Microbial Diversity and Ecological Structure of the Gut Microbiota in Children and Adults from an Indigenous Mexican Community

Microbial Diversity and Ecological Structure of the Gut Microbiota in Children and Adults from an Indigenous Mexican Community | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Microbial Diversity and Ecological Structure of the Gut Microbiota in Children and Adults from an Indigenous Mexican Community View ORCID Profile Sebastián Villada-Bedoya , Luisa I. Falcón , Osiris Gaona , Aída Elizondo García , View ORCID Profile Isaac G-Santoyo doi: https://doi.org/10.1101/2025.08.03.668344 Sebastián Villada-Bedoya 1 Neuroecology Lab, Facultad de Psicología , UNAM, Mexico City 04510, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sebastián Villada-Bedoya Luisa I. Falcón 2 Laboratorio de Ecología Bacteriana, Instituto de Ecología , UNAM, Mexico City 04510, Mexico 3 Instituto de Ecología, Campus Yucatán, Parque Científico y Tecnológico de Yucatán , Mérida 97302, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Osiris Gaona 2 Laboratorio de Ecología Bacteriana, Instituto de Ecología , UNAM, Mexico City 04510, Mexico 3 Instituto de Ecología, Campus Yucatán, Parque Científico y Tecnológico de Yucatán , Mérida 97302, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aída Elizondo García 1 Neuroecology Lab, Facultad de Psicología , UNAM, Mexico City 04510, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isaac G-Santoyo 1 Neuroecology Lab, Facultad de Psicología , UNAM, Mexico City 04510, Mexico Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isaac G-Santoyo For correspondence: isantoyo{at}psicologia.unam.mx Abstract Full Text Info/History Metrics Preview PDF Abstract Gut microbiota composition is shaped by environmental factors such as lifestyle, diet, socioeconomic and cultural context, as well as host-related factors like age and sex. Although microbiota research has expanded rapidly in recent years, most current paradigms are based on data from individuals in high-income, industrialized countries, particularly in Europe and North America. As a result, microbiota profiles from non-Westernized populations—especially those from Indigenous communities—remain underrepresented in global studies, limiting our understanding of the full ecological and evolutionary diversity of human gut microbial communities. In this study, we characterized the bacterial gut microbiota of Me’phaa individuals, a pre-Columbian Indigenous group from the south-central region of México. This rural community maintains a subsistence lifestyle centered on high-fiber, plant-based diets and limited exposure to industrialized foods. Using high-throughput 16S rRNA gene sequencing, we analyzed microbial profiles from 62 individuals aged 5 to 60 years, examining differences across age and sex to explore patterns of diversity and community structure. Despite sharing similar life conditions, noticeable differences in gut microbiota were observed; alpha and beta diversities revealed significant variation across age and sex. Children exhibited greater microbial richness and inter-individual variability, consistent with dynamic colonization processes that begin in early life. In contrast, adults showed more taxonomically uniform communities, with a higher abundance of taxa such as Prevotellaceae, Succinivibrio, and Dialister, which are associated with fiber fermentation and complex carbohydrate metabolism. Additionally, sex-related differences were evident, with adult males displaying the highest relative abundance of Prevotellaceae. Finally, the presence of taxonomic groups rarely observed in urban Western populations suggests that indigenous lifestyles may preserve microbial diversity with potential metabolic relevance. Our findings advance the ecological and evolutionary understanding of human gut microbiota and highlight the importance of integrating non-Westernized populations into microbiome research to better elucidate host-microbe coadaptation and its implications for health. Introduction The human body harbors a vast number of microorganisms, most of which reside in the gut and collectively are referred to as the gut microbiota (GM) ( 1 ). Among these microorganisms, bacteria represents the most diverse group, with ∼3180 genera described to date ( 2 ). The GM plays a crucial role in regulating key host biological host processes ( 3 ) that impact its health ( 4 – 6 ), including immunity, nutrient metabolism, and neuronal functioning ( 7 – 10 ). The host-microbiota relationship is essentially symbiotic: the host provides nutrients and ecological niches, while microorganisms contribute metabolic, protective, and immunomodulatory functions that the host cannot perform alone ( 11 , 12 ). Hence, alterations in this host-symbiont relationships have been observed in several human pathologies such as obesity, type II diabetes mellitus, bowel irritable bowel syndrome, cardiometabolic disorders, among others ( 13 ). Lifestyle practices play a critical role in shaping the structural ecology of the gut microbiota ( 14 , 15 ). Key factors include mode of delivery (cesarean vs. vaginal birth), infant feeding practices, access to allopathic medicine, and long-term dietary habits ( 10 ). Another key determinant is the host’s age. It has been reported that the rapid bacterial diversification observed in early childhood slows between the first and fifth years of life ( 16 ). Between the ages of seven and twelve, the gut microbiota becomes progressively more similar to that of adults ( 17 – 19 ). However, some evidence suggests that microbial communities may still retain taxonomic and functional distinctions during this stage ( 20 – 22 ). Despite the rapid growth and dynamism of microbiome research, most current paradigms regarding lifestyle-associated variation are based on data from populations residing in high-income countries in Europe and North America, grouped under what is commonly referred to as “Westernized lifestyles.” Limited efforts have been made to explore gut microbial ecology in populations whose lifestyles fall outside this category, including hunter-gatherers such as the Hadza of Tanzania ( 23 , 24 ), subsistence farmers in Bassa, Nigeria ( 25 ), indigenous agriculturalists in Bangladesh ( 3 ), farmers in Papua New Guinea ( 26 ), the agropastoral Wiwa community in Colombia ( 27 ), Amerindian groups in South America ( 28 , 29 ), and the Inuit from the Canadian Arctic ( 10 ). From these studies, it is now understood that such populations harbor a GM with greater phylogenetic diversity compared to Westernized groups ( 26 , 28 – 30 ). Additionally, these populations—regardless of continent—are enriched in a recurrent set of bacterial groups collectively known as VANISH taxa (Volatile or Associated Negatively with Industrialized Societies of Humans), including the families Prevotellaceae, Spirochaetaceae, and Succinivibrionaceae. These taxa are specialized in degrading complex plant-derived, fiber-rich carbohydrate due to their high abundance of carbohydrate-active enzymes (CAZymes) ( 14 , 24 ). In contrast, they show minimal or no presence of BIoSSUM taxa (Bloom or Selected in Urbanization/Modernization Societies), which include members of the Bacteroidaceae, Enterobacteriaceae, and Verrucomicrobiaceae families ( 14 , 30 ). Despite this progress, a large gap remains in our understanding of the gut microbial ecology of other world regions. For instance, aside from the studies in Venezuelan Amazonian Amerindian groups ( 29 ), very little is known about non-Westernized populations in Latin America’s Global South—particularly those representing pre-Hispanic indigenous communities. Currently, 826 indigenous groups are officially recognized across Latin America ( 31 ), encompassing between 42 and 50 million individuals ( 32 ) dispersed over approximately 20.1 million km² of territory. Due to a long history of colonialism, indigenous groups from Latin America’s Global South exhibits a wide spectrum of lifestyle practices, ranging from Westernized behaviors to highly pre-Columbian modes of living reflecting a profound ethnocultural mosaic, with populations at the farthest extremes of what is practiced in industrialized societies. In Mexico alone, at least 78 indigenous groups are recognized ( 31 ), whose lifestyles span this entire spectrum—from highly Westernized to minimally Westernized. One example is the Me’phaa people of the Montaña Alta region in Guerrero, an indigenous group residing in isolated communities of 50–80 families (each composed of five to ten members) ( 33 , 34 ). Their subsistence economy is based on the cultivation of maize and legumes (e.g., beans and lentils), collection of wild edible plants, and occasional fruit and vegetable farming. Animal protein is sourced through hunting and poultry farming, usually reserved for special occasions ( 33 ). All food is locally grown and harvested nearby ( 35 ). These communities lack sanitation infrastructure and potable water, relying on small wells, and have minimal access to allopathic medicine; vaginal births account for 98% of deliveries, and breastfeeding continues until at least two years of age. Residents speak only their native language, face extremely low income levels, and experience high infant and adult morbidity and mortality rates due to their geographic and social isolation ( 36 ). Thus, the aim of this study was to characterize the population structure, diversity, and taxonomic composition of the bacterial gut microbiota of a Me’phaa indigenous community in south-central Mexico, and to identify potential associations with age and sex. To this end, we addressed the following research questions: i) How do taxonomic assignments and diversity indices differ between age and sex groups within the Me’phaa community? and ii) Can age or sex predict higher or lower abundances of VANISH and BIoSSUM taxa in this non-Westernized population? We hypothesized that total microbial diversity would be lower in children compared to adults. Additionally, we anticipated a high prevalence of VANISH taxa—linked to fiber-rich diets—and low or absent representation of BIoSSUM taxa typically found in Westernized microbiomes. The study of this population offers an opportunity to understand how sociocultural practices shape gut microbial ecology in non-Westernized Latin American populations. Moreover, they allow us to compare findings not only with the few existing studies of other non-Westernized groups around the world but also with Westernized populations whose lifestyles may overlap in specific dimensions (e.g., fiber-rich diets yet high exposure to modern medicine). Ultimately, these insights expand our understanding of global health by offering a less biased view of human microbiome variation. Materials and methods Data collection The present study was conducted in two Me’phaa communities, Plan de Gatica (17° 7’44.364“ N, 99°7’22.695” W; 510 m a.s.l.) and El Naranjo (17°9’54.0036” N, 98°57’50.9832” W; 860 m a.s.l.) in the state of Guerrero, Mexico. The collection of biological material were conducted from September 7 to 29, 2017 and consisted of human fecal samples from children and adults belonging to Both indigenous communities are located approximately 30 km apart and share similar socioeconomic and cultural patterns ( 35 ). The diet in these communities is low in fat and animal protein and is characterized by the consumption of plant-derived carbohydrates from a fiber- and cereal-rich diet ( 14 , 37 ). The inclusion and exclusion criteria for this study were as follows: i) children aged 5–12 and adults aged 18–46, ii) born in the Plan de Gatica and Naranjo community and having lived there all their lives, iii) not having taken any type of allopathic medicine in the last 6 months or any type of antibiotic in the last 2 years, iv) in children, all born by natural childbirth and having been breastfed during early childhood and at least until the first 2 years of life, and v) not reporting any type of gastrointestinal or respiratory condition two months prior to sample collection. Fecal samples were obtained from children (aged between 5 and 10 years) and adults (aged between 19 and 76 years) in the Me’phaa community. A total of 62 fecal samples were collected, comprising 33 adults (15 males and 18 females) and 29 children (13 males and 16 females). Although the small sample size may be a limitation, other authors have worked with similar sample sizes and obtained robust results ( 25 , 38 ). Each participant was provided with a kit containing a sterile plastic container and a wooden tongue depressor to facilitate sample collection. Each container was labeled with a unique code assigned to each participant. Once the samples were returned by participants, they were processed: three aliquots of approximately 100 µL of fecal material were prepared using sterile pipette tips and transferred into sterile 1.5 mL microtubes. All fecal samples were frozen in liquid nitrogen and stored at -20 °C until DNA extraction. In addition to fecal samples, the study included anthropometric measurements (i.e., height, weight, BMI) for all participants, as well as a questionnaire on the nutritional status of both children and adults. Furthermore, information was collected regarding pregnancy, delivery, and breastfeeding duration. For the Me’phaa community, assistance from a translator was required, as this community does not speak Spanish. Fecal DNA extraction DNA was extracted from each of the 62 fecal samples using the DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA, USA), following the manufacturer’s instructions. Each sample (∼100 µL of fecal DNA) was resuspended in 30 µL of molecular-grade water and stored at -20 °C until PCR amplification. 16S rRNA gene amplification and sequencing Using the previously extracted DNA, the hypervariable V4 region of the 16S rRNA gene was amplified with universal bacterial/archaeal primers 515F/806R ( 14 , 39 ), following the procedures described by Caporaso et al. (2012) and Sánchez-Quinto et al. (2020) ( 38 , 40 ). PCR reactions were performed in triplicate for each sample, in a final volume of 25 µL, prepared as follows (reagents mixed in this order): 1) 15.67 µL of molecular-grade nuclease-free water, 2) 2.5 µL of Takara ExTaq 10× PCR buffer, 3) 0.7 µL of bovine serum albumin (BSA, 20 mg mL⁻¹), 4) 2 µL of Takara dNTP mix (2.5 mM), 5) 1 µL of 515F primer (10 µM), 6) 0.125 µL of Takara ExTaq DNA polymerase (5 U µL⁻¹) (TaKaRa, Shiga, Japan), 7) 2 µL of DNA template, and 8) 1 µL of 806R primer. The PCR protocol included an initial denaturation step at 95 °C for 3 minutes, followed by 35 cycles of 95 °C for 30 seconds, 52 °C for 40 seconds, and 72 °C for 90 seconds, with a final extension at 72 °C for 12 minutes. Once amplification was complete for all samples, triplicate reactions were pooled and purified using SPRI magnetic beads with the Agencourt AMPure XP PCR purification system (Beckman Coulter, Brea, CA, USA). The purified DNA was quantified using a Qubit 2.0 fluorometer to obtain approximately 20 ng per sample. The purified 16S rRNA amplicons (∼20 ng) were sequenced on an Illumina MiSeq platform at the Yale Center for Genome Analysis (New Haven, CT, USA), generating paired-end reads of approximately 250 bp. All sequences obtained were uploaded to the NCBI database under BioProject accession number PRJNA593240. Analysis of the sequence data Paired-end 2 × 250 reads were processed in QIIME2 and denoised using the DADA2 plugin to resolve amplicon sequence variants (ASVs) ( 41 ). Forward and reverse reads were truncated at 200 bp, and chimeric sequences were removed using the “consensus” method. Taxonomic assignment of representative ASV sequences was performed using the “classify consensus-vsearch” plugin ( 42 ), using the SILVA 132 database as a reference ( 43 ). Sequence alignment was performed using the MAFFT algorithm ( 44 ). After masking conserved positions and filtering gaps, a phylogeny was constructed with the FastTree algorithm ( 45 ). The abundance table and the phylogeny were exported into the R environment to perform the statistical analyses using phyloseq ( 46 ) and ggplot2 ( 47 ) packages. Plastic ASVs were filtered from the samples, which were then rarefied to a minimum sequencing of 21,000 reads per sample. A Venn diagram showing ASV distribution by age group was generated using the ‘ps_venn’ function from the MicEco package in R ( 48 ). Total diversity (α diversity) of ASVs was calculated using Faith’s phylogenetic diversity (PD) index, Shannon diversity index, Simpson’s dominance index, and the observed ASVs. Statistical analysis All statistical analyses were performed in R version 4.3.0 ( 49 ) using RStudio ( 50 ). For alpha (α) diversity analysis, observed richness (observed ASVs), Shannon index, inverse Simpson index, and Faith’s PD were calculated to assess overall richness and diversity of the sequenced samples across age groups and sexes. Statistical differences between these factors were determined using Type III analysis of variance tables (GLM; generalized linear models) with the ‘Anova’ function from the car package ( 51 ). Data were summarized and organized using the dplyr package ( 52 ), and figures were generated with ggplot2 ( 47 ). Beta (β) diversity analysis between age groups and sexes was estimated by calculating weighted UniFrac and Bray-Curtis distances. Statistical differences were assessed through permutational multivariate analysis of variance (PERMANOVA) using distance matrices. Additionally, a linear discriminant analysis (LDA) effect size (LEfSe) was performed. This algorithm identifies and explains high-dimensional biomarkers, detecting genomic features (genes, pathways, or taxa) that characterize differences between two or more biological conditions or classes. This analysis emphasizes statistical significance, biological consistency, and effect relevance, allowing identification of differentially abundant features that are also consistent with biologically meaningful categories ( 53 ). Moreover, differences in the abundance of VANISH families (Prevotellaceae, Spirochaetaceae, and Succinivibrionaceae) and BIoSSUM families (Bacteroidaceae, Enterobacteriaceae, and Verrucomicrobiaceae) ( 14 ) between age groups and sexes were explored using GLMs or zero-inflated regression models (ZAGA), as appropriate, implemented with the gamlss package ( 54 ). Ethics All procedures for testing and recruitment were approved (25 September 2017) by the National Autonomous University of Mexico Committee on Research Ethics (FPSI/CE/01/2016) and run in accordance with the ethical principles and guidelines of the Mexican Law (NOM-012-SSA3-2012). All participants read and signed a written informed consent. Additionally, we received signed approval from all participants (or their parents) who were photographed for the manuscript. Results Taxonomic comparison of the GM between indigenous Me’phaa children and adults This study generated a dataset of 62 fecal samples, corresponding to 29 children aged 5 to 10 years (16 girls and 13 boys) and 33 adults aged 19 to 76 years (18 females and 15 males) from the Me’phaa indigenous group (high mountain region of Guerrero). A total of 1,302,000 sequences were recovered after quality filtering and chimera removal. Firmicutes and Bacteroidota were the dominant phylum in both children and adults ( Fig 1a ). Within each age group, Firmicutes was the dominant phylum, showing the highest relative abundance (females = 68.86%, males = 52.15%, girls = 76.73%, and boys = 74.78%), followed by Bacteroidota (females = 22.10%, males = 40.46%, girls = 13.97%, and boys = 13.00%). A general pattern was observed in which children (both girls and boys) exhibited substantially lower relative abundances of Bacteroidota compared to adults. Download figure Open in new tab Fig 1. (a) Relative abundance of the gut microbiota and distribution of bacterial composition (16S rRNA V4) at the Phylum level in children and adults from the Me’phaa community. Phyla with relative abundances < 1% were grouped into the “< 1%” category. (b) Venn diagram of ASVs from the Me’phaa community. The phylum Proteobacteria showed considerable relative abundances in males (4.61%) and girls (5.22%). Conversely, the phylum Euryarchaetota displayed higher relative abundances in females (2.98%) and boys (3.01%), while Spirochaetota was more abundant in boys (2.99%) ( Fig 1a ). Some phyla were exclusively present in children (both boys and girls), including Chloroflexi (0.028%), Deinococcota (0.017%), Myxococcota (0.014%), Planctomycetota (0.013%), and Gemmatimonadota (0.008%). Moreover, the Venn diagram ( Fig 1b ) revealed that half (50.05%) of the total ASVs were shared between age groups. The ASVs specific to adults and children (25.28% and 24.67%, respectively) indicated that shared diversity was greater than the diversity specific to each age group ( Fig 1b ). This same pattern persisted when further discriminated by both age group and sex, where the shared diversity was substantially higher (ASVs = 50.86%) than the ASVs specific to each subgroup (females = 11.56%, males = 11.01%, girls = 10.31%, and boys = 10.26%). At the family level, the dominant families were Ruminococcaceae, Prevotellaceae, and Lachnospiraceae in both children and adults (S1 Fig). Within each age group, Ruminococcaceae was the dominant family, showing the highest relative abundance (females = 20.91%, males = 17.64%, girls = 21.25%, and boys = 22.05%). Notably, in females, Prevotellaceae exhibited the highest relative abundance (38.26%), whereas in the other age groups this family was the second most abundant (females = 18.57%, girls = 11.69%, and boys = 11.73%). Lachnospiraceae was the third most abundant family (females = 13.99%, males = 12.05%, girls = 12.54%, and boys = 13.60%). It was observed that the adult population (both females and males) had higher relative abundances of Prevotellaceae compared to children. The family Erysipelatoclostridiaceae showed considerable relative abundances in females (4.59%) and boys (5.66%). Conversely, the families Mycoplasmataceae, Streptococcaceae, and Enterobacteriaceae exhibited notable relative abundances in girls (9.15%, 5.20%, and 4.04%, respectively) (S1 Fig). Some families were exclusive to children (both boys and girls), including Woeseiaceae (0.173%), Xenococcaceae (0.476%), Hydrogenophilaceae (0.409%), Nitrosococcaceae (0.034%), and Desulfosarcinaceae (0.305%). Alpha and beta diversity of the GM in the indigenous Me’phaa populations When evaluating the observed ASVs in children and adults from the Me’phaa community, statistically significant differences were found for the interaction between age group × sex ( Table 1 ; Fig 2a ). Boys exhibited greater richness in their GM, with these differences primarily observed between boys and girls (t = 2.655, df = 54, P = 0.04) and between boys and females (t = 2.902, df = 54, P = 0.02). Download figure Open in new tab Fig 2. Alpha diversity indices of the gut microbiota in Me’phaa populations by age group and sex. (a) Observed OTUs; (b) Shannon index; (c) Simpson dominance index (inverse); (d) Faith’s phylogenetic diversity index. The line inside each box represents the median; boxes represent the first and third quartiles, and whiskers indicate the maximum and minimum values (excluding outliers). The black dot represents the mean. Each colored dot represents an individual/participant. Significant differences ( P < 0.05; see Table 1 ). In terms of the Shannon diversity index, statistically significant differences were found for age group and for the age group × sex interaction ( Table 1 ; Fig 2b ). Again, boys showed higher diversity values in their gut microbiota, with significant differences mainly between boys and girls (t = -3.358, df = 53, P < 0.01), between boys and males (t = -4.048, df = 53, P < 0.001), and between boys and females (t = -3.446, df = 53, P < 0.01). The Simpson dominance index indicated statistically significant differences for both explanatory variables and their interaction ( Table 1 ). These differences in dominant ASVs were mainly observed between boys and males (t = -2.864, df = 57, P = 0.02) and between boys and females (t = -2.774, df = 57, P = 0.03) ( Fig 2c ). Finally, for Faith’s PD index, only marginally significant differences were found between age groups, specifically between children and adults of both sexes (t = 1.801, df = 58, P = 0.08) ( Table 1 ; Fig 2d ). View this table: View inline View popup Download powerpoint Table 1. Results of the generalized linear models (GLM) for each diversity index evaluated. LR Chisq = likelihood ratio Chi-square. ( *) Significant differences; ( · ) Marginally significant differences, both indicated in bold. When evaluating beta diversity of the gut microbiota in children and adults from the Me’phaa community, no clear separation between age groups was evident. Using the weighted UniFrac method, marginally significant differences were found for the age group × sex interaction (PERMANOVA, F = 2.25, R² = 0.04, P = 0.080); children exhibited greater dispersion, while adults showed tighter clustering ( Fig 3a ). Using the Bray–Curtis method, the age group × sex interaction showed statistically significant differences (PERMANOVA, F = 2.26, R² = 0.04, P = 0.001), and a separation between children and adults was observed ( Fig 3b ), suggesting that the composition of the gut microbiota may vary by age group and sex in these indigenous populations. Download figure Open in new tab Fig 3. Non-metric multidimensional scaling (NMDS) analysis showing β diversity of Me’phaa indigenous populations. (a) Clustering based on the Weighted UniFrac method. (b) Clustering based on the Bray–Curtis index. Statistically significant differences were detected using PERMANOVA at a significance level of P < 0.05. Both distance metrics were independent of sex (PERMANOVA, weighted UniFrac: F = 0.64, R² = 0.01, P = 0.61; Bray–Curtis: F = 0.91, R² = 0.01, P = 0.52). Intergroup differences in gut microbiota composition between children and adults from the Me’phaa community were evident and are reflected in taxonomic differences between these two age groups. The heatmap of the most abundant ASVs illustrates similarities and differences between Me’phaa children and adults, as well as between sexes ( Fig 4 ), with a higher prevalence of the Prevotellaceae family in adults and genera such as Actinomyces and Akkermansia in children. Download figure Open in new tab Fig 4. Heatmap of the most abundant ASVs in Me’phaa children and adults. The left side of the plot displays the ASV clustering dendrogram, and the top shows the sample clustering dendrogram. Each color in the central heatmap grid represents the relative abundance value of ASVs in each taxon after normalization processing. Analysis of differences in the GM between Me’phaa indigenous children and adults To further investigate taxonomic distributions and clades in Me’phaa children and adults, we compared microbial community abundances at each taxonomic level. The histogram from the linear discriminant analysis (LDA) effect size (LEfSe) shows the LDA scores for microbial taxa with significant differences across age groups and sexes, indicating that females exhibited fewer microbial taxa compared to other groups ( Fig 5 ). Conversely, the LEfSe cladogram visualizes all detected microbial communities (abundance >1%) from phylum to genus ( Fig 6 ). Based on the inferred taxonomic profiles across all samples, LEfSe performed a statistical strategy to discover metagenomic biomarkers and identified 50 differentially abundant taxa (S1 Table). Download figure Open in new tab Fig 5. Histogram of the LEfSe analysis. The length of each bar represents the LDA score. The figure shows microbial taxa with significant differences across age groups and sexes. Females (red), males (green), girls (blue), and boys (purple). LDA score > 2. Download figure Open in new tab Fig 6. Cladogram from the LEfSe analysis, showing differences in the relative abundance of taxa at five levels (Phylum, Class, Order, Family, and Genus) between Me’phaa children and adults. Circles of different colors indicate that age groups and sexes exhibited differences in relative abundance. White circles represent non-significant taxa. Taxa that best characterized the females group included the family Atopobiaceae and the genus Libanicoccus . For males, the most representative taxa were from the families Prevotellaceae and Veillonellaceae, and the genera Alloprevotella and Dialister . In the children’s age group, the most abundant microbial taxa in girls belonged to the families Mycoplasmataceae and Ruminococcaceae, and the genera Candidatus Lumbricincola (a new lineage) and Ruminococcus , respectively. Finally, in boys, the most dominant families were Christensenellaceae, Ruminococcaceae, and Lachnospiraceae, with the group Christensenellaceae (R-7) and the genera Subdoligranulum and Blautia , respectively (S1 Table; Fig 6 ). Differences between VANISH and BIoSSUM groups in Me’phaa indigenous children and adults There were differences in the abundance of taxa belonging to VANISH groups (volatile and/or negatively associated with industrialized human societies) and BIoSSUM groups (positively associated with urban/modernized societies) ( 14 ). For this reason, in this study, we considered age group and sex of Me’phaa indigenous children and adults as predictors for the following families: Prevotellaceae, Spirochaetaceae, and Succinivibrionaceae (VANISH), and Bacteroidaceae, Enterobacteriaceae, and Verrucomicrobiaceae (BIoSSUM). It is worth noting that although the population studied is an indigenous community, and therefore BIoSSUM-associated taxa are likely not highly represented, we decided to carry out analyses for both groups. The GM of Me’phaa indigenous children and adults showed significant differences in two out of the three VANISH groups ( Table 2 ). For Prevotellaceae, significant differences were found for all explanatory variables, as well as for the age group × sex interaction ( Table 2 ), where males exhibited higher abundance compared to the other groups ( Fig 7a ). For the Spirochaetaceae family, there were significant differences for the age group × sex interaction ( Table 2 ), with boys showing the highest abundance compared to the other groups ( Fig 7b ). In the case of the Succinivibrionaceae family, no significant differences were detected for any of the variables or interactions evaluated ( Table 2 ; Fig 7c ). View this table: View inline View popup Download powerpoint Table 2. Results of the linear models for each family belonging to the VANISH and BIoSSUM groups. LR Chisq = likelihood ratio chi-squared statistic. t = t-test. (*) Significant differences; (·) Marginally significant differences, both shown in bold. Download figure Open in new tab Fig 7. Boxplots of VANISH and BIoSSUM GM groups in Me’phaa indigenous children and adults. The plots show family-level taxa in the fecal microbiota of Me’phaa children and adults. The line inside each box represents the median, the boxes represent the first and third quartiles, and the whiskers indicate minimum and maximum values. The black dot represents the mean. Each colored dot represents an individual/participant. Significant differences ( P < 0.05; see Table 2 ). Regarding families in the BIoSSUM group, significant differences were observed across all three families ( Table 2 ). For Bacteroidaceae, significant differences were found between sexes ( Table 2 ), with females exhibiting higher abundances than males, regardless of age group ( Fig 7d ). For the Enterobacteriaceae family, significant differences were observed between sexes, and marginally significant differences for the age group × sex interaction ( Table 2 ), with girls showing the highest abundances ( Fig 7e ). Finally, for the Verrucomicrobiaceae family, significant differences were observed between age groups, regardless of sex ( Table 2 ), with children exhibiting higher abundances than adults ( Fig 7f ). Discussion This study contributes to the limited body of knowledge regarding the GM of non-Westernized indigenous populations in the global south of Latin America. We observed that the Mexican Me’phaa population exhibits a remarkably diverse bacterial microbiota, which is consistent with findings in other non-Westernized populations across different geographic regions ( 10 , 27 ). This high diversity is mainly associated with a diet rich in plant-derived carbohydrates, particularly dietary fiber and cereals ( 37 , 38 ). Thus, our findings support the hypothesis that geography or host genetics may play a limited role in defining the microbial ecology of the human gut ( 55 ). Furthermore, the study provides insights into how the lifestyle and diet of the Me’phaa communities, contribute to the high diversity and unique microbial composition of their inhabitants. The second key finding here was the variation observed across participant age groups. In Westernized American populations, gut microbial diversity in children under 3 years is typically lower than in adults ( 16 ), and reaches a climax around 3–5 years of age, stabilizing thereafter, including into adulthood ( 56 ). In contrast, in this study, alpha diversity tended to be higher in boys compared to girls and adults (both male and female), even though the age range for the children (5– 12 years) is often underrepresented in microbiota studies, given the presumed stability reached by that age. Interestingly, the most common phyla among non-Westernized populations include Firmicutes, Bacteroidetes, Actinobacteria, and Verrucomicrobia ( 57 ). In Me’phaa children, we also observed representation from Actinobacteriota, Cyanobacteria, Euryarchaeota, Proteobacteria, and Spirochaetota ( Fig. 1a ). Notably, some phyla were detected exclusively in children (both boys and girls), albeit at very low abundances (0.008–0.028%), including Chloroflexi, Deinococcota, Myxococcota, Planctomycetota, and Gemmatimonadota. These groups, although uncommon in the human GM, have been reported in other non-Westernized populations and may reflect increased exposure to environmental microorganisms (e.g., soil, untreated water, and plant-based materials), as well as diets rich in plant fibers, characteristic of rural communities ( 29 , 38 ). The phylum Chloroflexi represents a low-abundance component (<1%) and remains poorly understood in terms of its physiology, host adaptation, or pathogenic potential ( 58 ). For Planctomycetota, some studies in Senegalese populations have reported antimicrobial activity, anaerobic fermentation capacity, and the ability to degrade various polysaccharides and glycoproteins ( 59 ). Deinococcota, Myxococcota, and Gemmatimonadota are rare taxa with little or no known human presence, yet their detection may be significant, as low-abundance taxa are thought to play crucial roles in maintaining gut homeostasis and function ( 57 ). We also observed in Me’phaa children a strong presence of the family Christensenellaceae, known for its high heritability and association with gut ecology and diseases such as obesity and IBD ( 60 ). The genera Actinomyces and Holdemanella were dominant among children of both sexes. Girls showed higher levels of Mycoplasmataceae and Ruminococcaceae, and the genera Ruminococcus , Akkermansia , Alistipes , and Phoenicibacter . In boys, Christensenellaceae, Ruminococcaceae, and Lachnospiraceae were more abundant, along with the genera Subdoligranulum and Blautia . Akkermansia has been associated with reduced weight gain and fat accumulation, similar to Christensenellaceae ( 60 ), and has the capacity to degrade intestinal mucin ( 61 ). It also improves glucose tolerance and reduces inflammation and metabolic endotoxemia in animal models ( 61 ). Although both taxa share similar metabolic and inflammatory roles, their long-term health effects on the host remain to be fully understood ( 38 ). The lower alpha diversity among adults may reflect a partial transition toward a more Westernized diet ( 10 ), characterized by reduced consumption of fiber-rich foods and increased intake of Western dietary components ( 14 ). This shift may be explained by the fact that adult men are often the ones who temporarily leave their communities to seek work in more urbanized or densely populated areas. Although short-term dietary changes can influence GM composition, evidence suggests that in healthy individuals, the bacterial profile tends to remain stable and is more significantly shaped by long-term dietary patterns ( 62 – 64 ). In adults from this community, the family Prevotellaceae and the genera Prevotella , Succinivibrio , and Dialister were predominant, consistent with fiber-rich diets and the fermentation of complex polysaccharides ( 65 , 66 ). Prevotella is a key biomarker of plant-based diets and has been linked to both metabolic protection and intestinal inflammation ( 67 ). Its abundance is associated with high intake of non-digestible polysaccharides due to its robust fermentative capabilities ( 68 ), whereas its loss correlates with reduced dietary fiber consumption ( 14 ). Short-term consumption of an animal-protein-rich diet has been shown to significantly alter the gut microbiota, increasing the abundance of Bacteroides , Bilophila , and Alistipes , while reducing fiber-degrading Firmicutes ( 15 ). Among the diverse gut microbiota, certain genera such as Coprococcus and Faecalibacterium have been associated with improved quality of life, largely through the production of butyrate, a short-chain fatty acid (SCFA) that helps reduce intestinal inflammation ( 69 , 70 ). Both genera tend to be depleted in individuals with inflammatory bowel disease (IBD) ( 71 ). Coprococcus has also been linked to hypertension and autism spectrum disorders ( 72 , 73 ), with sex-specific differences in prevalence ( 74 ). Dialister appears more abundant in males than females, and in adults compared to children, potentially reflecting greater dietary exposure to Western foods, especially among adult males. Dialister is a producer of SCFAs such as propionate and has been associated with immunomodulatory pathways and gut health ( 75 , 76 ). Overall, the elevated presence of these taxa suggests that adults with a pre-Columbian Mexican lifestyle harbor gut microbial profiles specialized in plant compound fermentation, potentially offering metabolic and immunological benefits. The GM is influenced by lifestyle, diet, ethnicity ( 15 , 77 , 78 ), and medical practices such as excessive antibiotic use ( 79 ). While diet is a major driver of GM structure, non-Westernized populations are often more vulnerable to malnutrition. When it occurs during breastfeeding or early childhood, it can lead to reduced levels of key gut bacteria such as Bifidobacterium longum , an important species associated with human milk ( 13 , 80 ). Here, we found low representation of this taxon in both children and adults. On the other hand, because VANISH taxa are rare in Western populations (on which most studies have been conducted), little is known about their role in the gut ( 14 ). Interestingly, the families Spirochaetaceae and Succinivibrionaceae contain highly motile members; however, the functions of these motile taxa within the microbiota remain largely unknown ( 14 ). The high abundance of Prevotella , a VANISH group, among Me’phaa males (and to a lesser extent in boys), is consistent with findings by Sánchez-Quinto et al. (2020) and other studies conducted in non-Westernized groups such as the Yanomami (Amerindians) and the BaAka (Africans) ( 29 , 81 ). In the human gastrointestinal tract, symbiotic relationships occur between the GM and the host, through which a large number of peptides are synthesized, surpassing those encoded by the human genome ( 89 ). The relevance of this interaction lies in its involvement in essential functions such as vitamin biosynthesis, polysaccharide fermentation, ion absorption, and the regulation of host metabolic pathways ( 82 , 83 ). It also contributes to the production of: i) metabolites such as folate (vitamin B9), indoles, secondary bile acids, and trimethylamine-N-oxide (linked to cardiovascular risk); ii) neurotransmitters such as serotonin and GABA; and viii) SCFAs, critical for gut health ( 84 ). These compounds play roles in immune, digestive, metabolic, and central nervous system (CNS) processes ( 76 , 84 ). The GM also contributes to fiber and starch catabolism, pathogen defense ( 85 – 88 ), and gut-brain axis communication, influencing motivation, stress response, and mood disorders ( 76 , 89 , 90 ). In conclusion, individuals from the Me’phaa community display high gut microbial diversity, exclusive low-abundance taxa, and clear age- and sex-related differences. The strong representation of bacterial families uncommon in Western populations reinforces the view that non-Westernized lifestyles preserve complex microbiotas with unexplored ecological and health-relevant functions. Future studies should include metagenomic and metabolomic analyses to characterize these functions and assess how lifestyle transitions influence gut microbial structure and function. Indigenous populations offer a unique opportunity to document and conserve human microbial diversity in the context of accelerating globalization and dietary change in the global south of the Americas. Author contributions Conceptualization: Isaac G.-Santoyo, Luisa I. Falcón. Data curation: Sebastián Villada-Bedoya, Luisa I. Falcón, Osiris Gaona, Aída Elizondo García, Isaac G.-Santoyo. Formal analysis: Sebastián Villada-Bedoya, Osiris Gaona, Luisa I. Falcón. Funding acquisition: Isaac G.-Santoyo. Investigation: Isaac G.-Santoyo, Luisa I. Falcón, Sebastián Villada-Bedoya, Osiris Gaona, Aída Elizondo García. Methodology: Sebastián Villada-Bedoya, Luisa I. Falcón, Osiris Gaona, Aída Elizondo García, Isaac G.-Santoyo. Project administration: Isaac G.-Santoyo. Resources: Isaac G.-Santoyo, Luisa I. Falcón. Writing – original draft: Sebastián Villada-Bedoya, Isaac G.-Santoyo. Writing – review & editing: Sebastián Villada-Bedoya, Isaac G.-Santoyo, Aída Elizondo García, Osiris Gaona, Luisa I. Falcón. Competing interests: The authors have declared that no competing interests exist. Funding This project was funded by UNAM-PAPIIT (grant numbers IA209416 and IA207019), CONACYT Ciencia Básica (grant number 241744), and Instituto de Ecología, UNAM (L.I.F)). Supporting information S1 Table . Biomarkers detected in Me’phaa children and adult populations. Differentially abundant clades among the age group and sex groups were identified by LEfSe using the highest LDA score. A total of 50 significantly different taxonomic biomarkers were detected. (PDF File) S1 Fig . Relative abundance of the GM and bacterial composition (16S rRNA V4) at the Family level in Me’phaa children and adults. Families with relative abundances < 1% were grouped into the “Others” category. (PDF File) Acknowledgments We are grateful to Margarita Muciño, Julio Naranjo, and Diego Hernández-Muciño from Xuajin Me’Phaa, a non-governmental association, for their help in the liaison with the Me’phaa community, and their help with sample collection and logistics. We also are grateful to Olga A. Rojas-Ramos, Ariatna Hernández Castillo, and Rosa M. de la Fuente Rodríguez for their contributions in sample collection. To Daniel Cerqueda-García for his help in bioinformatic analysis. We also thank to UNAM Posdoctoral Program (POSDOC), because during S.V.-B.’s postdoctoral stay it was possible to carry out this work in parallel to his stay in the Neuroecology Lab. References 1. ↵ Sommer F , Bäckhed F . The gut microbiota-masters of host development and physiology . Nat Rev Microbiol [Internet ]. 2013 ; 11 ( 4 ): 227 – 38 . Available from : doi: 10.1038/nrmicro2974 OpenUrl CrossRef PubMed 2. ↵ Falony G , Joossens M , Vieira-Silva S , Wang J , Darzi Y , Faust K , et al. Population-level analysis of gut microbiome variation . Science (80-). 2016 ; 352 ( 6285 ): 560 – 4 . OpenUrl Abstract / FREE Full Text 3. ↵ Ahammad I , Bhattacharjee A , Chowdhury ZM , Rahman A , Hossain MU , Dewan G , et al. Gut microbiome composition reveals the distinctiveness between the Bengali people and the Indigenous ethnicities in Bangladesh . Commun Biol . 2024 ; 7 ( 1 ): 1 – 12 . OpenUrl PubMed 4. ↵ Rojo D , Méndez-García C , Raczkowska BA , Bargiela R , Moya A , Ferrer M , et al. Exploring the human microbiome from multiple perspectives: Factors altering its composition and function . FEMS Microbiol Rev . 2017 ; 41 ( 4 ): 453 – 78 . OpenUrl CrossRef PubMed 5. Negi S , Das DK , Pahari S , Nadeem S , Agrewala JN . Potential role of gut microbiota in induction and regulation of innate immune memory . Front Immunol . 2019 ; 10 ( OCT ): 1 – 12 . OpenUrl CrossRef PubMed 6. ↵ Ramírez-Carrillo E , Gaona O , Nieto J , Sánchez-Quinto A , Cerqueda-García D , Falcón LI , et al. Disturbance in human gut microbiota networks by parasites and its implications in the incidence of depression . Sci Rep . 2020 ; 10 ( 1 ): 1 – 13 . OpenUrl CrossRef PubMed 7. ↵ Nicholson JK , Holmes E , Kinross J , Burcelin R , Gibson G , Jia W , et al. Host-gut microbiota metabolic interactions . Science (80-). 2012 ; 336 ( 6086 ): 1262 – 7 . OpenUrl Abstract / FREE Full Text 8. Turnbaugh PJ , Ley RE , Mahowald MA , Magrini V , Mardis ER , Gordon JI . An obesity-associated gut microbiome with increased capacity for energy harvest . Nature . 2006 ; 444 ( 7122 ): 1027 – 31 . OpenUrl CrossRef PubMed Web of Science 9. Cryan JF , Dinan TG . Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour . Nat Rev Neurosci [Internet ]. 2012 ; 13 ( 10 ): 701 – 12 . Available from : doi: 10.1038/nrn3346 OpenUrl CrossRef PubMed 10. ↵ Girard C , Tromas N , Amyot M , Shapiro BJ . Gut microbiome of the Canadian Arctic Inuit . mSphere . 2017 ; 2 ( 1 ). 11. ↵ Hitch TCA , Hall LJ , Walsh SK , Leventhal GE , Slack E , de Wouters T , et al. Microbiome-based interventions to modulate gut ecology and the immune system . Mucosal Immunol . 2022 ; 15 : 1095 – 113 . OpenUrl CrossRef PubMed 12. ↵ de Wouters T , Doré J , Lepage P . Does our food (environment) change our gut a potential role for inflammatory Bowel disease? Dig Dis . 2012 ; 30 ( suppl 3 ): 33 – 9 . OpenUrl PubMed 13. ↵ Fan Y , Pedersen O . Gut microbiota in human metabolic health and disease . Nat Rev Microbiol [Internet ]. 2021 ; 19 ( 1 ): 55 – 71 . Available from : doi: 10.1038/s41579-020-0433-9 OpenUrl CrossRef PubMed 14. ↵ Sonnenburg ED , Sonnenburg JL . The ancestral and industrialized gut microbiota and implications for human health . Nat Rev Microbiol [Internet ]. 2019 ; 17 ( 6 ): 383 – 90 . Available from : doi: 10.1038/s41579-019-0191-8 OpenUrl CrossRef PubMed 15. ↵ David LA , Maurice CF , Carmody RN , Gootenberg DB , Button JE , Wolfe BE , et al. Diet rapidly and reproducibly alters the human gut microbiome . Nature [Internet ]. 2014 ; 505 ( 7484 ): 559 – 63 . Available from : doi: 10.1038/nature12820 OpenUrl CrossRef PubMed Web of Science 16. ↵ Cheng J , Ringel-Kulka T , Heikamp-De Jong I , Ringel Y , Carroll I , De Vos WM , et al. Discordant temporal development of bacterial phyla and the emergence of core in the fecal microbiota of young children . ISME J . 2016 ; 10 ( 4 ): 1002 – 14 . OpenUrl CrossRef PubMed 17. ↵ Palmer C , Bik EM , DiGiulio DB , Relman DA , Brown PO . Development of the human infant intestinal microbiota . PLoS Biol . 2007 ; 5 ( 7 ): 1556 – 73 . OpenUrl CrossRef Web of Science 18. Rodríguez JM , Murphy K , Stanton C , Ross RP , Kober OI , Juge N , et al. The composition of the gut microbiota throughout life, with an emphasis on early life . Microb Ecol Heal Dis . 2015 ; 26 ( 1 ): 26050 . OpenUrl 19. ↵ Hollister EB , Riehle K , Luna RA , Weidler EM , Rubio-Gonzales M , Mistretta TA , et al. Structure and function of the healthy pre-adolescent pediatric gut microbiome . Microbiome [Internet ]. 2015 ; 3 : 36 . Available from : doi: 10.1186/s40168-015-0101-x OpenUrl CrossRef PubMed 20. ↵ Singh V , Lee G , Son H , Koh H , Kim ES , Unno T , et al. Butyrate producers, “The Sentinel of Gut ”: Their intestinal significance with and beyond butyrate, and prospective use as microbial therapeutics . Front Microbiol . 2023 ; 13 : 1103836 . 21. Agans R , Rigsbee L , Kenche H , Michail S , Khamis HJ , Paliy O . Distal gut microbiota of adolescent children is different from that of adults . FEMS Microbiol Ecol . 2011 ; 77 ( 2 ): 404 – 12 . OpenUrl CrossRef PubMed 22. ↵ Tomova A , Bukovsky I , Rembert E , Yonas W , Alwarith J . The Effects of vegetarian and vegan diets on gut microbiota . Front Nutr . 2019 ; 6 ( 47 ). 23. ↵ Schnorr SL , Candela M , Rampelli S , Centanni M , Consolandi C , Basaglia G , et al. Gut microbiome of the Hadza hunter-gatherers . Nat Commun [Internet ]. 2014 ; 5 : 1 – 12 . Available from : doi: 10.1038/ncomms4654 OpenUrl CrossRef 24. ↵ Smits SA , Leach J , Sonnenburg ED , Gonzalez CG , Lichtman JS , Reid G , et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania . Science (80-). 2017 ; 357 ( 6353 ): 802 – 5 . OpenUrl Abstract / FREE Full Text 25. ↵ Ayeni FA , Biagi E , Rampelli S , Fiori J , Soverini M , Audu HJ , et al. Infant and Adult Gut Microbiome and Metabolome in Rural Bassa and Urban Settlers from Nigeria . Cell Rep [Internet ]. 2018 ; 23 ( 10 ): 3056 – 67 . Available from : doi: 10.1016/j.celrep.2018.05.018 OpenUrl CrossRef PubMed 26. ↵ Martínez I , Stegen JC , Maldonado-Gómez MX , Eren MA , Siba PM , Greenhill AR , et al. The gut microbiota of rural Papua New Guineans: Composition, diversity patterns, and ecological processes . Cell Rep . 2015 ; 11 ( 4 ): 527 – 38 . OpenUrl CrossRef PubMed 27. ↵ Kann S , Eberhardt K , Hinz R , Schwarz NG , Dib JC , Aristizabal A , et al. The gut microbiome of an indigenous agropastoralist population in a remote area of Colombia with high rates of gastrointestinal infections and dysbiosis . Microorganisms . 2023 ; 11 ( 3 ). 28. ↵ Yatsunenko T , Rey FE , Manary MJ , Trehan I , Dominguez-Bello MG , Contreras M , et al. Human gut microbiome viewed across age and geography . Nature [Internet ]. 2012 ; 486 ( 7402 ):222–7. Available from : doi: 10.1038/nature11053 OpenUrl CrossRef PubMed Web of Science 29. ↵ Clemente JC , Pehrsson EC , Blaser MJ , Sandhu K , Gao Z , Wang B , et al. The microbiome of uncontacted Amerindians . Sci Adv . 2015 ; 1 ( 3 ). 30. ↵ Sonnenburg JL , Sonnenburg ED . Vulnerability of the industrialized microbiota . Science (80-) . 2019 ; 366 ( 6464 ). 31. ↵ CEPAL . Los pueblos indígenas en América Latina: avances en el último decenio y retos pendientes para la garantía de sus derechos [Internet] . Naciones Unidas. Santiago, Chile: Naciones Unidas ; 2014 . p. 128 . Available from: https://www.cepal.org/es/publicaciones/37050-pueblos-indigenas-america-latina-avances-ultimo-decenio-retos-pendientes-la 32. ↵ UNDP . The situation of Latin America’s indigenous population and the impact of COVID-19 [Internet] . 2021 . Available from: https://www.undp.org/latin-america/blog/situation-latin-americas-indigenous-population-and-impact-covid-19 33. ↵ Black CA . An Autosegmental Analysis of Me’phaa (Tlapanec) Noun Inflection. SIL México . Available online: https://mexico.sil.org/es/resources/archives/2860 [Internet]. (accessed on 06 August 2024). 2004. Available from: https://mexico.sil.org/es/resources/archives/2860 34. ↵ Miramontes O , DeSouza O , Hernández D , Ceccon E . Non-Lévy mobility patterns of Mexican Me’Phaa peasants searching for fuel wood . Hum Ecol . 2012 ; 40 ( 2 ): 167 – 74 . OpenUrl CrossRef 35. ↵ : Ceccon E Pérez DR Borda-Niño M , Carranza M , Diego Hernández-Muciño S , Muciño-Muciño M . Restauración productiva en la práctica: El caso de las comunidades indígenas MéPhaa de La Montaña de Guerrero, México . In : Ceccon E , Pérez DR , editors. Más allá de la ecologia de la restauración: Perspectivas sociales en América Latina y el Caribe . Buenos Aires : Vázquez Manzzini Editores ; 2016 . p. 245 – 55 . 36. ↵ UNDP . Índice de desarrollo humano para las entidades federativas, México 2015. Available online: https://www.mx.undp.org/content/mexico/es/home/library/poverty/indice-de-desarrollo-humano-para-las-entidades-federativas--mexi.html [Internet]. (accessed on 06 August 2024). 2015 . Available from: https://www.undp.org/es/mexico/publicaciones/indice-de-desarrollo-humano-para-las-entidades-federativas-mexico-2015 37. ↵ Martínez I , Lattimer JM , Hubach KL , Case JA , Yang J , Weber CG , et al. Gut microbiome composition is linked to whole grain-induced immunological improvements . ISME J . 2013 ; 7 ( 2 ): 269 – 80 . OpenUrl CrossRef PubMed 38. ↵ Sánchez-Quinto A , Cerqueda-García D , Falcón LI , Gaona O , Martínez-Correa S , Nieto J , et al. Gut microbiome in children from indigenous and urban communities in México: Different subsistence models, different microbiomes . Microorganisms . 2020 ; 8 ( 10 ): 1 – 16 . OpenUrl 39. ↵ Allaband C , McDonald D , Vázquez-Baeza Y , Minich JJ , Tripathi A , Brenner DA , et al. Microbiome 101: Studying, analyzing, and interpreting gut microbiome data for clinicians . Clin Gastroenterol Hepatol [Internet ]. 2019 ; 17 ( 2 ): 218 – 30 . Available from : doi: 10.1016/j.cgh.2018.09.017 OpenUrl CrossRef 40. ↵ Caporaso JG , Lauber CL , Walters WA , Berg-Lyons D , Huntley J , Fierer N , et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms . ISME J . 2012 ; 6 ( 8 ): 1621 – 4 . OpenUrl CrossRef PubMed Web of Science 41. ↵ Callahan BJ , McMurdie PJ , Rosen MJ , Han AW , Johnson AJA , Holmes SP . DADA2: High-resolution sample inference from Illumina amplicon data . Nat Methods . 2016 ; 13 ( 7 ): 581 – 3 . OpenUrl CrossRef PubMed 42. ↵ Rognes T , Flouri T , Nichols B , Quince C , Mahé F . VSEARCH: A versatile open source tool for metagenomics . PeerJ . 2016 ; 2016 ( 10 ): 1 – 22 . OpenUrl CrossRef 43. ↵ Quast C , Pruesse E , Yilmaz P , Gerken J , Schweer T , Yarza P , et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools . Nucleic Acids Res . 2013 ; 41 ( D1 ): 590 – 6 . OpenUrl CrossRef 44. ↵ Posada D Katoh K , Asimenos G , Toh H . Multiple Alignment of DNA Sequences with MAFFT BT - Bioinformatics for DNA Sequence Analysis . In: Posada D , editor. Totowa, NJ : Humana Press ; 2009 . p. 39 – 64 . Available from : doi: 10.1007/978-1-59745-251-9_3 OpenUrl CrossRef PubMed Web of Science 45. ↵ Price MN , Dehal PS , Arkin AP . FastTree 2 - Approximately maximum-likelihood trees for large alignments . PLoS One . 2010 ; 5 ( 3 ). 46. ↵ McMurdie PJ , Holmes S . Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data . PLoS One . 2013 ; 8 ( 4 ). 47. ↵ Wickham H . ggplot2: Elegant graphics for data analysis . Springer ; 2016 . 48. ↵ Russel J . Russel88/MicEco: v0.9.15 (v0.9.15), Zenodo [code] , doi: 10.5281/zenodo.4733747 , 2021. 2021 . OpenUrl CrossRef 49. ↵ R Core T. R : A language and environment for statistical computing . R Found Stat Comput Vienna, Austria . 2023 ; 50. ↵ R Studio T . RStudio: Integrated development for R. RStudio, Inc Boston, MA [Internet] . 2023 ; Available from: https://rstudio.com/ 51. ↵ Fox J , Weisberg S . An R companion to applied regression . Sage publications ; 2018 . 52. ↵ Wickham H , Francois R , Henry L , Muller K. dplyr: A grammar of data manipulation. Version R package version 0.7.6. 2018. 53. ↵ Segata N , Izard J , Waldron L , Gevers D , Miropolsky L , Garrett WS , et al. Metagenomic biomarker discovery and explanation. Genome Biol [Internet]. 2011 ; 12:R60 . Available from : http://genomebiology.com/2011/11/6/R60%0APage 54. ↵ Stasinopoulos MD , Rigby RA , Bastiani F De . GAMLSS: A distributional regression approach . Stat Modelling . 2018 ; 18 ( 3–4 ): 248 – 73 . OpenUrl CrossRef 55. ↵ Mollick SA , Maji S . Understanding the diversity of human gut microbes in indigenous populations across the world, 19 March 2024 , PREPRINT (Version 1) available at Research Square [ doi: 10.21203/rs.3.rs-3950664/v1 ]. 2024 ; OpenUrl CrossRef 56. ↵ Martino C , Dilmore AH , Burcham ZM , Metcalf JL , Jeste D , Knight R . Microbiota succession throughout life from the cradle to the grave . Nat Rev Microbiol . 2022 ; 20 : 707 – 20 . OpenUrl CrossRef PubMed 57. ↵ Arumugam M , Raes J , Pelletier E , Paslier D Le , Yamada T , Mende DR , et al. Enterotypes of the human gut microbiome . Nature . 2011 ; 473 (7346):174–80. 58. ↵ Campbell AG , Schwientek P , Vishnivetskaya T , Woyke T , Levy S , Beall CJ , et al. Genomics update diversity and genomic insights into the uncultured Chloroflexi from the human microbiota . Environ Microbiol . 2014 ; 16 ( 9 ): 2635 – 43 . OpenUrl CrossRef 59. ↵ van Niftrik L , Devos DP . Planctomycetes-Verrucomicrobia-Chlamydiae baterial superphylum: New model organisms for evolutionary cell biology . Front Microbiol . 2017 ; 8 : 1458 . OpenUrl PubMed 60. ↵ Waters JL , Ley RE . The human gut bacteria Christensenellaceae are widespread, heritable, and associated with health . BMC Biol . 2019 ; 17 ( 1 ): 1 – 11 . OpenUrl CrossRef PubMed 61. ↵ Cirstea M , Radisavljevic N , Finlay BB . Good bug, bad bug: Breaking through microbial stereotypes . Cell Host Microbe [Internet ]. 2018 ; 23 ( 1 ): 10 – 3 . Available from : doi: 10.1016/j.chom.2017.12.008 OpenUrl CrossRef PubMed 62. ↵ Turnbaugh PJ , Ridaura VK , Faith JJ , Rey FE , Knight R , Gordon JI . The effect of diet on the human gut microbiome: A metagenomic analysis in humanized gnotobiotic mice . Sci Transl Med . 2009 ; 1 ( 6 ). 63. Muegge BD , Kuczynski J , Knights D , Clemente JC , González A , Fontana L , et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans . Science (80-). 2011 ; 332 ( 6032 ): 970 – 4 . OpenUrl Abstract / FREE Full Text 64. ↵ Turnbaugh PJ , Bäckhed F , Fulton L , Gordon JI . Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome . Cell Host Microbe . 2008 ; 3 ( 4 ): 213 – 23 . OpenUrl CrossRef PubMed Web of Science 65. ↵ Graf D , Di Cagno R , Fak F , Flint HJ , Nyman M , Saarela M , et al. Contribution of diet to the composition of the human gut microbiota . Microb Ecol Heal Dis . 2015 ; 26 : 26164 . 66. ↵ Ou J , Carbonero F , Zoetendal EG , Delany JP , Wang M , Newton K , et al. Diet, microbiota, and microbial metabolites in colon cancer risk in rural Africans and African Americans . Am J Clin Nutr . 2013 ; 98 : 111 – 20 . OpenUrl Abstract / FREE Full Text 67. ↵ Tito RY , Chaffron S , Caenepeel C , Lima-mendez G , Wang J , Vieira-silva S , et al. Population-level analysis of Blastocystis subtype prevalence and variation in the human gut microbiota . Gut . 2019 ; 1180 – 9 . 68. ↵ De Filippo C , Cavalieri D , Di Paola M , Ramazzotti M , Poullet JB , Massart S , et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa . Proc Natl Acad Sci U S A . 2010 ; 107 ( 33 ): 14691 – 6 . OpenUrl Abstract / FREE Full Text 69. ↵ Rivière A , Selak M , Lantin D , Leroy F , De Vuyst L . Bifidobacteria and butyrate-producing colon bacteria: Importance and strategies for their stimulation in the human gut . Front Microbiol . 2016 ; 7 ( JUN ). 70. ↵ Louis P , Hold GL , Flint HJ . The gut microbiota, bacterial metabolites and colorectal cancer . Nat Rev Microbiol [Internet ]. 2014 ; 12 ( 10 ): 661 – 72 . Available from : doi: 10.1038/nrmicro3344 OpenUrl CrossRef PubMed 71. ↵ Gevers D , Kugathasan S , Denson LA , Vázquez-Baeza Y , Van Treuren W , Ren B , et al. The treatment-naive microbiome in new-onset Crohn’s disease . Cell Host Microbe . 2014 ; 15 ( 3 ): 382 – 92 . OpenUrl CrossRef PubMed Web of Science 72. ↵ Krajmalnik-Brown R , Lozupone C , Kang D-W , Adams JB . Gut bacteria in children with autism spectrum disorders: challenges and promise of studying how a complex community influences a complex disease . Microb Ecol Heal Dis . 2015 ; 26 ( 0 ). 73. ↵ Li J , Zhao F , Wang Y , Chen J , Tao J , Tian G , et al. Gut microbiota dysbiosis contributes to the development of hypertension . Microbiome [Internet ]. 2017 ; 5 ( 1 ): 1 – 19 . Available from : doi: 10.1186/s40168-016-0222-x OpenUrl CrossRef 74. ↵ Ho NT , Li F , Wang S , Kuhn L . MetamicrobiomeR: An R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effects models . BMC Bioinformatics . 2019 ; 20 ( 1 ): 1 – 15 . OpenUrl CrossRef PubMed 75. ↵ Shi J , Zhao D , Zhao F , Wang C , Zamaratskaia G , Li C . Chicken-eaters and pork-eaters have different gut microbiota and tryptophan metabolites . Sci Rep [Internet ]. 2021 ; 11 : 11934 . Available from : doi: 10.1038/s41598-021-91429-3 OpenUrl CrossRef 76. ↵ Valles-Colomer M , Falony G , Darzi Y , Tigchelaar EF , Wang J , Tito RY , et al. The neuroactive potential of the human gut microbiota in quality of life and depression . Nat Microbiol [Internet ]. 2019 ; 4 ( 4 ): 623 – 32 . Available from : doi: 10.1038/s41564-018-0337-x OpenUrl CrossRef PubMed 77. ↵ Wu GD , Chen J , Hoffmann C , Bittinger K , Chen YY , Keilbaugh SA , et al. Linking long-term dietary patterns with gut microbial enterotypes . Science (80-). 2011 ; 334 ( 6052 ): 105 – 8 . OpenUrl Abstract / FREE Full Text 78. ↵ Xu Z , Knight R . Dietary effects on human gut microbiome diversity . Br J Nutr . 2015 ; 113 : S1 – 5 . OpenUrl CrossRef PubMed 79. ↵ Francino MP . Antibiotics and the human gut microbiome: Dysbioses and accumulation of resistances . Front Microbiol . 2016 ; 6 ( JAN ): 1 – 11 . OpenUrl CrossRef 80. ↵ Smith MI , Yatsunenko T , Manary MJ , Trehan I , Mkakosya R , Cheng J , et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor . Science (80-). 2013 ; 339 ( 6119 ): 548 – 54 . OpenUrl Abstract / FREE Full Text 81. ↵ Gomez A , Petrzelkova KJ , Burns MB , Yeoman CJ , Amato KR , Vlckova K , et al. Gut microbiome of coexisting BaAka pygmies and Bantu reflects gradients of traditional subsistence patterns . Cell Rep . 2016 ; 14 : 2142 – 53 . OpenUrl CrossRef PubMed 82. ↵ Guarner F , Malagelada JR . Gut flora in health and disease . Lancet . 2003 ; 361 ( 9371 ): 512 – 9 . OpenUrl CrossRef PubMed Web of Science 83. ↵ Ghosh TS , Gupta S Sen , Bhattacharya T , Yadav D , Barik A , Chowdhury A , et al. Gut microbiomes of Indian children of varying nutritional status . PLoS One . 2014 ; 9 ( 4 ): 1 – 13 . OpenUrl CrossRef PubMed 84. ↵ Cani PD . Human gut microbiome: Hopes, threats and promises . Gut . 2018 ; 67 ( 9 ): 1716 – 25 . OpenUrl Abstract / FREE Full Text 85. ↵ Mazmanian SK , Round JL , Kasper DL . A microbial symbiosis factor prevents intestinal inflammatory disease . Nature . 2008 ; 453 ( 7195 ): 620 – 5 . OpenUrl CrossRef PubMed Web of Science 86. Lee YK , Mazmanian SK . Has the microbiota played a critical role in the evolution of the adaptive immune system? Science (80-). 2010 ; 330 ( 6012 ): 1768 – 73 . OpenUrl Abstract / FREE Full Text 87. Bäckhed F , Ley RE , Sonnenburg JL , Peterson DA , Gordon JI . Host-bacterial mutualism in the human intestine . Science (80-). 2005 ; 307 ( 5717 ): 1915 – 20 . OpenUrl Abstract / FREE Full Text 88. ↵ Dethlefsen L , Relman DA . Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation . Proc Natl Acad Sci U S A . 2011 ; 108 (SUPPL. 1 ): 4554 – 61 . OpenUrl Abstract / FREE Full Text 89. ↵ Mayer EA , Savidge T , Shulman RJ . Brain-gut microbiome interactions and functional bowel disorders . Gastroenterology [Internet ]. 2014 ; 146 ( 6 ): 1500 – 12 . Available from : doi: 10.1053/j.gastro.2014.02.037 OpenUrl CrossRef PubMed Web of Science 90. ↵ Sherwin E , Sandhu K V. , Dinan TG , Cryan JF . May the force be with you: The light and dark sides of the Microbiota–Gut–Brain Axis in neuropsychiatry . CNS Drugs . 2016 ; 30 ( 11 ): 1019 – 41 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 03, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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