WikiPathways: capturing the full diversity of pathway knowledge

MYLK*FLNB and DOCK1*LAMA2 gene-gene interactions associated with rheumatoid arthritis in the focal adhesion pathway

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO

The Gene Expression Omnibus (GEO) is a major open biomedical research repository for transcriptomics and other omics datasets. It currently contains millions of gene expression samples from tens of thousands of studies collected by many biomedical research laboratories from around the world. While users of the GEO repository can search the metadata describing studies for locating relevant datasets, there are currently no methods or resources that facilitate global search of GEO at the data level. To address this shortcoming, we developed RummaGEO, a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO. To develop the search engine, we performed offline automatic identification of sample conditions from the uniformly aligned GEO studies available from ARCHS4. We then computed differential expression signatures to extract gene sets from these studies. In total, RummaGEO currently contains 135,264 human and 158,062 mouse gene sets extracted from 23,395 GEO studies. Next, we analyzed the contents of the RummaGEO database to identify statistical patterns and perform various global analyses. The contents of the RummaGEO database are provided as a web-server search engine with signature search, PubMed search, and metadata search functionalities. Overall, RummaGEO provides an unprecedented resource for the biomedical research community enabling hypothesis generation for many future studies. The RummaGEO search engine is available from: https://rummageo.com/.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Comprehensive Genomic and Transcriptomic Analysis of Sclerosing Pneumocytoma

Sclerosing pneumocytoma is a rare and distinct lung neoplasm whose histogenesis and molecular alterations are the subject of ongoing research. Our recent study revealed that AKT1 internal tandem duplications (ITD), point mutations, and short indels were present in almost all tested sclerosing pneumocytomas, suggesting that AKT1 mutations are a major driving oncogenic event in this tumor. Although the pathogenic role of AKT1 point mutations is well established, the significance of AKT1 ITD in oncogenesis remains largely unexplored. We conducted comprehensive genomic and transcriptomic analyses of sclerosing pneumocytoma to address this knowledge gap. RNA-sequencing data from 23 tumors and whole-exome sequencing data from 44 tumors were used to obtain insights into their genetic and transcriptomic profiles. Our analysis revealed a high degree of genetic and transcriptomic similarity between tumors carrying AKT1 ITD and those with AKT1 point mutations. Mutational signature analysis revealed COSMIC signatures 1 and 5 as the prevailing signatures of sclerosing pneumocytoma, associated with the spontaneous deamination of 5-methylcytosine and an unknown etiology, respectively. RNA-sequencing data analysis revealed that the sclerosing pneumocytoma gene expression profile is characterized by activation of the PI3K/AKT/mTOR pathway, which exhibits significant similarity between tumors harboring AKT1 ITD and those with AKT1 point mutations. Notably, an upregulation of SOX9, a transcription factor known for its involvement in fetal lung development, was observed in sclerosing pneumocytoma. Specifically, SOX9 expression was prominent in the round cell component, whereas it was relatively lower in the surface cell component of the tumor. To the best of our knowledge, this is the first comprehensive investigation of the genomic and transcriptomic characteristics of sclerosing pneumocytoma. Results of the present study provide insights into the molecular attributes of sclerosing pneumocytoma and a basis for future studies of this enigmatic tumor.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC.

METHODS

The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression.

RESULTS

HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis.

CONCLUSIONS

Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Unraveling Connective Tissue Growth Factor as a Therapeutic Target and Assessing Kahweol as a Potential Drug Candidate in Triple-Negative Breast Cancer Treatment

Triple-negative breast cancer (TNBC) is characterized by aggressive behavior and limited treatment options, necessitating the identification of novel therapeutic targets. In this study, we investigated the clinical significance of connective tissue growth factor (CTGF) as a prognostic marker and explored the potential therapeutic effects of kahweol, a coffee diterpene molecule, in TNBC treatment. Initially, through a survival analysis on breast cancer patients from The Cancer Genome Atlas (TCGA) database, we found that CTGF exhibited significant prognostic effects exclusively in TNBC patients. To gain mechanistic insights, we performed the functional annotation and gene set enrichment analyses, revealing the involvement of CTGF in migratory pathways relevant to TNBC treatment. Subsequently, in vitro experiments using MDA-MB 231 cells, a representative TNBC cell line, demonstrated that recombinant CTGF (rCTGF) administration enhanced cell motility, whereas CTGF knockdown using CTGF siRNA resulted in reduced motility. Notably, rCTGF restored kahweol-reduced cell motility, providing compelling evidence for the role of CTGF in mediating kahweol's effects. At the molecular level, kahweol downregulated the protein expression of CTGF as well as critical signaling molecules, such as p-ERK, p-P38, p-PI3K/AKT, and p-FAK, associated with cell motility. In summary, our findings propose CTGF as a potential prognostic marker for guiding TNBC treatment and suggest kahweol as a promising antitumor compound capable of regulating CTGF expression to suppress cell motility in TNBC. These insights hold promise for the development of targeted therapies and improved clinical outcomes for TNBC patients.

Construction of an Exudative Age-Related Macular Degeneration Diagnostic and Therapeutic Molecular Network Using Multi-Layer Network Analysis, a Fuzzy Logic Model, and Deep Learning Techniques: Are Retinal and Brain Neurodegenerative Disorders Related?

Neovascular age-related macular degeneration (nAMD) is a leading cause of irreversible visual impairment in the elderly. The current management of nAMD is limited and involves regular intravitreal administration of anti-vascular endothelial growth factor (anti-VEGF). However, the effectiveness of these treatments is limited by overlapping and compensatory pathways leading to unresponsiveness to anti-VEGF treatments in a significant portion of nAMD patients. Therefore, a system view of pathways involved in pathophysiology of nAMD will have significant clinical value. The aim of this study was to identify proteins, miRNAs, long non-coding RNAs (lncRNAs), various metabolites, and single-nucleotide polymorphisms (SNPs) with a significant role in the pathogenesis of nAMD. To accomplish this goal, we conducted a multi-layer network analysis, which identified 30 key genes, six miRNAs, and four lncRNAs. We also found three key metabolites that are common with AMD, Alzheimer's disease (AD) and schizophrenia. Moreover, we identified nine key SNPs and their related genes that are common among AMD, AD, schizophrenia, multiple sclerosis (MS), and Parkinson's disease (PD). Thus, our findings suggest that there exists a connection between nAMD and the aforementioned neurodegenerative disorders. In addition, our study also demonstrates the effectiveness of using artificial intelligence, specifically the LSTM network, a fuzzy logic model, and genetic algorithms, to identify important metabolites in complex metabolic pathways to open new avenues for the design and/or repurposing of drugs for nAMD treatment.

Donor white blood cell differential is the single largest determinant of whole blood gene expression patterns

It has become widely accepted that sample cellular composition is a significant determinant of the gene expression patterns observed in any transcriptomic experiment performed with bulk tissue. Despite this, many investigations currently performed with whole blood do not experimentally account for possible inter-specimen differences in cellularity, and often assume that any observed gene expression differences are a result of true differences in nuclear transcription. In order to determine how confounding of an assumption this may be, in this study, we recruited a large cohort of human donors (n = 138) and used a combination of next generation sequencing and flow cytometry to quantify and compare the underlying contributions of variance in leukocyte counts versus variance in other biological factors to overall variance in whole blood transcript levels. Our results suggest that the combination of donor neutrophil and lymphocyte counts alone are the primary determinants of whole blood transcript levels for up to 75% of the protein-coding genes expressed in peripheral circulation, whereas the other factors such as age, sex, race, ethnicity, and common disease states have comparatively minimal influence. Broadly, this infers that a majority of gene expression differences observed in experiments performed with whole blood are driven by latent differences in leukocyte counts, and that cell count heterogeneity must be accounted for to meaningfully biologically interpret the results.

A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank

Bone mineral density (BMD) is an essential predictor of osteoporosis and fracture. We conducted a genome-wide trajectory analysis of BMD and analyzed the BMD change.

PURPOSE

This study aimed to identify the genetic architecture and potential biomarkers of BMD.

METHODS

Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform.

RESULTS

A total of 52 genes associated with BMD trajectory mean were identified, of which the top three significant genes were WNT16 (P = 1.31 × 10-126), FAM3C (P = 4.18 × 10-108), and CPED1 (P = 8.48 × 10-106). In addition, 114 genes associated with BMD within-subject variability were also identified, such as AC092079.1 (P = 2.72 × 10-13) and RGS7 (P = 4.72 × 10-10). The associations for these candidate genes were confirmed in the previous GWASs and replicated successfully in the Asian participants. GSEA results of BMD change identified multiple GO terms related to skeletal development, such as SKELETAL SYSTEM DEVELOPMENT (Padjusted = 2.45 × 10-3) and REGULATION OF OSSIFICATION (Padjusted = 2.45 × 10-3). KEGG enrichment analysis showed that these genes were mainly enriched in WNT SIGNALING PATHWAY.

CONCLUSIONS

Our findings indicated that the CPED1-WNT16-FAM3C locus plays a significant role in BMD mean trajectories and identified several novel candidate genes contributing to BMD within-subject variability, facilitating the understanding of the genetic architecture of BMD.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Multidimensional quantitative phenotypic and molecular analysis reveals neomorphic behaviors of p53 missense mutants

Mutations in the TP53 tumor suppressor gene occur in >80% of the triple-negative or basal-like breast cancer. To test whether neomorphic functions of specific TP53 missense mutations contribute to phenotypic heterogeneity, we characterized phenotypes of non-transformed MCF10A-derived cell lines expressing the ten most common missense mutant p53 proteins and observed a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and 3D mammosphere architecture. The p53 mutants R248W, R273C, R248Q, and Y220C are the most aggressive while G245S and Y234C are the least, which correlates with survival rates of basal-like breast cancer patients. Interestingly, a crucial amino acid difference at one position-R273C vs. R273H-has drastic changes on cellular phenotype. RNA-Seq and ChIP-Seq analyses show distinct DNA binding properties of different p53 mutants, yielding heterogeneous transcriptomics profiles, and MD simulation provided structural basis of differential DNA binding of different p53 mutants. Integrative statistical and machine-learning-based pathway analysis on gene expression profiles with phenotype vectors across the mutant cell lines identifies quantitative association of multiple pathways including the Hippo/YAP/TAZ pathway with phenotypic aggressiveness. Further, comparative analyses of large transcriptomics datasets on breast cancer cell lines and tumors suggest that dysregulation of the Hippo/YAP/TAZ pathway plays a key role in driving the cellular phenotypes towards basal-like in the presence of more aggressive p53 mutants. Overall, our study describes distinct gain-of-function impacts on protein functions, transcriptional profiles, and cellular behaviors of different p53 missense mutants, which contribute to clinical phenotypic heterogeneity of triple-negative breast tumors.

A network map of GDNF/RET signaling pathway in physiological and pathological conditions

Glial cell line-derived neurotrophic factor (GDNF) signals through a multi-component receptor system predominantly consisting of glycosyl-phosphatidylinositol-anchored GDNF family receptor alpha-1 (GFRα1) and the Rearranged during transfection (RET) receptor tyrosine kinase. GDNF/RET signaling is vital to the central and peripheral nervous system, kidney morphogenesis, and spermatogenesis. In addition, the dysregulation of the GDNF/RET signaling has been implicated in the pathogenesis of cancers. Despite the extensive research on GDNF/RET signaling, a molecular network of reactions induced by GDNF reported across the published literature. However, a comprehensive GDNF/RET pathway resource is currently unavailable. We describe an integrated signaling pathway reaction map of GDNF/RET consisting of 1151 molecular reactions. These include information pertaining to 52 molecular association events, 70 enzyme catalysis events, 36 activation/inhibition events, 22 translocation events, 856 gene regulation events, and 115 protein-level expression events induced by GDNF in diverse cell types. We developed a comprehensive GDNF/RET signaling network map based on these molecular reactions. The pathway map was made accessible through WikiPathways database ( https://www.wikipathways.org/index.php/Pathway:WP5143 ). Biocuration and development of gene regulatory network map of GDNF/RET signaling pathway.

Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice

To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.

A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank (N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74-2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.

Pleiotropic loci for cannabis use disorder severity in multi-ancestry high-risk populations

Cannabis use disorder (CUD) is common and has in part a genetic basis. The risk factors underlying its development likely involve multiple genes that are polygenetic and interact with each other and the environment to ultimately lead to the disorder. Co-morbidity and genetic correlations have been identified between CUD and other disorders and traits in select populations primarily of European descent. If two or more traits, such as CUD and another disorder, are affected by the same genetic locus, they are said to be pleiotropic. The present study aimed to identify specific pleiotropic loci for the severity level of CUD in three high-risk population cohorts: American Indians (AI), Mexican Americans (MA), and European Americans (EA). Using a previously developed computational method based on a machine learning technique, we leveraged the entire GWAS catalog and identified 114, 119, and 165 potentially pleiotropic variants for CUD severity in AI, MA, and EA respectively. Ten pleiotropic loci were shared between the cohorts although the exact variants from each cohort differed. While majority of the pleiotropic genes were distinct in each cohort, they converged on numerous enriched biological pathways. The gene ontology terms associated with the pleiotropic genes were predominately related to synaptic functions and neurodevelopment. Notable pathways included Wnt/β-catenin signaling, lipoprotein assembly, response to UV radiation, and components of the complement system. The pleiotropic genes were the most significantly differentially expressed in frontal cortex and coronary artery, up-regulated in adipose tissue, and down-regulated in testis, prostate, and ovary. They were significantly up-regulated in most brain tissues but were down-regulated in the cerebellum and hypothalamus. Our study is the first to attempt a large-scale pleiotropy detection scan for CUD severity. Our findings suggest that the different population cohorts may have distinct genetic factors for CUD, however they share pleiotropic genes from underlying pathways related to Alzheimer's disease, neuroplasticity, immune response, and reproductive endocrine systems.

The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.

Transcriptome Discovery of Genes in the Three Phases of Autophagy That Are Upregulated During Atrial Fibrillation

Background: Autophagy may contribute to the maintenance of atrial fibrillation (AF), but no previous study has concurrently surveyed all 3 phases of autophagy, namely autophagosome formation, lysosome formation, and autophagosome-lysosome fusion. Here we aimed to identify disorders involving various phases of autophagy during AF. Methods and Results: We used bioinformatic techniques to analyze publicly available DNA microarray datasets from the left atrium (LA) and right atrium (RA) of 7 patients with AF and 6 patients with normal sinus rhythm who underwent valvular surgeries. We compared gene expression levels in the LA (AF-LA) and RA of patients with AF with those in the LA and RA of patients with normal sinus rhythm. Several differentially expressed genes in the AF-LA sample were significantly associated with the Gene Ontogeny term 'Autophagy', indicating that the expression of autophagic genes was specifically altered in this dataset. In particular, the expression of genes known or suspected to be involved in autophagosome formation (autophagy related 5 [ATG5], autophagy related 10 [ATG10], autophagy related 12 [ATG12], and light chain 3B [LC3B]), lysosome formation (lysosomal associated membrane protein 1 [LAMP1] and lysosomal associated membrane protein 2 [LAMP2]), and autophagosome-lysosome fusion (synaptosome associated protein 29 [SNAP29], SNAP associated protein [SNAPIN], and syntaxin 17 [STX17]) was significantly upregulated in the LA-AF dataset. Conclusions: Autophagy is activated excessively in, and may perpetuate, AF.

Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose

The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h² = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.

Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview

The ability to assess various cellular events consequent to perturbations, such as genetic mutations, disease states and therapies, has been recently revolutionized by technological advances in multiple "omics" fields. The resulting deluge of information has enabled and necessitated the development of tools required to both process and interpret the data. While of tremendous value to basic researchers, the amount and complexity of the data has made it extremely difficult to manually draw inference and identify factors key to the study objectives. The challenges of data reduction and interpretation are being met by the development of increasingly complex tools that integrate disparate knowledge bases and synthesize coherent models based on current biological understanding. This chapter presents an example of how genomics data can be integrated with biological network analyses to gain further insight into the developmental consequences of genetic perturbations. State of the art methods for conducting similar studies are discussed along with modern methods used to analyze and interpret the data.

RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes

MOTIVATION

Functional interpretation of high-throughput metabolomic and transcriptomic results is a crucial step in generating insight from experimental data. However, pathway and functional information for genes and metabolites are distributed among many siloed resources, limiting the scope of analyses that rely on a single knowledge source.

RESULTS

RaMP-DB 2.0 is a web interface, relational database, API and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotations (ClassyFire, LIPID MAPS, SMILES, InChIs, etc.), with new data types related to metabolites and lipids incorporated. To streamline entity resolution across multiple source databases, we have implemented a new semi-automated process, thereby lessening the burden of harmonization and supporting more frequent updates. The associated RaMP-DB 2.0 R package now supports queries on pathways, common reactions (e.g. metabolite-enzyme relationship), chemical functional ontologies, chemical classes and chemical structures, as well as enrichment analyses on pathways (multi-omic) and chemical classes. Lastly, the RaMP-DB web interface has been completely redesigned using the Angular framework.

AVAILABILITY AND IMPLEMENTATION

The code used to build all components of RaMP-DB 2.0 are freely available on GitHub at https://github.com/ncats/ramp-db, https://github.com/ncats/RaMP-Client/ and https://github.com/ncats/RaMP-Backend. The RaMP-DB web application can be accessed at https://rampdb.nih.gov/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

Investigation of cell signalings and therapeutic targets in PTPRK-RSPO3 fusion-positive colorectal cancer

Introduction

Colorectal cancer (CRC) is one of the most deadly and common diseases in the world, accounting for over 881,000 casualties in 2018. The PTPRK-RSPO3 (P:R) fusion is a structural variation in CRC and well known for its ability to activate WNT signaling and tumorigenesis. However, till now, therapeutic targets and actionable drugs are limited in this subtype of cancer.

Materials and method

The purpose of this study is to identify key genes and cancer-related pathways specific for P:R fusion-positive CRC. In addition, we also inferred the actionable drugs in bioinformatics analysis using the Cancer Genome Atlas (TCGA) data.

Results

2,505 genes were altered in RNA expression specific for P:R fusion-positive CRC. By pathway analysis based on the altered genes, ten major cancer-related signaling pathways (Apoptosis, Direct p53, EGFR, ErbB, JAK-STAT, tyrosine kinases, Pathways in Cancer, SCF-KIT, VEGFR, and WNT-related Pathway) were significantly altered in P:R fusion-positive CRC. Among these pathways, the most altered cancer genes (ALK, ACSL3, AXIN, MYC, TP53, GNAQ, ACVR2A, and FAS) specific for P:R fusion and involved in multiple cancer pathways were considered to have a key role in P:R fusion-positive CRC. Based on the drug-target network analysis, crizotinib, alectinib, lorlatinib, brigatinib, ceritinib, erdafitinib, infigratinib and pemigatinib were selected as putative therapeutic candidates, since they were already used in routine clinical practice in other cancer types and target genes of the drugs were involved in multiple cancer-pathways.

Identification and validation of roles of lysyl oxidases in the predictions of prognosis, chemotherapy and immunotherapy in glioma

Background: Previous investigations have illustrated that lysyl oxidase family enzymes (LOXs) are contributing factors for tumor progression and remodeling immunomicroenvironment. However, it is scarce regarding comprehensive analysis of LOXs in the predictions of prognosis, chemotherapy and immunotherapy in glioma, the highly invasive brain tumor. Our present work aimed to explore the prognostic value, chemotherapeutic drug sensitivity and immunotherapy according to distinct LOXs expressions in glioma through bioinformatics analysis and experimental verification.

Methods: We collected gene expression data and clinical characteristics from the public databases including Chinese Glioma Genome Atlas (CGGA)-325, CGGA-693, the Cancer Genome Atlas (TCGA), IMvigor210 and Van Allen 2015 cohorts. The correlations between the clinicopathological factors and differential LOXs expressions were analyzed. The ROC curve and Kaplan-Meier analysis were conducted to evaluate the prediction ability of prognosis. Chemotherapeutic drug sensitivity via distinct LOXs expression levels was predicted using the pRRophetic package. Immune score, immune cell infiltration and immune checkpoint expression levels were also analyzed through diverse algorithms in R software. Finally, mRNA and protein expressions of LOXs were validated in glioma cells (T98G and A172) by real-time quantitative PCR and Western blot, respectively.

Results: Our results demonstrated that high levels of LOXs expressions were positively associated with glioma grades, older age and MGMT unmethylated status while elevations of LOXs were negatively correlated with IDH mutation or 1p/19q co-deletion. Furthermore, the glioma patients with low levels of LOXs also exhibited better prognosis. Also, differential LOXs expressions were associated with at least 12 chemotherapeutic drug sensitivity. Besides, it was also found that glioma patients with high LOXs expressions showed higher enrichment scores for immune cell infiltration and increased levels of immune checkpoints, suggesting the critical role of distinct LOXs expression levels for glioma immunotherapy. The predictive roles of LOXs expression in tumor immunotherapy were also validated in two immunotherapy cohorts including IMvigor 210 and Van Allen 2015. Experimental results revealed that expressions of LOX, LOXL1, LOXL2, and LOXL3 were higher in glioma cell lines at mRNA and protein levels.

Conclusion: Our findings altogether indicate that LOXs have potent predictive value for prognosis, chemotherapy and immunotherapy in glioma patients.

Pan-sarcoma characterization of lncRNAs in the crosstalk of EMT and tumour immunity identifies distinct clinical outcomes and potential implications for immunotherapy

The epithelial-to-mesenchymal transition (EMT) is a reversible process that may interact with tumour immunity through multiple approaches. There is increasing evidence demonstrating the interconnections among EMT-related processes, the tumour microenvironment, and immune activity, as well as its potential influence on the immunotherapy response. Long non-coding RNAs (lncRNAs) are emerging as critical modulators of gene expression. They play fundamental roles in tumour immunity and act as promising biomarkers of immunotherapy response. However, the potential roles of lncRNA in the crosstalk of EMT and tumour immunity are still unclear in sarcoma. We obtained multi-omics profiling of 1440 pan-sarcoma patients from 19 datasets. Through an unsupervised consensus clustering approach, we categorised EMT molecular subtypes. We subsequently identified 26 EMT molecular subtype and tumour immune-related lncRNAs (EILncRNA) across pan-sarcoma types and developed an EILncRNA signature-based weighted scoring model (EILncSig). The EILncSig exhibited favourable performance in predicting the prognosis of sarcoma, and a high-EILncSig was associated with exclusive tumour microenvironment (TME) characteristics with desert-like infiltration of immune cells. Multiple altered pathways, somatically-mutated genes and recurrent CNV regions associated with EILncSig were identified. Notably, the EILncSig was associated with the efficacy of immune checkpoint inhibition (ICI) therapy. Using a computational drug-genomic approach, we identified compounds, such as Irinotecan that may have the potential to convert the EILncSig phenotype. By integrative analysis on multi-omics profiling, our findings provide a comprehensive resource for understanding the functional role of lncRNA-mediated immune regulation in sarcomas, which may advance the understanding of tumour immune response and the development of lncRNA-based immunotherapeutic strategies for sarcoma.

Genome-wide Meta-analysis Reveals New Gene Signatures and Potential Drug Targets of Hypertension

The prevalence of hypertension reported around the world is increasing and is an important public health challenge. This study was designed to explore the disease's genetic variations and to identify new hypertension-related genes and target proteins. We analyzed 22 publicly available Affymetrix cDNA datasets of hypertension using an integrated system-level framework involving differential expression genetic (DEG) analysis, data mining, gene enrichment, protein-protein interaction, microRNA analysis, toxicogenomics, gene regulation, molecular docking, and simulation studies. We found potential DEGs after screening out the extracellular proteins. We studied the functional role of seven shortlisted DEGs (ADM, EDN1, ANGPTL4, NFIL3, MSR1, CEBPD, and USP8) in hypertension after disease gene curation analysis. The expression profiling and cluster analysis showed significant variations and enriched GO terms. hsa-miR-365a-3p, hsa-miR-2052, hsa-miR-3065-3p, hsa-miR-603, hsa-miR-7113-3p, hsa-miR-3923, and hsa-miR-524-5p were identified as hypertension-associated miRNA targets for each gene using computational algorithms. We found functional interactions of source DEGs with target and important gene signatures including EGFR, AGT, AVP, APOE, RHOA, SRC, APOB, STAT3, UBC, LPL, APOA1, and AKT1 associated with the disease. These DEGs are mainly involved in fatty acid metabolism, myometrial pathways, MAPK, and G-alpha signaling pathways linked with hypertension pathogenesis. We predicted significantly disordered regions of 71.2, 48.8, and 45.4% representing the mutation in the sequence of NFIL3, USP8, and ADM, respectively. Regulation of gene expression was performed to find upregulated genes. Molecular docking analysis was used to evaluate Food and Drug Administration-approved medicines against the four DEGs that were overexpressed. For each elevated target protein, the three best drug candidates were chosen. Furthermore, molecular dynamics (MD) simulation using the target's active sites for 100 ns was used to validate these 12 complexes after docking. This investigation establishes the worth of systems genetics for finding four possible genes as potential drug targets for hypertension. These network-based approaches are significant for finding genetic variant data, which will advance the understanding of how to hasten the identification of drug targets and improve the understanding regarding the treatment of hypertension.

Getting Started with LINCS Datasets and Tools

The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Navigating L1000 tools and data in CLUE.io Basic Protocol 2: Computing signatures from the L1000 data with the CD method Basic Protocol 3: Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA-seq Appyter Basic Protocol 4: Utilizing the L1000FWD resource for drug discovery Basic Protocol 5: KINOMEscan and the KINOMEscan Appyter Basic Protocol 6: LINCS P100 and GCP Proteomics Assays Basic Protocol 7: The LINCS Joint Project (LJP) Basic Protocol 8: The LINCS Data Portals and SigCom LINCS Basic Protocol 9: Creating and analyzing signatures with iLINCS.

Overcoming Sparseness of Biomedical Networks to Identify Drug Repositioning Candidates

Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing relies on exploration of diverse biochemical concepts and their relationships, including drug's adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs matrix completion to overcome the sparseness of biomedical data and to enrich the set of relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The algorithms is available at http://bioinfo.cs.uni.edu/AEONET.html.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Background

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.

Results

We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software.

Conclusion

The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions

To grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of, for example, signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format making it easier for humans to interpret. Still, these diagrams can be represented in machine readable formats, as done in the KEGG, Reactome, and WikiPathways databases. However, while humans are good at interpreting the message of the creators of diagrams, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles which need harmonizing to offer semantically enriched access. Particularly challenging, here, are the interactions between the biological entities that underlie the biological causality. These interactions provide information about the biological process (metabolic conversion, inhibition, etc.), the direction, and the participating entities. Availability of the interactions in a semantic and harmonized format is essential for searching the full network of biological interactions. We here study how the graphically-modelled biological knowledge in diagrams can be semantified and harmonized, and exemplify how the resulting data is used to programmatically answer biological questions. We find that we can translate graphically modelled knowledge to a sufficient degree into a semantic model and discuss some of the current limitations. We then use this to show that reproducible notebooks can be used to explore up- and downstream targets of MECP2 and to analyse the sphingolipid metabolism. Our results demonstrate that most of the graphical biological knowledge from WikiPathways is modelled into the semantic layer with the semantic information intact and connectivity information preserved. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.

OmicsView: omics data analysis through interactive visual analytics

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org.

This platform comes preloaded with 1000 s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohn’s disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

The m6A(m)-independent role of FTO in regulating WNT signaling pathways

FTO and ALKBH5 are the two enzymes responsible for mRNA demethylation. Hence, the functional study of FTO has been focused on its mechanistic role in dynamic mRNA modification, and how this post-transcriptional regulation modulates signaling pathways. Here, we report that the functional landscape of FTO is largely associated with WNT signaling pathways but in a manner that is independent of its enzymatic activity. Re-analyses of public datasets identified the bifurcation of canonical and noncanonical WNT pathways as the major role of FTO. In FTO-depleted cells, we find that the canonical WNT/β-Catenin signaling is attenuated in a non-cell autonomous manner via the up-regulation of DKK1. Simultaneously, this up-regulation of DKK1 promotes cell migration via activating the noncanonical WNT/PCP pathway. Unexpectedly, this regulation of DKK1 is independent of its RNA methylation status but operates at the transcriptional level, revealing a noncanonical function of FTO in gene regulation. In conclusion, this study places the functional context of FTO at the branch point of multiple WNT signaling pathways and extends its mechanistic role in gene regulation.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Combination therapy with novel androgen receptor antagonists and statin for castration-resistant prostate cancer

BACKGROUND

One of the growth mechanisms of castration-resistant prostate cancer (CRPC) is de novo androgen synthesis from intracellular cholesterol, and statins may be able to inhibit this mechanism. In addition, statins have been reported to suppress the expression of androgen receptors (ARs) in prostate cancer cell lines. In this study, we investigated a combination therapy of novel AR antagonists and statin, simvastatin, for CRPC.

METHODS

LNCaP, 22Rv1, and PC-3 human prostate cancer cell lines were used. We developed androgen-independent LNCaP cells (LNCaP-LA). Microarray analysis was performed, followed by pathway analysis, and mRNA and protein expression was evaluated by quantitative real-time polymerase chain reaction and Western blot analysis, respectively. Cell viability was determined by MTS assay and cell counts. All evaluations were performed on cells treated with simvastatin and with or without AR antagonists (enzalutamide, apalutamide, and darolutamide).

RESULTS

The combination of darolutamide and simvastatin most significantly suppressed proliferation in LNCaP-LA and 22Rv1 cells. In a 22Rv1-derived mouse xenograft model, the combination of darolutamide and simvastatin enhanced the inhibition of cell proliferation. In LNCaP-LA cells, the combination of darolutamide and simvastatin led to reduction in the mRNA expression of the androgen-stimulated genes, KLK2 and PSA; however, this reduction in expression did not occur in 22Rv1 cells. The microarray data and pathway analyses showed that the number of differentially expressed genes in the darolutamide and simvastatin-treated 22Rv1 cells was the highest in the pathway termed "role of cell cycle." Consequently, we focused our efforts on the cell cycle regulator polo-like kinase 1 (PLK1), cyclin-dependent kinase 2 (CDK2), and cell cycle division 25C (CDC25C). In 22Rv1 cells, the combination of darolutamide and simvastatin suppressed the mRNA and protein expression of these three genes. In addition, in PC-3 cells (which lack AR expression), the combination of simvastatin and darolutamide enhanced the suppression of cell proliferation and expression of these genes.

CONCLUSIONS

Simvastatin alters the expression of many genes involved in the cell cycle in CRPC cells. Thus, the combination of novel AR antagonists (darolutamide) and simvastatin can potentially affect CRPC growth through both androgen-dependent and androgen-independent mechanisms.

Are Alzheimer's and coronary artery diseases genetically related to longevity?

INTRODUCTION

In the last decade researchers have attempted to investigate the shared genetic architecture of longevity and age-related diseases and assess whether the increased longevity in certain people is due to protective alleles in the risk genes for a particular condition or whether there are specific "longevity" genes increasing the lifespan independently of age-related conditions' risk genes. The aim of this study was to investigate the shared genetic component between longevity and two age-related conditions.

METHODS

We performed a cross-trait meta-analysis of publicly available genome-wide data for Alzheimer's disease, coronary artery disease and longevity using a subset-based approach provided by the R package ASSET.

RESULTS

Despite the lack of strong genetic correlation between longevity and the two diseases, we identified 38 genome-wide significant lead SNPs across 22 independent genomic loci. Of them 6 were found to be potentially shared among the three traits mapping to genes including DAB2IP, DNM2, FCHO1, CLPTM1, and SNRPD2. We also identified 19 novel genome-wide associations for the individual traits in this study. Functional annotations and biological pathway enrichment analyses suggested that pleiotropic variants are involved in clathrin-mediated endocytosis and plasma lipoprotein and neurotransmitter clearance processes.

DISCUSSION

In summary, we have been able to advance in the knowledge of the genetic overlap existing among longevity and the two most common age-related disorders.

Survival estimation in patients with stomach and esophageal carcinoma using miRNA expression profiles

Identifying a miRNA signature associated with survival will open a new window for developing miRNA-targeted treatment strategies in stomach and esophageal cancers (STEC). Here, using data from The Cancer Genome Atlas on 516 patients with STEC, we developed a Genetic Algorithm-based Survival Estimation method, GASE, to identify a miRNA signature that could estimate survival in patients with STEC. GASE identified 27 miRNAs as a survival miRNA signature and estimated the survival time with a mean squared correlation coefficient of 0.80 ± 0.01 and a mean absolute error of 0.44 ± 0.25 years between actual and estimated survival times, and showed a good estimation capability on an independent test cohort. The miRNAs of the signature were prioritized and analyzed to explore their roles in STEC. The diagnostic ability of the identified miRNA signature was analyzed, and identified some critical miRNAs in STEC. Further, miRNA-gene target enrichment analysis revealed the involvement of these miRNAs in various pathways, including the somatotrophic axis in mammals that involves the growth hormone and transforming growth factor beta signaling pathways, and gene ontology annotations. The identified miRNA signature provides evidence for survival-related miRNAs and their involvement in STEC, which would aid in developing miRNA-target based therapeutics.

Dietary omega-3 fatty acid intake impacts peripheral blood DNA methylation -anti-inflammatory effects and individual variability in a pilot study

Omega-3 or n-3 polyunsaturated fatty acids (PUFAs) are widely studied for health benefits that may relate to anti-inflammatory activity. However, mechanisms mediating an anti-inflammatory response to n-3 PUFA intake are not fully understood. Of interest is the emerging role of fatty acids to impact DNA methylation (DNAm) and thereby modulate mediating inflammatory processes. In this pilot study, we investigated the impact of n-3 PUFA intake on DNAm in inflammation-related signaling pathways in peripheral blood mononuclear cells (PBMCs) of women at high risk of breast cancer. PBMCs of women at high risk of breast cancer (n=10) were obtained at baseline and after 6 months of n-3 PUFA (5 g/d EPA+DHA dose arm) intake in a previously reported dose finding trial. DNA methylation of PBMCs was assayed by reduced representation bisulfite sequencing (RRBS) to obtain genome-wide methylation profiles at the single nucleotide level. We examined the impact of n-3 PUFA on genome-wide DNAm and focused upon a set of candidate genes associated with inflammation signaling pathways and breast cancer. We identified 24,842 differentially methylated CpGs (DMCs) in gene promoters of 5507 genes showing significant enrichment for hypermethylation in both the candidate gene and genome-wide analyses. Pathway analysis identified significantly hypermethylated signaling networks after n-3 PUFA treatment, such as the Toll-like Receptor inflammatory pathway. The DNAm pattern in individuals and the response to n-3 PUFA intake are heterogeneous. PBMC DNAm profiling suggests a mechanism whereby n-3 PUFAs may impact inflammatory cascades associated with disease processes including carcinogenesis.

In Silico Investigation of the Biological Implications of Complex DNA Damage with Emphasis in Cancer Radiotherapy through a Systems Biology Approach

Different types of DNA lesions forming in close vicinity, create clusters of damaged sites termed as “clustered/complex DNA damage” and they are considered to be a major challenge for DNA repair mechanisms resulting in significant repair delays and induction of genomic instability. Upon detection of DNA damage, the corresponding DNA damage response and repair (DDR/R) mechanisms are activated. The inability of cells to process clustered DNA lesions efficiently has a great impact on the normal function and survival of cells. If complex lesions are left unrepaired or misrepaired, they can lead to mutations and if persistent, they may lead to apoptotic cell death. In this in silico study, and through rigorous data mining, we have identified human genes that are activated upon complex DNA damage induction like in the case of ionizing radiation (IR) and beyond the standard DNA repair pathways, and are also involved in cancer pathways, by employing stringent bioinformatics and systems biology methodologies. Given that IR can cause repair resistant lesions within a short DNA segment (a few nm), thereby augmenting the hazardous and toxic effects of radiation, we also investigated the possible implication of the most biologically important of those genes in comorbid non-neoplastic diseases through network integration, as well as their potential for predicting survival in cancer patients.

Gene Ontology Enrichment Analysis of Renal Agenesis: Improving Prenatal Molecular Diagnosis

Uni- or bilateral renal agenesis (RA) is a commonly occurring major congenital anomaly impacting fetal and neonatal outcomes. Since the etiology is highly heterogeneous, our aim was to provide a logically structured approach by highlighting the genes in which variants have been identified to be associated with RA and to define the pathways involved in this type of abnormal kidney development. We used Phenolyzer to collect a list of all the genes known as causative for RA. Using ClueGO gene enrichment analysis, we classified the relationship between these genes and the biological processes defined by gene ontology. We identified 287 genes and 69 groups of enriched biological processes. About 50% included pathways directly related to the development of urogenital organ tissues. Several ciliary, axis specification, hindgut development, and endocrine pathways were enriched, which may relate to different clinical presentations of RA. Our gene ontology enrichment analysis shows that genes representing distinct biological pathways are significantly enriched. This knowledge will lead to an improved molecular diagnosis in clinical care when applying genome-wide sequencing approaches. The findings will also allow to further study the biological pathways involved in RA and to identify novel candidate genes and pathways.

Association of low-frequency and rare coding variants with information processing speed

Measures of information processing speed vary between individuals and decline with age. Studies of aging twins suggest heritability may be as high as 67%. The Illumina HumanExome Bead Chip genotyping array was used to examine the association of rare coding variants with performance on the Digit-Symbol Substitution Test (DSST) in community-dwelling adults participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. DSST scores were available for 30,576 individuals of European ancestry from nine cohorts and for 5758 individuals of African ancestry from four cohorts who were older than 45 years and free of dementia and clinical stroke. Linear regression models adjusted for age and gender were used for analysis of single genetic variants, and the T5, T1, and T01 burden tests that aggregate the number of rare alleles by gene were also applied. Secondary analyses included further adjustment for education. Meta-analyses to combine cohort-specific results were carried out separately for each ancestry group. Variants in RNF19A reached the threshold for statistical significance (p = 2.01 × 10^-6) using the T01 test in individuals of European descent. RNF19A belongs to the class of E3 ubiquitin ligases that confer substrate specificity when proteins are ubiquitinated and targeted for degradation through the 26S proteasome. Variants in SLC22A7 and OR51A7 were suggestively associated with DSST scores after adjustment for education for African-American participants and in the European cohorts, respectively. Further functional characterization of its substrates will be required to confirm the role of RNF19A in cognitive function.

Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

Universal concept signature analysis: genome-wide quantification of new biological and pathological functions of genes and pathways

Identifying new gene functions and pathways underlying diseases and biological processes are major challenges in genomics research. Particularly, most methods for interpreting the pathways characteristic of an experimental gene list defined by genomic data are limited by their dependence on assessing the overlapping genes or their interactome topology, which cannot account for the variety of functional relations. This is particularly problematic for pathway discovery from single-cell genomics with low gene coverage or interpreting complex pathway changes such as during change of cell states. Here, we exploited the comprehensive sets of molecular concepts that combine ontologies, pathways, interactions and domains to help inform the functional relations. We first developed a universal concept signature (uniConSig) analysis for genome-wide quantification of new gene functions underlying biological or pathological processes based on the signature molecular concepts computed from known functional gene lists. We then further developed a novel concept signature enrichment analysis (CSEA) for deep functional assessment of the pathways enriched in an experimental gene list. This method is grounded on the framework of shared concept signatures between gene sets at multiple functional levels, thus overcoming the limitations of the current methods. Through meta-analysis of transcriptomic data sets of cancer cell line models and single hematopoietic stem cells, we demonstrate the broad applications of CSEA on pathway discovery from gene expression and single-cell transcriptomic data sets for genetic perturbations and change of cell states, which complements the current modalities. The R modules for uniConSig analysis and CSEA are available through https://github.com/wangxlab/uniConSig.

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.

Embryonic Stem Cell Differentiation Is Regulated by SET through Interactions with p53 and β-Catenin

The multifunctional histone chaperone, SET, is essential for embryonic development in the mouse. Previously, we identified SET as a factor that is rapidly downregulated during embryonic stem cell (ESC) differentiation, suggesting a possible role in the maintenance of pluripotency. Here, we explore SET's function in early differentiation. Using immunoprecipitation coupled with protein quantitation by LC-MS/MS, we uncover factors and complexes, including P53 and β-catenin, by which SET regulates lineage specification. Knockdown for P53 in SET-knockout (KO) ESCs partially rescues lineage marker misregulation during differentiation. Paradoxically, SET-KO ESCs show increased expression of several Wnt target genes despite reduced levels of active β-catenin. Further analysis of RNA sequencing datasets hints at a co-regulatory relationship between SET and TCF proteins, terminal effectors of Wnt signaling. Overall, we discover a role for both P53 and β-catenin in SET-regulated early differentiation and raise a hypothesis for SET function at the β-catenin-TCF regulatory axis.

NDEx: Accessing Network Models and Streamlining Network Biology Workflows

NDEx, the Network Data Exchange (https://www.ndexbio.org) is a web-based resource where users can find, store, share and publish network models of any type and size. NDEx is integrated with Cytoscape, the widely used desktop application for network analysis and visualization. NDEx and Cytoscape are the pillars of the Cytoscape Ecosystem, a diverse environment of resources, tools, applications and services for network biology workflows. In this article, we introduce researchers to NDEx and highlight how it can simplify common tasks in network biology workflows as well as streamline publication and access to). Finally, we show how NDEx can be used programmatically via Python with the 'ndex2' client library, and point readers to additional examples for other popular programming languages such as JavaScript and R. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Getting started with NDEx Basic Protocol 2: Using NDEx and Cytoscape in a publication-oriented workflow Basic Protocol 3: Manipulating networks in NDEx via Python.

Intratumoral injection of caerin 1.1 and 1.9 peptides increases the efficacy of vaccinated TC-1 tumor-bearing mice with PD-1 blockade by modulating macrophage heterogeneity and the activation of CD8+ T cells in the tumor microenvironment

OBJECTIVES

Developing a vaccine formula that alters the tumor-infiltrating lymphocytes to be more immune active against a tumor is key to the improvement of clinical responses to immunotherapy. Here, we demonstrate that, in conjunction with E7 antigen-specific immunotherapy, and IL-10 and PD-1 blockade, intratumoral administration of caerin 1.1/1.9 peptides improves TC-1 tumor microenvironment (TME) to be more immune active than injection of a control peptide.

METHODS

We compared the survival time of vaccinated TC-1 tumor-bearing mice with PD-1 and IL-10 blockade, in combination with a further injection of caerin 1.1/1.9 or control peptides. The tumor-infiltrating haematopoietic cells were examined by flow cytometry. Single-cell transcriptomics and proteomics were used to quantify changes in cellular activity across different cell types within the TME.

RESULTS

The injection of caerin 1.1/1.9 increased the efficacy of vaccinated TC-1 tumor-bearing mice with anti-PD-1 treatment and largely expanded the populations of macrophages and NK cells with higher immune activation level, while reducing immunosuppressive macrophages. More activated CD8+ T cells were induced with higher populations of memory and effector-memory CD8+ T subsets. Computational integration of the proteome with the single-cell transcriptome supported activation of Stat1-modulated apoptosis and significant reduction in immune-suppressive B-cell function following caerin 1.1 and 1.9 treatment.

CONCLUSIONS

Caerin 1.1/1.9-containing treatment results in improved antitumor responses. Harnessing the novel candidate genes preferentially enriched in the immune active cell populations may allow further exploration of distinct macrophages, T cells and their functions in TC-1 tumors.