Characteristics of carbohydrates determine the shape of the gut microbiota in a chicken cecal in-vitro model

Characteristics of carbohydrates determine the shape of the gut microbiota in a chicken cecal in-vitro model | 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 Characteristics of carbohydrates determine the shape of the gut microbiota in a chicken cecal in-vitro model Miriam J. Oost, Kahlile Youssef Abboud, Francisca C. Velkers, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4254410/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The intestinal microbiota is crucial for intestinal and overall animal health. Coccidiosis and necrotic enteritis poses significant economic burden on poultry farming. Such inflammatory intestinal diseases disrupt the gut microbiota and the addition of carbohydrates to feed can promote and sustain a stable gut microbiota. We compared the effects on microbiota composition and metabolites during fermentation of isomalto/malto-polysaccharides and high- and low methyl-esterified pectins (HMP, LMP), against a positive control, mannan-oligosaccharide (MOS), using the Chicken ALIMEntary tRact mOdel-2 (CALIMERO-2). CALIMERO-2 mimic fermentation in healthy ceca, and by spiking it with C. perfringens, we aimed to mimic fermentation in diseased chicken ceca. Pectins showed minor differences in monosaccharide composition and molecular weight. SPE8 had degree of methyl-esterification (DM) of 26 (LMP), and SPE6 and SPE7 DM of 63 (HMP). Beta-diversity was significantly similar between HMP’s SPE6 and SPE7. Bacteroidetes was dominant phylum, except for SIEM and MOS, where Firmicutes prevailed. Beneficial bacteria particularly Lactobacillus , remained stable across samples. This study advances our comprehension of the fermentability and structural impact of diverse carbohydrates on the broiler gut microbiota. Our findings underscore the potential of isomalto/malto-polysaccharides and pectins to promote intestinal health in poultry, warranting further investigations to optimize its inclusion in chicken feed. microbiota cecum broilers CALIMERO-2 IMMP pectins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The chicken gastrointestinal tract is colonized by approximately 10 13 bacteria, and the densest and most diverse population is found in the cecum (Oakley et al., 2014; Yeoman et al., 2012; X. Y. Zhu, Zhong, Pandya, & Joerger, 2002)). The intestinal microbiota plays a crucial role in maintaining intestinal and animal health, and intestinal homeostasis. Dysbiosis of the intestinal microbiota has been linked to many enteric diseases in humans and animals (Q. Yang et al., 2021). In broiler chickens, the intestinal disease coccidiosis caused by the protozoa Eimeria , is one of the most common diseases with large economic significance (Blake et al., 2020). Damage to the intestinal mucosa, caused by coccidiosis, predisposes for colonization and proliferation of Clostridium perfringens (C. perfringens) . Pathogenic C. perfringens strains can cause necrotic enteritis (NE), an intestinal inflammatory disease that causes a shift in microbiota composition (Immerseel et al., 2004; Parish, 1961; L. Timbermont, Haesebrouck, Ducatelle, & Van Immerseel, 2011; Leen Timbermont et al., 2009). Subclinical and clinical NE also results in economic losses in the poultry industry, estimated to be 6 billion US dollars worldwide (Yeoman et al., 2012). Antimicrobial drugs were commonly used for the treatment and prevention of NE. However, since the use of antibiotics has been linked to the development of drug-resistant bacteria, regulations to reduce antimicrobial use in livestock have increased the need for alternatives to control and prevent NE in broiler chickens (Castanon, 2007). Currently, there is much interest in maintaining or restoring a stable state of the intestinal microbiota, by adding potential prebiotics to chicken feed. Prebiotics are defined as “a selectively fermented ingredient that allows specific changes, both in the composition and/or activity in the gastrointestinal microbiota that confers benefits upon host wellbeing and health” (Gibson et al., 2017). The potential prebiotic effect of non-digestible carbohydrates is largely overlooked, especially in broilers. Non-digestible carbohydrates, such as mannan-oligosaccharides (MOS) have been reported to have several health benefits, for example, by competing for attachment sites with pathogens like C. perfringens preventing their colonization in the colon (Ofek & Beachey, 1978). Moreover, they can increase the population of commensal bacteria and thereby restore the dysbiotic microbiota, caused by C . perfringens (Baurhoo, Phillip, & Ruiz-Feria, 2007; Fernandez, Hinton, & Van Gils, 2000; Spring, Wenk, Dawson, & Newman, 2000; Y. Yang, Iji, Kocher, Mikkelsen, & Choct, 2008). MOS, as a fermentable dietary fiber, can also increase the total short-chain fatty acids (SCFAs) concentrations in the cecum which have been shown to improve intestinal morphology (Pan, Chen, Wu, Tang, & Zhao, 2009). Pectins are also known for their beneficial effects on the intestinal microbiota and for their metabolites upon fermentation in the cecum (Bang et al., 2018; Gómez, Gullón, Yáñez, Schols, & Alonso, 2016). Pectins are quite diverse in their structural characteristics and their functionalities and can contribute to a variety of health benefits (Jermendi, Beukema, van den Berg, de Vos, & Schols, 2022). Isomalto/malto-polysaccharides (IMMP), are soluble dietary fibers derived from potato starch and are produced by modification of starch with 4,6-α-glucanotransferease enzyme and consist of α-(1→4), and α-(1→6) linked glucose (Leemhuis et al., 2014; van der Zaal, Klostermann, Schols, Bitter, & Buwalda, 2019; van der Zaal, Schols, Bitter, & Buwalda, 2018). IMMP has been shown to have potential prebiotic effects in mammals, by significantly increasing the relative abundance of Bifidobacterium and Lactobacillus (Gu et al., 2018). This study evaluated three selected citrus pectins and IMMP for their potential prebiotic effect on the microbiota composition and its produced metabolites in a healthy and diseased chicken in-vitro model. We used the Chicken ALIMEntary tRact mOdel-2 (CALIMERO-2) (Oost, Velkers, Kraneveld, & Venema, 2021), in which we mimicked C. perfringens overgrowth, to simulate NE, as the diseased model. Material and methods 4.2.1 Intervention carbohydrates In total, six interventions were used for the fermentation, of which SIEM and MOS served as controls. MOS, from Saccharomyces cerevisiae , was provided by Nutrition Sciences N.V., Drongen, Belgium. The other four carbohydrates consisted of Isomalto/Malto-Polysaccharide-87 (IMMP), obtained from potato starch (provided by Royal Avebe, Veendam, the Netherlands), and three different orange pectins, called SPE8, a low methyl-esterified pectin, SPE6 and SPE7, highly methyl-esterified pectins (provided by Nutrition Sciences N.V.). SPE8 was formed from the de-esterification of SPE6 by the supplier. 4.2.2 Collection of cecal samples and standardization The cecal contents of broiler chickens (Ross 308) were obtained from slaughterhouse van der Linden Poultry Products B.V. (Beringe, the Netherlands), as previously reported (Oost et al., 2021). Briefly, the days before slaughter, the broiler chickens were fed a coccidiostat-free diet and were not treated with antibiotics. The cecal content was removed, cooled, transported under anaerobic conditions, and pooled under strictly anaerobic conditions in an anaerobic cabinet (Sheldon Lab- Bactron IV, Gomelius, OR, USA). A total amount of 945 g was 1:1 diluted with dialysis liquid [see section “Medium and reagents”] and 15% (w/v, final concentration) glycerol was added as a cryo-protective agent. The cecal samples were aliquoted (35 mL), snap-frozen in liquid nitrogen, and stored at -80°C. 4.2.3 Chicken ALIMEntary tRact mOdel-2 The Chicken ALIMEntary tRact mOdel-2 (CALIMERO-2, Figure S1) mimics the chicken ceca and has been described before (Oost et al., 2021). Shortly, this dynamic in-vitro system closely resemble the in vivo situation by keeping the temperature (41 ° C), the pH (6.6), and the anaerobic environment as in the chicken ceca. Moreover, the metabolites produced by the microbiota are continuously removed of the lumen by making use of a semi-permeable membrane that functions as a dialysis system, simulating the uptake by the chicken’s intestinal cells. CAMIMERO-2 was inoculated with the standardized anaerobic cecal microbiota of broiler chickens, obtained as described above, and the microbiota was fed with different non-digestible carbohydrates, as described in the next section. 4.2.4 Experimental setup In total twelve duplicate experiments in CALIMERO-2 were performed. During each experiment, four independent fermentation units were run simultaneously in parallel. Simulated ileal-efflux medium (SIEM; (Bussolo de Souza et al., 2014) was taken along as a control medium and MOS served as a positive control. The experiments started with the inoculation of 60 mL of the standardized anaerobic cecal microbiota, to which 90 mL of pre-reduced dialysis liquid was added (Fig 1). After inoculation, an adaptation period of 16 h followed for the microbiota to adapt to the system while being fed with SIEM (2.5 mL/h). Thereafter, the SIEM feeding stopped for 3 h, in which the microbiota fermented the remaining carbohydrates from SIEM (starvation period). After starvation, instead of the standard carbohydrates in SIEM (Oost et al., 2021) the intervention carbohydrates were added to SIEM, and these different media were subsequently continuously supplemented for 72 h with a constant flow (2.5 mL/h). There were four sample time points, 0 h (after starvation, and just before supplementation with the intervention carbohydrates), 24, 48, and 72 hours, from the lumen and dialysate. Microbial composition were analyzed form lumen and metabolite composition were analyzed in both, lumen and dialysate. All samples were snap-frozen in liquid nitrogen and stored at -80°C until further analysis. After 24 and 48h, a total volume of 25 mL of lumen sample was removed from the system to simulate the passage of chyme and replaced with pre-reduced dialysis liquid to keep a constant volume. To mimic C. perfringens infections, all runs were also performed with C. perfringens spiked into the system [see section 2.5]. 4.2.5 Strain and preparation of Clostridium perfringens In this study a toxin type A, netB- positive Clostridium perfringens strain (GD-Animal health, Deventer, the Netherlands) was used. To prepare the inoculum for spiking CALIMERO-2, C. perfringens was cultured on liver broth agar base (Biotrading, Mijdrecht, the Netherlands) followed by 48 h anaerobic incubation at 37 °C. Subsequently, a single colony was transferred to 10 mL liver broth medium, and CALIMERO-2 was inoculated with 1 mL of C. perfringens at 1.5*10 8 colony-forming units (CFU)/mL to mimic C. perfringens -infected chickens. 4.2.6 Medium and reagents 4.2.6.1 Dialysis liquid Dialysis liquid consisted of the following compounds (content gram per liter): 2.5 K 2 HPO 4 ·3H 2 O, 4.5 NaCl, 0.005 FeSO 4 ·7H 2 O, 0.5 MgSO 4 ·7H 2 O, 0.45 CaCl 2 ·2H 2 O, 0.05 ox bile (Sigma, Zwijndrecht, the Netherlands), and 0.4 cysteine.HCl, plus 1 mL of vitamin mixture containing (mg per liter): 1 menadione, 0.5 vitamin B12, 2 D-biotin, 10 pantothenate, 5 p -aminobenzoic acid, 4 thiamine, and 5 nicotinamide acid. 4.2.6.2 Standard Ileal Efflux Medium SIEM was prepared as described by De Souza et al. (26) with the following components (grams per liter): 9 citrus peel pectin, 9 beechwood xylan, 9 larch arabinogalactan, 9 potato amylopectin, 74.6 potato starch, 31.5 Tween 80, 43.7 casein, 0.7 ox-bile, 43.7 bactopepton, 4.7 K 2 HPO 4 .3H 2 O, 0.009 FeSO 4 .7H 2 O, 8.4 NaCl, 0.8 CaCl 2 .2H 2 O, 0.7 MgSO 4 .7H 2 O, 0.02 hemin, and 0.3 cysteine.HCl, plus 1.5 mL of the vitamin mixture. The pH was adjusted to 6.6 to mimic the chicken ceca and 60 mL/day was administered. Standard carbohydrates in SIEM were replaced with 7.5 g of MOS, IMMP, or pectins. 4.2.7 Analysis of the carbohydrates 4.2.7.1 Analysis of IMMP The IMMP used in this study was named after its percentage of total α-(1→6) glucosyl linkages. The total α-(1→6) linked glucosyl content, consisting of both α-(1→6) and α-(1→4) linked glucosyl residues, and was determined by hydrogen-1 nuclear magnetic resonance (1H NMR) spectroscopy, with the methodology and results already published previously (van der Zaal et al., 2018). IMMP-87 (87% α-(1→6) linkages) originates from potato starch (Royal Avebe,) modified with L. reuteri 121 GTFB 4,6-α-glucanotransferase and pullulanase (Leemhuis et al., 2014). 4.2.7.2 Characterization of pectins Neutral monosaccharide composition was determined after Seaman hydrolysis and derivatization into alditol acetates. Briefly, samples were pre-treated for 1 hour at 30 ˚C with 72% (w/w) H 2 SO 4 , and further submitted to acid hydrolysis with 1 M H 2 SO 4 (3h, 100 ˚C). The released neutral sugars were derivatized into their alditol acetates to be analyzed by gas chromatography (GC) (Englyst & Cummings, 1984). Inositol was used as an internal standard. The acidic monosaccharide, galacturonic Acid (GalA) was analyzed spectrophotometrically using the m -hydroxydiphenyl autoanalyser (Blumenkrantz & Asboe-Hansen, 1973; Thibault, 1979). The degree of methyl-esterification (DM) was estimated by saponification of pectins (NaOH 0.1 M; 1 h at 4 ˚C, followed by 23 h at room temperature). The methanol released was analyzed by head-space GC and DM was calculated (Huisman, Oosterveld, & Schols, 2004). 4.2.7.3 Enzymatic hydrolysis and determination of degree of blockiness of pectins The hydrolysis of pectin samples was conducted as follows: samples were dissolved in 50 mM sodium acetate buffer (pH 5.2), incubated under stirring at 40 ˚C with pectin lyase (PL, EC 4.2.2.10; ID: 1043) from Aspergillus niger for 6 h, adding endo -polygalacturonase (endo-PG, EC 3.2.1.15; ID 1027) from Kluyveromyces fragilis and incubating for another 18 h. After incubation, enzymes were inactivated at 100 ˚C for 10 min. Pectin digests were centrifuged and supernatant were analyzed by high-perfomance size exclusion chromatography (HPSEC), high performance anion exchange chromatography (HPAEC) and, ultra-high pressure liquid chromatography (UHPLC) HILIC-ESI-IT-MS, as described by Jermendi et al, 2022. The parameters and equations “degree of blockines (DB), the absolute degree of blockiness (DB ABS ), the DB of methyl-esterified oligomers by endo-PG (DB PGme ) and PL (DB PLme )” to describe methyl-ester have been also described by Jermendi et al, 2022. 4.2.8 Microbial DNA extraction DNA was extracted from 250 µl of the lumen samples taken during the CALIMERO-2 experiments as described before using the 2x300 bp protocol (Oost et al., 2021). Briefly, 1000 µl InhibitEx buffer (Qiagen, Venlo, the Netherlands) was added and samples were transferred to Precellys tubes containing 0.5 mm microbeads and treated in a bead beater (Precellys 24, Bertin Technologies, Montigny le-Bretonneux, France). Thereafter, the samples were incubated at 95°C for 7 min and centrifuged at 13500 x g for 1 min to pellet stool particles and cell wall fragments. From this point on, the QIAamp DNA stool Mini kit (Qiagen) was used following the manufacturer’s protocol from step 4 onwards. 4.2.9 Bacterial composition of the cecal microbiota The composition of the cecal bacteria was evaluated by 16S rRNA gene sequencing using Illumina Miseq (Illumina, San Diego, CA, United States) as described before (Oost et al., 2021). 16S rRNA gene amplicon libraries of the V3-V4 region were generated following the 16S Metagenomic Sequencing Library preparation manual of Illumina Miseq systems using the Nextera XT kit, using a 2-step PCR. A mock community was run along with the samples to guarantee sequence quality. 4.2.10 Short-Chain Fatty Acids, Branched-Chain Fatty Acids, and Organic Acids Quantification in Lumen and Dialysate samples SCFAs, branched-chain fatty acids (BCFAs), and organic acids were quantified through ion exclusion chromatography by Brightlabs (Venlo, the Netherlands). Briefly, an 883 Ion Chromatograph was used (IC; Metrohm, Switzerland), with a Transgenomic IC Sep ICE-ION-300 column (30 cm length, 7.8 mm diameter, and 7 mm particles) and a MetroSep RSPE6 Guard column. The mobile phase consisted of 1.5 mM aqueous sulfuric acid and the column had a flow rate of 24 mL/h and a temperature of 65°C. The acids were detected using suppressed conductivity detection. Samples were centrifuged at 13500 g for 10 min, and the clear supernatant was filtered through a 0.45 mm PFTE filter and diluted with mobile phase (for lumen 1:5, for dialysate 1:2). Ten μl were loaded on the column by an autosampler 730 (Metrohm). Molecules were eluted according to their pKa. 4.2.11 Bioinformatics analysis Microbiota bioinformatics was performed with QIIME2 2019.4 (Bolyen et al., 2019). Shortly, the raw sequencing data were demultiplexed, quality filtered, and denoised by using the q2-demux plugin and DADA2 (Callahan et al., 2016). In the DADA2 step, the first 9 bases were trimmed off and for the forward reads there was an additional truncation at 290 base pairs and for the reverse reads, this was at 280 base pairs. Taxonomy was assigned using the SILVA 128 16S rRNA gene reference database. Further analysis was conducted with R (version 4.2.0) within R-studio, with packages microViz (Barnett, 2021), microbiome (Lahti, 2017), and phyloseq (McMurdie & Holmes, 2013) for analysis and visualization of microbiome sequencing data. Microbial alpha-diversity was determined using observed number of OTUs and the Shannon index. Beta-diversity was determined using weighted and unweighted UniFrac distance metrics. Differences in taxonomic profiles were analyzed using Statistical Analysis Metagenomic Profiles (STAMP) software v.2.1.3 with Kruskal-Wallis test, followed by a Tukey-Kramer post-hoc test (Parks, Tyson, Hugenholtz, & Beiko, 2014). P values were corrected using the Benjamini-Hochberg method and a corrected P value < 0.05 was considered significant. Statistical significance of alpha-diversity was assessed with Kruskal-Wallis test, followed by a Dunns’ post-hoc test. For the statistical analysis of the beta-diversity for the different carbohydrates and comparison with the controls permutational multivariate analysis of variance (PERMANOVA) (Anderson, 2017) was performed. The statistical analysis of the remaining data was performed using GraphPad Prism 9.5.1 (GraphPad Software, San Diego, USA). The metabolites were assessed with two-way ANOVA analysis, followed by Bonferroni’s post-hoc test with selected pairs. For the relative abundance, phylum, and genus levels statistical significance was determined, using multivariate analysis for the different types of carbohydrates and a Welch’s t-test for the two groups healthy and diseased (the latter for the samples spiked with C. perfringens ). Results 4.3.1 Composition and structural properties of Pectins and IMMP The three different pectins and IMMP were analyzed regarding their monosaccharide composition, molecular weight distribution, and DM (only pectins (Table 1). The pectins are mainly composed of Galacturonic Acid (GalA), typically present in homogalacturonan (HG) type pectins, with slight differences in neutral sugar content. The molecular weight (M w ) distribution among the samples ranged from 115 kDa to 136 kDa. The DM, defined as the percentage of methyl-esters distributed within GalA residues over the HG backbone, was 63% for pectins SPE6 and SPE7, and 26% for pectin SPE8. Besides the similarities in sugar content and M w , pectins SPE6 and SPE7 are featured as highly methyl-esterified (HMP; DM > 50% DM) and pectin SPE8 is low methyl-esterified (LMP; DM < 50% DM). Table 1. Structural characteristics of pectins and isomalto/malto-polysaccharide (IMMP) Chemical features of Pectins and IMMP Sample Rha a Ara Xyl Man Gal Glc GalA b Total GalA M w DM mol% (w/w%) c (kDa) d (%) e SPE8 0.7 1.7 0.2 0.6 7.0 0.4 89.4 76.9 115 26 SPE6 1.0 2.2 0.2 0.9 7.7 0.7 87.4 68.2 131 63 SPE7 0.8 3.5 0.1 1.1 3.7 0.6 90.2 72.0 136 63 IMMP 100 95 10 - a Rha: Rhamnose; Ara: Arabinose; Xyl: xylose; Man: Mannose; Gal: Galactose; Glc: Glucose; GalA: Galacturonic Acid. b Determined spectrophotometrically using the m -hydroxydiphenyl automated skalar method. c Total galacturonic acid content anhydrous in w/w%. d Average molecular weight (Mw) determined by HPSEC based on the pectin standards. Average Mw for IMMP was determined by Multi-angle light scattering detector (21). e Degree of methyl-esterification (DM): mol of methanol per 100 mol of the total GalA in the sample. Pectin presenting DM lower than 50% is considered low methyl-esterified and DM above 50% is considered highly methyl-esterified. To have a better understanding of the methyl-esterification pattern over the pectin backbone, the analysis of distribution of non-methyl-esterified GalA residues can be useful. As a first step, the pectins were enzymatically degraded with pure, and well-defined enzymes polygalacturonase (endo-PG) and pectin lyase (PL) and analyzed by HPSEC with RI detector. PL can cleave glycosidic linkages in vicinal methyl-esterified GalA units by introducing a double bond (unsaturated), while endo-PG requires four consecutive non-esterified GalA units to act (Jermendi et al., 2022). Figure 2 confirm the virtually complete degradation of pectins by both enzymes. The three different pectins demonstrated rather similar M w before PL and Endo-PG digestion. After digestion, pectins were degraded into low molecular weight oligomers. HMP SPE6 and SPE7 had a very similar degradation pattern indicating a similar methyl ester level and distribution. For the LMP SPE8, the peak shape in the oligomer region (12.3 – 14.5 min) reflects the presence of different degradation products and shows indeed a different DM and distribution (Jermendi et al., 2022). In order to have a deeper overview of pectin digestion products, HPAEC-PAD/UV allowed separation, identification and quantification of oligomers ranging from degree of polymerization (DP) 1 to 7. Most oligomers formed after SPE8 digestion were saturated, demonstrating that endo-PG had greater activity over the SPE8 backbone (Figure 3). Contrary, SPE6 and SPE7 had a more complex but very similar chromatogram. Peaks of saturated and unsaturated oligomers appeared for SPE6 and SPE7, and the intensity of saturated DP 1 to 3 were lower when compared to SPE8. Peaks of unsaturated DP 2 to 6 were seen in SPE6 and SPE7, while SPE8 only had formation of unsaturated DP 2 and 3. This is as expected due to their higher DM. In addition, a random distribution of methyl-esters in SPE6 and SPE7 led to the formation of different diagnostic oligomers after pectin digestion. Table 2. Descriptive parameters of pectins with different DM Pectin DB a DB ABS b DG PGme c DB PLme d (%) (%) (%) (%) SPE8 25 19 45 0.6 SPE6 22 8 22 31 SPE7 20 7 22 35 a Degree of blockiness: amount of non-esterified mono-, di-, and triGalA per 100 mol of the non-esterified GalA in the sample. b Absolute degree of blockiness: amount of non-esterified mono-, di-, and triGalA per 100 mol of total GalA in the sample. c Degree of blockiness by endo-PG (DG PGme ): amount of saturated methyl-esterified galacturonic residues per 100 mol of total galacturonic acid in the sample. d Degree of blockiness by PL (DB PLme ): amount of unsaturated methyl-esterified galacturonic oligomers per 100 mol of total galacturonic acid in the sample. To further investigate the features that differentiate one pectin from another we characterized their degree of blockiness (Table 2), that is, the amount of released non-esterified mono-, di- and trisaccharides of GalA relative to the total non-esterified GalA in the pectin backbone (Jermendi et al., 2022). The elegant study from Jermendi et al (2022) demonstrate the formulas to calculate each DB parameter considering the DM, GalA content, HPAEC and HILIC measurements. Even though DB values were quite similar for all pectins, the patterns of methyl-esterification is different. DB ABS , which regards the amount of non-esterified oligomers related to total amount of GalA in the pectin was higher in SPE8, meaning that SPE8 has more “blocky” regions of non-esterified GalA units. The values for DB PLme and DB PGme are in consonance with the low DM of SPE8 when compared to SPE6 and SPE7, but also indicate that SPE8 had the fewest methylated vicinal GalA units and highest amount of “blocky” regions. SPE6 and SPE7 exhibit almost the same values for all parameters indicating that both have similar DM and distribution of methyl-esters over the pectin backbone. Interestingly, SPE7 had a slightly higher value for DB PLme , when compared to the other pectins, meaning that it possess more methylated vicinal GalA units. 4.3.2 Alpha- and beta-diversity of the cecal microbiota in CALIMERO-2 The effect of the different carbohydrates on microbial diversity was assessed by comparing alpha- and beta-diversity between the three different pectins and IMMP, and to the controls SIEM and MOS. Also, the diversity for the diseased model samples from CALIMERO-2, in which necrotic enteritis was mimicked by spiking in C. perfringens , was compared to the healthy model (Figure 4). For the alpha-diversity, both the Shannon index (Figure 4a) and the observed Operational Taxonomic Units (OTUs) were calculated for the fermented carbohydrates every 24h up to 72h (Figure 4b). The Shannon index and the observed OTUs indicate a significant decrease in microbiota for SIEM and MOS in the C. perfringens inoculated model, compared to the corresponding healthy model (Figure 4a-b). This difference between samples from the healthy and diseased models was not observed for the other substrates. When comparing the different types of carbohydrates in the healthy groups, the Shannon index shows that SIEM results in more diverse microbiota communities compared to SPE8 (P < 0.05). Also, MOS produced more diverse microbiota communities compared to IMMP (P < 0.05), SPE8 (P < 0.001), SPE6 (P < 0.05), and SPE7 (P < 0.01). Based on the observed OTUs, a similar pattern was found, but here SIEM showed higher observed OTUs compared to SPE6 instead of SPE8. The similarity in community structure between samples was studied with the beta-diversity metrics unweighted and weighted UniFrac measures. Principal coordinate analysis (PcoA) of unweighted UniFrac demonstrates an overlap of the two HMP SPE6 and SPE7. A significant difference in beta-diversity is observed for MOS compared to IMMP, SPE6, and SPE7 (P values, see Table 3, Figure 4c). PcoA of the weighted UniFrac also shows an overlap of HMP SPE6 and SPE7, indicating the high similarity of these substrates. LMP SPE8 shows a significant difference compared to MOS, IMMP, and HMP SPE7 (Table 3; Figure 4d). Table 3. P values of PERMANOVA on unweighted and weighted UniFrac. * P < 0.05. SIEM = Simulated ileal-efflux medium, MOS = mannan oligosaccharides, IMMP = isomalto/malto-polysaccharide, P = pectin. Unweighted UniFrac SIEM MOS IMMP SPE8 SPE6 SPE7 SIEM - 0.120 0.779 0.522 0.626 0.993 MOS - - 0.008* 0.084* 0.022* 0.002* IMMP - - - 0.434 0.610 0.484 SPE8 - - - - 0.753 0.178 SPE6 - - - - - 0.232 SPE7 - - - - - - Weighted Unifrac SIEM MOS IMMP SPE8 SPE6 SPE7 SIEM 0.640 0.090 0.142 0.650 0.380 MOS - - 0.110 0.027* 0.336 0.583 IMMP - - - 0.020* 0.056 0.180 SPE8 - - - - 0.377 0.015* SPE6 - - - - - 0.191 SPE7 - - - - - - 4.3.3 Changes in composition at the phylum and genus taxonomic levels The microbiota composition was determined at time point 0 and after 72h of fermentation in CALIMERO-2 in which the microbiota was exposed to carbohydrates. Phylum and genus levels, showed small changes in microbiota between the different experimental groups after 72h fermentation (Figure 5A). Bacteroidetes was the most abundant phylum for most of the groups, except for SIEM and MOS in the healthy model, in which Firmicutes was dominant. Within the phylum Firmicutes , the genera Lachnospiraceae UCG-010 , Anaerofilum , and Intestinimonas were significantly higher in the healthy, compared to the diseased samples (P < 0.05; Figure S2). At genus level, Bacteroides was significantly lower in the MOS and SIEM fermentations compared to Bacteroides for the other carbohydrates in the healthy model after 72 h fermentation (Figure 5C). Moreover, for MOS in the diseased model Bacteroides was also higher compared to MOS in the healthy model (Figure 5C). The genus Lachnoclostridium was significantly higher in the MOS samples compared to the other substrates (Figure 5D). The pectins LMP SPE8 and HMP SPE6 showed an increase of the genus Akkermansia compared to SIEM and MOS in the healthy model (Figure 5E). Akkermansia also increased in response to the addition of pectin LMP SPE8 in the diseased model. Lactobacillus levels were maintained for all carbohydrates, except for MOS, that demonstrated a significant increase of Lactobacillus in the healthy model (Figure 5F). Bifidobacterium showed the same relative abundance for all carbohydrates, only for SIEM in the diseased model there was a significant decrease (Figure 5G). 4.3.4 Cecal production of SCFAs in CALIMERO-2 Acetate, propionate, and butyrate are the three main SCFAs produced during the in-vitro fermentation of carbohydrates. Lactate and succinate, the intermediate products of carbohydrate fermentation, were present in very low concentrations compared to SCFA concentrations in the samples. The total SCFAs is the sum of acetate, propionate, and butyrate. The addition of C. perfringens did not affect metabolite production, compared to the healthy model (Fig S3). Figure 5A shows the total SCFAs over time for the different intervention carbohydrates. Fermentation of LMP SPE8 yielded a lower amount of total SCFAs when compared to SIEM, IMMP, and HMP SPE7 after 72h of fermentation (P < 0.05). When comparing the carbohydrates on the separated SCFAs, acetate showed a significantly higher cumulative production after 72h of fermentation for HMP SPE7 (P < 0.01) and IMMP (P < 0.05) compared to MOS. HMP SPE6 did not show any significant difference. HMP SPE7 also lead to significantly higher acetate compared to LMP SPE8 (Figure 6B). Butyrate was also significantly increased under SIEM and MOS fermentation at 48h and 72h, compared to the three pectins (Figure 6C). After 72h of fermentation, this was also shown for IMMP compared to LMP SPE8. Propionate showed significantly higher production after 48h of SIEM compared to MOS (P < 0.01) (Figure 6D). Propionate production was also higher for SIEM compared to the three pectins (P < 0.001). Propionate demonstrated similar production on IMMP and SIEM, and they were both significantly higher compared to MOS (P < 0.05) and SPE8, SPE6 and SPE7 (P < 0.001). After 72h, also MOS produced more propionate compared to LMP SPE8 (P < 0.05). Both lactate and succinate did show a significant difference at 24h and 48h for SIEM compared to the pectins and IMMP, which vanished after 72h (Figure 6). The negative cumulative production indicates that these are converted into the other SCFA (primarily propionate and butyrate). The amount of iso-butyrate, one of the BCFAs, was significantly higher upon providing SIEM and MOS, compared to the three pectins at 48h and 72h. For the other measured BCFA, iso-valerate, MOS had the highest cumulative amount, and this was significantly different from the other four carbohydrates at 48h, and after 72h also significantly higher compared to SIEM. SIEM only showed a significant increase compared to LMP SPE8 and HMP SPE6 at time points 48 and 72h. Discussion In this study, we aimed to investigate the potential prebiotic effect of three citrus pectins and the ten times smaller polymer IMMP. Therefore, it was crucial to know the chemical features of these carbohydrates to understand the correlation between structure and their fermentability. Pectins analysis revealed that, despite similarities, these pectins distinctively affected the gut microbiota of broilers in CALIMERO-2. The compositional differences, the related glycosidic bonds, the DM and DB of pectins, each impose a challenge to their fermentation by microorganisms. The molecular machinery needed for dietary fibre degradation is structure-specific, thus the complex and diverse structure of pectins may require many steps for enzymatic catalysis and SCFA production (Cronin, Joyce, O'Toole, & O'Connor, 2021). The DM is the most important structural feature of pectins, and more recently the DM-related parameter “DB” became subject of research. These features may add another barrier for their fermentation. Langhout and Schutte (1996) concluded that the health effects of pectins are source, amount and DM-related. Less complex carbohydrates and with lower molecular weight, such as IMMP and MOS, may be degraded in less enzymatic steps by a broad range of bacteria due to the common molecular machinery (Wardman, Bains, Rahfeld, & Withers, 2022). Consequently, both the different pectins as well as oligosaccharides can distinctively affect the gut microbiota. Pectins SPE8 and SPE6 were quite similar regarding their monosaccharide composition, because SPE8 was made from SPE6 by de-esterification. SPE7 has a higher percentage of arabinose and a lower percentage of galactose when compared to SPE8 and SPE6. The HM SPE7 had a significantly higher cumulative production of acetate compared to LMP SPE8 and also had a higher propionate concentration compared to both other two pectins after fermentation. The higher percentage of arabinose has been linked to an increase of acetate and propionate producers in earlier research (Tomioka et al., 2022). In studies with humans and animals, pectin fermentation leads to formation of acetate over propionate and butyrate (Firrman et al., 2022; Langhout & Schutte, 1996; Larsen et al., 2019). We compared the effects of the pectins and IMMP on bacterial composition and metabolite production. In these comparisons we also took along a fermentation control SIEM and the prebiotic compound MOS, the latter of which has been studied widely for its beneficial effects on intestinal health in chickens. Firstly, SIEM and MOS induced an increase in gut microbiota diversity and also promoted a higher number of observed OTUs in the healthy model. However, SIEM and MOS were not able to prevent the loss of microbes in numbers in the diseased model. The reduction of alpha-diversity (species richness) of the cecal microbiota can be linked to the addition C. perfringens, which normally shifts the intestinal microbiota, and reduces the alpha-diversity within the samples (Q. Yang et al., 2021). This might take longer than 72h of fermentation, explaining the initial decrease of alpha-diversity. The control SIEM is rich in several carbohydrates, such as arabinogalactan and xylan, and might have promoted growth of a wider range of different bacteria. The diversity in both the healthy and the diseased group is lower for the pectins and IMMP compared to SIEM. For pectin, this could be related to their complex structure and while the gut microbiota might need to adapt to a pectin-degrading microbiota, thus fermentation can be slower (Wardman et al., 2022). For IMMP the lower diversity compared to SIEM and MOS might be related to the delayed and slow-fermentation behavior compared to other prebiotics, because of the presence of the α 1,6 glycosidic linkages in IMMP (Gu et al., 2018; L. Tian et al., 2017). Beta-diversity (community structure) was significantly different for MOS compared to IMMP, SPE6, and SPE7, which can be explained by the changes in microbiota composition towards a microbiota that can degrade the complex structures of the pectins and IMMP. SPE8, however, showed significant differences compared to MOS, IMMP, and SPE7, which could be related to its low methyl-esterified and higher amounts of non-esterified (blocky) regions. The microbiota composition was determined, and small differences between the experimental carbohydrates were shown at the phylum and genus levels. MOS is known for creating a diverse gut microbiota, by supporting the growth of beneficial bacteria, such as Lactobacillus and Bifidobacterium, and decreasing the presence of pathogens such as C. perfringens (Baurhoo et al., 2007; Corrigan, Leeuw, Penaud-Frézet, Dimova, & Murphy, 2015; Fernandez et al., 2000; Ghasemian & Jahanian, 2016; Ofek & Beachey, 1978; Spring et al., 2000; Y. Yang et al., 2008). Lactobacillus was also increased in CALIMERO-2 in the MOS-treated samples. However, when C. perfringens was added to the system, Lactobacillus was significantly decreased in the MOS group. Bifidobacterium was present in all the samples. An observation that stood out at the phylum level, was that all samples, except the MOS and SIEM in the healthy model, had Bacteroidetes as the most abundant bacteria, whereas Firmicutes was dominant in MOS and SIEM in the healthy model.This is in line with previous research, wherein MOS also promoted Firmicutes population in the chicken cecal microbiota (Pourabedin, Xu, Baurhoo, Chevaux, & Zhao, 2014). The genus Akkermansia, which is associated with gut health in humans, was increased by SPE8 and SPE6 compared to SIEM and MOS in the healthy model. However, whether Akkermansia has the same beneficial effects in vivo in chickens, is still under debate. For example, it has been shown to have a protective effect on the intestinal barrier, but it has also been linked to a higher number of necrotic enteritis cases, and significant overgrowth and colonization of C. perfringens (W.-Y. Yang, Chou, & Wang, 2022; L. Zhu et al., 2020). Additionally, it is also interesting that Akkermansia survived in CALIMERO-2, with the lack of mucus in the system. The fermentation of non-digestible carbohydrates by anaerobic bacteria in the gut yields SCFAs, and these metabolites are related to health benefits to the host. In broilers, SCFAs production is related to protection against pathogens by building a balanced gut community, and improvements of gut immunity, gut barrier, and mucin secretion and may enhance broiler production performance (Liu, Li, Yang, & Guo, 2021). In our research, we showed that different non-digestible carbohydrates promote different cumulative production of metabolites. For instance, fermentation of SPE8, which is a low DM pectin, provided the lowest amounts of total SCFAs, organic acids, and BCFAs. It raises the question of whether this outcome was due to a slower fermentation rate of the pectin or if the gut bacteria were not able access the non-esterified GalA of this pectin. It requires more time for the gut microbiota to adapt to SPE8. Similarly, Tian et al. (L. Tian et al., 2017; Lingmin Tian et al., 2016), tested LMP and HMP, and also found quite different fermentation patterns for each pectin. Comparing the three pectins, SPE6 exhibited intermediate levels of metabolites, and SPE7 yielded the highest amounts of metabolites, which could imply the gut microbiota expressed enzymes able to degrade HM pectin, therefore a more extensive fermentation. The different DM and DBs, the compositional variations of the three pectins likely contribute to the divergent fermentation patterns observed. Interestingly, the values of total SCFAs, organic acids, and BCFAs were found to be very similar between the SIEM, IMMP and SPE7, with IMMP and SPE7 exhibiting even greater similarity. Apparently, our HM pectins are more easily used by gut bacteria in this study when compared to the LM pectin. Also, our results suggest that the fermentation patterns associated with IMMP and SPE7 might share common metabolic pathways. However, it is worth noting that the gut microbiota was differently modulated by these two substrates, once more demonstrating that the fermentation metabolites’ similarity in amounts does not necessarily reflect identical microbial community responses. Our study revealed that succinate and lactate were not dominantly present in the samples, also not by the fermentation of IMMP. Gu et al. (2018) reported that IMMP-94 and IMMP-96 predominately produce the intermediate SCFA, succinate, next to the SCFAs in a batch fermentation model using human inoculum. Our results showed that succinate and lactate were mainly converted to SCFAs, which might have happened faster because the microbiota of chickens is different compared to the human inoculum. Moreover, this might also be related to the decrease in pH in their model (Gu et al., 2018), compared to CALIMERO-2, in which the pH was constantly regulated. In conclusion, our results enhance our understanding of the correlation between carbohydrate structure and fermentability, emphasizing that the complexity of carbohydrates leads to contrasting outcomes on the gut microbiota and the production of metabolites. Although carbohydrates, especially IMMP, affect the relative abundance of bacteria and the total SCFAs production, future research is needed to determine if IMMP or the different pectins are beneficial for chicken gut health. Declarations Acknowledgments We thank Royal GD Deventer, the Netherlands, for providing the Clostridium perfringens strain in this research. We would like to thank Natalia Hutnik for her support with the pectin analysis, and Rob van Dinter, Jessica Verhoeven and Sanne Verbruggen for their technical support with the CALIMERO-2 experiments and 16S rRNA sequencing. Funding Declaration This research was performed in the public-private partnership 'CarboBiotics' coordinated by the Carbohydrate Competence Center (CCC, www.cccresearch.nl ). CarboBiotics is jointly financed by participating industrial partners Royal Avebe U.A., FrieslandCampina Nederland B.V., Nutrition Sciences N.V., and allowances of The Dutch Research Council (NWO). Furthermore, the study was also partly funded by the Centre for Healthy Eating & Food Innovation (HEFI) of Maastricht University – Campus Venlo. This research has been made possible with the support of the Dutch Province of Limburg with a grant to HEFI. Author contribution Miriam J. 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Oost","email":"","orcid":"","institution":"Maastricht University Campus Venlo","correspondingAuthor":false,"prefix":"","firstName":"Miriam","middleName":"J.","lastName":"Oost","suffix":""},{"id":292011322,"identity":"bb91e17e-af87-43d5-becb-47614552916f","order_by":1,"name":"Kahlile Youssef Abboud","email":"data:image/png;base64,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","orcid":"","institution":"Maastricht University Campus Venlo","correspondingAuthor":true,"prefix":"","firstName":"Kahlile","middleName":"Youssef","lastName":"Abboud","suffix":""},{"id":292011323,"identity":"113611ae-e890-4c0b-ac51-1da5e8039ed9","order_by":2,"name":"Francisca C. Velkers","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Francisca","middleName":"C.","lastName":"Velkers","suffix":""},{"id":292011324,"identity":"20166a48-6557-4096-be8c-0872ae4e06ff","order_by":3,"name":"Hans Leemhuis","email":"","orcid":"","institution":"Roya Avebe U.A.","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Leemhuis","suffix":""},{"id":292011325,"identity":"1d5322cd-b529-4d14-852c-e1b167aa850d","order_by":4,"name":"Geert Bruggeman","email":"","orcid":"","institution":"Nutrition Sciences N.V","correspondingAuthor":false,"prefix":"","firstName":"Geert","middleName":"","lastName":"Bruggeman","suffix":""},{"id":292011326,"identity":"88369e62-75bc-400c-a023-34ad4a152559","order_by":5,"name":"Aletta D. Kraneveld","email":"","orcid":"","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Aletta","middleName":"D.","lastName":"Kraneveld","suffix":""},{"id":292011327,"identity":"d3254623-f0f2-4d73-ad94-20a4e8b8dfd5","order_by":6,"name":"Henk A. Schols","email":"","orcid":"","institution":"Wageningen University","correspondingAuthor":false,"prefix":"","firstName":"Henk","middleName":"A.","lastName":"Schols","suffix":""},{"id":292011328,"identity":"5ad75c30-d64c-4c76-8853-549763d6e0d8","order_by":7,"name":"Koen Venema","email":"","orcid":"","institution":"Maastricht University Campus Venlo","correspondingAuthor":false,"prefix":"","firstName":"Koen","middleName":"","lastName":"Venema","suffix":""}],"badges":[],"createdAt":"2024-04-11 21:44:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4254410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4254410/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54993818,"identity":"f1d51a41-d2ad-47fb-b153-3e09c912740c","added_by":"auto","created_at":"2024-04-19 17:42:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the experimental setup of CALIMERO-2\u003c/strong\u003e. Experiments started with inoculation of the system, with or without spiking of Clostridium perfringens, and were followed by an adaptation period of 16 h, in which the system was fed with SIEM. After the adaptation, a period of 3 h of starvation followed, in which the system is not fed. At time point 0, test products; IsoMalto/MaltoPolysaccharide (IMMP), three pectins, Mannan-oligosaccharide (MOS), and SIEM were added to the system (7.5 g/day) and every 24 h samples were collected from the lumen and the dialysate.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4254410/v1/2db82a94f978f614ec9146c8.png"},{"id":54993816,"identity":"232746d7-9538-4ef1-b379-099c289c1f62","added_by":"auto","created_at":"2024-04-19 17:42:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHPSEC elution pattern \u003c/strong\u003eof\u003cstrong\u003e \u003c/strong\u003epectins SPE8, SPE6 and SPE7 before (dots) and after (straight line) pectin lyase and endo-polygalacturonase digestion. Average molecular weight is indicated as kDa.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4254410/v1/7561372a46bd0a777dfa87bd.png"},{"id":54994901,"identity":"e1cb9aa3-0376-43be-865a-b82a371d7345","added_by":"auto","created_at":"2024-04-19 17:50:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65738,"visible":true,"origin":"","legend":"\u003cp\u003eSPE8, SPE6 and SPE7 HPAEC-PAD elution patterns after digestion with \u003cem\u003eendo\u003c/em\u003e-PG and PL. Peak annotation: DP refers to degree of polymerization and the number denotes GalA units present in the oligomer. uDP refers to unsaturated oligomers released by PL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4254410/v1/4738b09f8f5dfb23a68f23dc.png"},{"id":54993820,"identity":"3d12f569-a489-44a7-aeef-ca94e9dab468","added_by":"auto","created_at":"2024-04-19 17:42:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial diversity\u003c/strong\u003e for the alpha-diversity, \u003cstrong\u003eA\u003c/strong\u003e Shannon indices, and \u003cstrong\u003eB\u003c/strong\u003e observed operational taxonomic units (OTUs) were\u003cstrong\u003e \u003c/strong\u003ecalculated to determine the abundance and evenness of the species present in the\u003cstrong\u003e \u003c/strong\u003eChicken ALIMEntary tRact mOdel-2 (CALIMERO-2) samples. Points are colored by the time points samples were taken. SIEM = Simulated ileal-efflux medium, MOS = mannan oligosaccharides, IMMP = isomalto/malto-polysaccharide, P = pectin. – represent the healthy model, and + the diseased model, in which \u003cem\u003eClostridium perfringens \u003c/em\u003ewas added. Data are presented as mean (n=2) ± SD. (* P \u0026lt; 0.05; ** P \u0026lt; 0.01; *** P \u0026lt; 0.001). The beta-diversity is represented as principal coordinate analysis (PcoA) using the \u003cstrong\u003eC\u003c/strong\u003e unweighted UniFrac and \u003cstrong\u003eD\u003c/strong\u003eweighted UniFrac for the cecal microbiota of chickens from the CALIMERO-2 model. Points are colored by carbohydrate intervention, and the shape of points represent different time points samples were taken. Samples in which \u003cem\u003eClostridium perfringens\u003c/em\u003e was added, are marked with a grey sphere.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4254410/v1/9aea31c93fee014806ee8765.png"},{"id":54993821,"identity":"859920a7-4ab1-4f8b-b1f2-7b3a8dcd7629","added_by":"auto","created_at":"2024-04-19 17:42:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":257728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial composition. \u003c/strong\u003eRelative abundance of bacterial \u003cstrong\u003eA\u003c/strong\u003e phyla and \u003cstrong\u003eB\u003c/strong\u003egenera in Chicken ALIMEntary tRact mOdel-2 samples in which different carbohydrates were added. \u003cstrong\u003eC-G\u003c/strong\u003e Relative abundance of genera, \u003cem\u003eBacteroides\u003c/em\u003e,\u003cem\u003eLachnoclostridium\u003c/em\u003e,\u003cem\u003e Akkermansia\u003c/em\u003e,\u003cem\u003e Lactobacillus \u003c/em\u003eand \u003cem\u003eBifidobacterium\u003c/em\u003e,\u003cem\u003e \u003c/em\u003ethat showed significant differences between the intervention carbohydrates. SIEM = Simulated ileal-efflux medium, MOS = mannan oligosaccharides, IMMP = isomalto/malto-polysaccharide, P = pectin. – \u003cem\u003eC. perfringens \u003c/em\u003erepresent the healthy model, and + \u003cem\u003eC. perfringens \u003c/em\u003ethe diseased model, in which the pathogen\u003cem\u003e \u003c/em\u003ewas added. * P \u0026lt; 0.05, ** P \u0026lt; 0.01, P \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4254410/v1/9f3428bdf18febe06ff10bc4.png"},{"id":54993819,"identity":"cb1f270e-7118-4f46-b365-f65159b078bf","added_by":"auto","created_at":"2024-04-19 17:42:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":185433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative short chain fatty (SCFAs) and branched chain fatty acid levels mmol for the different carbohydrates produced during in-vitro fermentation in the Chicken ALIMEntary tRact mOdel-2.\u003c/strong\u003e Total SCFAs are the sum of acetate, propionate, and butyrate. Error bars represent the SEM, n=2. The color of the line represents the carbohydrate interventions. Significant difference are shown in the marked area and those marked with * are compared to MOS, and marked with # are compared to SIEM. (P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4254410/v1/11b6fdcd4997d77cd4978727.png"},{"id":55719113,"identity":"3f701c82-d09b-4586-b511-7e55573e56f4","added_by":"auto","created_at":"2024-05-02 08:26:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1718544,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4254410/v1/8a4a3683-64af-448e-87d8-6cb6fba8354a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics of carbohydrates determine the shape of the gut microbiota in a chicken cecal in-vitro model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe chicken gastrointestinal tract is colonized by approximately 10\u003csup\u003e13\u003c/sup\u003e bacteria, and the densest and most diverse population is found in the cecum (Oakley et al., 2014; Yeoman et al., 2012; X. Y. Zhu, Zhong, Pandya, \u0026amp; Joerger, 2002)). The intestinal microbiota plays a crucial role in maintaining intestinal and animal health, and intestinal homeostasis. Dysbiosis of the intestinal microbiota has been linked to many enteric diseases in humans and animals (Q. Yang et al., 2021). In broiler chickens, the intestinal disease coccidiosis caused by the protozoa \u003cem\u003eEimeria\u003c/em\u003e, is one of the most common diseases with large economic significance (Blake et al., 2020). Damage to the intestinal mucosa, caused by coccidiosis, predisposes for colonization and proliferation of \u003cem\u003eClostridium perfringens (C. perfringens)\u003c/em\u003e. Pathogenic \u003cem\u003eC. perfringens\u003c/em\u003e strains can cause necrotic enteritis (NE), an intestinal inflammatory disease that causes a shift in microbiota composition (Immerseel et al., 2004; Parish, 1961; L. Timbermont, Haesebrouck, Ducatelle, \u0026amp; Van Immerseel, 2011; Leen Timbermont et al., 2009). Subclinical and clinical NE also results in economic losses in the poultry industry, estimated to be 6 billion US dollars worldwide (Yeoman et al., 2012). Antimicrobial drugs were commonly used for the treatment and prevention of NE. However, since the use of antibiotics has been linked to the development of drug-resistant bacteria, regulations to reduce antimicrobial use in livestock have increased the need for alternatives to control and prevent NE in broiler chickens (Castanon, 2007).\u003c/p\u003e\n\u003cp\u003eCurrently, there is much interest in maintaining or restoring a stable state of the intestinal microbiota, by adding potential prebiotics to chicken feed. Prebiotics are defined as “a selectively fermented ingredient that allows specific changes, both in the composition and/or activity in the gastrointestinal microbiota that confers benefits upon host wellbeing and health” (Gibson et al., 2017). The potential prebiotic effect of non-digestible carbohydrates is largely overlooked, especially in broilers. Non-digestible carbohydrates, such as mannan-oligosaccharides (MOS) have been reported to have several health benefits, for example, by competing for attachment sites with pathogens like \u003cem\u003eC. perfringens\u003c/em\u003e preventing their colonization in the colon (Ofek \u0026amp; Beachey, 1978). Moreover, they can increase the population of commensal bacteria and thereby restore the dysbiotic microbiota, caused by \u003cem\u003eC\u003c/em\u003e. \u003cem\u003eperfringens\u003c/em\u003e (Baurhoo, Phillip, \u0026amp; Ruiz-Feria, 2007; Fernandez, Hinton, \u0026amp; Van Gils, 2000; Spring, Wenk, Dawson, \u0026amp; Newman, 2000; Y. Yang, Iji, Kocher, Mikkelsen, \u0026amp; Choct, 2008). MOS, as a fermentable dietary fiber, can also increase the total short-chain fatty acids (SCFAs) concentrations in the cecum which have been shown to improve intestinal morphology (Pan, Chen, Wu, Tang, \u0026amp; Zhao, 2009). Pectins are also known for their beneficial effects on the intestinal microbiota and for their metabolites upon fermentation in the cecum (Bang et al., 2018; Gómez, Gullón, Yáñez, Schols, \u0026amp; Alonso, 2016). Pectins are quite diverse in their structural characteristics and their functionalities and can contribute to a variety of health benefits (Jermendi, Beukema, van den Berg, de Vos, \u0026amp; Schols, 2022). Isomalto/malto-polysaccharides (IMMP), are soluble dietary fibers derived from potato starch and are produced by modification of starch with 4,6-α-glucanotransferease enzyme and consist of α-(1→4), and α-(1→6) linked glucose (Leemhuis et al., 2014; van der Zaal, Klostermann, Schols, Bitter, \u0026amp; Buwalda, 2019; van der Zaal, Schols, Bitter, \u0026amp; Buwalda, 2018). IMMP has been shown to have potential prebiotic effects in mammals, by significantly increasing the relative abundance of \u003cem\u003eBifidobacterium\u0026nbsp;\u003c/em\u003eand \u003cem\u003eLactobacillus\u003c/em\u003e (Gu et al., 2018).\u003c/p\u003e\n\u003cp\u003eThis study evaluated three selected citrus pectins and IMMP for their potential prebiotic effect on the microbiota composition and its produced metabolites in a healthy and diseased chicken \u003cem\u003ein-vitro\u0026nbsp;\u003c/em\u003emodel. We used the Chicken ALIMEntary tRact mOdel-2 (CALIMERO-2) (Oost, Velkers, Kraneveld, \u0026amp; Venema, 2021), in which we mimicked \u003cem\u003eC. perfringens\u003c/em\u003e overgrowth, to simulate NE, as the diseased model.\u003c/p\u003e"},{"header":"Material and methods ","content":"\u003cp\u003e4.2.1 Intervention carbohydrates\u003c/p\u003e\n\u003cp\u003eIn total, six interventions were used for the fermentation, of which SIEM and MOS served as controls. MOS, from \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, was provided by Nutrition Sciences N.V., Drongen, Belgium. The other four carbohydrates consisted of Isomalto/Malto-Polysaccharide-87 (IMMP), obtained from potato starch (provided by Royal Avebe, Veendam, the Netherlands), and three different orange pectins, called SPE8, a low methyl-esterified pectin, SPE6 and SPE7, highly methyl-esterified pectins (provided by Nutrition Sciences N.V.). SPE8 was formed from the de-esterification of SPE6 by the supplier.\u003c/p\u003e\n\u003ch3\u003e4.2.2 Collection of cecal samples and standardization\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe cecal contents of broiler chickens (Ross 308) were obtained from slaughterhouse van der Linden Poultry Products B.V. (Beringe, the Netherlands), as previously reported\u0026nbsp;(Oost et al., 2021). Briefly, the days before slaughter, the broiler chickens were fed a coccidiostat-free diet and were not treated with antibiotics. The cecal content was removed, cooled, transported under anaerobic conditions, and pooled under strictly anaerobic conditions in an anaerobic cabinet (Sheldon Lab- Bactron IV, Gomelius, OR, USA). A total amount of 945 g was 1:1 diluted with dialysis liquid [see section \u0026ldquo;Medium and reagents\u0026rdquo;] and 15% (w/v, final concentration) glycerol was added as a cryo-protective agent. The cecal samples were aliquoted (35 mL), snap-frozen in liquid nitrogen, and stored at -80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e4.2.3 Chicken ALIMEntary tRact mOdel-2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Chicken ALIMEntary tRact mOdel-2 (CALIMERO-2, Figure S1) mimics the chicken ceca and has been described before\u0026nbsp;\u003c/em\u003e(Oost et al., 2021). Shortly, this dynamic in-vitro system closely resemble the in vivo situation by keeping the temperature (41\u003cem\u003e\u0026deg;\u003c/em\u003eC), the pH (6.6), and the anaerobic environment as in the chicken ceca. Moreover, the metabolites produced by the microbiota are continuously removed of the lumen by making use of a semi-permeable membrane that functions as a dialysis system, simulating the uptake by the chicken\u0026rsquo;s intestinal cells. CAMIMERO-2 was inoculated with the standardized anaerobic cecal microbiota of broiler chickens, obtained as described above, and the microbiota was fed with different non-digestible carbohydrates, as described in the next section.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e4.2.4 Experimental setup\u003c/h3\u003e\n\u003cp\u003eIn total twelve duplicate experiments in CALIMERO-2 were performed. During each experiment, four independent fermentation units were run simultaneously in parallel. Simulated ileal-efflux medium (SIEM; (Bussolo de Souza et al., 2014) was taken along as a control medium and MOS served as a positive control. The experiments started with the inoculation of 60 mL of the standardized anaerobic cecal microbiota, to which 90 mL of pre-reduced dialysis liquid was added (Fig 1). After inoculation, an adaptation period of 16 h followed for the microbiota to adapt to the system while being fed with SIEM (2.5 mL/h). Thereafter, the SIEM feeding stopped for 3 h, in which the microbiota fermented the remaining carbohydrates from SIEM (starvation period). After starvation, instead of the standard carbohydrates in SIEM (Oost et al., 2021) the intervention carbohydrates were added to SIEM, and these different media were subsequently continuously supplemented for 72 h with a constant flow (2.5 mL/h). There were four sample time points, 0 h (after starvation, and just before supplementation with the intervention carbohydrates), 24, 48, and 72 hours, from the lumen and dialysate. Microbial composition were analyzed form lumen and metabolite composition were analyzed in both, lumen and dialysate. All samples were snap-frozen in liquid nitrogen and stored at -80\u0026deg;C until further analysis. After 24 and 48h, a total volume of 25 mL of lumen sample was removed from the system to simulate the passage of chyme and replaced with pre-reduced dialysis liquid to keep a constant volume. To mimic \u003cem\u003eC. perfringens\u0026nbsp;\u003c/em\u003einfections, all runs were also performed with \u003cem\u003eC. perfringens\u0026nbsp;\u003c/em\u003espiked into the system [see section 2.5].\u003c/p\u003e\n\u003ch3\u003e4.2.5 Strain and preparation of Clostridium perfringens\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eIn this study a toxin type A, \u003cem\u003enetB-\u003c/em\u003epositive \u003cem\u003eClostridium perfringens\u0026nbsp;\u003c/em\u003estrain (GD-Animal health, Deventer, the Netherlands) was used. To prepare the inoculum for spiking CALIMERO-2, \u003cem\u003eC. perfringens\u003c/em\u003e was cultured on liver broth agar base (Biotrading, Mijdrecht, the Netherlands) followed by 48 h anaerobic incubation at 37 \u0026deg;C. Subsequently, a single colony was transferred to 10 mL liver broth medium, and CALIMERO-2 was inoculated with 1 mL of \u003cem\u003eC. perfringens\u003c/em\u003e at 1.5*10\u003csup\u003e8\u003c/sup\u003e colony-forming units (CFU)/mL to mimic \u003cem\u003eC. perfringens\u003c/em\u003e-infected\u003cem\u003e\u0026nbsp;\u003c/em\u003echickens.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e4.2.6 Medium and reagents\u0026nbsp;\u003c/h3\u003e\n\u003ch4\u003e\u003cem\u003e4.2.6.1 Dialysis liquid\u0026nbsp;\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eDialysis liquid consisted of the following compounds (content gram per liter): 2.5 K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e\u0026middot;3H\u003csub\u003e2\u003c/sub\u003eO, 4.5\u0026nbsp;NaCl, 0.005\u0026nbsp;FeSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO, 0.5\u0026nbsp;MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO, 0.45\u0026nbsp;CaCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;2H\u003csub\u003e2\u003c/sub\u003eO, 0.05 ox bile (Sigma, Zwijndrecht, the Netherlands), and 0.4 cysteine.HCl, plus 1 mL of vitamin mixture containing (mg per liter): 1 menadione, 0.5 vitamin B12, 2 D-biotin, 10 pantothenate, 5 \u003cem\u003ep\u003c/em\u003e-aminobenzoic acid, 4 thiamine, and 5 nicotinamide acid.\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e4.2.6.2 Standard Ileal Efflux Medium\u0026nbsp;\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eSIEM was prepared as described by De Souza \u003cem\u003eet al.\u003c/em\u003e (26) with the following components (grams per liter): 9 citrus peel pectin, 9 beechwood xylan, 9 larch arabinogalactan, 9 potato amylopectin, 74.6 potato starch, 31.5 Tween 80, 43.7 casein, 0.7 ox-bile, 43.7 bactopepton, 4.7 K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e.3H\u003csub\u003e2\u003c/sub\u003eO, 0.009 FeSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 8.4 NaCl, 0.8 CaCl\u003csub\u003e2\u003c/sub\u003e.2H\u003csub\u003e2\u003c/sub\u003eO, 0.7 MgSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 0.02 hemin, and 0.3 cysteine.HCl, plus 1.5 mL of the vitamin mixture. The pH was adjusted to 6.6 to mimic the chicken ceca and 60 mL/day was administered. Standard carbohydrates in SIEM were replaced with 7.5 g of MOS, IMMP, or pectins.\u003c/p\u003e\n\u003cp\u003e4.2.7 Analysis of the carbohydrates\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e4.2.7.1 Analysis of IMMP\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThe IMMP used in this study was named after its percentage of total \u0026alpha;-(1\u0026rarr;6) glucosyl linkages. The total \u0026alpha;-(1\u0026rarr;6) linked glucosyl content, consisting of both \u0026alpha;-(1\u0026rarr;6) and \u0026alpha;-(1\u0026rarr;4) linked glucosyl residues, and was determined by hydrogen-1 nuclear magnetic resonance (1H NMR) spectroscopy, with the methodology and results already published previously\u0026nbsp;(van der Zaal et al., 2018). IMMP-87 (87% \u0026alpha;-(1\u0026rarr;6) linkages) originates from potato starch (Royal Avebe,) modified with \u003cem\u003eL. reuteri\u003c/em\u003e 121 GTFB 4,6-\u0026alpha;-glucanotransferase and pullulanase\u0026nbsp;(Leemhuis et al., 2014).\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e4.2.7.2 Characterization of pectins\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eNeutral monosaccharide composition was determined after Seaman hydrolysis and derivatization into alditol acetates. Briefly, samples were pre-treated for 1 hour at 30 ˚C with 72% (w/w) H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, and further submitted to acid hydrolysis with 1 M H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e (3h, 100 ˚C). The released neutral sugars were derivatized into their alditol acetates to be analyzed by gas chromatography (GC) (Englyst \u0026amp; Cummings, 1984). Inositol was used as an internal standard. The acidic monosaccharide, galacturonic Acid (GalA) was analyzed spectrophotometrically using the \u003cem\u003em\u003c/em\u003e-hydroxydiphenyl autoanalyser (Blumenkrantz \u0026amp; Asboe-Hansen, 1973; Thibault, 1979). The degree of methyl-esterification (DM) was estimated by saponification of pectins (NaOH 0.1 M; 1 h at 4 ˚C, followed by 23 h at room temperature). The methanol released was analyzed by head-space GC and DM was calculated (Huisman, Oosterveld, \u0026amp; Schols, 2004).\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e4.2.7.3 Enzymatic hydrolysis and determination of degree of blockiness of pectins\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThe hydrolysis of pectin samples was conducted as follows: samples were dissolved in 50 mM sodium acetate buffer (pH 5.2), incubated under stirring at 40 ˚C with pectin lyase (PL, EC 4.2.2.10; ID: 1043) from \u003cem\u003eAspergillus niger\u003c/em\u003e for 6 h, adding \u003cem\u003eendo\u003c/em\u003e-polygalacturonase (endo-PG, EC 3.2.1.15; ID 1027) from\u003cem\u003e\u0026nbsp;Kluyveromyces fragilis\u0026nbsp;\u003c/em\u003eand incubating for another 18 h. After incubation, enzymes were inactivated at 100 ˚C for 10 min. Pectin digests were centrifuged and supernatant were analyzed by high-perfomance size exclusion chromatography (HPSEC), high performance anion exchange chromatography (HPAEC) and, ultra-high pressure liquid chromatography (UHPLC) HILIC-ESI-IT-MS, as described by Jermendi et al, 2022. The parameters and equations \u0026ldquo;degree of blockines (DB), the absolute degree of blockiness (DB\u003csub\u003eABS\u003c/sub\u003e), the DB of methyl-esterified oligomers by endo-PG (DB\u003csub\u003ePGme\u003c/sub\u003e) and PL (DB\u003csub\u003ePLme\u003c/sub\u003e)\u0026rdquo; to describe methyl-ester have been also described by Jermendi et al, 2022.\u003c/p\u003e\n\u003ch3\u003e4.2.8 Microbial DNA extraction\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from 250 \u0026micro;l of the lumen samples taken during the CALIMERO-2 experiments as described before using the 2x300 bp protocol (Oost et al., 2021). Briefly, 1000 \u0026micro;l InhibitEx buffer (Qiagen, Venlo, the Netherlands) was added and samples were transferred to Precellys tubes containing 0.5 mm microbeads and treated in a bead beater (Precellys 24, Bertin Technologies, Montigny le-Bretonneux, France). Thereafter, the samples were incubated at 95\u0026deg;C for 7 min and centrifuged at 13500 x g for 1 min to pellet stool particles and cell wall fragments. From this point on, the QIAamp DNA stool Mini kit (Qiagen) was used following the manufacturer\u0026rsquo;s protocol from step 4 onwards.\u003c/p\u003e\n\u003ch3\u003e4.2.9 Bacterial composition of the cecal microbiota\u003c/h3\u003e\n\u003cp\u003eThe composition of the cecal bacteria was evaluated by 16S rRNA gene sequencing using Illumina Miseq (Illumina, San Diego, CA, United States) as described before\u0026nbsp;(Oost et al., 2021). 16S rRNA gene amplicon libraries of the V3-V4 region were generated following the 16S Metagenomic Sequencing Library preparation manual of Illumina Miseq systems using the Nextera XT kit, using a 2-step PCR. A mock community was run along with the samples to guarantee sequence quality.\u003c/p\u003e\n\u003ch3\u003e4.2.10 Short-Chain Fatty Acids, Branched-Chain Fatty Acids, and Organic Acids Quantification in Lumen and Dialysate samples\u003c/h3\u003e\n\u003cp\u003eSCFAs, branched-chain fatty acids (BCFAs), and organic acids were quantified through ion exclusion chromatography by Brightlabs (Venlo, the Netherlands). Briefly, an 883 Ion Chromatograph was used (IC; Metrohm, Switzerland), with a Transgenomic IC Sep ICE-ION-300 column (30 cm length, 7.8 mm diameter, and 7 mm particles) and a MetroSep RSPE6 Guard column. The mobile phase consisted of 1.5 mM aqueous sulfuric acid and the column had a flow rate of 24 mL/h and a temperature of 65\u0026deg;C. The acids were detected using suppressed conductivity detection. Samples were centrifuged at 13500 g for 10 min, and the clear supernatant was filtered through a 0.45 mm PFTE filter and diluted with mobile phase (for lumen 1:5, for dialysate 1:2). Ten \u0026mu;l were loaded on the column by an autosampler 730 (Metrohm). Molecules were eluted according to their pKa.\u003c/p\u003e\n\u003ch3\u003e4.2.11 Bioinformatics analysis\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eMicrobiota bioinformatics was performed with QIIME2 2019.4 (Bolyen et al., 2019). Shortly, the raw sequencing data were demultiplexed, quality filtered, and denoised by using the q2-demux plugin and DADA2\u0026nbsp;(Callahan et al., 2016). In the DADA2 step, the first 9 bases were trimmed off and for the forward reads there was an additional truncation at 290 base pairs and for the reverse reads, this was at 280 base pairs. Taxonomy was assigned using the SILVA 128 16S rRNA gene reference database. Further analysis was conducted with R (version 4.2.0) within R-studio, with packages microViz\u0026nbsp;(Barnett, 2021), microbiome\u0026nbsp;(Lahti, 2017), and phyloseq\u0026nbsp;(McMurdie \u0026amp; Holmes, 2013)\u0026nbsp;for analysis and visualization of microbiome sequencing data. Microbial alpha-diversity was determined using observed number of OTUs and the Shannon index. Beta-diversity was determined using weighted and unweighted UniFrac distance metrics. Differences in taxonomic profiles were analyzed using Statistical Analysis Metagenomic Profiles (STAMP) software v.2.1.3 with Kruskal-Wallis test, followed by a Tukey-Kramer post-hoc test\u0026nbsp;(Parks, Tyson, Hugenholtz, \u0026amp; Beiko, 2014). P values were corrected using the Benjamini-Hochberg method and a corrected P value \u0026lt; 0.05 was considered significant. Statistical significance of alpha-diversity was assessed with Kruskal-Wallis test, followed by a Dunns\u0026rsquo; post-hoc test. For the statistical analysis of the beta-diversity for the different carbohydrates and comparison with the controls permutational multivariate analysis of variance (PERMANOVA) (Anderson, 2017) was performed. The statistical analysis of the remaining data was performed using GraphPad Prism 9.5.1 (GraphPad Software, San Diego, USA). The metabolites were assessed with two-way ANOVA analysis, followed by Bonferroni\u0026rsquo;s post-hoc test with selected pairs. For the relative abundance, phylum, and genus levels statistical significance was determined, using multivariate analysis for the different types of carbohydrates and a Welch\u0026rsquo;s t-test for the two groups healthy and diseased (the latter for the samples spiked with \u003cem\u003eC. perfringens\u003c/em\u003e).\u003c/p\u003e"},{"header":"Results ","content":"\u003ch3\u003e4.3.1 Composition and structural properties of Pectins and IMMP\u003c/h3\u003e\n\u003cp\u003eThe three different pectins and IMMP were analyzed regarding their monosaccharide composition, molecular weight distribution, and DM (only pectins (Table 1). The pectins are mainly composed of Galacturonic Acid (GalA), typically present in homogalacturonan (HG) type pectins, with slight differences in neutral sugar content. The molecular weight (M\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e) distribution among the samples ranged from 115 kDa to 136 kDa. The DM, defined as the percentage of methyl-esters distributed within GalA residues over the HG backbone, was 63% for pectins SPE6 and SPE7, and 26% for pectin SPE8. Besides the similarities in sugar content and M\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e, pectins SPE6 and SPE7 are featured as highly methyl-esterified (HMP; DM \u0026gt; 50% DM) and pectin SPE8 is low methyl-esterified (LMP; DM \u0026lt; 50% DM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Structural characteristics of pectins and isomalto/malto-polysaccharide (IMMP)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eChemical features of Pectins and IMMP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.234910277324634%\" valign=\"top\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRha\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAra\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXyl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGalA\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal GalA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003cem\u003ew\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.298531810766722%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.195121951219512%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.82113821138211%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003emol%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\" valign=\"top\"\u003e\n \u003cp\u003e(w/w%)\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.357723577235772%\" valign=\"top\"\u003e\n \u003cp\u003e(kDa)\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.268292682926829%\" valign=\"top\"\u003e\n \u003cp\u003e(%)\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.234910277324634%\" valign=\"top\"\u003e\n \u003cp\u003eSPE8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e89.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e76.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.298531810766722%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.234910277324634%\" valign=\"top\"\u003e\n \u003cp\u003eSPE6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e87.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.298531810766722%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.234910277324634%\" valign=\"top\"\u003e\n \u003cp\u003eSPE7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e90.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.298531810766722%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.234910277324634%\" valign=\"top\"\u003e\n \u003cp\u003eIMMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.66721044045677%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.398042414355627%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.298531810766722%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003eRha: Rhamnose; Ara: Arabinose; Xyl: xylose; Man: Mannose; Gal: Galactose; Glc: Glucose; GalA: Galacturonic Acid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003eDetermined spectrophotometrically using the \u003cem\u003em\u003c/em\u003e-hydroxydiphenyl automated skalar method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003eTotal galacturonic acid content anhydrous in w/w%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003eAverage molecular weight (Mw) determined by HPSEC based on the pectin standards. Average Mw for IMMP was determined by Multi-angle light scattering detector (21). \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003eDegree of methyl-esterification (DM): mol of methanol per 100 mol of the total GalA in the sample. Pectin presenting DM lower than 50% is considered low methyl-esterified and DM above 50% is considered highly methyl-esterified.\u003c/p\u003e\n\u003cp\u003eTo have a better understanding of the methyl-esterification pattern over the pectin backbone, the analysis of distribution of non-methyl-esterified GalA residues can be useful. As a first step, the pectins were enzymatically degraded with pure, and well-defined enzymes polygalacturonase (endo-PG) and pectin lyase (PL) and analyzed by HPSEC with RI detector. PL can cleave glycosidic linkages in vicinal methyl-esterified GalA units by introducing a double bond (unsaturated), while endo-PG requires four consecutive non-esterified GalA units to act (Jermendi et al., 2022). Figure 2 confirm the virtually complete degradation of pectins by both enzymes. The three different pectins demonstrated rather similar M\u003cem\u003ew\u003c/em\u003e before PL and Endo-PG digestion. After digestion, pectins were degraded into low molecular weight oligomers. HMP SPE6 and SPE7 had a very similar degradation pattern indicating a similar methyl ester level and distribution. For the LMP SPE8, the peak shape in the oligomer region (12.3 \u0026ndash; 14.5 min) reflects the presence of different degradation products and shows indeed a different DM and distribution (Jermendi et al., 2022).\u003c/p\u003e\n\u003cp\u003eIn order to have a deeper overview of pectin digestion products, HPAEC-PAD/UV allowed separation, identification and quantification of oligomers ranging from degree of polymerization (DP) 1 to 7. Most oligomers formed after SPE8 digestion were saturated, demonstrating that endo-PG had greater activity over the SPE8 backbone (Figure 3). Contrary, SPE6 and SPE7 had a more complex but very similar chromatogram. Peaks of saturated and unsaturated oligomers appeared for SPE6 and SPE7, and the intensity of saturated DP 1 to 3 were lower when compared to SPE8. Peaks of unsaturated DP 2 to 6 were seen in SPE6 and SPE7, while SPE8 only had formation of unsaturated DP 2 and 3. This is as expected due to their higher DM. In addition, a random distribution of methyl-esters in SPE6 and SPE7 led to the formation of different diagnostic oligomers after pectin digestion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Descriptive parameters of pectins with different DM\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePectin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDB\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.941176470588236%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDB\u003csub\u003eABS\u003c/sub\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDG\u003csub\u003ePGme\u003c/sub\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDB\u003csub\u003ePLme\u003c/sub\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.823529411764707%\" valign=\"top\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.941176470588236%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.941176470588236%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.941176470588236%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003eDegree of blockiness: amount of non-esterified mono-, di-, and triGalA per 100 mol of the non-esterified GalA in the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003eAbsolute degree of blockiness: amount of non-esterified mono-, di-, and triGalA per 100 mol of total GalA in the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003eDegree of blockiness by endo-PG (DG\u003csub\u003ePGme\u003c/sub\u003e): amount of saturated methyl-esterified galacturonic residues per 100 mol of total galacturonic acid in the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003eDegree of blockiness by PL (DB\u003csub\u003ePLme\u003c/sub\u003e): amount of unsaturated methyl-esterified galacturonic oligomers per 100 mol of total galacturonic acid in the sample.\u003c/p\u003e\n\u003cp\u003eTo further investigate the features that differentiate one pectin from another we characterized their degree of blockiness (Table 2), that is, the amount of released non-esterified mono-, di- and trisaccharides of GalA relative to the total non-esterified GalA in the pectin backbone\u0026nbsp;(Jermendi et al., 2022). The elegant study from Jermendi et al (2022) demonstrate the formulas to calculate each DB parameter considering the DM, GalA content, HPAEC and HILIC measurements. Even though DB values were quite similar for all pectins, the patterns of methyl-esterification is different. DB\u003csub\u003eABS\u003c/sub\u003e, which regards the amount of non-esterified oligomers related to total amount of GalA in the pectin was higher in SPE8, meaning that SPE8 has more \u0026ldquo;blocky\u0026rdquo; regions of non-esterified GalA units.\u0026nbsp;The values for DB\u003csub\u003ePLme\u003c/sub\u003e and DB\u003csub\u003ePGme\u003c/sub\u003e are in consonance with the low DM of SPE8 when compared to SPE6 and SPE7, but also indicate that SPE8 had the fewest methylated vicinal GalA units and highest amount of \u0026ldquo;blocky\u0026rdquo; regions. SPE6 and SPE7 exhibit almost the same values for all parameters indicating that both have similar DM and distribution of methyl-esters over the pectin backbone. Interestingly, SPE7 had a slightly higher value for DB\u003csub\u003ePLme\u003c/sub\u003e, when compared to the other pectins, meaning that it possess more methylated vicinal GalA units.\u003c/p\u003e\n\u003ch3\u003e4.3.2 Alpha- and beta-diversity of the cecal microbiota in CALIMERO-2\u003c/h3\u003e\n\u003cp\u003eThe effect of the different carbohydrates on microbial diversity was assessed by comparing alpha- and beta-diversity between the three different pectins and IMMP, and to the controls SIEM and MOS. Also, the diversity for the diseased model samples from CALIMERO-2, in which necrotic enteritis was mimicked by spiking in \u003cem\u003eC. perfringens\u003c/em\u003e, was compared to the healthy model (Figure 4). For the alpha-diversity, both the Shannon index (Figure 4a) and the observed Operational Taxonomic Units (OTUs) were calculated for the fermented carbohydrates every 24h up to 72h (Figure 4b). The Shannon index and the observed OTUs indicate a significant decrease in microbiota for SIEM and MOS in the \u003cem\u003eC. perfringens\u0026nbsp;\u003c/em\u003einoculated model, compared to the corresponding healthy model (Figure 4a-b). This difference between samples from the healthy and diseased models was not observed for the other substrates. When comparing the different types of carbohydrates in the healthy groups, the Shannon index shows that SIEM results in more diverse microbiota communities compared to SPE8 (P \u0026lt; 0.05). Also, MOS produced more diverse microbiota communities compared to IMMP (P \u0026lt; 0.05), SPE8 (P \u0026lt; 0.001), SPE6 (P \u0026lt; 0.05), and SPE7 (P \u0026lt; 0.01). Based on the observed OTUs, a similar pattern was found, but here SIEM showed higher observed OTUs compared to SPE6 instead of SPE8.\u003c/p\u003e\n\u003cp\u003eThe similarity in community structure between samples was studied with the beta-diversity metrics unweighted and weighted UniFrac measures. Principal coordinate analysis (PcoA) of unweighted UniFrac demonstrates an overlap of the two HMP SPE6 and SPE7. A significant difference in beta-diversity is observed for MOS compared to IMMP, SPE6, and SPE7 (P\u003cem\u003e\u0026nbsp;\u003c/em\u003evalues, see Table 3, Figure 4c). PcoA of the weighted UniFrac also shows an overlap of HMP SPE6 and SPE7, indicating the high similarity of these substrates. LMP SPE8 shows a significant difference compared to MOS, IMMP, and HMP SPE7 (Table 3; Figure 4d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eP values of PERMANOVA on unweighted and weighted UniFrac. * P \u0026lt; 0.05.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSIEM = Simulated ileal-efflux medium, MOS = mannan oligosaccharides, IMMP = isomalto/malto-polysaccharide, P = pectin.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"397\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eUnweighted UniFrac\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMMP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eSIEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eMOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.084*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.022*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eIMMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eSPE8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eSPE6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eSPE7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eWeighted Unifrac\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMMP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"NaN%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.027*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMMP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPE7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e\n\u003ch3\u003e4.3.3 Changes in composition at the phylum and genus taxonomic levels\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe microbiota composition was determined at time point 0 and after 72h of fermentation in CALIMERO-2 in which the microbiota was exposed to carbohydrates. Phylum and genus levels, showed small changes in microbiota between the different experimental groups after 72h fermentation (Figure 5A). \u003cem\u003eBacteroidetes\u003c/em\u003e was the most abundant phylum for most of the groups, except for SIEM and MOS in the healthy model, in which \u003cem\u003eFirmicutes\u003c/em\u003e was dominant. Within the phylum \u003cem\u003eFirmicutes\u003c/em\u003e, the genera \u003cem\u003eLachnospiraceae UCG-010\u003c/em\u003e, \u003cem\u003eAnaerofilum\u003c/em\u003e, and \u003cem\u003eIntestinimonas\u003c/em\u003e were significantly higher in the healthy, compared to the diseased samples (P \u0026lt; 0.05; Figure S2). At genus level, \u003cem\u003eBacteroides\u0026nbsp;\u003c/em\u003ewas significantly lower in the MOS and SIEM fermentations compared to \u003cem\u003eBacteroides\u003c/em\u003e for the other carbohydrates in the healthy model after 72 h fermentation (Figure 5C). Moreover, for MOS in the diseased model \u003cem\u003eBacteroides\u003c/em\u003e was also higher compared to MOS in the healthy model (Figure 5C). The genus \u003cem\u003eLachnoclostridium\u0026nbsp;\u003c/em\u003ewas significantly higher in the MOS samples compared to the other substrates (Figure 5D). The pectins LMP SPE8 and HMP SPE6 showed an increase of the genus \u003cem\u003eAkkermansia\u003c/em\u003e compared to SIEM and MOS in the healthy model (Figure 5E). \u003cem\u003eAkkermansia\u003c/em\u003e also increased in response to the addition of pectin LMP SPE8 in the diseased model. \u003cem\u003eLactobacillus\u003c/em\u003e levels were maintained for all carbohydrates, except for MOS, that demonstrated a significant increase of \u003cem\u003eLactobacillus\u003c/em\u003e in the healthy model (Figure 5F). \u003cem\u003eBifidobacterium\u0026nbsp;\u003c/em\u003eshowed the same relative abundance for all carbohydrates, only for SIEM in the diseased model there was a significant decrease (Figure 5G).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e4.3.4 Cecal production of SCFAs in CALIMERO-2\u003c/h3\u003e\n\u003cp\u003eAcetate, propionate, and butyrate are the three main SCFAs produced during the \u003cem\u003ein-vitro\u003c/em\u003e fermentation of carbohydrates. Lactate and succinate, the intermediate products of carbohydrate fermentation, were present in very low concentrations compared to SCFA concentrations in the samples. The total SCFAs is the sum of acetate, propionate, and butyrate. The addition of \u003cem\u003eC. perfringens\u0026nbsp;\u003c/em\u003edid not affect metabolite production, compared to the healthy model (Fig S3). Figure 5A shows the total SCFAs over time for the different intervention carbohydrates. Fermentation of LMP SPE8 yielded a lower amount of total SCFAs when compared to SIEM, IMMP, and HMP SPE7 after 72h of fermentation (P \u0026lt; 0.05). When comparing the carbohydrates on the separated SCFAs, acetate showed a significantly higher cumulative production after 72h of fermentation for HMP SPE7 (P \u0026lt; 0.01) and IMMP (P \u0026lt; 0.05) compared to MOS. HMP SPE6 did not show any significant difference. HMP SPE7 also lead to significantly higher acetate compared to LMP SPE8 (Figure 6B). Butyrate was also significantly increased under SIEM and MOS fermentation at 48h and 72h, compared to the three pectins (Figure 6C). After 72h of fermentation, this was also shown for IMMP compared to LMP SPE8. Propionate showed significantly higher production after 48h of SIEM compared to MOS (P \u0026lt; 0.01) (Figure 6D). Propionate production was also higher for SIEM compared to the three pectins (P \u0026lt; 0.001). Propionate demonstrated similar production on IMMP and SIEM, and they were both significantly higher compared to MOS (P \u0026lt; 0.05) and SPE8, SPE6 and SPE7 (P \u0026lt; 0.001). After 72h, also MOS produced more propionate compared to LMP SPE8 (P \u0026lt; 0.05). Both lactate and succinate did show a significant difference at 24h and 48h for SIEM compared to the pectins and IMMP, which vanished after 72h (Figure 6). The negative cumulative production indicates that these are converted into the other SCFA (primarily propionate and butyrate). The amount of iso-butyrate, one of the BCFAs, was significantly higher upon providing SIEM and MOS, compared to the three pectins at 48h and 72h. For the other measured BCFA, iso-valerate, MOS had the highest cumulative amount, and this was significantly different from the other four carbohydrates at 48h, and after 72h also significantly higher compared to SIEM. SIEM only showed a significant increase compared to LMP SPE8 and HMP SPE6 at time points 48 and 72h.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we aimed to investigate the potential prebiotic effect of three citrus pectins and the ten times smaller polymer IMMP. Therefore, it was crucial to know the chemical features of these carbohydrates to understand the correlation between structure and their fermentability. Pectins analysis revealed that, despite similarities, these pectins distinctively affected the gut microbiota of broilers in CALIMERO-2. The compositional differences, the related glycosidic bonds, the DM and DB of pectins, each impose a challenge to their fermentation by microorganisms. The molecular machinery needed for dietary fibre degradation is structure-specific, thus the complex and diverse structure of pectins may require many steps for enzymatic catalysis and SCFA production\u0026nbsp;(Cronin, Joyce, O'Toole, \u0026amp; O'Connor, 2021). The DM is the most important structural feature of pectins, and more recently the DM-related parameter “DB” became subject of research. These features may add another barrier for their fermentation. Langhout and Schutte (1996) concluded that the health effects of pectins are source, amount and DM-related.\u003c/p\u003e\n\u003cp\u003eLess complex carbohydrates and with lower molecular weight, such as IMMP and MOS, may be degraded in less enzymatic steps by a broad range of bacteria due to the common molecular machinery\u0026nbsp;(Wardman, Bains, Rahfeld, \u0026amp; Withers, 2022). Consequently, both the different pectins as well as oligosaccharides can distinctively affect the gut microbiota.\u003c/p\u003e\n\u003cp\u003ePectins SPE8 and SPE6 were quite similar regarding their monosaccharide composition, because SPE8 was made from SPE6 by de-esterification. SPE7 has a higher percentage of arabinose and a lower percentage of galactose when compared to SPE8 and SPE6. The HM SPE7 had a significantly higher cumulative production of acetate compared to LMP SPE8 and also had a higher propionate concentration compared to both other two pectins after fermentation. The higher percentage of arabinose has been linked to an increase of acetate and propionate producers in earlier research\u0026nbsp;(Tomioka et al., 2022). In studies with humans and animals, pectin fermentation leads to formation of acetate over propionate and butyrate\u0026nbsp;(Firrman et al., 2022; Langhout \u0026amp; Schutte, 1996; Larsen et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe compared the effects of the pectins and IMMP on bacterial composition and metabolite production. In these comparisons we also took along a fermentation control SIEM and the prebiotic compound MOS, the latter of which has been studied widely for its beneficial effects on intestinal health in chickens. Firstly, SIEM and MOS induced an increase in gut microbiota diversity and also promoted a higher number of observed OTUs in the healthy model. However, SIEM and MOS were not able to prevent the loss of microbes in numbers in the diseased model. The reduction of alpha-diversity (species richness) of the cecal microbiota can be linked to the addition C. perfringens, which normally shifts the intestinal microbiota, and reduces the alpha-diversity within the samples\u0026nbsp;(Q. Yang et al., 2021). This might take longer than 72h of fermentation, explaining the initial decrease of alpha-diversity. The control SIEM is rich in several carbohydrates, such as arabinogalactan and xylan, and might have promoted growth of a wider range of different bacteria. The diversity in both the healthy and the diseased group is lower for the pectins and IMMP compared to SIEM. For pectin, this could be related to their complex structure and while the gut microbiota might need to adapt to a pectin-degrading microbiota, thus fermentation can be slower\u0026nbsp;(Wardman et al., 2022). For IMMP the lower diversity compared to SIEM and MOS might be related to the delayed and slow-fermentation behavior compared to other prebiotics, because of the presence of the α 1,6 glycosidic linkages in IMMP\u0026nbsp;(Gu et al., 2018; L. Tian et al., 2017).\u003c/p\u003e\n\u003cp\u003eBeta-diversity (community structure) was significantly different for MOS compared to IMMP, SPE6, and SPE7, which can be explained by the changes in microbiota composition towards a microbiota that can degrade the complex structures of the pectins and IMMP. SPE8, however, showed significant differences compared to MOS, IMMP, and SPE7, which could be related to its low methyl-esterified and higher amounts of non-esterified (blocky) regions. The microbiota composition was determined, and small differences between the experimental carbohydrates were shown at the phylum and genus levels. MOS is known for creating a diverse gut microbiota, by supporting the growth of beneficial bacteria, such as Lactobacillus and Bifidobacterium, and decreasing the presence of pathogens such as C. perfringens\u0026nbsp;(Baurhoo et al., 2007; Corrigan, Leeuw, Penaud-Frézet, Dimova, \u0026amp; Murphy, 2015; Fernandez et al., 2000; Ghasemian \u0026amp; Jahanian, 2016; Ofek \u0026amp; Beachey, 1978; Spring et al., 2000; Y. Yang et al., 2008). Lactobacillus was also increased in CALIMERO-2 in the MOS-treated samples. However, when C. perfringens was added to the system, Lactobacillus was significantly decreased in the MOS group. Bifidobacterium was present in all the samples. An observation that stood out at the phylum level, was that all samples, except the MOS and SIEM in the healthy model, had Bacteroidetes as the most abundant bacteria, whereas Firmicutes was dominant in MOS and SIEM in the healthy model.This is in line with previous research, wherein MOS also promoted\u003cem\u003e\u0026nbsp;Firmicutes\u0026nbsp;\u003c/em\u003epopulation in the chicken cecal microbiota (Pourabedin, Xu, Baurhoo, Chevaux, \u0026amp; Zhao, 2014).\u003c/p\u003e\n\u003cp\u003eThe genus Akkermansia, which is associated with gut health in humans, was increased by SPE8 and SPE6 compared to SIEM and MOS in the healthy model. However, whether Akkermansia has the same beneficial effects in vivo in chickens, is still under debate. For example, it has been shown to have a protective effect on the intestinal barrier, but it has also been linked to a higher number of necrotic enteritis cases, and significant overgrowth and colonization of C. perfringens\u0026nbsp;(W.-Y. Yang, Chou, \u0026amp; Wang, 2022; L. Zhu et al., 2020). Additionally, it is also interesting that Akkermansia survived in CALIMERO-2, with the lack of mucus in the system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe fermentation of non-digestible carbohydrates by anaerobic bacteria in the gut yields SCFAs, and these metabolites are related to health benefits to the host. In broilers, SCFAs production is related to protection against pathogens by building a balanced gut community, and improvements of gut immunity, gut barrier, and mucin secretion and may enhance broiler production performance\u0026nbsp;(Liu, Li, Yang, \u0026amp; Guo, 2021). In our research, we showed that different non-digestible carbohydrates promote different cumulative production of metabolites. For instance, fermentation of SPE8, which is a low DM pectin, provided the lowest amounts of total SCFAs, organic acids, and BCFAs. It raises the question of whether this outcome was due to a slower fermentation rate of the pectin or if the gut bacteria were not able access the non-esterified GalA of this pectin. It requires more time for the gut microbiota to adapt to SPE8. Similarly, Tian et al.\u0026nbsp;(L. Tian et al., 2017; Lingmin Tian et al., 2016), tested LMP and HMP, and also found quite different fermentation patterns for each pectin.\u003c/p\u003e\n\u003cp\u003eComparing the three pectins, SPE6 exhibited intermediate levels of metabolites, and SPE7 yielded the highest amounts of metabolites, which could imply the gut microbiota expressed enzymes able to degrade HM pectin, therefore a more extensive fermentation. The different DM and DBs, the compositional variations of the three pectins likely contribute to the divergent fermentation patterns observed. Interestingly, the values of total SCFAs, organic acids, and BCFAs were found to be very similar between the SIEM, IMMP and SPE7, with IMMP and SPE7 exhibiting even greater similarity. Apparently, our HM pectins are more easily used by gut bacteria in this study when compared to the LM pectin. Also, our results suggest that the fermentation patterns associated with IMMP and SPE7 might share common metabolic pathways. However, it is worth noting that the gut microbiota was differently modulated by these two substrates, once more demonstrating that the fermentation metabolites’ similarity in amounts does not necessarily reflect identical microbial community responses.\u003c/p\u003e\n\u003cp\u003eOur study revealed that succinate and lactate were not dominantly present in the samples, also not by the fermentation of IMMP. Gu et al. (2018) reported that IMMP-94 and IMMP-96 predominately produce the intermediate SCFA, succinate, next to the SCFAs in a batch fermentation model using human inoculum. Our results showed that succinate and lactate were mainly converted to SCFAs, which might have happened faster because the microbiota of chickens is different compared to the human inoculum. Moreover, this might also be related to the decrease in pH in their model\u0026nbsp;(Gu et al., 2018), compared to CALIMERO-2, in which the pH was constantly regulated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, our results enhance our understanding of the correlation between carbohydrate structure and fermentability, emphasizing that the complexity of carbohydrates leads to contrasting outcomes on the gut microbiota and the production of metabolites. Although carbohydrates, especially IMMP, affect the relative abundance of bacteria and the total SCFAs production, future research is needed to determine if IMMP or the different pectins are beneficial for chicken gut health.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Royal GD Deventer, the Netherlands, for providing the \u003cem\u003eClostridium perfringens\u003c/em\u003e strain in this research. We would like to thank Natalia Hutnik for her support with the pectin analysis, and Rob van Dinter, Jessica Verhoeven and Sanne Verbruggen for their technical support with the CALIMERO-2 experiments and 16S rRNA sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis research was performed in the public-private partnership \u0026apos;CarboBiotics\u0026apos; coordinated by the Carbohydrate Competence Center (CCC,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewww.cccresearch.nl\u003c/strong\u003e\u003cstrong\u003e). CarboBiotics is jointly financed by participating industrial partners Royal Avebe U.A., FrieslandCampina Nederland B.V., Nutrition Sciences N.V., and allowances of The Dutch Research Council (NWO).\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFurthermore, the study was also partly funded by the Centre for Healthy Eating \u0026amp; Food Innovation (HEFI) of Maastricht University \u0026ndash; Campus Venlo. This research has been made possible with the support of the Dutch Province of Limburg with a grant to HEFI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMiriam J. Oost:\u0026nbsp;\u003c/strong\u003eMethodology, investigation, writing-original draft,\u003cstrong\u003e\u0026nbsp;Kahlile Youssef Abboud:\u0026nbsp;\u003c/strong\u003emethodology, investigation, writing-original draft, \u003cstrong\u003eFrancisca C. Velkers:\u0026nbsp;\u003c/strong\u003eSupervision, Project administration, writing \u0026ndash; reviewing \u0026amp; editing, \u003cstrong\u003eHans Leemhuis:\u003c/strong\u003e Writing \u0026ndash; reviewing \u0026amp; editing, \u003cstrong\u003eGeert Bruggeman:\u003c/strong\u003e Writing \u0026ndash; reviewing \u0026amp; editing, \u003cstrong\u003eAletta D. Kraneveld:\u0026nbsp;\u003c/strong\u003eSupervision, writing \u0026ndash; reviewing \u0026amp; editing, \u003cstrong\u003eHenk A. Schols:\u0026nbsp;\u003c/strong\u003eSupervision, writing \u0026ndash; reviewing \u0026amp; editing, \u003cstrong\u003eKoen Venema:\u0026nbsp;\u003c/strong\u003eSupervision, conceptualization, writing \u0026ndash; reviewing \u0026amp; editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, M. J. (2017). Permutational Multivariate Analysis of Variance (PERMANOVA). 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(2002). 16S rRNA-Based Analysis of Microbiota from the Cecum of Broiler Chickens. \u003cem\u003eApplied and Environmental Microbiology, 68\u003c/em\u003e(1), 124-137. doi:doi:10.1128/AEM.68.1.124-137.2002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"microbiota, cecum, broilers, CALIMERO-2, IMMP, pectins","lastPublishedDoi":"10.21203/rs.3.rs-4254410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4254410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe intestinal microbiota is crucial for intestinal and overall animal health. Coccidiosis and necrotic enteritis poses significant economic burden on poultry farming. Such inflammatory intestinal diseases disrupt the gut microbiota and the addition of carbohydrates to feed can promote and sustain a stable gut microbiota. We compared the effects on microbiota composition and metabolites during fermentation of isomalto/malto-polysaccharides and high- and low methyl-esterified pectins (HMP, LMP), against a positive control, mannan-oligosaccharide (MOS), using the Chicken ALIMEntary tRact mOdel-2 (CALIMERO-2). CALIMERO-2 mimic fermentation in healthy ceca, and by spiking it with C. perfringens, we aimed to mimic fermentation in diseased chicken ceca. Pectins showed minor differences in monosaccharide composition and molecular weight. SPE8 had degree of methyl-esterification (DM) of 26 (LMP), and SPE6 and SPE7 DM of 63 (HMP). Beta-diversity was significantly similar between HMP’s SPE6 and SPE7. \u003cem\u003eBacteroidetes\u003c/em\u003e was dominant phylum, except for SIEM and MOS, where \u003cem\u003eFirmicutes \u003c/em\u003eprevailed. Beneficial bacteria particularly \u003cem\u003eLactobacillus\u003c/em\u003e, remained stable across samples. This study advances our comprehension of the fermentability and structural impact of diverse carbohydrates on the broiler gut microbiota. Our findings underscore the potential of isomalto/malto-polysaccharides and pectins to promote intestinal health in poultry, warranting further investigations to optimize its inclusion in chicken feed.\u003c/p\u003e","manuscriptTitle":"Characteristics of carbohydrates determine the shape of the gut microbiota in a chicken cecal in-vitro model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 17:42:53","doi":"10.21203/rs.3.rs-4254410/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e39d6ee3-673d-4cef-95f9-459c0457ed2a","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-02T08:25:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 17:42:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4254410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4254410","identity":"rs-4254410","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}