ABSTRACT Ethnicity has a significant role in shaping the composition of the gut microbiome, which has implications in human physiology. This study intends to investigate the gut microbiome

of Bengali people as well as several indigenous ethnicities (Chakma, Marma, Khyang, and Tripura) residing in the Chittagong Hill Tracts areas of Bangladesh. Following fecal sample collection

from each population, part of the bacterial 16 s rRNA gene was amplified and sequenced using Illumina NovaSeq platform. Our findings indicated that Bangladeshi gut microbiota have a

distinct diversity profile when compared to other countries. We also found out that Bangladeshi indigenous communities had a higher _Firmicutes_ to _Bacteroidetes_ ratio than the Bengali

of the gut microbial diversity of the Bangladeshi population and its potential health implications. However, our study will help establish a basic understanding of the gut microbiome of the

Bangladeshi population. SIMILAR CONTENT BEING VIEWED BY OTHERS ELUCIDATING THE GUT MICROBIOME ALTERATIONS OF TRIBAL COMMUNITY OF ARUNACHAL PRADESH: PERSPECTIVES ON THEIR LIFESTYLE OR FOOD

HABITS Article Open access 31 October 2022 COMPARATIVE ANALYSIS OF GUT MICROBIOTA IN FREE RANGE AND HOUSE FED YAKS FROM LINZHOU COUNTY Article Open access 24 April 2025 GUT MICROBIOMES OF

AGROPASTORAL CHILDREN FROM THE ADADLE REGION OF ETHIOPIA REFLECT THEIR UNIQUE DIETARY HABITS Article Open access 01 December 2023 INTRODUCTION The human gut microbiome which is composed of

an enormous collection of microorganisms plays a crucial role in modulating various physiological processes, metabolic diseases, and immunological dysregulation. The composition of the gut

microbiome is influenced by a multitude of factors, including diet, lifestyle, environment, genetics, and ethnicity1. They forge a symbiotic relationship with the host in the

gastrointestinal tract, where they are monitored by the innate immune system using pattern recognition receptors such as toll-like receptors and Nucleotide-binding oligomerization

domain-containing protein (NOD)-like receptors2. A number of these bacteria are involved in regulating host metabolism by producing metabolites such as folate, indoles, secondary bile acids,

trimethylamine-N-oxide, neurotransmitters (e.g., serotonin, gamma amino butyric acid), and short-chain fatty acids3. Alteration in the host–symbiont relationships in the gut has been

observed in diseases like obesity, type 2 diabetes mellitus, non-alcoholic liver disease, cardio-metabolic diseases, and so on4. Since different ethnic groups have been reported to have

variations in genetics, food habits, lifestyle, their alterations can be greatly influenced by ethnicity. As a result, some ethnic groups are predisposed to certain metabolic diseases, while

others are inclined to others5. For example, high-risk variants of _NOD2_ are less prevalent in African Americans which makes their microbiome less prone to Crohn’s Disease6,7. Similarly,

the _lactase_ gene varies in different populations8. In the Japanese population higher abundance of _Bifidobacterium_ has been observed due to their characteristic _LCT_ genotypes9,10.

Hence, gut microbiome studies on ethnic groups can be helpful in identifying prognostic microbial biomarkers for various diseases. The genetic makeup, dietary patterns, and geographic

regions shape the gut microbiome of various ethnicities in a distinct pattern11,12,13,14. Previously, a number of gut microbiome studies in South-East Asia have been conducted. Gut

microbiomes of populations living in India and China which are geographically close to Bangladesh, showed an abundance of _Prevotella_ and _Bacteroidetes_ respectively5. In India, gut

microbiomes from the Adi, Apatani, and Nyshi tribes of Arunachal Pradesh (a state in Northeastern India) demonstrated the predominance of _Firmicutes_, _Bacteroidates_, _Actinobacteria_, and

_Proteobacteria_. On the other hand, Mongoloid and Proto-Australoid tribes of India were dominated by _Prevotella_15,16. However, studies from ethnic groups in Bangladesh are still not

available. Bangladeshi ethnic groups have diverse dietary habits. Bengali food customs stem from their agrarian culture, which is defined by a profusion of rice, fish, and different

vegetables. Dishes like biryani and kebabs are examples of the historical effects of Mughal and British dynasties on the cuisine. Seasonal fruit preparations, street snacks like shingara and

samosas, and traditional cakes called “Pitha” are all common17. Bangladeshi tribal people use forest resources to eat diversely. They feed on ‘bans koral,’ bamboo shoots from _Melocanna

baccifera_ and _Bambusa tulda_ during the rainy season. Fresh or dried wild plants commonly accompany staple grain recipes. Acidic leaves are often eaten as salad or chutney. Woody

perennials including _Albizia procera_, wild mango, and _Daemonorops jenkinsianus_ are vegetables for these ethnic groups. Lentinus, Shizophyllum, and Jew’s Ear mushrooms from decomposing

wood are eaten. Wild banana inflorescences and leaf sheath soft cores are also edible. During food shortages, banana core cooked with rice or bran is crucial18. Bangladesh is a country

inhabited by many different ethnicities. Bengalis are the largest among them. Other major groups are Chakma, Marma, Khyang, and Tripura with distinguishable food habits and lifestyles. The

Chittagong Hill Tracts in the southeastern parts of Bangladesh are home to the Chakma, Marma, Khyang, and Tripura people. Bengali people (Bengali language speakers) have Indian ancestry with

Proto-Australoid, Caucasoid, and Mongoloid genetics19,20,21. On the other hand, Chakma, Marma, Khyang, and Tripura people (Tibeto-Burman language speakers) have predominance of

Tibeto-Burman genetics. Tibeto-Burman populations originated from northwestern China and moved to the South. They have interbred with numerous southern tribes for the past 2600 years,

creating specific genetic traits among southern Tibeto-Burman populations22,23. Hence, Tibeto-Burman speakers from Northeast India are more distinctive than the ones living in the

Himalayas23. In Bangladesh, the Tibeto-Burman populations (e.g., Chakma, Marma, Khyang, Tripura etc.) have higher similarity with Northeast Indian Tibeto-Burman but they contain more

mainland Indian ancestry24. In this study, we investigated the gut microbiomes of Bengali, Chakma, Marma, Khyang, and Tripura populations in order to determine whether Bangladeshi

Tibeto-Burman populations differ from Bengali people in terms of their gut microbiomes. METHODS SAMPLE COLLECTION Prior to sample collection, written informed consent was obtained from all

participants. Ethical approval and necessary permits were obtained from the National Institute of Biotechnology Ethical Review Committee, Bangladesh (NIBERC2022-01). All ethical regulations

relevant to human research participants were followed. Supplementary Data 1 contains detailed sample information regarding age, gender, and cohort. All of the indigenous volunteers (Chakma,

Marma, Khyang, and Tripura) in this study reside in the Rangamati district (22°37’60 N 92°12’0E) of the Chittagong Hill Tracts region. Bengali samples were collected from Dhaka Division

(23.9536° N, 90.1495° E). Based on ethnicity, the participants were divided into five groups (Bengali, Chakma, Marma, Khyang, and Tripura). A total of 55 individuals were sampled, of which

13 were Bengali, 15 were Chakma, 10 were Khyang, 6 were Marma, and 11 were Tripura. Fecal samples of the participants were collected using sterile stool collection tubes. The samples were

then transported to the National Institute of Biotechnology using Icebox and subsequently stored at −80 °C temperature. Fecal DNA extraction was executed using the PureLink™ Microbiome DNA

Purification Kit (Catalog number: A29790). Specialized beads were used along with a combination of heat, chemical, and mechanical disruption to lyse the microorganisms efficiently.

Precipitation with a proprietary cleaning buffer removed inhibitors. After that, the samples were placed in spin columns, and the DNA attached to the column was washed once before elution.

DNA concentration and purity were estimated by Thermo Scientific NanoDrop 2000/2000c and then stored at −20 °C. AMPLICON GENERATION AND LIBRARY PREPARATION FOR SEQUENCING Amplicons were

generated using the 341 F (5’-CCTAYGGGRBGCASCAG-3’) and 806 R (5’-GGACTACNNGGGTATCTAAT-3’) primers that targeted the V3-V4 region of the bacterial 16 S rRNA gene. All PCR reactions were

performed with 15 μL of Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 μM of forward and reverse primers, and around 10 ng template DNA. For thermal cycling, initial

denaturation at 98 °C for 1 min was followed by 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 30 s. Final elongation was carried out

for 5 min at 72 °C. TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) was used as per the manufacturer’s protocol for sequencing library preparation, and index codes were appended.

The quality of the library was evaluated by employing the [email protected] Fluorometer (Thermo Scientific) and the Agilent Bioanalyzer 2100 system. Finally, the library was sequenced on an

Illumina NovaSeq platform, resulting in 250 bp paired-end reads. DATA PRE-PROCESSING AND QUALITY CONTROL The paired end sequences were converted into QIIME2 format for data pre-processing,

quality control, taxonomic assignment, differential abundance identification, functional analysis using QIIME2 platform (version 2021.4.0)25. More specifically, the data pre-processing of

paired end sequences were performed using the DADA2 plug-in within QIIME226. DADA2 filtered noisy reads, performed error correction in marginal sequences, removed chimeras and singletons,

joined denoised paired-end reads, and also de-replicated the filtered reads. The features produced by DADA2 are denoted as amplicon sequence variants. TAXONOMIC ASSIGNMENT For taxonomic

assignment, a pre-trained classifier based on the Naive Bayes machine-learning model was implemented. This model was trained on 99% sequence similarity of Greengenes 13_8 data. This

classifier was then deployed for taxonomy assignment of amplicon sequence variants. DIVERSITY ANALYSIS Several diversity metrics in QIIME2 require a rooted phylogenetic tree generated from

the amplicon sequence variants of the sampled data. A reference-based fragment insertion method, using q2-fragment-insertion tool, was applied to construct the rooted tree for this purpose.

Greengenes 13_8 data was used as a reference database in the q2-fragment-insertion tool27,28. The sequencing depth of the samples was 3525 to observe the richness. This phenomenon was

checked with the alpha rarefaction curve generated by the q2-diversity tool. The microbiome within and between samples was calculated by the core-metric-phylogenetic method of the

q2-diversity tool. This method computes several alpha (Observed features, Shanon diversity, Faith’s phylogenetic diversity, Pielou evenness) and beta (Jaccard distance, Bray–Curtis distance,

unweighted UniFrac distance, and weighted UniFrac distance) diversity metrics altogether29. Based on each beta diversity metric, this command also performed principal coordinates analysis

(PCoA)30. To visualize the PCoA plots for every beta diversity metric, EMPeror visualization tool was utilized to generate the figures31. Several statistical tests were conducted during

diversity analysis such as Kruskal–Wallis H test, two-way ANOVA, paired _t_-test and PERMANOVA test32,33,34,35. The boxplot() function in R was used to draw boxplots based on alpha diversity

values. This function follows the 1.5 IQR method for detecting outliers which are placed above and below the whiskers on the boxplots. Here, only one sample was identified as outlier

(Sample ID: BBT19). The result including the outlier has been provided in Supplementary Fig. 1. After removal of the outlier, the number of samples used for analysis were reduced from 55 –

54. DIFFERENTIAL ABUNDANCE TEST To classify the features that were differentially abundant across various sample groups the analysis of the composition of microbiomes (ANCOM) method was

applied by the q2-composition tool36. This statistical framework was deployed at the genus level. The minimum sample size for each feature was set to 27 (half of the total samples) because

ANCOM fails to manage false discovery rates at sample sizes <10 as well as to remove very lowly abundant features37. For linear discriminant analysis by the LEfSe, the feature table was

collapsed at the genus level and the minimum sample size was chosen at 2738. This tool first performs the non-parametric Kruskal-Wallis (KW) sum-rank test to identify the features which had

significant differential abundance across different metadata categories. Finally, LEfSe applies linear discriminant analysis to compute the effect size of each differentially abundant

feature and plot the linear discriminant analysis score in the log10 scale. Result of both ANCOM and LEfSe analysis conducted for all samples including the previously mentioned outliers has

been provided in Supplementary Fig. 2. FUNCTIONAL ANALYSIS BURRITO, an interactive visualization web server (http://elbo-spice.cs.tau.ac.il/shiny/burrito/), was utilized to explore the

taxa-function relationship within the samples of the study39. To acquire gene contents and functional annotations, this tool adopts the PICRUSt and KEGG Orthology databases,

respectively40,41. At first, features with a sample size of <27 were filtered from the original feature table to remove very low abundant taxon. The q2-vsearch tool was employed for

closed-reference clustering of retrieved features at 97% identity based on greengenes 97% OTU IDs as reference 42,43. Thus the acquired OTU table was then converted to the appropriate table

format for BURRITO input. COMPARATIVE ANALYSIS WITH TROPICAL AND SUBTROPICAL COUNTRIES Since Bangladesh is a tropical country, we compared the gut microbiome of Bangladeshi samples with

several tropical and subtropical countries to see if there is any similarity between them. To do this we took sequence data from publicly available 16 s rRNA amplicon sequence data from the

NCBI Sequence Read Archive and MG-RAST databases. Only healthy / control samples were taken from the selected datasets. Countries included in the comparative analysis were Australia, Egypt,

India, Indonesia, Malaysia, Mexico, Thailand, and Vietnam. Information about the samples taken from NCBI and MG-RAST has been presented in Supplementary Data 2 and Supplementary Data 3

respectively. The samples downloaded from MG-RAST were processed using q2-deblur while the samples downloaded from NCBI were processed using q2-dada234,44. This is because the samples were

taken from different regions and were prepared and sequenced using different methods. Amplicon sequencing using the Illumina MiSeq technology was done on Indian samples, encompassing the

V3-V4 region of bacterial 16 S rDNA. On the other hand, the V4 region of bacterial 16 S ribosomal RNA genes from Malawi, Amerindians, and the United States was amplified and sequenced using

an Illumina HiSeq 2000. The V1-V3 region of the 16 S ribosomal RNA (rRNA) gene of the Mongolian samples was amplified and sequenced using pyrosequencing on a Roche GS FLX. The samples were

merged with the qiime feature-table merge method. All the samples were rarefied to the same depth (3525). These foreign samples (_n_ = 181) were then compared with all Bangladeshi samples

(_n_ = 55) using the q2-diversity tool via Alpha diversity and Beta Diversity analysis. Afterwards, a phylogenetic rooted tree was generated by the sepp fragment insertion approach. This

rooted phylogeny was used to create a Unweighted Pair Group Method with Arithmetic Mean tree based on the Unweighted UniFrac metric by beta-rarefaction command of the q2-diversity tool45.

REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. RESULTS BENGALI POPULATION POSSESSED LOWER

_FIRMICUTES_ TO _BACTEROIDETES_ RATIO COMPARED TO INDIGENOUS GROUPS Among the 19 classified phyla, _Firmicutes_ and _Bacteroidetes_ were the most prevalent (48% and 34% of the total) in the

gut microbiome of all Bangladeshi populations. Other phyla with higher abundance were Proteobacteria (14%), Actinobacteria (3%), Tenericutes (0.5%), etc (Supplementary Fig. 3).

Interestingly, 100% of taxa from the _Bacteroidetes_ phylum belonged to the _Bacteroidia_ class. On the other hand, considering _Firmicutes_ phylum, 87% of features are _Clostridia_ class,

11% _Bacilli_, and 2% are _Erysipelotrichi_ (Supplementary Fig. 4). In Supplementary Data 4, the taxonomic classification of each feature id is documented, along with the confidence value.

The normalized abundance of the top ten genera across different cohorts is represented in cpm (counts per million) (Fig. 1a). The _Prevotella_ genus abundance was relatively similar in all

cohorts. Nevertheless, the prevalence of the _Bacteroides_ genus was drastically higher in the Chakma population and very low in Marma and Tripura samples. Moreover, Chakma tribal group also

contained _Faecalibacterium_, _Roseburia_, and uncharacterized genera from the _Ruminococcaceae_ family and _Bacteroidales_ order in a relatively higher amount. On the other hand,

_Streptococcus_ and _Megasphaera_ genera were highly abundant in the Bengali and Tripura populations and the Marma cohort contained these genera in relatively lower quantities. The Khyang

group contained the genus _Dialister_ and uncharacterized genera from _Enterobacteriaceae_ and _Ruminococcaceae_ families. About 0.05% amplicon sequence variants were classified as archaea

of the _Methanobacteriaceae_ family, of which only 0.1% were from the _Methanosphaera_ genus, and the rest of the features belonged to the _Methanobrevibacter_ genus (Fig. 1b). Twenty-four

families of features were found as core features and the frequency heatmap of these features revealed that there were two main clusters, one with higher frequency and another with lower

frequency (Fig. 1c). The _Streptococcaceae_, _Prevotellaceae_, _Veillonellaceae_, _Lachnospiraceae_, _Ruminococcaceae_, _Enterobacteriaceae_, and _Bacteroidaceae_ families were in the higher

frequency cluster. Most of the samples from the Bengali population clustered together and contained these higher frequency microbial families in relatively lower amounts. The median value

of the _Firmicutes_ to _Bacteroidetes_ ratio was highest (2.018) in the case of Tripura samples, while the Bengali population had the lowest (0.877) median ratio (Fig. 1d). All other cohorts

(Chakma, Khyang, Tripura) had a median _Firmicutes_ to _Bacteroidetes_ ratio of more than one. THE BENGALI POPULATION SHOWED INFERIOR MICROBIOME RICHNESS THAN OTHER ETHNIC COHORTS Alpha

diversity depicts the diversity within the sample. Alpha diversity is measured via Shannon diversity, Observed features, Faith pd and Pielou evenness parameters. Shannon diversity calculates

both the number of species in a community and their relative abundance. The median of Shannon diversity was relatively lower in the Bengali group compared to others (Fig. 2). For ethnicity

based cohorts, Kruskal-Wallis test for all groups had a _p-_value of 0.0033 (Supplementary Data 5). Observed features counted the number of distinct features present in the cohorts (Fig. 2).

Chakma, Marma, Khyang and Tripura populations had higher species richness than the Bengalis (_p-_value = 0.0003) (Supplementary Data 6). Faith pd incorporated information of the

evolutionary relationships between different bacterial species. Bengali samples had lower Fatih _p_-values than indigenous populations (Fig. 3). However, the difference in Faith pd score was

statistically insignificant (_p_-value = 0.3816) (Supplementary Data 7). Pielou evenness measured how evenly different species’ abundances were distributed within a community (Fig. 3).

There was no significant difference in Pielou evenness profile between indigenous populations (_p-_value = 0.0631) (Supplementary Data 8). PRESENCE AND ABSENCE OF VARIOUS SPECIES MADE

BENGALI MICROBIOME DISTINCT The PCoA plot revealed that the Bengali cohort was distinct from others in terms of Jaccard distance based on ethnicity (Fig. 4b) Jaccard distance is a binary

distance matrix that only considers whether a taxon is present or not. In other metrics, such as Bray–Curtis, weighted and unweighted UniFrac distances calculate presence and relative

abundances of microbial taxa but no separated clusters were observed there (Figs. 4a, c, and d). _ALISTIPES_ GENUS IS DIFFERENTIALLY ABUNDANT IN CHAKMA POPULATION ANCOM analysis revealed the

microbial community compositions between the various Bangladeshi ethnic groups. It used a log-ratio test to compare the relative abundance using non-parametric methods. Here, only the

Bengali vs. Non-Bengali and the Chakma vs. Non-Chakma comparisons demonstrated differentially abundant taxa between the groups. In terms of ANCOM, the other groups (Marma, Khyang, and

Tripura) did not reveal any differentially abundant bacteria. In the Bengali group, an unclassified bacteria showed a negative centered log ratio (clr) with higher W score (Fig. 5a).

Whereas, the Chakma population depicted abundance for _Alistipes_ and _Odoribacter_ (Fig. 5b). Another tool, LEfSe implemented statistical methods to identify microbial features that were

differentially abundant. LEfSe unveiled differentially abundant genus _Paraprevotella_, _Lactonifactor, Barnesiella, Bacteroides_, _Alistipes_, and _Ruminococcus_ within the Chakma

population (Fig. 5c). The results from ANCOM and LEfSe analysis complemented each other as they both unveiled that the genus _Alistipes_ is differentially abundant in the Chakma population.

BENGALI GUT MICROBIOME CONTRIBUTED TO MARKEDLY DIVERSE BIOLOGICAL PATHWAYS Relationships between microbiome composition and its effect on different biological functions were also explored in

this study. Among all metadata categories, only the Bengali, Chakma, and Marma groups showed differential enrichment of function based on their gut microbiome composition (Fig. 6). All

differentially abundant functions along with related contributing taxa and BH (Benjamini and Hochberg) FDR-adjusted _p_-values are tabulated in Supplementary Data 9. Thirty-three pathways

were differentially abundant in Bengali samples. Several of them were highly enriched in Bengali samples such as peptidases, Ribosomes, Purine, and Pyrimidine metabolism (Fig. 6a). The genus

_Bacteroides_, _Blautia_, _Collinsella_, _Coprococcus_, _Dorea_, _Parabacteroides_, SMB53, and _Slackia_ were the top significantly contributing taxa in the enrichment of these functions.

Chakma samples seemed to be enriched with histidine metabolism, arginine biosynthesis, and transcription machinery (Fig. 6b). But, in the case of the Marma samples histidine metabolism,

arginine biosynthesis functions were in relatively lower abundance than other Non-Marma samples, and transcription machinery function was in higher frequency (Fig. 6c). For both categories,

the genus _Bacteroides_, _Blautia_, and _Parabacteroides_ were the most contributing taxa for function abundance. Detailed statistics for significant pathways and taxa contributions for each

pathway are documented in Supplementary Data 10. BANGLADESHI GUT MICROBIOME HAS A DISTINCT COMPOSITION COMPARED TO TROPICAL AND SUBTROPICAL POPULATIONS When compared with the gut microbiome

of several other countries, the Bangladeshi samples formed a vividly distinct cluster based on bacterial composition and abundance (Fig. 7a, b). Cluster for Bray–Curtis and Jaccard distance

indicates that bacterial composition and their abundances are unique for Bangladeshi populations. Their species richness was higher than other countries (Fig. 7f). The unweighted UniFrac,

which incorporates phylogenetic information, demonstrated that most of the samples from the Bangladeshi population clustered closer to the Indian samples (Fig. 7c). All numerical source data

of Fig. 7 can be found in Supplementary Data 11a–h. Furthermore, the Unweighted Pair Group Method with Arithmetic Mean based tree showed that most of the indigenous samples of Bangladesh

have clustered with different Indian indigenous groups while the Bangladeshi samples as a whole were scattered across different phylogenetic clades (Fig. 8). DISCUSSION The human gut

microbiome is an integral part of human growth and development and it is highly influenced by ethnicity14,46. Diverse ethnic populations reside in Bangladesh. The majority of the population

is Bengali whereas large indigenous communities such as the Chakma, Marma, Tripura, and Khyang live in Chittagong Hill Tracts region of the country. The eating habits of these communities

substantially differ from Bengali people. Their gut microbiomes, which may have an impact on important public health issues, can be influenced by their genetic make-up, geographic location,

and lifestyle. In this study, we conducted a pilot survey to shed light of these areas. Here, we have performed a 16 s rRNA gene amplicon sequencing to identify the gut microbiome

composition. To execute this, we have collected fecal samples from Bengali, Chakma, Marma, Khyang, and Tripura populations. After collecting the microbial DNA from fecal samples, parts of

the 16 S rRNA genes of the bacterial species were amplified. The amplified products were then sequenced. The sequence data was then processed and analyzed to draw interpretations. After all

the analysis, we found that at the phylum level, Bengali population has lower _Firmicutes_ to _Bacteroidetes_ ratio compared to indigenous groups. Among _Firmicutes_ phylum, _Dialister_ and

_Faecalibacterium_ are highly abundant in Bangladeshi microbiomes (Fig. 1a). On the other hand, _Prevotella_, a member of the _Bacteroidetes_ phylum, was distributed evenly across each

cohort. People from the Indian subcontinent usually have _Prevotella_ abundance in their gut47. Here, we have explored the alpha and beta diversity of the gut microbiome. Alpha diversity

depicted within-group diversity and beta diversity showed different diversity ratios between the groups. The alpha diversity of the Bengali population was lower compared to others according

to the Shannon diversity and species richness (Observed features) (Fig. 2). The overall species evenness was enriched for indigenous populations (Fig. 3). Based on the phylogenetic diversity

of the gut microbes, the Bengali and the people of the Chittagong Hill Tracts were found not to be very distant. However, our findings indicate that the overall gut microbial population and

abundance is unique for Bangladesh when compared with international datasets. However, most of the indigenous samples from Bangladesh (Chakma, Marma, Khyang, Tripura) clustered separately

with a common origin with the Indian indigenous population whereas Bengali samples were spread across various clades (Fig. 8). Some bacterial species were found to be abundant in indigenous

groups that were not enriched in Bengali samples or vice versa. To identify this differential abundance, we employed several statistical approaches using LEfSe and ANCOM. Both of these tools

identified an unclassified bacteria, which can be studied further, that is significantly abundant in Bengali samples whereas Chakma samples had enriched levels of _Alistipes_ species.

_Alistipes_ is a newly discovered genus under _Bacteroidetes_ phylum. The gut microbial pathways play a critical role in health and diseases48. In Bengali samples DNA repair system,

pyrimidine and purine metabolism, lipopolysaccharide (LPS) biosynthesis, peptidases, ribosome, etc functions were enriched. On the other hand, Chakma samples were enriched with histidine

metabolism and arginine biosynthesis (Fig. 6b) whereas in the Marma group abundance of these pathways were lower (Fig. 6c). Furthermore, Bangladeshi gut microbes possessed a distinctive

diversity profile. Further research is needed to investigate the specific differences in responses and underlying mechanisms associated with distinct gut microbiota profiles. LIMITATIONS OF

THE STUDY The primary limitation of the study is small sample size, which exhibits significant variability among individuals sampled. With a sample size of _n_ = 55 divided into five ethnic

groups, two genders, and three large age groups (20–40, 40–60, and 60–80 years), the representativeness for each combination is low. The gut microbiome (GM) is well-known to be highly

influenced by factors such as sex, age, current health status and so on. In our study, the representativeness of each of these groups is limited. A higher sample size would certainly enhance

the credibility of the differences between these groups and also help draw more meaningful conclusions. However, the primary goal of our study was to explore and characterize the gut

microbiome of Bangladeshi population for the first time and thus establish the baseline data for further gut microbiome research in Bangladesh. During the course of the study, as a secondary

goal, we also wanted to see if there is any difference in the gut microbiome of the various ethnicities. Since our main priority was to characterize the Bangladeshi population as a whole,

the difference between various subgroups based on age, sex, and health status was given less importance. We want to reiterate that the number of participants in the current study was not

sufficient to provide a comprehensive representation of the overall gut microbial diversity within neither the Bangladeshi population as a whole nor the ethnicities studied (Bengali, Chakma,

Marma, Khyang, and Tripura). As a result, the findings from this study may lack statistical power and robustness, leading to potential biases and limited generalizability of the results.

Moreover, the small sample size may hinder the identification of subtle microbial variations that could be crucial in understanding complex interactions between the gut microbiota and

various health conditions. Consequently, caution must be exercised when interpreting the results of this gut microbiome study due to its limited sample size. Further research with larger

cohorts is absolutely necessary to draw more definitive and reliable conclusions about the role of gut microbiota in the Bangladeshi population. In conclusion, the current study indicates

that the indigenous gut microbiome was more diverse and distinct from the Bengali population. Our study will help establish the baseline data for the gut microbiome of the Bangladeshi

population. DATA AVAILABILITY All newly generated 16rRNA amplicon sequencing data used in this study can be freely accessed via NCBI BioProject number PRJNA876782. Source data have been

included in the supplementary data files. All other data are available from the corresponding author on reasonable request. CODE AVAILABILITY Code can be accessed from the corresponding

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Target. Ther._ 7, 1–28 (2022). Google Scholar  Download references ACKNOWLEDGEMENTS This study was funded under the “Establishment of National Gene Bank Project”, National Institute of

Biotechnology (NIB), Ministry of Science and Technology, Government of the People’s Republic of Bangladesh. We would like to thank Md. Amjad Hossain, Ritaren Chakma, Tina Tripura, Barshan

Chakma, and Suborna Dash for their assistance with sample and metadata collection. AUTHOR INFORMATION Author notes * These authors contributed equally: Ishtiaque Ahammad, Arittra

Bhattacharjee, Zeshan Mahmud Chowdhury, Anisur Rahman, and Mohammad Uzzal Hossain. AUTHORS AND AFFILIATIONS * Bioinformatics Division, National Institute of Biotechnology, Ganakbari,

Ashulia, Savar, Dhaka, 1349, Bangladesh Ishtiaque Ahammad, Arittra Bhattacharjee, Zeshan Mahmud Chowdhury, Anisur Rahman & Mohammad Uzzal Hossain * Rangamati Medical College, Hospital

Road, Rangamati-4500, Rangamati, Bangladesh Gourab Dewan & Shiny Talukder * Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349,

Bangladesh Keshob Chandra Das & Md Salimullah * Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, 1229, Bangladesh Chaman Ara Keya Authors *

Ishtiaque Ahammad View author publications You can also search for this author inPubMed Google Scholar * Arittra Bhattacharjee View author publications You can also search for this author

inPubMed Google Scholar * Zeshan Mahmud Chowdhury View author publications You can also search for this author inPubMed Google Scholar * Anisur Rahman View author publications You can also

search for this author inPubMed Google Scholar * Mohammad Uzzal Hossain View author publications You can also search for this author inPubMed Google Scholar * Gourab Dewan View author

publications You can also search for this author inPubMed Google Scholar * Shiny Talukder View author publications You can also search for this author inPubMed Google Scholar * Keshob

Chandra Das View author publications You can also search for this author inPubMed Google Scholar * Chaman Ara Keya View author publications You can also search for this author inPubMed 

Google Scholar * Md Salimullah View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Conceptualization: Mohammad Uzzal Hossain, Keshob Chandra

Das, Chaman Ara Keya, Md Salimullah, Analysis: Ishtiaque Ahammad, Arittra Bhattacharjee, Zeshan Mahmud Chowdhury, Anisur Rahman, Resources: Gourab Dewan, Shiny Talukder, Keshob Chandra Das,

Md Salimullah. Writing—Original Draft: Ishtiaque Ahammad, Arittra Bhattacharjee, Zeshan Mahmud Chowdhury, Anisur Rahman, Writing—Review & Editing: Ishtiaque Ahammad, Arittra

Bhattacharjee, Zeshan Mahmud Chowdhury, Anisur Rahman, Mohammad Uzzal Hossain, Chaman Ara Keya, Md Salimullah. Supervision: Keshob Chandra Das, Chaman Ara Keya, Md Salimullah. Funding

acquisition: Md Salimullah. CORRESPONDING AUTHOR Correspondence to Md Salimullah. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW

INFORMATION _Communications Biology_ thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Kevin Theis and Tobias Goris. ADDITIONAL

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reveals the distinctiveness between the Bengali people and the Indigenous ethnicities in Bangladesh. _Commun Biol_ 7, 500 (2024). https://doi.org/10.1038/s42003-024-06191-9 Download

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