The coastal participants came from fishing and coastal communities including Sadras, Kalpakkam, Pudupattinam, Pattinampakkam, Kasimedu, Venpurusham and Ennore, areas where residents are routinely exposed to marine environments and, in some locations, to polluted water and coastal sediments that can harbor pharmaceutical chemicals and other organic and inorganic pollutants. Most participants were aged 19 to 59 and followed a mixed South Indian rice-based diet. Regular seafood consumption was more common among coastal participants, who also reported higher rates of alcohol consumption and were predominantly employed in fishing-related occupations. These demographic and lifestyle differences between the groups were not statistically adjusted for in the analysis, a limitation the authors acknowledge explicitly.

After quality control, the researchers analyzed 30 fecal and 28 skin swab samples from the coastal group and 10 fecal and 10 skin swab samples from the non-coastal group. Sequencing on the Illumina MiSeq platform targeting the V3-V4 region of the 16S rRNA gene yielded 1,577,705 high-quality read pairs from fecal samples and 528,957 from skin swabs. Taxonomic assignment against the SILVA database identified 34 taxa in non-coastal fecal samples and 43 in coastal fecal samples, while skin swabs yielded 41 taxa in the non-coastal group and a striking 137 in the coastal group, spanning 23 phyla compared with 10 in the non-coastal skin samples.

In fecal samples, the two groups shared the same dominant phyla but in reversed proportions. Coastal gut microbiota were dominated by Firmicutes, whereas non-coastal samples were enriched in Bacteroidota. Within Firmicutes, the classes Bacilli and Clostridia were more prevalent in the coastal group, with elevated abundances of the orders Lachnospirales, Oscillospirales, Clostridia UCG-014, Erysipelotrichales, Lactobacillales and Selenomonadales. A differential abundance analysis using LEfSe, based on 47 amplicon sequence variants, further highlighted enrichment in the coastal group of the Lachnospiraceae family, including the genera Coprococcus and Lachnospira, along with the Succinivibrionaceae family and the genus Succinivibrio within Proteobacteria. The non-coastal population showed significant enrichment of Bacteroidota, particularly the class Bacteroidia and the genus Bacteroides.

These taxonomic differences carry potential functional implications, though the study did not measure host physiology directly. Members of Lachnospiraceae are commonly associated with butyrate production, while Bacteroides species are associated with propionate production, and both short-chain fatty acid classes have been linked in previous research to mucosal immune regulation, metabolic stability and gut barrier integrity. Predictive functional profiling with PICRUSt2 identified 12 MetaCyc pathways differing between the groups, with glycogen biosynthesis relatively more represented in coastal samples. Nine pathways were predicted to be enriched in the coastal group, including the superpathway of glucose and xylose degradation, pyruvate fermentation to propanoate, the pentose phosphate pathway and L-histidine degradation, while polyamine biosynthesis II, glycol metabolism and degradation, and photorespiration were predicted to be less expressed. The authors stress that these PICRUSt2-based inferences are predictive and would require metabolomic and biochemical validation.

Statistically, the fecal results were more nuanced. Alpha diversity metrics, including observed species, Chao1, Shannon and Simpson indices, showed no significant differences between coastal and non-coastal gut samples after outlier removal and Benjamini-Hochberg correction for multiple comparisons. However, beta diversity analysis using PERMANOVA on Bray-Curtis dissimilarity with 999 permutations revealed a significant difference in overall community composition between the groups, with an F statistic of 2.24, an R-squared of 5.7 percent and a p-value of 0.001. The authors note that the R-squared value indicates coastal exposure explained only a limited share of the variation, and a significant test of multivariate dispersion suggests the PERMANOVA result may partly reflect differences in within-group variability rather than composition alone.

The skin results were, in some respects, the more compelling half of the study. Among coastal participants, 15 of 28 skin swab donors had clinically normal skin, while 7 had eczema, 5 had tinea and 1 had dry skin; none of the non-coastal participants had any visible skin condition. Because skin disease could confound the comparison, the researchers repeated their alpha diversity analysis using only clinically normal skin from both groups. Even after this restriction, and after outlier removal and Benjamini-Hochberg correction, significant differences persisted in observed OTUs, Chao1, Shannon and Simpson indices, suggesting that the diversity differences were not explained solely by the presence of skin conditions. PERMANOVA on skin swab Bray-Curtis dissimilarity also showed a significant difference in community composition, with F equal to 1.29, R-squared of 3.5 percent and p equal to 0.041, and in this case the dispersion test was not significant, supporting a genuine compositional difference.

At the phylum level, Firmicutes and Proteobacteria dominated skin swabs in both groups, but the coastal samples showed greater abundance of the classes Bacilli and Clostridia, and within Proteobacteria, elevated Gammaproteobacteria and Alphaproteobacteria. At the order level, coastal skin harbored more Peptostreptococcales-Tissierellales, Clostridiales, Staphylococcales, Bacillales, Lactobacillales, Pseudomonadales, Enterobacterales, Rhodobacterales and Sphingomonadales. Staphylococcales was the predominant order in most individuals from both groups, followed by different companion taxa depending on region. Notably, the LEfSe analysis found no statistically significant differential markers for skin swabs, underscoring that the skin differences emerged at the community and diversity level rather than through individual indicator taxa.

The authors frame their findings against a growing literature on how environment shapes the human microbiome. Previous work has reported that Firmicutes, Proteobacteria and Actinobacteria predominate in the skin of Indian individuals and that geographic location strongly influences skin microbial composition across Indian families. Environmental contaminants such as heavy metals and organic pollutants have been associated in prior studies with gut dysbiosis through oxidative stress, altered cytokine responses, reduced microbial diversity and impaired epithelial barrier integrity, and swimming in polluted ocean water has been reported to disrupt the skin microbiome. The present study, however, did not measure pollutant exposure, host inflammatory markers or metabolic parameters, so the mechanisms linking coastal environment to microbial composition remain speculative.

The study’s limitations are considerable and clearly stated. The cross-sectional design cannot establish causal relationships between environmental exposure and microbiome alterations, no formal power calculation was performed, the coastal and non-coastal groups differed in sex distribution, age structure, alcohol consumption and occupation, and no positive or negative controls were included during DNA extraction, library preparation or sequencing. Fecal samples also may not fully represent mucosal-associated or small-intestinal microbial populations. The authors conclude that coastal and non-coastal populations in Tamil Nadu harbor distinct microbial community structures across both gut and skin, and they call for larger, balanced studies integrating metagenomics, functional analyses and environmental assessments to clarify how coastal living, from contaminated water to seafood-rich diets and fishing occupations, shapes the human microbiome and, ultimately, human health.

Subject of Research: Comparative 16S rRNA sequencing of gut and skin microbiota in coastal versus non-coastal populations of Tamil Nadu, India

Article Title: Comparative analysis of gut and skin microbiota from the coastal and non-coastal populations in Tamil Nadu using 16S rRNA gene sequencing: a pilot study

Article References: Thangamani, R. A., Jayachandran, J., Jayavel, A., Ponnaiya, S. K. M., Mitra, A., Shanmugam, P., Radhakrishnan, A., Banerjee, A., & Duttaroy, A. K. (2026). Comparative analysis of gut and skin microbiota from the coastal and non-coastal populations in Tamil Nadu using 16S rRNA gene sequencing: a pilot study. Frontiers in Microbiology, 17, Article 1849751. https://doi.org/10.3389/fmicb.2026.1849751

Image Credits: AI Generated

DOI: 10.3389/fmicb.2026.1849751

Keywords: gut microbiome, skin microbiome, 16S rRNA sequencing, coastal populations, Tamil Nadu, India, dysbiosis, Firmicutes, Bacteroidota, Proteobacteria, environmental exposure, pilot study