Gut Microbiome Analysis of Snails: A Biotechnological Approach

Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.

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Regorafenib plus toripalimab in patients with metastatic colorectal cancer: a phase Ib/II clinical trial and gut microbiome analysis

Cell Reports Medicine ◽

10.1016/j.xcrm.2021.100383 ◽

2021 ◽

pp. 100383

Author(s):

Feng Wang ◽

Ming-Ming He ◽

Yi-Chen Yao ◽

Xia Zhao ◽

Zhi-Qiang Wang ◽

...

Keyword(s):

Colorectal Cancer ◽

Clinical Trial ◽

Metastatic Colorectal Cancer ◽

Gut Microbiome ◽

Phase Ib ◽

Microbiome Analysis

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Geographical Relationships between Long-Tailed Goral (Naemorhedus caudatus) Populations Based on Gut Microbiome Analysis

Microorganisms ◽

10.3390/microorganisms9092002 ◽

2021 ◽

Vol 9 (9) ◽

pp. 2002

Author(s):

Chang Eon Park ◽

Bum-Joon Cho ◽

Min-Ji Kim ◽

Hee Cheon Park ◽

Jae-Ho Shin

Keyword(s):

Gut Microbiome ◽

Alpha Diversity ◽

Migration Route ◽

Ecological Studies ◽

Northern Asia ◽

Microbiome Composition ◽

Microbiome Analysis ◽

Northern Regions ◽

Regional Population ◽

Relational Network

The long-tailed goral (Naemorhedus caudatus) is an endangered species found in the mountains of eastern and northern Asia. Its populations have declined for various reasons, and this species has been designated as legally protected in South Korea. Although various ecological studies have been conducted on long-tailed gorals, none have investigated the gut microbiome until now. In the present study, we compared the diversity and composition of the gut microbiome of seven populations of Korean long-tailed gorals. By analyzing the gut microbiome composition for each regional population, it was found that four phyla—Firmicutes, Actinobacteriota, Bacteroidota, and Proteobacteria—were the most dominant in all regions on average. The alpha diversity of the gut microbiome of the goral population in the northern regions was high, while that in the southern regions was low. Through the analysis of beta diversity, the seven long-tailed goral populations have been divided into three groups: the Seoraksan population, the Samcheock population, and the Wangpicheon population. It was possible to confirm the regional migration of the animals using the gut microbiome based on the site-relational network analysis. It was found that the most stable population of long-tailed gorals in Korea was the Seoraksan population, and the closely related groups were the Samcheok and Wangpicheon populations, respectively. Wangpicheon appeared to be a major point of dispersal in the migration route of Korean long-tailed gorals.

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Microbial Dysbiosis and polyamine metabolism as predictive markers for early detection of pancreatic cancer

10.1101/342634 ◽

2018 ◽

Author(s):

Roberto Mendez ◽

Kousik Kesh ◽

Nivedita Arora ◽

Leá Di Martino ◽

Florencia McAllister ◽

...

Keyword(s):

Pancreatic Cancer ◽

Survival Rate ◽

Early Detection ◽

Gut Microbiota ◽

Gut Microbiome ◽

Ductal Adenocarcinoma ◽

Metabolic Reconstruction ◽

Microbiome Analysis ◽

Early Stages ◽

Kpc Mice

AbstractPurposeThe lack of tools for early detection of pancreatic ductal adenocarcinoma (PDAC) is directly correlated to the abysmal survival rate in patients. In addition to several potential detection tools under active investigation, we tested the gut microbiome and its metabolic complement as one of the earliest detection tools that could be useful in patients at high-risk for PDAC.Experimental DesignA combination of 16s pyrosequencing and whole-genome sequencing of gut microbiota was used in a spontaneous genetically engineered PDAC murine model (KRASG12DTP53R172HPdxCre or KPC). Metabolic reconstruction of microbiome was done using the HUmanN2 pipeline. Serum polyamine levels were measured from murine and patient samples using standard methods.ResultsResults showed a progressive Proteobacterial and Firmicutes dominance in gut microbiota in early stages of PDAC development. Upon in silico reconstruction of active metabolic pathways within the altered microbial flora, polyamine and nucleotide biosynthetic pathways were significantly elevated. These metabolic products are known to be actively assimilated by the host and eventually utilized by rapidly dividing cells for proliferation validating their importance in the context of tumorigenesis. In KPC mice, as well as PDAC patients, we show significantly elevated serum polyamine concentration. Therefore, at the early stages of tumorigenesis, the gut microbial composition changes in a way to release metabolites that foster host tumorigenesis, thereby fulfilling the ‘vicious cycle hypothesis’ of the role of the microbiome in health and disease states.ConclusionsOur results provide a potential, precise, non-invasive tool for early detection of PDAC, which will result in improved outcomes.SynopsisGut microbiota changes during early stages of pancreatic ductal adenocarcinoma (PDAC) progression and contributes towards host polyamine pool. Both changes can be used as an early predictive marker for PDAC.Translational RelevancePancreatic carcinogenesis progresses through pre-cancerous PanIN lesions to invasive cancer. Even though these morphological changes are histologically distinct, imaging techniques are not able to distinguish the pre-invasive PanINs from normal pancreas, making detection of a tumor at a precancerous stage impossible. Thus, majority of cases (85–90%) present with advanced pancreatic cancer at the time of diagnosis. This contributes to the dismal survival rate in this disease. Our study of gut microbiome analysis on KPC mice during tumor progression followed by metabolic reconstruction and experimental validation in human samples indicate that gut-microbiome analysis along with an analysis of the microbial metabolites can be developed as potential biomarkers for detection of PDAC at early stages when histological changes are not yet grossly apparent.

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Gut Microbiome Variation Modulates the Effects of Dietary Fiber on Host Metabolism

10.21203/rs.3.rs-97357/v1 ◽

2020 ◽

Author(s):

Sofia M Murga-Garrido ◽

Qilin Qilin Hong ◽

Tzu-Wen L Cross ◽

Evan Hutchison ◽

Jessica Han ◽

...

Keyword(s):

Dietary Fiber ◽

Gut Microbiome ◽

Interindividual Variation ◽

Cardiometabolic Health ◽

Metabolic Phenotypes ◽

Microbiome Analysis ◽

Host Genetic ◽

Gnotobiotic Mice ◽

Isocaloric Diets ◽

Host Metabolism

Abstract BackgroundThere is general consensus that consumption of dietary fermentable fiber improves cardiometabolic health, in part by promoting mutualistic microbes and by increasing production of beneficial metabolites in the distal gut. However, human studies have reported variations in the observed benefits among individuals consuming the same fiber.Several factors likely contribute to this variation, including host genetic and gut microbial differences. We hypothesized that gut microbial metabolism of dietary fiber represents an important and differential factor that modulates how dietary fiber impacts the host.ResultsWe examined genetically identical gnotobiotic mice harboring two distinct complex gut microbial communities and exposed to four isocaloric diets, each containing different fibers: (i) cellulose, (ii) inulin, (iii) pectin, (iv) a mix of 5 fermentable fibers (assorted fiber). Gut microbiome analysis showed that each transplanted community preserved a core of common taxa across diets that differentiated it from the other community, but there were variations in richness and bacterial taxa abundance within each community among the different diet treatments. Host epigenetic, transcriptional and metabolomic analyses revealed diet-directed differences between microbiome groups, including variation in amino acids and lipid pathways that were associated with divergent health outcomes.ConclusionThis study demonstrates that interindividual variation in the gut microbiome is causally linked to differential effects of dietary fiber on host metabolic phenotypes and suggests that a one-fits-all fiber supplementation approaches to promote health is unlikely to elicit consistent effects across individuals Overall, the presented results underscore the importance of microbe-diet interactions on host metabolism and suggest that gut microbes modulate dietary fiber efficacy.

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Gut microbiome diversity detected by high-coverage 16S and shotgun sequencing of paired stool and colon sample

Scientific Data ◽

10.1038/s41597-020-0427-5 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 3

Author(s):

Joan Mas-Lloret ◽

Mireia Obón-Santacana ◽

Gemma Ibáñez-Sanz ◽

Elisabet Guinó ◽

Miguel L. Pato ◽

...

Keyword(s):

Gut Microbiome ◽

Complex Structure ◽

High Coverage ◽

Cross Sectional ◽

Microbiome Analysis ◽

Hypervariable Regions ◽

16S Gene ◽

Faecal Samples ◽

Health And Disease ◽

And Function

AbstractThe gut microbiome has a fundamental role in human health and disease. However, studying the complex structure and function of the gut microbiome using next generation sequencing is challenging and prone to reproducibility problems. Here, we obtained cross-sectional colon biopsies and faecal samples from nine participants in our COLSCREEN study and sequenced them in high coverage using Illumina pair-end shotgun (for faecal samples) and IonTorrent 16S (for paired feces and colon biopsies) technologies. The metagenomes consisted of between 47 and 92 million reads per sample and the targeted sequencing covered more than 300 k reads per sample across seven hypervariable regions of the 16S gene. Our data is freely available and coupled with code for the presented metagenomic analysis using up-to-date bioinformatics algorithms. These results will add up to the informed insights into designing comprehensive microbiome analysis and also provide data for further testing for unambiguous gut microbiome analysis.

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