Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.
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ABSTRACT:
SUBMITTER: Duran C
PROVIDER: S-EPMC7997970 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective: Dysbiosis of gastric microbiota such as Helicobacter pylori plays a significant role in pathogenesis and progression of gastric cancer. Our aim was to evaluate the composition and functional effects of gastric microbiota in superficial gastritis (SG) and advanced gastric adenocarcinoma (GC). Methods: We carried out shotgun metagenomic sequencing on gastric wash samples from 6 patients with GC and 5 patients with SG. The taxonomic composition was profiled using MetaPhlAn2 and functional gene pathway was profiled using HUMAnN2. Differences in microbial composition and pathways between the two patient groups were assessed via LEfSe. Results: The gastric microbiota in GC patients was characterized by reduced species richness, enrichment of 13 bacterial taxa and depletion of 31 taxa (q < 0.05). The most representative taxa which were abundant in GC corresponded to the commensals or opportunistic pathogens that usually colonize the oral cavity, including genera Neisseria, Alloprevotella, and Aggregatibacter, species Streptococcus_mitis_oralis_pneumoniae and strain Porphyromonas_endodontalis.t_GCF_000174815. Each of the three GC-associated genera could separate GC from SG completely. In particular, Sphingobium yanoikuyae, a bacterium capable of degrading carcinogenic compounds, was depleted in GC. Functionally, pathways associated with the biosynthesis of lipopolysaccharide (LPS) and L-arginine were enriched in GC, whereas pathways involved in the fermentation of short chain fatty acids (SCFAs) and branched amino acid metabolism were more abundant in SG. Conclusions: Our results present new alterations in the gastric microbiome in patients with GC from a whole-genome perspective, suggesting that microbiome composition and function can be used for prognosis and diagnosis of GC.
| S-EPMC6299874 | biostudies-literature
Project description:Chronic inflammation has been associated with an increased risk of several human malignancies, a classic example being gastric adenocarcinoma (GC). Development of GC is known to result from infection of the gastric mucosa by Helicobacter pylori, which initially induces acute inflammation and, in a subset of patients, progresses over time to chronic inflammation, gastric atrophy, intestinal metaplasia, dysplasia, and finally intestinal-type GC. Germ-line encoded receptors known as pattern-recognition receptors (PRRs) are critical for generating mature pro-inflammatory cytokines that are crucial for both Th1 and Th2 responses. Given that H. pylori is initially targeted by PRRs, it is conceivable that dysfunction within genes of this arm of the immune system could modulate the host response against H. pylori infection, and subsequently influence the emergence of GC. Current evidence suggests that Toll-like receptors (TLRs) (TLR2, TLR3, TLR4, TLR5, and TLR9), nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) (NOD1, NOD2, and NLRP3), a C-type lectin receptor (DC-SIGN), and retinoic acid-inducible gene (RIG)-I-like receptors (RIG-I and MDA-5), are involved in both the recognition of H. pylori and gastric carcinogenesis. In addition, polymorphisms in genes involved in the TLR (TLR1, TLR2, TLR4, TLR5, TLR9, and CD14) and NLR (NOD1, NOD2, NLRP3, NLRP12, NLRX1, CASP1, ASC, and CARD8) signaling pathways have been shown to modulate the risk of H. pylori infection, gastric precancerous lesions, and/or GC. Further, the modulation of PRRs has been suggested to suppress H. pylori-induced inflammation and enhance GC cell apoptosis, highlighting their potential relevance in GC therapeutics. In this review, we present current advances in our understanding of the role of the TLR and NLR signaling pathways in the pathogenesis of GC, address the involvement of other recently identified PRRs in GC, and discuss the potential implications of PRRs in GC immunotherapy.
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Project description:Distinguishing infectious pathogens from harmless microorganisms is essential for animal health. The mechanisms used to identify infectious microbes are not fully understood, particularly in metazoan hosts that eat bacteria as their food source. Here, we characterized a non-canonical pattern-recognition system in Caenorhabditis elegans (C. elegans) that assesses the relative threat of virulent Pseudomonas aeruginosa (P. aeruginosa) to activate innate immunity. We discovered that the innate immune response in C. elegans was triggered by phenazine-1-carboxamide (PCN), a toxic metabolite produced by pathogenic strains of P. aeruginosa. We identified the nuclear hormone receptor NHR-86/HNF4 as the PCN sensor in C. elegans and validated that PCN bound to the ligand-binding domain of NHR-86/HNF4. Activation of NHR-86/HNF4 by PCN directly engaged a transcriptional program in intestinal epithelial cells that protected against P. aeruginosa. Thus, a bacterial metabolite is a pattern of pathogenesis surveilled by nematodes to identify a pathogen in its bacterial diet.
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Project description:PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from Jan 1, 2000 to Dec 31, 2014 is used as the training data set, and the data set from Jan 1, 2015 to Oct 30, 2020 is used to verify the forecasting effect. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead; these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Empirical results show that applying different machine learning methods to two-day and three-day patterns for one-day-ahead forecasts can be profitable.
| S-EPMC8345893 | biostudies-literature
Project description:Inflammasomes are important multiprotein regulatory complexes of innate immunity and have recently emerged as playing divergent roles in numerous inflammation-associated cancers. Among these include gastric cancer (GC), the third leading cause of cancer-associated death worldwide, and we have previously discovered a pro-tumorigenic role for the key inflammasome adaptor apoptosis-associated speck-like protein containing a CARD (ASC) in the spontaneous genetic gp130 F/F mouse model for GC. However, the identity of the specific pattern recognition receptors (PRRs) that activate tumor-promoting inflammasomes during GC is unknown. Here, we investigated the role of the best-characterized inflammasome-associated PRR, nucleotide-binding domain, and leucine-rich repeat containing receptor, pyrin domain-containing (NLRP) 3, in GC. In gastric tumors of gp130 F/F mice, although NLRP3 expression was elevated at the mRNA (qPCR) and protein (immunohistochemistry) levels, genetic ablation of NLRP3 in gp130 F/F:Nlrp3 -/- mice did not alleviate the development of gastric tumors. Similarly, cellular processes associated with tumorigenesis in the gastric mucosa, namely, proliferation, apoptosis, and inflammation, were comparable between gp130 F/F and gp130 F/F:Nlrp3 -/- mice. Furthermore, inflammasome activation levels, determined by immunoblotting and immunohistochemistry for cleaved Caspase-1, which along with ASC is another integral component of inflammasome complexes, were unchanged in gp130 F/F and gp130 F/F:Nlrp3 -/- gastric tumors. We also observed variable NLRP3 expression levels (mRNA and protein) among independent GC patient cohorts, and NLRP3 was not prognostic for survival outcomes. Taken together, these data suggest that NLRP3 does not play a major role in promoting inflammasome-driven gastric tumorigenesis, and thus pave the way for further investigations to uncover the key inflammasome-associated PRR implicated in GC.
| S-EPMC8921257 | biostudies-literature
Project description:The peptidoglycan fragments γ-d-glutamyl- meso-diaminopimelic acid (iE-DAP) and muramyl-dipeptide (MDP) are microbial-specific metabolites that activate intracellular pattern recognition receptors and stimulate immune signaling pathways. While extensive structure-activity studies have demonstrated that these bacterial cell wall metabolites trigger NOD1- and NOD2-dependent signaling, their direct binding to these innate immune receptors or other proteins in mammalian cells has not been established. To characterize these fundamental microbial metabolite-host interactions, we synthesized a series of peptidoglycan metabolite photoaffinity reporters and evaluated their cross-linking to NOD1 and NOD2 in mammalian cells. We show that active iE-DAP and MDP photoaffinity reporters selectively cross-linked NOD1 and NOD2, respectively, and not their inactive mutants. We also discovered MDP reporter cross-linking to Arf GTPases, which interacted most prominently with GTP-bound Arf6 and coimmunoprecipitated with NOD2 upon MDP stimulation. Notably, MDP binding to NOD2 and Arf6 was abrogated with loss-of-function NOD2 mutants associated with Crohn's disease. Our studies demonstrate peptidoglycan metabolite photoaffinity reporters can capture their cognate immune receptors in cells and reveal unpredicted ligand-induced interactions with other cellular cofactors. These photoaffinity reporters should afford useful tools to discover and characterize other peptidoglycan metabolite-interacting proteins.
| S-EPMC6943928 | biostudies-literature
Project description:Nanophotonics finds ever broadening applications requiring complex components with many parameters to be simultaneously designed. Recent methodologies employing optimization algorithms commonly focus on a single performance objective, provide isolated designs, and do not describe how the design parameters influence the device behaviour. Here we propose and demonstrate a machine-learning-based approach to map and characterize the multi-parameter design space of nanophotonic components. Pattern recognition is used to reveal the relationship between an initial sparse set of optimized designs through a significant reduction in the number of characterizing parameters. This defines a design sub-space of lower dimensionality that can be mapped faster by orders of magnitude than the original design space. The behavior for multiple performance criteria is visualized, revealing the interplay of the design parameters, highlighting performance and structural limitations, and inspiring new design ideas. This global perspective on high-dimensional design problems represents a major shift in modern nanophotonic design and provides a powerful tool to explore complexity in next-generation devices.
| S-EPMC6803653 | biostudies-literature
Project description:Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid profile of a specific oil type can be mimicked by a mixture of 2 or more oil types. This has led to fraudulent oil adulteration and intentional mislabeling of edible oils threatening food safety and endangering public health. Here, we present a machine learning method to uncover fatty acid patterns discriminative for ten different plant oil types and their intra-variability. We also describe a supervised end-to-end learning method that can be generalized to oil composition of any given mixtures. Trained on a large number of simulated oil mixtures, independent test dataset validation demonstrates that the model has a 50th percentile absolute error between 1.4-1.8% and a 90th percentile error of 4-5.4% for any 3-way mixtures of the ten oil types. The deep learning model can also be further refined with on-line training. Because oil-producing plants have diverse geographical origins and hence slightly varying fatty acid profiles, an online-training method provides also a way to capture useful knowledge presently unavailable. Our method allows the ability to control product quality, determining the fair price of purchased oils and in-turn allowing health-conscious consumers the future of accurate labeling.
| S-EPMC7584611 | biostudies-literature
Project description:ObjectiveIn this study, we analyzed the metabolite profile of the tongue coating of patients having gastric precancerous lesion (GPL) with damp phlegm pattern and proposed a mechanism of pathological transition.MethodsThe changes in tongue-coating metabolites in patients with GPL damp phlegm pattern were analyzed using GC-TOF-MS and UHPLC-QE-MS metabolomics methods.ResultsWhen compared with 20 patients who did not exhibit a nondamp phlegm pattern, 12 metabolites were highly expressed and 10 metabolites were under expressed in 40 cases of damp phlegm pattern, of which involved 9 metabolic pathways. Compared with 15 healthy people, 134 metabolites were upregulated and 3 metabolites were downregulated in 40 cases exhibiting a damp phlegm pattern, of which involved 17 metabolic pathways. The patients with damp phlegm pattern were compared with nondamp phlegm pattern patients and healthy people, the main differential metabolites were primarily lipids and lipid-like molecules, and the main differential metabolic pathways were related to glycerophospholipid metabolism. In the glycerophospholipid metabolism, the metabolites with changes were phosphatidylethanolamine and lysoPC(18 : 1 (9z)). Among them, phosphatidylethanolamine exists in the synthesis stage of glycerophospholipid metabolism.ConclusionsAbnormal expression of lipids and lipid-like molecules, as the major metabolic change, was involved in the formation of GPL patients with damp phlegm pattern.
| S-EPMC8189775 | biostudies-literature
Project description:Extensive structure-activity studies of iE-DAP and MDP have demonstrated their selective activation of NOD1- and NOD2-expressing cells, respectively. Nonetheless, the direct binding of iE-DAP and MDP to NOD1 and NOD2, respectively, as well as other proteins in mammalian cells has not been systematically investigated. To address the direct interaction of iE-DAP and MDP with NOD1 and NOD2 in living cells, we synthesized a series of peptidoglycan metabolite photoaffinity chemical reporters containing a diazirine for photocrosslinking in cells and an alkyne tag for bioorthogonal detection of covalently-labeled proteins. The analysis of these peptidoglycan metabolite photoaffinity reporters demonstrate that iE-DAP and MDP can directly bind to NOD1 and NOD2 in mammalian cells. The crosslinking of these peptidoglycan metabolite photoaffinity reporters only occur with active variants of NOD1 and NOD2. Beyond direct binding to NOD2, the active MDP photoaffinity reporter selectively crosslinked Arf GTPases in NOD2-expressing cells and did so most prominently with GTP-bound Arf6, revealing the Arf proteins as novel direct pattern recognition receptors. Additional labeling and co-precipitation experiments suggest that MDP induces a complex between NOD2 and GTP-Arf6. This study highlights the utility of peptidoglycan metabolite photoaffinity reporters for characterizing their direct binding to intracellular pattern recognition receptors and identifying unpredicted cellular cofactors. The applications of these microbial-specific metabolite reporters should enable the discovery of other receptors, transporters and regulatory enzymes in host cells and microbes.
2020-05-26 | PXD012350 | Pride