A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits.

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.

The personalized cancer network explorer (PeCaX) as a visual analytics tool to support molecular tumor boards.

BACKGROUND: Personalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process. RESULTS: The Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene-drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker .

Therapeutic targeting of the TPX2/TTK network in colorectal cancer.

BACKGROUND: While the increased screening, changes in lifestyle, and recent advances in treatment regimen have decreased colorectal cancer (CRC) mortality, metastatic disease and recurrence remains a major clinical challenge. In the era of precision medicine, the identification of actionable novel therapeutic targets could ultimately offer an alternative treatment strategy for CRC. METHODS: RNA-Seq was conducted using the illumina platform, while bioinformatics analyses were conducted using CLC genomics workbench and iDEP.951. Colony forming unit, flow cytometry, and fluorescent microscopy were used to assess cell proliferation, cell cycle distribution, and cell death, respectively. The growth potential of CRC cells under 3-dimensional (3D) conditions was assessed using Matrigel. STRING database (v11.5) and Ingenuity Pathway Analysis (IPA) tool were used for network and pathway analyses. CRISPR-Cas9 perturbational effects database was used to identify potential therapeutic targets for CRC, through integration with gene-drug interaction database. Structural modeling and molecular docking were used to assess the interaction between candidate drugs and their targets. RESULTS: In the current study, we investigated the therapeutic potential of targeting TPX2, TTK, DDX39A, and LRP8, commonly upregulated genes in CRC identified through differential expression analysis in CRC and adjacent non-cancerous tissue. Targeted depletion of TPX2 and TTK impaired CRC proliferation, cell cycle progression, and organoid formation under 3D culture conditions, while suppression of DDX39A and LRP8 had modest effects on CRC colony formation. Differential expression analysis and bioinformatics on TPX2 and TTK-deficient cells identified cell cycle regulation as the hallmark associated with loss of TPX2 and TTK. Elevated expression of TPX2 and TTK correlated with an oncogenic state in tumor tissue from patients with colon adenocarcinoma, thus corroborating an oncogenic role for the TPX2/TTK network in the pathogenesis of CRC. Gene set enrichment and pathway analysis of TPX2high/TTKhigh CRC identified numerous additional gene targets as integral components of the TPX2/TTK network. Integration of TPX2/TTK enriched network with CRISPR-Cas9 functional screen data identified numerous novel dependencies for CRC. Additionally, gene-drug interaction analysis identified several druggable gene targets enriched in the TPX2/TTK network, including AURKA, TOP2A, CDK1, BIRC5, and many others. CONCLUSIONS: Our data has implicated an essential role for TPX2 and TTK in CRC pathogenesis and identified numerous potential therapeutic targets and their drug interactions, suggesting their potential clinical use as a novel therapeutic strategy for patients with CRC. Video Abstract.

Implementation of pharmacogenetic testing in medication reviews in a hospital setting.

AIM: To investigate whether it is feasible to perform pharmacogenetic testing and implement the test results as part of medication reviews during hospitalization of multimorbid patients. METHODS: Patients with ≥2 chronic conditions and ≥5 regular drugs with at least one potential gene-drug interaction (GDI) were included from one geriatric and one cardiology ward for pharmacogenetic testing. After inclusion by the study pharmacist, blood samples were collected and shipped to the laboratory for analysis. For patients still hospitalized at the time when the pharmacogenetic test results were available, the information was used in medication reviews. Recommendations from the pharmacist on actionable GDIs were communicated to the hospital physicians, who subsequently decided on potential immediate changes or forwarded suggestions in referrals to general practitioners. RESULTS: The pharmacogenetic test results were available for medication review in 18 of the 46 patients (39.1%), where median length of hospital stay was 4.7 days (1.6-18.3). The pharmacist recommended medication changes for 21 of 49 detected GDIs (42.9%). The hospital physicians accepted 19 (90.5%) of the recommendations. The most commonly detected GDIs involved metoprolol (CYP2D6 genotype), clopidogrel (CYP2C19 genotype) and atorvastatin (CYP3A4/5 and SLCOB1B1 genotype). CONCLUSIONS: The study shows that implementation of pharmacogenetic testing for medication review of hospitalized patients has the potential to improve drug treatment before being transferred to primary care. However, the logistics workflow needs to be further optimized, as test results were available during hospitalization for less than half of the patients included in the study.

Integrated Bioinformatics Analysis of DNA Methylation Biomarkers in Thyroid Cancer Based on TCGA Database.

Previous studies have reported a cluster of aberrant promoter methylation changes associated with silencing of tumor suppressor genes in thyroid cancer (TC), but these results of individual genes are far from enough. In this work, we aimed to investigate the onset and pattern of methylation changes during the progression of TC by informatics analysis. We downloaded the DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas focusing on TC. Abnormally methylated differentially expressed genes (DEGs) were sorted and pathways were analyzed. The KEGG and GO were then used to perform enrichment and functional analysis of identified pathways and genes. Gene-drug interaction network and human protein atlas were applied to obtain feature DNA methylation biomarkers. In total, we identified 2170 methylation-driven DEGs, including 1054 hypermethylatedlow-expression DEGs and 1116 hypomethylated-high-expression DEGs at the screening step. Further analysis screened total of eight feature DNA methylation biomarkers (RXRG, MET, PDGFRA, FCGR3A, VEGFA, CSF1R, FCGR1A and C1QA). Pathway analysis showed that aberrantly methylated DEGs mainly associated with transcriptional misregulation in cancer, MAPK signaling, and intrinsic apoptotic signaling in TC. Taken together, we have identified novel aberrantly methylated genes and pathways linked to TC, which might serve as novel biomarkers for precision diagnosis and disease treatment.

A circular RNAs dataset landscape reveals potential signatures for the detection and prognosis of early-stage lung adenocarcinoma.

BACKGROUND: This study aimed to identify potential circular ribonucleic acid (circRNA) signatures involved in the pathogenesis of early-stage lung adenocarcinoma (LAC). METHODS: The circRNA sequencing dataset of early-stage LAC was downloaded from the Gene Expression Omnibus database. First, the differentially expressed circRNAs (DEcircRNAs) between tumour and non-tumour tissues were screened. Then, the corresponding miRNAs and their target genes were predicted. In addition, prognosis-related genes were identified using survival analysis and further used to build a network of competitive endogenous RNAs (ceRNAs; DEcircRNA-miRNA-mRNA). Finally, the functional analysis and drug-gene interaction analysis of mRNAs in the ceRNA network was performed. RESULTS: A total of 35 DEcircRNAs (30 up-regulated and 5 down-regulated circRNAs) were identified. Moreover, 135 DEcircRNA-miRNA and 674 miRNA-mRNA pairs were predicted. The survival analysis of these target mRNAs revealed that 60 genes were significantly associated with survival outcomes in early-stage LAC. Of these, high levels of PSMA 5 and low levels of NAMPT, CPT 2 and TNFSF11 exhibited favourable prognoses. In addition, the DEcircRNA-miRNA-mRNA network was constructed, containing 5 miRNA-circRNA (hsa_circ_0092283/hsa-miR-762/hsa-miR-4685-5p; hsa_circ_0070610/hsa-let-7a-2-3p/hsa-miR-3622a-3p; hsa_circ_0062682/hsa-miR-4268) and 60 miRNA-mRNA pairs. Functional analysis of the genes in the ceRNA network showed that they were primarily enriched in the Wnt signalling pathway. Moreover, PSMA 5, NAMPT, CPT 2 and TNFSF11 had strong correlations with different drugs. CONCLUSION: Three circRNAs (hsa_circ_0062682, hsa_circ_0092283 and hsa_circ_0070610) might be potential novel targets for the diagnosis of early-stage LAC.

Hypermutation signature reveals a slippage and realignment model of translesion synthesis by Rev3 polymerase in cisplatin-treated yeast.

Gene-gene or gene-drug interactions are typically quantified using fitness as a readout because the data are continuous and easily measured in high throughput. However, to what extent fitness captures the range of other phenotypes that show synergistic effects is usually unknown. Using Saccharomyces cerevisiae and focusing on a matrix of DNA repair mutants and genotoxic drugs, we quantify 76 gene-drug interactions based on both mutation rate and fitness and find that these parameters are not connected. Independent of fitness defects, we identified six cases of synthetic hypermutation, where the combined effect of the drug and mutant on mutation rate was greater than predicted. One example occurred when yeast lacking RAD1 were exposed to cisplatin, and we characterized this interaction using whole-genome sequencing. Our sequencing results indicate mutagenesis by cisplatin in rad1Δ cells appeared to depend almost entirely on interstrand cross-links at GpCpN motifs. Interestingly, our data suggest that the following base on the template strand dictates the addition of the mutated base. This result differs from cisplatin mutation signatures in XPF-deficient Caenorhabditis elegans and supports a model in which translesion synthesis polymerases perform a slippage and realignment extension across from the damaged base. Accordingly, DNA polymerase ζ activity was essential for mutagenesis in cisplatin-treated rad1Δ cells. Together these data reveal the potential to gain new mechanistic insights from nonfitness measures of gene-drug interactions and extend the use of mutation accumulation and whole-genome sequencing analysis to define DNA repair mechanisms.

Identification of gene-drug interactions that impact patient survival in TCGA.

BACKGROUND: With the advent of large scale biological data collection for various diseases, data analysis pipelines and workflows need to be established to build frameworks for integrative analysis. Here the authors present a pipeline for identifying disease specific gene-drug interactions using CNV (Copy Number Variation) and clinical data from the TCGA (The Cancer Genome Atlas) project. Two cancer types were selected for analysis, LGG (Brain lower grade glioma) and GBM (Glioblastoma multiforme), due to the possible progression from LGG to GBM in some cases. The copy number and clinical data were then used to preform survival analysis on a gene by gene basis on sub-populations of patients exposed to a given drug. RESULTS: Several gene-drug interactions are identified, where the copy number of a gene is associated to survival of a patient exposed to a certain drug. Both Irinotecan/HAS2 (Hyaluronan synthase 2) and Bevacizumab/PGAM1 (Phosphoglycerate mutase 1) are interactions found in this study with independent confirmation. Independent work in colon, breast cancer and leukemia (Györffy, Breast Cancer Res Treat 123:725-731, 2010; Mueller, Mol Cancer Ther 11:3024-3032, 2010; Hitosugi, Cancer Cell 13:585-600, 2012) showed these two interactions can lead to increased survival. CONCLUSION: While the pipeline produced several possible interactions where increased survival is linked to normal or increased copy number of a given gene for patients treated with a given drug, no instance of low copy number or full deletion was linked to increased survival. The development of this pipeline shows a promising utility to identify possible beneficial gene-drug interactions that could improve patient survival and may illustrate some of the problems inherent in this kind of analysis on these data.

Systems pharmacology modeling: an approach to improving drug safety.

Advances in systems biology in conjunction with the expansion in knowledge of drug effects and diseases present an unprecedented opportunity to extend traditional pharmacokinetic and pharmacodynamic modeling/analysis to conduct systems pharmacology modeling. Many drugs that cause liver injury and myopathies have been studied extensively. Mitochondrion-centric systems pharmacology modeling is important since drug toxicity across a large number of pharmacological classes converges to mitochondrial injury and death. Approaches to systems pharmacology modeling of drug effects need to consider drug exposure, organelle and cellular phenotypes across all key cell types of human organs, organ-specific clinical biomarkers/phenotypes, gene-drug interaction and immune responses. Systems modeling approaches, that leverage the knowledge base constructed from curating a selected list of drugs across a wide range of pharmacological classes, will provide a critically needed blueprint for making informed decisions to reduce the rate of attrition for drugs in development and increase the number of drugs with an acceptable benefit/risk ratio.

Pharmacokinetic interactions between tacrolimus and Wuzhi capsule in liver transplant recipients: Genetic polymorphisms affect the drug interaction.

Wuzhi capsule (WZC), a commonly used Chinese patent medicine to treat various types of liver dysfunction in China, increases the exposure of tacrolimus (TAC) in liver transplant recipients. However, this interaction has inter-individual variability, and the underlying mechanism remains unclear. Current research indicates that CYP3A4/5 and drug transporters influence the disposal of both drugs. This study aims to evaluate the association between TAC dose-adjusted trough concentration (C/D) and specific genetic polymorphisms of CYP3A4/5, drug transporters and pregnane x receptor (PXR), and plasma levels of major WZC components, deoxyschisandrin and γ-schisandrin, in liver transplant patients receiving both TAC and WZC. Liquid chromatography-tandem-mass spectrometry was used to detect the plasma levels of deoxyschisandrin and γ-schisandrin, and nine polymorphisms related to metabolic enzymes, transporters and PXR were genotyped by sequencing. A linear mixed model was utilized to assess the impact of the interaction between genetic variations and WZC components on TAC lnC/D. Our results indicate a significant association of TAC lnC/D with the plasma levels of deoxyschisandrin and γ-schisandrin. Univariate analysis demonstrated three polymorphisms in the genes ABCB1 (rs2032582), ABCC2 (rs2273697), ABCC2 (rs3740066), and PXR (rs3842689) interact with both deoxyschisandrin and γ-schisandrin, influencing the TAC lnC/D. In multiple regression model analysis, the interactions between deoxyschisandrin and both ABCB1 (rs2032582) and ABCC2 (rs3740066), post-operative day (ß < 0.001, p < 0.001), proton pump inhibitor use (ß = -0.152, p = 0.008), body mass index (ß = 0.057, p < 0.001), and ABCC2 (rs717620, ß = -0.563, p = 0.041), were identified as significant factors of TAC lnC/D, accounting for 47.89% of the inter-individual variation. In summary, this study elucidates the influence of the interaction between ABCB1 and ABCC2 polymorphisms with WZC on TAC lnC/D. These findings offer a scientific basis for their clinical interaction, potentially aiding in the individualized management of TAC therapy in liver transplant patients.

Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation.

INTRODUCTION AND OBJECTIVES: Oral diseases, including gingivitis and periodontitis, are linked to the Wnt signaling pathway, vital for bone metabolism, cementum homeostasis, and mesenchymal stem cell differentiation. Advances in generative AI techniques, such as variational autoencoders (VAEs) and quantum variational classifiers (QVCs), offer promising tools for predicting gene associations between drugs and diseases. This study aims to compare the predictive performance of VAEs and QVCs in modeling drug-disease gene networks within the Wnt signaling pathway in periodontal inflammation. METHODS: Genes associated with Wnt-related periodontal inflammation were identified through comprehensive literature reviews and genomic databases. Their roles in various biological processes were evaluated using gene set enrichment analysis, employing tools like Enrichr, which integrates diverse gene sets from sources such as DSigDB, DisGeNET, and Lincs_l1000.drug. The study then applied VAEs and QVCs to predict gene-disease associations related to the Wnt signaling pathway. RESULTS: The analysis revealed an extensive network comprising 1738 nodes and 1498 edges, averaging 1.992 neighbors per node. The network exhibited a diameter of 2, a radius of 1, and a characteristic path length of 1.992, indicating limited interconnectivity. The VQA model demonstrated a high accuracy rate of 97.5%, although it only detected 50% of anomalies. The VQC model achieved a precision of 78%, with Class 1 samples showing improved recall and a balanced F1 score. CONCLUSION: VQC and VAE models exhibit strong potential for discovering FDA-approved drugs by predicting gene-drug associations in periodontitis based on the Wnt signaling pathway. CLINICAL RELEVANCE: This study highlights the potential of VAEs and QVCs in predicting gene-drug associations for periodontal inflammation. This could lead to more targeted therapies for oral diseases like periodontitis, improving patient outcomes and advancing personalized treatment strategies in clinical practice.

Case report: Avoiding intolerance to antipsychotics through a personalized treatment approach based on pharmacogenetics.

Introduction: The standard approach to treatment in psychiatry is known as "treatment as usual" (TAU), in which the same types of treatment are administered to a group of patients. TAU often requires numerous dose adjustments and medication changes due to ineffectiveness and/or the occurrence of adverse drug reactions (ADRs). This process is not only time-consuming but also costly. Antipsychotic medications are commonly used to treat various psychiatric disorders such as schizophrenia and mood disorders. Some of the inter-individual differences in efficacy and ADRs observed in psychopharmacotherapy can be explained by genetic variability in the pharmacokinetics and pharmacodynamics of antipsychotics. A better understanding of (in)efficacy and possible ADRs can be achieved by pharmacogenetic analysis of genes involved in the metabolism of antipsychotics. Most psychotropic drugs are metabolized by genetically variable CYP2D6, CYP1A2, CYP3A4, and CYP2C19 enzymes. To demonstrate the utility of pharmacogenetic testing for tailoring antipsychotic treatment, in this paper, we present the case of a patient in whom a pharmacogenetic approach remarkably altered an otherwise intolerant or ineffective conventional TAU with antipsychotics. Methods: In this case report, we present a 60-year-old patient with psychotic symptoms who suffered from severe extrapyramidal symptoms and a malignant neuroleptic syndrome during treatment with risperidone, fluphenazine, aripiprazole, brexpiprazole, and olanzapine. Therefore, we performed a pharmacogenetic analysis by genotyping common functional variants in genes involved in the pharmacokinetic pathways of prescribed antipsychotics, namely, CYP2D6, CYP3A4, CYP3A5, CYP1A2, ABCB1, and ABCG2. Treatment recommendations for drug-gene pairs were made according to available evidence-based pharmacogenetic recommendations from the Dutch Pharmacogenetics Working Group (DPWG) or Clinical Pharmacogenetics Implementation Consortium (CPIC). Results: Pharmacogenetic testing revealed a specific metabolic profile and pharmacokinetic phenotype of the patient, which in retrospect provided possible explanations for the observed ADRs. Based on the pharmacogenetic results, the choice of an effective and safe medication proved to be much easier. The psychotic symptoms disappeared after treatment, while the negative symptoms persisted to a lesser extent. Conclusion: With the case presented, we have shown that taking into account the pharmacogenetic characteristics of the patient can explain the response to antipsychotic treatment and associated side effects. In addition, pharmacogenetic testing enabled an informed choice of the most appropriate drug and optimal dose adjustment. This approach makes it possible to avoid or minimize potentially serious dose-related ADRs and treatment ineffectiveness. However, due to the complexity of psychopathology and the polypharmacy used in this field, it is of great importance to conduct further pharmacokinetic and pharmacogenetic studies to better assess gene-drug and gene-gene-drug interactions.

Assessment of key regulatory genes and identification of possible drug targets for Leprosy (Hansen's disease) using network-based approach.

Leprosy is a major health concern and continues to be a source of fear and stigma among people worldwide. Despite remarkable achievements in the treatment, understanding of pathogenesis and transmission, epidemiology of leprosy still remains inadequate. The prolonged incubation period, slow rates of occurrence in those exposed and deceptive clinical presentation pose challenges to develop reliable strategies to stop transmission. Hence, there is a need for improved diagnostics and therapies to prevent mortality caused by leprosy. The objectives of this study are to identify significant genes from protein-protein interactions (PPIs) network of leprosy and to choose the most effective therapeutic targets. Fifty genes related with leprosy were discovered by literature mining. These genes were used to construct a primary network. Leading Eigen Vector method was used to break down the primary network into various sub-networks or communities. It was found that the primary network was divided into many sub-networks at the 6 levels. Seed genes were traced at each level till key regulatory genes were identified. Three seed genes, namely, GNAI3, NOTCH1, and HIF1A, were able to make their way till the final motif stage. These genes along with their interacting partners were considered key regulators of the leprosy network. This study provides leprosy-associated key genes which can lead to improved diagnosis and therapies for leprosy patients.

Identification of Molecular Signatures and Candidate Drugs in Vascular Dementia by Bioinformatics Analyses.

Vascular dementia (VaD) is considered to be the second most common form of dementia after Alzheimer's disease, and no specific drugs have been approved for VaD treatment. We aimed to identify shared transcriptomic signatures between the frontal cortex and temporal cortex in VaD by bioinformatics analyses. Gene ontology and pathway enrichment analyses, protein-protein interaction (PPI) and hub gene identification, hub gene-transcription factor interaction, hub gene-microRNA interaction, and hub gene-drug interaction analyses were performed. We identified 159 overlapping differentially expressed genes (DEGs) between the frontal cortex and temporal cortex that were enriched mainly in inflammation and innate immunity, synapse pruning, regeneration, positive regulation of angiogenesis, response to nutrient levels, and positive regulation of the digestive system process. We identified 10 hub genes in the PPI network (GNG13, CD163, C1QA, TLR2, SST, C1QB, ITGB2, CCR5, CRH, and TAC1), four central regulatory transcription factors (FOXC1, CREB1, GATA2, and HINFP), and four microRNAs (miR-27a-3p, miR-146a-5p, miR-335-5p, and miR-129-2-3p). Hub gene-drug interaction analysis found four drugs (maraviroc, cenicriviroc, PF-04634817, and efalizumab) that could be potential drugs for VaD treatment. Together, our results may contribute to understanding the underlying mechanisms in VaD and provide potential targets and drugs for therapeutic intervention.

Warfarin Pharmacogenomics for Precision Medicine in Real-Life Clinical Practice in Southern Africa: Harnessing 73 Variants in 29 Pharmacogenes.

Pharmacogenomics is universally relevant for worldwide modern therapeutics and yet needs further development in resource-limited countries. While there is an abundance of genetic association studies in controlled medical settings, there is a paucity of studies with a naturalistic design in real-life clinical practice in patients with comorbidities and under multiple drug treatment regimens. African patients are often burdened with communicable and noncommunicable comorbidities, yet the application of pharmacogenomics in African clinical settings remains limited. Using warfarin as a model, this study aims at minimizing gaps in precision/personalized medicine research in African clinical practice. We present, therefore, pharmacogenomic profiles of a cohort of 503 black Africans (n = 252) and Mixed Ancestry (n = 251) patients from Southern Africa, on warfarin and co-prescribed drugs in a naturalized noncontrolled environment. Seventy-three (n = 73) single nucleotide polymorphisms (SNPs) in 29 pharmacogenes were characterized using a combination of allelic discrimination, Sanger sequencing, restriction fragment length polymorphism, and Sequenom Mass Array. The common comorbidities were hypertension (43-46%), heart failure (39-45%), diabetes mellitus (18%), arrhythmia (25%), and HIV infection (15%). Accordingly, the most common co-prescribed drugs were antihypertensives, antiarrhythmic drugs, antidiabetics, and antiretroviral therapy. We observed marked variation in major pharmacogenes both at interethnic levels and within African subpopulations. The Mixed Ancestry group presented a profile of genetic variants reflecting their European, Asian, and African admixture. Precision medicine requires that African populations begin to capture their own pharmacogenetic SNPs as they cannot always infer with absolute certainty from Asian and European populations. In the current historical moment of the COVID-19 pandemic, we also underscore that the spectrum of drugs interacting with warfarin will likely increase, given the systemic and cardiovascular effects of COVID-19, and the anticipated influx of COVID-19 medicines in the near future. This observational clinical pharmacogenomics study of warfarin, together with past precision medicine research, collectively, lends strong support for incorporation of pharmacogenetic profiling in clinical settings in African patients for effective and safe administration of therapeutics.

Identification of potential drug targets for vascular dementia and carotid plaques by analyzing underlying molecular signatures shared by them.

Background: Vascular dementia (VaD) and carotid atherosclerotic plaques are common in the elderly population, conferring a heavy burden on families and society. Accumulating evidence indicates carotid atherosclerotic plaques to be a risk factor for VaD. However, the underlying mechanisms for this association are mainly unknown. Materials and methods: We analyzed temporal cortex gene expression data of the GSE122063 dataset and gene expression data of the GSE163154 dataset to identify commonly differentially expressed genes (DEGs). Then we performed functional enrichment analysis, immune cell infiltration and evaluation, correlation analysis between differentially expressed immune-related genes (DEIRGs) and immune cells, receiver operating characteristic (ROC) analysis, and drug-gene analysis. Results: We identified 41 overlapped DEGs between the VaD and carotid atherosclerosis plaque datasets. Functional enrichment analyses revealed that these overlapped DEGs were mainly enriched in inflammatory and immune-related processes. Immunocyte infiltration and evaluation results showed that M0 macrophages, M2 macrophages, and T cells gamma delta had a dominant abundance in carotid atherosclerosis plaque samples, and M0 macrophages showed a significantly different infiltration percentage between the early and advanced stage plaques group. Resting CD4 memory T cells, M2 macrophages, and naive B cells were the top three highest infiltrating fractions in VaD. Furthermore, B cells and NK cells showed a different infiltration percentage between VaD and matched controls. We identified 12 DEIRGs, and the result of correlation analysis revealed that these DEIRGs were closely related to differentially expressed immune cells. We identified five key DEIRGs based on ROC analysis. The drug-gene interaction analysis showed that four drugs (avacopan, CCX354, BMS-817399, and ASK-8007) could be potential drugs for VaD and carotid atherosclerotic plaques treatment. Conclusion: Collectively, these findings indicated that inflammatory and immune-related processes be a crucial common pathophysiological mechanism shared by VaD and carotid plaques. This study might provide new insights into common molecular mechanisms between VaD and carotid plaques and potential targets for the treatment.

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track.

BACKGROUND: Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. BASIC PROCEDURES: We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. MAIN FINDINGS: We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions. PRINCIPAL CONCLUSIONS: We show the benchmarking results of novel blockchain-based methods for institutions to share patient outcomes related to gene-drug interactions. Our findings support blockchain utilization in healthcare, genomic and biomedical applications. The source code is publicly available at https://github.com/tsungtingkuo/genedrug.

Weighted gene co-expression network analysis and drug-gene interaction bioinformatics uncover key genes associated with various presentations of malaria infection in African children and major drug candidates.

Malaria is a fatal parasitic disease with unelucidated pathogenetic mechanism. Herein, we aimed to uncover genes associated with different clinical aspects of malaria based on the GSE1124 dataset that is publicly accessible by using WGCNA. We obtained 16 co-expression modules and their correlations with clinical features. Using the MCODE tool, we identified THEM4, STYX, VPS36, LCOR, KIAA1143, EEA1, RAPGEF6, LOC439994, ZBTB33, PTPN22, ESCO1, and KLF3 as hub genes positively associated with Plasmodium falciparum infection (ASPF). These hub genes were involved in the biological processes of endosomal transport, regulation of natural killer cell proliferation, and KEGG pathways of endocytosis and fatty acid elongation. For the purple module negatively correlated with ASPF, we identified 19 hub genes that were involved in the biological processes of positive regulation of cellular protein catabolic process and KEGG pathways of other glycan degradation. For the salmon module positively correlated with severe malaria anemia (SMA), we identified 17 hub genes that were among those driving the biological processes of positive regulation of erythrocyte differentiation. For the brown module positively correlated with cerebral malaria (CM), we identified eight hub genes and these genes participated in phagolysosome assembly and positive regulation of exosomal secretion, and animal mitophagy pathway. For the tan module negatively correlated with CM, we identified four hub genes that were involved in CD8-positive, alpha-beta T cell differentiation and notching signaling pathway. These findings may provide new insights into the pathogenesis of malaria and help define new diagnostic and therapeutic approaches for malaria patients.

Common Nodes of Virus-Host Interaction Revealed Through an Integrated Network Analysis.

Viruses are one of the major causes of acute and chronic infectious diseases and thus a major contributor to the global burden of disease. Several studies have shown how viruses have evolved to hijack basic cellular pathways and evade innate immune response by modulating key host factors and signaling pathways. A collective view of these multiple studies could advance our understanding of virus-host interactions and provide new therapeutic perspectives for the treatment of viral diseases. Here, we performed an integrative meta-analysis to elucidate the 17 different host-virus interactomes. Network and bioinformatics analyses showed how viruses with small genomes efficiently achieve the maximal effect by targeting multifunctional and highly connected host proteins with a high occurrence of disordered regions. We also identified the core cellular process subnetworks that are targeted by all the viruses. Integration with functional RNA interference (RNAi) datasets showed that a large proportion of the targets are required for viral replication. Furthermore, we performed an interactome-informed drug re-purposing screen and identified novel activities for broad-spectrum antiviral agents against hepatitis C virus and human metapneumovirus. Altogether, these orthogonal datasets could serve as a platform for hypothesis generation and follow-up studies to broaden our understanding of the viral evasion landscape.

Scalable biclustering - the future of big data exploration?

Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners. We also try to explain why biclustering may soon become one of the standards for big data analytics.