Modulation and metabolism of obesity-associated microbiota in a dynamic simulator of the human gut microbiota

LWT - Food Science and Technology 141 (2021) 110921 Available online 18 January 2021 0023-6438/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Modulation and metabolism of obesity-associated microbiota in a dynamic simulator of the human gut microbiota Teresa Requena a , * , Ya Song b , Carmen Pel´ aez a , M. Carmen Martínez-Cuesta a a Instituto de Investigaci´ on en Ciencias de la Alimentaci´ on CIAL (CSIC), Nicol´ as Cabrera 9, Campus de Cantoblanco, Universidad Aut´ onoma de Madrid, 28049, Madrid, Spain b Guangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou, 510642, China ARTICLE INFO Keywords: Microbiota Obesity Gut model Propionate Food ingredients ABSTRACT The in vitro development of representative gut microbiota from obese individuals has been approached in the present study by using a three-stage dynamic simulator of the human gut microbiota (BFBL Gut Model). During the stabilization period, aimed to reach the microbiota steady state, initial differences found between normalweight (Nw) and obese (Ob) faecal samples were reproduced. The differentiation included lower values of Akkermansia, Enterococcus and Faecalibacterium and higher values of Lactobacillus for the Ob microbiota when compared with the Nw microbiota. The change of food ingredients to simulate a high consumption of readilyfermented carbohydrates and fructose-enriched beverages maintained these differences and additionally produced a decrease of Bifidobacterium in the Ob microbiota. Related to obesity metabolic signatures, a lower capacity to produce propionate was characteristic of the Ob microbiota under all the tested conditions. This could be useful in the selection of food ingredients for targeted production of propionate by the human gut microbiota prior to their assessment in nutritional intervention studies related to obesity. 1. Introduction The scientific literature is continuously accumulating evidences that associate gut microbiome alterations with the human health (Zuo et al., 2020), although more critical scientific approaches for establishing causality for the gut microbiome in diseases are still required (Walter, Armet, Finlay, & Shanahan, 2020). Regarding obesity, the gut microbiota has been related to increased energy harvest and the formation of signalling molecules able to influence the adipose depots (Heiss & Olofsson, 2018), and to have a significant role in the onset and establishment of obesity (Rosenbaum, Knight, & Leibel, 2015). Specific obesity-related microbial groups, aside from the early reported shift between the phyla Bacteroidetes and Firmicutes, have associated Bifidobacterium, Faecalibacterium, Christensenella and Akkermansia muciniphila, among others, with a lean phenotype and healthy status (Bianchi, Duque, Saad, & Sivieri, 2019; Dao et al., 2016; Wang et al., 2019). On the other hand, certain gut bacterial species such as Enterobacter chloacae have demonstrated to induce the obese phenotype in germ-free mice (Fei & Zhao, 2013). Overall, the assumption of a taxonomic signature of obesity seems more likely at the species level than at phylum level (Peters et al., 2018) and also taking into consideration that some species can be organized as guilds providing similar functions to the human host (Lam, Zhang, & Zhao, 2018). Diet is the main source of substrates for the gut microbiota, and the ingestion of high/dense-energy diets involves changes in the composition of the gut microbiota that can be associated with obesity (Zmora, Suez, & Elinav, 2019). Besides the intake of high fat diets, sugar-sweetened beverages also increase the risk of obesity, and can exert alterations of both oral and gut microbiotas (Keller, Kressirer, Belstrom, Twetman, & Tanner, 2017; Payne, Chassard, & Lacroix, 2012). Likewise, there are strong associations between the faecal metabolome and obesity (Zierer et al., 2018). The gut microbiota of obese individuals seems to be more effective in extracting energy from food (e.g. capability to produce short chain fatty acids, SCFA) than that of lean individuals (Turnbaugh et al., 2006). Then, increased capacity for energy harvest can be redirected toward energy accumulation via hepatic de novo lipogenesis and body fat partitioning (Goffredo et al., 2016). The description of species that drive a characteristic obese microbiota core able to extract more energy than microbiota from lean individuals for similar amounts of dietary energy, can be tested in vitro in a host-free environment. The characterization of representative gut * Corresponding author. E-mail address: [email protected] (T. Requena). Contents lists available at ScienceDirect LWT journal homepage: www.elsevier.com/locate/lwt https://doi.org/10.1016/j.lwt.2021.110921 Received 7 September 2020; Received in revised form 13 November 2020; Accepted 13 January 2021 LWT 141 (2021) 110921 2 microbiota from obese individuals has been tried in the present study using a dynamic simulator of the gastrointestinal tract equipped with three-stage continuous reactors for reproducing the colon regionspecific microbiota and its metabolism (Barroso, Cueva, Pelaez, Martinez-Cuesta, & Requena, 2015; Martínez-Cuesta, Pel´ aez, & Requena, 2019). The model was tested to check differences in microbiological and metabolic characteristics during stabilization of faecal microbiota from obese and normal-weight individuals under standard diet conditions (Barroso, Cueva, Pel´ aez, Martínez-Cuesta, & Requena, 2015) and using a high energy-content medium (Barroso et al., 2015). Based on previously observed differences in abundance of distinct communities of microorganism from these faecal samples (Martínez-Cuesta, del Campo, Garriga-García, Pel´ aez, & Requena, 2020), the model can be instrumental in identifying microbial taxa that might play a metabolic role in obesity. 2. Materials and methods 2.1. Samples The faecal samples used in this study were collected from normalweight (Nw) and obese (Ob) adult volunteers recruited at the Endocrine Department of the University Hospital Ram´ on y Cajal (Madrid, Spain) and stored at −80 ◦C, as previously described (Martínez-Cuesta et al., 2020). The study was approved by the Clinical Ethics Committee of Hospital Ram´ on y Cajal with the code 394/14 and Spanish Council of Scientific Research (CSIC), and an informed written consent was obtained from each volunteer. Based on bacterial (operational taxonomic units, OTU) abundances (%) yielded by 16S rRNA gen amplicon-based metagenomics (Martínez-Cuesta et al., 2020), five similar samples selected from each phenotype (Nw or Ob) were thawed, pooled and homogenized with the saline dialysate described by Aguirre et al. (Aguirre et al., 2015). The faecal slurry was aliquoted, snap-frozen in liquid nitrogen and stored at −80 ◦C. The procedure allows the use of an equivalent microbiota in terms of composition and activity (Aguirre, Ramiro-García, Koenen, & Venema, 2014) for experimental replicates and comparison among different food ingredients. 2.2. Dynamic simulator of the colon microbiome (BFBL gut model) The BFBL gut model is a four-stage reactors system aimed to simulate in vitro the small intestine (SI) and the microbial conditions of three regions (R1, R2, R3) of the human colon (Martínez-Cuesta et al., 2019). At the beginning of the experiment, the three colon reactors were filled and pre-conditioned with regular (normal energy, NE) nutritive medium (Barroso et al., 2015) and each of them simultaneously inoculated with the same faecal pooled sample from either the Nw or the Ob human volunteers. The inoculated colon units were allowed to equilibrate overnight in batch conditions at 37 ◦C and continuously flushed with nitrogen. The pH in the colonic reactors was controlled by addition of 0.5 M NaOH and 0.5 M HCl to keep values of 5.7 ±0.2 in R1, 6.3 ±0.2 in R2 and 6.8 ±0.2 in R3. The development and stabilization of the microbial community until steady-state conditions in the three colon reactors was operated by feeding three times a day (during 14 days) the SI unit with NE nutritive medium (pH 2) mixed with pancreatic juice (Barroso et al., 2015), containing a mix of bile salts, pancreatin from porcine pancreas and NaHCO 3 that neutralized the acidic pH. After 2 h of digestion at 37 ◦C, the whole content of the SI unit was automatically transferred to the R1 colon compartment at a flow rate of 5 mL/min. The transfer of colonic content between the R1, R2, and R3 reactors was controlled with level sensors that keep their volumes at 250, 400, and 300 mL, respectively. Therefore, the R1 vessel operates at an acidic pH and has a high availability of substrates, representing the ascending colon, the R2 vessel simulates the transverse colon, and the final vessel (R3) resembles the neutral pH and low substrate availability conditions of the descending colon (Martínez-Cuesta et al., 2019). In order to keep the vessels under anaerobic conditions, nitrogen flow was automatically activated with each feeding cycle, and it was active during the 2 h incubation in the SI unit and the following nutritive medium transfer (40 min) between the colon reactors. During the experimental set up, samples were collected every 24 h from the three colon reactors and centrifuged (11,200×g during 10 min at 4 ◦C), and the pellet and supernatant were stored separately at −20 ◦C until further analysis. Each experiment using frozen aliquots of the pooled samples from either Nw or Ob individuals was repeated twice. 2.3. High energy medium The development of the Nw and Ob microbiotas was also analysed using a high energy (HE) nutritive medium, which was characterized by a high content of high-glycaemic index carbohydrates (digestible starch) and simple carbohydrates (fructose), to reach 45% more fermentable carbohydrates than the NE nutritive medium, as indicated by Payne et al. (Payne et al., 2012). Therefore, the three colon reactors were fed with the HE nutritive medium that contained arabinogalactan (1 g/L), pectin from apple (2 g/L), xylan (1 g/L), potato starch (6 g/L), maize starch (4 g/L), fructose (6 g/L), glucose (0.4 g/L), yeast extract (3 g/L), peptone (1 g/L), mucin (4 g/L) and L-cysteine (0.5 g/L). The development and stabilization of the microbial community were as described above. 2.4. Microbiological analysis DNA was extracted from pelleted samples following the noncommercial IHMS Protocol Q recommended by the International Human Microbiome Consortium (Costea et al., 2017). Quantitative microbiological analysis of DNA samples was carried out by qPCR using SYBR® green methodology in a ViiA7 Real-Time PCR System (Life Technologies, Carlsbad, CA, USA). Standards, primers, amplicon size, and annealing temperature for Akkermansia, Bacteroides, Bifidobacterium, Blautia coccoides-Eubacterium rectale Cluster XIVa, Clostridium leptum subgroup specific cluster IV, Enterobacteriaceae, Enterococcus, Faecalibacterium, Lactobacillus, Prevotella, Roseburia, and Ruminococcus Cluster IV have been described previously (Lozano-Ojalvo et al., 2019). Additional primers and qPCR conditions were selected for the analysis of Alistipes (Roager, Licht, Poulsen, Larsen, & Bahl, 2014), Atopobium (Matsuki, Watanabe, Fujimoto, Takada, & Tanaka, 2004), Bilophila (Baldwin et al., 2016) and sulphate-reducing bacteria (SRB) based on the dissimilatory sulphite reductase (dsr) gene (Kondo, Nedwell, Purdy, & Silva, 2004). Standards for these groups were derived from the targeted cloned genes using the pGEM-T cloning vector system kit (Promega, Madison, WI, USA) as described previously (Barroso et al., 2013). 2.5. Analysis of SCFA and ammonium SCFA in the sample supernatants from the R1, R2 and R3 reactors were analysed by HLPC as described earlier (Barroso et al., 2015). SCFA were separated in a Rezex ROA Organic Acids column (Phenomenex, Torrance, CA, USA) by using a linear gradient of 0.005 mM sulphuric acid at 0.6 mL/min. The ammonium content was determined using the Nessler’s reagent (Sigma) as described by Doo et al. (Doo, Chassard, Schwab, & Lacroix, 2017). 2.6. Statistical analysis Results were expressed as media ±SD. Student’s t-test (p ≤0.05) was applied for pairwise comparisons of qPCR, SCFA and ammonium results between Nw and Ob microbiotas during the stabilization in the colon reactors. T. Requena et al. LWT 141 (2021) 110921 3 3. Results and discussion 3.1. Microbiological differences between the Nw and Ob microbiotas The BFBL gut model has been used in this study to evaluate its utility for developing representative microbiota associated to either obese or normal-weight phenotype. Based on this, the model could be instrumental in evaluating differences in microbial taxa that might play a role in generating differences in the production of metabolites, e.g. SCFA, for similar amount of ingredients or energy in the growth medium. Furthermore, dynamic gut models allow culturing of microbiota in steady-state conditions, thereby increasing the microbial stability and the physiological relevance of experiments (Dupont et al., 2019). The 16S rRNA gene amplicon sequencing data (operational taxonomic units, OTU) obtained from 26 normal-weight and obese individuals (Martínez-Cuesta et al., 2020) were examined to select faecal donors with similar taxonomic profiles that could be representative for lean and obese phenotypes. Five donors from each phenotype were selected and pooled to inoculate the BFBL gut model (Nw and Ob runs). Regarding the inoculum, Table 1 shows the mean OTU abundance (%) of the targeted microbial groups checked in this study for the 5 faecal samples combined into the Nw and Ob pools and the results (log copy number/mL) obtained by quantitative PCR (qPCR) using specific primers of the inoculum in the colon reactors. Compared to the Nw faecal pool, the Ob faecal pool had lower (p <0.05) relative OTU abundance and qPCR counts of Akkermansia, Alistipes, Enterococcus, and Enterobacteriaceae and higher (p <0.05) relative abundance of Bilophila and Roseburia. The qPCR analysis showed additional lower qPCR Table 1 Abundance (%) of the operational taxonomic units (OTU), representing the targeted microbial groups analysed in this study, determined in the normalweight (Nw) and obese (Ob) faecal pools and the qPCR values (log copy number/mL) of these groups at the inoculum in the BFL gut model. Group OTU (%) qPCR (log copy number/mL) Nw Ob Nw Ob Akkermansia 1.196 0.020* 6.75 ±0.17 3.29 ±0.41* Alistipes 2.660 0.364* 7.29 ±0.24 6.19 ±0.21* Atopobium 0.002 0.005 6.60 ±0.48 6.44 ±0.23 Bacteroides 3.400 2.840 8.52 ±0.26 7.86 ±0.22* Bifidobacterium 7.200 6.860 7.32 ±0.23 7.25 ±0.29 Bilophila 0.077 0.259* 5.16 ±0.24 4.99 ±0.27 C. leptum a – – 7.70 ±0.14 7.47 ±0.19 B. coccoides b 7.102 9.044 8.01 ±0.17 7.83 ±0.56 SRB c 0.330 0.354 7.47 ±0.17 7.37 ±0.20 Enterobacteriaceae 1.294 0.204* 6.01 ±0.19 5.87 ±0.13 Enterococcus 0.021 0.002* 6.01 ±0.40 5.89 ±0.17 Faecalibacterium 10.400 10.800 7.26 ±0.20 6.44 ±0.34* Lactobacillus 0.592 0.222 5.78 ±0.22 7.30 ±0.49* Prevotella 2.370 2.713 5.46 ±0.60 4.70 ±0.56* Roseburia 2.940 8.000* 7.55 ±0.12 7.89 ±0.14* Ruminococcus 8.000 9.312 7.90 ±0.28 7.63 ±0.28 *Values significantly different (p <0.05) between Nw and Ob. a Clostridium leptum subgroup specific cluster IV. b Blautia coccoides-Eubacterium rectale cluster XIVa. c Sulphate-reducing bacteria. Table 2 qPCR values (mean log copy number/mL ±SD) of the targeted microbial groups observed at the end of the first and second weeks of the development of the normalweight (Nw) and obese (Ob) microbiotas in the three-colonic reactors (R) of the BFBL gut model. Group First Week Second Week Nw Ob Nw Ob R1 R2 R3 R1 R2 R3 R1 R2 R3 R1 R2 R3 Akkermansia 5.94 ± 0.43 5.98 ± 0.06 6.55 ± 0.33 3.64 ± 0.34* 4.14 ± 0.34* 4.78 ± 0.45* 3.67 ± 0.58 7.53 ± 0.40 7.37 ± 0.48 3.97 ± 0.77 5.99 ± 0.87* 5.78 ± 0.55* Alistipes 6.32 ± 0.13 7.45 ± 0.90 7.74 ± 0.43 6.00 ± 0.50 6.98 ± 0.64 7.76 ± 0.69 6.04 ± 0.23 7.67 ± 0.21 7.37 ± 0.54 5.94 ± 0.32 8.08 ± 0.38 7.57 ± 0.42 Atopobium 7.56 ± 0.62 7.50 ± 0.67 7.35 ± 0.48 7.34 ± 0.79 7.65 ± 0.72 7.76 ± 0.31 5.83 ± 1.08 6.82 ± 0.81 6.72 ± 0.75 5.37 ± 1.05 6.61 ± 0.78 6.72 ± 0.88 Bacteroides 6.06 ± 0.46 9.74 ± 0.22 9.81 ± 0.11 5.75 ± 0.22* 8.85 ± 0.91* 9.68 ± 0.27 7.08 ± 0.46 9.54 ± 0.17 9.19 ± 0.07 6.28 ± 0.99* 9.84 ± 0.39 9.60 ± 0.25 Bifidobacterium 8.25 ± 0.48 8.30 ± 0.38 8.28 ± 0.18 8.02 ± 0.61 8.54 ± 0.57 8.38 ± 0.51 8.56 ± 0.28 8.46 ± 0.32 8.03 ± 0.52 8.08 ± 0.23 8.25 ± 0.13 8.27 ± 0.25 Bilophila 4.63 ± 0.20 5.02 ± 0.16 5.55 ± 0.39 4.62 ± 0.20 5.13 ± 0.92 5.85 ± 1.04 5.01 ± 0.35 8.34 ± 0.43 8.23 ± 0.46 5.46 ± 1.07 8.66 ± 0.24 8.49 ± 0.26 C. leptum 6.82 ± 0.46 8.08 ± 0.22 8.22 ± 0.21 6.53 ± 0.41 8.60 ± 0.15 8.50 ± 0.12 4.39 ± 0.94 7.95 ± 0.77 7.72 ± 0.84 3.92 ± 0.55 7.28 ± 0.56 7.51 ± 0.60 B. coccoides 7.09 ± 0.59 8.44 ± 0.31 8.61 ± 0.13 5.87 ± 0.49* 7.86 ± 0.64* 7.92 ± 0.29* 8.20 ± 0.24 8.17 ± 0.39 7.97 ± 0.39 6.48 ± 1.04* 8.01 ± 0.40 7.82 ± 0.39 SRB 7.67 ± 0.16 7.73 ± 0.64 7.79 ± 0.40 7.69 ± 0.15 7.71 ± 0.16 7.82 ± 0.18 7.59 ± 0.27 9.51 ± 0.30 9.34 ± 0.37 7.72 ± 0.19 9.53 ± 0.17 9.40 ± 0.18 Enterobacteriaceae 8.49 ± 0.24 8.49 ± 0.55 8.48 ± 0.48 7.20 ± 1.02* 7.17 ± 0.66* 7.66 ± 0.49* 8.48 ± 0.20 8.35 ± 0.28 8.09 ± 0.36 8.82 ± 0.25 8.68 ± 0.58 8.41 ± 0.48 Enterococcus 9.74 ± 0.86 9.71 ± 0.75 9.84 ± 0.48 8.89 ± 0.42* 8.39 ± 0.41* 8.66 ± 0.39* 8.51 ± 0.50 8.62 ± 0.42 8.69 ± 0.55 7.44 ± 0.48* 7.38 ± 0.19* 7.62 ± 0.30* Faecalibacterium 6.56 ± 0.56 7.56 ± 0.64 7.83 ± 0.47 5.74 ± 0.45* 7.36 ± 0.19 7.39 ± 0.14* 4.19 ± 0.75 7.88 ± 0.12 7.60 ± 0.30 3.96 ± 0.33 6.72 ± 0.85* 6.97 ± 0.58 Lactobacillus 6.34 ± 0.11 6.28 ± 0.10 6.01 ± 0.09 9.14 ± 0.13* 8.87 ± 0.14* 9.00 ± 0.13* 6.05 ± 0.13 6.37 ± 0.05 6.23 ± 0.24 6.20 ± 0.29 7.04 ± 0.49* 7.51 ± 0.40* Prevotella 4.35 ± 0.28 4.73 ± 0.08 5.23 ± 0.16 4.79 ± 0.33 4.96 ± 0.20 4.87 ± 0.19 4.89 ± 0.23 4.70 ± 0.06 4.55 ± 0.15 5.02 ± 0.31 4.71 ± 0.39 4.58 ± 0.10 Roseburia 5.45 ± 0.63 6.53 ± 0.39 6.52 ± 0.34 5.84 ± 0.62 7.33 ± 0.42* 7.38 ± 0.26* 3.75 ± 0.33 6.23 ± 0.12 6.02 ± 0.17 3.71 ± 0.43 6.28 ± 0.25 6.10 ± 0.32 Ruminococcus 7.08 ± 0.61 7.68 ± 0.23 8.03 ± 0.30 7.07 ± 0.59 7.56 ± 0.43 7.91 ± 0.61 4.45 ± 0.19 7.74 ± 0.19 7.54 ± 0.13 4.54 ± 0.17 6.90 ± 0.78 7.29 ± 0.38 1 Clostridium leptum subgroup specific cluster IV. 2 Blautia coccoides-Eubacterium rectale cluster XIVa. 3 Sulphate-reducing bacteria. *Values significantly different (p <0.05) between Nw and Ob. T. Requena et al. LWT 141 (2021) 110921 4 counts of Bacteroides, Faecalibacterium and Prevotella and higher Lactobacillus values in the Ob pool than in the Nw pool (Table 1). Overall, these microbial results agreed with published information describing differences in specific microbial groups associated to obesity (Bianchi et al., 2019; Dao et al., 2016; Peters et al., 2018); although divergent results between different studies describing bacterial groups associated to obesity can also be observed (Crovesy, Masterson, & Rosado, 2020). In general, Akkermansia has been consistently reported to be negatively associated to obesity (Crovesy et al., 2020; Everard et al., 2013; Xu et al., 2020), whereas Bilophila has been described to increase after high intake of saturated fat (Devkota et al., 2012). On the other hand, some studies indicate that intestinal Enterococcus abundance correlates inversely with excessive weight gain (Laursen et al., 2020) and Lactobacillus association with obesity could be species-specific (Casta˜ neda-M´ arquez et al., 2020; Crovesy et al., 2020). In our study, we verified that primers for Enterococcus and Lactobacillus (Matsuda et al., 2009; Rinttil¨ a, Kassinen, Malinen, Krogius, & Palva, 2004) were genus-specific and able to quantify these genera separately (results not shown). Table 2 shows mean qPCR values (log copy number/mL) observed at the end (the last 3 days) of the first and second weeks of the development of Nw and Ob microbiotas in the BFBL gut model. During this microbiota stabilization period aimed to reach the steady state, initial differences found between the Nw and Ob inocula could be still reproduced. That includes the lower values of Akkermansia, Enterococcus, and Faecalibacterium and the higher values of Lactobacillus for the Ob microbiota when compared with the Nw microbiota. The initial differences quantified for Enterobacteriaceae, Bacteroides and Roseburia were observed mainly during the first week of the stabilization period or only in the R1 unit (Table 2). Overall, the conditions of acidic pH in reactor R1 (setting of pH at 5.7) limited the development of pH-sensitive microbial groups, whereas most analysed bacterial groups developed in the R2 and R3 reactors. The pH is a driver of community diversification in dynamic three-stage gut simulators (Martínez-Cuesta et al., 2019). Also, as previously observed (Barroso et al., 2016), the microbial community represented by clostridial cluster IV (C. leptum, Faecalibacterium, and Ruminoccocus) and cluster XVIa (Roseburia) decreased in the proximal reactor during stabilization (Table 2). On the other hand, the regular delivery of bile salts for the in vitro-adapted conditions could be responsible for the increasing of bile-tolerant microorganisms such as Alistipes, Bilophila, Bacteroides and Enterobacteriaceae. 3.2. Increase of energy in the nutritive medium The HE medium used in the study represents the high consumption of readily-fermented carbohydrates and fructose-enriched beverages that is associated with the increase of obesity (Barroso et al., 2016; Charrez, Qiao, & Hebbard, 2015; Payne et al., 2012). Table 3 shows the qPCR counts of the targeted microbial groups at the end of the second week of the development of the normal-weight (Nw) and obese (Ob) microbiotas when the BFBL gut model was fed with the high energy medium. The stabilization of the Nw and Ob microbiotas with the HE medium maintained the differences previously observed between these two microbiotas for Akkermansia, Enterococcus, Faecalibacterium and Lactobacillus (Table 3). Overall, the increase in the nutritive medium of readily-fermentable sugars produced a decrease of Bifidobacterium in the Ob microbiota at the end of the stabilization stage and of Alistipes, C. leptum subgroup specific cluster IV, Lactobacillus and Ruminococcus during the Nw microbiota stabilization. Among well adapted bacteria to the human gut environment, Bifidobacterium genomes encode several ABC-type transporters for fructans and complex carbohydrates uptake, but monosaccharide transporters are less frequently identified (Egan & Van Sinderen, 2018), being some species unable to growth with fructose as the only carbohydrate source (Janer et al., 2004). Moreover, counts of Atopobium, Bilophila and Enterococcus decreased in both Nw and Ob microbiotas with the HE medium when compared with the NE nutritive medium (Table 3). Among them, Bilophila has been described to proliferate during periodic-fasting (Mesnage, Grundler, Schwiertz, Le Maho, & Wilhelmi de Toledo, 2019), and to decrease with augmented SCFA formation in the environment (Vandeputte et al., 2017). 3.3. Differences in SCFA and ammonium formation by the Nw and Ob microbiotas and effect of the HE nutritive medium Fig. 1 shows results of maximal values of acetate, propionate and butyrate produced by the Nw and Ob microbiotas during the stabilization with basal NE nutritive medium and HE medium. Maximal values were observed for both microbiotas and nutritive media during the first three days of incubation. Total SCFA formation has a tendency to reach stable values during the second week of stabilization (Barroso et al., Table 3 qPCR values (mean log copy number/mL ±SD) of the targeted microbial groups at the end of the second week of the development of the normal-weight (Nw) and obese (Ob) microbiotas in the three-colonic reactors (R) of the BFBL gut model fed with the high energy medium. Group Nw Ob R1 R2 R3 R1 R2 R3 Akkermansia 3.74 ± 0.81 7.81 ± 0.06 7.69 ± 0.27 3.89 ± 0.79 7.25 ± 0.52* 7.09 ± 0.88 Alistipes 5.66 ± 0.10 7.26 ± 0.09 6.61 ± 0.40 6.26 ± 0.07* 7.70 ± 0.07* 8.07 ± 0.06* Atopobium 6.40 ± 0.26 6.49 ± 0.21 6.24 ± 0.08 5.31 ± 0.30* 5.95 ± 0.16* 6.19 ± 0.30 Bacteroides 6.41 ± 0.31 9.39 ± 0.30 9.15 ± 0.15 5.97 ± 0.45 9.14 ± 0.21 9.13 ± 0.20 Bifidobacterium 8.55 ± 0.16 8.28 ± 0.27 8.01 ± 0.28 7.09 ± 0.33* 7.52 ± 0.14* 7.62 ± 0.35 Bilophila 4.85 ± 0.23 8.19 ± 0.15 8.00 ± 0.13 4.55 ± 0.27 8.03 ± 0.13 8.17 ± 0.14 C. leptum 3.73 ± 0.27 8.10 ± 0.10 7.74 ± 0.15 3.70 ± 0.17 7.35 ± 0.02* 7.03 ± 0.02 B. coccoides 8.41 ± 0.21 8.47 ± 0.29 8.00 ± 0.22 5.82 ± 0.15* 8.19 ± 0.20 8.27 ± 0.14 SRB 6.79 ± 0.15 9.42 ± 0.11 9.22 ± 0.08 6.98 ± 0.11 9.30 ± 0.07 9.35 ± 0.09 Enterobacteriaceae 8.09 ± 0.08 7.97 ± 0.08 7.54 ± 0.14 8.97 ± 0.24* 8.86 ± 0.18* 8.67 ± 0.32* Enterococcus 7.31 ± 0.13 7.63 ± 0.29 8.05 ± 0.63 7.12 ± 0.70 6.86 ± 0.30* 6.85 ± 0.23* Faecalibacterium 3.86 ± 0.18 7.95 ± 0.08 7.75 ± 0.01 3.84 ± 0.44 6.48 ± 0.53* 6.63 ± 0.66* Lactobacillus 5.80 ± 0.29 5.66 ± 0.11 5.67 ± 0.15 5.57 ± 0.37 6.49 ± 0.46* 6.70 ± 0.64* Prevotella 3.28 ± 0.08 3.36 ± 0.48 3.24 ± 0.52 3.82 ± 0.35 3.76 ± 0.84 3.53 ± 0.67 Roseburia 3.34 ± 0.12 6.22 ± 0.49 5.76 ± 0.12 3.92 ± 0.18 6.24 ± 0.38 6.35 ± 0.13* Ruminococcus 4.02 ± 0.42 7.15 ± 0.12 6.92 ± 0.19 4.89 ± 0.23 6.72 ± 0.17 6.86 ± 0.33 1 Clostridium leptum subgroup specific cluster IV. 2 Blautia coccoides-Eubacterium rectale cluster XIVa. 3 Sulphate-reducing bacteria. *Values significantly different (p <0.05) between Nw and Ob. T. Requena et al. LWT 141 (2021) 110921 5 2015, 2016). It is remarkable that the highest production of acetate and butyrate was observed with the Ob microbiota during the first week of stabilization with the HE diet (Fig. 1). Obese microbiota has been described to be enriched in metabolic pathways involved in the major formation of butyrate and acetate (Turnbaugh et al., 2006). On the other hand, the production of propionate by the Ob microbiota during the whole period of stabilization was lower (p <0.05) than that of the Nw microbiota under all conditions and in all reactors, being even no detectable in R1 with the HE medium (Fig. 1). Analysis of SCFA content in faeces from morbid obese individuals has described less propionate amount than butyrate and acetate (Farup & Valeur, 2020). Propionate has attracted attention because of its capacity to trigger the secretion of glucagon-like peptide-1 and peptide YY which are involved in the regulation of appetite and glucose metabolism (Chambers et al., 2015). In our study, reduced counts at the onset of the experiments in the Ob microbiota of Akkermansia and Bacteroides could be associated with low values of propionate. Also, oral administration of Akkermansia muciniphila has demonstrated to decrease food energy efficiency (Depommier et al., 2020). When comparing the two nutritive media used in the study (NE and HE), we observed that the HE diet caused an increase of total SCFA and a decrease in Atopobium and Bilophila in both Nw and Ob microbiotas. The increase of SCFA formation in the human gut has been linked with the decrease of Bilophila abundance, genus that was associated with constipation and reduced quality of life (Vandeputte et al., 2017). Bilophila has also demonstrated pro-inflammatory effects and association with the progression of Parkinson’s disease symptoms based on its ability to degrade bile acids (Baldini et al., 2020). Based on its susceptibility to SCFA content, Bilophila seems a plausible target for testing colonic-fermentable ingredients to be used as potential prebiotics. Ammonium production was similar for both Nw and Ob microbiotas, which increased through the stabilization period, reaching average values (±SD) of 6.8 ±1.1 mM and 5.2 ±0.7 mM in R1, 11.0 ±1.9 mM and 9.6 ±1.6 mM in R2 and 12.3 ±2.2 mM and 10.5 ±2.0 mM in R3, respectively, at the second week of stabilization with the NE medium. With the HE medium, there was a decrease (p <0.05) of ammonium production by the Ob microbiota in the three reactors (2.5 ±0.1 mM in R1, 6.7 ±0.8 mM in R2 and 7.7 ±1.0 mM in R3). The production of ammonium by the Nw microbiota with the HE medium tended also to decrease when compared with the NE diet, but the differences were only significant in the R1 (5.0 ±1.3 mM). We previously observed that the increase in readily-fermentable carbohydrates in the medium has a significant effect in decreasing the proteolytic metabolism in the colonic reactors, which is inhibited in favour of carbohydrate fermentation by the intestinal microbiota (Barroso et al., 2016). 4. Conclusions The use of dynamic colonic models for developing the human gut microbiota in steady-state conditions increases the physiological relevance of the experiments and facilitates the estimation of bacterial behaviour under dietary changes (Poeker et al., 2018). The three-stage continuous reactors, characterized by decreased fermentable substrates and increased pH values from first to last reactor, configured in the BFBL gut model to simulate the human gut microbiota has been tested to define if the differences between normal-weight (Nw) and obese (Ob) microbiotas are maintained during the period to develop until steady-state conditions. This could be instrumental to outline the species that drive a characteristic obese microbiota core. Within the microbial groups evaluated in the study, we observed lower values of Akkermansia, Enterococcus and Faecalibacterium and higher values of Lactobacillus for the Ob microbiota when compared with the Nw microbiota during the development of the microbiotas in the BFBL gut model, which reproduced the initial differences found between the Nw and Ob faecal microbiotas. Related to metabolic signatures, a lower capacity to produce propionate was characteristic of the Ob microbiota under all the tested conditions. This could be useful for assessing the fermentability of potential prebiotics in the selection of food ingredients for specific production of propionate by the human gut microbiota prior to their assessment in nutritional intervention studies related to obesity. CRediT authorship contribution statement Teresa Requena: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Ya Song: Methodology, Investigation, Writing-. Carmen Pel´ aez: Conceptualization, Writing – review & editing. M. Carmen Martínez-Cuesta: Conceptualization, Writing – review & editing, Funding acquisition. Fig. 1. SCFA (mM) maximal production of the normal-weight (Nw) and obese (Ob) microbiotas in the three-colonic reactors (R) of the BFBL gut model fed with normal (NE) and high energy (HE) media. *Values significantly different (p <0.05) between Nw and Ob microbiotas. T. Requena et al. LWT 141 (2021) 110921 6 Declaration of competing interest Please check the following as appropriate: ✓All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. ✓ This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. ✓ The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript o The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript: Acknowledgments This research was funded by Spanish Ministry of Science and Innovation, grant number AGL2016-75951-R. Y.S. received a scholarship under the Graduate Student Overseas Study Program from South China Agricultural University. The authors acknowledge technical support of Esteban S´ aez and David Gal´ an. References Aguirre, M., Eck, A., Koenen, M. E., Savelkoul, P. H., Budding, A. E., & Venema, K. (2015). Evaluation of an optimal preparation of human standardized fecal inocula for in vitro fermentation studies. Journal of Microbiological Methods, 117, 78–84. https://doi.org/10.1016/j.mimet.2015.07.019 Aguirre, M., Ramiro-García, J., Koenen, M. E., & Venema, K. (2014). To pool or not to pool? Impact of the use of individual and pooled fecal samples for in vitro fermentation studies. Journal of Microbiological Methods, 107, 1–7. https://doi.org/ 10.1016/j.mimet.2014.08.022 Baldini, F., Hertel, J., Sandt, E., Thinnes, C. C., Neuberger-Castillo, L., Pavelka, L., et al. (2020). Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions. BMC Biology, 18, 62. https://doi. org/10.1186/s12915-020-00775-7 Baldwin, J., Collins, B., Wolf, P. G., Martinez, K., Shen, W., Chuang, C. C., et al. (2016). Table grape consumption reduces adiposity and markers of hepatic lipogenesis and alters gut microbiota in butter fat-fed mice. Journal of Nutritional Biochemistry, 27, 123–135. https://doi.org/10.1016/j.jnutbio.2015.08.027 Barroso, E., Cueva, C., Pel´ aez, C., Martínez-Cuesta, M. C., & Requena, T. (2015). Development of human colonic microbiota in the computer-controlled dynamic SIMulator of the GastroIntestinal tract SIMGI. LWT-Food Science and Technology, 61, 283–289. https://doi.org/10.1016/j.lwt.2014.12.014 Barroso, E., Montilla, A., Corzo, N., Pel´ aez, C., Martínez-Cuesta, M. C., & Requena, T. (2016). Effect of lactulose-derived oligosaccharides on intestinal microbiota during the shift between media with different energy contents. Food Research International, 89, 302–308. https://doi.org/10.1016/j.foodres.2016.08.025 Barroso, E., S´ anchez-Pat´ an, F., Martín-Alvarez, P. J., Bartolom´ e, B., MorenoArribas, M. V., Pel´ aez, C., et al. (2013). Lactobacillus plantarum IFPL935 favors the initial metabolism of red wine polyphenols when added to a colonic microbiota. Journal of Agricultural and Food Chemistry, 61, 10163–10172. https://doi.org/ 10.1021/jf402816r Bianchi, F., Duque, A., Saad, S. M. I., & Sivieri, K. (2019). Gut microbiome approaches to treat obesity in humans. Applied Microbiology and Biotechnology, 103, 1081–1094. https://doi.org/10.1007/s00253-018-9570-8 Casta˜ neda-M´ arquez, A. C., Díaz-Benítez, C. E., Bahena-Rom´ an, M., CampuzanoBenítez, G. E., Galv´ an-Portillo, M., Campuzano-Rinc´ on, J. C., et al. (2020). Lactobacillus paracasei as a protective factor of obesity induced by an unhealthy diet in children. Obesity Research & Clinical Practice, 14, 271–278. https://doi.org/ 10.1016/j.orcp.2020.04.005 Chambers, E. S., Viardot, A., Psichas, A., Morrison, D. J., Murphy, K. G., ZacVarghese, S. E., et al. (2015). Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults. Gut, 64, 1744–1754. https://doi.org/10.1136/gutjnl-2014-307913 Charrez, B., Qiao, L., & Hebbard, L. (2015). The role of fructose in metabolism and cancer. Hormone Molecular Biology and Clinical Investigation, 22, 79–89. https://doi. org/10.1515/hmbci-2015-0009 Costea, P. I., Zeller, G., Sunagawa, S., Pelletier, E., Alberti, A., Levenez, F., et al. (2017). Towards standards for human fecal sample processing in metagenomic studies. Nature Biotechnology, 35, 1069–1076. https://doi.org/10.1038/nbt.3960 Crovesy, L., Masterson, D., & Rosado, E. L. (2020). Profile of the gut microbiota of adults with obesity: A systematic review. European Journal of Clinical Nutrition, 74, 1251–1262. https://doi.org/10.1038/s41430-020-0607-6 Dao, M. C., Everard, A., Aron-Wisnewsky, J., Sokolovska, N., Prifti, E., Verger, E. O., et al. (2016). Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: Relationship with gut microbiome richness and ecology. Gut, 65, 426–436. https://doi.org/10.1136/gutjnl-2014-308778 Depommier, C., Van Hul, M., Everard, A., Delzenne, N. M., De Vos, W. M., & Cani, P. D. (2020). Pasteurized Akkermansia muciniphila increases whole-body energy expenditure and fecal energy excretion in diet-induced obese mice. Gut Microbes, 11, 1231–1245. https://doi.org/10.1080/19490976.2020.1737307 Devkota, S., Wang, Y., Musch, M. W., Leone, V., Fehlner-Peach, H., Nadimpalli, A., et al. (2012). Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10 -/- mice. Nature, 487, 104–108. https://doi.org/10.1038/nature11225 Doo, E. H., Chassard, C., Schwab, C., & Lacroix, C. (2017). Effect of dietary nucleosides and yeast extracts on composition and metabolic activity of infant gut microbiota in PolyFermS colonic fermentation models. FEMS Microbiology Ecology, 93, fix088. https://doi.org/10.1093/femsec/fix088 Dupont, D., Alric, M., Blanquet-Diot, S., Bornhorst, G., Cueva, C., Deglaire, A., et al. (2019). Can dynamic in vitro digestion systems mimic the physiological reality? Critical Reviews in Food Science and Nutrition, 59, 1546–1562. https://doi.org/ 10.1080/10408398.2017.1421900 Egan, M., & Van Sinderen, D. (2018). Carbohydrate metabolism in bifidobacteria. In P. Mattarelli, B. Biavati, W. H. Holzapfel, & B. J. B. Wood (Eds.), Bifidobacteria and related organisms: Biology, taxonomy, applications (pp. 145–164). London: Academic Press Ltd-Elsevier Science Ltd. Everard, A., Belzer, C., Geurts, L., Ouwerkerk, J. P., Druart, C., Bindels, L. B., et al. (2013). Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proceedings of the National Academy of Sciences of the United States of America, 110, 9066–9071. https://doi.org/10.1073/ pnas.1219451110 Farup, P. G., & Valeur, J. (2020). Changes in faecal short-chain fatty acids after weightloss interventions in subjects with morbid obesity. Nutrients, 12, 802. https://doi. org/10.3390/nu12030802 Fei, N., & Zhao, L. (2013). An opportunistic pathogen isolated from the gut of an obese human causes obesity in germfree mice. The ISME Journal, 7, 880–884. https://doi. org/10.1038/ismej.2012.153 Goffredo, M., Mass, K., Parks, E. J., Wagner, D. A., McClure, E. A., Graf, J., et al. (2016). Role of gut microbiota and short chain fatty acids in modulating energy harvest and fat partitioning in youth. Journal of Clinical Endocrinology & Metabolism, 101, 4367–4376. https://doi.org/10.1210/jc.2016-1797 Heiss, C. N., & Olofsson, L. E. (2018). Gut microbiota-dependent modulation of energy metabolism. Journal of Innate Immunity, 10, 163–171. https://doi.org/10.1159/ 000481519 Janer, C., Rohr, L. M., Pel´ aez, C., Laloi, M., Cleusix, V., Requena, T., et al. (2004). Hydrolysis of oligofructoses by the recombinant beta-fructofuranosidase from Bifidobacterium lactis. Systematic & Applied Microbiology, 27, 279–285. https://doi. org/10.1078/0723-2020-00274 Keller, M. K., Kressirer, C. A., Belstrom, D., Twetman, S., & Tanner, A. C. R. (2017). Oral microbial profiles of individuals with different levels of sugar intake. Journal of Oral Microbiology, 9, 1355207. https://doi.org/10.1080/20002297.2017.1355207 Kondo, R., Nedwell, D. B., Purdy, K. J., & Silva, S. D. (2004). Detection and enumeration of sulphate-reducing bacteria in estuarine sediments by competitive PCR. Geomicrobiology Journal, 21, 145–157. https://doi.org/10.1080/ 01490450490275307 Lam, Y. Y., Zhang, C., & Zhao, L. (2018). Causality in dietary interventions-building a case for gut microbiota. Genome Medicine, 10, 62. https://doi.org/10.1186/s13073018-0573-y Laursen, M. F., Larsson, M. W., Lind, M. V., Larnkjaer, A., Molgaard, C., Michaelsen, K. F., et al. (2020). Intestinal Enterococcus abundance correlates inversely with excessive weight gain and increased plasma leptin in breastfed infants. FEMS Microbiology Ecology, 96. https://doi.org/10.1093/femsec/fiaa066. fiaa066. Lozano-Ojalvo, D., Martínez-Blanco, M., P´ erez-Rodríguez, L., Molina, E., Pel´ aez, C., Requena, T., et al. (2019). Egg white peptide-based immunotherapy enhances vitamin A metabolism and induces RORγt + regulatory T cells. Journal of Functional Foods, 52, 204–211. https://doi.org/10.1016/j.jff.2018.11.012 Martínez-Cuesta, M., del Campo, R., Garriga-García, M., Pel´ aez, C., & Requena, T. (2020). Taxonomic characterization and metabolic activity of the obese microbiota. Frontiers in Cellular and Infection Microbiology (submitted for publication. Martínez-Cuesta, M., Pel´ aez, C., & Requena, T. (2019). Laboratory simulators of the colon microbiome. In J. Faintuch, & S. Faintuch (Eds.), Microbiome and metabolome in diagnosis, therapy, and other strategic applications (pp. 61–67). Academic Press, Elsevier. Matsuda, K., Tsuji, H., Asahara, T., Matsumoto, K., Takada, T., & Nomoto, K. (2009). Establishment of an analytical system for the human fecal microbiota, based on reverse transcription-quantitative PCR targeting of multicopy rRNA molecules. Applied and Environmental Microbiology, 75, 1961–1969. https://doi.org/10.1128/ AEM.01843-08 Matsuki, T., Watanabe, K., Fujimoto, J., Takada, T., & Tanaka, R. (2004). Use of 16S rRNA gene-targeted group-specific primers for real-time PCR analysis of predominant bacteria in human feces. Applied and Environmental Microbiology, 70, 7220–7228. https://doi.org/10.1128/AEM.70.12.7220-7228.2004 Mesnage, R., Grundler, F., Schwiertz, A., Le Maho, Y., & Wilhelmi de Toledo, F. (2019). Changes in human gut microbiota composition are linked to the energy metabolic switch during 10 d of Buchinger fasting. Journal of Nutrition Sciences, 8, e36. https:// doi.org/10.1017/jns.2019.33 T. Requena et al. LWT 141 (2021) 110921 7 Payne, A. N., Chassard, C., & Lacroix, C. (2012). Gut microbial adaptation to dietary consumption of fructose, artificial sweeteners and sugar alcohols: Implications for host-microbe interactions contributing to obesity. Obesity Reviews, 13, 799–809. https://doi.org/10.1111/j.1467-789X.2012.01009.x Peters, B. A., Shapiro, J. A., Church, T. R., Miller, G., Trinh-Shevrin, C., Yuen, E., … Ahn, J. (2018). A taxonomic signature of obesity in a large study of American adults. Scientific Reports, 8, 9749. https://doi.org/10.1038/s41598-018-28126-1 Poeker, S. A., Geirnaert, A., Berchtold, L., Greppi, A., Krych, L., Steinert, R. E., … Lacroix, C. (2018). Understanding the prebiotic potential of different dietary fibers using an in vitro continuous adult fermentation model (PolyFermS). Scientific Reports, 8, 4318. https://doi.org/10.1038/s41598-018-22438-y Rinttil¨ a, T., Kassinen, A., Malinen, E., Krogius, L., & Palva, A. (2004). Development of an extensive set of 16S rDNA-targeted primers for quantification of pathogenic and indigenous bacteria in faecal samples by real-time PCR. Journal of Applied Microbiology, 97, 1166–1177. https://doi.org/10.1111/j.1365-2672.2004.02409.x Roager, H. M., Licht, T. R., Poulsen, S. K., Larsen, T. M., & Bahl, M. I. (2014). Microbial enterotypes, inferred by the prevotella-to-bacteroides ratio, remained stable during a 6-month randomized controlled diet intervention with the new nordic diet. Applied and Environmental Microbiology, 80, 1142–1149. https://doi.org/10.1128/ AEM.03549-13 Rosenbaum, M., Knight, R., & Leibel, R. L. (2015). The gut microbiota in human energy homeostasis and obesity. Trends in Endocrinology and Metabolism, 26, 493–501. https://doi.org/10.1016/j.tem.2015.07.002 Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., & Gordon, J. I. (2006). An obesity-associated gut microbiome with increased capacity for energy harvest. Nature, 444(7122), 1027–1031. https://doi.org/10.1038/nature05414 Vandeputte, D., Falony, G., Vieira-Silva, S., Wang, J., Sailer, M., Theis, S., et al. (2017). Prebiotic inulin-type fructans induce specific changes in the human gut microbiota. Gut, 66, 1968–1974. https://doi.org/10.1136/gutjnl-2016-313271 Walter, J., Armet, A. M., Finlay, B. B., & Shanahan, F. (2020). Establishing or exaggerating causality for the gut microbiome: Lessons from human microbiotaassociated rodents. Cell, 180, 221–232. https://doi.org/10.1016/j.cell.2019.12.025 Wang, K., Liao, M., Zhou, N., Bao, L., Ma, K., Zheng, Z., et al. (2019). Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Reports, 26, 222–235. https://doi.org/10.1016/j. celrep.2018.12.028. e225. Xu, Y., Wang, N., Tan, H. Y., Li, S., Zhang, C., & Feng, Y. (2020). Function of Akkermansia muciniphila in obesity: Interactions with lipid metabolism, immune response and gut systems. Frontiers in Microbiology, 11, 219. https://doi.org/10.3389/ fmicb.2020.00219 Zierer, J., Jackson, M. A., Kastenmuller, G., Mangino, M., Long, T., Telenti, A., et al. (2018). The fecal metabolome as a functional readout of the gut microbiome. Nature Genetics, 50, 790–795. https://doi.org/10.1038/s41588-018-0135-7 Zmora, N., Suez, J., & Elinav, E. (2019). You are what you eat: Diet, health and the gut microbiota. Nature Reviews Gastroenterology & Hepatology, 16, 35–56. https://doi. org/10.1038/s41575-018-0061-2 Zuo, T., Zhang, F., Lui, G. C. Y., Yeoh, Y. K., Li, A. Y. L., Zhan, H., et al. (2020). Alterations in gut microbiota of patients with COVID-19 during time of hospitalization. Gastroenterology. https://doi.org/10.1053/j.gastro.2020.05.048 T. Requena et al.