scholarly journals Analysis of Human Gut Microbiome: Taxonomy and Metabolic Functions in Thai Adults

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 331
Author(s):  
Nachon Raethong ◽  
Massalin Nakphaichit ◽  
Narissara Suratannon ◽  
Witida Sathitkowitchai ◽  
Wanlapa Weerapakorn ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Asian Country ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Human Gut ◽  
Thai Population ◽  
Shotgun Metagenomics ◽  
Human Gut Microbiome ◽  
Metabolic Functions

The gut microbiome plays a major role in the maintenance of human health. Characterizing the taxonomy and metabolic functions of the human gut microbiome is necessary for enhancing health. Here, we analyzed the metagenomic sequencing, assembly and construction of a meta-gene catalogue of the human gut microbiome with the overall aim of investigating the taxonomy and metabolic functions of the gut microbiome in Thai adults. As a result, the integrative analysis of 16S rRNA gene and whole metagenome shotgun (WMGS) sequencing data revealed that the dominant gut bacterial families were Lachnospiraceae and Ruminococcaceae of the Firmicutes phylum. Consistently, across 3.8 million (M) genes annotated from 163.5 gigabases (Gb) of WMGS sequencing data, a significant number of genes associated with carbohydrate metabolism of the dominant bacterial families were identified. Further identification of bacterial community-wide metabolic functions promisingly highlighted the importance of Roseburia and Faecalibacterium involvement in central carbon metabolism, sugar utilization and metabolism towards butyrate biosynthesis. This work presents an initial study of shotgun metagenomics in a Thai population-based cohort in a developing Southeast Asian country.

scholarly journals MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome

2021 ◽  
Vol 17 (1) ◽  
pp. e1008487
Author(s):  
Preecha Patumcharoenpol ◽  
Massalin Nakphaichit ◽  
Gianni Panagiotou ◽  
Anchalee Senavonge ◽  
Narissara Suratannon ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Rrna Gene ◽  
16S Rrna Gene Sequences ◽  
Functional Capability ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Metabolic Functions ◽  
Scale Models

Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.

scholarly journals Whole-Genome Metagenomic Analysis of the Gut Microbiome in HIV-1-Infected Individuals on Antiretroviral Therapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiangning Bai ◽  
Aswathy Narayanan ◽  
Piotr Nowak ◽  
Shilpa Ray ◽  
Ujjwal Neogi ◽  
...  
Keyword(s):  
Antiretroviral Therapy ◽  
Gut Microbiome ◽  
Alpha Diversity ◽  
16S Rrna Gene Sequencing ◽  
Taxonomic Resolution ◽  
Rrna Gene ◽  
Human Gut ◽  
Shotgun Metagenomics ◽  
Human Gut Microbiome ◽  
Hiv 1

Gut microbiome plays a significant role in HIV-1 immunopathogenesis and HIV-1-associated complications. Previous studies have mostly been based on 16S rRNA gene sequencing, which is limited in taxonomic resolution at the genus level and inferred functionality. Herein, we performed a deep shotgun metagenomics study with the aim to obtain a more precise landscape of gut microbiome dysbiosis in HIV-1 infection. A reduced tendency of alpha diversity and significantly higher beta diversity were found in HIV-1-infected individuals on antiretroviral therapy (ART) compared to HIV-1-negative controls. Several species, such as Streptococcus anginosus, Actinomyces odontolyticus, and Rothia mucilaginosa, were significantly enriched in the HIV-1-ART group. Correlations were observed between the degree of immunodeficiency and gut microbiome in terms of microbiota composition and metabolic pathways. Furthermore, microbial shift in HIV-1-infected individuals was found to be associated with changes in microbial virulome and resistome. From the perspective of methodological evaluations, our study showed that different DNA extraction protocols significantly affect the genomic DNA quantity and quality. Moreover, whole metagenome sequencing depth affects critically the recovery of microbial genes, including virulome and resistome, while less than 5 million reads per sample is sufficient for taxonomy profiling in human fecal metagenomic samples. These findings advance our understanding of human gut microbiome and their potential associations with HIV-1 infection. The methodological assessment assists in future study design to accurately assess human gut microbiome.

scholarly journals A unique and reliable fecal DNA extraction method for 16S rRNA gene and shotgun metagenomic sequencing in the analysis of the human gut microbiome

2020 ◽  
Author(s):  
Céline Elie ◽  
Magali Perret ◽  
Karen Louis ◽  
Asmaà Fritah-Lafont ◽  
Philippe Leissner ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Dna Extraction ◽  
Gut Microbiome ◽  
High Throughput Sequencing ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Shotgun Metagenomic Sequencing

Abstract Background: The gut microbiome is widely analyzed using high-throughput sequencing, such as 16S rRNA gene amplicon sequencing and shotgun metagenomic sequencing (SMS). DNA extraction is known to have a large impact on the metagenomic analyses. The aim of this study was to select a unique and best performing DNA extraction protocol for both metagenomic sequencing methods. In that context, four commonly used DNA extraction methods were compared for the analysis of the gut microbiota. Commercial versions were evaluated against modified protocols using a stool preprocessing device (SPD, bioMérieux) in order to facilitate DNA extraction. Stool samples from nine healthy volunteers and nine patients with a Clostridium difficile infection were extracted with all protocols and sequenced with both metagenomic methods. Protocols were ranked using wet- and dry-lab criteria, including quality controls of the extracted genomic DNA, alpha-diversity, accuracy using a mock community of known composition and repeatability across technical replicates.Results: Independently of the sequencing methods used, SPD significantly improved efficiency of the four tested protocols compared with their commercial version, in terms of extracted DNA quality, accuracy of the predicted composition of the microbiota (notably for Gram-positive bacteria), sample alpha-diversity, and experimental repeatability. The best overall performance was obtained for the S-DQ protocol, SPD combined to the DNeasy PowerLyser PowerSoil protocol from QIAGEN.Conclusion: Based on this evaluation, we recommend to use the S-DQ protocol, to obtain standardized and high quality extracted DNA in the human gut microbiome studies.

scholarly journals Yoghurt Consumption is Associated With Transient Changes in the Composition of the Human Gut Microbiome

2020 ◽  
Author(s):  
Caroline Ivanne Le Roy ◽  
Alexander Kurilshikov ◽  
Emily Leeming ◽  
Alessia Visconti ◽  
Ruth Bowyer ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
Visceral Fat ◽  
Gut Microbiome ◽  
Body Weight Gain ◽  
Healthy Eating Index ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Shotgun Metagenomic Sequencing

Abstract Background: Yoghurt contains live bacteria that could contribute via modulation of the gut microbiota to its reported beneficial effects such as reduced body weight gain and lower incidence of type 2 diabetes. To date, the association between yoghurt consumption and the composition of the gut microbiota is underexplored. Here we used clinical variables, metabolomics, 16S rRNA and shotgun metagenomic sequencing data collected on over 1000 predominantly female UK twins to define the link between the gut microbiota and yoghurt-associated health benefits. Results: According to food frequency questionnaires (FFQ), 73% of subjects consumed yoghurt. Consumers presented a healthier diet pattern (healthy eating index: beta = 2.17±0.34; P = 2.72x10-10) and improved metabolic health characterised by reduced visceral fat (beta = -28.18±11.71 g; P = 0.01). According to 16S rRNA gene analyses and whole shotgun metagenomic sequencing approach consistent taxonomic variations were observed with yoghurt consumption. More specifically, we identified higher abundance of species used as yoghurt starters Streptococcus thermophilus (beta = 0.41±0.051; P = 6.14x10-12) and sometimes added Bifidobacterium animalis subsp. lactis (beta = 0.30±0.052; P = 1.49x10-8) in the gut of yoghurt consumers. Replication in 1103 volunteers from the LifeLines-DEEP cohort confirmed the increase of S. thermophilus among yoghurt consumers. Using food records collected the day prior to faecal sampling we showed that increase in these two yoghurt bacteria could be transient. Metabolomics analysis revealed that B. animalis subsp. lactis was associated with 13 faecal metabolites including a 3-hydroxyoctanoic acid, known to be involved in the regulation of gut inflammation.Conclusions: Yoghurt consumption is associated with reduced visceral fat mass and changes in gut microbiome including transient increase of yoghurt-contained species (i.e. S. thermophilus and B. lactis).

scholarly journals Correction: 16S rRNA gene sequencing and healthy reference ranges for 28 clinically relevant microbial taxa from the human gut microbiome

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0212474 ◽  
Author(s):  
Daniel E. Almonacid ◽  
Laurens Kraal ◽  
Francisco J. Ossandon ◽  
Yelena V. Budovskaya ◽  
Juan Pablo Cardenas ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Reference Ranges ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Rrna Gene Sequencing

scholarly journals Pet-Human Gut Microbiome Host Classifier Using Data from Different Studies

2020 ◽  
Vol 8 (10) ◽  
pp. 1591
Author(s):  
Nadia Bykova ◽  
Nikita Litovka ◽  
Anna Popenko ◽  
Sergey Musienko
Keyword(s):  
Gut Microbiome ◽  
Environmental Data ◽  
Domestic Cat ◽  
Sequencing Data ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Feature Importance ◽  
Importance Analysis ◽  
Using Data ◽  
Sources Of Contamination

(1) Background: microbiome host classification can be used to identify sources of contamination in environmental data. However, there is no ready-to-use host classifier. Here, we aimed to build a model that would be able to discriminate between pet and human microbiomes samples. The challenge of the study was to build a classifier using data solely from publicly available studies that normally contain sequencing data for only one type of host. (2) Results: we have developed a random forest model that distinguishes human microbiota from domestic pet microbiota (cats and dogs) with 97% accuracy. In order to prevent overfitting, samples from several (at least four) different projects were necessary. Feature importance analysis revealed that the model relied on several taxa known to be key components in domestic cat and dog microbiomes (such as Fusobacteriaceae and Peptostreptococcaeae), as well as on some taxa exclusively found in humans (as Akkermansiaceae). (3) Conclusion: we have shown that it is possible to make a reliable pet/human gut microbiome classifier on the basis of the data collected from different studies.

scholarly journals Natural and after colon washing fecal samples: the two sides of the coin for investigating the human gut microbiome

2021 ◽  
Author(s):  
Elisabetta Piancone ◽  
Bruno Fosso ◽  
Mariangela De Robertis ◽  
Elisabetta Notario ◽  
Annarita Oranger ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Digital Pcr ◽  
Shannon Index ◽  
Individual Variability ◽  
Rrna Gene ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Taxonomic Analysis ◽  
Paired Samples

To date there are several studies focusing on the importance of gut microbiome for human health, however the selection of a universal sampling matrix representative of the microbial biodiversity associated to the gastrointestinal (GI) tract, still represents a challenge. Here we present a study in which, through a deep metabarcoding analysis of the 16S rRNA gene, we compared two sampling matrices, feces (F) and colonic lavage liquid (LL), in order to evaluate their accuracy to represent the complexity of the human gut microbiome. A training set of 37 volunteers was attained and paired F and LL samples were collected from each subject. A preliminary absolute quantification of total 16S rDNA, performed by droplet digital PCR (ddPCR), confirmed that sequencing and taxonomic analysis were performed on same total bacterial abundance obtained from the two sampling methods. The taxonomic analysis of paired samples revealed that, although specific taxa were predominantly or exclusively observed in LL samples, as well as other taxa were detectable only or were predominant in stool, the microbiomes of the paired samples F and LL in the same subject hold overlapping taxonomic composition. Moreover, LL samples revealed a higher biodiversity than stool at all taxonomic ranks, as demonstrated by the Shannon Index and the Inverse Simpson's Index. We also found greater inter-individual variability than intra-individual variability in both sample matrices. Finally, functional differences were unveiled in the gut microbiome detected in the F and LL samples. A significant overrepresentation of 22 and 13 metabolic pathways, mainly occurring in Firmicutes and Proteobacteria, was observed in gut microbiota detected in feces and LL samples, respectively. This suggests that LL samples may allow for the detection of microbes adhering to the intestinal mucosal surface as members of the resident flora that are not easily detectable in stool, most likely representative of a diet-influenced transient microbiota. This first comparative study on feces and LL samples for the study of the human gut microbiome demonstrates that the use of both types of sample matrices may represent a possible choice to obtain a more complete view of the human gut microbiota in response to different biological and clinical questions.

scholarly journals Yoghurt consumption is associated with transient changes in the composition of the human gut microbiome

2020 ◽  
Author(s):  
Caroline Ivanne Le Roy ◽  
Alexander Kurilshikov ◽  
Emily Leeming ◽  
Alessia Visconti ◽  
Ruth Bowyer ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
Visceral Fat ◽  
Gut Microbiome ◽  
Body Weight Gain ◽  
Healthy Eating Index ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Shotgun Metagenomic Sequencing

Abstract Background: Yoghurt contains live bacteria that could contribute via modulation of the gut microbiota to its reported beneficial effects such as reduced body weight gain and lower incidence of type 2 diabetes. To date, the association between yoghurt consumption and the composition of the gut microbiota is underexplored. Here we used clinical variables, metabolomics, 16S rRNA and shotgun metagenomic sequencing data collected on over 1000 predominantly female UK twins to define the link between the gut microbiota and yoghurt-associated health benefits. Results: According to food frequency questionnaires (FFQ), 73% of subjects consumed yoghurt. Consumers presented a healthier diet pattern (healthy eating index: beta = 2.17±0.34; P = 2.72x10 -10 ) and improved metabolic health characterised by reduced visceral fat (beta = -28.18±11.71 g; P = 0.01). According to 16S rRNA gene analyses and whole shotgun metagenomic sequencing approach consistent taxonomic variations were observed with yoghurt consumption. More specifically, we identified higher abundance of species used as yoghurt starters Streptococcus thermophilus (beta = 0.41±0.051; P = 6.14x10 -12 ) and sometimes added Bifidobacterium animalis subsp. lactis (beta = 0.30±0.052; P = 1.49x10 -8 ) in the gut of yoghurt consumers. Replication in 1103 volunteers from the LL-DEEP cohort confirmed the increase of S. thermophilus among yoghurt consumers. Using food records collected the day prior to faecal sampling we showed that increase in these two yoghurt bacteria could be transient. Metabolomics analysis revealed that B. animalis subsp. lactis was associated with 13 faecal metabolites including a 3-hydroxyoctanoic acid, known to be involved in the regulation of gut inflammation. Conclusions: Yoghurt consumption is associated with reduced visceral fat mass and changes in gut microbiome including transient increase of yoghurt-contained species ( i.e. S. thermophilus and B. lactis ).

scholarly journals Captivity humanizes the primate microbiome

2016 ◽  
Vol 113 (37) ◽  
pp. 10376-10381 ◽  
Author(s):  
Jonathan B. Clayton ◽  
Pajau Vangay ◽  
Hu Huang ◽  
Tonya Ward ◽  
Benjamin M. Hillmann ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Meta Analysis ◽  
Human Microbiome ◽  
16S Rrna Gene Sequencing ◽  
Modern Human ◽  
Primate Species ◽  
Rrna Gene ◽  
Human Gut ◽  
Dietary Analysis ◽  
Human Gut Microbiome

The primate gastrointestinal tract is home to trillions of bacteria, whose composition is associated with numerous metabolic, autoimmune, and infectious human diseases. Although there is increasing evidence that modern and Westernized societies are associated with dramatic loss of natural human gut microbiome diversity, the causes and consequences of such loss are challenging to study. Here we use nonhuman primates (NHPs) as a model system for studying the effects of emigration and lifestyle disruption on the human gut microbiome. Using 16S rRNA gene sequencing in two model NHP species, we show that although different primate species have distinctive signature microbiota in the wild, in captivity they lose their native microbes and become colonized with Prevotella and Bacteroides, the dominant genera in the modern human gut microbiome. We confirm that captive individuals from eight other NHP species in a different zoo show the same pattern of convergence, and that semicaptive primates housed in a sanctuary represent an intermediate microbiome state between wild and captive. Using deep shotgun sequencing, chemical dietary analysis, and chloroplast relative abundance, we show that decreasing dietary fiber and plant content are associated with the captive primate microbiome. Finally, in a meta-analysis including published human data, we show that captivity has a parallel effect on the NHP gut microbiome to that of Westernization in humans. These results demonstrate that captivity and lifestyle disruption cause primates to lose native microbiota and converge along an axis toward the modern human microbiome.

scholarly journals Impact of Different Exercise Modalities on the Human Gut Microbiome

Sports ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 14
Author(s):  
Dierdra Bycura ◽  
Anthony C. Santos ◽  
Arron Shiffer ◽  
Shari Kyman ◽  
Kyle Winfree ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Sequence Data ◽  
Exercise Program ◽  
Rrna Gene ◽  
Human Gut ◽  
Post Intervention ◽  
Cross Sectional ◽  
Intervention Phase ◽  
Human Gut Microbiome

In this study we examined changes to the human gut microbiome resulting from an eight-week intervention of either cardiorespiratory exercise (CRE) or resistance training exercise (RTE). Twenty-eight subjects (21 F; aged 18–26) were recruited for our CRE study and 28 subjects (17 F; aged 18–33) were recruited for our RTE study. Fecal samples for gut microbiome profiling were collected twice weekly during the pre-intervention phase (three weeks), intervention phase (eight weeks), and post-intervention phase (three weeks). Pre/post VO2max, three repetition maximum (3RM), and body composition measurements were conducted. Heart rate ranges for CRE were determined by subjects’ initial VO2max test. RTE weight ranges were established by subjects’ initial 3RM testing for squat, bench press, and bent-over row. Gut microbiota were profiled using 16S rRNA gene sequencing. Microbiome sequence data were analyzed with QIIME 2. CRE resulted in initial changes to the gut microbiome which were not sustained through or after the intervention period, while RTE resulted in no detectable changes to the gut microbiota. For both CRE and RTE, we observe some evidence that the baseline microbiome composition may be predictive of exercise gains. This work suggests that the human gut microbiome can change in response to a new exercise program, but the type of exercise likely impacts whether a change occurs. The changes observed in our CRE intervention resemble a disturbance to the microbiome, where an initial shift is observed followed by a return to the baseline state. More work is needed to understand how sustained changes to the microbiome occur, resulting in differences that have been reported in cross sectional studies of athletes and non-athletes.

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