Gene: a gene-centered information resource at NCBI

GlioMarker: An integrated database for knowledge exploration of diagnostic biomarkers in gliomas

Gliomas are the most frequent malignant and aggressive tumors in the central nervous system. Early and effective diagnosis of glioma using diagnostic biomarkers can prolong patients' lives and aid in the development of new personalized treatments. Therefore, a thorough and comprehensive understanding of the diagnostic biomarkers in gliomas is of great significance. To this end, we developed the integrated and web-based database GlioMarker (http://gliomarker.prophetdb.org/), the first comprehensive database for knowledge exploration of glioma diagnostic biomarkers. In GlioMarker, accurate information on 406 glioma diagnostic biomarkers from 1559 publications was manually extracted, including biomarker descriptions, clinical information, associated literature, experimental records, associated diseases, statistical indicators, etc. Importantly, we integrated many external resources to provide clinicians and researchers with the capability to further explore knowledge on these diagnostic biomarkers based on three aspects. (1) Obtain more ontology annotations of the biomarker. (2) Identify the relationship between any two or more components of diseases, drugs, genes, and variants to explore the knowledge related to precision medicine. (3) Explore the clinical application value of a specific diagnostic biomarker through online analysis of genomic and expression data from glioma cohort studies. GlioMarker provides a powerful, practical, and user-friendly web-based tool that may serve as a specialized platform for clinicians and researchers by providing rapid and comprehensive knowledge of glioma diagnostic biomarkers to subsequently facilitates high-quality research and applications.

Mechanosensitive Piezo channels mediate the physiological and pathophysiological changes in the respiratory system

Mechanosensitive Piezo ion channels were first reported in 2010 in a mouse neuroblastoma cell line, opening up a new field for studying the composition and function of eukaryotic mechanically activated channels. During the past decade, Piezo ion channels were identified in many species, such as bacteria, Drosophila, and mammals. In mammals, basic life activities, such as the sense of touch, proprioception, hearing, vascular development, and blood pressure regulation, depend on the activation of Piezo ion channels. Cumulative evidence suggests that Piezo ion channels play a major role in lung vascular development and function and diseases like pneumonia, pulmonary hypertension, apnea, and other lung-related diseases. In this review, we focused on studies that reported specific functions of Piezos in tissues and emphasized the physiological and pathological effects of their absence or functional mutations on the respiratory system.

Molecular Insights into the Role of Pathogenic nsSNPs in GRIN2B Gene Provoking Neurodevelopmental Disorders

The GluN2B subunit of N-methyl-D-aspartate receptors plays an important role in the physiology of different neurodevelopmental diseases. Genetic variations in the GluN2B coding gene (GRIN2B) have consistently been linked to West syndrome, intellectual impairment with focal epilepsy, developmental delay, macrocephaly, corticogenesis, brain plasticity, as well as infantile spasms and Lennox–Gastaut syndrome. It is unknown, however, how GRIN2B genetic variation impacts protein function. We determined the cumulative pathogenic impact of GRIN2B variations on healthy participants using a computational approach. We looked at all of the known mutations and calculated the impact of single nucleotide polymorphisms on GRIN2B, which encodes the GluN2B protein. The pathogenic effect, functional impact, conservation analysis, post-translation alterations, their driving residues, and dynamic behaviors of deleterious nsSNPs on protein models were then examined. Four polymorphisms were identified as phylogenetically conserved PTM drivers and were related to structural and functional impact: rs869312669 (p.Thr685Pro), rs387906636 (p.Arg682Cys), rs672601377 (p.Asn615Ile), and rs1131691702 (p.Ser526Pro). The combined impact of protein function is accounted for by the calculated stability, compactness, and total globularity score. GluN2B hydrogen occupancy was positively associated with protein stability, and solvent-accessible surface area was positively related to globularity. Furthermore, there was a link between GluN2B protein folding, movement, and function, indicating that both putative high and low local movements were linked to protein function. Multiple GRIN2B genetic variations are linked to gene expression, phylogenetic conservation, PTMs, and protein instability behavior in neurodevelopmental diseases. These findings suggest the relevance of GRIN2B genetic variations in neurodevelopmental problems.

GeneToList: A Web Application to Assist with Gene Identifiers for the Non-Bioinformatics-Savvy Scientist

Simple Summary

With the increasing popularity of omics technologies, many scientists are dealing with large sets of genomic data for the first time. While there are many amazing bioinformatics tools available to help analyze this data, one common difficulty these scientists face is that not all gene data or analysis tools use the same genomic nomenclature. Another issue is that many publications still use obsolete or colloquial gene aliases instead of official gene identifiers. Common gene ID conversion tools and other downstream analysis software struggle with these gene aliases. Therefore, we developed a free and publicly available web application, GeneToList, to assist in gene ID disambiguation and gene ID conversion, with a specific focus on a user-friendly interface for the non-bioinformatics-savvy scientist.

Abstract

The increasing incorporation of omics technologies into biomedical research and translational medicine presents challenges to end users of the large and complex datasets that are generated by these methods. A particular challenge in genomics is that the nomenclature for genes is not uniform between large genomic databases or between commonly used genetic analysis tools. Furthermore, outdated genomic nomenclature can still be found amongst scientific communications, including peer-reviewed manuscripts. Therefore, a web application (GeneToList) was developed to assist in gene ID conversion and alias matching, with a specific focus on achieving a user-friendly interface for the non-bioinformatics-savvy scientist. It currently includes gene information for over 38,000 different taxa retrieved from the National Center for Biotechnology and Information (NCBI) Gene resource. Supported databases of gene IDs include NCBI Gene Symbols, NCBI Gene IDs (Entrez IDs), OMIM IDs, HGNC IDs, Ensembl IDs, and 28 other taxa-specific identifiers. GeneToList is available at genetolist.com. The tool is a web application that is compatible with many standard browsers. The gene ID conversion feature of this application was found to outcompete the common gene ID conversion tools. Specifically, it was able to successfully convert all tested IDs, whereas the others were not able to recognize the gene aliases. Therefore, the gene ID disambiguation provided by this application should be beneficial for many scientists dealing with gene data when the uniformity of gene nomenclature is important for downstream analysis.

Neuroprotective Drug Discovery From Phytochemicals and Metabolites for CNS Viral Infection: A Systems Biology Approach With Clinical and Imaging Validation

Background

Recent studies have reported that pulmo-neurotropic viruses can cause systemic invasion leading to acute respiratory failure and neuroinfection. The tetracycline class of secondary metabolites of microorganisms is effective against several migrating neurotropic viral disorders, as Japanese-Encephalitis (JE), Severe-Acute-Respiratory-Syndrome Coronavirus-2 (SARS-COV2), Human-Immunodeficiency-Virus (HIV), and Simian-Immunodeficiency-Virus (SIV). Another microbial secondary metabolite, cephalosporin, can be used for anti-viral combination therapy. However, a substantial public health debacle is viral resistance to such antibiotics, and, thus, one needs to explore the antiviral efficiency of other secondary metabolites, as phytochemicals. Hence, here, we investigate phytochemicals like podophyllotoxin, chlorogenic acid, naringenin, and quercetin for therapeutic efficiency in neurotropic viral infections.

Methods

To investigate the possibility of the afferent neural pathway of migrating virus in man, MRI scanning was performed on human subjects, whereby the connections between cranial nerves and the brain-stem/limbic-region were assessed by fiber-tractography. Moreover, human clinical-trial assessment (n = 140, p = 0.028) was done for formulating a quantitative model of antiviral pharmacological intervention. Furthermore, docking studies were performed to identify the binding affinity of phytochemicals toward antiviral targets as (i) host receptor [Angiotensin-converting Enzyme-2], (ii) main protease of SARS-COV2 virus (iii) NS3-Helicase/Nucleoside triphosphatase of Japanese-encephalitis-virus, and the affinities were compared to standard tetracycline and cephalosporin antibiotics. Then, network pharmacology analysis was utilized to identify the possible mechanism of action of those phytochemicals.

Results

Human MRI-tractography analysis showed fiber connectivity, as: (a) Path-1: From the olfactory nerve to the limbic region (2) Path-2: From the peripheral glossopharyngeal nerve and vagus nerves to the midbrain-respiratory-center. Docking studies revealed comparable binding affinity of phytochemicals, tetracycline, and cephalosporin antibiotics toward both (a) virus receptors, (b) host cell receptors where virus-receptor binds. The phytochemicals effectively countered the cytokine storm-induced neuroinflammation, a critical pathogenic pathway. We also found that a systems-biology-based double-hit mathematical bi-exponential model accounts for patient survival-curve under antiviral treatment, thus furnishing a quantitative-clinical framework of secondary metabolite action on virus and host cells.

Conclusion

Due to the current viral resistance to antibiotics, we identified novel phytochemicals that can have clinical therapeutic application to neurotropic virus infection. Based on human MRI scanning and clinical-trial analysis, we demarcated the anatomical pathway and systems-biology-based quantitative formulation of the mechanism of antiviral action.

TogoID: an exploratory ID converter to bridge biological datasets

MOTIVATION

Understanding life cannot be accomplished without making full use of biological data, which are scattered across databases of diverse categories in life sciences. To connect such data seamlessly, identifier (ID) conversion plays a key role. However, existing ID conversion services have disadvantages, such as covering only a limited range of biological categories of databases, not keeping up with the updates of the original databases and outputs being hard to interpret in the context of biological relations, especially when converting IDs in multiple steps.

RESULTS

TogoID is an ID conversion service implementing unique features with an intuitive web interface and an application programming interface (API) for programmatic access. TogoID currently supports 65 datasets covering various biological categories. TogoID users can perform exploratory multistep conversions to find a path among IDs. To guide the interpretation of biological meanings in the conversions, we crafted an ontology that defines the semantics of the dataset relations.

AVAILABILITY AND IMPLEMENTATION

The TogoID service is freely available on the TogoID website (https://togoid.dbcls.jp/) and the API is also provided to allow programmatic access. To encourage developers to add new dataset pairs, the system stores the configurations of pairs at the GitHub repository (https://github.com/togoid/togoid-config) and accepts the request of additional pairs.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Helicobacter pylori serology is associated with worse overall survival in patients with melanoma treated with immune checkpoint inhibitors

The microbiome is now regarded as one of the hallmarks of cancer and several strategies to modify the gut microbiota to improve immune checkpoint inhibitor (ICI) activity are being evaluated in clinical trials. Preliminary data regarding the upper gastro-intestinal microbiota indicated that Helicobacter pylori seropositivity was associated with a negative prognosis in patients amenable to ICI. In 97 patients with advanced melanoma treated with ICI, we assessed the impact of H. pylori on outcomes and microbiome composition. We performed H. pylori serology and profiled the fecal microbiome with metagenomics sequencing. Among the 97 patients, 22% were H. pylori positive (Pos). H. pylori Pos patients had a significantly shorter overall survival (p = .02) compared to H. pylori negative (Neg) patients. In addition, objective response rate and progression-free survival were decreased in H. pylori Pos patients. Metagenomics sequencing did not reveal any difference in diversity indexes between the H. pylori groups. At the taxa level, Eubacterium ventriosum, Mediterraneibacter (Ruminococcus) torques, and Dorea formicigenerans were increased in the H. pylori Pos group, while Alistipes finegoldii, Hungatella hathewayi and Blautia producta were over-represented in the H. pylori Neg group. In a second independent cohort of patients with NSCLC, diversity indexes were similar in both groups and Bacteroides xylanisolvens was increased in H. pylori Neg patients. Our results demonstrated that the negative impact of H. pylori on outcomes seem to be independent from the fecal microbiome composition. These findings warrant further validation and development of therapeutic strategies to eradicate H. pylori in immuno-oncology arena.

Combined multi-omics and network pharmacology approach reveals the role of Tripterygium Wilfordii Hook F in treating HIV immunological non-responders

BACKGROUND

The HIV-1 infected immunological non-responders (INRs) are characterized by poor immune reconstitution after long-term treatment. Tripterygium Wilfordii Hook F (TwHF) pill is a traditional Chinese patent drug with extensive immunosuppressive effects and has been clinically proven efficacy in treating INRs.

PURPOSE

The therapeutic mechanism of TwHF pills in the treatment of INRs was investigated by the combined multi-omics analysis on clinical samples and network pharmacology approach.

METHODS

Clinically, the peripheral blood mononuclear cells (PBMC) samples of TwHF-treated INRs from different time points were collected to conduct the transcriptomic and proteomic profiling. Key effector pathways of TwHF were enriched and analyzed by the ingenuity pathway analysis (IPA). Computationally, the TwHF-related compounds were obtained from traditional Chinese medicine databases, and literature search and structural prediction were performed to identify TwHF-related targets. Integrated with the INR-related targets, the 'TwHF-compounds-targets-INR' network was constructed to analyze core effector targets by centrality measurement. Experimentally, the effects of TwHF compounds on the T cells activation and expression of identified targets were evaluated with in vitro cell culture.

RESULTS

33 INRs were included and treated with TwHF pills for 17 (IQR, 12-24) months. These patients experienced rapid growth in the CD4⁺ T cell counts and decreased T cell activation. The multi-omics analysis showed that the interferon (IFN)-signaling pathway was significantly inhibited after taking TwHF pills. The network pharmacology predicted the central role of the signal transducer and activator of transcription 1 (STAT1) in the 'TwHF-compounds-targets-INR' network. Further bioinformatic analysis predicted STAT1 would regulate over 58.8% of identified down-regulated genes. Cell experiments validated that triptolide (TPL) would serve as the major bioactivity compound of TwHF pills to inhibit the immune cell activation, the production of IFN-γ, the expression of downstream IFN-stimulated genes, and the phosphorylation of STAT1.

CONCLUSION

Our research is the first to systemic verify the mechanisms of TwHF in treating INRs. The IFN signaling pathway and the STAT1 would be the major effector targets of TwHF pills in treating INRs. The TPL would be the major bioactive compound to inhibit the IFN response and the phosphorylation of STAT1. Our observations suggest the basis for further application of TPL analogous in treating INRs.

Articulating target-mining techniques to disinter Alzheimer's specific targets for drug repurposing

BACKGROUND AND OBJECTIVES

Alzheimer's Disease (AD), an extremely progressive neurodegenerative disorder is an amalgamation of numerous intricate pathological networks. This century old disease is still an unmet medical condition owing to the modest efficacy of existing therapeutic agents in antagonizing the multi-targeted pathological pathways underlying AD. Given the paucity in AD specific drugs, fabricating comprehensive research strategies to envision disease specific targets to channelize and expedite drug discovery are mandated. However, the dwindling approval rates and stringent regulatory constraints concerning the approval of a new chemical entity is daunting the pharmaceutical industries from effectuating de novo research. To bridge the existing gaps in AD drug research, a promising contemporary way out could be drug repurposing. This drug repurposing investigation is intended to envisage AD specific targets and create drug libraries pertinent to the shortlisted targets via a series of avant-garde bioinformatics and computational strategies.

METHODS

Transcriptomic analysis of three AD specific datasets viz., GSE122063, GSE15222 and GSE5281 revealed significant Differentially Expressed Genes (DEGs) and subsequent Protein-Protein Interactions (PPI) network analysis captured crucial AD targets. Later, homology model was constructed through I-TASSER for a shortlisted target protein which lacked X-ray crystallographic structure and the built protein model was validated by molecular dynamic simulations. Further, drug library was created for the shortlisted target based on structural and side effect similarity with respective standard drugs. Finally, molecular docking, binding energy calculations and molecular dynamics studies were carried out to unravel the interactions exhibited by drugs from the created library with amino acids in active binding pocket of RGS4.

RESULTS

SST and RGS4 were shortlisted as potentially significant AD specific targets, however, the less explored target RGS4 was considered for further sequential analysis. Homology model constructed for RGS4 displayed best quality when validated through Ramachandran plot and ERRAT plot. Subsequent docking and molecular dynamics studies showcased substantial affinity demonstrated by three drugs viz., Ziprasidone, Melfoquine and Metaxalone from the created drug libraries, towards RGS4.

CONCLUSION

This virtual analysis forecasted the repurposable potential of Ziprasidone, Melfoquine and Metaxalone against AD based on their affinity towards RGS4, a key AD-specific target.

Comparison of High-Throughput Technologies in the Classification of Adult-Onset Still's Disease Patients

A meta-analysis study was conducted to compare high-throughput technologies in the classification of Adult-Onset Still's Disease patients, using differentially expressed genes from independent profiling experiments. We exploited two publicly available datasets from the Gene Expression Omnibus and performed a separate differential expression analysis on each dataset to extract statistically important genes. We then mapped the genes of the two datasets and subsequently we employed well-established machine learning algorithms to evaluate the denoted genes as candidate biomarkers. Using next-generation sequencing data, we managed to achieve the maximum (100%) classification accuracy, sensitivity and specificity with the Gradient Boosting and the Random Forest classifiers, compared to the 83% of the DNA microarray data. Clinical Relevance- When biomarkers derived from one study are applied to the data of another, in many cases the results may diverge significantly. Here we establish that in cross-profiling meta-analysis approaches based on differential expression analysis, next-generation sequencing data provide more accurate results than microarray experiments in the classification of Adult-Onset Still's Disease patients.

RegEl corpus: identifying DNA regulatory elements in the scientific literature

High-throughput technologies led to the generation of a wealth of data on regulatory DNA elements in the human genome. However, results from disease-driven studies are primarily shared in textual form as scientific articles. Information extraction (IE) algorithms allow this information to be (semi-)automatically accessed. Their development, however, is dependent on the availability of annotated corpora. Therefore, we introduce RegEl (Regulatory Elements), the first freely available corpus annotated with regulatory DNA elements comprising 305 PubMed abstracts for a total of 2690 sentences. We focus on enhancers, promoters and transcription factor binding sites. Three annotators worked in two stages, achieving an overall 0.73 F1 inter-annotator agreement and 0.46 for regulatory elements. Depending on the entity type, IE baselines reach F1-scores of 0.48–0.91 for entity detection and 0.71–0.88 for entity normalization. Next, we apply our entity detection models to the entire PubMed collection and extract co-occurrences of genes or diseases with regulatory elements. This generates large collections of regulatory elements associated with 137 870 unique genes and 7420 diseases, which we make openly available.

Database URL: https://zenodo.org/record/6418451#.YqcLHvexVqg

Exploring automatic inconsistency detection for literature-based gene ontology annotation

Motivation

Literature-based gene ontology annotations (GOA) are biological database records that use controlled vocabulary to uniformly represent gene function information that is described in the primary literature. Assurance of the quality of GOA is crucial for supporting biological research. However, a range of different kinds of inconsistencies in between literature as evidence and annotated GO terms can be identified; these have not been systematically studied at record level. The existing manual-curation approach to GOA consistency assurance is inefficient and is unable to keep pace with the rate of updates to gene function knowledge. Automatic tools are therefore needed to assist with GOA consistency assurance. This article presents an exploration of different GOA inconsistencies and an early feasibility study of automatic inconsistency detection.

Results

We have created a reliable synthetic dataset to simulate four realistic types of GOA inconsistency in biological databases. Three automatic approaches are proposed. They provide reasonable performance on the task of distinguishing the four types of inconsistency and are directly applicable to detect inconsistencies in real-world GOA database records. Major challenges resulting from such inconsistencies in the context of several specific application settings are reported. This is the first study to introduce automatic approaches that are designed to address the challenges in current GOA quality assurance workflows. The data underlying this article are available in Github at https://github.com/jiyuc/AutoGOAConsistency.

RPpocket: An RNA–Protein Intuitive Database with RNA Pocket Topology Resources

RNA–protein complexes regulate a variety of biological functions. Thus, it is essential to explore and visualize RNA–protein structural interaction features, especially pocket interactions. In this work, we develop an easy-to-use bioinformatics resource: RPpocket. This database provides RNA–protein complex interactions based on sequence, secondary structure, and pocket topology analysis. We extracted 793 pockets from 74 non-redundant RNA–protein structures. Then, we calculated the binding- and non-binding pocket topological properties and analyzed the binding mechanism of the RNA–protein complex. The results showed that the binding pockets were more extended than the non-binding pockets. We also found that long-range forces were the main interaction for RNA–protein recognition, while short-range forces strengthened and optimized the binding. RPpocket could facilitate RNA–protein engineering for biological or medical applications.

Genome-Wide Characterization of Superoxide Dismutase (SOD) Genes in Daucus carota: Novel Insights Into Structure, Expression, and Binding Interaction With Hydrogen Peroxide (H2O2) Under Abiotic Stress Condition

Superoxide dismutase (SOD) proteins are important antioxidant enzymes that help plants to grow, develop, and respond to a variety of abiotic stressors. SOD gene family has been identified in a number of plant species but not yet in Daucus carota. A total of 9 DcSOD genes, comprising 2 FeSODs, 2 MnSODs, and 5 Cu/ZnSODs, are identified in the complete genome of D. carota, which are dispersed in five out of nine chromosomes. Based on phylogenetic analysis, SOD proteins from D. carota were categorized into two main classes (Cu/ZnSODs and MnFeSODs). It was predicted that members of the same subgroups have the same subcellular location. The phylogenetic analysis was further validated by sequence motifs, exon-intron structure, and 3D protein structures, with each subgroup having a similar gene and protein structure. Cis-regulatory elements responsive to abiotic stresses were identified in the promoter region, which may contribute to their differential expression. Based on RNA-seq data, tissue-specific expression revealed that DcCSD2 had higher expression in both xylem and phloem. Moreover, DcCSD2 was differentially expressed in dark stress. All SOD genes were subjected to qPCR analysis after cold, heat, salt, or drought stress imposition. SODs are antioxidants and play a critical role in removing reactive oxygen species (ROS), including hydrogen peroxide (H2O2). DcSODs were docked with H2O2 to evaluate their binding. The findings of this study will serve as a basis for further functional insights into the DcSOD gene family.

A Network Pharmacology Analysis of Cytotoxic Triterpenes Isolated from Euphorbia abyssinica Latex Supported by Drug-likeness and ADMET Studies

Euphorbia plants have been identified as potential sources of antitumor lead compounds. The current study aimed to isolate and identify the main active constituents of Euphorbia abyssinica latex followed by a cytotoxic evaluation. A network pharmacology approach was employed to predict the underlying mechanism. Finally, drug-likeness and ADMET studies were conducted for active compounds. The phytochemical investigation of the latex of E. abyssinica resulted in the isolation of two triterpenes, 3-acetyloxy-(3α)-urs-12-en-28-oic methyl ester (1) and lup-20(29)-en-3α,23-diol (2). The dichloromethane extract displayed potent cytotoxic activity against the MCF7 cell line with an IC₅₀ value of 4.27 ± 0.12 μg/mL but weak activity against HepG2 and HeLa cell lines (IC₅₀ = 20.47 ± 1.17 and 26.73 ± 2.99 μg/mL, respectively) compared to doxorubicin. Compound 1 showed an encouraging cytotoxic effect against MCF7 with IC₅₀ = 4.20 ± 0.20 μg/mL, followed by compound 2 (IC₅₀ = 5.8 ± 0.35 μg/mL). The network analysis revealed that the two isolated compounds are linked to 68 targets of human nature, among which 51 genes are linked to breast carcinomas and 5 targets (AR, CYP19A1, EGFR, PGR, and PTGS2) might be the top therapeutic targets of isolated compounds on breast cancer. Furthermore, the gene-enrichment analysis revealed that E. abyssinica could play a role in the treatment of breast cancer by striking 51 potential targets via mainly three signaling pathways: P13K-AKT, Wnt, and VEGF. Therefore, isolated triterpenes could be considered effective antitumor agents for breast cancer by elucidating their candidate target to alleviate breast cancer and related signaling pathways of the targets.

Yusof NF, Mohd Yunos RI, Ishak M, Saidin S, Rose IM, Sagap I, Mazlan L, Mohd Azman ZA, Mazlan M, Ab Rahim S, Wan Ngah WZ, Nathan S, Hashim NAA, Mohamed-Hussein ZA, Jamal R. TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

Colorectal cancer (CRC) ranks second among the most commonly occurring cancers in Malaysia, and unfortunately, its pathobiology remains unknown. CRC pathobiology can be understood in detail with the implementation of omics technology that is able to generate vast amounts of molecular data. The generation of omics data has introduced a new challenge for data organization. Therefore, a knowledge-based repository, namely TCGA-My, was developed to systematically store and organize CRC omics data for Malaysian patients. TCGA-My stores the genome and metabolome of Malaysian CRC patients. The genome and metabolome datasets were organized using a Python module, pandas. The variants and metabolites were first annotated with their biological information using gene ontologies (GOs) vocabulary. The TCGA-My relational database was then built using HeidiSQL PorTable 9.4.0.512, and Laravel was used to design the web interface. Currently, TCGA-My stores 1,517,841 variants, 23,695 genes, and 167,451 metabolites from the samples of 50 CRC patients. Data entries can be accessed via search and browse menus. TCGA-My aims to offer effective and systematic omics data management, allowing it to become the main resource for Malaysian CRC research, particularly in the context of biomarker identification for precision medicine.

Mechanism of Peitu Shengjin Formula Shenlingbaizhu Powder in Treating Bronchial Asthma and Allergic Colitis through Different Diseases with Simultaneous Treatment Based on Network Pharmacology and Molecular Docking

Background

Shenlingbaizhu powder (SLBZP), one of the classic Earth-cultivating and gold-generating prescriptions of traditional Chinese medicine, is widely used to treat various diseases. However, the pharmacological mechanisms of SLBZP on bronchial asthma (BA) and allergic colitis (AC) remain to be elucidated.

Methods

Network pharmacology and molecular docking technology were used to explore the potential mechanism of SLBZP in treating BA and AC with the simultaneous treatment of different diseases. The potential active compounds of SLBZP and their corresponding targets were obtained from BATMAN-TCM, ETCM, SymMap TCM@TAIWAN, and TCMSP databases. BA and AC disease targets were collected through DisGeNET, TTD, GeneCards, PharmGKB, OMIM, NCBI, The Human Phenotype Ontology, and DrugBank databases. Common targets for drugs and diseases were screened by using the bioinformatics and evolutionary genomics platform. The analyses and visualizations of Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of common targets were carried out by R software. The key targets were screened by using the plug-in “cytoHubba” of Cytoscape software, and the “active compound-key target” network was constructed. Molecular docking analysis was performed using AutoDock software. The miRTarBase database was used to predict microRNAs (miRNAs) targeting key targets, and the key target-miRNA network was constructed.

Result

Through screening, 246 active compounds and 281 corresponding targets were obtained. Common targets were mainly enriched in 2933 biological processes and 182 signal pathways to play the role of treating BA and AC. There were 131 active compounds related to key targets. The results of molecular docking showed that the important active compounds in SLBZP had good binding ability with the key targets. The key target-miRNA network showed that 94 miRNAs were predicted.

Conclusion

SLBZP has played the role of treating different diseases with the same treatment on BA and AC through the characteristics of multicompound, multitarget, and multipathway of traditional Chinese medicine, which provides a theoretical basis for explaining the mechanism and clinical application of SLBZP treating different diseases with the same treatment in BA and AC.

SigCom LINCS: data and metadata search engine for a million gene expression signatures

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Solute carrier family 2 members 1 and 2 as prognostic biomarkers in hepatocellular carcinoma associated with immune infiltration

BACKGROUND

Metabolic reprogramming has been identified as a core hallmark of cancer. Solute carrier family 2 is a major glucose carrier family. It consists of 14 members, and we mainly study solute carrier family 2 member 1 (SLC2A1) and solute carrier family 2 member 2 (SLC2A2) here. SLC2A1, mainly existing in human erythrocytes, brain endothelial cells, and normal placenta, was found to be increased in hepatocellular carcinoma (HCC), while SLC2A2, the major transporter of the normal liver, was decreased in HCC.

AIM

To identify if SLC2A1 and SLC2A2 were associated with immune infiltration in addition to participating in the metabolic reprogramming in HCC.

METHODS

The expression levels of SLC2A1 and SLC2A2 were tested in HepG2 cells, HepG215 cells, and multiple databases. The clinical characteristics and survival data of SLC2A1 and SLC2A2 were examined by multiple databases. The correlation between SLC2A1 and SLC2A2 was analyzed by multiple databases. The functions and pathways in which SLC2A1, SLC2A2, and frequently altered neighbor genes were involved were discussed in String. Immune infiltration levels and immune marker genes associated with SLC2A1 and SLC2A2 were discussed from multiple databases.

RESULTS

The expression level of SLC2A1 was up-regulated, but the expression level of SLC2A2 was down-regulated in HepG2 cells, HepG215 cells, and liver cancer patients. The expression levels of SLC2A1 and SLC2A2 were related to tumor volume, grade, and stage in HCC. Interestingly, the expression levels of SLC2A1 and SLC2A2 were negatively correlated. Further, high SLC2A1 expression and low SLC2A2 expression were linked to poor overall survival and relapse-free survival. SLC2A1, SLC2A2, and frequently altered neighbor genes played a major role in the occurrence and development of tumors. Notably, SLC2A1 was positively correlated with tumor immune infiltration, while SLC2A2 was negatively correlated with tumor immune infiltration. Particularly, SLC2A2 methylation was positively correlated with lymphocytes.

CONCLUSION

SLC2A1 and SLC2A2 are independent therapeutic targets for HCC, and they are quintessential marker molecules for predicting and regulating the number and status of immune cells in HCC.

BigMPI4py: Python Module for Parallelization of Big Data Objects Discloses Germ Layer Specific DNA Demethylation Motifs

Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 2³¹ bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and High Performance Computer systems. We use BigMPI4py to speed-up the search for germ line specific de novo DNA methylated/unmethylated motifs from the 59 whole genome bisulfite sequencing DNA methylation samples from 27 human tissues of the ENCODE project. We developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores allowing us an efficient identification of a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues and finally, the discovery of germ layer specific DNA demethylation motifs. Our results point out that DNA methylation signal provide a higher degree of information for the demethylated state than for the methylated state. BigMPI4py is available at https://https://www.arauzolab.org/tools/bigmpi4py and https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis.

Mining Similar Aspects for Gene Similarity Explanation Based on Gene Information Network

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful.

The vomeronasal system in semiaquatic beavers

In contrast to the main olfactory system that detects volatile chemicals in the nasal air, the vomeronasal system can detect nonvolatile chemicals as well as volatiles. In the vomeronasal system, chemicals are perceived by the vomeronasal organ (VNO) projecting axons to the accessory olfactory bulb (AOB). Beavers (Castor spp.) are semiaquatic mammals that have developed chemical communication. It is possible that the beaver's anal gland secretions, nonvolatile and insoluble substances, may work as a messenger in the water and that beavers may detect the nonvolatile chemicals floating on the water surface via the VNO. The present study aimed to clarify the specificities of the beaver vomeronasal system by histologically and immunohistochemically analyzing the VNO and AOB of 12 Eurasian beavers (C. fiber). The VNO directly opened to the nasal cavity and was independent of a narrow nasopalatine duct connecting the oral and nasal cavities. The VNO comprised soft tissues including sensory and nonsensory epithelium, glands, a venous sinus, an artery, as well as cartilage inner, and bone outer enclosures. The AOB had distinct six layers, and anti-G protein α-i2 and α-o subunits were, respectively, immunoreactive in rostral and caudal glomeruli layers indicating expressions of V1Rs and V2Rs. According to gene repertories analysis, the beavers had 23 and six intact V1R and V2R genes respectively. These findings suggested that beavers recognize volatile odorants and nonvolatile substances using the vomeronasal system. The beaver VNO was developed as well as in other rodents, and it had two specific morphological features, namely, disadvantaged contact with the oral cavity because of a tiny nasopalatine duct, and a double bone and cartilage envelope. Our results highlight the importance of the vomeronasal system in beaver chemical communication and support the possibility that beavers can detect chemicals floating on the water surface via the VNO.

Inflammation-Related Genes Serve as Prognostic Biomarkers and Involve in Immunosuppressive Microenvironment to Promote Gastric Cancer Progression

Gastric cancer (GC) is a typical inflammatory-related malignant tumor which is closely related to helicobacter pylori infection. Tumor inflammatory microenvironment plays a crucial role in tumor progression and affect the clinical benefit from immunotherapy. In recent years, immunotherapy for gastric cancer has achieved promising outcomes, but not all patients can benefit from immunotherapy due to tumor heterogeneity. In our study, we identified 29 differentially expressed and prognostic inflammation-related genes in GC and normal samples. Based on those genes, we constructed a prognostic model using a least absolute shrinkage and selection operator (LASSO) algorithm, which categorized patients with GC into two groups. The high-risk group have the characteristics of "cold tumor" and have a poorer prognosis. In contrast, low-risk group was "hot tumor" and had better prognosis. Targeting inflammatory-related genes and remodeling tumor microenvironment to turn "cold tumor" into "hot tumor" may be a promising solution to improve the efficacy of immunotherapy for patients with GC.

Biological mechanisms of aging predict age-related disease co-occurrence in patients

Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity.

Computational and Experimental Analysis of Genetic Variants

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.

Uncovering the Pharmacological Mechanisms of Gexia-Zhuyu Formula (GXZY) in Treating Liver Cirrhosis by an Integrative Pharmacology Strategy

Liver cirrhosis (LC) is a fibrotic lesion of liver tissue caused by the repeated progression of chronic hepatitis. The traditional Chinese medicine Gexia-Zhuyu formula (GXZY) has a therapeutic effect on LC. However, its pharmacological mechanisms on LC remain elucidated. Here, we used the network pharmacology approach to explore the action mechanisms of GXZY on LC. The compounds of GXZY were from the traditional Chinese medicine systems pharmacology (TCMSP) database, and their potential targets were from SwissTargetPrediction and STITCH databases. The disease targets of LC came from GeneCards, DisGeNET, NCBI gene, and OMIM databases. Then we constructed the protein-protein interaction (PPI) network to obtain the key target genes. And the gene ontology (GO), pathway enrichment, and expression analysis of the key genes were also performed. Subsequently, the potential action mechanisms of GXZY on LC predicted by the network pharmacology analyses were experimentally validated in LC rats and LX2 cells. A total of 150 components in GXZY were obtained, among which 111 were chosen as key compounds. The PPI network included 525 targets, and the key targets were obtained by network topological parameters analysis, whereas the predicted key genes of GXZY on LC were AR, JUN, MYC, CASP3, MMP9, GAPDH, and RELA. Furthermore, these key genes were related to pathways in cancer, hepatitis B, TNF signaling pathway, and MAPK signaling pathway. The in vitro and in vivo experiments validated that GXZY inhibited the process of LC mainly via the regulation of cells proliferation and migration through reducing the expression of MMP9. In conclusion, through the combination of network pharmacology and experimental verification, this study offered more insight molecular mechanisms of GXZY on LC.

MicroRNA childhood cancer catalog (M3Cs): a resource for translational bioinformatics toward health informatics in pediatric cancer

MicroRNA childhood Cancer Catalog (M3Cs) is a high-quality curated collection of published miRNA research studies on 16 pediatric cancer diseases. M3Cs scope was based on two approaches: data-driven clinical significance and data-driven human pediatric cell line models. Based on the translational bioinformatics spectrum, the main objective of this study is to bring miRNA research into clinical significance in both pediatric cancer patient care and drug discovery toward health informatics in childhood cancer. M3Cs development passed through three phases: 1. Literature Mining: It includes external database search and screening. 2. Data processing that includes three steps: (a) Data Extraction, (b) Data Curation and annotation, (c) Web Development. 3. Publishing: Shinyapps.io was used as a web interface for the deployment of M3Cs. M3Cs is now available online and can be accessed through https://m3cs.shinyapps.io/M3Cs/. For data-driven clinical significance approach, 538 miRNAs from 268 publications were reported in the clinical domain while 7 miRNAs from 5 publications were reported in the clinical & drug domain. For data-driven human pediatric cell line models approach, 538 miRNAs from 1268 publications were reported in the cell line domain while 211 miRNAs from 177 publications in the cell line & drug domain. M3Cs acted to fill the gap by applying translational bioinformatics general pathway to transfer data-driven research toward data-driven clinical care and/or hypothesis generation. Aggregated and well-curated data of M3Cs will enable stakeholders in health care to incorporate miRNA in the clinical policy.

Database URL:https://m3cs.shinyapps.io/M3Cs/

Positively selected genes in the hoary bat (Lasiurus cinereus) lineage: prominence of thymus expression, immune and metabolic function, and regions of ancient synteny

Background

Bats of the genus Lasiurus occur throughout the Americas and have diversified into at least 20 species among three subgenera. The hoary bat (Lasiurus cinereus) is highly migratory and ranges farther across North America than any other wild mammal. Despite the ecological importance of this species as a major insect predator, and the particular susceptibility of lasiurine bats to wind turbine strikes, our understanding of hoary bat ecology, physiology, and behavior remains poor.

Methods

To better understand adaptive evolution in this lineage, we used whole-genome sequencing to identify protein-coding sequence and explore signatures of positive selection. Gene models were predicted with Maker and compared to seven well-annotated and phylogenetically representative species. Evolutionary rate analysis was performed with PAML.

Results

Of 9,447 single-copy orthologous groups that met evaluation criteria, 150 genes had a significant excess of nonsynonymous substitutions along the L. cinereus branch (P < 0.001 after manual review of alignments). Selected genes as a group had biased expression, most strongly in thymus tissue. We identified 23 selected genes with reported immune functions as well as a divergent paralog of Steep1 within suborder Yangochiroptera. Seventeen genes had roles in lipid and glucose metabolic pathways, partially overlapping with 15 mitochondrion-associated genes; these adaptations may reflect the metabolic challenges of hibernation, long-distance migration, and seasonal variation in prey abundance. The genomic distribution of positively selected genes differed significantly from background expectation by discrete Kolmogorov-Smirnov test (P < 0.001). Remarkably, the top three physical clusters all coincided with islands of conserved synteny predating Mammalia, the largest of which shares synteny with the human cat-eye critical region (CECR) on 22q11. This observation coupled with the expansion of a novel Tbx1-like gene family may indicate evolutionary innovation during pharyngeal arch development: both the CECR and Tbx1 cause dosage-dependent congenital abnormalities in thymus, heart, and head, and craniodysmorphy is associated with human orthologs of other positively selected genes as well.

Transmembrane Helices Are an Over-Presented and Evolutionarily Conserved Source of Major Histocompatibility Complex Class I and II Epitopes

Cytolytic T cell responses are predicted to be biased towards membrane proteins. The peptide-binding grooves of most alleles of histocompatibility complex class I (MHC-I) are relatively hydrophobic, therefore peptide fragments derived from human transmembrane helices (TMHs) are predicted to be presented more often as would be expected based on their abundance in the proteome. However, the physiological reason of why membrane proteins might be over-presented is unclear. In this study, we show that the predicted over-presentation of TMH-derived peptides is general, as it is predicted for bacteria and viruses and for both MHC-I and MHC-II, and confirmed by re-analysis of epitope databases. Moreover, we show that TMHs are evolutionarily more conserved, because single nucleotide polymorphisms (SNPs) are present relatively less frequently in TMH-coding chromosomal regions compared to regions coding for extracellular and cytoplasmic protein regions. Thus, our findings suggest that both cytolytic and helper T cells are more tuned to respond to membrane proteins, because these are evolutionary more conserved. We speculate that TMHs are less prone to mutations that enable pathogens to evade T cell responses.

Multimodal reasoning based on knowledge graph embedding for specific diseases

MOTIVATION

Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases.

RESULTS

This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model's reliability in discovering new knowledge by manually proofreading predicted drug-gene, gene-disease and disease-drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models.

AVAILABILITY AND IMPLEMENTATION

The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome Nexus: A Comprehensive Resource for the Annotation and Interpretation of Genomic Variants in Cancer

PURPOSE

Interpretation of genomic variants in tumor samples still presents a challenge in research and the clinical setting. A major issue is that information for variant interpretation is fragmented across disparate databases, and aggregation of information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one-stop shop for variant annotation with a user-friendly interface for cancer researchers and clinicians.

METHODS

Genome Nexus (1) aggregates variant information from sources that are relevant to cancer research and clinical applications, (2) allows high-performance programmatic access to the aggregated data via a unified application programming interface, (3) provides a reference page for individual cancer variants, (4) provides user-friendly tools for annotating variants in patients, and (5) is freely available under an open source license and can be installed in a private cloud or local environment and integrated with local institutional resources.

RESULTS

Genome Nexus is available at https://www.genomenexus.org. It displays annotations from more than a dozen resources including those that provide variant effect information (variant effect predictor), protein sequence annotation (Uniprot, Pfam, and dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, and SIFT), population prevalences (gnomAD, dbSNP, and ExAC), cancer population prevalences (Cancer hotspots and SignalDB), and clinical actionability (OncoKB, CIViC, and ClinVar). We describe several use cases that demonstrate the utility of Genome Nexus to clinicians, researchers, and bioinformaticians. We cover single-variant annotation, cohort analysis, and programmatic use of the application programming interface. Genome Nexus is unique in providing a user-friendly interface specific to cancer that allows high-performance annotation of any variant including unknown ones.

CONCLUSION

Interpretation of cancer genomic variants is improved tremendously by having an integrated resource for annotations. Genome Nexus is freely available under an open source license.

ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects

Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), much research on these topics is available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers which lack standardization, integration and curation. To address these challenges, we built a pilot database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specializes in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 31 712 VMs entries, 105 624 VISs, 16 310 viral target genes and 1 110 015 virus sequences of eight viruses in 77 human diseases obtained from the public domain. Furthermore, in ViMIC users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1358 transcription regulators and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.

Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks

Quantitative Structure-Activity Relationship (QSAR) modeling is a common computational technique for predicting chemical toxicity, but a lack of new methodological innovations has impeded QSAR performance on many tasks. We show that contemporary QSAR modeling for predictive toxicology can be substantially improved by incorporating semantic graph data aggregated from open-access public databases, and analyzing those data in the context of graph neural networks (GNNs). Furthermore, we introspect the GNNs to demonstrate how they can lead to more interpretable applications of QSAR, and use ablation analysis to explore the contribution of different data elements to the final models' performance.

Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT

MOTIVATION

Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.

METHOD

We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models-dubbed PPI-BioBERT-x10-to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.

RESULTS AND CONCLUSION

The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter [Formula: see text] (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts.

Using GenBank and SRA

GenBank^® and the Sequence Read Archive (SRA) are comprehensive databases of publicly available DNA sequences. GenBank contains data for 480,000 named organisms, more than 176,000 within the embryophyta, obtained through submissions from individual laboratories and batch submissions from large-scale sequencing projects. SRA contains reads from next-generation sequencing studies from over 110,000 species. Daily data exchange with the European Nucleotide Archive (ENA) in Europe and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage for both databases. GenBank and SRA data are accessible through the NCBI Entrez retrieval system that integrates these data with other data at NCBI, such as genomes, taxonomy, and the biomedical literature. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. Usage scenarios for both GenBank and SRA ranging from local and cloud analyses to online analyses supported by the NCBI web-based tools are discussed. Both GenBank and SRA, along with their related retrieval and analysis services, are available from the NCBI homepage at www.ncbi.nlm.nih.gov .

Two-Component System Genes in Sorghum bicolor: Genome-Wide Identification and Expression Profiling in Response to Environmental Stresses

The two-component signal transduction system (TCS) acts in a variety of physiological processes in lower organisms and has emerged as a key signaling system in both prokaryotes and eukaryotes, including plants. TCS genes assist plants in processes such as stress resistance, cell division, nutrition signaling, leaf senescence, and chloroplast division. In plants, this system is composed of three types of proteins: response regulators (RRs), histidine kinases (HKs), and histidine phosphotransfer proteins (HPs). We aimed to study the Sorghum bicolor genome and identified 37 SbTCS genes consisting of 13 HKs, 5 HPs, and 19 RRs (3 type-A RRs, 7 type-B RRs, 2 type-C RRs, and 7 pseudo-RRs). The structural and phylogenetic comparison of the SbTCS members with their counterparts in Arabidopsis thaliana, Oryza sativa, Cicer arietinum, and Glycine max showed group-specific conservations and variations. Expansion of the gene family members is mostly a result of gene duplication, of both the tandem and segmental types. HKs and RRs were observed to be originated from segmental duplication, while some HPs originated from tandem duplication. The nuclear genome of S. bicolor contain 10 chromosomes and these SbTCS genes are randomly distributed on all the chromosomes. The promoter sequences of the SbTCS genes contain several abiotic stress-related cis-elements. RNA-seq and qRT-PCR-based expression analysis demonstrated most of the TCS genes were responsive to drought and salt stresses in leaves, which suggest their role in leaf development. This study lays a foundation for further functional study of TCS genes for stress tolerance and developmental improvement in S. bicolor.

Weighted Cox regression for the prediction of heterogeneous patient subgroups

BACKGROUND

An important task in clinical medicine is the construction of risk prediction models for specific subgroups of patients based on high-dimensional molecular measurements such as gene expression data. Major objectives in modeling high-dimensional data are good prediction performance and feature selection to find a subset of predictors that are truly associated with a clinical outcome such as a time-to-event endpoint. In clinical practice, this task is challenging since patient cohorts are typically small and can be heterogeneous with regard to their relationship between predictors and outcome. When data of several subgroups of patients with the same or similar disease are available, it is tempting to combine them to increase sample size, such as in multicenter studies. However, heterogeneity between subgroups can lead to biased results and subgroup-specific effects may remain undetected.

METHODS

For this situation, we propose a penalized Cox regression model with a weighted version of the Cox partial likelihood that includes patients of all subgroups but assigns them individual weights based on their subgroup affiliation. The weights are estimated from the data such that patients who are likely to belong to the subgroup of interest obtain higher weights in the subgroup-specific model.

RESULTS

Our proposed approach is evaluated through simulations and application to real lung cancer cohorts, and compared to existing approaches. Simulation results demonstrate that our proposed model is superior to standard approaches in terms of prediction performance and variable selection accuracy when the sample size is small.

CONCLUSIONS

The results suggest that sharing information between subgroups by incorporating appropriate weights into the likelihood can increase power to identify the prognostic covariates and improve risk prediction.

RefSeq Functional Elements as experimentally assayed nongenic reference standards and functional interactions in human and mouse

Eukaryotic genomes contain many nongenic elements that function in gene regulation, chromosome organization, recombination, repair, or replication, and mutation of those elements can affect genome function and cause disease. Although numerous epigenomic studies provide high coverage of gene regulatory regions, those data are not usually exposed in traditional genome annotation and can be difficult to access and interpret without field-specific expertise. The National Center for Biotechnology Information (NCBI) therefore provides RefSeq Functional Elements (RefSeqFEs), which represent experimentally validated human and mouse nongenic elements derived from the literature. The curated data set is comprised of richly annotated sequence records, descriptive records in the NCBI Gene database, reference genome feature annotation, and activity-based interactions between nongenic regions, target genes, and each other. The data set provides succinct functional details and transparent experimental evidence, leverages data from multiple experimental sources, is readily accessible and adaptable, and uses a flexible data model. The data have multiple uses for basic functional discovery, bioinformatics studies, genetic variant interpretation; as known positive controls for epigenomic data evaluation; and as reference standards for functional interactions. Comparisons to other gene regulatory data sets show that the RefSeqFE data set includes a wider range of feature types representing more areas of biology, but it is comparatively smaller and subject to data selection biases. RefSeqFEs thus provide an alternative and complementary resource for experimentally assayed functional elements, with future data set growth expected.

Author-sourced capture of pathway knowledge in computable form using Biofactoid

Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.

The cytosolic iron-sulfur cluster assembly (CIA) pathway is required for replication stress tolerance of cancer cells to Chk1 and ATR inhibitors

The relationship between ATR/Chk1 activity and replication stress, coupled with the development of potent and tolerable inhibitors of this pathway, has led to the clinical exploration of ATR and Chk1 inhibitors (ATRi/Chk1i) as anticancer therapies for single-agent or combinatorial application. The clinical efficacy of these therapies relies on the ability to ascertain which patient populations are most likely to benefit, so there is intense interest in identifying predictive biomarkers of response. To comprehensively evaluate the components that modulate cancer cell sensitivity to replication stress induced by Chk1i, we performed a synthetic-lethal drop-out screen in a cell line derived from a patient with triple-negative breast cancer (TNBC), using a pooled barcoded shRNA library targeting ~350 genes involved in DNA replication, DNA damage repair, and cycle progression. In addition, we sought to compare the relative requirement of these genes when DNA fidelity is challenged by clinically relevant anticancer breast cancer drugs, including cisplatin and PARP1/2 inhibitors, that have different mechanisms of action. This global comparison is critical for understanding not only which agents should be used together for combinatorial therapies in breast cancer patients, but also the genetic context in which these therapies will be most effective, and when a single-agent therapy will be sufficient to provide maximum therapeutic benefit to the patient. We identified unique potentiators of response to ATRi/Chk1i and describe a new role for components of the cytosolic iron-sulfur assembly (CIA) pathway, MMS19 and CIA2B-FAM96B, in replication stress tolerance of TNBC.

The Aliment to Bodily Condition knowledgebase (ABCkb): a database connecting plants and human health

Objective

Overconsumption of processed foods has led to an increase in chronic diet-related diseases such obesity and type 2 diabetes. Although diets high in fresh fruits and vegetables are linked with healthier outcomes, the specific mechanisms for these relationships are poorly understood. Experiments examining plant phytochemical production and breeding programs, or separately on the health effects of nutritional supplements have yielded results that are sparse, siloed, and difficult to integrate between the domains of human health and agriculture. To connect plant products to health outcomes through their molecular mechanism an integrated computational resource is necessary.

Results

We created the Aliment to Bodily Condition Knowledgebase (ABCkb) to connect plants to human health by creating a stepwise path from plant \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document}→ plant product \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document}→ human gene \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document}→ pathways \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document}→ indication. ABCkb integrates 11 curated sources as well as relationships mined from Medline abstracts by loading into a graph database which is deployed via a Docker container. This new resource, provided in a queryable container with a user-friendly interface connects plant products with human health outcomes for generating nutritive hypotheses. All scripts used are available on github (https://github.com/atrautm1/ABCkb) along with basic directions for building the knowledgebase and a browsable interface is available (https://abckb.charlotte.edu).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05835-x.

AroCageDB: A Web-Based Resource for Aromatic Cage Binding Sites and Their Intrinsic Ligands

While aromatic cages have extensively been investigated in the context of structural biology, molecular recognition, and drug discovery, there exist to date no comprehensive resource for proteins sharing this conserved structural motif. To this end, we parsed the Protein Data Bank and thus constructed the Aromatic Cage Database (AroCageDB), a database for investigating the binding pocket descriptors and ligand binding space of aromatic-cage-containing proteins (ACCPs). AroCageDB contains 487 unique ACCPs bound to 890 unique ligands, for a total of 1636 complexes. This web-accessible database provides a user-friendly interface for the interactive visualization of ligand-bound ACCP structures, with a variety of search options that will open up opportunities for structural analyses and drug discovery campaigns. AroCageDB is freely available at http://www.pharmbioinf.uni-freiburg.de/arocagedb/.

Epione application: An integrated web‑toolkit of clinical genomics and personalized medicine in systemic lupus erythematosus

Genome wide association studies (GWAS) have identified autoimmune disease-associated loci, a number of which are involved in numerous disease-associated pathways. However, much of the underlying genetic and pathophysiological mechanisms remain to be elucidated. Systemic lupus erythematosus (SLE) is a chronic, highly heterogeneous auto-immune disease, characterized by differences in autoantibody profile, serum cytokines and a multi-system involvement. This study presents the Epione application, an integrated bioinformatics web-toolkit, designed to assist medical experts and researchers in more accurately diagnosing SLE. The application aims to identify the most credible gene variants and single nucleotide polymorphisms (SNPs) associated with SLE susceptibility, by using patient's genomic data to aid the medical expert in SLE diagnosis. The application contains useful knowledge of >70,000 SLE-related publications that have been analyzed, using data mining and semantic techniques, towards extracting the SLE-related genes and the corresponding SNPs. Probable genes associated with the patient's genomic profile are visualized with several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, to obtain a representative number of the most credible candidate genes and biological pathways associated with the SLE. Furthermore, an evaluation study was performed on a patient diagnosed with SLE and is presented herein. Epione has also been expanded in family-related candidate patients to evaluate its predictive power. All the recognized gene variants that were previously considered to be associated with SLE were accurately identified in the output profile of the patient, and by comparing the results, novel findings have emerged. The Epione application may assist and facilitate in early stage diagnosis by using the patients' genomic profile to compare against the list of the most predictable candidate gene variants related to SLE. Its diagnosis-oriented output presents the user with a structured set of results on variant association, position in genome and links to specific bibliography and gene network associations. The overall aim of the present study was to provide a reliable tool for the most effective study of SLE. This novel and accessible webserver tool of SLE is available at http://geneticslab.aua.gr/epione/.

Characterization and comparison of gene-centered human interactomes

The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature. It is therefore unclear to which extent the various interactomes yield similar results when used in the context of interactome-based approaches in network biology. We compared a comprehensive list of human interactomes on the basis of topology, protein complexes, molecular pathways, pathway cross-talk and disease gene prediction. In a general context of relevant heterogeneity, our study provides a series of qualitative and quantitative parameters that describe the state of the art of human interactomes and guidelines for selecting interactomes in future applications.

Exposure to extracellular vesicles from Pseudomonas aeruginosa result in loss of DNA methylation at enhancer and DNase hypersensitive site regions in lung macrophages

Various pathogens use differing strategies to evade host immune response including modulating the host's epigenome. Here, we investigate if EVs secreted from P. aeruginosa alter DNA methylation in human lung macrophages, thereby potentially contributing to a dysfunctional innate immune response. Using a genome-wide DNA methylation approach, we demonstrate that P. aeruginosa EVs alter certain host cell DNA methylation patterns. We identified 1,185 differentially methylated CpGs (FDR < 0.05), which were significantly enriched for distal DNA regulatory elements including enhancer regions and DNase hypersensitive sites. Notably, all but one of the 1,185 differentially methylated CpGs were hypomethylated in association with EV exposure. Significantly hypomethylated CpGs tracked to genes including AXL, CFB and CCL23. Gene expression analysis identified 310 genes exhibiting significantly altered expression 48 hours post P. aeruginosa EV treatment, with 75 different genes upregulated and 235 genes downregulated. Some CpGs associated with cytokines such as CSF3 displayed strong negative correlations between DNA methylation and gene expression. Our infection model illustrates how secreted products (EVs) from bacteria can alter DNA methylation of the host epigenome. Changes in DNA methylation in distal DNA regulatory regions in turn can modulate cellular gene expression and potential downstream cellular processes.

Gene Ontology curation of the blood-brain barrier to improve the analysis of Alzheimer's and other neurological diseases

The role of the blood-brain barrier (BBB) in Alzheimer's and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer's research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins. Database URLhttps://www.ebi.ac.uk/QuickGO/.

Mouse Genome Informatics (MGI): latest news from MGD and GXD

The Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation standards. MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease. MGI is the authoritative source for mouse gene, allele, and strain nomenclature and is the primary source of mouse phenotype annotations, functional annotations, developmental gene expression information, and annotations of mouse models with human diseases. MGI maintains mouse anatomy and phenotype ontologies and contributes to the development of the Gene Ontology and Disease Ontology and uses these ontologies as standard terminologies for annotation. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are MGI's two major knowledgebases. Here, we highlight some of the recent changes and enhancements to MGD and GXD that have been implemented in response to changing needs of the biomedical research community and to improve the efficiency of expert curation. MGI can be accessed freely at http://www.informatics.jax.org .

Revelation of Pivotal Genes Pertinent to Alzheimer's Pathogenesis: A Methodical Evaluation of 32 GEO Datasets

Alzheimer’s disease (AD), a dreadful neurodegenerative disorder that affects cognitive and behavioral function in geriatric populations, is characterized by the presence of amyloid deposits and neurofibrillary tangles in brain regions. The International D World Alzheimer Report2018 noted a global prevalence of 50 million AD cases and forecasted a threefold rise to 139 million by 2050. Although there exist numerous genetic association studies pertinent to AD in different ethnicities, critical genetic factors and signaling pathways underlying its pathogenesis remain ambiguous. This study was aimed to analyze the genetic data retrieved from 32 Gene Expression Omnibus datasets belonging to diverse ethnic cohorts in order to identify overlapping differentially expressed genes (DEGs). Stringent selection criteria were framed to shortlist appropriate datasets based on false discovery rate (FDR) p-value and log FC, and relevant details of upregulated and downregulated DEGs were retrieved. Among the 32 datasets, only six satisfied the selection criteria. The GEO2R tool was employed to retrieve significant DEGs. Nine common DEGs, i.e., SLC5A3, BDNF, SST, SERPINA3, RTN3, RGS4, NPTX, ENC1 and CRYM were found in more than 60% of the selected datasets. These DEGs were later subjected to protein–protein interaction analysis with 18 AD-specific literature-derived genes. Among the nine common DEGs, BDNF, SST, SERPINA3, RTN3 and RGS4 exhibited significant interactions with crucial proteins including BACE1, GRIN2B, APP, APOE, COMT, PSEN1, INS, NEP and MAPT. Functional enrichment analysis revealed involvement of these genes in trans-synaptic signaling, chemical transmission, PI3K pathway signaling, receptor–ligand activity and G protein signaling. These processes are interlinked with AD pathways.

Genetically altered animal models for ATP1A3-related disorders

Within the past 20 years, particularly with the advent of exome sequencing technologies, autosomal dominant and de novo mutations in the gene encoding the neurone-specific α3 subunit of the Na+,K+-ATPase (NKA α3) pump, ATP1A3, have been identified as the cause of a phenotypic continuum of rare neurological disorders. These allelic disorders of ATP1A3 include (in approximate order of severity/disability and onset in childhood development): polymicrogyria; alternating hemiplegia of childhood; cerebellar ataxia, areflexia, pes cavus, optic atrophy and sensorineural hearing loss syndrome; relapsing encephalopathy with cerebellar ataxia; and rapid-onset dystonia-parkinsonism. Some patients present intermediate, atypical or combined phenotypes. As these disorders are currently difficult to treat, there is an unmet need for more effective therapies. The molecular mechanisms through which mutations in ATP1A3 result in a broad range of neurological symptoms are poorly understood. However, in vivo comparative studies using genetically altered model organisms can provide insight into the biological consequences of the disease-causing mutations in NKA α3. Herein, we review the existing mouse, zebrafish, Drosophila and Caenorhabditis elegans models used to study ATP1A3-related disorders, and discuss their potential contribution towards the understanding of disease mechanisms and development of novel therapeutics.

CovPDB: a high-resolution coverage of the covalent protein-ligand interactome

In recent years, the drug discovery paradigm has shifted toward compounds that covalently modify disease-associated target proteins, because they tend to possess high potency, selectivity, and duration of action. The rational design of novel targeted covalent inhibitors (TCIs) typically starts from resolved macromolecular structures of target proteins in their apo or holo forms. However, the existing TCI databases contain only a paucity of covalent protein–ligand (cP–L) complexes. Herein, we report CovPDB, the first database solely dedicated to high-resolution cocrystal structures of biologically relevant cP–L complexes, curated from the Protein Data Bank. For these curated complexes, the chemical structures and warheads of pre-reactive electrophilic ligands as well as the covalent bonding mechanisms to their target proteins were expertly manually annotated. Totally, CovPDB contains 733 proteins and 1,501 ligands, relating to 2,294 cP–L complexes, 93 reactive warheads, 14 targetable residues, and 21 covalent mechanisms. Users are provided with an intuitive and interactive web interface that allows multiple search and browsing options to explore the covalent interactome at a molecular level in order to develop novel TCIs. CovPDB is freely accessible at http://www.pharmbioinf.uni-freiburg.de/covpdb/ and its contents are available for download as flat files of various formats.