \\u003cem\\u003eK. phaffii\\u003c/em\\u003e [67] found contrasting results, linking reduced biomass yields to increased maintenance-energy requirements in such conditions.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePartial blockage of methanol dissimilatory pathway to reduce carbon loss via CO\\u003csub\\u003e2\\u003c/sub\\u003e release\\u003c/h2\\u003e \\u003cp\\u003eIn \\u003cem\\u003eK. phaffii\\u003c/em\\u003e, methanol is oxidized to formaldehyde by alcohol oxidase (\\u003cem\\u003eAOX\\u003c/em\\u003e) in the peroxisomes. Several studies have revealed that less than half of the formaldehyde is assimilated into dihydroxyacetone (DHA) and glyceraldehyde 3-phosphate (G3P) for biomass synthesis [71,72], while the majority enters the dissimilation pathway, where it is further oxidized to CO\\u003csub\\u003e2\\u003c/sub\\u003e, generating NADH and ATP (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Although this pathway is essential for formaldehyde detoxification and energy production, it leads to a huge loss of carbon atoms through CO\\u003csub\\u003e2\\u003c/sub\\u003e release [73].\\u003c/p\\u003e \\u003cp\\u003eThe combination of different metabolic engineering strategies described so far resulted in a shift of carbon flux from the dissimilatory pathway towards 3-HP production. In particular, strain PpCβ21-PEJY, which overexpresses a mutated NADP-dependent formate dehydrogenase (PseFDH(V9)), two monocarboxylate transporters (Esbp6 and Jen1), and pyruvate carboxylase isoform 2 (Pyc2), showed a 16% reduction in CO\\u003csub\\u003e2\\u003c/sub\\u003e yield and a 27% increase in 3-HP yield in methanol fed-batch cultures compared to the parental strain (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). To further decrease CO\\u003csub\\u003e2\\u003c/sub\\u003e formation, the \\u003cem\\u003eK. phaffii FDH1\\u003c/em\\u003e endogenous gene, encoding for a NAD-dependent formate dehydrogenase, was deleted in strains PpCβ21-PEJ and PpCβ21-PEJY, resulting in strains PpCβ21-PEJΔ\\u003cem\\u003efdh1\\u003c/em\\u003e and PpCβ21-PEJYΔ\\u003cem\\u003efdh1\\u003c/em\\u003e, respectively. This strategy aimed to channel formate oxidation to CO\\u003csub\\u003e2\\u003c/sub\\u003e exclusively through the NADP-dependent PseFDH(V9) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eStrains PpCβ21-PEJΔ\\u003cem\\u003efdh1\\u003c/em\\u003e and PpCβ21-PEJYΔ\\u003cem\\u003efdh1\\u003c/em\\u003e were cultivated in triplicate alongside their parental strains in 24-deep well plates containing BMM medium. Both 3-HP production and cell growth were drastically reduced by over 90% compared to their respective parental strains (see Additional file 4). 3-HP titers as low as 0.10 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e were observed in strains with \\u003cem\\u003eFDH1\\u003c/em\\u003e deletion, while strains PpCβ21-PEJ and PpCβ21-PEJY produced up to 1.50 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e 3-HP. This suggests that NADH generated from dissimilated formaldehyde via \\u003cem\\u003eFLD\\u003c/em\\u003e was not sufficient to make up for the loss of NADH (and consequently, ATP) typically produced by native \\u003cem\\u003eFDH\\u003c/em\\u003e, leading to an energy imbalance and suboptimal growth on methanol. Notably, around 1 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of formic acid (FA) accumulated, indicating that PseFDH(V9) expression alone was not enough to fully oxidize FA to CO\\u003csub\\u003e2\\u003c/sub\\u003e, creating a bottleneck in the dissimilatory pathway. The accumulation of toxic intermediates, such as FA (and formaldehyde), likely contributed to the impaired growth observed. In fact, it has been postulated that the main physiological role of \\u003cem\\u003eFDH\\u003c/em\\u003e is detoxifying formate rather than enhancing energy production [74]. Moreover, partial blocking of the dissimilatory pathway also led to up to 4.3 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of residual methanol (Additional file 4), suggesting additional metabolic bottlenecks downstream the methanol assimilatory pathway.\\u003c/p\\u003e \\u003cp\\u003eSimilar challenges were reported by Guo et al. [34] when the methanol dissimilation pathway was completely blocked through \\u003cem\\u003eFDH\\u003c/em\\u003e deletion in a \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain engineered to produce malic acid from methanol. In contrast, a recent study found no significant growth differences between a \\u003cem\\u003eK. phaffii\\u003c/em\\u003e GS115 strain and an \\u003cem\\u003eFDH\\u003c/em\\u003e-deficient GS115 strain grown on 1% YPM, suggesting context-dependent effects of \\u003cem\\u003eFDH\\u003c/em\\u003e deletion on methanol metabolism. Moreover, comparative transcriptomic analysis revealed that the impact of \\u003cem\\u003eFDH\\u003c/em\\u003e deletion was less pronounced than that caused by the deletion of other genes involved in the methanol dissimilatory pathway (\\u003cem\\u003eFLD\\u003c/em\\u003e, \\u003cem\\u003eFGH\\u003c/em\\u003e) [75].\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn this study, we enhanced the performance of our previously developed 3-HP-producing strains through several metabolic engineering strategies focusing on the improvement of yields and productivities. These strategies included: i) overexpressing the upstream module of the β-alanine pathway, ii) partially blocking the methanol dissimilation pathway, and iii) reducing intracellular 3-HP accumulation. To our knowledge, this is the first time the \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u0026rsquo;s gene encoding for the lactate transporter Esbp6 has been expressed in \\u003cem\\u003eK. phaffii\\u003c/em\\u003e, proving its effectiveness in facilitating 3-HP export. Overall, co-overexpression of \\u003cem\\u003eESBP6\\u003c/em\\u003e and \\u003cem\\u003eJEN1\\u003c/em\\u003e, encoding two lactate transporters, along with \\u003cem\\u003ePYC2\\u003c/em\\u003e to enhance oxaloacetate supply, led to a final 3-HP concentration of 27.0 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in 39.3 h, with a product yield of 0.19 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and a volumetric productivity of 0.56 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. However, deleting \\u003cem\\u003eFDH1\\u003c/em\\u003e in \\u003cem\\u003eK. phaffii\\u003c/em\\u003e impaired growth, probably due to an energy imbalance.\\u003c/p\\u003e \\u003cp\\u003eNotably, we further demonstrated 3-HP production under industrially relevant cultivation conditions, specifically at a low pH of 3.5, and highlighted the beneficial effects of overexpressing genes encoding lactate transporters, such as Esbp6 and Jen1, to support 3-HP production at pH 3.5.\\u003c/p\\u003e \\u003cp\\u003eWhile this work highlights the potential of using efficient monocarboxylate transporters to achieve high productivities, yields, and titers of the target carboxylic acid, further improvements are needed to reach industrially relevant metrics. Furthermore, a deeper understanding of the export mechanisms, substrate specificity, and regulation of carboxylate transporters is crucial for the successful development of microbial cell factories for industrial carboxylic acid production, regardless of the pH conditions used in the fermentation processes.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary information\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe online version contains supplementary material available at:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank Enrique V\\u0026aacute;zquez-Pereira and Jordi Reig for their assistance in designing specific engineered loci and for their contributions to the corresponding cloning procedures.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 1 was created by SAC in BioRender. BioRender.com/w53i486\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSAC, JA, and PF conceived and designed the research project. SAC performed all experiments and analyzed the data. MPT performed the NMR analyses and metabolite identification. PF and JA contributed to data interpretation. SAC wrote the first draft of the manuscript and PF contributed to the manuscript final version. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by project \\u0026lsquo;innoVative bIo-based chains for CO\\u003csub\\u003e2\\u003c/sub\\u003e VALorisation as aDded-value organic acids\\u0026rsquo; \\u0026ndash; VIVALDI (ID: 101000441) from the Horizon 2020 Program of the European Commission; 2021-SGR-00143 from the Ag\\u0026egrave;ncia de Gesti\\u0026oacute; d\\u0026rsquo;Ajuts Universitaris i de Recerca (AGAUR) of the Catalan Government. SAC was supported by a FI fellowship (2022FI_B1_00173) from AGAUR.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data that supports the findings of this study are included in this published article/Additional files. Further datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eOlah GA. Beyond oil and gas: the methanol economy. Angew Chem Int Ed. 2005;44:2636\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eSarwar A, Lee EY. Methanol-based biomanufacturing of fuels and chemicals using native and synthetic methylotrophs. Synth Syst Biotechnol. 2023;8:396\\u0026ndash;415.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eBachleitner S, Ata \\u0026Ouml;, Mattanovich D. The potential of CO\\u003csub\\u003e2\\u003c/sub\\u003e-based production cycles in biotechnology to fight the climate crisis. Nat Commun. 2023;14:6978.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eV\\u0026aacute;squez Castro E, Memari G, Ata \\u0026Ouml;, Mattanovich D. Carbon efficient production of chemicals with yeasts. Yeast. 2023;40:583\\u0026ndash;93.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eLv X, Yu W, Zhang C, Ning P, Li J, Liu Y, Du G, Liu L. C1-based biomanufacturing: Advances, challenges and perspectives. Bioresour Technol. 2023;367:128259.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eJiang X, Meng X, Xian M. Biosynthetic pathways for 3-hydroxypropionic acid production. Appl Microbiol Biotechnol. 2009;82:995\\u0026ndash;1003.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eRathnasingh C, Raj SM, Jo JE, Park S. Development and evaluation of efficient recombinant Escherichia coli strains for the production of 3-hydroxypropionic acid from glycerol. Biotechnol Bioeng. 2009;104:729\\u0026ndash;39.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eWerpy T, Petersen G. Top value added chemicals from biomass: Volume I \\u0026mdash; Results of screening for potential candidates from sugars and synthesis gas. Springfield (VA): US Department of Energy; 2004. Report No. DOE/GO-102004-1992.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eBozell JJ, Petersen GR. Technology development for the production of biobased products from biorefinery carbohydrates\\u0026mdash;the US Department of Energy\\u0026rsquo;s \\u0026ldquo;Top 10\\u0026rdquo; revisited. Green Chem. 2010;12:539\\u0026ndash;55.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eDella Pina C, Falletta E, Rossi M. A green approach to chemical building blocks. The case of 3-hydroxypropanoic acid. Green Chem. 2011;13:1624\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eKumar V, Ashok S, Park S. Recent advances in biological production of 3-hydroxypropionic acid. Biotechnol Adv. 2013;31:945\\u0026ndash;61.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003ede Fouch\\u0026eacute;cour F, S\\u0026aacute;nchez-Casta\\u0026ntilde;eda AK, Saulou-B\\u0026eacute;rion C, Spinnler H\\u0026Eacute;. Process engineering for microbial production of 3-hydroxypropionic acid. Biotechnol Adv. 2018;36:1207\\u0026ndash;22.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eJers C, Kalantari A, Garg A, Mijakovic I. Production of 3-hydroxypropanoic acid from glycerol by metabolically engineered bacteria. Front Bioeng Biotechnol. 2019;7:124.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eKim JW, Ko YS, Chae TU, Lee SY. High-level production of 3-hydroxypropionic acid from glycerol as a sole carbon source using metabolically engineered Escherichia coli. Biotechnol Bioeng. 2020;117:2139\\u0026ndash;52.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eZhao P, Ma C, Xu L, Tian P. Exploiting tandem repetitive promoters for high-level production of 3-hydroxypropionic acid. Appl Microbiol Biotechnol. 2019;103:4017\\u0026ndash;31.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eJi RY, Ding Y, Shi TQ, Lin L, Huang H, Gao Z, Ji XJ. Metabolic engineering of yeast for the production of 3-hydroxypropionic acid. Front Microbiol. 2018;9:2185.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eKildegaard KR, Jensen NB, Schneider K, Czarnotta E, \\u0026Ouml;zdemir E, Klein T, Maury J, Ebert BE, Christensen HB, Chen Y, Kim IK, Herrg\\u0026aring;rd MJ, Blank LM, Forster J, Nielsen J, Borodina I. Engineering and systems-level analysis of Saccharomyces cerevisiae for production of 3-hydroxypropionic acid via malonyl-CoA reductase-dependent pathway. Microb Cell Fact. 2016;15:53.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eYu W, Cao X, Gao J, Zhou YJ. Overproduction of 3-hydroxypropionate in a super yeast chassis. Bioresour Technol. 2022;361:127690.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eSuyama A, Higuchi Y, Urushihara M, Maeda Y, Takegawa K. Production of 3-hydroxypropionic acid via the malonyl-CoA pathway using recombinant fission yeast strains. J Biosci Bioeng. 2017;124:392\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eTakayama S, Ozaki A, Konishi R, Otomo C, Kishida M, Hirata Y, Matsumoto T, Tanaka T, Kondo A. Enhancing 3-hydroxypropionic acid production in combination with sugar supply engineering by cell surface-display and metabolic engineering of Schizosaccharomyces pombe. Microb Cell Fact. 2018;17:176.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eFina A, Heux S, Albiol J, Ferrer P. Combining metabolic engineering and multiplexed screening methods for 3-hydroxyprionic acid production in Pichia pastoris. Front Bioeng Biotechnol. 2022;10:942304.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eKildegaard KR, Wang Z, Chen Y, Nielsen J, Borodina I. Production of 3-hydroxypropionic acid from glucose and xylose by metabolically engineered Saccharomyces cerevisiae. Metab Eng Commun. 2015;2:132\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eBorodina I, Kildegaard KR, Jensen NB, Blicher TH, Maury J, Sherstyk S, Schneider K, Lamosa P, Herrg\\u0026aring;rd MJ, Rosenstand I, \\u0026Ouml;berg F, Forster J, Nielsen J. Establishing a synthetic pathway for high-level production of 3-hydroxypropionic acid in Saccharomyces cerevisiae via \\u0026beta;-alanine. Metab Eng. 2015;27:57\\u0026ndash;64.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eLis AV, Schneider K, Weber J, Keasling JD, Jensen MK, Klein T. Exploring small-scale chemostats to scale up microbial processes: 3-hydroxypropionic acid production in S. cerevisiae. Microb Cell Fact. 2019;18:50.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eCregg JM, Vedvick TS, Raschke WC. Recent advances in the expression of foreign genes in Pichia pastoris. Biotechnology (N Y). 1993;11:905\\u0026thinsp;\\u0026minus;\\u0026thinsp;10.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eWerten MWT, Van Den Bosch TJ, Wind RD, Mooibroek H, De Wolf FA. High-yield secretion of recombinant gelatins by Pichia pastoris. Yeast. 1999;15:1087\\u0026ndash;96.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eBudavari S. The Merck index: An encyclopedia of chemicals, drugs, and biologicals. 11th ed. Rahway: Merck; 1989.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003evan Maris AJ, Konings WN, Dijken JPV, Pronk JT. Microbial export of lactic and 3-hydroxypropanoic acid: Implications for industrial fermentation processes. Metab Eng. 2004;6:245\\u0026ndash;55.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eGuo F, Qiao Y, Xin F, Zhang W, Jiang M. Bioconversion of C1 feedstocks for chemical production using Pichia pastoris. Trends Biotechnol. 2023;41:1066-79.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eLiu X Bin, Liu M, Tao XY, Zhang ZX, Wang FQ, Wei DZ. Metabolic engineering of Pichia pastoris for the production of dammarenediol-II. J Biotechnol. 2015;216:47\\u0026ndash;55.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eGao J, Zuo Y, Xiao F, Wang Y, Li D, Xu J, Ye C, Feng L, Jiang L, Liu T, Gao D, Ma B, Huang L, Xu Z, Lian J. Biosynthesis of catharanthine in engineered Pichia pastoris. Nat Synth. 2023;2:231\\u0026ndash;42.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eCai P, Li Y, Zhai X, Yao L, Ma X, Jia L, Zhou YJ. Microbial synthesis of long-chain \\u0026alpha;-alkenes from methanol by engineering Pichia pastoris. Bioresour Bioprocess. 2022;9:58.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eCai P, Wu X, Deng J, Gao L, Shen Y, Yao L, Zhou YJ. Methanol biotransformation toward high-level production of fatty acid derivatives by engineering the industrial yeast Pichia pastoris. Proc Natl Acad Sci U S A. 2022;119:e2201711119.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eGuo F, Dai Z, Peng W, Zhang S, Zhou J, Ma J, Dong W, Xin F, Zhang W, Jiang M. Metabolic engineering of Pichia pastoris for malic acid production from methanol. Biotechnol Bioeng. 2021;118:357\\u0026ndash;71.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eYamada R, Ogura K, Kimoto Y, Ogino H. Toward the construction of a technology platform for chemicals production from methanol: D-lactic acid production from methanol by an engineered yeast Pichia pastoris. World J Microbiol Biotechnol. 2019;35:37.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eSeverinsen MM, Bachleitner S, Modenese V, Ata \\u0026Ouml;, Mattanovich D. Efficient production of itaconic acid from the single-carbon substrate methanol with engineered Komagataella phaffii. Biotechnol Biofuels Bioprod. 2024;17:98.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eWu X, Cai P, Gao L, Li Y, Yao L, Zhou YJ. Efficient bioproduction of 3-hydroxypropionic acid from methanol by a synthetic yeast cell factory. ACS Sustain Chem Eng. 2023;11:6445\\u0026ndash;53.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003e\\u0026Agrave;vila-Cabr\\u0026eacute; S, P\\u0026eacute;rez-Trujillo M, Albiol J, Ferrer P. Engineering the synthetic \\u0026beta;-alanine pathway in Komagataella phaffii for conversion of methanol into 3-hydroxypropionic acid. Microb Cell Fact. 2023;22:237.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003e\\u0026Agrave;vila-Cabr\\u0026eacute; S, P\\u0026eacute;rez-Trujillo M, Albiol J, Ferrer P. Correction to: Engineering the synthetic \\u0026beta;-alanine pathway in Komagataella phaffii for conversion of methanol into 3-hydroxypropionic acid. Microb Cell Fact. 2024;23:235.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eMazzoli R. Current progress in production of building-block organic acids by consolidated bioprocessing of lignocellulose. Fermentation. 2021;7:248.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eSoares-Silva I, Ribas D, Sousa-Silva M, Azevedo-Silva J, Rendulić T, Casal M. Membrane transporters in the bioproduction of organic acids: State of the art and future perspectives for industrial applications. FEMS Microbiol Lett. 2020;367:fnaa118.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eWu T, Li J, Tian C. Fungal carboxylate transporters: Recent manipulations and applications. Appl Microbiol Biotechnol. 2023;107:5909\\u0026ndash;22.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eMakuc J, Paiva S, Schauen M, Kr\\u0026auml;mer R, Andr\\u0026eacute; B, Casal M, Le\\u0026atilde;o C, Boles E. The putative monocarboxylate permeases of the yeast Saccharomyces cerevisiae do not transport monocarboxylic acids across the plasma membrane. Yeast. 2001;18:1131\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eCasal M, Paiva S, Andrade RP, Gancedo C. The lactate-proton symport of Saccharomyces cerevisiae is encoded by JEN1. J Bacteriol. 1999;181:2620-3.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eSugiyama M, Akase S pei, Nakanishi R, Kaneko Y, Harashima S. Overexpression of ESBP6 improves lactic acid resistance and production in Saccharomyces cerevisiae. J Biosci Bioeng. 2016;122:415\\u0026ndash;20.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eQin N, Li L, Wan X, Ji X, Chen Y, Li C, Liu P, Zhang Y, Yang W, Jiang J, Xia J, Shi S, Tan T, Nielsen J, Chen Y, Liu Z. Increased CO\\u003csub\\u003e2\\u003c/sub\\u003e fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast. Nat Commun. 2024;15:1591.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003ePorro D, Bianchi M, Ranzi B, Frontali L, Vai M, Winkler A, Alberghina L. Yeast strains for the production of lactic acid. Patent WO1999014335A1; 1999.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eBranduardi P, Sauer M, De Gioia L, Zampella G, Valli M, Mattanovich D, Porro D. Lactate production yield from engineered yeasts is dependent from the host background, the lactate dehydrogenase source and the lactate export. Microb Cell Fact. 2006;5:4.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003ePacheco A, Talaia G, S\\u0026aacute;-Pessoa J, Bessa D, Gon\\u0026ccedil;alves MJ, Moreira R, Paiva S, Casal M, Queir\\u0026oacute;s O. Lactic acid production in Saccharomyces cerevisiae is modulated by expression of the monocarboxylate transporters Jen1 and Ady2. FEMS Yeast Res. 2012;12:375\\u0026ndash;81.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eZhu P, Luo R, Li Y, Chen X. Metabolic engineering and adaptive evolution for efficient production of L-lactic acid in Saccharomyces cerevisiae. Microbiol Spectr. 2022;10:e0227722.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eLima PBA, Mulder KCL, Melo NTM, Carvalho LS, Menino GS, Mulinari E, de Castro VH, Dos Reis TF, Goldman GH, Magalh\\u0026atilde;es BS, Parachin NS. Novel homologous lactate transporter improves L-lactic acid production from glycerol in recombinant strains of Pichia pastoris. Microb Cell Fact. 2016;15:158.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eGassler T, Heistinger L, Mattanovich D, Gasser B, Prielhofer R. CRISPR/Cas9-mediated homology-directed genome editing in Pichia pastoris. Methods Mol Biol. 2019;1923:211\\u0026ndash;25.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eLabun K, Montague TG, Gagnon JA, Thyme SB, Valen E. CHOPCHOP v2: A web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 2016;44:W272\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eSears IB, O\\u0026rsquo;connor J, Rossanese OW, Glick BS. A versatile set of vectors for constitutive and regulated gene expression in Pichia pastoris. Yeast. 1998;14:783\\u0026ndash;90.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003ePrielhofer R, Barrero JJ, Steuer S, Gassler T, Zahrl R, Baumann K, Sauer M, Mattanovich D, Gasser B, Marx H. GoldenPiCS: A Golden Gate-derived modular cloning system for applied synthetic biology in the yeast Pichia pastoris. BMC Syst Biol. 2017;11:123.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eJensen NB, Strucko T, Kildegaard KR, David F, Maury J, Mortensen UH, Forster J, Nielsen J, Borodina I. EasyClone: Method for iterative chromosomal integration of multiple genes in Saccharomyces cerevisiae. FEMS Yeast Res. 2014;14:238\\u0026ndash;48.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eMaurer M, K\\u0026uuml;hleitner M, Gasser B, Mattanovich D. Versatile modeling and optimization of fed batch processes for the production of secreted heterologous proteins with Pichia pastoris. Microb Cell Fact. 2006;5:37.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eTom\\u0026agrave;s-Gamisans M, Ferrer P, Albiol J. Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources. Microb Biotechnol. 2018;11:224\\u0026ndash;37.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eNoorman HJ, Rornein B, Ch M Luyben KA, Heijnen JJ. Classification, error detection, and reconciliation of process information in complex biochemical systems. Biotechnol Bioeng. 1996;49:364\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003evan der Heijden RT, Heijnen JJ, Hellinga C, Romein B, Luyben KC. Linear constraint relations in biochemical reaction systems: I. Classification of the calculability and the balanceability of conversion rates. Biotechnol Bioeng. 1994;43:3\\u0026ndash;10.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003ePonte X, Montesinos-Segu\\u0026iacute; JL, Valero F. Bioprocess efficiency in Rhizopus oryzae lipase production by Pichia pastoris under the control of PAOX1 is oxygen tension dependent. Process Biochem. 2016;51:1954\\u0026ndash;63.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eBecker SA, Feist AM, Mo ML, Hannum G, Palsson B, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox. Nat Protoc. 2007;2:727\\u0026ndash;38.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eMiao L, Li Y, Zhu T. Metabolic engineering of methylotrophic Pichia pastoris for the production of \\u0026beta;-alanine. Bioresour Bioprocess. 2021;8:89.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eTong T, Tao Z, Chen X, Gao C, Liu H, Wang X, Liu GQ, Liu L. A biosynthesis pathway for 3-hydroxypropionic acid production in genetically engineered Saccharomyces cerevisiae. Green Chem. 2021;23:4502\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eMalubhoy Z, Bahia FM, de Valk SC, de Hulster E, Rendulić T, Ortiz JPR, Xiberras J, Klein M, Mans R, Nevoigt E. Carbon dioxide fixation via production of succinic acid from glycerol in engineered Saccharomyces cerevisiae. Microb Cell Fact. 2022;21:102.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eChemarin F, Ath\\u0026egrave;s V, Bedu M, Loty T, Allais F, Trelea IC, Moussa M. Towards an in situ product recovery of bio-based 3-hydroxypropionic acid: Influence of bioconversion broth components on membrane-assisted reactive extraction. J Chem Technol Biotechnol. 2019;94:964\\u0026ndash;72.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eFina A, Millard P, Albiol J, Ferrer P, Heux S. High throughput \\u003csup\\u003e13\\u003c/sup\\u003eC-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux. Microb Cell Fact. 2023;22:117.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003ePeetermans A, Foulqui\\u0026eacute;-Moreno MR, Thevelein JM. Mechanisms underlying lactic acid tolerance and its influence on lactic acid production in Saccharomyces cerevisiae. Microb Cell. 2021;8:111\\u0026ndash;30.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003ePiper P, Calderon CO, Hatzixanthis K, Mollapour M. Weak acid adaptation: The stress response that confers yeasts with resistance to organic acid food preservatives. Microbiology (Reading). 2001;147:2635-42.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eHakkaart X, Liu Y, Hulst M, el Masoudi A, Peuscher E, Pronk J, et al. Physiological responses of Saccharomyces cerevisiae to industrially relevant conditions: Slow growth, low pH, and high CO\\u003csub\\u003e2\\u003c/sub\\u003e levels. Biotechnol Bioeng. 2020;117:721\\u0026ndash;35.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eJord\\u0026agrave; J, Suarez C, Carnicer M, ten Pierick A, Heijnen JJ, van Gulik W, Ferrer P, Albiol J, Wahl A. Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary \\u003csup\\u003e13\\u003c/sup\\u003eC flux analysis. BMC Syst Biol. 2013;7:17.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eJord\\u0026agrave; J, De Jesus SS, Peltier S, Ferrer P, Albiol J. Metabolic flux analysis of recombinant Pichia pastoris growing on different glycerol/methanol mixtures by iterative fitting of NMR-derived \\u003csup\\u003e13\\u003c/sup\\u003eC-labelling data from proteinogenic amino acids. N Biotechnol. 2014;31:120\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eYurimoto H, Kato N, Sakai Y. Assimilation, dissimilation, and detoxification of formaldehyde, a central metabolic intermediate of methylotrophic metabolism. Chem Rec. 2005;5:367\\u0026ndash;75.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eSakai Y, Murdanoto AP, Konishi T, Iwamatsu A, Kato N. Regulation of the formate dehydrogenase gene, FDH1, in the methylotrophic yeast Candida boidinii and growth characteristics of an FDH1-disrupted strain on methanol, methylamine, and choline. J Bacteriol. 1997;179:4480-5.\\u003c/span\\u003e\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cspan\\u003eYu YF, Yang J, Zhao F, Lin Y, Han S. Comparative transcriptome and metabolome analyses reveal the methanol dissimilation pathway of Pichia pastoris. BMC Genomics. 2022;23:366.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-biological-engineering\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jbie\",\"sideBox\":\"Learn more about [Journal of Biological Engineering](http://jbioleng.biomedcentral.com/)\",\"snPcode\":\"13036\",\"submissionUrl\":\"https://submission.nature.com/new-submission/13036/3\",\"title\":\"Journal of Biological Engineering\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"3-hydroxypropionic acid, Pichia pastoris, Komagataella phaffii, methanol, β-alanine pathway, metabolic engineering, lactate transporters\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5386323/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5386323/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eBioconversion of methanol derived from CO\\u003csub\\u003e2\\u003c/sub\\u003e reduction into value-added chemicals is crucial for mitigating global warming and reducing fossil fuels dependence within a circular economy. Production of 3-hydroxypropionic (3-HP) acid, a key building block for the development of biobased products such as acrylates and 1,3-propanediol, has been successfully achieved using methanol as the sole carbon and energy source in the methylotrophic yeast \\u003cem\\u003eKomagataella phaffii\\u003c/em\\u003e (syn. \\u003cem\\u003ePichia pastoris\\u003c/em\\u003e). However, challenges remain in meeting commercially relevant concentrations, yields and productivities of 3-HP, prompting further strain optimization. In the present study, we have combined metabolic engineering strategies aiming at increasing metabolic precursors supply and redirecting carbon flux towards 3-HP production.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA combinatorial metabolic engineering strategy targeted to increase precursor supply and 3-HP export was applied to the original 3-HP producing \\u003cem\\u003eK. phaffii\\u003c/em\\u003e strain harboring the synthetic b-alanine pathway and a heterologous NADP-dependent formate dehydrogenase. To do so, several genes encoding for enzymes catalyzing reactions immediately upstream of the β-alanine pathway were overexpressed to enhance the pathway\\u0026rsquo;s precursors supply. However, only the overexpression of the pyruvate carboxylase \\u003cem\\u003ePYC2\\u003c/em\\u003e gene significantly increased the 3-HP yield on biomass (Y\\u003csub\\u003eP/X\\u003c/sub\\u003e) in small-scale cultivations. Co-overexpression of \\u003cem\\u003ePYC2\\u003c/em\\u003e and the lactate permeases \\u003cem\\u003eESBP6\\u003c/em\\u003e and \\u003cem\\u003eJEN1\\u003c/em\\u003e genes led to a 55% improvement in titer (1.5 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) and product yield (0.13 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) compared to the reference strain, mostly due to Esbp6 activity, proving its effectiveness as a 3-HP transporter. Deletion of the native formate dehydrogenase gene \\u003cem\\u003eFDH1\\u003c/em\\u003e did not increase methanol flux entering the assimilatory pathway. Instead, knockout strains showed severe growth defects due to toxic intermediates accumulation. Co-expression of a gene encoding for a mutated NADP-dependent formate dehydrogenase in these strains failed to compensate for the loss of native \\u003cem\\u003eFDH\\u003c/em\\u003e. The strain combining \\u003cem\\u003ePYC2\\u003c/em\\u003e, \\u003cem\\u003eESBP6\\u003c/em\\u003e and \\u003cem\\u003eJEN1\\u003c/em\\u003e overexpression was further tested in fed-batch cultures at pH 5, achieving a final 3-HP concentration of 27.0 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in 39.3 h, with a product yield of 0.19 g g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and a volumetric productivity of 0.56 g l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. These results represent a 42% increase in final concentration and over 20% improvement in volumetric productivity compared to the original 3-HP producing strain. Furthermore, bioreactor-scale cultivations at pH 3.5 revealed increased robustness of the strains overproducing monocarboxylate transporters.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eOur results point out the potential of lactate transporters to efficiently drive 3-HP export in \\u003cem\\u003eK. phaffii\\u003c/em\\u003e, leading to higher titers, yields, and productivities, even at lower pH conditions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Leveraging lactate transporters for superior 3-hydroxypropionic (3-HP) acid production from methanol in Komagataella phaffii\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-11-18 13:54:56\",\"doi\":\"10.21203/rs.3.rs-5386323/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-12-15T06:48:25+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-12-14T15:54:32+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-12-13T04:33:19+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-12-06T04:00:22+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"142101648134471629402348228687406279706\",\"date\":\"2024-12-05T00:56:41+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"4430152818722663090726229394929396396\",\"date\":\"2024-12-05T00:41:33+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"141778765498251331189376113172920710009\",\"date\":\"2024-12-04T18:06:27+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-12-04T13:38:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"59892522895391940735574191999618337396\",\"date\":\"2024-11-25T01:22:25+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-11-19T20:54:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-11-05T06:32:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-11-05T06:31:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Journal of Biological Engineering\",\"date\":\"2024-11-04T08:36:29+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-biological-engineering\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jbie\",\"sideBox\":\"Learn more about [Journal of Biological Engineering](http://jbioleng.biomedcentral.com/)\",\"snPcode\":\"13036\",\"submissionUrl\":\"https://submission.nature.com/new-submission/13036/3\",\"title\":\"Journal of Biological Engineering\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a414474f-ddaf-4c88-b439-340df0ea483c\",\"owner\":[],\"postedDate\":\"November 18th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-02-24T16:55:23+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5386323\",\"link\":\"https://doi.org/10.1186/s13036-025-00488-x\",\"journal\":{\"identity\":\"journal-of-biological-engineering\",\"isVorOnly\":false,\"title\":\"Journal of Biological Engineering\"},\"publishedOn\":\"2025-02-20 15:57:40\",\"publishedOnDateReadable\":\"February 20th, 2025\"},\"versionCreatedAt\":\"2024-11-18 13:54:56\",\"video\":\"\",\"vorDoi\":\"10.1186/s13036-025-00488-x\",\"vorDoiUrl\":\"https://doi.org/10.1186/s13036-025-00488-x\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5386323\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5386323\",\"identity\":\"rs-5386323\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}