Investigation of the Potential Mechanisms of Limonene Resistance in Meyerozyma Caribbica by Comparative Genomics

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Bioprospecting offers a cost-effective strategy to overcome this limitation and through this approach a strain of Meyerozyma caribbica , with a profile of resistance to the monoterpene limonene, was isolated from Solanum lycocarpum . This study aimed to elucidate the molecular mechanisms underlying this resistance through comparative genomics. These in silico analyses against five food-industry relevant yeasts ( Saccharomyces cerevisiae, Kluyveromyces marxianus, Spathaspora passalidarum, S. arborariae , and Scheffersomyces stipitis ) identified 58 orthologs gene clusters and highlighted 26 proteins belonging to the Major Facilitator Superfamily (MFS) transporter in M. caribbica . Homology modeling and molecular docking analyses demonstrated a satisfactory binding affinity between limonene and the generated MFS protein models, supporting their potential role as specific efflux pumps. Quantitative PCR analysis identified a specific clade within the MFS transporter superfamily that was significantly upregulated under limonene stress. However, experimental validation revealed that intracellular limonene concentrations in M. caribbica following exposure to 4% (v/v) limonene were not significantly lower than those measured in susceptible yeast strains. The confluence of in silico and experimental evidence strongly indicates that the limonene resistance in M. caribbica is mediated by the active efflux of the compound, facilitated by specific MFS transporters. These findings establish M. caribbica as a promising biological chassis for the industrial bioconversion of limonene into value-added products. Limonene Meyerozyma caribbica comparative genomics MFS transporter molecular docking bioconversion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The production of aroma compounds is traditionally achieved through natural extraction or chemical synthesis, both of which are typically associated with limiting factors such as low productivity, dependence on climatic conditions, extreme process requirements, and the generation of racemic mixtures[ 1 ]. Biotechnological processes offer a viable alternative for the production of aroma compounds, as they circumvent the need for seasonal raw materials, operate under milder conditions, exhibit high specificity, and can be considered environmentally sustainable[ 2 ]. Among the substrates extensively studied and employed in bioprocesses, monoterpenes hold particular significance[ 3 ]. In this group, limonene (empirical formula: C₁₀H₁₆) stands out due to its industrial relevance, attributed to its widespread availability, low cost, and role as a precursor for various high-value-added compounds[ 1 ]. However, terpene bioconversion is constrained by several factors, particularly the inherent toxicity of these compounds to microorganisms when used as substrates in biotechnological processes[ 2 ]. A potential strategy to overcome the challenges associated with the physicochemical properties of limonene in bioconversion processes is the use of robust microorganisms exhibiting resistance phenotypes to terpene toxicity and other organic solvents[ 4 ]. Through bioprospecting efforts, a strain of Meyerozyma caribbica displaying limonene resistance was isolated from a plant-derived source and identified via genetic markers by the research group at the Food Biotechnology Laboratory of the Federal University of Vales do Jequitinhonha and Mucuri, in collaboration with the Synthetic Biology and Biological Systems Modeling Laboratory at the Federal University of Viçosa. The yeast M. caribbica demonstrates significant biotechnological potential, as recent studies have shown that isolates of this species are capable of bioremediating aqueous media containing manganese ions[ 5 ]. Furthermore, it has been identified as a promising candidate for ethanol production from various carbohydrate sources[ 6 ]. The increasing availability of genomic data and bioinformatics tools enables the discovery of novel biological insights and facilitates the establishment of correlations between organisms and their phenotypic traits[ 7 ]. In this context, the present study aims to investigate the potential biological mechanisms underlying the limonene resistance phenotype observed in M. caribbica through comparative genomics. 2. Material and methods 2.1 Microorganisms and culture medium The Meyerozyma caribbica yeast strain used in this study was isolated by the research group at the Food Biotechnology Laboratory (LBA) of the Institute of Science and Technology at the Federal University of Vales do Jequitinhonha and Mucuri (ICT-UFVJM) from the fruit of Solanum lycocarpum . The Saccharomyces cerevisiae , Kluyveromyces marxianus , Spathaspora passalidarum , Spathaspora arborariae , and Scheffersomyces stipitis strains were kindly provided by Dr. Luciano Gomes Fietto from the Department of Biochemistry and Molecular Biology at the Federal University of Viçosa. For cultivation experiments, an adapted Yeast Malt (YM) medium was used, consisting of bacteriological peptone (0.5%), glucose (1%), yeast extract (0.3%), and distilled water. When a solid medium was required, 2% bacteriological agar was added to the formulation. 2.2 Limonene resistance assay The cultivation strategies employed in this study were based on the methodology used in the isolation and selection of Meyerozyma caribbica by the LBA research group, as described by Bicas & Pastore[ 8 ]. Microorganisms were activated in YM medium on Petri dishes for 48 hours at 30°C in a bacteriological incubator (Thelga, TE64CB). Following the activation period, a bacteriological loop was used to transfer the isolates to a tube of 16 × 100 mm (13.5 mL) containing 6 mL of sterile YM medium. The tubes were incubated in an orbital shaker (Tecnal, model TE-421) at 30°C with agitation at 150 rpm for 48 hours. Following cultivation, microbial growth rates were indirectly assessed by measuring absorbance at 600 nm (UA) using 200 µL aliquots of culture medium containing biomass in a digital spectrophotometer (Versa Max Microplate Reader, Molecular Devices). The absorbance values obtained were used to normalize the initial inoculum for yeast cultivation in the presence of limonene. Finally, microorganisms were cultivated in liquid YM medium in 13.5 mL test tubes containing 0, 2, and 4% limonene ((R)-(+)-limonene (Sigma-Aldrich, ≥ 93% purity)) (v/v), with the initial inoculum adjusted to 0.6 UA. Cultures were maintained at 30°C with agitation at 150 rpm in an orbital incubator. Absorbance measurements were taken every 24 hours for up to 72 hours. All assays were performed in technical triplicates. 2.3 Comparative genomics The genomes used in this study are available in the National Center for Biotechnology Information (NCBI) database, and the accession numbers for each assembly are listed in Table 1 . In general, the comparative genomics methodology can be summarized in three main steps: (i) prediction of all genes from the assembly of a reference genome for each microorganism, (ii) identification and clustering of orthologous genes, and (iii) annotation of the potential proteins encoded by the genes of interest. These steps were conducted in a Linux virtual environment, and both the software and the resulting analysis data were hosted on the Júpiter server of the Information Technology Directorate at UFV. Table 1 Species and NCBI accession numbers of the genomes analyzed. Microorganism Accession Number Saccharomyces cerevisiae GCF_000146045.2 Kluyveromyces marxianus GCF_001417885.1 Spathaspora passalidarum GCF_000223485.1 Spathaspora arborariae GCA_000497715.1 Scheffersomyces stipitis GCF_000209165.1 Meyerozyma caribbica GCA_000755205.1 Gene prediction for each selected genome was performed using Augustus software (version 3.2.2, available at: http://augustus.gobics.de/ ) [ 9 ], with parameters set for protein and coding sequence (CDS) retrieval. Orthologous gene clustering followed the methodology described by Mendes et al.[ 10 ] and was conducted using OrthoMCL[ 11 ]. Subsequently, annotation of the clustered genes classified as orthologous to M. caribbica was performed using InterProScan 5.30[ 12 ], which predicts protein domains and families based on the CDS obtained from Augustus, correlating them with the InterPro database[ 13 ]. 2.4 Sample preparation for cell viability test and volatile compound extraction Initially, the yeasts S. cerevisiae , K. marxianus , S. passalidarum , and M. caribbica were cultivated in 13.5 mL test tubes containing approximately 5.8 mL of YM medium at 30°C with orbital shaking at 150 rpm until the cultures reached an optical density of 1 AU. A 300 µL aliquot of each culture was reserved for cell viability testing through serial dilution and spot plating. Subsequently, 240 µL of limonene (final concentration: 4% (v/v)) was added to each tube, followed by vortexing for 1 minute. The tubes were then returned to the incubator at 30°C with orbital shaking at 150 rpm. After 15 minutes, another 300 µL aliquot of each culture was collected for a second cell viability test. Two 600 µL aliquots of each culture exposed to limonene were reserved for volatile compound extraction in an organic solvent, adapting the methodology described by Molina et al.[ 14 ]. 2.5 Cell viability test The cell viability assay was conducted at two points, before and after exposure to 4% (v/v) limonene for 15 minutes. Serial dilutions 1:10, 1:100, 1:1,000, 1:10,000, and 1:20,000 were prepared from the 300 µL aliquots and spotted (10 µL per spot) onto petri dishes containing solid YM medium. The plates were incubated at 30°C for 48 hours and photographed using a gel documentation system (UVB Transilluminator LTB-20X20HE, Loccus do Brasil, Cotia, São Paulo, Brazil). 2.6 Volatile compound extraction For volatile compound extraction, 600 µL of culture medium exposed to limonene for 15 minutes was transferred to 1.5 mL microcentrifuge tubes and centrifuged at 3,000 rpm for 5 minutes (Nova Instrument, NI1802). The supernatant was separated from the precipitate into new 1.5 mL tubes. Ethyl acetate was used as the solvent, added at a 1:1 ratio to the initial sample volume (600 µL) in each tube (supernatant and precipitate), followed by vortexing for 1 minute. A second centrifugation was performed under the same conditions, and the organic phase (clear layer) was transferred to new 1.5 mL tubes containing 100 mg of anhydrous sodium sulfate. This process (solvent addition, vortexing, centrifugation, and organic phase transfer) was repeated three times. The final volume of the extracted sample in ethyl acetate was approximately 1.8 mL. A calibration curve was generated to correlate limonene peak areas with biological sample concentrations, using eight reference points (0, 0.1, 0.2, 0.5, 1, 2, 3, and 4 µL of limonene per mL of ethyl acetate). 2.7 Limonene quantification by gas chromatography-mass spectrometry (GC-MS) Limonene quantification was performed using a gas chromatograph coupled with a mass spectrometer (SHIMADZU, GCMS-QP2010 Ultra) equipped with a Restek Rtx-5MS fused silica column (30 m length, 0.25 mm internal diameter, 0.25 µm film thickness), composed of 5% diphenyl and 95% dimethyl polysiloxane. Helium was used as the carrier gas at a constant flow rate of 1.8 mL/min. The split-splitless injector was maintained at 220°C and operated in split mode with a split ratio of 40:1. The detector was set at a transfer line temperature of 240°C, an ionization energy of + 70 eV, and a mass range of 35–400 m/z. The gas chromatograph oven temperature was initially held at 40°C for 2 minutes, then increased to 200°C at a rate of 10°C/min, where it was maintained for an additional 5 minutes. 2.8 Gene expression quantification To design primers targeting the Major Facilitator Superfamily (MFS) transporters in M. caribbica , we first identified nucleotide sequences of annotated transporter orthologs. These sequences were clustered and a phylogenetic tree was constructed using MEGA software[ 15 ]. Consensus sequences from the resulting clades were used to design 18 primer pairs, ensuring coverage of all 26 annotated MFS transporter genes. The nucleotide specificity of all primers was subsequently evaluated using Primer-BLAST[ 16 ]. M. caribbica cells used for RNA isolation were grown in YM medium overnight at 30°C and stirring at 120 rpm. Each inoculum was divided into three 125 mL Erlenmeyer flasks containing 20 mL of fresh medium, and the final monoterpene concentrations of 0 (control), 2 and 4% (v/v). The initial OD 600 was adjusted to 0.6. The flasks were incubated for 72 h under the conditions described above. The samples were collected at 0, 24 and 72 hours of stress exposure to perform the expression analysis. Experiments were conducted in three biological replicates. The harvested cells were treated with Lyticase from Arthrobacter luteus (Sigma-Aldrich) for 1 hour at 30°C under gentle stirring. Total RNA was extracted using the RNeasy Mini kit (Qiagen). RNA concentration was quantified with the Qubit 3.0 Fluorometer (Invitrogen). Samples were treated with RNase-free DNase I (Promega M6101), and cDNA synthesis was performed with the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems 4374967) according to the following cycle: 10 min at 25°C, 120 min at 37°C, and 5 min at 85°C. Genomic DNA was extracted using the lithium acetate/single-stranded carrier DNA/polyethylene glycol (LiAc/ss-DNA/PEG)[ 17 ]. The qPCR analysis was performed using GoTaq qPCR Master Mix (Promega A6002) with StepOne™ Real-Time PCR System (Applied Biosystems). The assays were carried out in technical duplicate using the conditions as follows: 10 min at 95 ºC and 40 cycles of 30 s at 95 ºC, 1 min at 60 ºC. Relative mRNA quantification was performed using the standard curve method, in which genomic DNA was serially diluted to obtain a standard curve for each target by plotting the average Ct versus the log10 of DNA concentrations from the control sample (1.56–100.0 ng.µL-1). The results were normalized using actin as internal control. 2.9 Molecular docking The nucleotide sequences of predicted proteins from clades B and I were selected for molecular docking analysis and translated in silico into their corresponding polypeptides using the ExPASy translate tool[ 18 ], thereby generating the amino acid sequences employed in all subsequent analyses. As a first step in structural characterization, the predicted proteins were analyzed for the presence of signal peptides and transmembrane helices. Signal peptide prediction was carried out using the SignalP 5.0 server[ 19 ], configured for the "Eukarya" organism group with an extended output format. Concurrently, the prediction of transmembrane helices was conducted using the TMHMM 2.0 server[ 20 ], which provided an extensive output that included graphical representations of topology. Following this initial characterization, three-dimensional protein structures were modeled by homology using the Phyre2 server[ 21 ] in intensive mode with 20 references templates models, including the crystal structure of E. coli multidrug transporter MdfA (PDB accession number 4ZP0), and then subjected to energy minimization for structural refinement. This optimization step was performed using the YASARA energy minimization server[ 22 ] with the YASARA force field. Following refinement, the optimized structures underwent a rigorous quality assessment. This comprehensive evaluation included analysis of residue stereochemistry via Ramachandran plots, as well as validation of the three-dimensional models using the ProSA, ERRAT, and Verify 3D programs. For the docking simulations, the ligand structure of (+)-limonene was acquired from the LigandBox chemical database[ 23 ]. Finally, protein-ligand interactions were evaluated through molecular docking simulations performed with AutoDock4[ 24 , 25 ] and AutoDock Vina[ 26 ] within the PyRx platform. This integrated environment enabled the streamlined preparation of both the protein and ligand for comprehensive binding analysis. All three-dimensional resultant models were visualized with PyMOL. 2.10 Statistical analysis To assess and determine whether there were significant differences in limonene quantification values and gene expression quantification, an Analysis of Variance (ANOVA) with Bonferroni correction for multiple hypothesis testing was performed using GraphPad Prism software (version 8.0.1). A significance level of 5% (p < 0.05) was adopted. 3. Results 3.1 Comparative genomics One of the most successful strategies used to investigate phenotypic characteristics of living organisms is comparative genomics[ 27 , 28 ]. Through computational methods, it is possible to identify groups of genes from different organisms that share ancestry and biological functions, as well as similarities and differences at the genetic level. After predicting the genes and classifying them into orthologous groups based on the genomes listed in Table 1 , a total of 58 orthologous gene clusters were found in the genome of M. caribbica ; 39 for S. cerevisiae ; 33 for S. arborariae ; 30 for S. passalidarum; 25 for K. marxianus and none for S. stipitis (Fig. 1 ). Seeking to understand which biological processes might be involved in the phenotypic resistance to limonene observed in M. caribbica and considering that this characteristic could be related to genes exclusively present in this microorganism, the annotation of the proteins within the 58 clusters unique to this yeast was performed. The Fig. 1 displays the categorization of all these 153 proteins present in the 58 orthologous gene groups of M. caribbica . Seven categories of proteins were identified: cell adhesion (8), autophagy (3), enzymes (22, with 15 related to carbohydrates), transcription factors (10), leucine-rich repeat (24), redox proteins (16), and transporters (26), along with hypothetical proteins (44), which are of unknown function. The category with the highest representation corresponded to transporters, comprising 26 proteins. All of these belong to the MFS, the largest family of secondary transporters, which is widely conserved across all forms of life[ 29 ]. This finding is particularly relevant, as efflux pumps are known to contribute to reduced toxicity and increased tolerance to organic solvents in microorganisms. The toxicity of limonene toward microbial cells is largely attributed to its lipophilic nature, which leads to its accumulation in the cell membrane, thereby compromising plasma membrane integrity and inhibiting specific cellular functions. Thus, the high number of transporter proteins identified in the M. caribbica genome suggests that resistance mechanisms in this microorganism may be associated with limonene transporters. 3.2 Limonene quantification Based on the hypothesis that the resistance phenotype observed in M. caribbica may be associated with transporter proteins, intracellular limonene quantification was performed using gas chromatography and compared with three other yeasts ( S. cerevisiae , K. marxianus , and S. passalidarum ). Cell viability assays were conducted before and after exposure of the cultures to 4% (v/v) limonene for 15 minutes (Fig. 2 ). Prior to exposure, cell growth was observed for all yeast strains across all dilutions, confirming their viability. After exposure, microbial growth was detected only in K. marxianus and M. caribbica . Statistical analysis of intracellular limonene quantification couldn’t reach a significant difference between M. caribbica and the other yeast species, although the absolute intracellular limonene levels measured in this microorganism were consistently lower than those detected in the remaining yeast strains under investigation. While a bacteriostatic effect was observed for K. marxianus , M. caribbica demonstrated continued viability after the 15-minute limonene treatment. 3.3 Gene expression quantification Nine clades were established (Fig. 3 ) based on the phylogenetic tree constructed from the protein sequence data of MFS-type transporters identified in M. caribbica . The clades were designated as A, B, C, D, E, F, G, H, and I. The qPCR experiments were successfully performed for clades A, B, G, H, and I. The resulting data were normalized to establish relative expression across replicates and subsequently plotted after statistical analysis (Fig. 4 ). A statistically significant increase in gene expression was observed only for clade I when the yeast was cultivated in the presence of 4% (v/v) limonene for 72 hours. 3.4 Docking The prediction of signal peptide presence yielded very low probabilities across all models, with values not exceeding 0.0046. Specifically, the model cluster5734_mc|g4624.t1 exhibited the highest probability of 0.0046, while all remaining models displayed a probability of 0.0005. Analysis of transmembrane helix topology revealed a degree of variation among the models. The cluster4139_mc|g4970.t1 model was predicted to possess 5 transmembrane helices, cluster5734_mc|g4624.t1 was predicted to have 12, and the remaining models consistently featured a predicted count of 10 transmembrane helices. Of the optimized models, all exhibited Ramachandran plot quality parameters exceeding 95% for the combined allowed and favored regions. Regarding the ProSA quality parameters, the models yielded z-scores proximate to established quality benchmarks, particularly models cluster4139_mc|4968.t1 (-4.53) and cluster4139_mc|4969.t1 (-3.99). In the ERRAT analysis, model cluster4139_mc|4971.t1 was the sole construct displaying an overall quality factor below 90%, whereas model cluster4139_mc|4968.t1 achieved a score of 95.693%. Conversely, for the Verify 3D assessment, none of the models attained the recommended threshold (3D-1D score ≥ 0.2) indicative of high quality. The highest performance in this category was demonstrated by model cluster5734_mc|g4624.t1, which reached 63.18%, a value substantially below the 80% benchmark associated with satisfactory structural integrity. Homology modeling yielded six three-dimensional protein structures, which exhibited an overall architecture highly similar to one of the templates models, the crystal structure of E. coli multidrug transporter MdfA. A structural superposition of the resulting models is presented in Fig. 7, with alpha-helices and beta-sheets depicted in red and yellow, respectively. Subsequent molecular docking analyses revealed binding affinity values for the protein-ligand complexes ranging from − 5.4 kcal/mol to -6.7 kcal/mol. The model cluster4139_mc|g4968.t1 demonstrated the weakest binding affinity (-5.4 kcal/mol), while models cluster5734_mc|g4624.t1 and cluster4139_mc|g4970.t1 exhibited the strongest binding affinities, both at -6.7 kcal/mol. 4. Discussion The present study was motivated by the critical need to overcome the inherent toxicity of limonene, a major bottleneck in the microbial valorization of this monoterpene into high-value compounds such as perillyl alcohol[ 30 ]. The isolation of a Meyerozyma caribbica strain exhibiting unprecedented resistance to limonene concentrations up to 4% (v/v) provided a pivotal starting point for this investigation. Consequently, we employed an integrative multi-methodological approach to elucidate the molecular underpinnings of this exceptional phenotype. Our initial comparative genomic analysis suggested that membrane transporters could be key genetic determinants of limonene resistance, a hypothesis drawn from evidence in other microorganisms[ 31 ]. To empirically test the postulate that active efflux mitigates intracellular accumulation, we quantified limonene levels across several yeast strains. Although the results indicated no statistically significant difference between the mean intracellular limonene concentrations, the absolute values quantified in M. caribbica were consistently lower than those measured in the other yeast strains under investigation. A plausible explanation for this outcome could involve the predetermined exposure time utilized in the experiment; a longer duration might have revealed a more pronounced disparity in limonene accumulation by the cells. Kluyveromyces marxianus also exhibited viability post-exposure, its high intracellular limonene load suggests a tolerance mechanism distinct from active efflux, potentially related to the duration of the assay or other intrinsic cellular defenses. Subsequent transcriptomic analysis aimed to identify which, if any, of the putative transporters identified through genomics were functionally recruited under limonene stress. Notably, the expression levels of most candidate clades (A, B, G, H) remained low and inconclusive. However, a significant and specific upregulation of clade I was observed following prolonged exposure to 4% (v/v) limonene. This targeted transcriptional response strongly implicates the clade I encoded transporter could be directly involved in the cellular defense against limonene toxicity, offering crucial mRNA-level evidence for its role in the resistance phenotype. To further interrogate the function of the leading candidates, molecular docking simulations were performed on the proteins encoded by clades B and I. The computational models revealed two critical findings: firstly, the predicted structures showed significant homology to known transporter proteins, and secondly, both models demonstrated a robust and favorable binding affinity for limonene. These in silico results provide compelling structural evidence that these putative transporters possess the necessary architecture to interact with and potentially facilitate the export of limonene molecules. 5. Conclusion In conclusion, the convergence of genomic, empirical, transcriptomic, and computational evidence presented herein supports a coherent model for limonene resistance in M. caribbica . We propose that this phenotype could be related by the action of specific membrane transporters, most notably the protein encoded by clade I, which is transcriptionally induced by limonene stress. The function of this and other putative pumps is substantiated by their computationally predicted capacity for ligand binding. This work not only elucidates a key survival strategy in a robu31st yeast isolate but also establishes a foundational framework for future strain engineering. The identification and validation of these efflux transporters open promising avenues for developing superior microbial platforms capable of withstanding high concentrations of toxic monoterpenes, thereby enhancing the biotechnological production of valuable compounds. Declarations Acknowledgments We gratefully acknowledge Dr. Sérgio Antônio Fernandes from the Department of Chemistry at UFV for his guidance in the methodology for intracellular limonene quantification using gas chromatography-mass spectrometry (GC-MS). We also extend our sincere thanks to Dr. Luciano Gomes Fietto from the Department of Biochemistry and Molecular Biology at the Federal University of Viçosa for generously providing the yeast strains Saccharomyces cerevisiae , Kluyveromyces marxianus , Spathaspora passalidarum , Spathaspora arborariae , and Scheffersomyces stipites . Author contributions Conceptualization: Filipe Augusto Teixeira, Gustavo Molina and Tiago Antônio de Oliveira Mendes; Methodology: Filipe Augusto Teixeira, Fernanda Matias Albuini, Ananda Aguilar and Patrick Neves Squizato; Formal analysis and investigation: Filipe Augusto Teixeira and Tiago Antônio de Oliveira Mendes; Writing - original draft preparation: Filipe Augusto Teixeira; Writing - review and editing: Filipe Augusto Teixeira, Gustavo Molina, Fernanda Matias Albuini, Ananda Aguilar, Patrick Neves Squizato and Tiago Antônio de Oliveira Mendes; Resources: Gustavo Molina and Tiago Antônio de Oliveira Mendes; Supervision: Gustavo Molina and Tiago Antônio de Oliveira Mendes. Competing Interests The authors have no competing interests to declare that are relevant to the content of this article. References Felipe L, de O, Oliveira AM, Bicas JL (2017) Bioaromas – Perspectives for sustainable development. 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Proteins: Structure, Function and Bioinformatics 77:114–122 Kawabata T, Sugihara Y, Fukunishi Y, Nakamura H (2013) LigandBox: A database for 3D structures of chemical compounds. Biophys (Japan) 9:113–121. https://doi.org/10.2142/biophysics.9.113 Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28:1145–1152. https://doi.org/10.1002/jcc.20634 Morris GM, Goodsell DS, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14%3C1639::AID-JCC10%3E3.0.CO;2-B Trott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334 Grueber CE (2015) Comparative genomics for biodiversity conservation. Comput Struct Biotechnol J 13:370–375. https://doi.org/10.1016/j.csbj.2015.05.003 Coutinho TJD, Franco GR, Lobo FP (2015) Homology-Independent Metrics for Comparative Genomics. Comput Struct Biotechnol J 13:352–357. https://doi.org/10.1016/j.csbj.2015.04.005 Yan N (2013) Structural advances for the major facilitator superfamily (MFS) transporters. Trends Biochem Sci 38:151–159. https://doi.org/10.1016/j.tibs.2013.01.003 Alonso-Gutierrez J, Chan R, Batth TS et al (2013) Metabolic engineering of Escherichia coli for limonene and perillyl alcohol production. Metab Eng 19:33–41. https://doi.org/10.1016/j.ymben.2013.05.004 Wang Y, Lim L, Diguistini S et al (2013) A specialized ABC efflux transporter GcABC-G1 confers monoterpene resistance to Grosmannia clavigera, a bark beetle-associated fungal pathogen of pine trees. New Phytol 197:886–898 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 29 Jan, 2026 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. 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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-8732531","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593908427,"identity":"f9dddda6-ead1-4ec6-a94a-837c1f134b3c","order_by":0,"name":"Filipe Augusto Teixeira","email":"","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Filipe","middleName":"Augusto","lastName":"Teixeira","suffix":""},{"id":593908428,"identity":"7d884396-e2b0-4175-a813-5869776fa6b7","order_by":1,"name":"Gustavo Molina","email":"","orcid":"","institution":"Universidade Federal dos Vales do Jequitinhonha e Mucuri","correspondingAuthor":false,"prefix":"","firstName":"Gustavo","middleName":"","lastName":"Molina","suffix":""},{"id":593908429,"identity":"4bd08705-c1ef-4778-bb1b-35a6af2f3717","order_by":2,"name":"Fernanda Matias Albuini","email":"","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"Matias","lastName":"Albuini","suffix":""},{"id":593908430,"identity":"737ecad0-5099-4e92-ba47-519da8ce1251","order_by":3,"name":"Ananda Aguilar","email":"","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Ananda","middleName":"","lastName":"Aguilar","suffix":""},{"id":593908432,"identity":"a62e464a-7c06-49f8-a065-1688386c9860","order_by":4,"name":"Patrick Neves Squizato","email":"","orcid":"","institution":"Institute of Biosynthetic and Fiber Innovation","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"Neves","lastName":"Squizato","suffix":""},{"id":593908434,"identity":"e21aeaf0-fa21-4b9e-ab7f-d002bc4a0938","order_by":5,"name":"Tiago Antônio de Oliveira Mendes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZgTTgIGhgoGfgYEHvw4eVC1nGCQbCGpBYhswMLYRocWenfnxiw81NtH80s0bH/6cd1jC4ADvsQ/4HcZmZjnjWFruzDnHio15t4G08CXPIOAXM2MetsO5G27kmEkzbjtcJ9nAY4zfL8zs34x5/h3O3X8jx/znzzmHJYjQwmP8mLcNaItEjhkDb8NhCX4GQloO85QxzuxLy51xI61YmudYugQ/M18yXi3s/cc3f/jwzSa3f0byxo8/aqwl2Nh7D+PVAgRsEqh8ZuzKUJTgjYVRMApGwSgYBQwAN1lDlbzDj7wAAAAASUVORK5CYII=","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":true,"prefix":"","firstName":"Tiago","middleName":"Antônio de Oliveira","lastName":"Mendes","suffix":""}],"badges":[],"createdAt":"2026-01-29 14:10:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8732531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8732531/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103067504,"identity":"4ed53de4-372f-4aa9-a3ad-3c7d9583dd31","added_by":"auto","created_at":"2026-02-20 11:34:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":361818,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of orthologous gene clusters found for each yeast submitted to comparative genomics and categorization of proteins unique to \u003cem\u003eM. caribbica\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8732531/v1/973e1e3dca657d69db3a802b.png"},{"id":103067508,"identity":"6576a5e3-f667-4bd2-a447-c372f9a5f473","added_by":"auto","created_at":"2026-02-20 11:34:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3000306,"visible":true,"origin":"","legend":"\u003cp\u003eIntracellular limonene quantification. A: Sample preparation methodology and growth curves of the four tested yeast strains following 72-hour cultivation. B: Cell viability assay before and after 15-minute limonene exposure, performed via serial dilution of the cultures. C: Intracellular limonene quantification by gas chromatography.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8732531/v1/7f04d6d39b39e97a0398bf3f.png"},{"id":103067505,"identity":"b7f120a1-c07a-4e61-8b74-a352418b08b4","added_by":"auto","created_at":"2026-02-20 11:34:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226661,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic distribution of the nine clades formed from the 26 MFS-type transporter sequences identified in \u003cem\u003eM. caribbica\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8732531/v1/e253d3971d4b42fb8c12eadb.png"},{"id":103067507,"identity":"81317f8e-e163-4d20-8b55-266deb134cbc","added_by":"auto","created_at":"2026-02-20 11:34:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":236902,"visible":true,"origin":"","legend":"\u003cp\u003eRelative expression of genes from clades A, B, G, H, and I. Significant relative expression was observed solely for clade I when \u003cem\u003eM. caribbica\u003c/em\u003e was cultivated for 72 hours in the presence of 4% (v/v) limonene.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8732531/v1/e2add3da8b3d4f7a0696196c.png"},{"id":103067509,"identity":"3ff29b3f-769c-48ef-84ef-861124615849","added_by":"auto","created_at":"2026-02-20 11:34:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2970872,"visible":true,"origin":"","legend":"\u003cp\u003eOptimized predicted models from molecular docking. A) Top view and B) cross-sectional view of each optimized model. The limonene molecule is highlighted in blue.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8732531/v1/d0699a56ede2d510b75bf722.png"},{"id":103504255,"identity":"7dd9f8d5-b5a2-41fe-ac79-23d441c6d71d","added_by":"auto","created_at":"2026-02-26 13:18:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8294371,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8732531/v1/de9bd6f8-338c-489e-aef5-1135f254df6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInvestigation of the Potential Mechanisms of Limonene Resistance in Meyerozyma Caribbica by Comparative Genomics\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe production of aroma compounds is traditionally achieved through natural extraction or chemical synthesis, both of which are typically associated with limiting factors such as low productivity, dependence on climatic conditions, extreme process requirements, and the generation of racemic mixtures[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Biotechnological processes offer a viable alternative for the production of aroma compounds, as they circumvent the need for seasonal raw materials, operate under milder conditions, exhibit high specificity, and can be considered environmentally sustainable[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the substrates extensively studied and employed in bioprocesses, monoterpenes hold particular significance[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In this group, limonene (empirical formula: C₁₀H₁₆) stands out due to its industrial relevance, attributed to its widespread availability, low cost, and role as a precursor for various high-value-added compounds[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, terpene bioconversion is constrained by several factors, particularly the inherent toxicity of these compounds to microorganisms when used as substrates in biotechnological processes[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA potential strategy to overcome the challenges associated with the physicochemical properties of limonene in bioconversion processes is the use of robust microorganisms exhibiting resistance phenotypes to terpene toxicity and other organic solvents[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Through bioprospecting efforts, a strain of \u003cem\u003eMeyerozyma caribbica\u003c/em\u003e displaying limonene resistance was isolated from a plant-derived source and identified via genetic markers by the research group at the Food Biotechnology Laboratory of the Federal University of Vales do Jequitinhonha and Mucuri, in collaboration with the Synthetic Biology and Biological Systems Modeling Laboratory at the Federal University of Vi\u0026ccedil;osa.\u003c/p\u003e \u003cp\u003eThe yeast \u003cem\u003eM. caribbica\u003c/em\u003e demonstrates significant biotechnological potential, as recent studies have shown that isolates of this species are capable of bioremediating aqueous media containing manganese ions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, it has been identified as a promising candidate for ethanol production from various carbohydrate sources[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe increasing availability of genomic data and bioinformatics tools enables the discovery of novel biological insights and facilitates the establishment of correlations between organisms and their phenotypic traits[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this context, the present study aims to investigate the potential biological mechanisms underlying the limonene resistance phenotype observed in \u003cem\u003eM. caribbica\u003c/em\u003e through comparative genomics.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Microorganisms and culture medium\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eMeyerozyma caribbica\u003c/em\u003e yeast strain used in this study was isolated by the research group at the Food Biotechnology Laboratory (LBA) of the Institute of Science and Technology at the Federal University of Vales do Jequitinhonha and Mucuri (ICT-UFVJM) from the fruit of \u003cem\u003eSolanum lycocarpum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, \u003cem\u003eKluyveromyces marxianus\u003c/em\u003e, \u003cem\u003eSpathaspora passalidarum\u003c/em\u003e, \u003cem\u003eSpathaspora arborariae\u003c/em\u003e, and \u003cem\u003eScheffersomyces stipitis\u003c/em\u003e strains were kindly provided by Dr. Luciano Gomes Fietto from the Department of Biochemistry and Molecular Biology at the Federal University of Vi\u0026ccedil;osa.\u003c/p\u003e \u003cp\u003eFor cultivation experiments, an adapted Yeast Malt (YM) medium was used, consisting of bacteriological peptone (0.5%), glucose (1%), yeast extract (0.3%), and distilled water. When a solid medium was required, 2% bacteriological agar was added to the formulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Limonene resistance assay\u003c/h2\u003e \u003cp\u003eThe cultivation strategies employed in this study were based on the methodology used in the isolation and selection of \u003cem\u003eMeyerozyma caribbica\u003c/em\u003e by the LBA research group, as described by Bicas \u0026amp; Pastore[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Microorganisms were activated in YM medium on Petri dishes for 48 hours at 30\u0026deg;C in a bacteriological incubator (Thelga, TE64CB).\u003c/p\u003e \u003cp\u003eFollowing the activation period, a bacteriological loop was used to transfer the isolates to a tube of 16 \u0026times; 100 mm (13.5 mL) containing 6 mL of sterile YM medium. The tubes were incubated in an orbital shaker (Tecnal, model TE-421) at 30\u0026deg;C with agitation at 150 rpm for 48 hours.\u003c/p\u003e \u003cp\u003eFollowing cultivation, microbial growth rates were indirectly assessed by measuring absorbance at 600 nm (UA) using 200 \u0026micro;L aliquots of culture medium containing biomass in a digital spectrophotometer (Versa Max Microplate Reader, Molecular Devices). The absorbance values obtained were used to normalize the initial inoculum for yeast cultivation in the presence of limonene.\u003c/p\u003e \u003cp\u003eFinally, microorganisms were cultivated in liquid YM medium in 13.5 mL test tubes containing 0, 2, and 4% limonene ((R)-(+)-limonene (Sigma-Aldrich, \u0026ge;\u0026thinsp;93% purity)) (v/v), with the initial inoculum adjusted to 0.6 UA. Cultures were maintained at 30\u0026deg;C with agitation at 150 rpm in an orbital incubator. Absorbance measurements were taken every 24 hours for up to 72 hours. All assays were performed in technical triplicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Comparative genomics\u003c/h2\u003e \u003cp\u003eThe genomes used in this study are available in the National Center for Biotechnology Information (NCBI) database, and the accession numbers for each assembly are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In general, the comparative genomics methodology can be summarized in three main steps: (i) prediction of all genes from the assembly of a reference genome for each microorganism, (ii) identification and clustering of orthologous genes, and (iii) annotation of the potential proteins encoded by the genes of interest. These steps were conducted in a Linux virtual environment, and both the software and the resulting analysis data were hosted on the J\u0026uacute;piter server of the Information Technology Directorate at UFV.\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\u003eSpecies and NCBI accession numbers of the genomes analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroorganism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccession Number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCF_000146045.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKluyveromyces marxianus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCF_001417885.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpathaspora passalidarum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCF_000223485.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpathaspora arborariae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCA_000497715.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eScheffersomyces stipitis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCF_000209165.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMeyerozyma caribbica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCA_000755205.1\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\u003eGene prediction for each selected genome was performed using Augustus software (version 3.2.2, available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://augustus.gobics.de/\u003c/span\u003e\u003cspan address=\"http://augustus.gobics.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], with parameters set for protein and coding sequence (CDS) retrieval. Orthologous gene clustering followed the methodology described by Mendes et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and was conducted using OrthoMCL[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubsequently, annotation of the clustered genes classified as orthologous to \u003cem\u003eM. caribbica\u003c/em\u003e was performed using InterProScan 5.30[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which predicts protein domains and families based on the CDS obtained from Augustus, correlating them with the InterPro database[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sample preparation for cell viability test and volatile compound extraction\u003c/h2\u003e \u003cp\u003eInitially, the yeasts \u003cem\u003eS. cerevisiae\u003c/em\u003e, \u003cem\u003eK. marxianus\u003c/em\u003e, \u003cem\u003eS. passalidarum\u003c/em\u003e, and \u003cem\u003eM. caribbica\u003c/em\u003e were cultivated in 13.5 mL test tubes containing approximately 5.8 mL of YM medium at 30\u0026deg;C with orbital shaking at 150 rpm until the cultures reached an optical density of 1 AU. A 300 \u0026micro;L aliquot of each culture was reserved for cell viability testing through serial dilution and spot plating. Subsequently, 240 \u0026micro;L of limonene (final concentration: 4% (v/v)) was added to each tube, followed by vortexing for 1 minute. The tubes were then returned to the incubator at 30\u0026deg;C with orbital shaking at 150 rpm. After 15 minutes, another 300 \u0026micro;L aliquot of each culture was collected for a second cell viability test.\u003c/p\u003e \u003cp\u003eTwo 600 \u0026micro;L aliquots of each culture exposed to limonene were reserved for volatile compound extraction in an organic solvent, adapting the methodology described by Molina et al.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cell viability test\u003c/h2\u003e \u003cp\u003eThe cell viability assay was conducted at two points, before and after exposure to 4% (v/v) limonene for 15 minutes. Serial dilutions 1:10, 1:100, 1:1,000, 1:10,000, and 1:20,000 were prepared from the 300 \u0026micro;L aliquots and spotted (10 \u0026micro;L per spot) onto petri dishes containing solid YM medium. The plates were incubated at 30\u0026deg;C for 48 hours and photographed using a gel documentation system (UVB Transilluminator LTB-20X20HE, Loccus do Brasil, Cotia, S\u0026atilde;o Paulo, Brazil).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Volatile compound extraction\u003c/h2\u003e \u003cp\u003eFor volatile compound extraction, 600 \u0026micro;L of culture medium exposed to limonene for 15 minutes was transferred to 1.5 mL microcentrifuge tubes and centrifuged at 3,000 rpm for 5 minutes (Nova Instrument, NI1802). The supernatant was separated from the precipitate into new 1.5 mL tubes. Ethyl acetate was used as the solvent, added at a 1:1 ratio to the initial sample volume (600 \u0026micro;L) in each tube (supernatant and precipitate), followed by vortexing for 1 minute. A second centrifugation was performed under the same conditions, and the organic phase (clear layer) was transferred to new 1.5 mL tubes containing 100 mg of anhydrous sodium sulfate. This process (solvent addition, vortexing, centrifugation, and organic phase transfer) was repeated three times. The final volume of the extracted sample in ethyl acetate was approximately 1.8 mL.\u003c/p\u003e \u003cp\u003eA calibration curve was generated to correlate limonene peak areas with biological sample concentrations, using eight reference points (0, 0.1, 0.2, 0.5, 1, 2, 3, and 4 \u0026micro;L of limonene per mL of ethyl acetate).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Limonene quantification by gas chromatography-mass spectrometry (GC-MS)\u003c/h2\u003e \u003cp\u003eLimonene quantification was performed using a gas chromatograph coupled with a mass spectrometer (SHIMADZU, GCMS-QP2010 Ultra) equipped with a Restek Rtx-5MS fused silica column (30 m length, 0.25 mm internal diameter, 0.25 \u0026micro;m film thickness), composed of 5% diphenyl and 95% dimethyl polysiloxane. Helium was used as the carrier gas at a constant flow rate of 1.8 mL/min. The split-splitless injector was maintained at 220\u0026deg;C and operated in split mode with a split ratio of 40:1. The detector was set at a transfer line temperature of 240\u0026deg;C, an ionization energy of +\u0026thinsp;70 eV, and a mass range of 35\u0026ndash;400 m/z. The gas chromatograph oven temperature was initially held at 40\u0026deg;C for 2 minutes, then increased to 200\u0026deg;C at a rate of 10\u0026deg;C/min, where it was maintained for an additional 5 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene expression quantification\u003c/h2\u003e \u003cp\u003eTo design primers targeting the Major Facilitator Superfamily (MFS) transporters in \u003cem\u003eM. caribbica\u003c/em\u003e, we first identified nucleotide sequences of annotated transporter orthologs. These sequences were clustered and a phylogenetic tree was constructed using MEGA software[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consensus sequences from the resulting clades were used to design 18 primer pairs, ensuring coverage of all 26 annotated MFS transporter genes. The nucleotide specificity of all primers was subsequently evaluated using Primer-BLAST[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eM. caribbica\u003c/em\u003e cells used for RNA isolation were grown in YM medium overnight at 30\u0026deg;C and stirring at 120 rpm. Each inoculum was divided into three 125 mL Erlenmeyer flasks containing 20 mL of fresh medium, and the final monoterpene concentrations of 0 (control), 2 and 4% (v/v). The initial OD\u003csub\u003e600\u003c/sub\u003e was adjusted to 0.6. The flasks were incubated for 72 h under the conditions described above. The samples were collected at 0, 24 and 72 hours of stress exposure to perform the expression analysis. Experiments were conducted in three biological replicates.\u003c/p\u003e \u003cp\u003eThe harvested cells were treated with Lyticase from \u003cem\u003eArthrobacter luteus\u003c/em\u003e (Sigma-Aldrich) for 1 hour at 30\u0026deg;C under gentle stirring. Total RNA was extracted using the RNeasy Mini kit (Qiagen). RNA concentration was quantified with the Qubit 3.0 Fluorometer (Invitrogen). Samples were treated with RNase-free DNase I (Promega M6101), and cDNA synthesis was performed with the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems 4374967) according to the following cycle: 10 min at 25\u0026deg;C, 120 min at 37\u0026deg;C, and 5 min at 85\u0026deg;C. Genomic DNA was extracted using the lithium acetate/single-stranded carrier DNA/polyethylene glycol (LiAc/ss-DNA/PEG)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe qPCR analysis was performed using GoTaq qPCR Master Mix (Promega A6002) with StepOne\u0026trade; Real-Time PCR System (Applied Biosystems). The assays were carried out in technical duplicate using the conditions as follows: 10 min at 95 \u0026ordm;C and 40 cycles of 30 s at 95 \u0026ordm;C, 1 min at 60 \u0026ordm;C. Relative mRNA quantification was performed using the standard curve method, in which genomic DNA was serially diluted to obtain a standard curve for each target by plotting the average Ct versus the log10 of DNA concentrations from the control sample (1.56\u0026ndash;100.0 ng.\u0026micro;L-1). The results were normalized using actin as internal control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Molecular docking\u003c/h2\u003e \u003cp\u003eThe nucleotide sequences of predicted proteins from clades B and I were selected for molecular docking analysis and translated \u003cem\u003ein silico\u003c/em\u003e into their corresponding polypeptides using the ExPASy translate tool[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], thereby generating the amino acid sequences employed in all subsequent analyses. As a first step in structural characterization, the predicted proteins were analyzed for the presence of signal peptides and transmembrane helices. Signal peptide prediction was carried out using the SignalP 5.0 server[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], configured for the \"Eukarya\" organism group with an extended output format. Concurrently, the prediction of transmembrane helices was conducted using the TMHMM 2.0 server[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which provided an extensive output that included graphical representations of topology. Following this initial characterization, three-dimensional protein structures were modeled by homology using the Phyre2 server[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] in intensive mode with 20 references templates models, including the crystal structure of \u003cem\u003eE. coli\u003c/em\u003e multidrug transporter MdfA (PDB accession number 4ZP0), and then subjected to energy minimization for structural refinement. This optimization step was performed using the YASARA energy minimization server[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] with the YASARA force field. Following refinement, the optimized structures underwent a rigorous quality assessment. This comprehensive evaluation included analysis of residue stereochemistry via Ramachandran plots, as well as validation of the three-dimensional models using the ProSA, ERRAT, and Verify 3D programs. For the docking simulations, the ligand structure of (+)-limonene was acquired from the LigandBox chemical database[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Finally, protein-ligand interactions were evaluated through molecular docking simulations performed with AutoDock4[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and AutoDock Vina[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] within the PyRx platform. This integrated environment enabled the streamlined preparation of both the protein and ligand for comprehensive binding analysis. All three-dimensional resultant models were visualized with PyMOL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo assess and determine whether there were significant differences in limonene quantification values and gene expression quantification, an Analysis of Variance (ANOVA) with Bonferroni correction for multiple hypothesis testing was performed using GraphPad Prism software (version 8.0.1). A significance level of 5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was adopted.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparative genomics\u003c/h2\u003e \u003cp\u003eOne of the most successful strategies used to investigate phenotypic characteristics of living organisms is comparative genomics[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Through computational methods, it is possible to identify groups of genes from different organisms that share ancestry and biological functions, as well as similarities and differences at the genetic level. After predicting the genes and classifying them into orthologous groups based on the genomes listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 58 orthologous gene clusters were found in the genome of \u003cem\u003eM. caribbica\u003c/em\u003e; 39 for \u003cem\u003eS. cerevisiae\u003c/em\u003e; 33 for \u003cem\u003eS. arborariae\u003c/em\u003e; 30 for \u003cem\u003eS. passalidarum;\u003c/em\u003e 25 for \u003cem\u003eK. marxianus\u003c/em\u003e and none for \u003cem\u003eS. stipitis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeeking to understand which biological processes might be involved in the phenotypic resistance to limonene observed in \u003cem\u003eM. caribbica\u003c/em\u003e and considering that this characteristic could be related to genes exclusively present in this microorganism, the annotation of the proteins within the 58 clusters unique to this yeast was performed. The Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the categorization of all these 153 proteins present in the 58 orthologous gene groups of \u003cem\u003eM. caribbica\u003c/em\u003e. Seven categories of proteins were identified: cell adhesion (8), autophagy (3), enzymes (22, with 15 related to carbohydrates), transcription factors (10), leucine-rich repeat (24), redox proteins (16), and transporters (26), along with hypothetical proteins (44), which are of unknown function.\u003c/p\u003e \u003cp\u003eThe category with the highest representation corresponded to transporters, comprising 26 proteins. All of these belong to the MFS, the largest family of secondary transporters, which is widely conserved across all forms of life[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This finding is particularly relevant, as efflux pumps are known to contribute to reduced toxicity and increased tolerance to organic solvents in microorganisms. The toxicity of limonene toward microbial cells is largely attributed to its lipophilic nature, which leads to its accumulation in the cell membrane, thereby compromising plasma membrane integrity and inhibiting specific cellular functions. Thus, the high number of transporter proteins identified in the \u003cem\u003eM. caribbica\u003c/em\u003e genome suggests that resistance mechanisms in this microorganism may be associated with limonene transporters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Limonene quantification\u003c/h2\u003e \u003cp\u003eBased on the hypothesis that the resistance phenotype observed in \u003cem\u003eM. caribbica\u003c/em\u003e may be associated with transporter proteins, intracellular limonene quantification was performed using gas chromatography and compared with three other yeasts (\u003cem\u003eS. cerevisiae\u003c/em\u003e, \u003cem\u003eK. marxianus\u003c/em\u003e, and \u003cem\u003eS. passalidarum\u003c/em\u003e). Cell viability assays were conducted before and after exposure of the cultures to 4% (v/v) limonene for 15 minutes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Prior to exposure, cell growth was observed for all yeast strains across all dilutions, confirming their viability. After exposure, microbial growth was detected only in \u003cem\u003eK. marxianus\u003c/em\u003e and \u003cem\u003eM. caribbica\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eStatistical analysis of intracellular limonene quantification couldn\u0026rsquo;t reach a significant difference between \u003cem\u003eM. caribbica\u003c/em\u003e and the other yeast species, although the absolute intracellular limonene levels measured in this microorganism were consistently lower than those detected in the remaining yeast strains under investigation. While a bacteriostatic effect was observed for \u003cem\u003eK. marxianus\u003c/em\u003e, \u003cem\u003eM. caribbica\u003c/em\u003e demonstrated continued viability after the 15-minute limonene treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Gene expression quantification\u003c/h2\u003e \u003cp\u003eNine clades were established (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) based on the phylogenetic tree constructed from the protein sequence data of MFS-type transporters identified in \u003cem\u003eM. caribbica\u003c/em\u003e. The clades were designated as A, B, C, D, E, F, G, H, and I.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe qPCR experiments were successfully performed for clades A, B, G, H, and I. The resulting data were normalized to establish relative expression across replicates and subsequently plotted after statistical analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A statistically significant increase in gene expression was observed only for clade I when the yeast was cultivated in the presence of 4% (v/v) limonene for 72 hours.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Docking\u003c/h2\u003e \u003cp\u003eThe prediction of signal peptide presence yielded very low probabilities across all models, with values not exceeding 0.0046. Specifically, the model cluster5734_mc|g4624.t1 exhibited the highest probability of 0.0046, while all remaining models displayed a probability of 0.0005. Analysis of transmembrane helix topology revealed a degree of variation among the models. The cluster4139_mc|g4970.t1 model was predicted to possess 5 transmembrane helices, cluster5734_mc|g4624.t1 was predicted to have 12, and the remaining models consistently featured a predicted count of 10 transmembrane helices. Of the optimized models, all exhibited Ramachandran plot quality parameters exceeding 95% for the combined allowed and favored regions. Regarding the ProSA quality parameters, the models yielded z-scores proximate to established quality benchmarks, particularly models cluster4139_mc|4968.t1 (-4.53) and cluster4139_mc|4969.t1 (-3.99). In the ERRAT analysis, model cluster4139_mc|4971.t1 was the sole construct displaying an overall quality factor below 90%, whereas model cluster4139_mc|4968.t1 achieved a score of 95.693%. Conversely, for the Verify 3D assessment, none of the models attained the recommended threshold (3D-1D score\u0026thinsp;\u0026ge;\u0026thinsp;0.2) indicative of high quality. The highest performance in this category was demonstrated by model cluster5734_mc|g4624.t1, which reached 63.18%, a value substantially below the 80% benchmark associated with satisfactory structural integrity. Homology modeling yielded six three-dimensional protein structures, which exhibited an overall architecture highly similar to one of the templates models, the crystal structure of \u003cem\u003eE. coli\u003c/em\u003e multidrug transporter MdfA. A structural superposition of the resulting models is presented in Fig.\u0026nbsp;7, with alpha-helices and beta-sheets depicted in red and yellow, respectively. Subsequent molecular docking analyses revealed binding affinity values for the protein-ligand complexes ranging from \u0026minus;\u0026thinsp;5.4 kcal/mol to -6.7 kcal/mol. The model cluster4139_mc|g4968.t1 demonstrated the weakest binding affinity (-5.4 kcal/mol), while models cluster5734_mc|g4624.t1 and cluster4139_mc|g4970.t1 exhibited the strongest binding affinities, both at -6.7 kcal/mol.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study was motivated by the critical need to overcome the inherent toxicity of limonene, a major bottleneck in the microbial valorization of this monoterpene into high-value compounds such as perillyl alcohol[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The isolation of a \u003cem\u003eMeyerozyma caribbica\u003c/em\u003e strain exhibiting unprecedented resistance to limonene concentrations up to 4% (v/v) provided a pivotal starting point for this investigation. Consequently, we employed an integrative multi-methodological approach to elucidate the molecular underpinnings of this exceptional phenotype.\u003c/p\u003e \u003cp\u003eOur initial comparative genomic analysis suggested that membrane transporters could be key genetic determinants of limonene resistance, a hypothesis drawn from evidence in other microorganisms[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To empirically test the postulate that active efflux mitigates intracellular accumulation, we quantified limonene levels across several yeast strains. Although the results indicated no statistically significant difference between the mean intracellular limonene concentrations, the absolute values quantified in \u003cem\u003eM. caribbica\u003c/em\u003e were consistently lower than those measured in the other yeast strains under investigation. A plausible explanation for this outcome could involve the predetermined exposure time utilized in the experiment; a longer duration might have revealed a more pronounced disparity in limonene accumulation by the cells. \u003cem\u003eKluyveromyces marxianus\u003c/em\u003e also exhibited viability post-exposure, its high intracellular limonene load suggests a tolerance mechanism distinct from active efflux, potentially related to the duration of the assay or other intrinsic cellular defenses.\u003c/p\u003e \u003cp\u003eSubsequent transcriptomic analysis aimed to identify which, if any, of the putative transporters identified through genomics were functionally recruited under limonene stress. Notably, the expression levels of most candidate clades (A, B, G, H) remained low and inconclusive. However, a significant and specific upregulation of clade I was observed following prolonged exposure to 4% (v/v) limonene. This targeted transcriptional response strongly implicates the clade I encoded transporter could be directly involved in the cellular defense against limonene toxicity, offering crucial mRNA-level evidence for its role in the resistance phenotype.\u003c/p\u003e \u003cp\u003eTo further interrogate the function of the leading candidates, molecular docking simulations were performed on the proteins encoded by clades B and I. The computational models revealed two critical findings: firstly, the predicted structures showed significant homology to known transporter proteins, and secondly, both models demonstrated a robust and favorable binding affinity for limonene. These \u003cem\u003ein silico\u003c/em\u003e results provide compelling structural evidence that these putative transporters possess the necessary architecture to interact with and potentially facilitate the export of limonene molecules.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the convergence of genomic, empirical, transcriptomic, and computational evidence presented herein supports a coherent model for limonene resistance in \u003cem\u003eM. caribbica\u003c/em\u003e. We propose that this phenotype could be related by the action of specific membrane transporters, most notably the protein encoded by clade I, which is transcriptionally induced by limonene stress. The function of this and other putative pumps is substantiated by their computationally predicted capacity for ligand binding. This work not only elucidates a key survival strategy in a robu31st yeast isolate but also establishes a foundational framework for future strain engineering. The identification and validation of these efflux transporters open promising avenues for developing superior microbial platforms capable of withstanding high concentrations of toxic monoterpenes, thereby enhancing the biotechnological production of valuable compounds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge Dr. Sérgio Antônio Fernandes from the Department of Chemistry at UFV for his guidance in the methodology for intracellular limonene quantification using gas chromatography-mass spectrometry (GC-MS). We also extend our sincere thanks to Dr. Luciano Gomes Fietto from the Department of Biochemistry and Molecular Biology at the Federal University of Viçosa for generously providing the yeast strains \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, \u003cem\u003eKluyveromyces marxianus\u003c/em\u003e, \u003cem\u003eSpathaspora passalidarum\u003c/em\u003e, \u003cem\u003eSpathaspora arborariae\u003c/em\u003e, and \u003cem\u003eScheffersomyces stipites\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Filipe Augusto Teixeira, Gustavo Molina and Tiago Antônio de Oliveira Mendes; Methodology: Filipe Augusto Teixeira, Fernanda Matias Albuini, Ananda Aguilar and Patrick Neves Squizato; Formal analysis and investigation: Filipe Augusto Teixeira and Tiago Antônio de Oliveira Mendes; Writing - original draft preparation: Filipe Augusto Teixeira; Writing - review and editing: Filipe Augusto Teixeira, Gustavo Molina, Fernanda Matias Albuini, Ananda Aguilar, Patrick Neves Squizato and Tiago Antônio de Oliveira Mendes; Resources: Gustavo Molina and Tiago Antônio de Oliveira Mendes; Supervision: Gustavo Molina and Tiago Antônio de Oliveira Mendes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFelipe L, de O, Oliveira AM, Bicas JL (2017) Bioaromas \u0026ndash; Perspectives for sustainable development. 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New Phytol 197:886\u0026ndash;898\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"current-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Current Microbiology","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Limonene, Meyerozyma caribbica, comparative genomics, MFS transporter, molecular docking, bioconversion","lastPublishedDoi":"10.21203/rs.3.rs-8732531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8732531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTerpene bioconversion presents a sustainable alternative to chemical synthesis for producing high-value compounds, yet its application is constrained by the inherent toxicity of terpenes to microbial biocatalysts. Bioprospecting offers a cost-effective strategy to overcome this limitation and through this approach a strain of \u003cem\u003eMeyerozyma caribbica\u003c/em\u003e, with a profile of resistance to the monoterpene limonene, was isolated from \u003cem\u003eSolanum lycocarpum\u003c/em\u003e. This study aimed to elucidate the molecular mechanisms underlying this resistance through comparative genomics. These \u003cem\u003ein silico\u003c/em\u003e analyses against five food-industry relevant yeasts (\u003cem\u003eSaccharomyces cerevisiae, Kluyveromyces marxianus, Spathaspora passalidarum, S. arborariae\u003c/em\u003e, and \u003cem\u003eScheffersomyces stipitis\u003c/em\u003e) identified 58 orthologs gene clusters and highlighted 26 proteins belonging to the Major Facilitator Superfamily (MFS) transporter in \u003cem\u003eM. caribbica\u003c/em\u003e. Homology modeling and molecular docking analyses demonstrated a satisfactory binding affinity between limonene and the generated MFS protein models, supporting their potential role as specific efflux pumps. Quantitative PCR analysis identified a specific clade within the MFS transporter superfamily that was significantly upregulated under limonene stress. However, experimental validation revealed that intracellular limonene concentrations in \u003cem\u003eM. caribbica\u003c/em\u003e following exposure to 4% (v/v) limonene were not significantly lower than those measured in susceptible yeast strains. The confluence of \u003cem\u003ein silico\u003c/em\u003e and experimental evidence strongly indicates that the limonene resistance in \u003cem\u003eM. caribbica\u003c/em\u003e is mediated by the active efflux of the compound, facilitated by specific MFS transporters. These findings establish \u003cem\u003eM. caribbica\u003c/em\u003e as a promising biological chassis for the industrial bioconversion of limonene into value-added products.\u003c/p\u003e","manuscriptTitle":"Investigation of the Potential Mechanisms of Limonene Resistance in Meyerozyma Caribbica by Comparative Genomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 11:34:11","doi":"10.21203/rs.3.rs-8732531/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:48:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236186930423571220822737227917176725242","date":"2026-05-07T16:17:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272470956227221337073362900415887810540","date":"2026-05-06T20:42:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19055131948579172367507637445970861822","date":"2026-05-06T20:28:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302063366203565937668037943978437870037","date":"2026-05-05T04:21:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T20:45:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152883477441852502022148611504609455394","date":"2026-03-30T16:07:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-18T03:48:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T20:52:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T16:34:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Current Microbiology","date":"2026-01-29T13:38:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"current-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Current Microbiology","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6464d4bf-0652-491b-abb9-6a041467bae9","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:48:11+00:00","index":69,"fulltext":""},{"type":"reviewerAgreed","content":"236186930423571220822737227917176725242","date":"2026-05-07T16:17:20+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"272470956227221337073362900415887810540","date":"2026-05-06T20:42:08+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"19055131948579172367507637445970861822","date":"2026-05-06T20:28:06+00:00","index":64,"fulltext":""},{"type":"reviewerAgreed","content":"302063366203565937668037943978437870037","date":"2026-05-05T04:21:49+00:00","index":61,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T11:34:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 11:34:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8732531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8732531","identity":"rs-8732531","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}