Gut microbiota of healthy Asians and their discriminative features revealed by metagenomics approach

Abstract

This study is conducted to identify the microbial architecture and its functional capacity in the Asian population via the whole metagenomics approach. A brief comparison of the Asian countries namely Malaysia, India, China, and Thailand, was conducted, giving a total of 916 taxa under observation. Results show a close representation of the taxonomic diversity in the gut microbiota of Malaysia, India, and China, where Bacteroidetes, Firmicutes, and Actinobacteria were more predominant compared to other phyla. Mainly due to the multi-racial population in Malaysia, which also consists of Malays, Indian, and Chinese, the population tend to share similar dietary preferences, culture, and lifestyle, which are major influences that shapes the structure of the gut microbiota. Moreover, Thailand showed a more distinct diversity in the gut microbiota which was highly dominated by Firmicutes. Meanwhile, functional profiles show 1034 gene families that are common between the four countries. The Malaysia samples are having the most unique gene families with a total of 67,517 gene families, and 51 unique KEGG Orthologs, mainly dominated by the metabolic pathways, followed by microbial metabolism in diverse environments. In conclusion, this study provides some general overview on the structure of the Asian gut microbiota, with some additional highlights on the Malaysian population.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03671-3.

Introduction

Microorganisms live symbiotically with humans, forming various distinct communities in different parts of the human body known as the microbiota. One of the most important microbiota in the human body resides in the gut, playing significant and complex roles in the host system. For instance, regulating physiological processes such as degradation of food source (Rowland et al. 2018; Valdes et al. 2018; Vernocchi et al. 2020), production of enzymes and hormones (Neuman et al. 2015; Fukui et al. 2018; Martin et al. 2019), modulates the host’s metabolism (Holmes et al. 2012; Martin et al. 2019; Zhang et al. 2019a, b), maintaining homeostasis in the gut environment (Gagnière et al. 2016; Dicks et al. 2017; Lazar et al. 2018), and regulating the immune system (Holmes et al. 2012; Martin et al. 2019; Zhang et al. 2019a, b). Moreover, by evidence of previous studies, the role and function of the gut microbiota is not limited to the gut, but also influences other parts of the body such as the brain via the gut–brain axis, where the gastrointestinal tract communicates through a bidirectional network of signaling pathways consisting of multiple connections involving the immune system and bacterial metabolites and products (Calvani et al. 2018; Huang et al. 2019; Ma et al. 2019; Morais et al. 2020).

In general, the gastrointestinal tract harbors a diverse microbial community along the mouth, esophagus, stomach, small and large intestine, rectum, and anus. Food digestion and nutrients absorption occurs mainly in the small intestine, where it then passes to the colon for further water absorption, production of vitamins and waste solidification. There are many factors that contribute to the structure of the host’s gut microbiota, such as genetics, age, mode of birth, diet, medicine, and stress (Kelsen and Wu 2012; Gagnière et al. 2016; Bajinka et al. 2020). For instance, previous studies have shown how stress and depression influence the composition of the gut microbiota which, in turn, changes the gut permeability, and results in a leaky gut. Commonly dysbiosis occurs with over representation of intestinal pathobionts which accelerate systemic inflammation in the host by translocating across the epithelial barrier to reach the extraintestinal tissue (Kiecolt-glaser et al. 2018). This condition allows microorganisms to penetrate into the blood circulation and produce an inflammatory response (Madison and Kiecolt-Glaser 2019). Dietary pattern is also one of the main factors that influences the composition of the gut microbiota. Studies have reported the gut microbiota response toward different type of diets such as Western diet and also plant-rich diet. The studies showed that a western diet implicated a less healthy diet, resulting in a reduced intestinal epithelial defenses and promotion of pathogenic bacteria that affects carcinogenic pathways (Bolte et al. 2021; Ibragimova et al. 2021). Meanwhile, a plant-based diet causes an increment of microorganisms that produce short-chain fatty acids (SCFA), which have shown to improve metabolic markers and reduce cancer risk (Bolte et al. 2021; Ibragimova et al. 2021).

In correspond to these various factors, it is difficult to obtain a concrete justification on the composition of the gut microbiota either in healthy or unhealthy individuals. Many studies have been conducted to study on the unique composition of the gut microbiota and their association with the host’s demographic properties such as ethnicity, lifestyle, sex and age, and geographic location (Zeller et al. 2014; Rampelli et al. 2020; Zhang et al. 2019a, b; Kaur et al. 2020; Luan et al. 2020) However, these studies require a large number of samples to get an overview of the gut microbiota for the population. Furthermore, due to their complex role and importance to human health, the gut microbiota has reached the forefront in research studies. Many studies have reported the relationship between the gut microbiota with various gastrointestinal diseases such as ulcerative colitis, irritable bowel syndrome, inflammatory bowel disease, and colorectal cancer (Manichanh et al. 2012; Wang et al. 2018; Hills et al. 2019; Ortigao et al. 2020; Fan et al. 2021). However, not limited to the gastrointestinal tract, some studies also show the relationship between the gut microbiota and other diseases such as kidney failure diabetes, autism spectrum disorder (ASD), and also Parkinson’s (Bliss and Whiteside 2018; Sherwin et al. 2018; Stavropoulou et al. 2021).

With recent findings in hand, knowledge on the association of microorganisms in the gut community with their host, including the influence of various factors shows the uniqueness of different population. Nevertheless, there is still a scarcity in fundamental knowledge for understanding the unique gut microbiota in humans. As most studies would directly link the gut microbiota with disease-related individuals (Zeller et al. 2014; Thomas et al. 2019; Wirbel et al. 2019), it is still crucial to understand the gut microbiota within a healthy human body. For this purpose, whole metagenomic analysis on the gut microbiota of normal individuals was conducted to analyze the distinct gut community between different geographic population.

Moreover, in favor of the advanced technology in next generation sequencing, whole metagenomics sequencing or also known as shotgun metagenomics sequencing would provide a finer view of the gut community in terms of coverage and depth on identifying microbial genomes in the sample (Laudadio et al. 2018; Tyagi et al. 2019). In comparison to the more favorable metagenomics approach which focuses on the 16S region of the microbial genome (16S metagenomic sequencing), the whole metagenomics sequencing is less prone to primer bias during polymerase chain reaction (PCR) as direct sequencing on the samples are feasible (Poretsky et al. 2014). Furthermore, another advantage of the shotgun approach is the ability to predict potential genes for getting a basic understanding on the functional contribution of the gut microbiota to the host (Di Guglielmo et al. 2019; Kwon et al. 2019; Medina et al. 2019).

In short, gut microbiota plays vital roles in our body mechanisms and immune system to maintain a healthy condition. Although, numerous studies have been conducted to understand their symbiotic relationship in the human body, it is still unclear due to the complexity of the microbial community followed by their extensive roles and physiological properties. Thus, it is essential to study on the architecture of the microbial community as a fundamental knowledge for further understanding their significant contribution in the human body. Hence, to explore the structure of the human gut community, this paper reports on the taxonomic and functional profiles of gut microbiota representing several distinct populations in Asia via the whole metagenomics approach.

Methodology

Data collection

In this study, we randomly selected 33 samples of healthy individuals from Asia countries namely Malaysia, Thailand (Raethong et al. 2021), India (Gupta et al. 2019), and China (Yu et al. 2017). Whole metagenomic sequences of the gut microbiome were retrieved from NCBI by the following accession IDs: PRJNA637175, PRJNA531273, and PRJEB10878. Meanwhile, samples from Malaysia were collected from University Malaya Medical Centre (UMMC), Petaling Jaya, Selangor and sequenced in this study. Ethics approval have been approved by Medical Research Ethics Committee, University of Malaya Medical Center, Malaysia, with reference MRECID.NO: 201914-6975. The Malaysian metagenome sequences have been deposited in NCBI Sequence Read Archive, with BioProject accession ID: PRJNA872758.

Metagenomics analyses

Data pre-processing

Sequence quality of the raw sequences were analyzed and cleaned using FastQC v.0.11.9 (Andrews 2010), and Trimmomatic v.0.36 (Bolger et al. 2014) to obtain high-quality data. The sequences were then screened for host contamination by aligning to the human reference genome, Hg38 via Bowtie2 (Caspi et al. 2014) and SAMtools (Caspi et al. 2014). Quality checking was repeated to ensure high-quality sequences are obtained.

Taxonomic and functional profiling

High-quality sequences were then classified using Metagenomic Phylogenetic Analysis 3, MetaPhlAn3 v.3.0.7 (Beghini et al. 2021), a taxonomic profiling tool that implements a marker-gene mapping algorithm. MetaPhlAn3 was run using default parameters and mapped to the CHOCOPhlAn pangenome database. Functional profiling of the high-quality sequences was conducted using the HMP Unified Metabolic Analysis Network 3, HUMAnN3 v.3.0.0.alpha.4 (Beghini et al. 2021) pipeline with default parameters, the gene families were identified based on the UniRef90 database, a sub-cluster of the UniProt Reference Clusters, UniRef (Suzek et al. 2007). Moreover, functional groups were analyzed with reference to KEGG Orthology, KO (Kanehisa et al. 2008), and mapping of the metabolic pathway profiles via MetaCyc (Caspi et al. 2014), a metabolic pathway database.

Statistical validation

Statistical validation was conducted using Bioconductor packages in R software (v.4.0.5). Alpha diversity was analyzed based on Observed and Chao for species richness, and Shannon, and Simpson indices representing species diversity. Linear discriminant analysis (LDA) and effect size (LEfSe) (Segata et al. 2011) were conducted with Kruskal–Wallis test at alpha-value = 0.05, and LDA threshold of 2.0.

Results and discussion

Gut microbiota is a complex and dynamic structure in the human body that plays crucial roles in maintaining an optimum health condition. Characterizing the architecture of the gut microbiota in healthy individuals is an important initial step in understanding the association of the microbiota with health and disease. Generally, the gut microbiota of a healthy adult may harbor thousands of bacterial species which are dominantly from Bacteroidetes and Firmicutes phyla (Candela et al. 2012; Rinninella et al. 2019). Studies have shown the gut inhabiting a unique and diverse microbiota of which the taxonomic composition varies among individuals.

In this study, we aim to look at the different structure of the gut microbiota among healthy individuals coming from different countries in Asia, namely Malaysia, Thailand, India, and China. Table 1 shows a summary of the taxonomic composition of the different countries which is also represented in Fig. 1. The 4 Asian countries share 81 common species, while Thailand have the highest number of unique species, followed by China, India, while Malaysia showed the least with a total of 10 unique species, namely Bacteroides sp. CAG_144, Bacteroides sp. OM08_11, Blautia coccoides, Clostridium clostridioforme, Clostridium sp. D5, Firmicutes bacterium CAG_424, Lactonifactor longoviformis, Parabacteroides sp. CAG_409, Prevotella disiens, and Streptococcus pasteurianus. Based on the results, the gut microbiota of the Asian population is dominated by four major phyla namely Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria as shown in Fig. 2. However, Thailand has shown a smaller abundance of the Bacteroidetes, while being dominated by Firmicutes, which was also reported similarly by La-ongkham et al. (2020) and Raethong et al. (2021), whereas India has a larger composition of Bacteroidetes than the other three major phyla mentioned earlier.

Table 1.

Taxonomic comparison of the gut microbiota between Asian countries

Country	Taxa size	Unique species	Common species
Malaysia	161	10	81
Chinaa	266	30
Indiab	144	26
Thailandc	345	103

^aRaw sequences were obtained from Yu et al. (2017)

^bRaw sequences were obtained from Gupta et al. (2019)

^cRaw sequences were obtained from Raethong et al. (2021)

Fig. 1.

Taxonomic comparison between Asian countries showing unique and common species in the gut microbiota shared between Malaysia, China, Thailand and India population

Fig. 2.

Taxonomic comparison between Asian gut microbiota at phylum level based on the relative abundances

Furthermore, Tables 2, 3, 4, 5 show list of top 20 most abundant species in the gut microbiota of each population. In general, the diversity of the gut microbiota in each population was predominated by normal flora and commensals. Malaysia had greater abundance for commensal bacteria such as Bacteroidetes vulgatus and Eubacterium eligens, compared to Thailand, India and China. Well-known probiotics from actinobacteria phyla such as Bifidobacterium pseudocatenulatum and Bifidobacterium bifidum were also ranked in the top ten in the Malaysian gut microbiota. Meanwhile, Faecalibacterium prausnitzii and Eubacterium species such as Eubacterium rectale, Eubacterium sp. CAG_180, were found relatively high in Thailand population. This finding is also reported by La-ongkham et al. (2020), where F. prausnitzii showed the highest abundance among healthy groups of Thai population, suggesting this species as a potential biomarker for a healthy gut in Thai individuals. F. prausnitzii are capable of producing high amounts of butyrate and produces anti-inflammatory effects, which are vital for human health (Li et al. 2008; Sokol et al. 2008; Miquel et al. 2014; La-ongkham et al. 2020). The results also show that Proteobacteria such as Escherichia coli and Klebsiella pneumoniae were also abundant in the gut microbiota for Thailand, India, and China population. Interestingly, in the Indian population, Megasphaera elsdenii was found to be highly abundant with a relative abundance of 4.55%, and shared by 70% of the population. The genus Megasphaera was also found in the other countries of interest, but their relative abundance was lower at less than 1.5%. This lactate-utilizing bacterium is one of the important commensals in the gut microbiota as they ferment lactate to produce propionate via the acrylate pathway (Reichardt et al. 2014; Louis et al. 2022). Moreover, a previous study by Bhute et al. (2016) also reported on the distinct abundance of M. elsdenii species in healthy Indian individuals. Hence, also indicating that this species can also be a potential biomarker for healthy Indians. In addition, India also showed higher abundance of a diverse Prevotella sp. namely Prevotella copri, Prevotella stercorea, Prevotella sp. CAG_279, and Prevotella sp. CAG_5226, which may suggest the influence of vegetarian practice in their culture. Meanwhile, China showed the highest abundance of E. coli compared to the other countries with a relative abundance of 8.598%. A pectin-utilizing bacterium Lachnospira pectinoschiza, was also relatively high in the gut microbiota of China population. Pectin, a complex carbohydrate is found in the plant cell wall, and fruits such as carrots, apples, and citrus (Flint et al. 2012; Van der Merwe 2021).

Table 2.

Top 20 most abundant species in Malaysian gut microbiota

Phylum	Species	Relative abundance (%)
Firmicutes	Eubacterium rectale	11.298
Bacteroidetes	Bacteroides vulgatus	11.068
Bacteroidetes	Prevotella copri	9.070
Bacteroidetes	Bacteroides stercoris	6.631
Actinobacteria	Bifidobacterium adolescentis	5.073
Firmicutes	Faecalibacterium prausnitzii	3.248
Bacteroidetes	Bacteroides uniformis	2.667
Actinobacteria	Bifidobacterium pseudocatenulatum	2.584
Firmicutes	Eubacterium eligens	2.399
Actinobacteria	Bifidobacterium bifidum	1.964
Firmicutes	Roseburia faecis	1.874
Firmicutes	Fusicatenibacter saccharivorans	1.760
Firmicutes	Dialister sp. CAG_357	1.698
Bacteroidetes	Parabacteroides distasonis	1.582
Firmicutes	Dorea longicatena	1.574
Actinobacteria	Bifidobacterium longum	1.529
Bacteroidetes	Bacteroides eggerthii	1.438
Bacteroidetes	Bacteroides ovatus	1.341
Firmicutes	Holdemanella biformis	1.341
Firmicutes	Coprococcus comes	1.330

Table 3.

Top 20 most abundant species in Thailand gut microbiota

Phylum	Species	Relative abundance (%)
Firmicutes	Faecalibacterium prausnitzii	11.997
Firmicutes	Eubacterium rectale	10.568
Firmicutes	Eubacterium sp. CAG_180	6.411
Actinobacteria	Bifidobacterium adolescentis	5.445
Proteobacteria	Escherichia coli	5.378
Firmicutes	Roseburia inulinivorans	4.653
Firmicutes	Streptococcus salivarius	3.658
Firmicutes	Fusicatenibacter saccharivorans	3.266
Firmicutes	Ruminococcus torques	2.636
Firmicutes	Roseburia faecis	2.219
Firmicutes	Roseburia intestinalis	1.897
Firmicutes	Ruminococcus bromii	1.733
Actinobacteria	Collinsella aerofaciens	1.619
Firmicutes	Holdemanella biformis	1.593
Bacteroidetes	Prevotella sp. CAG_1031	1.346
Proteobacteria	Klebsiella pneumoniae	1.277
Firmicutes	Coprococcus comes	1.215
Firmicutes	Streptococcus parasanguinis	1.178
Bacteroidetes	Bacteroides uniformis	1.169
Firmicutes	Anaerostipes hadrus	1.087

Raw sequences were obtained from Raethong et al. (2021)

Table 4.

Top 20 most abundant species in India gut microbiota

Phylum	Species	Relative abundance (%)
Bacteroidetes	Prevotella copri	36.051
Bacteroidetes	Prevotella stercorea	4.680
Firmicutes	Megasphaera elsdenii	4.554
Bacteroidetes	Bacteroides vulgatus	4.175
Firmicutes	Dialister succinatiphilus	4.153
Actinobacteria	Bifidobacterium adolescentis	3.015
Bacteroidetes	Prevotella sp. CAG_279	2.747
Proteobacteria	Escherichia coli	2.715
Firmicutes	Eubacterium rectale	2.619
Proteobacteria	Klebsiella pneumoniae	2.461
Firmicutes	Butyrivibrio crossotus	2.455
Firmicutes	Faecalibacterium prausnitzii	2.228
Firmicutes	Clostridium sp. CAG_510	2.159
Actinobacteria	Collinsella aerofaciens	1.607
Bacteroidetes	Prevotella sp. CAG_5226	1.557
Firmicutes	Lactobacillus ruminis	1.352
Firmicutes	Ruminococcus bromii	1.247
Actinobacteria	Bifidobacterium longum	1.134
Proteobacteria	Klebsiella variicola	1.114
Firmicutes	Eubacterium sp. CAG_180	0.933

Raw sequences were obtained from Gupta et al. (2019)

Table 5.

Top 20 most abundant species in China gut microbiota

Phylum	Species	Relative abundance (%)
Bacteroidetes	Bacteroides vulgatus	9.414
Proteobacteria	Escherichia coli	8.598
Bacteroidetes	Prevotella copri	6.590
Bacteroidetes	Bacteroides stercoris	6.245
Firmicutes	Faecalibacterium prausnitzii	5.404
Bacteroidetes	Bacteroides uniformis	4.640
Proteobacteria	Klebsiella pneumoniae	3.933
Firmicutes	Lachnospira pectinoschiza	2.959
Firmicutes	Roseburia inulinivorans	2.363
Bacteroidetes	Parabacteroides merdae	2.090
Bacteroidetes	Bacteroides ovatus	2.045
Firmicutes	Ruminococcus bromii	1.961
Firmicutes	Eubacterium rectale	1.924
Actinobacteria	Bifidobacterium adolescentis	1.742
Firmicutes	Eubacterium eligens	1.655
Bacteroidetes	Bacteroides thetaiotaomicron	1.554
Firmicutes	Roseburia faecis	1.528
Actinobacteria	Collinsella aerofaciens	1.464
Bacteroidetes	Bacteroides eggerthii	1.459
Firmicutes	Anaerostipes hadrus	1.451

Raw sequence were obtained from Yu et al. (2017)

Alpha and beta diversity was conducted to validate the taxonomic diversity in the gut microbiota across different population. Figure 3 demonstrates alpha diversity analyses which evaluates the structure of microbial community within each population by measuring the species richness (number of OTUs present in the population) and species diversity (abundances of the OTUs present in the population). The results show a greater species richness and diversity in Thailand population compared to the other countries, indicating a more diverse microbial community in the gut microbiota of the Thailand population. Moreover, Malaysian population had a relatively smaller species richness and diversity which was mainly due to the small sample size in comparison to the other populations (Thailand, India, and China). Meanwhile, to compare diversity across the different populations, beta diversity was conducted and the different taxonomic composition between the Asian countries is shown in Figs. 4 and 5. For this purpose, Jaccard index was conducted to show differences in the microbial community based on the presence/absence of OTUs, whereas Bray Curtis dissimilarity index analyzes the differences by incorporating the relative abundances of the microbial community in each population.

Fig. 3.

Alpha diversity of the gut microbiota of the Asian countries, consisting of different metrices namely Observed and Chao1 for estimating species richness, whereas Shannon and Simpson for evaluating species diversity

Fig. 4.

Principal coordinate analysis (PCoA) of the gut microbiota of the Asian countries based on Jaccard Index metrics, clustered at 95% confidence interval. The PCoA shows the distance of microbial diversity based on the presence/absence of the microbial OTUs in the gut community between Malaysia, India, China, and Thailand. a Jaccard index on the whole taxa. b Jaccard index on taxa size N = 100. c Jaccard index on taxa size N = 90

Fig. 5.

Principal coordinate analysis (PCoA) of the gut microbiota of the Asian countries based on Bray Curtis Dissimilarity metrics, clustered at 95% confidence interval. The PCoA shows the distance of microbial diversity in the gut community between Malaysia, India, China, and Thailand. a Bray Curtis dissimilarity based on the whole taxa. b Bray Curtis dissimilarity based on taxa size N = 100. c Bray Curtis dissimilarity based on taxa size N = 90

Beta diversity was conducted on the whole taxa, and narrowed to a smaller taxa size consisting of N = 100 and N = 90 to observe their differences in clustering and diversity. Results in Figs. 4a, b, 5a, b show that the taxa diversity between the gut microbiota of the Asian countries do have some common diversity when compared at the whole taxa level, but starts to differ significantly when the size of taxa was narrowed down to N = 90 as shown in Figs. 4c, 5c. This shows that the diversity in the gut microbiota across Malaysia, Thailand, India and China may share some common taxonomic structure within the intermediate species as shown at the whole taxa level and N = 100. Interestingly, the gut microbiota of Malaysian population was clustered with both India and China clusters, and the diversity differs with Thailand as it does not fall into Thailand cluster. Thailand had a more distinct diversity compared to Malaysia, India, and China, as it was separated into a distinct cluster at N = 90. As Malaysia consist of a wide variety of races, which includes Indian and Chinese, the results may indicate that the clustering was due to a common lifestyle or dietary habit between the different races which shapes a common trait in the structure of the gut microbiota between Malaysia, China, and India. In this case, although Thailand is a neighboring country to Malaysia, the culture and dietary preference differs from the Malaysian culture. A Thai meal consist of rice, eaten with a variety of side dishes that commonly consist of vegetables, seafood, eggs, and meat, and balanced with different flavors such as sweet, sour, salty, and spicy (La-ongkham et al. 2015; Phoonlapdacha et al. 2022). Meanwhile, results of the dietary pattern represented in this study, in Supplementary Table 1, suggest that Malaysians prefer to eat more vegetables and white meat, with an average of 2.00, and 1.67 times per day. Compared to other type of diet namely fruits, dairy, fish, and red meat with the consumption at an average 1.00 times per day. However, due to the very limited sample size, the dietary information based on our samples may only suggest on the food preference, and may not reflect the actual dietary pattern of the population. More samples should be included in future studies to get a more significant picture on the dietary preference for the Malaysian population.

Although the dietary preference in Asian countries is most likely similar, the food in each country or region is unique based on the culture and availability of the raw ingredients that are indigenous to the area. Asians are well known to practice high fiber and carbohydrate foods, such as rice and wheat as staple food, and higher consumption of fruits and vegetables, compared to animal proteins (La-ongkham et al. 2015; Nitisinprasert et al. 2016; Shondelmyer et al. 2018; Dhakan et al. 2019; Lu et al. 2021; Phoonlapdacha et al. 2022). High abundances of Prevotella sp. in this study may also reflect the high consumption of plant-rich diet as many studies have also reported on the correlation between the abundance of Prevotella sp. with diet rich in vegetables (Bhute et al. 2016; Nitisinprasert et al. 2016; Kisuse et al. 2018; Pareek et al. 2019; Phoonlapdacha et al. 2022). Meanwhile, the food preference in India are mostly wheat, grains, and rice as their staple food, which are consumed with legumes, assorted spices, vegetables, and yogurt (Shondelmyer et al. 2018). Similarly, rice and wheat are the staple food in China, and they also prefer a high fiber diet mainly vegetables, accompanied with animal and vegetable protein such as pork, chicken, and soy beans (Lu et al. 2021). Studies also showed a close relation between Bacteroides and Prevotella sp., and high consumption of fiber, protein, and animal fat (Singh et al. 2017; Costea et al. 2018). Bacteroides enterotypes was also reported to increase in the gut microbiota of individuals who practice a western diet which comprises high intake of fat and proteins (Singh et al. 2017; Phoonlapdacha et al. 2022). Moreover, high abundance of Bifidobacterium can be observed in individuals who practice rice and wheat diet, whereas, the species decreases in low wheat content diet such as gluten-free diet, low-gluten diet, and diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) (Singh et al. 2017; Lu et al. 2021). Moreover, Asians also practice high consumption of fermented foods such as tempeh, cheese, fermented soy beans, yogurt, budu, belacan, ragi, and tapai (Dimidi et al. 2019; Tamang et al. 2020; Leeuwendaal et al. 2022).

In addition, functional profile was constructed by evaluating potential gene families and pathways associated to the gut microbiota. Gene families were identified and compared among the four countries of interest. As shown in Fig. 6, Malaysia has the highest number of unique gene families among the four Asian countries, with a total of 67,517 gene families, followed by India, China, and Thailand, while they share a total of 1034 common gene families. The gene families were also mapped to KEGG Orthology (KO) database for further functional ortholog classification, and compared between Malaysia, India, and Thailand. China had no gene families mapped to KO; hence, it was not included in the analysis. The functional orthologs provide information on the molecular-level functions of genes and proteins. Visualization of the comparison between KOs associated to the gut microbiota of each country is illustrated in Fig. 7. Malaysia had mapped to a total of 51 KOs that are unique to the other countries as listed in Table 6, while sharing a total of 2281 KOs with India and Thailand. The unique KOs are mostly involved in metabolism, signaling and cellular processes, and genetic and environmental information processing. From the results, a total number of 34 major pathways have been associated with Malaysia’s unique KOs, having metabolic pathways as the top pathway with 14 hits of sub-pathways, followed by microbial metabolism in diverse environments as listed in Supplementary Table 2 and summarized in Fig. 8.

Fig. 6.

Comparison of gene families associated with the gut microbiota in Asian population obtained based on UniRef 90 clusters from UniRef database

Fig. 7.

Comparison of KEGG Orthologs (KO) associated with the gut microbiota in Asian population (Malaysia, India and Thailand)

Table 6.

List of unique KEGG Orthologs (KO) associated with Malaysian gut microbiota

KO ID	Symbol	Name	EC no.
K00663	aacA	Aminoglycoside 6ʹ-N-acetyltransferase	EC:2.3.1.82
K00666	K00666	Fatty-acyl-CoA synthase	EC:6.2.1.-
K01174	nuc	Micrococcal nuclease	EC:3.1.31.1
K01482	DDAH, ddaH	Dimethylargininase	EC:3.5.3.18
K01501	E3.5.5.1	Nitrilase	EC:3.5.5.1
K01848	E5.4.99.2A, mcmA1	Methylmalonyl-CoA mutase, N-terminal domain	EC:5.4.99.2
K01849	E5.4.99.2B, mcmA2	Methylmalonyl-CoA mutase, C-terminal domain	EC:5.4.99.2
K02945	RP-S1, rpsa	Small subunit ribosomal protein S1	–
K02958	RP-S15e, RPS15	Small subunit ribosomal protein S15e	–
K03048	rpoE	DNA-directed RNA polymerase subunit delta	–
K03328	TC.PST	Polysaccharide transporter, PST family	–
K03336	iolD	3D-(3,5/4)-Trihydroxycyclohexane-1,2-dione acylhydrolase (decyclizing)	EC:3.7.1.22
K03337	iolB	5-Deoxy-glucuronate isomerase	EC:5.3.1.30
K03366	butA, budC	Meso-butanediol dehydrogenase /(S,S)-butanediol dehydrogenase / diacetyl reductase	EC:1.1.1.- 1.1.1.76 1.1.1.304
K04767	acuB	Acetoin utilization protein AcuB	–
K04940	odh	Opine dehydrogenase	EC:1.5.1.28
K05020	opuD, betL	Glycine betaine transporter	–
K05303	K05303	O-Methyltransferase	EC:2.1.1.-
K05305	FUK	Fucokinase	EC:2.7.1.52
K06973	K06973	Uncharacterized protein	–
K06975	K06975	Uncharacterized protein	–
K06978	K06978	Uncharacterized protein	–
K06980	ygfZ	tRNA-modifying protein YgfZ	–
K06987	K06987	Uncharacterized protein	–
K06989	nadX, ASPDH	Apartate dehydrogenase	EC:1.4.1.21
K06993	K06993	Ribonuclease H-related protein	–
K06997	yggS, PROSC	PLP dependent protein	–
K07218	nosD	Nitrous oxidase accessory protein	–
K07651	resE	Two-component system, OmpR family, sensor histidine kinase ResE	EC:2.7.13.3
K07696	nreC	Two-component system, NarL family, response regulator NreC	–
K09859	K09859	Uncharacterized protein	–
K10240	cebE	Cellobiose transport system substrate-binding protein	–
K11145	mrnC	Mini-ribonuclease III	EC:3.1.26.-
K11175	purN	Phosphoribosylglycinamide formyltransferase 1	EC:2.1.2.2
K11177	yagR	Xanthine dehydrogenase YagR molybdenum-binding subunit	EC:1.17.1.4
K11178	yagS	Xanthine dehydrogenase YagS FAD-binding subunit	EC:1.17.1.4
K11179	tusE, dsrC	tRNA 2-thiouridine synthesizing protein E	EC:2.8.1.-
K11180	dsrA	Dissimilatory sulfite reductase alpha subunit	EC:1.8.99.5
K11181	dsrB	Dissimilatory sulfite reductase beta subunit	EC:1.8.99.5
K11183	fruB, fpr	Multiphosphoryl transfer protein	EC:2.7.1.202
K11184	chr, crh	Catabolite repression HPr-like protein	–
K11689	dctQ	C4-dicarboxylate transporter, DctQ subunit	–
K12218	icmP, trbA	Intracellular multiplication protein IcmP	–
K14665	amhX	Amidohydrolase	EC:3.5.1.-
K15855	csxA	Exo-1,4-beta-d-glucosaminidase	EC:3.2.1.165
K16928	mtaT	Energy-coupling factor transport system substrate-specific component	–
K17108	GBA2	Non-lysosomal glucosylceramidase	EC:3.2.1.45
K18197	yesW	Rhamnogalacturonan endolyase	EC:4.2.2.23
K18846	npmA	16S rRNA (adenine(1408)-N(1))-methyltransferase	EC:2.1.1.180
K19134	csx10	CRISPR-associated protein Csx10	–
K21405	acoR	Sigma-54 dependent transcriptional regulator, acetoin dehydrogenase operon transcriptional activator AcoR	–

Fig. 8.

The figure shows a list of pathways from MetaCyc database mapped to Malaysia’s unique KO, with number of hits are being represented at the end of each bar. The number of hits represent the number of sub-pathways being complemented to the respective major pathways

Meanwhile, since there is scarce information on the gut microbiota of the Malaysian population for healthy individuals, further comparative insights on Malaysia and other countries have been done. Significant differentially abundant species have been identified based on linear discriminant analysis (LDA) effect size (LEfSe). This approach was conducted to calculate taxa that is best discriminated between Malaysia and each of the other countries. Figures 9 and 10 show the LEfSe results, indicating significant discriminative taxa between the gut microbiota of Malaysian individuals and those from Thailand, India, and China, respectively. These results are also supported with a list of significant differentially abundant species derived from the LEfSe analysis in Supplementary Table 3–5. Interestingly, Malaysia showed a higher number of significant species when compared to each of the Asian countries involved, regardless of the smaller sample size. Thus, suggesting, Malaysians have a more unique gut microbiota compared to Thailand, India, and China. Furthermore, the functional contribution of the significant differentially abundant species was also further analyzed by mapping to MetaCyc database to predict the respective pathway complement of the gut microbiota from the annotated metagenomes. Figure 11 represents the summary of the pathways associated with the microorganisms in the gut microbiota, and further details on the list of pathways are included in the Supplementary Table 6–8. Moreover, top ten pathways associated with the significant differentially abundant species obtained from the comparison between Malaysia and the other countries are also listed in Tables 7, 8, 9. In comparison to Malaysia, the top pathways represented by the significant differentially abundant species in China and Thailand were mainly associated with Proteobacteria such as K. pneumoniae and E. coli, respectively. Meanwhile, the top pathways observed in Malaysian population were mainly associated with Actinobacteria and Firmicutes such as Bifidobacterium, Megamonas, and Acidaminococcus. Most of the pathways listed are related to carbohydrate fermentation, and synthesizing amino acids and essential metabolites such as aromatic amino acids, vitamins, and energy, which are vital for the microorganisms to survive in the gut. However, further meta-transcriptomic and metabolomics analysis are required to further assess the functional contribution of the microbiota and their relationship to the human host.

Fig. 9.

The figures represent the linear discriminant features between the gut microbiota of Malaysia and other countries, with LDA score > 2.0, and p value < 0.05. The linear discriminant features represent taxa at phylum to species level. a Linear discriminant features between the gut microbiota of Malaysia and Thailand population. b Linear discriminant features between the gut microbiota of Malaysia and India population. c Linear discriminant features between the gut microbiota of Malaysia and China population

Fig. 10.

The figures represent the linear discriminant features between the gut microbiota of Malaysia and other countries from phylum to genus level, with LDA score > 2.0, and p value < 0.05 in a cladogram. a Linear discriminant feature between the gut microbiota of Malaysia and Thailand population. b Linear discriminant features between the gut microbiota of Malaysia and India population. c Linear discriminant features between the gut microbiota of Malaysia and China population

Fig. 11.

The figures represents the number of significant differentially abundant species and their complementary pathways based on LEfSe results. a Comparison of the significant differentially abundant species and their complementary pathways between the gut microbiota of Malaysia and Thailand population. b Comparison between Malaysia and India population. c Comparison between Malaysia and China population

Table 7.

Top 10 pathways associated with significant differentially abundant species between Malaysia and Thailand gut microbiota

Country	Top 10 pathways	Phylum	Genus	Species
Malaysia	ARO-PWY: chorismate biosynthesis I COA-PWY-1: coenzyme A biosynthesis II (mammalian) COA-PWY: coenzyme A biosynthesis I COMPLETE-ARO-PWY: superpathway of aromatic amino acid biosynthesis HISDEG-PWY: L-histidine degradation I PANTO-PWY: phosphopantothenate biosynthesis I PANTOSYN-PWY: pantothenate and coenzyme A biosynthesis I PEPTIDOGLYCANSYN-PWY: peptidoglycan biosynthesis I (meso-diaminopimelate containing) PWY-2942: L-lysine biosynthesis III PWY-1042: glycolysis IV (plant cytosol) PWY-4242: pantothenate and coenzyme A biosynthesis III	Firmicutes	Acidaminococcus	Acidaminococcus intestini
Thailanda	PWY0-1586: peptidoglycan maturation (meso-diaminopimelate containing) NONOXIPENT-PWY: pentose phosphate pathway (non-oxidative branch) PWY-7111: pyruvate fermentation to isobutanol (engineered) PWY-7663: gondoate biosynthesis (anaerobic) PWY-5667: CDP-diacylglycerol biosynthesis I PWY0-1319: CDP-diacylglycerol biosynthesis II VALSYN-PWY: L-valine biosynthesis ILEUSYN-PWY: L-isoleucine biosynthesis I (from threonine) PWY-6305: putrescine biosynthesis IV PWY-7220: adenosine deoxyribonucleotides de novo biosynthesis II	Proteobacteria	Escherichia	Escherichia coli

^aRaw sequences were obtained fromRaethong et al. (2021)

Table 8.

Top 10 pathways associated with significant differentially abundant species between Malaysia and India gut microbiota

Country	Top 10 pathways	Phylum	Genus	Species
Malaysia	PWY-7237: myo-, chiro- and scillo-inositol degradation PWY-6737: starch degradation V TRPSYN-PWY: L-tryptophan biosynthesis	Firmicutes	Megamonas	Megamonas hypermegale
	PWY0-1586: peptidoglycan maturation (meso-diaminopimelate containing) PWY-2942: L-lysine biosynthesis III COA-PWY-1: coenzyme A biosynthesis II (mammalian) ILEUSYN-PWY: l-isoleucine biosynthesis I (from threonine) VALSYN-PWY: l-valine biosynthesis DAPLYSINESYN-PWY: l-lysine biosynthesis I COA-PWY: coenzyme A biosynthesis I	Actinobacteria	Bifidobacterium	Bifidobacterium pseudocatenulatum
Indiaa	No pathway mapped to species

^aRaw sequence were obtained fromGupta et al. (2019)

Table 9.

Top 10 pathways associated with significant differentially abundant species between Malaysia and China gut microbiota

Country	Top 10 pathways	Phylum	Genus	Species
Malaysia	DTDPRHAMSYN-PWY: dTDP-l-rhamnose biosynthesis I PWY-5384: sucrose degradation IV (sucrose phosphorylase) PWY-7220: adenosine deoxyribonucleotides de novo biosynthesis II PWY-7222: guanosine deoxyribonucleotides de novo biosynthesis II	Actinobacteria	Bifidobacterium	Bifidobacterium bifidum
	PWY-6609: adenine and adenosine salvage III PWY-7197: pyrimidine deoxyribonucleotide phosphorylation PWY-7111: pyruvate fermentation to isobutanol (engineered) VALSYN-PWY: l-valine biosynthesis PWY-5686: UMP biosynthesis PANTO-PWY: phosphopantothenate biosynthesis I	Firmicutes	Megamonas	Megamonas funiformis cag 377
	PWY-7111: pyruvate fermentation to isobutanol (engineered)	Firmicutes	Acidaminococcus	Acidaminococcus intestini
Chinaa	PWY0-1586: peptidoglycan maturation (meso-diaminopimelate containing) PWY-7111: pyruvate fermentation to isobutanol (engineered) PWY-1042: glycolysis IV (plant cytosol) VALSYN-PWY: l-valine biosynthesis ILEUSYN-PWY: l-isoleucine biosynthesis I (from threonine) ASPASN-PWY: superpathway of l-aspartate and l-asparagine biosynthesis PWY-5913: TCA cycle VI (obligate autotrophs) FERMENTATION-PWY: mixed acid fermentation PWY-6803: phosphatidylcholine acyl editing PWY-7219: adenosine ribonucleotides de novo biosynthesis	Proteobacteria	Klebsiella	Klebsiella pneumoniae

^aRaw sequences were obtained from Yu et al. (2017)

In conclusion, the healthy gut microbiota in the Asian population namely, Malaysia, India, China, and Thailand are predominated by Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria. Top abundant species shows that the gut microbiota is colonized by normal flora and commensals such as B. vulgatus, Eubacterium, and Bifidobacterium species. Most of the gut microbiota observed are involved in the production of short-chain fatty acids (SCFA) by various carbohydrate fermentation pathways as represented in the functional profile. The taxonomic profile also proved findings from recent studies on the potential biomarkers for Thailand and India gut microbiota such as the high abundance of F. prausnitzii and M. elsdenii in their respective gut community. Moreover, this study also provides an overview on the gut microbiota of healthy Malaysian population, comprising a high abundance of commensals in the top species that are mainly shaped by Bacteroidetes, Firmicutes, and Actinobacteria. Information on the gut microbiota of healthy Malaysians generated by the whole metagenomics approach have not yet been reported. Hence, these provisional findings are a stepping stone for future research on the gut community of healthy Malaysian population. It is recommended to increase the number of samples, including samples from different regions or ethnicity in the future to get a wider perspective and coverage on the gut microbiota of Malaysian population. It is also recommended to relate the study with specific diseases and compare the gut microbiota between healthy and disease-related individuals, as it may give more insights to the roles of the gut microbiota in the human body. Since the microbial population in the gut environment is diverse and influenced by various factors, it is difficult to gain more information on the symbiotic association of the gut microbiota with human health. A comparison of an individual or a population by their health progression from healthy to unhealthy may provide a more significant differences in the gut microbiota. Hence, providing a clearer picture on how the different conditions of the host also shape the gut community and vice versa.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization: SBM, SB, HO, SFMT; Methodology: SFMT, SBM, SB, HO; Formal analysis and investigation: SFMT, SBM; Writing—original draft preparation: SFMT; Writing—review and editing: SFMT, SBM, SB, HO; Funding acquisition: SBM, SKPG; Resources: AR, CSR, SBM, SB; Supervision: SBM, SB, HO.

Data availability

Accession numbers: Sequences obtained in this present study have been deposited in the NCBI Sequence Read Archive (SRA) (BioProject accession number PRJNA872758).

Declarations

Conflict of interest

The authors declare that they have no conflict of interest in the publication.

Ethical statements

This study was approved by the Medical Research Ethics Committee, University of Malaya Medical Center, Malaysia with ethical approval number MRECID.NO: 201914-6975, and written informed consent was obtained from all participants.