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. 2023 Aug 30;13:14247. doi: 10.1038/s41598-023-36754-5

A display and analysis platform for gut microbiomes of minority people and phenotypic data in China

Jun Li 1,✉,#, Chunxue Wei 1,#, Ting Zhou 2, Chunfen Mo 3, Guanjun Wang 1, Feng He 1, Pengyu Wang 4, Ling Qin 1, Fujun Peng 5,✉
PMCID: PMC10469205  PMID: 37648696

Abstract

The minority people panmicrobial community database (MPPCD website: http://mppmcdb.cloudna.cn/) is the first microbe-disease association database of Chinese ethnic minorities. To research the relationships between intestinal microbes and diseases/health in the ethnic minorities, we collected the microbes of the Han people for comparison. Based on the data, such as age, among the different ethnic groups of the different regions of Sichuan Province, MPPCD not only provided the gut microbial composition but also presented the relative abundance value at the phylum, class, order, family and genus levels in different groups. In addition, differential analysis was performed in different microbes in the two different groups, which contributed to exploring the difference in intestinal microbe structures between the two groups. Meanwhile, a series of related factors, including age, sex, body mass index, ethnicity, physical condition, and living altitude, were included in the MPPCD, with special focus on living altitude. To date, this is the first intestinal microbe database to introduce altitude features. In conclusion, we hope that MPPCD will serve as a fundamental research support for the relationship between human gut microbes and host health and disease, especially in ethnic minorities.

Subject terms: Microbiology, Diseases, Gastroenterology

Introduction

In 2001, the Human Genome Project (HGP) was completed as a result of the efforts of six countries. The project highlights the importance of understanding the synergistic activities between humans and microbes1–3. Subsequently, many biologists, especially microbiologists, immunologists, and medical scientists, began to explore the relationship between human diseases and the gut microbiota4,5. Meanwhile, large research projects, such as the Human Microbiome Project (HMP) and MetaHIT, have provided new ideas for microbiome research and enhanced biologists’ understanding of host-microbe interactions, especially the human gut microbiome with clinical functions6,7.

In normal conditions, gut microbes maintain a symbiotic relationship with hosts to help humans to digest and absorb nutrients, to provide energy, to produce important metabolites and bioactive components, to affect the immune system development, to regulate immune mediators, to maintain a normal intestinal barrier, and to prevent the infestation of pathogens8,9. Therefore, the gut microbe genomes are also regarded as “our second genome”. Many countries and organizations have established gut microbiome projects, including the GUT Project in the United States, the Chinese Academy of Sciences Microbiome Project, and the Canadian Microbiome Project, aimed at better understanding gut ecosystems and their functions10. Dysbiosis in the microbiota can lead to a series of diseases, including asthma, polycystic ovary syndrome, obesity, type 2 diabetes, and hypertension4,11–15. Currently, there are several public platforms that relate the microbiome and these diseases, such as gcMeta16, MANTA17, HMDAD18, and HPMCD19.

The human gut is dominated by 150–170 species of bacteria that perform protective, metabolic and structural functions10. However, a large number of studies have demonstrated that the species and relative abundance of gut microbes are associated with a series of factors, including diet, age, sample type (health or disease), gender, ethnicity, and weight10,20–22. Numerous studies have found that differing dietary patterns could affect microbe compositions23–25. One case—control study showed that Enterobacteriaceae was obviously reduced in vegans compared to omnivorous control populations26. Experimental mice fed a high-fat diet (40–80% total caloric intake) exhibit decreased Bacteroidetes at the phylum level and increased Firmicutes24. The analyses of faecal samples in the four European study groups indicated age-related structural differences in the bacterial communities27. Bacteroides presented lower levels in elderly subjects than in younger subjects from Italy28. In addition, the diversity of gut microbes is caused by ethnicity and sex27,29, 30.

There are currently some common platforms for microbiological disease-related databases. For example, HMDAD sorts out the relationship between microbes and diseases from previously published microbiome studies, and studies how they change by integrating disease genes, symptoms, chemical fragments and drugs31. MicroPhenoDB describes the relationship between microbial core genes and disease phenotypes by integrating microbial disease association data32. HPMCD provides a metagenomic resource that enables analysis of the gut microbiome and can search for health or disease states associated with the presence of the microbiome33. However, these databases only emphasize the relationship between gut microbes and hosts or briefly classify the subjects who are an important effective factor. In this study, we focused on the associations among the populations in ethnic minorities of Sichuan Province of China and gut microbes. First, we recruited persons from different regions in Sichuan Province, gathered their faeces, and acquired their clinical information, such as age and weight. Then, genomic DNA of each individual was extracted, the 16S rRNA of which was sequenced. Furthermore, we performed a series of processing for these datasets from gut microbial communities. Finally, these results and analysis were presented in the MPPCD database. Previously, we have completed the evolutionary analysis of intestinal microbes of Tibetan, laying a foundation for the establishment of MPPCD.

Results

Clinical characteristics of all the samples

All the individuals belong to different ethnic groups and live in different areas of Sichuan province, ranging from 500 to 4001 m above sea level. The detailed and statistical results are shown in Fig. 1. Regarding age, we performed statistical distribution according to the < Chinese Modern Age Division Standard > and found that the populations of middle age (41–65 years old), old age (≥ 66 years old) and young age (18–40 years old) were the top three groups (Fig. 1a). In the different ethnic groups, the ordering was Tibetan, Han and Chiang Chinese (Fig. 1b). Most people live between 800 and 3600 m above sea level, and a few people live less than 800 m or more than 3600 m above sea level (Fig. 1c). The BMI ordering was 562 normal, 441 overweight, 151 undefined, and 97 underweight. Meanwhile, the overweight populations were divided into mild obesity, obesity, and severe obesity according to World Health Organization Standards (Fig. 1d). Arthritis, hyperlipidemia, hypertension, gastrosia, and hyperuric were the most common diseases among the participants (Fig. 1e).

Figure 1.

Figure 1

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Clinical characteristics of all samples. (a) Age distribution analysis of all samples. (b) Ethnicity distribution analysis of all the samples. (c) Analysis of living altitude distribution of all samples. (d) BMI distribution analysis of all samples. (e) Health status distribution analysis of all samples.

Disease association analysis

In order to further study the relationship between intestinal microbes and diseases, we selected 5 diseases with high prevalence in Tibetan population, including rheumatoid arthritis, hyperlipidemia, hypertension, chronic gastritis, and hyperuricemia. Based on the effective data of each sample: QIIME software (Version 1.9.1) was used to cluster OTUs. During the clustering process, the valid data of all samples were combined and the sequence was classified into an OTU with 97% similarity. OTU with significant difference in abundance between each disease and the normal group was found, and the network graph was constructed (Fig. 2a–e). In addition, OTU with differences in more than one disease were screened out to construct a network diagram (Fig. 2f). Correlation analysis is conducted between OTU and altitude. The red upward arrow represents a significant positive correlation between OTU and altitude, and the green downward arrow represents a significant negative correlation between OTU and altitude. The results show that that there was a significant OTU difference in the abundance of Bacteroides between the hypertensive group and the normal group, and it was positively correlated with altitude (Fig. 2a). OTU of Ruminococcaceae abundance was significantly different between gastritis group and normal group, and was positively correlated with altitude (Fig. 2c). The abundance of OTU such as Ruminococcaceae and Oscillospira was significantly different between the hyperlipemia group, the hyperuric group and the normal group, and was positively correlated with altitude (Fig. 2d–e). At the same time, Ruminococcaceae and Oscillospira show significant differences OTUs in a variety of diseases, and are positively correlated with altitude (Fig. 2f).

Figure 2.

Figure 2

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The relationship between living altitude, intestinal microbes and disease. (a) The relationship between hypertension and intestinal microbes and altitude. (b) The relationship between arthritis and intestinal microbes and altitude. (c) The relationship between chronic gastritis and intestinal microbes and altitude. (d) The relationship between hyperlipemia and intestinal microbes and altitude. (e) The relationship between hyperuria and intestinal microbes and altitude. (f) The relationship between these five diseases and the intestinal microbes and the altitude.

The influence of external factors on intestinal microbes

In order to further explore the influencing factors of intestinal microbes in Tibetan population, we measured the relationship between the influencing factors and the characteristic changes of intestinal microbes in Tibetan population from the perspectives of diet index, disease index, medication index and environmental index. Based on the relative abundance table and factor information table of different species, Spearman correlation analysis in R software (Version 3.3.3) was used to measure the potential association between intestinal microbes and changes in clinical indicators. We established the association network between external factors-microbiome and clinical indicators (Fig. 3) to reveal the association between microbiome and the occurrence and development of high-incidence diseases as well as the potential mechanism. As can be seen from the figure, with the increase of altitude, the relative abundance of Clostfridium increases as atmospheric pressure and oxygen partial pressure decrease. Klebsiella was on the contrary. With the increase of atmospheric pressure and oxygen partial pressure, the relative abundance of altitude decreased and increased. Citrobacter was positively correlated with atmospheric pressure and partial oxygen pressure, but negatively correlated with altitude, RBC count, hematocrit, and hemoglobin content, suggesting that the relative abundance of Citrobacter increased with the increase of atmospheric pressure and partial oxygen pressure and the decrease of altitude, which might lead to changes in RBC count, hematocrit, and hemoglobin content. It can also be seen from the figure that coffee, fried food, meat, and other dietary factors are also significantly correlated with bacteria. In order to more intuitively see the correlation between different species and environmental factors and clinical indicators, we constructed a network diagram among the three (Fig. 4).

Figure 3.

Figure 3

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Correlation analysis of gut microbiota with external factors in Tibetans with high Incidence. Association diagram of intestinal microbes with high incidence and external factors in Tibetan population: The horizontal axis represents each influencing factor and clinical indicators, and the vertical axis represents the differential microbes. “*” in the figure represents that there is a significant correlation between the corresponding species-factors (P < 0.05). The red and green blocks in the figure represent the magnitude of correlation R, ranging from − 1 to 1, and the blue (negative) represents the negative correlation between the two. (red) positive value indicates that there is a positive correlation between the two. The larger the absolute value is, the greater the correlation is and the redder the color is, indicating that this factor shows an upward trend with the increase of the abundance of bacteria. The bluer the color is, the opposite is true.

Figure 4.

Figure 4

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Correlations between different species and environmental factors and clinical indicators. Network diagram of intestinal microbes and significantly related factors in Tibetan population with high incidence. The blue line represents the negative correlation, the gray line represents the positive correlation; Line thickness on behalf of the correlation between size (here select|r|> 0.2, p < 0.05); Dots of different colors represent different factor types, and blue represents species.

Data search and navigation

MPPCD (website: http://mppmcdb.cloudna.cn/) provides users with a concise search engine and a user-friendly interface to access, browse and retrieve different data types and analysis results. The website interface is comprised of four sections: “Home”, “Tools”, “Documents”, and “Contact us” (Fig. 5a).

Figure 5.

Figure 5

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Screenshots and search functions of MPPCD. (a) The main modules of the website interface in MPPCD. (b) The interface for comparison search. (c) The results of the comparison search. (d) The relative abundance ratio of each sample is shown by a bar chart (upper) and heatmap (below). (e) Differential analysis of the abundance of each gut microbe among different groups. (f) Interface of microbiota annotation. (g) Annotation results of the different aspects in different databases and banks.

On the “Home” page, a brief introduction of MPPCD, search functionality, users’ scopes, a statistical summary of all samples, database update history, etc. were provided. The search functionality mainly had two search modules, including “comparison search” and “microbiota search”. The comparison search functionality is designed to provide the ability to search samples by particular characteristics, including sex, ethnicity, age, living altitude, BMI and health conditions (Fig. 5b,c). Then, a list of samples within the database that fulfilled the search criteria was presented (Fig. 5c). This database mainly allowed users to select two groups of samples and compare the gut microbe components, abundance, and differences in each group (Fig. 5c). If desired, it is also possible to use this functionality to search just a single group and obtain its associated data, such as genus abundance. Importantly, they were chosen without particular order or with cross selecting. Meanwhile, the number of selected samples was also presented in both Group 1 and Group 2 (Fig. 5c). Clicking the “analysis” button would show the following results: (1) the gut microbial composition and relative abundance value customized by one or two groups in the phylogenetic level of phylum, class, order, family and genus, which is displayed for browsing with bar charts (Fig. 5d). Placing the cursor over the bars in the graph will provide detailed information about the search results. Meanwhile, the shown number of gut microbes could be user-defined, and sometimes, the actual number of gut microbes may be less than the required number. (2) The relative enrichment of each microbe in each sample was displayed and viewed using a heatmap at any phylogenetic level (Fig. 5d). (3) The difference analysis of each microbe abundance among Group 1 and Group 2 was performed using the P value and FDR, which contributed to determining the association between the gut microbes and related factors (Fig. 5e). In the comparison search, living altitude also introduced MPPCD and was regarded as a related factor, which was distinguished from other databases of human gut microbiota, such as HPMCD19, gcMeta16, and MANTA17. In addition to the “comparison search” module, we further provided the microbiota search functionality. The gut microbe’s name can be input in the text field where we provided a fuzzy matching function, such as Verrucomicrobiae (Fig. 5f). Clicking the “Search” button, the MPPCD would present the detailed microbe annotation with a series of related databases and banks, including Taxonomy annotation (https://www.ncbi.nlm.nih.gov/taxonomy the Microbewiki database), (https://microbewiki.kenyon.edu/index.php), Genome annotation (https://www.ncbi.nlm.nih.gov/genome/), the Bacdive database34–36, the gutMDisorder database37, and HMDAD bank (http://www.cuilab.cn/hmdad) (Fig. 5g).

On the “Tools” page, to better understand and analyze human intestinal microbes, MPPCD presents many analysis tools that are made up of four sections, including sequencing processing, microbiology analysis, statistical analysis and gene annotation (Fig. 6 and Table 1). All the tools can be used by clicking the icons (Fig. 6). Table 1 presents the application scenarios for all human gut microbes.

Figure 6.

Figure 6

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Functional descriptions of the Tool module. The module contains four sections: sequencing processing, microbiology analysis, statistical analysis and gene annotation. The corresponding tools were also presented.

Table 1.

The analysis tools of human intestinal microbes.

Name Analysis tools Function or application
Sequence processing Fastq to fasta To convert fastq file to fasta file with program language
Align sequences Multiple sequence alignment by Muscle software
Phylogentic trees To build the evolutionary tree with PhyML software that approved many files format
Microbiology analysis Alpha diversity To analyze how many different sequences (microbes) are in a single sample
Beta diversity To research differential analysis among different groups with PCoA measures
Intestinal type The samples are divided into different types by clustering algorithm
Annotation To analysis the composition of gut microbes in every samples
Statistical analysis T-test Differential analysis among different groups
Rank sum test Differential analysis among different groups
Correlation analysis To research the relation for many factors
Gene annotation Antibiotic resistance analysis To research the drug resistance analysis for gut microbes
Virulence factor analysis Virulence factor analysis for gut microbes
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Discussion

In this study, we succeeded in constructing an MPPCD database of intestinal microbes of minority people in Sichuan Province, China. It contained 1251 self-produced data instead of public database sources. Compared to other gut-related databases, MPPCD has many advantages.

One feature is the complete related factors with gut microbes. The gut microbiota community structures are affected by factors such as age and sex. At present, there are some human microbe-disease databases. HMDAD is a resource that collects and curates human microbe-disease association data from microbiota studies and contains these related factors18. MicroPhenoDB is regarded as the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes in different body sites, which lack host lifestyle, age, and other factors38. HPMCD provides a detailed analysis of human microbial communities and supports research from basic microbiology and immunology for therapeutic development in human health and disease19. Not only do these related factors affect gut microbes, but it also contains ethnicity factors that were simply divided into European and Han Chinese. A larger number of studies have shown significant variations in microbiome composition in healthy individuals from different race and ethnicity categories10,39–43. In this MPPCD, these factors were included in MPPCD to clearly determine the association between microbes and diseases. Furthermore, we focused on specific regions, detailed race groups, and similar lifestyle and living environments. These factors contributed to exploring the relationship between microbes and diseases.

The second feature was altitude first imported into the MPPCD. Before, a string of studies had demonstrated that exposure to the hypoxic environment at high altitude played an important role in altering the gut microbial composition and its activities, which can cause gastrointestinal symptoms44–50. Adak et al.44 demonstrated that the gut microbial composition is altered during acclimatization to hypobaric hypoxia; the total aerobes decreased 50-fold, whereas total anaerobes increased 115-fold after 15 days of migration at high altitude. Kang et al.47 uncovered more Enterobacteriales, Enterobacteriaceae, Gammaproteobacteria, Porphyromonadaceae and Escherichia Shigella, and several other types of microbial species were found in the gut microbiota of the immigrant Han population living on the Tibetan Plateau than in that of the low-altitude Han population by sequencing the 16S rRNA V1–V3 regions. Lan et al.48 studied the correlations between the gut microbiota community structures of Tibetans and geography across six different locations ranging from 2800 to 4500 m and found significant differences in microbial diversity and richness, and Faecalibacterium, Bacteroides and Bifidobacterium were positively correlated with altitude, which suggested that gut microbiota may play important roles in regulating high altitude and geographical adaptations. Ma et al.49 found that bacterial taxa differed significantly among Tibetan sheep and sheep collected from high-altitude Tibetan Plateau (3000 m) and low altitudes (1800 m), respectively. Zhu et al.50 discovered that the gut microbiota of Han hypertensive patients from middle-altitude (2260 m) presented greater α—and β-diversities, a lower ratio of Firmicutes/Bacteroidetes, and a higher abundance of beneficial Verrucomicrobia and Akkermansia compared to Han hypertensive patients from low altitude (13 m). Among the healthy individuals, the gut microbiota also showed greater α-diversity and a lower ratio of Firmicutes/Bacteroidetes at middle altitudes than at low altitudes. These results suggest that altitude has a significant influence on gut microbiome compositions.

Another feature of this database is that we offer a set of data analysis and visualization platforms, including sequencing processing, microbiology analysis, statistical analysis, and gene annotation, which are made up of 13 analytical tools, such as correlation analysis and provide scientists with a robust site for powerful data analysis. Furthermore, the analytical tools are free, operate simply, and contain the samples.

In addition, several issues should be considered for MPPCD. First, the MPPCD is a relatively professional database and limits user groups, including microbiologists, immunologists and medical scientists. Second, the sample size is small. There are less than 1300 gut microbiota in MPPCD. As the diversity and size of samples increase to include diverse infections, disease conditions, ethnicity categories, and sample types in the future, the complexity and specificity of search functionality will be improved.

Third, the scope of the MPPCD database will also be widened from the current focus on the human gastrointestinal tract to encompass samples from other human body sites, including the lungs and bladder. In the next few years, the issues mentioned above are expected to be considered and solved in future versions.

In conclusion, MPPCD is the first association database between human intestinal microbes and diseases in China’s ethnic minorities and contains a series of search functionality and analysis tools. The search functionality mainly consists of “comparison search” and “microbiota annotation”. The analysis tools include four modules: sequencing processing, microbiology analysis, statistical analysis, and gene annotation. Importantly, the residential altitude was first regarded as a related factor introduced into the MPPCD database. Furthermore, we found differential gut microbes among customized groups at the phylum, class, order, family, and genus levels.

Methods

Ethics committee approval

All clinical and experimental steps were approved by the ethical committee of Chengdu Medical College (NO. 2017009). All experiments were performed in accordance with relevant named guidelines and regulations. All participants received written informed consent and signed informed consent.

Subject selection and sampling

Human subjects included in the MPPCD database were acquired from rural and urban people of Sichuan Province, including those from Chengdu city, Hongyuan County, Barkam County, and Wenchuan County. We visited the relevant areas and completed data collection with the assistance of local health authorities51. All the individuals (natural crowds) resided in different regions of Sichuan Province at altitudes ranging from 500 to 4001 m. They belonged to different ethnicities, including Tibetan, Han, Chiang, Hui, Yi and Mongol Chinese. The subjects completed a questionnaire survey, including demographic information (age, sex, birthplace, place of residence, ethnicity, etc.), health status(digestive tract diseases, type 2 diabetes, mental health, genetic diseases, etc.), diet (staple food, dietary intake, drinking habit, consumption of coffee, tea, yogurt, etc.) and exercise(daily physical activity, exercise frequency, etc.). A total of 1251 fecal samples were collected for analysis. These samples are included in the database, where they raised from human faecal samples and contain sufficient metadata to determine if they originate from a healthy or diseased individual, which were immediately transported to the laboratory of Chengdu Medical College with an ice pack and then frozen at − 20 °C. Meanwhile, all clinical information was collected according to standard procedures, including age, sex, body mass index (BMI), ethnicity, living altitude, height, weight, blood pressure and physical condition. Fasting peripheral venous blood was collected by trained personnel at all collection points according to standardized procedures for routine blood tests (hemoglobin, red blood cell, white blood cell and platelet counts) and biochemical tests (liver and kidney function, blood glucose and blood lipid levels). During the period of data collection, check the data regularly and carry out quality control.

DNA extraction and 16S ribosomal RNA sequencing

Microbe DNA of every sample was extracted at Novogene Bioinformatics Technology Co., Ltd. using a TianGen Kit (TIANGEN BIOTECH (BEUJING)Co., LTD) according to the manufacturer’s recommendations, and then DNA concentrations were quantified using a Qubit 2.0 Fluorometer (Invitrogen). The V3-V4 hypervariable regions of the 16S rRNA of bacteria and archaea were amplified using the common primers 341F (5’- ACTCCTACGGGA GGCAGCAG -3’) and 806R (5’- GGACTACHVGGGTWTCTAAT -3’). The purified PCR products were constructed with a short fragment library with a 2 × 250 paired-end (PE) configuration and sequenced using the Illumina HiSeq platform. The required sequencing results were performed by bioinformatic and statistical analysis. The dataset supporting the results of this article has been deposited in the EMBL European Nucleotide Archive (ENA) under BioProject accession code PRJEB44310.

The raw reads were filtered to remove low-quality and polyclonal sequences using QIIME (Version 1.9.1) software52. The filtered data were further compared with the Gold database, and the chimaera reads were detected by using the Uchime algorithm in Usearch software (Version 8.1.1861)53. The resulting reads for each sample were clustered into operational taxonomic units (OTUs) at the level of 97% similarity using the Uclust algorithm in QIIME52. Representative sequences for each OTU were selected, and the taxonomic information was annotated using the Greengenes database (Version 13.8)54. In addition, we selected 5 diseases with high prevalence in Tibetan population, including rheumatoid arthritis, hyperlipidemia, hypertension, chronic gastritis, and hyperuricemia. According to the available data of each sample, the difference of OTU abundance between each disease group and the normal group was analyzed. Finally, we also analyzed the influence of external factors on intestinal microbes of Tibetan population. Based on the relative abundance tables and factor information tables of different species, Spearman correlation analysis in R software (Version 3.3.3) was used to measure the potential association between intestinal microbes and changes in clinical indicators.

Statistical analysis

Based on the results of taxonomic analysis, the microbe types and relative abundance ratio of each sample are shown in the form of bar charts and heatmaps at the phylum, class, order, family, and genus levels using R programs. We also performed differential analysis of the microbe abundance among different groups with LEfSe software55. The P-value was determined by the Wilcox rank-sum test, indicating the differential analysis was performed by both the stats package of R language and the script package of python language, and then, it was adjusted by the false discovery rate (FDR) measures.

Database schema and implementation

The underlying MPPCD Web server was based on the MongoDB database and Galaxy platform to manage and schedule the analysis tools. Analysis applications were implemented with Python, Bash and R languages. The webpage and interactions of MPPCD were accomplished by the JavaScript and Cascading Style Sheets (CSS) languages.

Supplementary Information

Supplementary Information. (198.7KB, pdf)

Author contributions

J.L. mainly contributes to design this study and written the original manuscript. F.J.P. and L.Q. conceived and designed the study. C.X.W., T.Z and C.F.M responsible for the collection and processing of samples. F.H., G.J.W. and P.Y.W. supervised and administered the project. F.J.P. and L.Q. conceived involved in sample storage and processing. F.H., C.X.W. and T.Z. mainly involved in the revision of the manuscript, and were responsible for project implementation and quality control. All co-authors contributed substantially to manuscript revisions. All authors read and approved the final manuscript.

Funding

This study is supported by the National Natural Science Foundation of China (81702446), Department of Science and Technology of Sichuan Province (CN) (22NSFSC1601),Chengdu Science and Technology Bureau (2021-YF05-00975 -SN),Scientific Research Project of Health Commission of Sichuan Province (19PJ190).

Data availability

The dataset supporting the results of this article has been deposited in the EMBL European Nucleotide Archive (ENA) under BioProject accession code: PRJEB44310. Any remaining data are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Jun Li and Chunxue Wei.

Contributor Information

Jun Li, Email: lij523@mcmaster.ca.

Fujun Peng, Email: pengfujun901@163.com.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-023-36754-5.

References

  • 1.Carrasco-Ramiro F, Peiró-Pastor R, Aguado B. Human genomics projects and precision medicine. Gene Ther. 2017;24:551–561. doi: 10.1038/gt.2017.77. [DOI] [PubMed] [Google Scholar]
  • 2.McGuire AL, et al. Ethical, legal, and social considerations in conducting the human microbiome project. Genome Res. 2008;18:1861–1864. doi: 10.1101/gr.081653.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Relman DA, Falkow S. The meaning and impact of the human genome sequence for microbiology. Trends Microbiol. 2001;9:206–208. doi: 10.1016/S0966-842X(01)02041-8. [DOI] [PubMed] [Google Scholar]
  • 4.Michalovich D, et al. Obesity and disease severity magnify disturbed microbiome-immune interactions in asthma patients. Nat. Commun. 2019;10:5711. doi: 10.1038/s41467-019-13751-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Zhu W, et al. Gut microbes impact stroke severity via the trimethylamine N-oxide pathway. Cell Host. Microbe. 2021 doi: 10.1016/j.chom.2021.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature486, 207–214. 10.1038/nature11234 (2012) [DOI] [PMC free article] [PubMed]
  • 7.Qin J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65. doi: 10.1038/nature08821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lin L, Zhang J. Role of intestinal microbiota and metabolites on gut homeostasis and human diseases. BMC Immunol. 2017;18:2. doi: 10.1186/s12865-016-0187-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Takiishi T, Fenero CIM, Câmara NOS. Intestinal barrier and gut microbiota: Shaping our immune responses throughout life. Tissue Barriers. 2017;5:e1373208. doi: 10.1080/21688370.2017.1373208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Adak A, Khan MR. An insight into gut microbiota and its functionalities. Cell Mol. Life Sci. 2019;76:473–493. doi: 10.1007/s00018-018-2943-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Guo J, et al. Gut microbiota in patients with polycystic ovary syndrome: A systematic review. Reprod. Sci. 2022;29:69–83. doi: 10.1007/s43032-020-00430-0. [DOI] [PubMed] [Google Scholar]
  • 12.Ngowi EE, et al. Impact of the factors shaping gut microbiota on obesity. J. Appl. Microbiol. 2021;131:2131–2147. doi: 10.1111/jam.15036. [DOI] [PubMed] [Google Scholar]
  • 13.Sanchez-Carrillo S, et al. A body weight loss- and health-promoting gut microbiota is established after bariatric surgery in individuals with severe obesity. J. Pharm. Biomed. Anal. 2021;193:113747. doi: 10.1016/j.jpba.2020.113747. [DOI] [PubMed] [Google Scholar]
  • 14.Qin J, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. doi: 10.1038/nature11450. [DOI] [PubMed] [Google Scholar]
  • 15.Li J, et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome. 2017;5:14. doi: 10.1186/s40168-016-0222-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Shi W, et al. gcMeta: A global catalogue of metagenomics platform to support the archiving, standardization and analysis of microbiome data. Nucleic Acids Res. 2019;47:D637–D648. doi: 10.1093/nar/gky1008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Chen Y-A, et al. MANTA, an integrative database and analysis platform that relates microbiome and phenotypic data. PLoS ONE. 2020;15:e0243609. doi: 10.1371/journal.pone.0243609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Ma W, et al. An analysis of human microbe-disease associations. Brief Bioinform. 2017;18:85–97. doi: 10.1093/bib/bbw005. [DOI] [PubMed] [Google Scholar]
  • 19.Forster SC, et al. HPMCD: The database of human microbial communities from metagenomic datasets and microbial reference genomes. Nucleic Acids Res. 2016;44:D604–D609. doi: 10.1093/nar/gkv1216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Garcia-Mazcorro JF, Kawas JR, Licona Cassani C, Mertens-Talcott S, Noratto G. Different analysis strategies of 16S rRNA gene data from rodent studies generate contrasting views of gut bacterial communities associated with diet, health and obesity. PeerJ. 2020;8:e10372. doi: 10.7717/peerj.10372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Patterson E, et al. Gut microbiota, obesity and diabetes. Postgrad. Med. J. 2016;92:286–300. doi: 10.1136/postgradmedj-2015-133285. [DOI] [PubMed] [Google Scholar]
  • 22.A review of 10 years of human microbiome research activities at the US National Institutes of Health, Fiscal Years 2007–2016. Microbiome7, 31. 10.1186/s40168-019-0620-y (2019). [DOI] [PMC free article] [PubMed]
  • 23.Zmora N, Suez J, Elinav E. You are what you eat: Diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 2019;16:35–56. doi: 10.1038/s41575-018-0061-2. [DOI] [PubMed] [Google Scholar]
  • 24.Gentile CL, Weir TL. The gut microbiota at the intersection of diet and human health. Science. 2018;362:776–780. doi: 10.1126/science.aau5812. [DOI] [PubMed] [Google Scholar]
  • 25.Clements SJ, Carding RS. Diet, the intestinal microbiota, and immune health in aging. Crit. Rev. Food Sci. Nutr. 2018;58:651–661. doi: 10.1080/10408398.2016.1211086. [DOI] [PubMed] [Google Scholar]
  • 26.Zimmer J, et al. A vegan or vegetarian diet substantially alters the human colonic faecal microbiota. Eur. J. Clin. Nutr. 2012;66:53–60. doi: 10.1038/ejcn.2011.141. [DOI] [PubMed] [Google Scholar]
  • 27.Mueller S, et al. Differences in fecal microbiota in different European study populations in relation to age, gender, and country: A cross-sectional study. Appl. Environ. Microbiol. 2006;72:1027–1033. doi: 10.1128/AEM.72.2.1027-1033.2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Woodmansey EJ, McMurdo MET, Macfarlane GT, Macfarlane S. Comparison of compositions and metabolic activities of fecal microbiotas in young adults and in antibiotic-treated and non-antibiotic-treated elderly subjects. Appl. Environ. Microbiol. 2004;70:6113–6122. doi: 10.1128/AEM.70.10.6113-6122.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Yurkovetskiy L, et al. Gender bias in autoimmunity is influenced by microbiota. Immunity. 2013;39:400–412. doi: 10.1016/j.immuni.2013.08.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Fransen F, et al. The impact of gut microbiota on gender-specific differences in immunity. Front Immunol. 2017;8:754. doi: 10.3389/fimmu.2017.00754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ma W, et al. An analysis of human microbe-disease associations. Brief. Bioinform. 2017;18:85–97. doi: 10.1093/bib/bbw005. [DOI] [PubMed] [Google Scholar]
  • 32.Yao G, et al. MicroPhenoDB associates metagenomic data with pathogenic microbes, microbial core genes, and human disease phenotypes. Genomics Proteomics Bioinform. 2020;18:760–772. doi: 10.1016/j.gpb.2020.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Forster SC, et al. HPMCD: The database of human microbial communities from metagenomic datasets and microbial reference genomes. Nucleic Acids Res. 2016;44:D604–609. doi: 10.1093/nar/gkv1216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Reimer LC, et al. BacDive in 2019: Bacterial phenotypic data for High-throughput biodiversity analysis. Nucleic Acids Res. 2019;47:D631–D636. doi: 10.1093/nar/gky879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Söhngen C, et al. BacDive–the bacterial diversity metadatabase in 2016. Nucleic Acids Res. 2016;44:D581–D585. doi: 10.1093/nar/gkv983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Söhngen C, Bunk B, Podstawka A, Gleim D, Overmann J. BacDive–the bacterial diversity metadatabase. Nucleic Acids Res. 2014;42:D592–D599. doi: 10.1093/nar/gkt1058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Cheng L, Qi C, Zhuang H, Fu T, Zhang X. gutMDisorder: A comprehensive database for dysbiosis of the gut microbiota in disorders and interventions. Nucleic Acids Res. 2020;48:D554–D560. doi: 10.1093/nar/gkz843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Yao G, et al. MicroPhenoDB associates metagenomic data with pathogenic microbes, microbial core genes, and human disease phenotypes. Genomics Proteomics Bioinformatics. 2020;18:760–772. doi: 10.1016/j.gpb.2020.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Brooks AW, Priya S, Blekhman R, Bordenstein SR. Gut microbiota diversity across ethnicities in the United States. PLoS Biol. 2018;16:e2006842. doi: 10.1371/journal.pbio.2006842. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Sun Y, et al. Population-level configurations of gut mycobiome across 6 ethnicities in urban and rural China. Gastroenterology. 2021 doi: 10.1053/j.gastro.2020.09.014. [DOI] [PubMed] [Google Scholar]
  • 41.Zhang J, et al. A phylo-functional core of gut microbiota in healthy young Chinese cohorts across lifestyles, geography and ethnicities. ISME J. 2015;9:1979–1990. doi: 10.1038/ismej.2015.11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Zuo T, et al. Human-Gut-DNA virome variations across geography, ethnicity, and urbanization. Cell Host Microbe. 2020 doi: 10.1016/j.chom.2020.08.005. [DOI] [PubMed] [Google Scholar]
  • 43.Gupta VK, Paul S, Dutta C. Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. Front Microbiol. 2017;8:1162. doi: 10.3389/fmicb.2017.01162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Adak A, Maity C, Ghosh K, Pati BR, Mondal KC. Dynamics of predominant microbiota in the human gastrointestinal tract and change in luminal enzymes and immunoglobulin profile during high-altitude adaptation. Folia Microbiol. (Praha) 2013;58:523–528. doi: 10.1007/s12223-013-0241-y. [DOI] [PubMed] [Google Scholar]
  • 45.Adak A, Ghosh K, Mondal KC. Modulation of small intestinal homeostasis along with its microflora during acclimatization at simulated hypobaric hypoxia. Indian J. Exp. Biol. 2014;52:1098–1105. [PubMed] [Google Scholar]
  • 46.Adak A, Maity C, Ghosh K, Mondal KC. Alteration of predominant gastrointestinal flora and oxidative damage of large intestine under simulated hypobaric hypoxia. Z. Gastroenterol. 2014;52:180–186. doi: 10.1055/s-0033-1336007. [DOI] [PubMed] [Google Scholar]
  • 47.Li K, et al. Comparative analysis of gut microbiota of native Tibetan and han populations living at different altitudes. PLoS ONE. 2016;11:e0155863. doi: 10.1371/journal.pone.0155863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Lan D, et al. Correlations between gut microbiota community structures of Tibetans and geography. Sci. Rep. 2017;7:16982. doi: 10.1038/s41598-017-17194-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Ma Y, et al. Gut microbiota adaptation to high altitude in indigenous animals. Biochem. Biophys. Res. Commun. 2019;516:120–126. doi: 10.1016/j.bbrc.2019.05.085. [DOI] [PubMed] [Google Scholar]
  • 50.Zhu L-L, et al. Distinct features of gut microbiota in high-altitude Tibetan and middle-altitude han hypertensive patients. Cardiol. Res. Pract. 2020;2020:1957843. doi: 10.1155/2020/1957843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Li J, et al. Succession of the gut microbiome in the Tibetan population of Minjiang river basin. Front Microbiol. 2022;13:834335. doi: 10.3389/fmicb.2022.834335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Caporaso JG, et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods. 2010;7:335–336. doi: 10.1038/nmeth.f.303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Edgar RC. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods. 2013;10:996–998. doi: 10.1038/nmeth.2604. [DOI] [PubMed] [Google Scholar]
  • 54.DeSantis TZ, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 2006;72:5069–5072. doi: 10.1128/AEM.03006-05. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Segata N, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. doi: 10.1186/gb-2011-12-6-r60. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information. (198.7KB, pdf)

Data Availability Statement

The dataset supporting the results of this article has been deposited in the EMBL European Nucleotide Archive (ENA) under BioProject accession code: PRJEB44310. Any remaining data are available from the corresponding author upon reasonable request.

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