Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data.

BACKGROUND: Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. RESULTS: Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. CONCLUSIONS: Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors

Revealing missing human protein isoforms based on Ab initio prediction, RNA-seq and proteomics

Biological and biomedical research relies on comprehensive understanding of protein-coding transcripts. However, the total number of human proteins is still unknown due to the prevalence of alternative splicing. In this paper, we detected 31,566 novel transcripts with coding potential by filtering our ab initio predictions with 50 RNA-seq datasets from diverse tissues/cell lines. PCR followed by MiSeq sequencing showed that at least 84.1% of these predicted novel splice sites could be validated. In contrast to known transcripts, the expression of these novel transcripts were highly tissue-specific. Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples. We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought. Furthermore, we found that alternative splicing is extraordinarily widespread for genes involved in specific biological functions like protein binding, nucleoside binding, neuron projection, membrane organization and cell adhesion. In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.publishedVersio

Rapid detection of multiple resistance genes to last-resort antibiotics in Enterobacteriaceae pathogens by recombinase polymerase amplification combined with lateral flow dipstick

The worrying emergence of multiple resistance genes to last-resort antibiotics in food animals and human populations throughout the food chain and relevant environments has been increasingly reported worldwide. Enterobacteriaceae pathogens are considered the most common reservoirs of such antibiotic resistance genes (ARGs). Thus, a rapid, efficient and accurate detection method to simultaneously screen and monitor such ARGs in Enterobacteriaceae pathogens has become an urgent need. Our study developed a recombinase polymerase amplification (RPA) assay combined with a lateral flow dipstick (LFD) for simultaneously detecting predominant resistance genes to last-resort antibiotics of Enterobacteriaceae pathogens, including mcr-1, blaNDM-1 and tet(X4). It is allowed to complete the entire process, including crude DNA extraction, amplification as well as reading, within 40 min at 37°C, and the detection limit is 101 copies/μl for mcr-1, blaNDM-1 and tet(X4). Sensitivity analysis showed obvious association of color signals with the template concentrations of mcr-1, blaNDM-1 and tet(X4) genes in Enterobacteriaceae pathogens using a test strip reader (R2 = 0.9881, R2 = 0.9745, and R2 = 0.9807, respectively), allowing for quantitative detection using multiplex RPA-LFD assays. Therefore, the RPA-LFD assay can suitably help to detect multiple resistance genes to last-resort antibiotics in foodborne pathogens and has potential applications in the field

Biomolecular Network-Based Synergistic Drug Combination Discovery

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing

Biomolecular Network-Based Synergistic Drug Combination Discovery

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing

Targeting the Small- and Intermediate-Conductance Ca2+-Activated Potassium Channels: The Drug-Binding Pocket at the Channel/Calmodulin Interface

The small- and intermediate-conductance Ca2+-activated potassium (SK/IK) channels play important roles in the regulation of excitable cells in both the central nervous and cardiovascular systems. Evidence from animal models has implicated SK/IK channels in neurological conditions such as ataxia and alcohol use disorders. Further, genome-wide association studies have suggested that cardiovascular abnormalities such as arrhythmias and hypertension are associated with single nucleotide polymorphisms that occur within the genes encoding the SK/IK channels. The Ca2+ sensitivity of the SK/IK channels stems from a constitutively bound Ca2+-binding protein: calmodulin. Small-molecule positive modulators of SK/IK channels have been developed over the past decade, and recent structural studies have revealed that the binding pocket of these positive modulators is located at the interface between the channel and calmodulin. SK/IK channel positive modulators can potentiate channel activity by enhancing the coupling between Ca2+ sensing via calmodulin and mechanical opening of the channel. Here, we review binding pocket studies that have provided structural insight into the mechanism of action for SK/IK channel positive modulators. These studies lay the foundation for structure-based drug discovery efforts that can identify novel SK/IK channel positive modulators. © 2014 S. Karger AG, Base

Targeting the Small- and Intermediate Conductance Ca2+- Activated Potassium Channels: The Drug Binding Pocket at the Channel/Calmodulin Interface

The small- and intermediate-conductance Ca 2+ -activated potassium (SK/IK) channels play important roles in the regulation of excitable cells in both the central nervous and cardiovascular systems. Evidence from animal models has implicated SK/IK channels in neurological conditions such as ataxia and alcohol use disorders. Further, genome-wide association studies have suggested that cardiovascular abnormalities such as arrhythmias and hypertension are associated with single nucleotide polymorphisms that occur within the genes encoding the SK/IK channels. The Ca 2+ sensitivity of the SK/IK channels stems from a constitutively bound Ca 2+ -binding protein: calmodulin. Small-molecule positive modulators of SK/IK channels have been developed over the past decade, and recent structural studies have revealed that the binding pocket of these positive modulators is located at the interface between the channel and calmodulin. SK/IK channel positive modulators can potentiate channel activity by enhancing the coupling between Ca 2+ sensing via calmodulin and mechanical opening of the channel. Here, we review binding pocket studies that have provided structural insight into the mechanism of action for SK/IK channel positive modulators. These studies lay the foundation for structure-based drug discovery efforts that can identify novel SK/IK channel positive modulators

ToPP: Tumor online prognostic analysis platform for prognostic feature selection and clinical patient subgroup selection.

Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php

Exploring the RING-catalyzed ubiquitin transfer mechanism by MD and QM/MM calculations.

Ubiquitylation is a universal mechanism for controlling cellular functions. A large family of ubiquitin E3 ligases (E3) mediates Ubiquitin (Ub) modification. To facilitate Ub transfer, RING E3 ligases bind both the substrate and ubiquitin E2 conjugating enzyme (E2) linked to Ub via a thioester bond to form a catalytic complex. The mechanism of Ub transfer catalyzed by RING E3 remains elusive. By employing a combined computational approach including molecular modeling, molecular dynamics (MD) simulations, and quantum mechanics/molecular mechanics (QM/MM) calculations, we characterized this catalytic mechanism in detail. The three-dimensional model of dimeric RING E3 ligase RNF4 RING, E2 ligase UbcH5A, Ub and the substrate SUMO2 shows close contact between the substrate and Ub transfer catalytic center. Deprotonation of the substrate lysine by D117 on UbcH5A occurs with almost no energy barrier as calculated by MD and QM/MM calculations. Then, the side chain of the activated lysine gets close to the thioester bond via a conformation change. The Ub transfer pathway begins with a nucleophilic addition that forms an oxyanion intermediate of a 4.23 kcal/mol energy barrier followed by nucleophilic elimination, resulting in a Ub modified substrate by a 5.65 kcal/mol energy barrier. These results provide insight into the mechanism of RING-catalyzed Ub transfer guiding the discovery of Ub system inhibitors