Dataset Information

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

ABSTRACT: Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila, Rikenellaceae, Bacteroides, Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

SUBMITTER: Vernocchi P

PROVIDER: S-EPMC7699235 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

Vernocchi Pamela P Gili Tommaso T Conte Federica F Del Chierico Federica F Conta Giorgia G Miccheli Alfredo A Botticelli Andrea A Paci Paola P Caldarelli Guido G Nuti Marianna M Marchetti Paolo P Putignani Lorenza L

International journal of molecular sciences 20201119 22

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing ...[more]

PMID: 33227982

Similar Datasets

Project description:BACKGROUND:Fibromyalgia is a complex, relatively unknown disease characterised by chronic, widespread musculoskeletal pain. The gut-brain axis connects the gut microbiome with the brain through the enteric nervous system (ENS); its disruption has been associated with psychiatric and gastrointestinal disorders. To gain an insight into the pathogenesis of fibromyalgia and identify diagnostic biomarkers, we combined different omics techniques to analyse microbiome and serum composition. METHODS:We collected faeces and blood samples to study the microbiome, the serum metabolome and circulating cytokines and miRNAs from a cohort of 105 fibromyalgia patients and 54 age- and environment-matched healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from faeces samples. UPLC-MS metabolomics and custom multiplex cytokine and miRNA analysis (FirePlex™ technology) were used to examine sera samples. Finally, we combined the different data types to search for potential biomarkers. RESULTS:We found that the diversity of bacteria is reduced in fibromyalgia patients. The abundance of the Bifidobacterium and Eubacterium genera (bacteria participating in the metabolism of neurotransmitters in the host) in these patients was significantly reduced. The serum metabolome analysis revealed altered levels of glutamate and serine, suggesting changes in neurotransmitter metabolism. The combined serum metabolomics and gut microbiome datasets showed a certain degree of correlation, reflecting the effect of the microbiome on metabolic activity. We also examined the microbiome and serum metabolites, cytokines and miRNAs as potential sources of molecular biomarkers of fibromyalgia. CONCLUSIONS:Our results show that the microbiome analysis provides more significant biomarkers than the other techniques employed in the work. Gut microbiome analysis combined with serum metabolomics can shed new light onto the pathogenesis of fibromyalgia. We provide a list of bacteria whose abundance changes in this disease and propose several molecules as potential biomarkers that can be used to evaluate the current diagnostic criteria.

Project description:Loss of traditional diets by food globalization may have adverse impact on the health of human being through the alteration of gut microbial ecosystem. To address this notion, we compared the gut microbiota of urban (n = 17) and rural (n = 28) school-aged children in Thailand in association with their dietary habits. Dietary records indicated that children living in urban Bangkok tended to consume modern high-fat diets, whereas children in rural Buriram tended to consume traditional vegetable-based diets. Sequencing of 16S rRNA genes amplified from stool samples showed that children in Bangkok have less Clostridiales and more Bacteroidales and Selenomonadales compared to children in Buriram and bacterial diversity is significantly less in Bangkok children than in Buriram children. In addition, fecal butyrate and propionate levels decreased in Bangkok children in association with changes in their gut microbial communities. Stool samples of these Thai children were classified into five metabolotypes (MTs) based on their metabolome profiles, each characterized by high concentrations of short and middle chain fatty acids (MT1, n = 17), amino acids (MT2, n = 7), arginine (MT3, n = 6), amino acids, and amines (MT5, n = 8), or an overall low level of metabolites (MT4, n = 4). MT1 and MT4 mainly consisted of samples from Buriram, and MT2 and MT3 mainly consisted of samples from Bangkok, whereas MT5 contained three samples from Bangkok and five from Buriram samples. According to the profiles of microbiota and diets, MT1 and MT2 are characteristic of children in Buriram and Bangkok, respectively. Predicted metagenomics indicated the underrepresentation in MT2 of eight genes involved in pathways of butyrate biosynthesis, notably including paths from glutamate as well as pyruvate. Taken together, this study shows the benefit of high-vegetable Thai traditional diets on gut microbiota and suggests that high-fat and less-vegetable urban dietary habits alter gut microbial communities in Thai children, which resulted in the reduction of colonic short chain fatty acid fermentation.

Project description:Parkinson's disease is a neurodegenerative disorder characterized by the accumulation of intracellular aggregates of misfolded alpha-synuclein along the cerebral axis. Several studies report the association between intestinal dysbiosis and Parkinson's disease, although a cause-effect relationship remains to be established. Herein, the gut microbiota composition of 64 Italian patients with Parkinson's disease and 51 controls was determined using a next-generation sequencing approach. A real metagenomics shape based on gas chromatography-mass spectrometry was also investigated. The most significant changes within the Parkinson's disease group highlighted a reduction in bacterial taxa, which are linked to anti-inflammatory/neuroprotective effects, particularly in the Lachnospiraceae family and key members, such as Butyrivibrio, Pseudobutyrivibrio, Coprococcus, and Blautia The direct evaluation of fecal metabolites revealed changes in several classes of metabolites. Changes were seen in lipids (linoleic acid, oleic acid, succinic acid, and sebacic acid), vitamins (pantothenic acid and nicotinic acid), amino acids (isoleucine, leucine, phenylalanine, glutamic acid, and pyroglutamic acid) and other organic compounds (cadaverine, ethanolamine, and hydroxy propionic acid). Most modified metabolites strongly correlated with the abundance of members belonging to the Lachnospiraceae family, suggesting that these gut bacteria correlate with altered metabolism rates in Parkinson's disease.IMPORTANCE To our knowledge, this is one of the few studies thus far that correlates the composition of the gut microbiota with the direct analysis of fecal metabolites in patients with Parkinson's disease. Overall, our data highlight microbiota modifications correlated with numerous fecal metabolites. This suggests that Parkinson's disease is associated with gut dysregulation that involves a synergistic relationship between gut microbes and several bacterial metabolites favoring altered homeostasis. Interestingly, a reduction of short-chain fatty acid (SCFA)-producing bacteria influenced the shape of the metabolomics profile, affecting several metabolites with potential protective effects in the Parkinson group. On the other hand, the extensive impact that intestinal dysbiosis has at the level of numerous metabolic pathways could encourage the identification of specific biomarkers for the diagnosis and treatment of Parkinson's disease, also in light of the effect that specific drugs have on the composition of the intestinal microbiota.

Project description:BackgroundThe infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.ResultsStool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.ConclusionsOur results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

Dataset Information

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

Publications

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure