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Background
Metagenomic studies carried out in the past decade have led to an enhanced understanding of the gut microbiome in human health, however, the Indian gut microbiome is not well explored yet. We analysed the gut microbiome of 110 healthy individuals from two distinct locations (North-Central and Southern) in India using multi-omics approach...
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Context 1
... four population datasets. The co-inertia (Procrustes) analysis and the eigenvalues calculated from PCA using both core and accessory functions also showed that the Indian gut microbiome was significantly (FDR Adj. P-value = 6.4 x 10 -10 , 2 x 10 -16 and 0.05 with China, Denmark and USA, respectively for PC1) different from the other datasets ( Fig. 2A & B). These results also show the uniqueness of Indian gut microbial functions in composition and diversity at both core and accessory levels. The Indian gut microbiome was found to be enriched (FDR Adj. P<0.05, Log Odds Ratio >1.5) in functions for carbohydrate and energy metabolism including degradation of complex polysaccharides and ...
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Objective
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Background:
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Background
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Methods
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Citations
... To find altered ASVs without collapsing the features, DESeq2 analysis was used. Moreover, because zero-inflated data can lead to increased estimates of variance, zero-inflated count models appear to provide a better fit for 16S datasets displaying a bimodal distribution (i.e., point mass at zero and second mass separate from zero) [42,49]. Thus, we also confirmed our results using the R packages zinbwave (a zero-inflated count modelzero-inflated negative binomial (ZINB) model) and scran, followed by differential abundance testing analysis using DESeq2 (Fig. 1E). ...
Background
The exact mechanisms linking the gut microbiota and social behavior are still under investigation. We aimed to explore the role of the gut microbiota in shaping social behavior deficits using selectively bred mice possessing dominant (Dom) or submissive (Sub) behavior features. Sub mice exhibit asocial, depressive- and anxiety-like behaviors, as well as systemic inflammation, all of which are shaped by their impaired gut microbiota composition.
Methods
An age-dependent comparative analysis of the gut microbiota composition of Dom and Sub mice was performed using 16S rRNA sequencing, from early infancy to adulthood. Dom and Sub gastrointestinal (GI) tract anatomy, function, and immune profiling analyses were performed using histology, RT-PCR, flow cytometry, cytokine array, and dextran-FITC permeability assays. Short chain fatty acids (SCFA) levels in the colons of Dom and Sub mice were quantified using targeted metabolomics. To support our findings, adult Sub mice were orally treated with hyaluronic acid (HA) (30 mg/kg) or with the non-steroidal anti-inflammatory agent celecoxib (16 mg/kg).
Results
We demonstrate that from early infancy the Sub mouse gut microbiota lacks essential bacteria for immune maturation, including Lactobacillus and Bifidobacterium genera. Furthermore, from birth, Sub mice possess a thicker colon mucin layer, and from early adulthood, they exhibit shorter colonic length, altered colon integrity with increased gut permeability, reduced SCFA levels and decreased regulatory T-cells, compared to Dom mice. Therapeutic intervention in adult Sub mice treated with HA, celecoxib, or both agents, rescued Sub mice phenotypes. HA treatment reduced Sub mouse gut permeability, increased colon length, and improved mouse social behavior deficits. Treatment with celecoxib increased sociability, reduced depressive- and anxiety-like behaviors, and increased colon length, and a combined treatment resulted in similar effects as celecoxib administered as a single agent.
Conclusions
Overall, our data suggest that treating colon inflammation and decreasing gut permeability can restore gut physiology and prevent social deficits later in life. These findings provide critical insights into the importance of early life gut microbiota in shaping gut immunity, functionality, and social behavior, and may be beneficial for the development of future therapeutic strategies.
... Our methodology began by determining the underlying taxon abundance levels for the training and testing populations. From Figure 1, the two least overlapping datasets, Gupta 26,68 and Feng 25 , were chosen to be the template of training and testing sets, respectively. More specifically, 30 control samples and 183 species of the Gupta dataset were included for simulating the dataset for training, and 61 healthy samples and 468 species of the Feng dataset were included for simulating the dataset for testing. ...
The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.
... Earlier studies identified that the Indian gut microbiota is distinct, as it is rich in Prevotella, Dialister, and Megasphaera, [26][27][28][29][30][31][32] unlike westernized groups dominated by Bacteroides spp. 33 and Phocaeicola vulgatus 34 . ...
... An ordinary one-way Analysis of Variance (ANOVA) was used, and no significant differences in macronutrient intake were found between ESHA and EpiNu. The macronutrient compositions from ESHA were 30 Immigrants. (B) Significant differences in types of fat consumed by Indians and Indo-Immigrants were calculated between EpiNu vs. ESHA. ...
... Indians, known for their high-complex-carbohydrate diet, have been previously indicated to exhibit an increased expression of carbohydrate metabolism genes for complex polysaccharides in their gut. 32,26,30 Our findings reflected this relationship, as we detected enrichment of CAZy families that aid in the digestion of xylan and xyloglucan, with P. copri as the top contributing taxa. A similar pattern was in fact detected in another study on immigrants who migrated from Thailand to the US, where a decrease in Prevotella spp. ...
Objective
This study investigates the gut microbiome of Indians residing in India, Indian immigrants in Canada, and Canadians of Indian ancestry to understand the impact of westernization on their gut. Given the higher risk of Inflammatory Bowel Disease (IBD) among Indo-Canadians, these results provide insight into how westernization of the gut microbiome may influence incidence of IBD.
Design
Stool samples were collected from healthy Indians in India, Immigrants born in India, Indo-Canadians, plus Canadian-borne individuals with European descent and Euro-Immigrants as non-Indian/westernized controls (N = 174).16S and shotgun sequencing compared microbial taxa and functional profiles across groups. Dietary and demographic data were collected to assess lifestyle patterns.
Results
The microbiome of Indians and Indo-Immigrants were found to be distinct from westernized groups, with high abundances of Prevotella spp. and CAZymes known to facilitate the breakdown of plant-based foods, reflecting their traditional high complex carbohydrate diet. The Indian microbiome also showed characteristics of high bacterial cell turnover, pathogenic potential and stress tolerance, indicating their gut may be better primed to handle stressors on the gut. Indo-Canadians exhibited a transitional gut microbiome, harbouring taxa both common to Indians and westernized groups, which followed a pattern of increasing dietary acculturation.
Conclusions
The transition from a non-westernized to westernized microbiome in Canadian- borne Indian migrants highlights the significant influence that the westernized lifestyle and dietary acculturation has on the gut microbiome. Considering globalization and immigration to westernized countries is occurring at a rapid rate, with 40.6% of the Canadian population made up of first- and second-generation immigrants, ¹ future research should investigate the health implications that such microbiome transitions have on immigrant populations and newly industrialized nations.
KEY MESSAGES
What is already known on this topic
The Indian microbiota, known for its high Prevotella spp. abundances, differs from the westernized gut. Indians in westernized countries face increased risk for Inflammatory Bowel Disease (IBD), which may be linked to their gut microbiome. Yet, research on this demographic is scarce, leaving a gap in knowledge to elucidate the factors contributing to their increased IBD risk.
What this study adds
Our study, using both 16S and shotgun metagenomic analysis, reveal a decline in Prevotella spp. and a loss of gut robustness in Indian migrants, indicating a transition towards a westernized microbiome. This transition is characterized by the diminished capacity for carbohydrate-active enzymes (CAZymes) necessary for plant-based dietary products, which was associated with their transition away from the high-fiber Indian diet.
How this might affect research, practice, or policy
By highlighting the microbiome changes that occur in Indian immigrants upon westernization, this study underscores the urgent need for more inclusive microbiome research beyond westernized/European populations, which can inform healthcare strategies to mitigate disease risks associated with westernization.
... More importantly, NAD can upregulate BCAA-related fatty acid metabolism, while Prevotella, strongly correlated with BCAA levels, contains BCAA biosynthesis gene. 92,93 BCAAs involve three essential amino acid, including Leucine, Isoleucine and Valine, which can promote the repair of liver injury and restore its detoxification function. Given that branched-chain aminotransferase (BCAT) activity is strong in the colon, kidneys, and skeletal muscles but low in the liver, BCAA typically avoid the liver's first pass catabolism and instead convert into branched-chain a-keto acid (BCKA) in tissues like skeletal muscles. ...
Metabolic dysfunction–associated steatotic liver disease (MASLD), formerly named non-alcoholic fatty liver disease (NAFLD), is induced by alterations of hepatic metabolism. As a critical metabolites function regulator, nicotinamide adenine dinucleotide (NAD) nowadays has been validated to be effective in the treatment of diet-induced murine model of MASLD. Additionally, gut microbiota has been reported to have the potential to prevent MASLD by dietary NAD precursors metabolizing together with mammals. However, the underlying mechanism remains unclear. In this review, we hypothesized that NAD enhancing mitochondrial activity might reshape a specific microbiota signature, and improve MASLD progression demonstrated by fecal microbiota transplantation. Here, this review especially focused on the mechanism of Microbiota-Gut-Liver Axis together with NAD metabolism for the MASLD progress. Notably, we found significant changes in Prevotella associated with NAD in a gut microbiome signature of certain MASLD patients. With the recent researches, we also inferred that Prevotella can not only regulate the level of NAD pool by boosting the carbon metabolism, but also play a vital part in regulating the branched-chain amino acid (BCAA)-related fatty acid metabolism pathway. Altogether, our results support the notion that the gut microbiota contribute to the dietary NAD precursors metabolism in MASLD development and the dietary NAD precursors together with certain gut microbiota may be a preventive or therapeutic strategy in MASLD management.
... where a i is the relative abundance of gene in sample S, x i is the number of time gene i was detected in sample S (i.e. the number of mapped reads), L i is the length of gene i, j is all the genes (j number of genes) and b i is copy number of gene i in sequence data from sample S 43,44 . ...
Ocean microbiome is crucial for global biogeochemical cycles and primary productivity. Despite numerous studies investigating the global ocean microbiomes, the microbiome composition of the Andaman region of the Indian Ocean remains largely unexplored. While this region harbors pristine biological diversity, the escalating anthropogenic activities along coastal habitats exert an influence on the microbial ecology and impact the aquatic ecosystems. We investigated the microbiome composition in the coastal waters of the Andaman Islands by 16S rRNA gene amplicon and metagenomic shotgun sequencing approaches and compared it with the Tara Oceans Consortium. In the coastal waters of the Andaman Islands, a significantly higher abundance and diversity of Synechococcus species was observed with a higher abundance of photosynthesis pigment-related genes to adapt to variable light conditions and nutrition. In contrast, Prochlorococcus species showed higher abundance in open ocean water samples of the Indian Ocean region, with a relatively limited functional diversity. A higher abundance of antibiotic-resistance genes was also noted in the coastal waters region. We also updated the ocean microbiome gene catalog with 93,172 unique genes from the Andaman coastal water microbiome. This study provides valuable insights into the Indian Ocean microbiome and supplements the global marine microbial ecosystem studies.
... Peptoniphilus is an opportunistic pathogen which can cause bloodstream, diabetic skin and soft tissue infections [63]. Whereas Megamonas, Mitsuokella and Paraprevotella are previously reported as part of healthy gut microbiome in Indian population [64,65]. Other non SCFA producers which showed association with the co-variates included Solobacterium, Haemophilus, Klebsiella, Elusimicrobium, Corynebacterium. ...
Background
Pakistan is a multi-ethnic society where there is a disparity between dietary habits, genetic composition, and environmental exposures. The microbial ecology of healthy Pakistani gut in the context of anthropometric, sociodemographic, and dietary patterns holds interest by virtue of it being one of the most populous countries, and also being a Lower Middle Income Country (LMIC).
Methods
16S rRNA profiling of healthy gut microbiome of normo-weight healthy Pakistani individuals from different regions of residence is performed with additional meta-data collected through filled questionnaires. The current health status is then linked to dietary patterns through χ2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\chi }^{2}$$\end{document} test of independence and Generalized Linear Latent Variable Model (GLLVM) where distribution of individual microbes is regressed against all recorded sources of variability. To identify the core microbiome signature, a dynamic approach is used that considers into account species occupancy as well as consistency across assumed grouping of samples including organization by gender and province of residence. Fitting neutral modeling then revealed core microbiome that is selected by the environment.
Results
A strong determinant of disparity is by province of residence. It is also established that the male microbiome is better adapted to the local niche than the female microbiome, and that there is microbial taxonomic and functional diversity in different ethnicities, dietary patterns and lifestyle habits. Some microbial genera, such as, Megamonas, Porphyromonas, Haemophilus, Klebsiella and Finegoldia showed significant associations with consumption of pickle, fresh fruits, rice, and cheese. Our analyses suggest current health status being associated with the diet, sleeping patterns, employment status, and the medical history.
Conclusions
This study provides a snapshot of the healthy core Pakistani gut microbiome by focusing on the most populous provinces and ethnic groups residing in predominantly urban areas. The study serves a reference dataset for exploring variations in disease status and designing personalized dietary and lifestyle interventions to promote gut health, particularly in LMICs settings.
... (Supplementary Fig. S1, Supplementary Table S1). These 14 metagenomic studies [17,[26][27][28][29][30][31][32][33][34][35][36][37][38] encompassed six countries across four continents: America, Europe, Asia, and Africa. They included four studies from China (CHN) with 269 individuals, three from the United States (USA) with 151 individuals, including those from a study jointly conducted with the United Kingdom (GBR), three from India (IND) with 97 individuals, two from the Netherlands (NLD) with 904 individuals, and one from Madagascar (MDG) with 97 individuals. ...
Despite extensive research on the gut microbiome of healthy individuals from a single country, there are still a limited number of population-level comparative studies. Moreover, the sequencing approach used in most related studies involves 16 S ribosomal RNA (rRNA) sequencing with a limited resolution, which cannot provide detailed functional profiles. In the present study, we applied a combined analysis approach to analyze whole metagenomic shotgun sequencing data from 2035 healthy adult samples from six countries across four continents. Analysis of core species revealed that 13 species were present in more than 90 % of all investigated individuals, the majority of which produced short-chain fatty acids (SCFA)-producing bacteria. Our analysis revealed consistently significant differences in gut microbial species and pathways between Western and non-Western countries, such as Escherichia coli and the relation of MetaCyc pathways to the TCA cycle. Specific changes in microbial species and pathways are potentially related to lifestyle and diet. Furthermore, we identified several noteworthy microbial species and pathways that exhibit distinct characteristics specific to China. Interestingly, we observed that China (CHN) was more similar to the United States (USA) and United Kingdom (GBR) in terms of the taxonomic and functional composition of the gut microbiome than India (IND) and Madagascar (MDG), which were more similar to the China (CHN) diet. The current study identified consistent microbial features associated with population and geography, which will inspire further clinical translations that consider paying attention to differences in microbiota backgrounds and confounding factors.
... To replicate this scenario, we procured two 335 publicly available and geographically diverse datasets using the control samples from 336 GuptaA 2019 and FengQ 2015 as the template for our simulations, which is the same 337 with our previous analysis [11]. Specifically, we included 30 control samples and 183 338 species from the GuptaA 2019 dataset [18,78] for training purposes, and 61 healthy 339 samples and 468 species from the FengQ 2015 dataset [79] for testing purposes. For 340 each dataset, we were provided with a count table consisting of rows representing taxa 341 and columns representing samples. ...
Significant advancements have been made in metagenomic research for the prediction of phenotypes based on microbiome data. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes using 3 simulations and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in root mean squared error of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.
... In this study, the gut microbiome of the Mishing community was found to be dominated by Prevotella, a signature of the Indian population (39)(40)(41)(42)(43), which has been previously associated with a vegetarian or carbohydrate-rich diet (7). However, Poro drinkers had lower levels of Prevotella than non-drinkers and Nogin drinkers, although their gut microbiomes were colonized to a high extent by Prevotella. ...
... Lastly, we found that the gut microbiome of the Mishing population was colonized to a high extent by Succinivibrio, a bacterium not previously reported in the Indian population (39)(40)(41). This bacterium is commonly found among hunter-gatherers and foragers (45,46). ...
In this study, the impact of traditional rice-based fermented alcoholic beverages (two types of Apong drink: Poro and Nogin) on the gut microbiome and health of the Mishing community in India was examined. Two groups (n = 71 in each group, 58 females and 84 males) that consumed one of these beverages were compared to a control group (n = 24, all males) that did not consume either beverage. Gut microbial composition was analyzed by sequencing 16S rRNA of fecal metagenomes and analyzing untargeted fecal metabolites, and short-chain fatty acids (SCFAs). We also collected data on anthropometric measures and serum biochemical markers. Our results showed that Apong drinkers had higher blood pressure, but lower blood glucose and total protein levels than other non-drinkers. Also, gut microbiome composition was found to be affected by the choice of Apong, with Apong drinkers having a more diverse and distinct microbiome compared to non-drinkers. Apong drink type or being a drinker or not explained even a higher variation of fecal metabolome composition than microbiome composition and Apong drinkers had lower levels of the SCFA isovaleric acid than non-drinkers. Overall, this study shows that a single dietary factor can significantly impact the gut microbiome of a community and highlights the potential role of traditional fermented beverages in modulating gut bacteria.
IMPORTANCE
Our study investigated how a traditional drink called Apong, made from fermented rice, affects the gut and health of the Mishing community in India. We compared two groups of people who drink Apong to a group of people who do not drink it. To accomplish this, we studied the gut bacteria, fecal metabolites, and blood samples of the participants. It was found that the people who drank Apong had higher blood pressure but lower blood sugar and protein levels than people who did not drink it. We also found that the gut microbiome composition of people who drank Apong was different from those who did not drink it. Moreover, people who drank Apong had lower levels of isovaleric acid in their feces. Overall, this study shows that a traditional drink like Apong can affect the gut bacteria of a community.
... Our methodology began by determining the underlying taxon abundance levels for the training and testing populations. From Figure 1, the two least overlapping datasets, Gupta [68,26] and Feng [25], were chosen to be the template of training and testing sets, respectively. More specifically, 30 control samples and 183 species of the Gupta dataset were included for simulating the dataset for training, and 61 healthy samples and 468 species of the Feng dataset were included for simulating the dataset for testing. ...
... There are eight publicly accessible colorectal cancer (CRC) datasets shown in Table 1, including Feng [25], Gupta [26,68], Thomas [8], Vogtmann [28], Wirbel [29], Yachida [30], Yu [9], and Zeller [31]. In total, we included 1260 samples (625 controls, 635 CRC cases) from multiple countries such as the USA, China, France, etc. ...
The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM and RLE show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.
