ORIGINAL RESEARCH published: 17 October 2019 doi: 10.3389/fmicb.2019.02346 Cross-Regional View of Functional and Taxonomic Microbiota Composition in Obesity and Post-obesity Treatment Shows Country Specific Microbial Contribution Daniel A. Medina 1* , Tianlu Li 2,3 , Pamela Thomson 4 , Alejandro Artacho 5 , Vicente Pérez-Brocal 5 and Andrés Moya 5,6,7 1 Laboratorio de Biotecnología Aplicada, Facultad de Medicina Veterinaria, Universidad San Sebastián, Puerto Montt, Chile, 2 Chromatin and Disease Group, Cancer Epigenetics and Biology Programme (PEBC), Bellvitge Biomedical Research Edited by: Institute (IDIBELL), Barcelona, Spain, 3 Epigenetics and Immune Disease Group, Josep Carreras Leukaemia Research Qi Zhao, Institute (IJC), Barcelona, Spain, 4 Departamento de Ingeniería Química y Bioprocesos, Escuela de Ingeniería, Pontificia Shenyang Aerospace University, Universidad Católica de Chile, Santiago, Chile, 5 Genomics and Health Area, Fundación para el Fomento de la Investigación China Sanitaria y Biomédica de la Comunidad Valenciana (FISABIO)-Salud Pública, Valencia, Spain, 6 Integrative Systems Biology Reviewed by: Institute, University of Valencia, CSIC, Valencia, Spain, 7 Biomedical Research Centre Network for Epidemiology and Public Alexander V. Tyakht, Health (CIBEResp), Madrid, Spain Institute of Gene Biology (RAS), Russia Sofia Forslund, Gut microbiota has been shown to have an important influence on host health. Max Delbrück Center for Molecular The microbial composition of the human gut microbiota is modulated by diet and Medicine, Germany other lifestyle habits and it has been reported that microbial diversity is altered in *Correspondence: Daniel A. Medina obese people. Obesity is a worldwide health problem that negatively impacts the daniel.medina@uss.cl quality of life. Currently, the widespread treatment for obesity is bariatric surgery. Interestingly, gut microbiota has been shown to be a relevant factor in effective Specialty section: This article was submitted to weight loss after bariatric surgery. Since that the human gut microbiota of normal Systems Microbiology, subjects differs between geographic regions, it is possible that rearrangements of a section of the journal the gut microbiota in dysbiosis context are also region-specific. To better understand Frontiers in Microbiology how gut microbiota contribute to obesity, this study compared the composition of Received: 29 October 2018 Accepted: 26 September 2019 the human gut microbiota of obese and lean people from six different regions and Published: 17 October 2019 showed that the microbiota compositions in the context of obesity were specific to Citation: each studied geographic location. Furthermore, we analyzed the functional patterns Medina DA, Li T, Thomson P, Artacho A, Pérez-Brocal V and using shotgun DNA metagenomic sequencing and compared the results with other Moya A (2019) Cross-Regional View obesity-related metagenomic studies, we observed that microbial contribution to of Functional and Taxonomic functional pathways were country-specific. Nevertheless, our study showed that Microbiota Composition in Obesity and Post-obesity Treatment Shows although microbial composition of obese patients was country-specific, the overall Country Specific Microbial metabolic functions appeared to be the same between countries, indicating that different Contribution. Front. Microbiol. 10:2346. microbiota components contribute to similar metabolic outcomes to yield functional doi: 10.3389/fmicb.2019.02346 redundancy. Furthermore, we studied the microbiota functional changes of obese Frontiers in Microbiology | www.frontiersin.org 1 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota patients after bariatric surgery, by shotgun metagenomics sequencing and observed that changes in functional pathways were specific to the type of obesity treatment. In all, our study provides new insights into the differences and similarities of obese gut microbiota in relation to geographic location and obesity treatments. Keywords: human gut microbiota, bariatric surgery, obesity, functional redundancy, metagenomic, functional convergence INTRODUCTION short chain fatty acids, increased low-grade inflammation and altered bile acid metabolism (Qin et al., 2012; Karlsson et al., It has been established that the composition of human gut 2013; Khan et al., 2016). However, a recent study focusing microbiota greatly influences human health (Carding et al., on the meta-analysis of previously published data using forest 2015, reviewed by Hall et al., 2017). Although the microbiota machine learning models showed no significant differences in gut composition of each individual is relatively stable across adult microbiota composition between obese and healthy individuals life, it varies widely between individuals (Lozupone et al., 2012; (Sze and Schloss, 2016). Nevertheless, further research is required Kolde et al., 2018). It has been observed that the human gut to unravel the exact role of gut microbiota composition in microbiota is primarily dominated by the phyla Firmicutes and obesity context, and the factors, including geographical location, Bacteroidetes (Zhernakova et al., 2016), with lesser contribution that may have an influence on not only differential microbial from Actinobacteria, Proteobacteria and Verrucomicrobia (Qin abundance but also long-term patient outcome. et al., 2010). Nevertheless, the microbiota composition differs Gastric surgical procedures, commonly known as bariatric geographically, primarily based on a variety of factors including surgery, have been successful in mediating long-term weight loss host genetics, dietary habits, age, geographic location and lifestyle and reducing the incidence of related comorbidities (Sjöström, (Yatsunenko et al., 2012; Chilton, 2014; Nishijima et al., 2016; 2008; Eldar et al., 2011). Roux-en-Y gastric bypass (RYGB) is Fujio-Vejar et al., 2017; Gupta et al., 2017). To date, most of the one of the most common bariatric surgery procedures in the studies on human gut microbiota have focused on populations United States (Smoot et al., 2006), where a small stomach pouch from North America and Europe and although several of studies is connected to the proximal jejunum to directly bypass food have demonstrated associations between microbiota alterations to the small intestine, resulting in restrictive and malabsorptive and diseases, including obesity, the specific contribution of these nutrient intake (Tice et al., 2008; Tran et al., 2016). Another alterations to treatment response and how they differ across common bariatric surgery is sleeve gastrectomy (SG), where a geographic locations still need to be envisioned. significant portion of the stomach is removed to decrease its Worldwide, obesity has nearly tripled since 1980 (Stevens volume, leading to a significant reduction in the amount of et al., 2012). Information published by the World Health food consumed (Gumbs et al., 2007). Since their invention, a Organization (2016) shows that more than 1.9 billion adults, number of studies have observed changes in obesity-associated 18 years and older, have a body mass index (BMI) above 25 kg/m2 microbiota and functional gene richness following different among which, over 650 million have a BMI > 30 kg/m2 , hence types of bariatric surgery (Zhang et al., 2009; Graessler et al., classifying them as obese. Overweight and obesity are defined 2013; Kong et al., 2013; Tremaroli et al., 2015; Shao et al., as abnormal or excessive fat accumulation due to environmental 2016; Ilhan et al., 2017; Medina et al., 2017; Murphy et al., and genetic factors (Angelakis et al., 2012). While overweight 2017). Specific changes following surgical intervention include an individuals haves a BMI range between 25 and less than 30 kg/m2 , increase in Proteobacteria (Escherichia coli, Enterobacter spp.), obesity is classified by BMI as: obesity grade 1 (30 to 35 kg/m2 ), changes in Bacteroides and Prevotella abundance, accompanied grade 2 (35 to 40 kg/m2 ) and grade 3 (40 to 60 kg/m2 ) (NHLBI by an increase in Akkermansia and a decline in Clostridium Expert Panel, 1998). Diverse studies have associated obesity with genus, and global changes in Firmicutes/Bacteroidetes phyla altered gut microbiota and reduced functional potential (Ley ratios (Zhang et al., 2009; Furet et al., 2010; Li et al., 2011; et al., 2006; Turnbaugh et al., 2006; Tremaroli and Backhed, 2012; Kong et al., 2013; Damms-Machado et al., 2015; Louis et al., Karlsson et al., 2013; Le Chatelier et al., 2013; Damms-Machado 2016; Palleja et al., 2016; Ilhan et al., 2017; Medina et al., 2017). et al., 2015; Haro et al., 2016; Medina et al., 2017), which can be These microbial changes following bariatric surgery have shown partially reversed after surgical intervention (Palleja et al., 2016). to mediate different outcomes and, interestingly, such changes It has been originally observed that the relative abundance of appear to depend on the type of surgery performed (Ilhan et al., Firmicutes and Bacteroidetes can be altered in obese patients, 2017; Medina et al., 2017; Murphy et al., 2017). Specifically, where an over-representation of Firmicutes is observed, in metagenomic studies following RYGB showed an increase in contrast to their lean counterparts (Ley et al., 2006; Turnbaugh pathways involving aerobic respiration and glutathione transfer et al., 2009; Million et al., 2012; Walters et al., 2014; Kasai et al., and metabolism (Palleja et al., 2016). Both observations were 2015; Haro et al., 2016). These taxonomic differences between in agreement with the increase in Proteobacteria phylum lean and obese subjects may contribute to the development abundance, driven by facultative anaerobes, such as E. coli, and perpetuation of obesity in several ways, including fat as a result of lower gastric acid exposure during stomach storage, regulation of energy metabolism, energy extraction from transit. In the same study, the authors found an increase in Frontiers in Microbiology | www.frontiersin.org 2 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota the pathways that degrade putrescine to succinate to produce depletion were performed using Microbiome Helper and QIIME gamma-aminobutyric acid (GABA) as a byproduct. GABA is v1.9.1 scripts (Caporaso et al., 2010; Navas-Molina et al., 2013; known to act on receptors in the hypothalamus to promote Comeau et al., 2017). Chimera sequences were filtered using satiety and, additionally, is thought to promote GLP-1 release, a the Vsearch tool (Rognes et al., 2016). Operational taxonomic positive regulator of GABA production by pancreatic beta-cells, units (OTUs) were picked by open-reference command and which provides further involvement of this pathway following defined by clustering at 3% divergence (97% similarity) using RYGB (Palleja et al., 2016). In addition, a complementary study the GreenGenes database release 08-2013 as reference (DeSantis conducted 9 years after RYGB intervention showed that bacterial et al., 2006; McDonald et al., 2012). Diversity analyses were rearrangements were stable in the long run and surgically altered performed using QIIME v1.9.1 scripts under Microbiome microbiota promoted reduced fat deposition in recipient mice Helper v2.3 environment. The sequencing depth for even sub- (Tremaroli et al., 2015). sampling and maximum rarefaction depth was at least 10000 In this study, we compared the gut microbiota composition counts/sample, regard the minimum value obtained after OTU- and functional patterns of obese subjects from Chile with picking for each data set (Supplementary Table S2). Predictive published data from Italy, Denmark, United States, France, metagenomic functional profiling of microbial composition were and Saudi Arabia, in order to better understand the microbiota performed using PICRUSt (Langille et al., 2013), following contribution to obesity. We found obese subjects to show the instructions provided in their Metagenomic Prediction geographic specificity in regards to relative microbiota Tutorial. KEGG Orthology analyses were performed using abundance, similar to what has been previously observed in the “ko_to_pathway_map” PICRUSt database to identify the healthy individuals. Interestingly, the gut microbiota of obese enrichment of metabolic pathways. patients did not display differential enrichment of functional Taxonomic abundance and metagenomic prediction tables pathways between countries, indicating that the geography- were exported to R environment (R Core Team, 2013) for specific microbial compositions converge to perform similar statistical analysis and figures were represented using the package functions in obese individuals. Furthermore, this study analyzed LSD Lots of Superior Depictions (Schwalb et al., 2011). Volcano the metagenomic profiles of Chilean patients subjected to gastric plots were constructed using the adjusted FDR p-value obtained bypass and sleeve gastrectomy and observed changes in the from the unpaired t-test comparisons and the fold change of each functional capacity of gut microbiota after surgery. Specifically, condition. The taxonomic processed data obtained from QIIME we identified Akkermansia muciniphila as one of the bacteria and the metadata for the raw data files used in this study are that drives the change in metabolic pathways after surgical exhibited in Supplementary Table S2. intervention for obesity in Chilean patients. The PICRUSt functional prediction was validated comparing the KEGG Orthology abundances obtained with the Uniref90 metagenomic abundances from the HUMAnN2 output. For MATERIALS AND METHODS this, the metagenomic Uniref90 table was converted to KEGG Orthology using the script humann2_regroup_table from Sample and DNA Raw Data Collection HUMAnN2 (Abubucker et al., 2012). Both KEGG Orthology DNA raw data sequences from the stool of obese and lean datasets were merged by row and normalized using quantile subjects were obtained from studies carried out in Chile (Medina normalization to compare linear dependence between each data et al., 2017; Thomson et al., 2019), Italy (Tremaroli et al., 2015), set using Pearson and pairwise Spearman rank correlation in the Denmark (Palleja et al., 2016), United States (Ilhan et al., 2017), R environment (R Core Team, 2013). France and Saudi Arabia (Yasir et al., 2015), described in Table 1 and Supplementary Table S1. In addition, 12 DNA stool samples Functional Annotation and Metagenomic from Chilean obese patients before and after treatment obtained Profiling of Fecal DNA from a previous study (Medina et al., 2017) were sequenced A total of 12 DNA stool samples were sequenced using the using shotgun metagenomics. All experiments were conducted in Illumina HiSeq next-generation sequencing platform carried accordance with the Declaration of Helsinki and approved by the out at Genoma Mayor (Universidad Mayor, Chile) with an Ethics Committee of the Faculty of Medicine, Pontifical Catholic output of 2 × 100 pb and 20 × 106 paired-end reads per University of Chile (n◦ 15-337). sample. The raw data produced from DNA sequencing in this study were stored at the ENA-EMBL database under the Taxonomic Profiling From 16S rDNA accession number PRJEB29060. To evaluate the taxa abundance Gene Amplicon Sequencing and metagenomic composition, all metagenomic raw sequences Raw data of DNA sequences belonging to different studies were analyzed in parallel using the Microbiome Helper v2.3 were downloaded from ENA-EMBL or SRA-NCBI databases environment following the metagenomics SOP v2 tutorial. (Supplementary Table S1). To access the microbiota taxa and This pipeline allowed the calculation of microbiota abundance abundance, in this study we re-analyzed all raw data sequences using functions from MetaPhlAn2 (Truong et al., 2015) and using Microbiome Helper v2.3 OVA pipeline (Comeau et al., functional profile using HUMAnN2 (Abubucker et al., 2012). 2017). Briefly, MiSeq paired-end sequences were joined using DNA sequence quality control was performed using KneadData PEAR (Zhang et al., 2014). Joined sequences were filtered (McIver et al., 2018), in which low quality sequences were by quality and length, in which demultiplexing and barcode first removed by Trimmomatic (Bolger et al., 2014), followed Frontiers in Microbiology | www.frontiersin.org 3 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota TABLE 1 | Description of studies used in this work. Study Country Methodology N◦ lean subjects N◦ obese subjects DOI Medina et al., 2017 Chile 16S rDNA Amplicon 0 19 doi: 10.7717/peerj.3443 Thomson et al., 2019 Chile 16S rDNA Amplicon 28 0 doi: 10.1017/S0007114519001570 Yasir et al., 2015 France 16S rDNA Amplicon 12 16 doi: 10.1038/nutd.2015.3 Yasir et al., 2015 Saudi Arabia 16S rDNA Amplicon 9 9 doi: 10.1038/ismej.2017.71 Ilhan et al., 2017 United States 16S rDNA Amplicon 10 15 doi: 10.1038/ismej.2017.71 Medina, this study Chile Shotgun metagenomics 0 6 This study Palleja et al., 2016 Denmark Shotgun metagenomics 0 13 doi: 10.1186/s13073-016-0312-1 Tremaroli et al., 2015 Italy Shotgun metagenomics 0 7 doi: 10.1016/j.cmet.2015.07.009 by the application of Bowtie2 to screen out the contaminant interface Galaxy1 (Segata et al., 2011). Statistical differences DNA sequences mainly from human and viruses (Langmead between the KEGG Orthology abundances were calculated and Salzberg, 2012). Both programs were run simultaneously for using the unpaired t-test and adjusted by BH FDR as described all samples using the GNU Parallel tool to repeat concatenate above. –log10 FDR was plotted against log2 ratio of each country the entire process for each data set (First and Job, 2011). in respect to Chile and represented using the R package LSD Lots The paired-end files were merged using the script provided of Superior Depictions (Schwalb et al., 2011). by the Microbiome Helper pipeline and then taxonomic and functional profiling were performed using MetaPhlAn2 and HUMAnN2, respectively. Gene family and pathway abundances RESULTS were normalized for each sample and represented as a percentage. The resulting tables were exported to the R environment (R Core Gut Microbial Diversity Is Specific to Team, 2013) for statistical analysis and figure representation. Geographic Locations Metadata sets used for DNA shotgun sequencing to obtain The composition and function of the human gut microbiota stratified and unstratified metagenomic profiles are listed in represent one of most important factors involved in obesity Supplementary Table S3. and its treatment (Ley et al., 2006; Tremaroli and Backhed, 2012; Karlsson et al., 2013; Le Chatelier et al., 2013; Damms- Data Comparison and Statistical Machado et al., 2015; Medina et al., 2017). It has been previously Analysis established that the human gut microbiota displays pronounced Statistical analyses were conducted using the R environment differences between individuals residing in distinct geographic (R Core Team, 2013) version 3.4.4 (2018-03-15). Before locations (Yatsunenko et al., 2012; Fujio-Vejar et al., 2017; calculations between countries, data sets were scaled Gupta et al., 2017). It is therefore of great interest to compare proportionally, and using the R package preprocessCore the microbial diversity of obese individuals from different (Bolstad, 2019), quantile normalization was performed to reduce geographical locations around the world. In this line of research, batch effects between data belonging to different sources as was this study compared the gut microbial diversity of obese and previously used for genome-wide analyses (Sun et al., 2011; Guo lean subjects from Chile (Medina et al., 2017; Thomson et al., et al., 2014; Fei et al., 2018). For differential analysis of group 2019) with data published by other studies in different regions variance between lean and obese microbiota abundance, we used around the world, namely United States (Ilhan et al., 2017), Canonical Correspondence Analysis (CCA, a.k.a. constrained France and Saudi Arabia (Yasir et al., 2015), described in correspondence analysis) and Adonis tests, in which both were Table 1. Taxonomic microbiota abundance were collected by calculated using the R package vegan (Oksanen et al., 2018). The sequencing the 16S rDNA hypervariable regions V3–V4 or V4– Adonis test was used for permutational multivariate analysis of V5 using the Illumina MiSeq platform (Supplementary Table variance using distance matrices, in which samples were fitted S1), and raw data was processed as described in materials to linear models to calculate the whole compositional variability and methods. Taxonomic abundance comparisons at the genus taking into account different sources of variation as well as level from the data obtained showed significant diversity in the interactions between them. Spearman’s rank correlation microbiota composition in lean people as previously reported analyses were used to assess taxa abundance or functional (Yatsunenko et al., 2012; Nishijima et al., 2016; Fujio-Vejar abundance associations between the samples groups. To analyze et al., 2017; Gupta et al., 2017), but also is country specific differences in group means between lean and obese microbiota in gut microbiota of obese subjects. Specifically, data variance abundance, we applied Wilcoxon sum rank test with FDR analysis using CCA and Adonis tests showed significant adjustment utilizing the Benjamini Hochberg (BH) method. This differences (p-value < 0.05) in the gut microbiota composition was performed using the R command p.adjust(), in which we between Chile, United States, France, and Saudi Arabia, considered tests with FDR < 0.05 to be significant. In order to in which the lean and obese microbiota were clustered identify differentially enriched biomarkers among the compared groups, we applied the LEfSe analytic method using the online 1 http://huttenhower.sph.harvard.edu Frontiers in Microbiology | www.frontiersin.org 4 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota by country (Figures 1A,B). Utilizing a different approach, that taxonomic microbial distribution is specific to each pairwise Spearman rank correlation, we further demonstrated country (Figure 2). that the taxonomic abundance correlation of obese gut Next, using the PICRUSt tool (Langille et al., 2013) designed microbiota differed between countries (Supplementary Figure to infer the functional patterns from taxonomic profile, we S1A). Nevertheless, although obese microbiota composition compared predicted KEGG Orthology (KO) enrichment of obese is clustered by country, there is significant inter-individual subjects from United States, France, and Saudi Arabia with variability between subjects, indicated by high dispersion the KO obtained from the obese Chilean data as reference patterns observed in data variance analyses (Figure 1). In in order to identify potential functional differences. Here, we this regard, although some proximity is observed between observed significant differences (FDR < 0.05) in enriched France and Saudi Arabia microbiota, CCA and Adonis test pathways for all the countries studied with respect to Chile variance analyses between the two countries shows a significant (Figure 3). Downregulated pathways in Saudi Arabia displayed statistical difference (Supplementary Figure S1B). In all, these an enrichment in translation and energy metabolism, whereas comparisons indicate that the microbiota compositions of obese the United States and France showed a downregulation in patients and lean subjects were specific to each analyzed membrane transport. For upregulated pathways, United States geographic location and displayed significant differences between showed a specific enrichment in cellular processing and countries. Comparing taxonomic abundance of obese individuals signaling and amino acid metabolism, whereas France and with their lean counterparts from the same country we Saudi Arabia were found to enrich in pathways involving observed significant differences by multivariate variance analyses membrane transport (Supplementary Figure S3). In summary, for Chile, France, and Saudi Arabia (CCA p-value < 0.05); the differences observed by functional prediction suggested that however, no statistically significant differences were observed the human gut metagenome of obese subjects, like their healthy for United States (Supplementary Figure S2). Wilcoxon sum- counterparts (Yatsunenko et al., 2012), may differ according to rank tests showed significant differences (FDR < 0.05) between the geographical location. lean and obese microbiota in Chile and France data sets, but not in United States and Saudi Arabia. Interestingly, although CCA showed clustering by country in both cases, Metagenomic Variations Across Regional France and Chile shared some genus changes between lean Location in the Context of Obesity and obese microbiota (Megasphaera, Veillonella, Adlercreutzia Our initial analysis utilizing 16S rDNA amplicon sequencing and Lachnospira) (Supplementary Table S2). In addition, showed marked differences between the compositions of the gut heatmap clustering by complete linkage method and the microbiota of obese subjects from different countries, therefore, stacked bar plot of taxonomic abundances showed differential to further validate our initial observation, we analyzed stool patterns not only between obese and lean subjects, but also samples of 6 Chilean pre-treatment obese patients obtained between countries, which again highlights our observation from a previous study (Medina et al., 2017) by shotgun DNA FIGURE 1 | Taxonomic abundance comparison at genus level from 16S rDNA sequencing. Constrained Correspondence Analysis (CCA) and Adonis test were performed to assess the variance in microbiota profiles at the genus level in lean (A) and obese (B) subjects. Vectors represent quantitative explanatory variables with confidence circles depicted for each country. Corresponding p-values are shown for each analysis and p-value < 0.05 was considered statistically significant. x- and y-axis show CCA1 and CCA2 components, respectively. Green, blue, brown and red points indicate individuals from France, United States, Saudi Arabia, and Chile, respectively. Frontiers in Microbiology | www.frontiersin.org 5 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota FIGURE 2 | Heatmap clustering and stacked bar plot of taxonomic abundances of lean and obese subjects at genus level. (A) Taxonomy abundance at genus level between obese and lean subjects were clustered using complete linkage method. Values indicate abundance of microbiota at the genus level. (B) Average taxonomy abundance at genus level were represented as stacked bar plot to of lean and obese subject of each country. x-axis depicts percentage of abundance of each taxonomy. Dark and light blue represent obese and lean subjects, respectively. Red, blue, green, and brown represent individuals from Chile, United States, France, and Saudi Arabia, respectively. FIGURE 3 | Volcano plot of KEGG Orthology abundances obtained from metagenomic simulation. Fold change of United States (A), France (B), and Saudi Arabia (C) of KO abundance with regard to Chile are plotted against –log10 of FDR. Dotted lines in the x-axis denotes a two-fold change, while dotted lines in the y-axis delimit a FDR value of 0.05. sequencing in order to dissect the contribution of metabolic into account microbial composition, and observed no statistically functional changes and microbiota compositions. Additionally, significant differences between the three countries using CCA we compared the results obtained from the Chilean cohort with analysis and Adonis tests (p-value > 0.05), in which the variance similar studies carried out from patients in Italy (Tremaroli et al., plot displayed little dispersion between the samples from different 2015) and Denmark (Palleja et al., 2016), described in Table 1. countries (Figure 4A). Furthermore, the pairwise Spearman All raw data were analyzed using the Metagenomic SOAP v2 rank correlation showed overall good correlation between the from Microbiome Helper (Comeau et al., 2017) as described samples of all datasets (Figure 4B). Hence, in contrast to in “Materials and Methods.” First, we compared the functional PICRUSt prediction, these results showed that the overall composition of obese subjects from Chile (n = 6), Italy (n = 7) and microbial functionality had no differences between obese subjects Denmark (n = 7) before any medical intervention utilizing CCA, belonging to different world regions. Subsequently, we performed Adonis tests and pairwise Spearman rank correlation. Initially, a stratified data analysis, a type of analysis that distinguishes we performed an unstratified analysis, which corresponds to the functional contribution of each bacterial species to the the analysis of metabolic pathway enrichment without taking overall metabolic pathways. Interestingly, stratified data, unlike Frontiers in Microbiology | www.frontiersin.org 6 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota unstratified analysis, showed significant dispersion between the report an increase in Proteobacteria abundance and facultative countries (Figure 4C), and no correlation between datasets anaerobes, such as E. coli, and an enrichment in microbial was observed (Figure 4D), indicating that individual microbial pathways involving aerobic respiration, glutathione and gamma- metagenomic contribution to overall functional pathways is aminobutyric acid metabolism. Similarly, in a previous study different between countries, in agreement with functional from our laboratory using Chilean obese patients, we observed inferences obtained by PICRUSt (Figure 3). Also, we use significant changes in the human gut microbiota, and these the Spearman rank correlation values obtained to compare changes were specific to the type of surgery performed (Medina statistically into-countries and cross-country associations in et al., 2017). Here, utilizing the same cohort, we performed both unstratified and stratified data, finding no statistical shotgun DNA sequencing of stool samples before and after differences between Spearman values (p-value FDR adjusted surgical intervention in order to analyze changes in taxonomic of 0.75 and 1, respectively). Utilizing linear discriminant composition (Medina et al., 2017). Although no statistical testing analysis (LDA) to quantify phylogenetic and functional diversity, was performed due to insufficient cohort number (n = 2 pathways over 1.5 LDA score were found to be differentially for each group), our results suggested that the two kinds of enriched between countries in both stratified and unstratified bariatric surgery mediated different rearrangements of functional approaches (Figure 5). In addition, we validated the PICRUSt pathways. We found stratified and unstratified functional functional prediction comparing KEGG Orthology obtained with changes of several folds, 6 months following either Roux-en-Y HUMAnN2 output from metagenomic data using Pearson and gastric bypass (RYGB) or sleeve gastrectomy (SG) interventions pairwise Spearman rank Correlation, with both showing linear (Figure 6). More specifically, unstratified functional changes dependence between 3590 common KEGG Orthology families were more pronounced in RYGB–treated patients compared to (Supplementary Figure S4 and Supplementary Table S5), which SG-treated patients, represented by higher data variance with an proved the relationship between in silico predictions and our increase in log2 fold change (Figures 6B,D). Functional changes biological findings as was previously demonstrated by PICRUSt in unstratified RYGB data suggested an increase in pathways authors (Langille et al., 2013). Altogether, these results suggested related with acetyl-CoA biosynthesis, trehalose degradation, that microbial contribution to functional pathways was country- GABA shunt and phospholipid remodeling. Contrastingly, specific, indicating that there was redundancy in the functions changes observed in SG intervention included an increase in of these distinct microbial species found in obese subjects from metabolic pathways such as fatty acid β-oxidation, TCA cycle different countries, in which different microbiota components and acetyl-CoA biosynthesis (Supplementary Table S4). Top contribute to obtain similar metabolic outcomes. stratified functional changes after RYGB were mainly driven Furthermore, microbial taxonomy abundance data obtained by A. muciniphila, E. coli, Bacteroides vulgatus, Eubacterium from shotgun DNA sequencing showed that Chilean microbiota siraeum and Streptococcus salivarius, while in SG functional composition from obese subjects was different compared to changes were driven by Bacteroides cellulosilyticus, S. salivarius, Italian and Danish cohorts (Tremaroli et al., 2015; Palleja Eubacterium eligens, Lactococcus lactis, Alistipes finegoldii, E. coli et al., 2016). CCA analysis and Adonis tests showed significant and A. muciniphila (Supplementary Table S4). Altogether, these differences between the microbiota of analyzed subjects at species results suggested that bariatric surgery in Chilean patients also level (Supplementary Figure S5A), which strengthens our caused functional rearrangement in microbiota, in concordance previous observations obtained from the 16S rDNA sequencing to previous metagenomic studies (Graessler et al., 2013; taxonomic comparison at genus level (Figure 1B). In addition, Tremaroli et al., 2015; Palleja et al., 2016), and these changes LDA analysis showed differences in species abundance between appeared to be specific to the surgery performed. the three microbial datasets species with and LDA scores higher than 2, reinforcing the observation that the microbiota under dysbiosis has different compositions specific to each geographical DISCUSSION location (Supplementary Figure S5B). Obesity is a world-wide health problem whose global prevalence has increased at an accelerated rate since 1980 (Stevens et al., Gut Microbiota Functionality Changes 2012). It has been previously described that the composition Following Bariatric Surgery in Chilean and functionality of human gut microbiota plays a crucial Subjects role in mediating both disease and recovery following medical Several studies have shown that the regulation of energy and fat intervention (Turnbaugh et al., 2006; Tremaroli and Backhed, storage is influenced by intestinal microbes, and this composition 2012; Karlsson et al., 2013; Le Chatelier et al., 2013; Damms- may contribute to obesity (Qin et al., 2012; Karlsson et al., 2013). Machado et al., 2015). This study compared the gut microbiota In this regard, it has been reported that human gut microbiota compositions of obese subjects from Chile with previously composition varies between obese patients who underwent published data from the United States, France, and Saudi Arabia different types of surgical intervention (Zhang et al., 2009; Furet using 16S rDNA Illuminia MiSeq, as well as from Italy and et al., 2010; Schloss, 2016; Ilhan et al., 2017; Medina et al., 2017; Denmark using Illumina HiSeq shotgun metagenomics, and Murphy et al., 2017). Moreover, several of these studies describe found that the microbiota composition differs significantly functional changes after treatment and its prevalence across time between obese and lean subjects from these different countries, (Tremaroli et al., 2015; Palleja et al., 2016). More specifically, they showing country-specific clustering. It has previously been Frontiers in Microbiology | www.frontiersin.org 7 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota FIGURE 4 | Stratified and unstratified functional profiles of obese gut microbiota from three different countries. Unstratified (A,B) and stratified (C,D) analyses of obese gut microbiota abundance for Chile (red), Italy (blue) and Denmark (green). Scatterplots (A,C) represent CCA and Adonis test analysis, and heatmap (B,D) represent pairwise Spearman rank correlation of functional microbial abundance between countries. described that a number of factors can modulate the composition environmental factors (Gupta et al., 2017; Jones et al., 2018; of intestinal microbiota, one of which is geographic origin Rothschild et al., 2018). However, twin studies revealed more (reviewed by Rojo et al., 2017). A previous study compared highly correlated microbiota compositions between monozygotic the microbiota composition of a group of healthy subjects twins than dizygotic twins (Goodrich et al., 2014). Given from France with Saudi Arabia by 16S rDNA sequencing and subjects belonging to close location have less genetic variance suggested that the dietary habits of different regions or countries than subjects from distant regions, this may also contribute can generate changes in intestinal microbiota (Yasir et al., to the differences in microbiota composition observed between 2015). This idea was reinforced by other studies suggesting that geographical origins. different types of diets directly change microbiota composition Our work attempted to indicate the need for the global study (Wu et al., 2011; Fava et al., 2013). In fact, mice subjected to of human microbiota for a proper assessment of the microbial obesogenic diets displayed significant functional and taxonomic contribution in respect to geographic distribution. In this modifications in their gut microbiota (Tran et al., 2019). Other context, we compared the taxonomic gut microbiota abundance studies show that genetics play a secondary role compared to of lean and obese at genus level using multivariate variance Frontiers in Microbiology | www.frontiersin.org 8 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota FIGURE 5 | Linear discriminant analysis (LDA) effect size (LEfSe) of metabolic pathways. Unstratified (A) and stratified (B) functional enrichment analyses from microbiome of obese subjects belonging to Chile (red, n = 6), Denmark (green, n = 7) and Italy (blue, n = 7). Pathways with LDA scores higher than 1.5 (A) or 2 (B) are shown. For stratified functional enrichment (B), unmapped/unintegrated reads are denoted as the bacterial taxa in which the pathways belong to. FIGURE 6 | MA Plots for stratified and unstratified functional changes after bariatric surgery in Chilean patients. (A,B) Gastric bypass and (C,D) sleeve gastrectomy stratified (A,C) and unstratified (B,D) functional changes 6 months post-surgery. Values are expressed as mean (x-axis) and fold change (y-axis), and represented as log2 values. Dotted lines denote a two-fold change. Frontiers in Microbiology | www.frontiersin.org 9 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota analysis and non-parametric mean comparison. Although, using identify specific microbial composition and their individual Wilcoxon rank test, we found different statistical changes contribution to metabolic pathways, significant differences in France and Chile gut microbiota, this was, however, not between countries were identified, in agreement with our the case for United States and Saudi Arabia. Our results initial PICRUSt metabolic inference, because both methods suggest a country-specific signature for both lean and obese takes in account taxonomic composition to assess metabolic microbiota. Nevertheless, we cannot discard the possibility pathways abundance. Altogether, our observations indicate while that our observations may be influenced by technical and overall metabolic functions do not change, microbial functional methodological factors. First, it is known that non-parametric contribution to obesity are specific to each country. tests have less chance of detecting a true effect where one Although the lack of differences observed in metagenomic exists (Whitley and Ball, 2002). Hence, utilizing a different unstratified analysis may be due to small cohort numbers, our statistical approach to analyze differences in variance (CAA comparisons suggest that the gut microbiota of obese patients and Adonis test) between lean and obese subjects in France, from different countries have functional convergence, where the Chile, and Saudi Arabia, the subsequent results support the same essential metabolic functions are carried out by related notion that obese versus lean microbiota is different between and unrelated bacterial species. Nevertheless, it is not surprising countries. Second, we cannot discard that this observation may that bacterial compositions of obese individuals differ between be relatively influenced by the methodology differences between counties due to both genetic and environmental factors, as studies and also may be confounded by large interpersonal previously described by others (reviewed by Gupta et al., 2017), variation. A meta-analysis of the association between differences however, our observations relating to convergent metabolic in the microbiome and obesity status made between 10 published functions provide new insights into how the gut microbiota studies found that is difficult to classify a subject as obese by shape common disease phenotypes. The concept of intestinal its microbiota composition, suggesting the possibility that each functional redundancy has been previously explored, in which individual has their own bacterial signatures (Sze and Schloss, taxonomic diversity appears to be irrelevant for the inference 2016). Another meta-analysis that compared lean and obese of functional traits (reviewed by Moya and Ferrer, 2016; Rojo subject from five published studies also found that signatures et al., 2017). One such example was demonstrated by the Human of obesity were not consistent between studies, as just one of Microbiome Project Consortium (Huttenhower et al., 2012), the 5 studies analyzed showed diversity differences between where the authors observed the microbial metabolism to remain lean and obese subject (Walters et al., 2014). Nevertheless, constant across individuals over time despite high variability in the authors declared that it was possible to assess differences composition. One possible explanation of microbial functional between lean and obese subjects when they changed the redundancy can be due to the evolutionary convergence of method to identify OTUs (from “close reference” to “pick de unrelated taxa, in which variable combinations of species from novo”) or used supervised learning tools to categorize subjects different phyla can at least partially fulfill the metabolic functions according to lean and obese states with considerable accuracy of another, resulting in different species of bacteria behaving (Walters et al., 2014). Altogether, these observations suggest that similarly (Moya and Ferrer, 2016; Rojo et al., 2017). geographical differences between lean and obese subjects requires Bariatric surgeries have become a frequent choice of treatment additional validation using an international cohort that not only for obesity patients over the last years, being SG the most frequent takes into account confounding factors but maintain the same procedure in the world (Angrisani et al., 2017; reviewed by experimental design. ASMBS, 2018). Several studies have shown that the intestinal Our study performed a functional inference analysis based microbiota composition changes following surgical intervention on the data obtained by 16S rDNA sequencing and observed and these changes vary between patients who have undergone significant differences in KEGG Orthology enrichment in different kind of surgical intervention such as RYGB, SG and samples obtained from France, United States, and Saudi Arabia laparoscopic adjustable gastric banding (LAGB) (Tremaroli et al., compared to Chile. These results suggested the microbial 2015; Palleja et al., 2016; Ilhan et al., 2017; Medina et al., 2017). differences observed were associated with changes in functional One of the limitations of our metagenomic study after bariatric pathways, however, this kind of analysis was unable to surgery is our small cohort number (n = 2 for each group), differentiate the microbial contribution of each microorganism and therefore, further studies with larger cohort are required species to the identified functional pathways. Therefore, a to confirm that different treatments mediate differentially more detailed comparison of metagenomic DNA sequencing metagenomic rearrangements. However, this study suggest that data was used, which was obtained using the HiSeq platform the microbial functionality of patients changed 6 months to analyze stool samples of obese patients from Chile, Italy following surgical treatment. In agreement with previous studies, and Denmark. First, we performed an unstratified analysis, it also observed specificity in the changes regarding to the type a type of metabolic pathway enrichment analysis without of surgery performed, where functional changes were mainly taking into account microbial contribution, and revealed that mediated by A. muciniphila, E. coli, B. vulgatus, E. siraeum and the functionality in obesity context did not show statistical S. salivarius after RYGB, and by B. cellulosilyticus, S. salivarius, differences (in both CCA and Adonis testing) between patients E. eligens, L. lactis, A. finegoldii, E. coli and A. muciniphila species from Chile, Italy and Denmark, suggesting that gut microbiota after SG. Although it is impossible to determine from this study metabolic pathways do not change according to geographic whether the microbiota composition changes were a consequence origin. Conversely, using a stratified approach that could of dietary changes or weight modification, our results not only Frontiers in Microbiology | www.frontiersin.org 10 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota hint at a microbiota signature for different bariatric surgeries, providing new insights into the role of the gut microbiome in but also suggest the need for further microbiota meta- treatment strategies. analyses at world-wide levels to study metabolic disorders such as obesity. A common key factor in obesity is the low abundance of ETHICS STATEMENT A. muciniphila (Schneeberger et al., 2015; Palleja et al., 2016; Medina et al., 2017; Seck et al., 2018), a mucin-degrading All experiments were conducted in accordance with the bacterium that resides in the human gut mucus layer and whose Declaration of Helsinki and approved by the Ethics Committee abundance in healthy subjects represents 3–5% of the residential of the Faculty of Medicine, Pontifical Catholic University of microbial community (Derrien et al., 2004). Interestingly, it Chile (n◦ 15–337). DNA samples used here for metagenomic has been shown that A. muciniphila prevents inflammation and belongs from previous published studies (Medina et al., 2017). adipose tissue alterations in mice (Schneeberger et al., 2015). The 16S and metagenomic raw data sequences used belongs from The administration of A. muciniphila grown under mucin- previous published studies (Tremaroli et al., 2015; Yasir et al., depleted conditions is effective in reducing obesity and improve 2015; Palleja et al., 2016; Ilhan et al., 2017; Medina et al., 2017; intestinal barrier integrity in obese mice (Shin et al., 2019), Thomson et al., 2019). controls fat mass storage and glucose homeostasis in obese and type 2 diabetic mice (Everard et al., 2013), and it has been previously described that bariatric surgery improves its AUTHOR CONTRIBUTIONS abundance (Damms-Machado et al., 2015; Tremaroli et al., 2015; Palleja et al., 2016; Medina et al., 2017). Furthermore, overweight DM conceived, designed, performed the comparisons, and obese individuals with higher A. muciniphila abundance analyzed the data, and wrote the manuscript. TL wrote and is associated with a healthier metabolic status compared with edited the figures and manuscript. PT contributed to the lower abundance (Dao et al., 2016). Therefore, the enrichment discussion. AA provided the scripts to do the multivariate of A. muciniphila provides a possible therapy in the treatment data analysis. VP-B and AM critically read and edited of obesity, and this possibility has been explored in a recent the manuscript. study (Sheng et al., 2018). Here, we identified A. muciniphila as one of the bacteria that drove the changes in metabolic pathways after surgical intervention of Chilean obese patients, FUNDING an important observation considering that gut microbiota of This work was supported by CONICYT-Chile through healthy Chilean subjects has high abundance of Verrucomicrobia the FONDECYT [n◦ 3160525] (DM) and by Universidad bacteria, including A. muciniphila (Fujio-Vejar et al., 2017). San Sebastián, and by grants to AM from the Spanish Further studies to understand the underlying mechanisms Ministry of Economy and Competitiveness (projects involving A. muciniphila as a target of bacto-therapy to treat SAF2012-31187, SAF2013-49788-EXP, SAF2015-65878-R), obesity are required. Carlos III Health Institute (Projects PIE14/00045 Our results, together with previously published studies, and AC15/00022), Generalitat Valenciana (Project highlight the need to consider region-specific analysis PrometeoII/2014/065 and Prometeo/2018/A/133), Asociación of the gut microbiota in order to fully understand the Española Contra el Cancer (Project AECC 2017-1485) bacterial basis for the development of such diseases as and co-financed by the European Regional Development obesity and the response to surgical and non-surgical Fund (ERDF). treatment, opening to the possibility that probiotic development to treat different kinds of dysbiosis should be country-specific. ACKNOWLEDGMENTS We thank Daniel Garrido from Pontificia Universidad Católica CONCLUSION de Chile for kindly providing the DNA samples belonging to a previous study (Medina et al., 2017) to be used here for shotgun This study identified significant differences in the human gut DNA sequencing. We also thank the Departamento de Ingeniería microbiota of obese patients from around the world, and found Química y Bioprocesos of the Pontificia Universidad Católica de that functional dissimilarities were mediated by differences Chile for their contribution. in taxonomic microbiota composition, which were region- specific, rather than alterations in metabolic pathways. This indicates the presence of functional metabolic redundancy SUPPLEMENTARY MATERIAL between the microbiota of obese patients despite the bacterial differences and geographic origin. Furthermore, functional The Supplementary Material for this article can be found changes in gut microbiota following bariatric surgery were online at: https://www.frontiersin.org/articles/10.3389/fmicb. observed to be specific to the type of treatment received, 2019.02346/full#supplementary-material Frontiers in Microbiology | www.frontiersin.org 11 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota REFERENCES Furet, J., Kong, L., Tap, J., Poitou, C., Basdevant, A., Bouillot, J. L., et al. (2010). Differential adaptation of human gut microbiota to bariatric surgery-induced Abubucker, S., Segata, N., Goll, J., Schubert, A. M., Izard, J., Cantarel, B. L., et al. weight loss. Diabetes Metab. Res. Rev. 59, 3049–3057. doi: 10.2337/db10-0253 (2012). Metabolic reconstruction for metagenomic data and its application to Goodrich, J. K., Waters, J. L., Poole, A. C., Sutter, J. L., Koren, O., Blekhman, R., the human microbiome. PLoS Comput. Biol. 8:e1002358. doi: 10.1371/journal. et al. (2014). Human genetics shape the gut microbiome. Cell 159, 789–799. pcbi.1002358 doi: 10.1016/j.cell.2014.09.053 Angelakis, E., Armougom, F., Million, M., and Raoult, D. (2012). The relationship Graessler, J., Qin, Y., Zhong, H., Zhang, J., Licinio, J., Wong, M. L., et al. between gut microbiota and weight gain in humans. Future Microbiol. 7, (2013). Metagenomic sequencing of the human gut microbiome before and 91–109. doi: 10.2217/fmb.11.142 after bariatric surgery in obese patients with type 2 diabetes: correlation with Angrisani, L., Santonicola, A., Iovino, P., Vitiello, A., Zundel, N., Buchwald, H., inflammatory and metabolic parameters. Pharmacogenomics J. 13, 514–522. et al. (2017). Bariatric surgery and endoluminal procedures: IFSO worldwide doi: 10.1038/tpj.2012.43 survey 2014. Obes. Surg. 27, 2279–2289. doi: 10.1007/s11695-017-2666-x Gumbs, A. A., Gagner, M., Dakin, G., and Pomp, A. (2007). Sleeve gastrectomy for ASMBS (2018). American Society for Metabolic and Bariatric Surgery: Estimate morbid obesity. Obes. Surg. 17, 962–969. doi: 10.1007/s11695-007-9151-x of Bariatric Surgery Numbers, 2011–2016. Available at: https://asmbs.org/ Guo, Y., Zhao, S., Su, P. F., Li, C. I., Ye, F., Flynn, C. R., et al. (2014). Statistical resources/estimate-of-bariatric-surgery-numbers. strategies for microRNAseq batch effect reduction. Transl. Cancer Res. 3, Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible 260–265. doi: 10.3978/j.issn.2218-676X.2014.06.05 trimmer for illumina sequence data. Bioinformatics 30, 2114–2120. doi: 10.1093/ Gupta, V. K., Paul, S., and Dutta, C. (2017). Geography, ethnicity or subsistence- bioinformatics/btu170 specific variations in human microbiome composition and diversity. Front. Bolstad, B. (2019). Preprocesscore: A Collection of Pre-Processing Functions. Microbiol. 8:1162. doi: 10.3389/fmicb.2017.01162 Avalaible at: https://github.com/bmbolstad/preprocessCore. Hall, A. B., Tolonen, A. C., and Xavier, R. J. (2017). Human genetic variation and Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., the gut microbiome in disease. Nat. Rev. Genet. 18, 690–699. doi: 10.1038/nrg. Costello, E. K., et al. (2010). QIIME allows analysis of high-throughput 2017.63 community sequencing data. Nat. Publ. Gr. 7, 335–336. doi: 10.1038/nmeth.f. Haro, C., Rangel-Zúñiga, O. A., Alcalá-Díaz, J. F., Gómez-Delgado, F., Pérez- 303 Martínez, P., Delgado-Lista, J., et al. (2016). Intestinal microbiota is influenced Carding, S., Verbeke, K., Vipond, D. T., Corfe, B. M., and Owen, L. J. (2015). by gender and body mass index. PLoS One 2:11. doi: 10.1371/journal.pone. Dysbiosis of the gut microbiota in disease. Microb. Ecol. Health Dis. 26:26191. 0154090 doi: 10.3402/mehd.v26.26191 Huttenhower, C., Gevers, D., Knight, R., Abubucker, S., Badger, J. H., Chinwalla, Chilton, S. N. (2014). The effects of diet and the microbiome on reproduction and A. T., et al. (2012). Structure, function and diversity of the healthy human longevity: a comparative review across 5 continents. J. Nutr. Food Sci. 5:364. microbiome. Nature 486, 207–214. doi: 10.1038/nature11234 doi: 10.4172/2155-9600.1000364 Ilhan, Z. E., Dibaise, J. K., Isern, N. G., Hoyt, D. W., Marcus, A. K., Kang, D. W., Comeau, A. M., Douglas, G. M., and Langille, M. G. (2017). Microbiome helper: a et al. (2017). Distinctive microbiomes and metabolites linked with weight loss custom and streamlined workflow for microbiome. mSystems 3:2. doi: 10.1128/ after gastric bypass, but not gastric banding. ISME J. 11, 2047–2058. doi: 10. mSystems.00127-16 1038/ismej.2017.71 Damms-Machado, A., Mitra, S., Schollenberger, A. E., Kramer, K. M., Meile, T., Jones, R. B., Zhu, X., Moan, E., Murff, H. J., Ness, R. M., Seidner, D. L., et al. Königsrainer, A., et al. (2015). Effects of surgical and dietary weight loss therapy (2018). Inter-niche and inter-individual variation in gut microbial community for obesity on gut microbiota composition and nutrient absorption. Biomed Res. assessment using stool, rectal swab, and mucosal samples. Sci. Rep. 8:4139. Int. 2015:806248. doi: 10.1155/2015/806248 doi: 10.1038/s41598-018-22408-4 Dao, M. C., Everard, A., Aron-Wisnewsky, J., Sokolovska, N., Prifti, E., Verger, Karlsson, F., Tremaroli, V., Nielsen, J., and Backhed, F. (2013). Assessing the E. O., et al. (2016). Akkermansia muciniphila and improved metabolic health human gut microbiota in metabolic diseases. Diabetes Metab. Res. Rev. 62, during a dietary intervention in obesity: relationship with gut microbiome 3341–3349. doi: 10.2337/db13-0844 richness and ecology. Gut 65, 426–436. doi: 10.1136/gutjnl-2014-308778 Kasai, C., Sugimoto, K., Moritani, I., Tanaka, J., Oya, Y., Inoue, H., et al. Derrien, M., Vaughan, E. E., Plugge, C. M., and de Vos, W. M. (2004). Akkermansia (2015). Comparison of the gut microbiota composition between obese and municiphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. non-obese individuals in a Japanese population, as analyzed by terminal Int. J. Syst. Evol. Microbiol. 54(Pt 5), 1469–1476. doi: 10.1099/ijs.0.02873-0 restriction fragment length polymorphism and next-generation sequencing. DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., BMC Gastroenterol. 15:100. doi: 10.1186/s12876-015-0330-2 et al. (2006). Greengenes, a chimera-checked 16S rRNA gene database and Khan, M. J., Gerasimidis, K., Edwards, C. A., and Shaikh, M. G. (2016). Role of gut workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072. microbiota in the aetiology of obesity: proposed mechanisms and review of the doi: 10.1128/AEM.03006-5 literature. J. Obes. 2016, 1–27. doi: 10.1155/2016/7353642 Eldar, S., Heneghan, H. M., Brethauer, S. A., and Schauer, P. R. (2011). Bariatric Kolde, R., Franzosa, E. A., Rahnavard, G., Hall, A. B., Vlamakis, H., Stevens, C., surgery for treatment of obesity. Int. J. Obes. 35(Suppl. 3), S16–S21. doi: 10. et al. (2018). Host genetic variation and its microbiome interactions within the 1038/ijo.2011.142 human microbiome project. Genome Med. 10:6. doi: 10.1186/s13073-018-0515- Everard, A., Belzer, C., Geurts, L., Ouwerkerk, J. P., Druart, C., Bindels, L. B., et al. 8 (2013). Cross-talk between Akkermansia muciniphila and intestinal epithelium Kong, L. C., Tap, J., Aron-Wisnewsky, J., Pelloux, V., Basdevant, A., Bouillot, J. L., controls diet-induced obesity. Proc. Natl. Acad. Sci. U.S.A. 110, 9066–9071. et al. (2013). Gut microbiota after gastric bypass in human obesity: increased doi: 10.1073/pnas.1219451110 richness and associations of bacterial genera with adipose tissue genes. Am. Soc. Fava, F., Gitau, R., Griffin, B. A., Gibson, G. R., Tuohy, K. M., and Lovegrove, Nutr. 98, 16–24. doi: 10.3945/ajcn.113.058743 J. A. (2013). The type and quantity of dietary fat and carbohydrate alter faecal Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, microbiome and short-chain fatty acid excretion in a metabolic syndrome J. A., et al. (2013). Predictive functional profiling of microbial communities “at-risk” population. Int. J. Obes. 37, 216–223. doi: 10.1038/ijo.2012.33 using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821. doi: Fei, T., Zhang, T., Shi, W., and Yu, T. (2018). Mitigating the adverse impact of 10.1038/nbt.2676 batch effects in sample pattern detection. Bioinformatics 34, 2634–2641. doi: Langmead, B., and Salzberg, S. L. (2012). Fast gapped-read alignment with bowtie 10.1093/bioinformatics/bty117 2. Nat. Methods 9:357. doi: 10.1038/nmeth.1923 First, Y., and Job, P. (2011). GNU parallel: the command-line power tool. USENIX Le Chatelier, E., Nielsen, T., Qin, J., Prifti, E., Hildebrand, F., Falony, G., et al. Mag. 3, 42–47. doi: 10.5281/zenodo.16303 (2013). Richness of human gut microbiome correlates with metabolic markers. Fujio-Vejar, S., Vasquez, Y., Morales, P., Magne, F., Vera-Wolf, P., Ugalde, J. A., Nature 500, 541–546. doi: 10.1038/nature12506 et al. (2017). The gut microbiota of healthy chilean subjects reveals a high Ley, R. E., Turnbaugh, P. J., Klein, S., and Gordon, J. I. (2006). Microbial ecology: abundance of the phylum verrucomicrobia. Front. Microbiol. 8:1221. doi: 10. human gut microbes associated with obesity. Nature 444, 1022–1023. doi: 10. 3389/fmicb.2017.01221 1038/nature4441021a Frontiers in Microbiology | www.frontiersin.org 12 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota Li, J. V., Ashrafian, H., Bueter, M., Kinross, J., Sands, C., and le Roux, C. W. Schneeberger, M., Everard, A., Gómez-Valadés, A. G., Matamoros, S., Ramírez, S., (2011). Metabolic surgery profoundly influences gut microbial-host metabolic Delzenne, N. M., et al. (2015). Akkermansia muciniphila inversely correlates cross-talk. Gut 60, 1214–1223. doi: 10.1136/gut.2010.234708 with the onset of inflammation, altered adipose tissue metabolism and Louis, S., Tappu, R. M., Damms-Machado, A., Huson, D. H., and Bischoff, metabolic disorders during obesity in mice. Sci. Rep. 5:16643. doi: 10.1038/ S. C. (2016). Characterization of the gut microbial community of obese srep16643 patients following a weight-loss intervention using whole metagenome shotgun Schwalb, B., Tresch, A., Torkler, P., Duemcke, S., Demel, C., Ripley, B., et al. (2011). sequencing. PLoS One 11:1–18. doi: 10.1371/journal.pone.0149564 LSD: Lots of Superior Depictions. Available at: http://cran.r-project.org/web/ Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K., and Knight, R. packages/LSD/index.html (2012). Diversity, stability and resilience of the human gut microbiota. Nature Seck, E. H., Senghor, B., Merhej, V., Bachar, D., Cadoret, F., Robert, C., et al. 489, 220–230. doi: 10.1038/nature11550 (2018). Salt in stools is associated with obesity, gut halophilic microbiota and McDonald, D., Price, M. N., Goodrich, J., Nawrocki, E. P., DeSantis, T. Z., Probst, Akkermansia muciniphila depletion in humans. Int. J. Obes. 43, 862–871. doi: A., et al. (2012). An improved Greengenes taxonomy with explicit ranks 10.1038/s41366-018-0201-3 for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., 610–618. doi: 10.1038/ismej.2011.139 et al. (2011). Metagenomic biomarker discovery and explanation. Genome Biol. McIver, L. J., Abu-Ali, G., Franzosa, E. A., Schwager, R., Morgan, X. C., Waldron, 12:R60. doi: 10.1186/gb-2011-12-6-r60 L. et al. (2018). bioBakery: a meta’omic analysis environment. Bioinformatics 34, Shao, Y., Ding, R., Xu, B., Hua, R., Shen, Q., He, K., et al. (2016). Alterations 1235–1237. doi: 10.1093/bioinformatics/btx754 of gut microbiota after roux-en-Y gastric bypass and sleeve gastrectomy Medina, D. A., Pedreros, J. P., Turiel, D., Quezada, N., Pimentel, F., Escalona, A., in sprague-dawley rats. Obes. Surg. 27, 295–302. doi: 10.1007/s11695-016- et al. (2017). Distinct patterns in the gut microbiota after surgical or medical 2297-7 therapy in obese patients. PeerJ 5:e3443. doi: 10.7717/peerj.3443 Sheng, L., Jena, P. K., Liu, H.-X., Hu, Y., Nagar, N., Bronner, D. N., et al. (2018). Million, M., Maraninch, M., Henry, M., Armougom, F., Richet, H., Carrieri, P., Obesity treatment by epigallocatechin-3-gallate–regulated bile acid signaling et al. (2012). Obesity-associated gut microbiota is enriched in Lactobacillus and its enriched Akkermansia muciniphila. FASEB J. doi: 10.1096/fj.201800370R reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter [Epub ahead of print] smithii. Int. J. Obes. 36, 817–825. doi: 10.1038/ijo.2011.153 Shin, J., Noh, J.-R., Chang, D.-H., Kim, Y.-H., Kim, M. H., Lee, E. S., Moya, A., and Ferrer, M. (2016). Functional redundancy-induced stability of gut et al. (2019). Elucidation of akkermansia muciniphila probiotic traits driven microbiota subjected to disturbance. Trends Microbiol. 24, 402–413. doi: 10. by mucin depletion. Front. Microbiol. 10:1137. doi: 10.3389/fmicb.2019. 1016/j.tim.2016.02.002 01137 Murphy, R., Tsai, P., Jüllig, M., Liu, A., Plank, L., and Booth, M. (2017). Differential Sjöström, L. (2008). Bariatric surgery and reduction in morbidity and mortality: changes in gut microbiota after gastric bypass and sleeve gastrectomy bariatric experiences from the SOS study. Int. J. Obes. 32(Suppl. 7), S93–S97. doi: 10. surgery vary according to diabetes remission. Obes. Surg. 27, 917–925. doi: 1038/ijo.2008.244 10.1007/s11695-016-2399-2 Smoot, T. M., Xu, P., Hilsenrath, P., Kuppersmith, N. C., and Singh, K. P. (2006). Navas-Molina, J. A., Peralta-Sanchez, J. M., Gonza, Lez, A., McMurdie, P. J., Gastric bypass surgery in the united states, 1998-2002. Am. J. Public Health 96, Vázquez-Baeza, Y., et al. (2013). Advancing our understanding of the human 1187–1189. doi: 10.2105/AJPH.2004.060129 microbiome using QIIME. Methods Enzymol. 531, 371–444. doi: 10.1016/B978- Stevens, G. A., Singh, G. M., Lu, Y., Danaei, G., Lin, J. K., Finucane, M. M., et al. 0-12-407863-5.00019-8 (2012). National, regional, and global trends in adult overweight and obesity NHLBI Expert Panel (1998). Clinical guidelines on the identification, evaluation, prevalences. Popul Health Metr. 10:22. doi: 10.1186/1478-7954-10-22 and treatment of overweight and obesity in adults - the evidence report. Obes. Sun, Z., Chai, H. S., Wu, Y., White, W. M., Donkena, K. V., Klein, C. J., et al. Res. 6(Suppl. 2), 51S–209S. (2011). Batch effect correction for genome-wide methylation data with illumina Nishijima, S., Suda, W., Oshima, K., Kim, S.-W., Hirose, Y., Morita, H., et al. (2016). infinium platform. BMC Med Genom. 16:84. doi: 10.1186/1755-8794-4-84 The gut microbiome of healthy Japanese and its microbial and functional Sze, M. A., and Schloss, P. D. (2016). Looking for a signal in the noise: revisiting uniqueness. DNA Res. 23, 125–133. doi: 10.1093/dnares/dsw002 obesity and the microbiome. MBio 23, 7. doi: 10.1128/mBio.01018-6 Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Thomson, P., Santibañez, R., Aguirre, C., Galgani, J., and Garrido, D. (2019). et al. (2018). Vegan: Community Ecology Package. Available at: https://cran.r- Short-term impact of sucralose consumption on the metabolic response project.org/package=vegan. and gut microbiome of healthy adults. Br. J. Nutr. 1, 1–23. doi: 10.1017/ Palleja, A., Kashani, A., Allin, K. H., Nielsen, T., Zhang, C., Li, Y., et al. (2016). S0007114519001570 Roux-en-Y gastric bypass surgery of morbidly obese patients induces swift Tice, J. A., Karliner, L., Walsh, J., Petersen, A. J., and Feldman, M. D. (2008). and persistent changes of the individual gut microbiota. Genome Med. 8, 67. Gastric banding or bypass? a systematic review comparing the two most popular doi: 10.1186/s13073-016-0312-1 bariatric procedures. Am. J. Med. 121, 885–893. doi: 10.1016/j.amjmed.2008.05. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., et al. (2010). 036 A human gut microbial gene catalogue established by metagenomic sequencing. Tran, D. D., Nwokeabia, I. D., Purnell, S., Zafar, S. N., Ortega, G., Hughes, K., Nature 464, 59–65. doi: 10.1038/nature08821 et al. (2016). Revision of roux-en-y gastric bypass for weight regain: a systematic Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., et al. (2012). A metagenome- review of techniques and outcomes. Obes. Surg. 26, 1627–1634. doi: 10.1007/ wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60. s11695-016-2201-5 doi: 10.1038/nature11450 Tran, H. Q., Mills, R. H., Peters, N. V., Holder, M. K., de Vries, G. J., Knight, R Core Team (2013). R Core Team. R A Lang. Environ. Stat. Comput. R Found. Stat. R. et al. (2019). Associations of the fecal microbial proteome composition Comput. Vienna: R Core Team. and proneness to diet-induced obesity. Mol. Cell. Proteomics. 18, 1864–1879. Rognes, T., Flouri, T., Nichols, B., Quince, C., and Mahé, F. (2016). VSEARCH: a doi: 10.1074/mcp.ra119.001623 versatile open source tool for metagenomics. PeerJ 4:e2584. doi: 10.7717/peerj. Tremaroli, V., and Backhed, F. (2012). Functional interactions between the 2584 gut microbiota and host metabolism. Nature 489, 242–249. doi: 10.1038/ Rojo, D., Méndez-García, C., Raczkowska, B. A., Bargiela, R., Moya, A., Ferrer, nature11552 M., et al. (2017). Exploring the human microbiome from multiple perspectives: Tremaroli, V., Karlsson, F., Werling, M., Ståhlman, M., Kovatcheva-Datchary, factors altering its composition and function. FEMS Microbiol. Rev. 41, 453– P., Olbers, T., et al. (2015). Roux-en-Y gastric bypass and vertical banded 478. doi: 10.1093/femsre/fuw046 gastroplasty induce long-term changes on the human gut microbiome Rothschild, D., Weissbrod, O., Barkan, E., Kurilshikov, A., Korem, T., Zeevi, D., contributing to fat mass regulation. Cell Metab. 22, 228–238. doi: 10.1016/j. et al. (2018). Environment dominates over host genetics in shaping human gut cmet.2015.07.009 microbiota. Nature 555, 210–215. doi: 10.1038/nature25973 Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., Schloss, P. D. (2016). Looking for a signal in the noise: revisiting obesity and the. 7, et al. (2015). MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. 1–9. doi: 10.1128/mBio.01018-16.Editor Methods 12, 902–903. doi: 10.1038/nmeth.3589 Frontiers in Microbiology | www.frontiersin.org 13 October 2019 | Volume 10 | Article 2346 Medina et al. Cross-Regional View of Obesity Gut Microbiota Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B. L., Duncan, A., Ley, Zhang, H., DiBaise, J. K., Zuccolo, A., Kudrna, D., Braidotti, M., Yeisoo, Y., et al. R. E., et al. (2009). A core gut microbiome in obese and lean twins. Nature 457, (2009). Human gut microbiota in obesity and after gastric bypass. Proc. Natl. 480–484. doi: 10.1038/nature07540 Acad. Sci. U. S. A. 106, 2365–2370. doi: 10.1073/pnas.0812600106 Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., and Zhang, J., Kobert, K., Flouri, T., and Stamatakis, A. (2014). PEAR: a fast and Gordon, J. I. (2006). An obesity-associated gut microbiome with increased accurate illumina paired-end reAd mergeR. Bioinformatics 30, 614–620. doi: capacity for energy harvest. Nature 444, 1027–1131. doi: 10.1038/nature05414 10.1093/bioinformatics/btt593 Walters, W. A., Xu, Z., and Knight, R. (2014). Meta-analyses of human gut Zhernakova, A., Kurilshikov, A., Bonder, M. J., Tigchelaar, E. F., Schirmer, M., microbes associated with obesity and IBD. FEBS Lett. 588, 4223–4233. doi: Vatanen, T., et al. (2016). Population-based metagenomics analysis reveals 10.1016/j.febslet.2014.09.039 markers for gut microbiome composition and diversity. Science 352, 565–569. Whitley, E., and Ball, J. (2002). Statistics review 6: nonparametric methods. Crit. doi: 10.1126/science.aad3369 Care 6, 509–513. doi: 10.1186/cc1820 Wu, G. D., Chen, J., Hoffmann, C., Bittinger, K., Chen, Y. Y., Keilbaugh, Conflict of Interest: The authors declare that the research was conducted in the S. A., et al. (2011). Linking long-term dietary patterns with gut absence of any commercial or financial relationships that could be construed as a microbial enterotypes. Science 7 334, 105–108. doi: 10.1126/science.120 potential conflict of interest. 8344 Yasir, M., Angelakis, E., Bibi, F., Azhar, E. I., Bachar, D., Lagier, J. C., et al. (2015). Copyright © 2019 Medina, Li, Thomson, Artacho, Pérez-Brocal and Moya. This is an Comparison of the gut microbiota of people in france and saudi arabia. Nutr open-access article distributed under the terms of the Creative Commons Attribution Diabetes 27:e153. doi: 10.1038/nutd.2015.3 License (CC BY). The use, distribution or reproduction in other forums is permitted, Yatsunenko, T., Rey, F. E., Manary, M. J., Trehan, I., Dominguez-Bello, provided the original author(s) and the copyright owner(s) are credited and that the M. G., Contreras, M., et al. (2012). Human gut microbiome viewed original publication in this journal is cited, in accordance with accepted academic across age and geography. Nature 486, 222–227. doi: 10.1038/nature practice. 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