T helper 17 (Th17) cells are pathogenic in many inflammatory diseases, but also support the integrity of the intestinal barrier in a non-inflammatory manner. It is unclear what distinguishes ...inflammatory Th17 cells elicited by pathogens and tissue-resident homeostatic Th17 cells elicited by commensals. Here, we compared the characteristics of Th17 cells differentiating in response to commensal bacteria (SFB) to those differentiating in response to a pathogen (Citrobacter rodentium). Homeostatic Th17 cells exhibited little plasticity towards expression of inflammatory cytokines, were characterized by a metabolism typical of quiescent or memory T cells, and did not participate in inflammatory processes. In contrast, infection-induced Th17 cells showed extensive plasticity towards pro-inflammatory cytokines, disseminated widely into the periphery, and engaged aerobic glycolysis in addition to oxidative phosphorylation typical for inflammatory effector cells. These findings will help ensure that future therapies directed against inflammatory Th17 cells do not inadvertently damage the resident gut population.
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•Tissue-resident, SFB-elicited Th17 cells are non-inflammatory•Citrobacter-elicited Th17 cells show high plasticity towards inflammatory cytokines•SFB Th17 cells are metabolically similar to resting memory cells•Citrobacter Th17 cells are highly glycolytic effector cells
The distinctions between inflammatory Th17 cells elicited by pathogens and tissue-resident homeostatic Th17 cells elicited by commensals are unclear. Omenetti et al. show that tissue-resident Th17 cells, in contrast to pathogen-elicited Th17 cells, exhibit little plasticity towards inflammatory cytokines, show muted metabolism, and do not participate in inflammatory reactions. These findings highlight the link between metabolic fitness and functional state.
A subcellular map of the human proteome Thul, Peter J.; Åkesson, Lovisa; Wiking, Mikaela ...
Science (American Association for the Advancement of Science),
05/2017, Letnik:
356, Številka:
6340
Journal Article
Recenzirano
Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of ...subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.
Parkinson’s disease (PD) is the most common progressive neurological disorder compromising motor functions. However, nonmotor symptoms, such as gastrointestinal (GI) dysfunction, precede those ...affecting movement. Evidence of an early involvement of the GI tract and enteric nervous system highlights the need for better understanding of the role of gut microbiota in GI complications in PD. Here, we investigate the gut microbiome of patients with PD using metagenomics and serum metabolomics. We integrate these data using metabolic modeling and construct an integrative correlation network giving insight into key microbial species linked with disease severity, GI dysfunction, and age of patients with PD. Functional analysis reveals an increased microbial capability to degrade mucin and host glycans in PD. Personalized community-level metabolic modeling reveals the microbial contribution to folate deficiency and hyperhomocysteinemia observed in patients with PD. The metabolic modeling approach could be applied to uncover gut microbial metabolic contributions to PD pathophysiology.
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•Microbiome functional analysis reveals changes in carbohydrate-active enzymes in PD•Higher number of bacterial mucin and host degradation enzymes link to PD•Metabolic modeling reveals the contribution of bacterial-specific metabolism in PD•Gut-community modeling reveals the role of bacterial folate and homocysteine in PD
Rosario et al. reveal the role of gut microbiome in Parkinson’s disease (PD) through functional and compositional analysis and genome-scale metabolic modeling. Microbial capability of mucin and host glycans degradation is associated with disease severity. Gut-community metabolic modeling reveals the bacterial contribution to folic acid deficit and hyperhomocysteinemia in PD.
Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing ...technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.
The global threat of antimicrobial resistance has driven the use of high-throughput sequencing techniques to monitor the profile of resistance genes, known as the resistome, in microbial populations. ...The human oral cavity contains a poorly explored reservoir of these genes. Here we analyse and compare the resistome profiles of 788 oral cavities worldwide with paired stool metagenomes. We find country and body site-specific differences in the prevalence of antimicrobial resistance genes, classes and mechanisms in oral and stool samples. Within individuals, the highest abundances of antimicrobial resistance genes are found in the oral cavity, but the oral cavity contains a lower diversity of resistance genes compared to the gut. Additionally, co-occurrence analysis shows contrasting ARG-species associations between saliva and stool samples. Maintenance and persistence of antimicrobial resistance is likely to vary across different body sites. Thus, we highlight the importance of characterising the resistome across body sites to uncover the antimicrobial resistance potential in the human body.
To investigate the biological processes that are altered in obese subjects, we generated cell-specific integrated networks (INs) by merging genome-scale metabolic, transcriptional regulatory and ...protein-protein interaction networks. We performed genome-wide transcriptomics analysis to determine the global gene expression changes in the liver and three adipose tissues from obese subjects undergoing bariatric surgery and integrated these data into the cell-specific INs. We found dysregulations in mannose metabolism in obese subjects and validated our predictions by detecting mannose levels in the plasma of the lean and obese subjects. We observed significant correlations between plasma mannose levels, BMI, and insulin resistance (IR). We also measured plasma mannose levels of the subjects in two additional different cohorts and observed that an increased plasma mannose level was associated with IR and insulin secretion. We finally identified mannose as one of the best plasma metabolites in explaining the variance in obesity-independent IR.
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•We generated cell-specific integrated networks for lean and obese subjects•We found dysregulations in the mannose metabolism in obese subjects•Plasma mannose level was associated with insulin resistance independent of BMI•Mannose is used in explaining the variance in obesity-independent insulin resistance
Lee et al. merged genome-scale metabolic models (GEMs), transcriptional regulatory networks (TRNs), and protein-protein interaction networks (PPINs) to generate cell-specific integrated networks for hepatocytes, myocytes, and adipocytes of lean and obese subjects undergoing bariatric surgery. They identified, and independently validated, plasma mannose as highly associated with insulin resistance, independent of BMI.
Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale ...perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein-protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated
revealed
as a potential target for treating
-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.
The gut microbiota can modulate human metabolism through interactions with macronutrients. However, microbiota-diet-host interactions are difficult to study because bacteria interact in complex food ...webs in concert with the host, and many of the bacteria are not yet characterized. To reduce the complexity, we colonize mice with a simplified intestinal microbiota (SIM) composed of ten sequenced strains isolated from the human gut with complementing pathways to metabolize dietary fibers. We feed the SIM mice one of three diets (chow fiber rich, high-fat/high-sucrose, or zero-fat/high-sucrose diets both low in fiber) and investigate (1) how dietary fiber, saturated fat, and sucrose affect the abundance and transcriptome of the SIM community, (2) the effect of microbe-diet interactions on circulating metabolites, and (3) how microbiota-diet interactions affect host metabolism. Our SIM model can be used in future studies to help clarify how microbiota-diet interactions contribute to metabolic diseases.
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•Mice are colonized with ten bacterial strains to create a simple human microbiota model•Dietary changes alter colonization patterns of the simplified intestinal microbiota (SIM)•SIM-diet interactions affect some circulating metabolites in the host•The SIM affects host metabolism in a diet-specific manner
Kovatcheva-Datchary et al. develop a mouse model colonized with a simplified intestinal microbiota (SIM) to investigate the microbe-microbe and host-microbe interactions in the mammalian gut, focusing on host metabolism. They combine dietary interventions and different omics approaches to show the potential of the SIM model to study the microbe-diet-host interplay.
Background Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources ...of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations. Results In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences. Conclusions This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome. Keywords: Natural herb, Multi-compound multi-target (MCMT), Human protein interactome
Obesity has become a global public health and economic problem. Obesity is a major risk factor for a number of complications, such as type 2 diabetes, cardiovascular disease, fatty liver disease, and ...cancer. Serotonin (5-hydroxytryptamine 5-HT) is a biogenic monoamine that plays various roles in metabolic homeostasis. It is well known that central 5-HT regulates appetite and mood. Several 5-HT receptor agonists and selective serotonin receptor uptake inhibitors (SSRIs) have shown beneficial effects on appetite and mood control in clinics. Although several genetic polymorphisms related to 5-HT synthesis and its receptors are strongly associated with obesity, there is little evidence of the role of peripheral 5-HT in human metabolism. In this study, we performed a systemic analysis of transcriptome data from the Genotype-Tissue Expression (GTEX) database. We investigated the expression of 5-HT and tryptophan hydroxylase (TPH), the rate-limiting enzyme of 5-HT biosynthesis, in the human brain and peripheral tissues. We also performed differential gene expression analysis and predicted changes in metabolites by comparing gene expressions of tissues with high TPH expression to the gene expressions of tissues with low TPH expression. Our analyses provide strong evidence that serotonin plays an important role in the regulation of metabolic homeostasis in humans.
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