Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data

Nature Protocols, Journal Year: 2022, Volume and Issue: 17(8), P. 1735 - 1761

Published: June 17, 2022

Language: Английский

---

Tryptophan-derived microbial metabolites activate the aryl hydrocarbon receptor in tumor-associated macrophages to suppress anti-tumor immunity

Immunity, Journal Year: 2022, Volume and Issue: 55(2), P. 324 - 340.e8

Published: Feb. 1, 2022

The aryl hydrocarbon receptor (AhR) is a sensor of products tryptophan metabolism and potent modulator immunity. Here, we examined the impact AhR in tumor-associated macrophage (TAM) function pancreatic ductal adenocarcinoma (PDAC). TAMs exhibited high activity Ahr-deficient macrophages developed an inflammatory phenotype. Deletion Ahr myeloid cells or pharmacologic inhibition reduced PDAC growth, improved efficacy immune checkpoint blockade, increased intra-tumoral frequencies IFNγ+CD8+ T cells. Macrophage was not required for this effect. Rather, dependent on Lactobacillus metabolization dietary to indoles. Removal TAM promoted accumulation TNFα+IFNγ+CD8+ cells; provision indoles blocked In patients with PDAC, AHR expression associated rapid disease progression mortality, as well immune-suppressive phenotype, suggesting conservation regulatory axis human disease.

Language: Английский

---

Mass spectrometry-based metabolomics in microbiome investigations

Nature Reviews Microbiology, Journal Year: 2021, Volume and Issue: 20(3), P. 143 - 160

Published: Sept. 22, 2021

Language: Английский

---

MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 51(W1), P. W310 - W318

Published: May 11, 2023

Abstract Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, important biomarkers, potential activities. Here we introduce MicrobiomeAnalyst 2.0 support comprehensive statistics, visualization, interpretation, integrative analysis of data outputs commonly generated from microbiome studies. Compared the previous version, features three new modules: (i) a Raw Data Processing module for amplicon processing taxonomy annotation that connects directly Marker Profiling downstream statistical analysis; (ii) Metabolomics help dissect associations between community compositions metabolic activities through joint paired metabolomics datasets; (iii) Statistical Meta-Analysis identify consistent signatures by integrating across multiple Other improvements include added multi-factor differential interactive visualizations popular graphical outputs, updated methods prediction correlation analysis, expanded taxon set libraries based on latest literature. These are demonstrated using dataset recent type 1 diabetes study. is freely available at microbiomeanalyst.ca.

Language: Английский

---

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

Frontiers in Microbiology, Journal Year: 2021, Volume and Issue: 12

Published: Feb. 19, 2021

The number of microbiome-related studies has notably increased the availability data on human microbiome composition and function. These provide essential material to deeply explore host-microbiome associations their relation development progression various complex diseases. Improved data-analytical tools are needed exploit all information from these biological datasets, taking into account peculiarities data, i.e., compositional, heterogeneous sparse nature datasets. possibility predicting host-phenotypes based taxonomy-informed feature selection establish an association between predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights models that can be used outputs, such as classification prediction in microbiology, infer host phenotypes diseases use microbial communities stratify patients by characterization state-specific signatures. Here we review state-of-the-art ML methods respective software applied studies, performed part COST Action ML4Microbiome activities. This scoping focuses application related clinical diagnostics, prognostics, therapeutics. Although presented here more bacterial community, many algorithms could general, regardless type. literature covering broad topic aligned with methodology. manual identification sources been complemented with: (1) automated publication search through digital libraries three major publishers using natural language processing (NLP) Toolkit, (2) relevant repositories GitHub ranking research papers relying rank approach.

Language: Английский

---

Bifidobacterium breve CCFM1025 attenuates major depression disorder via regulating gut microbiome and tryptophan metabolism: A randomized clinical trial

Brain Behavior and Immunity, Journal Year: 2021, Volume and Issue: 100, P. 233 - 241

Published: Dec. 4, 2021

Language: Английский

---

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Computational and Structural Biotechnology Journal, Journal Year: 2021, Volume and Issue: 19, P. 1092 - 1107

Published: Jan. 1, 2021

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets taxonomic and functional diversity are key better understanding ecology. Machine learning has proven be a useful approach for analyzing community data making predictions about outcomes including human environmental health. applied profiles been used predict disease states health, quality presence contamination the environment, as trace evidence forensics. appeal powerful tool that can provide deep insights into communities identify patterns data. However, often machine models black boxes specific outcome, with little how arrived at predictions. Complex algorithms may value higher accuracy performance sacrifice interpretability. In order leverage more translational research related microbiome strengthen extract meaningful biological information, it is important interpretable. Here we review current trends applications ecology well some challenges opportunities broad application communities.

Language: Английский

---

Human milk oligosaccharide DSLNT and gut microbiome in preterm infants predicts necrotising enterocolitis

Gut, Journal Year: 2020, Volume and Issue: 70(12), P. 2273 - 2282

Published: Dec. 16, 2020

Objective Necrotising enterocolitis (NEC) is a devastating intestinal disease primarily affecting preterm infants. The underlying mechanisms are poorly understood: mother’s own breast milk (MOM) protective, possibly relating to human oligosaccharide (HMO) and infant gut microbiome interplay. We investigated the interaction between HMO profiles development its association with NEC. Design performed profiling of MOM in large cohort infants NEC (n=33) matched controls (n=37). In subset 48 (14 NEC), we also longitudinal metagenomic sequencing stool (n=644). Results Concentration single HMO, disialyllacto-N-tetraose (DSLNT), was significantly lower received by compared controls. A threshold level 241 nmol/mL had sensitivity specificity 0.9 for Metagenomic before onset showed relative abundance Bifidobacterium longum higher Enterobacter cloacae Longitudinal impacted low DSLNT associated reduced transition into community types dominated spp typically observed older Random forest analysis combining metagenome data accurately classified 87.5% as healthy or having Conclusion These results demonstrate importance HMOs health disease. findings offer potential targets biomarker development, risk stratification novel avenues supplements that may prevent life-threatening

Language: Английский

---

High-throughput cultivation and identification of bacteria from the plant root microbiota

Nature Protocols, Journal Year: 2021, Volume and Issue: 16(2), P. 988 - 1012

Published: Jan. 13, 2021

Language: Английский

---

Bile acids drive the newborn’s gut microbiota maturation

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: July 23, 2020

Abstract Following birth, the neonatal intestine is exposed to maternal and environmental bacteria that successively form a dense highly dynamic intestinal microbiota. Whereas effect of exogenous factors has been extensively investigated, endogenous, host-mediated mechanisms have remained largely unexplored. Concomitantly with microbial colonization, liver undergoes functional transition from hematopoietic organ central metabolic regulation immune surveillance. The aim present study was analyze influence developing hepatic function metabolism on early Here, we report characterization colonization dynamics in murine gastrointestinal tract ( n = 6–10 per age group) using metabolomic profiling combination multivariate analysis. We observed major age-dependent changes identified bile acids as potent drivers microbiota maturation. Consistently, oral administration tauro-cholic acid or β-tauro-murocholic newborn mice 7–14 accelerated postnatal

Language: Английский

---