Nonobese Subjects of Bacteroides and Prevotella Enterotypes

Home , Prevotella

bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 5 1 Nonobese subjects of Bacteroides and Prevotella enterotypes 6 7 2 responded differentially to calorie restriction intervention 8 9 1,2, 6,* 1,2,3, 6,* 2,3 2,3,5 2,3 10 3 Hua Zou , Dan Wang , Huahui Ren , Chao Fang , Zhun Shi , Pengfan 11 1,2 2.3 2,4 2,4 2,3,6# 12 4 Zhang , Peishan Chen , Jian Wang , Huanming Yang , Kaiye Cai , Huanzi 13 2,3,5,6# 14 5 Zhong 15 16 6 1 BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, 17 18 7 China 19 20 8 2 BGI-Shenzhen, Shenzhen 518083, China 21 22 23 9 3 China National Genebank, BGI-Shenzhen, Shenzhen 518120, China 24 25 10 4 James D. Watson Institute of Genome Sciences, Hangzhou 310058, China 26 27 28 11 5 Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University 29 30 12 of Copenhagen, 2100 Copenhagen Ø, Denmark 31 32 13 6 Shenzhen Key Laboratory of Human commensal microorganisms and Health Research 33 34 35 14 *These authors contributed equally to this work 36 37 15 #Corresponding authors: 38 39 40 16 Kaiye Cai. E-mail: [email protected]; 41 42 17 43 Huanzi Zhong. E-mail: [email protected] 44 45 18 Abstract 46 47 48 19 Background: Today, growing numbers of normal-weight people prefer low-calorie food 49 50 20 or snacks. However, to our knowledge, the effect of calorie restriction (CR) on different 51 52 21 enterotypes was poorly studied in nonobese individuals. 53 54 22 Results: A total of 41 nonobese volunteers received a 3-week CR intervention and their 55 56 23 fecal samples before and after the intervention were collected and sequenced. Before the 57 58 24 intervention, these 41 subjects could be robustly grouped into two enterotypes: 59 60 25 Bacteroides (ETB, n=28) and Prevotella (ETP, n=13). After the CR intervention, we 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 26 found that ETP subjects had a significantly larger body mass index (BMI) loss ratio than 5 6 27 ETB subjects. In addition, changes on composition and functional potential of gut 7 8 28 microbiome were also different between ETB and ETP subjects after the intervention. 9 10 29 Despite of the above different responses to CR intervention, a convergent shift in gut 11 12 30 microbiome was observed between ETB and ETP subjects after the intervention. 13 14 31 Conclusions: Our work highlights the potential application for microbiome stratification 15 16 32 in nutrition intervention. 17 18 33 19 keywords: gut microbiota, enterotype, calorie restriction intervention 20 21 34 Background 22 23 35 The gut microbiota containing trillions of microbes is a complex microbial ecosystem and 24 25 36 carries out functions that are important to host health, including dietary energy extraction 26 27 37 as well as development and maintenance of the immune system [1]. Enterotype which is a 28 29 38 way of stratifying the complex human gut microbial community was first described in 2011 30 31 39 [2]. Plenty of studies have shown that individuals with different enterotypes might have 32 40 different clinical parameters and gut microbiota responses under the same diet [3-7]. 33 34 35 41 Calorie Restriction (CR) without malnutrition has been demonstrated to extend lifespan 36 37 42 and retard age-related diseases in many studies [8-10]. For instance, in a 2-year nonobese 38 39 43 human trail, CR had larger decreased in Cardiometabolic risk factors [9]. Another CR 40 44 intervention that lasted 2 years reduced BMI and improved mood and sleep duration on 41 42 45 health-related quality of life in healthy nonobese adults [10]. 43 44 45 46 The association between CR intervention and gut microbiota has been explored in many 46 47 47 studies, particularly among overweight and obese individuals [11, 12]. A 10-week CR diet 48 48 plus physical activity changed the composition of the gut microbiota in overweight 49 50 49 adolescents [11]. A shorter CR intervention that lasted 4 weeks improved gut barrier 51 52 50 integrity and reduced systemic inflammation on gut microbial diversity in obese women 53 54 51 [12]. Compelling evidences suggest that CR intervention may improve the ability of gut 55 56 52 microbiota for synthesis of short-chain fatty acids (SCFAs) which are involved in energy 57 58 53 supplement for host [13, 14]. 59 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 54 Although plenty of studies have focused on the association between CR intervention and 5 6 55 gut microbiota (as demonstrated above), to our knowledge, no studies have evaluated the 7 8 56 impact of CR on nonobese individuals of different enterotypes. As the low-calorie diet 9 10 57 becomes trendy, studying the effect of CR on human gut microbiota for nonobese people 11 12 58 becomes essential. Under these circumstances, we designed our study (Figure 1), in which 13 59 a CR intervention were conducted on 41 nonobese subjects (Body Mass Index (BMI) < 28 14 15 60 for Chinese [15] ). All subjects underwent a 1-week normal calorie intake diet and 3-week 16 17 61 calorie restriction intake diet (Figure 1). BMI values and fecal samples were collected 18 19 62 before and after the CR intervention. We then conducted taxonomic and functional 20 21 63 profiling of all the fecal samples in silico. Based on their taxonomic profiles before the 22 23 64 intervention, subjects can be robustly clustered into two enterotypes: Bacteroides (ETB, n 24 65 = 28) and Prevotella (ETP, n = 13). After the 3-week CR intervention, we found that ETB 25 26 66 and ETP subjects responded differently to the CR intervention. Firstly, BMI loss ratio of 27 28 67 ETP subjects drops more significantly than ETB subjects. Secondly, changes on 29 30 68 composition and functional potential of gut microbiome were also different between ETB 31 32 69 and ETP subjects after the intervention. Despite of the above different responses to CR 33 34 70 intervention, a convergent shift on gut microbiome composition and functional potential 35 71 was observed between ETB and ETP subjects after the intervention, which suggests that 36 37 72 the same diet may make the gut microbiome more similar. 38 39 40 73 Results 41 42 43 74 Enterotyping and characterization of subjects before CR intervention 44 45 75 Our subjects can be robustly stratified into two enterotypes (See Methods): Bacteroides 46 76 (ETB, n = 28) and Prevotella (ETP, n = 13). The two enterotypes are visualized in Figure 47 48 77 2A. 49 50 51 78 After finishing enterotyping of subjects, we firstly compared the phenotypes between ETB 52 53 79 and ETP subjects using Wilcoxon rank-sum test or Pearson’s chi-squares test (See 54 80 Methods). The result revealed that the before-intervention phenotypes, including sex 55 56 81 distribution (female/male ratio), age, BMI and weight, showed no significant difference 57 58 82 between ETB and ETP subjects (Table 1). The significance threshold was set at p < 0.05 59 60 83 in this paper. 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 84 On the contrary, composition and functional potentials of gut microbiome showed 5 6 85 significant differences between these two enterotypes before the intervention. In general, 7 8 86 genus profiles between ETB and ETP before-intervention fecal samples were hugely 9 10 87 different with p < 0.0001 using permutational analysis of variance (PERMANOVA) (See 11 12 88 Methods). In detail, we performed Wilcoxon rank-sum test on the relative abundances of 13 89 common genera or species (See Methods) between ETB and ETP subjects. Among the 14 15 90 common genera, we identified a total of six genera with significantly different relative 16 17 91 abundances between two enterotypes before the intervention: Prevotella and 18 19 92 Paraprevotella were more abundant in samples belonging to ETP while relative 20 21 93 abundances of Bacteroides, Fusobacterium, Eggerthella, and Anaerostipes were 22 23 94 significantly higher in ETB samples (Wilcoxon rank-sum test, p < 0.05; Figure 2B). 24 95 Among the common species, Prevotella copri, Paraprevotella xylaniphila, Dorea 25 26 96 longicatena and other five bacteria were significantly more abundant in ETP samples 27 28 97 (Wilcoxon rank-sum test, p < 0.05; Figure 2C). Meanwhile, the relative abundances of 28 29 30 98 common species, such as Bacteroides dorei, Bacteroides uniformis, Parabacteroides 31 32 99 distasonis and Bacteroides ovatus, were significantly larger in ETB samples (Wilcoxon 33 34 100 rank-sum test, p < 0.05; Figure 2C). 35 36 101 On the function level, differentially enriched KEGG pathways between ETB and ETP 37 38 102 samples were identified according to their reporter Z-scores [16]. A reporter score |Z| > 39 40 103 1.96 (95% confidence according to the normal distribution) was used as the detection 41 42 104 threshold for significantly differentiating pathways (See Methods). We identified a total of 43 105 29 KEGG pathways which were differentially enriched between ETB and ETP samples 44 45 106 before the intervention (|Z|>1.96; Figure 2D). For example, nine pathways for carbohydrate 46 47 107 metabolism, such as propanoate and butanoate metabolism, were highly enriched in ETB 48 49 108 samples before the intervention (Figure 2D). For another instance, several pathways 50 51 109 belonging to membrane transport, metabolism of cofactors and vitamins and xenobiotics 52 110 biodegradation and metabolism were also enriched in ETB samples (Figure 2D). Only four 53 54 111 pathways including phenylalanine, tyrosine and tryptophan biosynthesis, methane 55 56 112 metabolism, peptidoglycan biosynthesis and terpenoid backbone biosynthesis were 57 58 113 enriched in ETP samples before the intervention (pathways highlighted in blue in Figure 59 60 114 2D). 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 115 ETB and ETP subjects responded differentially to CR Intervention 5 6 7 116 After the 3-week CR intervention, BMI of both ETB and ETP subjects decreased 8 9 117 significantly (Figure 3A; paired Wilcoxon rank-sum test, p<0.05, See Methods). 10 118 Subsequent analysis revealed that ETP subjects had a significantly larger BMI loss ratio 11 12 119 compared to ETB subjects (Wilcoxon rank-sum test, p <0.05; Figure 3B). The BMI loss 13 14 120 ratio of a subject was calculated based on the following equation: 15 16 퐵푀퐼푎푓푡푒푟 − 퐵푀퐼푏푒푓표푟푒 17 121 퐵푀퐼 푙표푠푠 푟푎푡푖표 = 18 퐵푀퐼푏푒푓표푟푒 19 20 21 122 where 퐵푀퐼푏푒푓표푟푒 and 퐵푀퐼푎푓푡푒푟 is the respective BMI of a subject before and after the 22 23 123 intervention. 24 25 124 What’s more, gut microbiota of ETB and ETP subjects changed differentially to CR 26 27 125 intervention for both microbiome composition and functional potentials (Figure 3C, 3D). 28 29 126 In terms of common species, relative abundances of Enterobacter cloacae, Enterobacter 30 31 127 hormaechei/cloacae and Klebsiella oxytoca significantly increased and Collinsella 32 33 128 aerofaciens significantly decreased only in ETP samples (Figure 3C, species in blue; Paired 34 129 Wilcoxon rank-sum test). Whereas, relative abundances of ten species significantly 35 36 130 changed in ETB samples specifically (Figure 3C, species in orange; Paired Wilcoxon rank- 37 38 131 sum test). Among these ten species, the relative abundances of seven species, like 39 40 132 Eubacterium rectale, Bacteroides plebeius, Ruminococcus lactaris, Prevotella copri, and 41 42 133 Eggerthella lenta etc., significantly increased while the remaining three species 43 44 134 (Bacteroides stercoris, Bacteroides coprocola and Veillonella parvula) significantly 45 135 decreased in ETB subjects. 46 47 48 136 On the function level, we observed a total of 49 KEGG pathways which changed 49 50 137 differentially between ETB and ETP samples (Figure 3D). Among these 49 pathways, 51 52 138 levels of 31 pathways (in orange in Figure 3D) changed significantly only in ETB samples, 53 139 17 pathways (in blue in Figure 3D) changed significantly only in ETP samples and 1 54 55 140 pathway (in green in Figure 3D) changed significantly in both ETB and ETP samples 56 57 141 (decreased in ETB samples while increased in ETP samples). In particular, for 58 59 142 carbohydrate metabolism, levels for propanoate and butanoate metabolism significantly 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 143 increased in ETB samples only (Figure 3D). A significant decrease of metabolism levels 5 6 144 for amino sugar, nucleotide sugar, fructose and mannose were observed in ETP samples 7 8 145 only (Figure 3D). For energy metabolism, three pathways including methane metabolism, 9 10 146 carbon fixation pathways in prokaryotes and nitrogen metabolism decreased in ETP 11 12 147 samples only (Figure 3D). Four pathways for the lipid metabolism significantly changed 13 148 in ETB samples only: an increase of levels for fatty acid and glycerolipid metabolism etc. 14 15 149 and a decrease of levels for fatty acid biosynthesis. For metabolism of cofactors and 16 17 150 vitamins, abundances of six out of the seven pathways shown in Figure 3D decreased in 18 19 151 either ETB or ETP samples, such as biotin and riboflavin metabolism. 20 21 152 ETB vs. ETP gut microbiota after CR intervention 22 23 24 153 After finding the different responses, we then compared microbiota of ETB and ETP after- 25 26 154 intervention samples. Firstly, we compared the relative abundances of common genera 27 28 155 using Wilcoxon rank-sum test. As shown in Figure 4A and Figure 2B, the six common 29 156 genera (marked with an asterisk in Figure 4A and Figure 2B) which were significantly 30 31 157 different between ETB and ETP before-intervention samples remained significantly 32 33 158 different after the intervention. In addition, relative abundances of ten genera which 34 35 159 showed no inter-enterotype difference before the intervention became significantly 36 37 160 different between ETB and ETP after-intervention samples, including nine genera 38 39 161 (Parabacteroides, Ruminococcus, Clostridium, Blautia, Bilophila, Coprobacillus, 40 162 Campylobacter, Staphylococcus and Anaerococcus) became more abundant in ETB 41 42 163 samples and one genus (Enterobacter) got more abundant in ETP samples after the 43 44 164 intervention (Figure 4A). On the species level, the relative abundances of a total of 42 45 46 165 species became significantly different between ETB and ETP samples after the intervention, 47 48 166 including 27 species (marked with an asterisk in Figure 4B and Figure 2C) which already 49 50 167 showed significant differences between ETB and ETP samples before the intervention 51 168 (Figure 4B). 15 species that showed no significant inter-enterotype difference before the 52 53 169 intervention became significant different between ETB and ETP samples: 13 species (in 54 55 170 orange with no asterisk in Figure 4B) became significantly more abundant in ETB samples 56 57 171 while 2 species, Enterobacter cloacae and Ruminococcus flavefaciens (in orange with no 58 59 172 asterisk in Figure 4B) got more abundant in ETP samples after the intervention. In addition, 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 173 the relative abundances of 9 species (with no asterisk in Figure 2C), including Bacteroides 5 6 174 intestinalis, Bacteroides salanitronis and Prevotella timonensis, etc., became no longer 7 8 175 significantly different between ETB and ETP samples after CR intervention. 9 10 176 We further studied inter-enterotype differences on functional potentials after the 11 12 177 intervention. There were a total of 28 KEGG pathways showing differential enrichment 13 14 178 between ETB and ETP samples after the intervention (Figure 4C), including 24 pathways 15 16 179 (marked with an asterisk in Figure 4C and Figure 2D) were already significantly different 17 18 180 before the intervention. 4 pathways (with no asterisk in Figure 4C) began to show 19 20 181 differential inter-enterotype enrichment after the intervention: valine, leucine and 21 182 isoleucine degradation and glutathione metabolism got more enriched in ETB subjects 22 23 183 while valine, leucine and isoleucine biosynthesis and d−glutamine and d−glutamate 24 25 184 metabolism became more enriched in ETP subjects. 5 pathways (with no asterisk in Figure 26 27 185 2D) which showed differential inter-enterotype enrichment before the intervention were no 28 29 186 longer significantly different between ETB and ETP samples and these five pathways were 30 31 187 lysine degradation, phenylalanine, tyrosine and tryptophan biosynthesis, flagellar assembly, 32 188 methane metabolism and bisphenol degradation. 33 34 35 189 A convergent shift on gut microbiota of ETB and ETP subjects 36 37 190 Interestingly, from the results shown in Figure 3C, we observed that in fecal samples 38 39 191 belonging to the Bacteroides enterotype, the relative abundance of one prevotella bacteria 40 41 192 (Prevotella copri) significantly increased while two Bacteroides bacterium (Bacteroides 42 43 193 stercoris and Bacteroides coprocola) significantly decreased. Additionally, three species 44 45 194 (Bacteroides intestinalis, Bacteroides salanitronis and Prevotella timonensis) of the 46 47 195 Bacteroides or Prevotella genus which were initially significantly different between ETB 48 196 and ETP samples showed no inter-enterotype difference after the intervention. On the 49 50 197 functional level, we found five pathways which changed simultaneously in ETB and ETP 51 52 198 samples (Figure 3E). All these above observations provided us a thought that the same 3- 53 54 199 week diet may reduce the gut microbiota dissimilarity between ETB and ETP subjects. 55 56 200 To confirm our hypothesis, we then calculated the inter-enterotype Bray-Curtis distances 57 58 201 of gene, genera and species profiles before and after the intervention (Figure 5 A-C). The 59 60 202 results revealed that the inter-enterotype distances in gene, genera and species profiles had 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 203 significantly decreased after the intervention (paired Wilcoxon rank-sum test, p < 0.05; 5 6 204 Figure 5 A-C). But it should be noted that the inter-enterotype distances in gene, genera 7 8 205 and species profiles were still significantly larger than that of intra-enterotype after the 9 10 206 intervention (Wilcoxon rank-sum test, p < 0.05; Figure 5 D-E). 11 12 207 13 Discussion 14 15 208 The most important finding in our study is that ETB and ETP subjects responded 16 17 209 differentially to CR intervention in both BMI and gut microbiota. As for BMI, although 18 19 210 CR intervention resulted in a BMI loss for both ETB and ETP subjects, subjects of ETP 20 21 211 enterotype had a significantly larger BMI loss ratio than ETB subjects. This finding is 22 212 consistent with a previous study that high P/B group had higher weight loss than low P/B 23 24 213 group when participants received the New Nordic Diet [6]. For gut microbiota, both 25 26 214 microbiome composition and functional potentials changes differentially between ETB and 27 28 215 ETP samples. Particularly, we observed that relative abundances of species and functional 29 30 216 pathways (Eubacterium rectal, butanoate and propanoate metabolisms) which are related 31 32 217 to SCFA-synthesis and involved in energy supplement for host significantly increased in 33 218 ETB gut microbiomes only [17]. In addition, relative abundances of pathways for 34 35 219 metabolism of cofactors and vitamins changed differentially between ETB and ETP 36 37 220 samples: biotin metabolism, folate, pantothenate and CoA biosynthesis levels only 38 39 221 decreased in ETB samples while riboflavin (vitamin B2), nicotinate and nicotinamide 40 41 222 metabolism levels decreased in ETP samples only. Despite of these different responses, 42 43 223 both ETB and ETP gut microbiome exhibited decreased levels for cofactors and vitamins 44 224 metabolism, which suggested that extra supplements for cofactors and vitamins may be 45 46 225 needed for low-calorie diet. Riboflavin (vitamin B2) metabolism level decreased after CR 47 48 226 in fecal samples of ETP was also reported in a previous study [18]. 49 50 227 Another interesting finding in our study is that a convergent shift of the ETB and ETP gut 51 52 228 microbiota was observed after this short-term CR intervention, which agreed with a 53 229 54 published finding that human gut microbiome functions were driven convergence by 55 230 dietary intervention [19]. Inter-enterotype Bray-Curtis distances of gene/genus/species 56 57 231 profiles decreased significantly after CR intervention. The relative abundances of several 58 59 232 species including Bacteroides intestinalis, Bacteroides salanitronis and Prevotella 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 233 timonensis showed no significant inter-enterotype differences after the intervention. Some 5 6 234 previous studies also showed that diet history contributed to enterotype clustering and 7 8 235 metagenomics content and enterotype of some persons even switched to other enterotypes 9 10 236 because of dietary interventions [3, 20-22]. 11 12 237 Our study provides new evidences for the potential feasibility of gut microbiota 13 238 stratification for personalized BMI loss guidance with CR intervention. Future work can 14 15 239 utilize more information, such as human genome and metabolome, to conduct a systematic 16 17 240 study on CR intervention. 18 19 241 Conclusions 20 21 22 242 Our study reveals that BMI and gut microbiota of ETB and ETP individuals changed 23 24 243 differentially to a short-term calorie-restriction intervention. Moreover, a convergent shift 25 244 on ETB and ETP gut microbiota was observed after the intervention. Our results highlight 26 27 245 the potential application for microbiome stratification in nutrition intervention. 28 29 30 246 Methods 31 32 33 247 Subject enrollment 34 35 248 Volunteer-wanted posters were propagated at the China National Gene Bank in Shenzhen 36 249 from March to April 2017. A volunteer was considered if his/her BMI was less than 28 37 38 250 kg/m2. In addition, recruited volunteers should meet all the following criteria: 1) had not 39 40 251 taken antibiotics in the recent 2 months; 2) had no hypertension, diabetes mellitus and 41 42 252 intestinal disease; 3) had regular eating and lifestyle patterns; 4) could follow the controlled 43 44 253 feeding trial. Finally, 41 subjects (24 females and 17 males aged 30 ± 6 years old) met all 45 46 254 the criteria and were recruited in this study (Table 2). 47 48 255 Study design 49 50 51 256 As shown in Figure 1, all subjects underwent a 1-week normal-calorie diet and 3-week 52 257 low-calorie diet. During this 4-week study, all participants ate the meals at our study center. 53 54 258 The low-calorie diet, which was of ~50% calories of the normal-calorie diet (female, 55 56 259 1000kcal/day; male, 1200kcal/day), was designed according to the Dietary Guidelines for 57 58 260 Chinese Residents (2016) [23] and nutritionally balanced. The respective proportion of 59 60 261 protein, carbohydrates and fat was approximately 35%, 50% and 15%. Before the CR 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 262 intervention, BMI and fecal samples were collected at our study center (Figure 1). In the 5 6 263 last week of CR intervention, BMI values for each subject were collected multiple times 7 8 264 and an averaged BMI values was used in our study (Figure 1). Fecal samples were also 9 10 265 collected after the CR intervention (Figure 1). 11 12 266 Fecal DNA extraction and metagenomic sequencing 13 14 15 267 Fecal samples were transferred to the laboratory on dry ice and kept frozen at -80°C. The 16 17 268 stool DNA was extracted following the MetaHIT protocol as described previously [24]. 18 269 19 The DNA concentration was estimated by Qubit (Invitrogen). Sequencing was performed 20 270 based on the BGISEQ-500 protocol in the SE100 mode. 21 22 23 271 Data processing, gut microbiota profiling and enterotyping 24 25 272 Raw reads from BGISEQ-500 were trimmed by an overall accuracy (OA) control strategy 26 27 273 for quality adjustment [25]. 98.15% of the raw reads remained as high-quality reads by 28 29 274 using this approach. To remove human reads, the high-quality reads were aligned to hg19 30 31 275 using SOAP2.22 [25]. The retained clean reads were aligned to the integrated non- 32 33 276 redundant gene catalog (IGC) using SOAP2.22 (identity ≥ 0.95) [26]. As shown in 34 35 277 Supplemental Table 1, the clean reads reached an average IGC mapping rate of 80.18% 36 278 and an average unique mapping rate of 65.76%. The relative abundances of genes, genera, 37 38 279 species and KOs were annotated by IGC reference [26]. For enterotyping, we applied a 39 40 280 universal classifier (http://enterotypes.org/) which circumvents many of the problems in 41 42 281 enterotyping (no standardization, such as sample size is small and clustering a single 43 44 282 sample is impossible) and provides a more comparable result [27, 28]. From the 45 283 classification result, all our samples fitted the models of this classifier with 28 subjects 46 47 284 belonging to ETB and 13 subjects belonging to ETP. The enterotype for each subject was 48 49 285 shown in Supplemental Table 2. 50 51 52 286 Wilcoxon rank-sum test 53 54 287 Wilcoxon rank-sum test was performed by a R built-in function named wilcox.test with 55 56 288 default parameters. Paired Wilcoxon rank-sum test was also calculated by the wilcox.test 57 58 289 function with the parameter paired = T. The R version we used in this study is version 59 60 290 3.5.0. 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 291 Pearson’s chi-square test 5 6 7 292 To evaluate whether the female/male ratio was significantly different between subjects of 8 9 293 the two enterotypes, Pearson’s chi-square test was performed by R chisq.test function with 10 294 default parameters. 11 12 13 295 Permutational multivariate analysis of variance (PERMANOVA) 14 15 16 296 To evaluate the correlation level between genus profiles and enterotypes, we performed a 17 297 permutational multivariate analysis of variance (PERMANOVA) with 9,999 permutations 18 19 298 on enterotypes and the Bray-Curtis distance matrix calculated from the genus profiles (R 20 21 299 vegan package). 22 23 24 300 Common genus or species 25 26 301 Genus or species with occurrence rate > 80% and median relative abundance > 1e-6 in all 27 28 302 samples was defined as common genus or species. The detailed information for common 29 30 303 genus/species is shown in Supplemental Table 3-4. 31 32 304 Reporter score 33 34 35 305 Differentially enriched KEGG pathways were identified according to their reporter Z-score 36 37 306 [29]. A reporter score |Z| > 1.96 (95% confidence according to normal distribution) was 38 307 used as a detection threshold for significantly differentiating pathways. 39 40 41 308 List of abbreviations 42 43 44 45 309 CR: Calorie restriction; BMI: Body mass index; KO: Kyoto Encyclopedia of Genes and 46 47 310 Genomes Orthology; PERMANOVA: permutational analysis of variance 48 49 50 311 Declarations 51 52 53 54 312 Ethics approval and consent to participate 55 56 57 313 The study protocol was approved by the institutional review board on bioethics and 58 59 314 biosafety of BGI (NO. BGI-IRB 17020). 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 315 Consent for publication 5 6 7 316 Not applicable. 8 9 10 11 317 Availability of data and material 12 13 14 318 The metagenomics sequencing reads of this study can be found in the China National 15 16 319 Genebank Nucleotide Sequence Archive (CNSA) (https://db.cngb.org/cnsa/) under the 17 18 19 320 BioProject number CNP0000247. The scripts used to perform statistical analysis and 20 21 321 figure plotting are available at https://github.com/HuaZou/NonobeseBP. The gut 22 23 322 microbiota profiles are also available at https://github.com/HuaZou/NonobeseBP. 24 25 26 27 323 Competing interests 28 29 30 324 The authors declare that they have no competing interests. 31 32 33 34 325 Funding 35 36 37 326 This work is supported by National Key Research and Development Program of China 38 39 327 (No.2017YFC0909703) and Shenzhen Municipal Government of China (No. 40 41 42 328 CXB201108250098A). 43 44 45 329 Authors' contributions 46 47 48 330 P.C and K.C collected the samples and provided the fecal microbiome data. H.Z, D.W, 49 50 51 331 Z.H.Z, H.R, Z.S, P.Z, C.F, H.Y and J.W established the concept and analysis framework 52 53 332 of the study. H.Z and D.W performed the bioinformatic analysis. H.Z wrote the first draft 54 55 333 of the manuscript and D.W and Z.H.Z revised and edited the manuscript. All authors 56 57 58 334 read and approved the final manuscript. 59 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 335 Acknowledgements 5 6 7 336 We are very grateful to acknowledge colleagues from the China National Genebank for 8 9 10 337 collecting fecal samples and metadata and as well as DNA extraction, library construction, 11 12 338 sequencing by colleagues in BGI-Shenzhen. 13 14 15 16 339 References 17 18 19 340 1. Sommer F, Bäckhed F: The gut microbiota — masters of host development and 20 341 physiology. Nature Reviews Microbiology 2013, 11(4):227-238. 21 22 342 2. 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Uncovering transcriptional regulation of metabolism by using metabolic network 39 40 386 topology. 41 42 387 17. Damms-Machado A, Mitra S, Schollenberger AE, Kramer KM, Meile T, 43 388 Konigsrainer A, Huson DH, Bischoff SC: Effects of surgical and dietary weight loss 44 45 389 therapy for obesity on gut microbiota composition and nutrient absorption. Biomed 46 47 390 Res Int 2015, 2015:806248. 48 49 391 18. Heinsen FA, Fangmann D, Muller N, Schulte DM, Ruhlemann MC, Turk K, Settgast 50 392 U, Lieb W, Baines JF, Schreiber S et al: Beneficial Effects of a Dietary Weight Loss 51 52 393 Intervention on Human Gut Microbiome Diversity and Metabolism Are Not 53 54 394 Sustained during Weight Maintenance. Obes Facts 2016, 9(6):379-391. 55 395 19. 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Wang J, Linnenbrink M, Kunzel S, Fernandes R, Nadeau MJ, Rosenstiel P, Baines 16 404 JF: Dietary history contributes to enterotype-like clustering and functional 17 18 405 metagenomic content in the intestinal microbiome of wild mice. Proc Natl Acad Sci 19 20 406 U S A 2014, 111(26):E2703-2710. 21 407 22. Moeller AH, Ochman H: Microbiomes are true to type. Proc Natl Acad Sci U S A 22 23 408 2014, 111(26):9372-9373. 24 25 409 23. Wang SS, Lay S, Yu HN, Shen SR: Dietary Guidelines for Chinese Residents 26 27 410 (2016): comments and comparisons. J Zhejiang Univ Sci B 2016, 17(9):649-656. 28 411 24. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D et al: 29 30 412 A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 31 32 413 2012, 490(7418):55-60. 33 414 25. Fang C, Zhong H, Lin Y, Chen B, Han M, Ren H, Lu H, Luber JM, Xia M, Li W et 34 35 415 al: Assessment of the cPAS-based BGISEQ-500 platform for metagenomic 36 37 416 sequencing. Gigascience 2018, 7(3):1-8. 38 417 26. Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, Arumugam M, Kultima JR, Prifti 39 40 418 E, Nielsen T et al: An integrated catalog of reference genes in the human gut 41 42 419 microbiome. Nat Biotechnol 2014, 32(8):834-841. 43 420 27. Costea PI, Hildebrand F, Arumugam M, Bäckhed F, Blaser MJ, Bushman FD, 44 45 421 de Vos WM, Ehrlich SD, Fraser CM, Hattori M et al: Enterotypes in the landscape 46 47 422 of gut microbial community composition. Nature Microbiology 2017, 3(1):8-16. 48 49 423 28. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, Almeida M, 50 424 Arumugam M, Batto JM, Kennedy S et al: Richness of human gut microbiome 51 52 425 correlates with metabolic markers. Nature 2013, 500(7464):541-546. 53 54 426 29. Patil KR, Nielsen J: Uncovering transcriptional regulation of metabolism by using 55 427 metabolic network topology. Proceedings of the National Academy of Sciences of 56 57 428 the United States of America 2005, 102(8):2685-2689. 58 59 429 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 430 5 Figures, tables and additional files 6 7 8 431 Figure 1. Overview of Experimental design. CR intervention was conducted in the last 9 10 432 three weeks of our study. 11 12 433 Figure 2. Gut microbiota of ETB vs. ETP before the intervention. A, NMDS plot of 13 14 434 ETB and ETP samples. B, common genera whose relative abundances were significantly 15 16 435 different between ETB and ETP samples before the intervention. C, common species 17 18 436 whose relative abundances were significantly different between ETB and ETP samples 19 437 20 before the intervention. D, KEGG pathways which were differentially enriched between 21 438 ETB and ETP samples before the intervention. Genus/species/pathways marked with an 22 23 439 asterisk are genus/species/pathways which remained significant inter-enterotype different 24 25 440 before and after the intervention, which will be discussed later. 26 27 28 441 Figure 3. Differential responses of ETB and ETP to CR on BMI and gut microbiota. 29 442 A, within-group changes on BMI. B, comparison of the BMI loss ratio between ETB and 30 31 443 ETP subjects. C, common species whose relative abundances changed differentially 32 33 444 between ETB and ETP samples. D, KEGG pathways whose levels changed differentially 34 35 445 between ETB and ETP samples. E, KEGG pathways whose levels changed simultaneously 36 37 446 between ETB and ETP samples. Species/pathways in orange are species/pathways which 38 447 changed significantly in ETB samples only. Species/pathways in blue are species/pathways 39 40 448 which changed significantly in ETP samples only. Species/pathways in green are 41 42 449 species/pathways which changed significantly in both ETB and ETP samples. *, p<0.5; **, 43 44 450 p<0.01 45 46 451 47 Figure 4. Gut microbiota of ETB vs. ETP after the intervention. A, common genera 48 452 whose relative abundances were significantly different between ETB and ETP samples 49 50 453 after the intervention. B, common species whose relative abundances were significantly 51 52 454 different between ETB and ETP samples after the intervention. C, KEGG pathways which 53 54 455 were differentially enriched between ETB and ETP samples after the intervention. 55 56 456 Genus/species/pathways marked with an asterisk are genus/species/pathways which 57 457 remained significant inter-enterotype different before and after the intervention. 58 59 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 3 4 458 Figure 5. Bray-Curtis distance of profiles for genes, genera and species. A-C, 5 6 459 Comparison of inter-enterotype distance before and after the intervention on gene, genus 7 8 460 and species level. D-F, Comparison of inter- and intra- enterotype distance after the 9 10 461 intervention on gene. 11 12 462 Table 1. Comparison of phenotypes of ETB and ETP subjects before CR intervention 13 14 15 463 Table 2. Cohort description 16 17 464 Supplemental Table 1. Metagenomic sequencing statistics for fecal samples 18 19 20 465 Supplemental Table 2. Enterotyping results 21 22 466 Supplemental Table 3. Common genus 23 24 25 467 Supplemental Table 4. Common species 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Table 1. Comparison of phenotypes of ETB and ETP subjects before CR intervention ETB Group ETP Group P value (Mean ± SD) (Mean ± SD) ETB vs ETP Number of subjects 28 13 Sex (female/male ) 16/12 8/5 1 Age 29 ± 6 30 ± 7 0.44 BMI (kg/m2) 23.50 ± 2.81 24.21 ± 2.85 0.42 Weight (kg) 64.40 ± 11.37 65.81 ± 12.39 0.62

Table 2. Cohort Description Cohort (Mean ± SD) Number of subjects 41 Sex (female/male) 24/17 Age 30 ± 6 BMI (kg/m2) 23.72 ± 2.81 Weight(kg) 64.84 ± 11.57 bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Fecal sample

BMI measure Volunteer recruitment Meals Calorie Restricted dietary intervention trail

Start 1 2 3 4 week A ● B Bacteroides ● ＊ ● ● Fusobacterium ● ● ● ＊ ●● ● ● ● ● ETP ●● ● ● Eggerthella ● ● ● ＊ ● ● ● ● ● ● ● ● ● MDS2 ● ● ● Anaerostipes bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. ●The copyright holder for this preprint (which was not ＊ certified by peer review) is the author/funder, who has granted bioRxiv a license● to display the preprint in perpetuity.ETB It is made available under aCC-BY-NC-ND 4.0 International license. ● ● ● ● ● Prevotella＊ ● ETB Paraprevotella

−0.4 −0.2 0.0 0.2 0.4 ＊ ● ETP

−0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 −6 −5 −4 −3 −2 −1 0 MDS1 Relative abundance (log10) C D Bacteroides dorei＊ Amino Acid Metabolism ETB vs ETP Bacteroides uniformis＊ Histidine metabolism＊ Parabacteroides distasonis＊ Lysine degradation Bacteroides ovatus＊ Phenylalanine, tyrosine and tryptophan biosynthesis Bacteroides xylanisolvens Carbohydrate Metabolism ＊ Pyruvate metabolism Bacteroides thetaiotaomicron＊＊ Bacteroides fragilis＊ Pentose phosphate pathway＊ Bacteroides finegoldii＊ Amino sugar and nucleotide sugar metabolism＊ Bacteroides intestinalis Fructose and mannose metabolism＊ Bacteroides eggerthii＊ Glyoxylate and dicarboxylate metabolism＊ Bacteroides clarus＊ Ascorbate and aldarate metabolism＊ Bacteroides salanitronis Butanoate metabolism＊ Bacteroides fluxus＊ Galactose metabolism＊ Clostridium bolteae＊ Propanoate metabolism＊ Lachnospiraceae bacterium_3_1_57FAA_CT1 Cell Motility Clostridiales bacterium_1_7_47FAA Flagellar assembly ＊ Energy Metabolism Prevotella timonensis Nitrogen metabolism＊ Lachnospiraceae bacterium_1_4_56FAA ＊ Methane metabolism Bacteroides helcogenes＊ Glycan Biosynthesis and Metabolism Clostridium sp._HGF2＊ Glycosaminoglycan degradation＊ Lachnospiraceae bacterium_9_1_43BFAA＊ Other glycan degradation＊ Clostridium ramosum＊ Peptidoglycan biosynthesis＊ Lachnospiraceae bacterium_4_1_37FAA＊ Lipid Metabolism Eggerthella lenta＊ Secondary bile acid biosynthesis＊ Membrane Transport Anaerostipes caccae ABC transporters＊ Porphyromonas asaccharolytica＊ Phosphotransferase system (PTS)＊ Fusobacterium ＊ Bacterial secretion system＊ Clostridium spiroforme Metabolism of Cofactors and Vitamins Prevotella copri＊ Porphyrin and chlorophyll metabolism＊ Paraprevotella xylaniphila＊ Lipoic acid metabolism＊ Dorea longicatena Biotin metabolism＊ Prevotella bivia＊ Metabolism of Terpenoids and Polyketides Terpenoid backbone biosynthesis Coprococcus eutactus＊＊ Enterococcus faecium Signal Transduction Two−component system Eubacterium biforme ＊ Xenobiotics Biodegradation and Metabolism Prevotella bergensis＊ ETB Bisphenol degradation ETP Chloroalkane and chloroalkene degradation＊ −6 −4 −2 0 2 4 6 −8 −6 −4 −2 0 Reporter score Relative abundance (log10) ETB Before vs After ETP Before vs After D Amino Acid Metabolism A ETB ETP B ETB ETP Phenylalanine metabolism 0.0 Cysteine and methionine metabolism Histidine metabolism ** ** Valine, leucine and isoleucine biosynthesis 28 −0.5 Lysine degradation Valine, leucine and isoleucine degradation Phenylalanine, tyrosine and tryptophan biosynthesis −1.0 Arginine and proline metabolism 26bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made availableBiosynthesis under of Other Secondary Metabolites ) aCC-BY-NC-ND 4.0 International license. 2 −1.5 Streptomycin biosynthesis

m Carbohydrate Metabolism 24 Propanoate metabolism kg −2.0 Glycolysis / Gluconeogenesis Starch and sucrose metabolism

BMI ( 22 −2.5 Butanoate metabolism Amino sugar and nucleotide sugar metabolism Fructose and mannose metabolism BMI loss ratio (100%) 20 −3.0 Pyruvate metabolism Cell Motility −3.5 Bacterial chemotaxis 18 Energy Metabolism Methane metabolism * Carbon fixation pathways in prokaryotes Before After Before After Nitrogen metabolism Glycan Biosynthesis and Metabolism Glycosaminoglycan degradation Lipopolysaccharide biosynthesis Lipid Metabolism Fatty acid metabolism Glycerolipid metabolism C Fatty acid biosynthesis ETB ETP Synthesis and degradation of ketone bodies Membrane Transport

ns ABC transporters Veillonella parvula * Bacterial secretion system Phosphotransferase system (PTS) ns ● ● ● Metabolism of Cofactors and Vitamins

● ● Bacteroides coprocola ** Ubiquinone and other terpenoid−quinone biosynthesis Folate biosynthesis ns ●

* One carbon pool by folate Bacteroides stercoris ●● ● Pantothenate and CoA biosynthesis Biotin metabolism ns

●● ●● ● * Eggerthella lenta ● ● ● Riboflavin metabolism Nicotinate and nicotinamide metabolism Metabolism of Other Amino Acids ns ● ● ● ●

Phascolarctobacterium succinatutens ● ● ● * beta−Alanine metabolism D−Alanine metabolism

ns Glutathione metabolism

● ● ●

● ● ● ● ●● Prevotella copri ** Metabolism of Terpenoids and Polyketides Biosynthesis of siderophore group nonribosomal peptides

ns Terpenoid backbone biosynthesis ● Ruminococcus lactaris * Signal Transduction Two−component system ns

● ● Xenobiotics Biodegradation and Metabolism Alistipes sp_HGB5 * Chlorocyclohexane and chlorobenzene degradation Aminobenzoate degradation ns Benzoate degradation Bacteroides plebeius * Bisphenol degradation Caprolactam degradation

ns Fluorobenzoate degradation ● Eubacterium rectale ** Dioxin degradation

ns −6 −4 −2 0 2 4 6 −6 −4 −2 0 2 4 6 ●

● ● Collinsella aerofaciens ** Reporter score ns

● ● E * Klebsiella oxytoca ● ● ETB Before vs After ETP Before vs After Carbohydrate Metabolism Pentose and glucuronate interconversions

ns Galactose metabolism

● Enterobacter hormaechei/cloacae ** Cell Motility Flagellar assembly Before Glycan Biosynthesis and Metabolism ns ● ● ● Enterobacter cloacae ●● ● ● * Other glycan degradation After Metabolism of Cofactors and Vitamins Porphyrin and chlorophyll metabolism −8 −6 −4 −2 0 −8 −6 −4 −2 0 −6 −4 −2 0 2 4 6 −6 −4 −2 0 2 4 6 Relative abundance (log10) Reporter score A Parabacteroides ETB Ruminococcus ETP Clostridium bioRxiv preprint doi: https://doi.org/10.1101/514596Blautia ; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Bilophila aCC-BY-NC-ND 4.0 International license. Coprobacillus Campylobacter Staphylococcus Anaerococcus Enterobacter C Bacteroides＊ Eggerthella＊ Fusobacterium＊ Amino Acid Metabolism After ETB vs ETP Anaerostipes＊ Valine, leucine and isoleucine degradation Prevotella＊ Valine, leucine and isoleucine biosynthesis Paraprevotella＊ Metabolism of Other Amino Acids Glutathione metabolism −6 −4 −2 0 D−Glutamine and D−glutamate metabolism Relative abundance (log10) Amino Acid Metabolism Histidine metabolism＊ B Carbohydrate Metabolism Bilophila wadsworthia Ascorbate and aldarate metabolism＊ Ruminococcus sp_5_1_39BFAA Glyoxylate and dicarboxylate metabolism＊ Ruminococcus sp_SR1.5 Propanoate metabolism＊ Bifidobacterium longum Amino sugar and nucleotide sugar metabolism＊ Clostridium scindens Butanoate metabolism＊ Campylobacter jejuni Pentose phosphate pathway＊ Galactose metabolism Lachnospiraceae bacterium_2_1_46FAA ＊ Fructose and mannose metabolism Eubacterium limosum ＊ Pyruvate metabolism Streptococcus gallolyticus ＊ Peptostreptococcus anaerobius Energy Metabolism Bifidobacterium breve Nitrogen metabolism＊ Bifidobacterium animalis Glycan Biosynthesis and Metabolism Megasphaera micronuciformis Glycosaminoglycan degradation＊ Enterobacter cloacae Other glycan degradation＊ Ruminococcus flavefaciens Peptidoglycan biosynthesis＊ Bacteroides dorei＊ Lipid Metabolism Bacteroides uniformis＊ Parabacteroides distasonis＊ Secondary bile acid biosynthesis＊ Bacteroides ovatus＊ Membrane Transport Bacteroides thetaiotaomicron＊ ABC transporters＊ Bacteroides xylanisolvens＊ Phosphotransferase system (PTS)＊ Bacteroides fragilis＊ Bacterial secretion system＊ Bacteroides finegoldii＊ Metabolism of Cofactors and Vitamins Bacteroides eggerthii＊ Bacteroides clarus＊ Porphyrin and chlorophyll metabolism＊ Bacteroides fluxus＊ Biotin metabolism＊ Clostridium bolteae＊ Lipoic acid metabolism＊ Clostridiales bacterium_1_7_47FAA＊ Metabolism of Terpenoids and Polyketides Lachnospiraceae bacterium_1_4_56FAA＊ Terpenoid backbone biosynthesis Clostridium sp._HGF2＊＊ Bacteroides helcogenes＊ Signal Transduction Lachnospiraceae bacterium_9_1_43BFAA＊ Two−component system＊ Eggerthella lenta＊ Xenobiotics Biodegradation and Metabolism Clostridium ramosum＊ Lachnospiraceae bacterium_4_1_37FAA＊ Chloroalkane and chloroalkene degradation＊ Porphyromonas asaccharolytica＊ −6 −4 −2 0 2 4 6 Fusobacterium＊ Prevotella copri＊ Reporter score Paraprevotella xylaniphila＊ Prevotella bivia＊ Coprococcus eutactus ＊ ETB Prevotella bergensis ＊ ETP

−8 −6 −4 −2 Relative abundance (log10) bioRxiv preprint doi: https://doi.org/10.1101/514596; this version posted January 9, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A ** B 0.8 **

0.9 0.6 0.6

0.8 0.4 0.4

0.7 bray distance ( gene ) bray

bray distance ( genus ) bray 0.2 bray distance ( species ) bray

0.2 0.6

0.0 Before After Before After Before After (ETB vs ETP) (ETB vs ETP) (ETB vs ETP) (ETB vs ETP) (ETB vs ETP) (ETB vs ETP)

D ** E ** F 0.8 ** 1.0 ** ** ** 0.75

0.6

0.8 0.50

0.4

0.25 0.6 0.2 Bray distance in after ( gene ) Bray Bray distance in after ( genus ) Bray Bray distance in after ( species ) Bray

0.00 ETB vs ETP ETB vs ETB ETP vs ETP ETB vs ETP ETB vs ETB ETP vs ETP ETB vs ETP ETB vs ETB ETP vs ETP