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NPJ Genomic Medicine May 2024Kawasaki disease (KD) is a multisystem inflammatory illness of infants and young children that can result in acute vasculitis. The mechanism of coronary artery aneurysms...
Kawasaki disease (KD) is a multisystem inflammatory illness of infants and young children that can result in acute vasculitis. The mechanism of coronary artery aneurysms (CAA) in KD despite intravenous gamma globulin (IVIG) treatment is not known. We performed a Whole Genome Sequencing (WGS) association analysis in a racially diverse cohort of KD patients treated with IVIG, both using AHA guidelines. We defined coronary aneurysm (CAA) (N = 234) as coronary z ≥ 2.5 and large coronary aneurysm (CAA/L) (N = 92) as z ≥ 5.0. We conducted logistic regression models to examine the association of genetic variants with CAA/L during acute KD and with persistence >6 weeks using an additive model between cases and 238 controls with no CAA. We adjusted for age, gender and three principal components of genetic ancestry. The top significant variants associated with CAA/L were in the intergenic regions (rs62154092 p < 6.32E-08 most significant). Variants in SMAT4, LOC100127, PTPRD, TCAF2 and KLRC2 were the most significant non-intergenic SNPs. Functional mapping and annotation (FUMA) analysis identified 12 genomic risk loci with eQTL or chromatin interactions mapped to 48 genes. Of these NDUFA5 has been implicated in KD CAA and MICU and ZMAT4 has potential functional implications. Genetic risk score using these 12 genomic risk loci yielded an area under the receiver operating characteristic curve (AUC) of 0.86. This pharmacogenomics study provides insights into the pathogenesis of CAA/L in IVIG-treated KD and shows that genomics can help define the cause of CAA/L to guide management and improve risk stratification of KD patients.
PubMed: 38816462
DOI: 10.1038/s41525-024-00419-7 -
NPJ Systems Biology and Applications May 2024Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding...
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.
Topics: Chromatin; Humans; Machine Learning; Genome-Wide Association Study; Enhancer Elements, Genetic; Computational Biology; Transcriptome; Transcription Factors; Gene Expression Profiling; Pharmacogenetics
PubMed: 38816426
DOI: 10.1038/s41540-024-00388-8 -
PloS One 2024A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to...
A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of protein-protein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/.
Topics: Humans; Mutation, Missense; Genome, Human; Models, Molecular; Software; Internet; Proteins
PubMed: 38814966
DOI: 10.1371/journal.pone.0303604 -
Frontiers in Pharmacology 2024Chronic pain is a major socioeconomic burden in the Mediterranean region. However, we noticed an under-representation of these populations in the pharmacogenetics of...
BACKGROUND
Chronic pain is a major socioeconomic burden in the Mediterranean region. However, we noticed an under-representation of these populations in the pharmacogenetics of pain management studies. In this context, we aimed 1) to decipher the pharmacogenetic variant landscape among Mediterranean populations compared to worldwide populations in order to identify therapeutic biomarkers for personalized pain management and 2) to better understand the biological process of pain management through investigation of pharmacogenes pathways.
MATERIALS AND METHODS
We collected genes and variants implicated in pain response using the Prisma guidelines from literature and PharmGK database. Next, we extracted these genes from genotyping data of 829 individuals. Then, we determined the variant distribution among the studied populations using multivariate (MDS) and admixture analysis with R and STRUCTURE software. We conducted a Chi2 test to compare the interethnic frequencies of the identified variants. We used SNPinfo web server, miRdSNP database to identify miRNA-binding sites. In addition, we investigated the functions of the identified genes and variants using pathway enrichment analysis and annotation tools. Finally, we performed docking analysis to assess the impact of variations on drug interactions.
RESULTS
We identified 63 variants implicated in pain management. MDS analysis revealed that Mediterranean populations are genetically similar to Mexican populations and divergent from other populations. STRUCTURE analysis showed that Mediterranean populations are mainly composed of European ancestry. We highlighted differences in the minor allele frequencies of three variants (rs633, rs4680, and rs165728) located in the gene. Moreover, variant annotation revealed ten variants with potential miRNA-binding sites. Finally, protein structure and docking analysis revealed that two missense variants (rs4680 and rs6267) induced a decrease in COMT protein activity and affinity for dopamine.
CONCLUSION
Our findings revealed that Mediterranean populations diverge from other ethnic groups. Furthermore, we emphasize the importance of pain-related pathways and miRNAs to better implement these markers as predictors of analgesic responses in the Mediterranean region.
PubMed: 38813106
DOI: 10.3389/fphar.2024.1380613 -
Frontiers in Pharmacology 2024
PubMed: 38813104
DOI: 10.3389/fphar.2024.1373056 -
Nature Communications May 2024G protein-coupled receptors (GPCRs) are sophisticated signaling machines able to simultaneously elicit multiple intracellular signaling pathways upon activation....
G protein-coupled receptors (GPCRs) are sophisticated signaling machines able to simultaneously elicit multiple intracellular signaling pathways upon activation. Complete (in)activation of all pathways can be counterproductive for specific therapeutic applications. This is the case for the serotonin 2 A receptor (5-HTR), a prominent target for the treatment of schizophrenia. In this study, we elucidate the complex 5-HTR coupling signature in response to different signaling probes, and its physiological consequences by combining computational modeling, in vitro and in vivo experiments with human postmortem brain studies. We show how chemical modification of the endogenous agonist serotonin dramatically impacts the G protein coupling profile of the 5-HTR and the associated behavioral responses. Importantly, among these responses, we demonstrate that memory deficits are regulated by G protein activation, whereas psychosis-related behavior is modulated through G stimulation. These findings emphasize the complexity of GPCR pharmacology and physiology and open the path to designing improved therapeutics for the treatment of stchizophrenia.
Topics: Animals; Female; Humans; Male; Mice; Brain; GTP-Binding Protein alpha Subunits, Gq-G11; HEK293 Cells; Memory Disorders; Psychotic Disorders; Receptor, Serotonin, 5-HT2A; Schizophrenia; Serotonin; Signal Transduction
PubMed: 38811567
DOI: 10.1038/s41467-024-48196-2 -
Briefings in Bioinformatics May 2024The development of deep learning models plays a crucial role in advancing precision medicine. These models enable personalized medical treatments and interventions based...
The development of deep learning models plays a crucial role in advancing precision medicine. These models enable personalized medical treatments and interventions based on the unique genetic, environmental and lifestyle factors of individual patients, and the promotion of precision medicine is achieved mainly through genomic data analysis, variant annotation and interpretation, pharmacogenomics research, biomarker discovery, disease typing, clinical decision support and disease mechanism interpretation. Extensive research has been conducted to address precision medicine challenges using attention mechanism models such as SAN, GAT and transformers. Especially, the recent popularity of ChatGPT has significantly propelled the application of this model type to a new height. Therefore, I propose a Special Issue for Briefings in Bioinformatics about the topic 'Attention Mechanism Models for Precision Medicine'. This Special Issue aims to provide a comprehensive overview and presentation of innovative researches on the application of graph attention mechanism models in precision medicine.
Topics: Precision Medicine; Humans; Deep Learning; Computational Biology; Genomics
PubMed: 38811359
DOI: 10.1093/bib/bbae156 -
ELife May 2024Alterations in the function of K channels such as the voltage- and Ca-activated K channel of large conductance (BK) reportedly promote breast cancer (BC) development and...
Alterations in the function of K channels such as the voltage- and Ca-activated K channel of large conductance (BK) reportedly promote breast cancer (BC) development and progression. Underlying molecular mechanisms remain, however, elusive. Here, we provide electrophysiological evidence for a BK splice variant localized to the inner mitochondrial membrane of murine and human BC cells (mitoBK). Through a combination of genetic knockdown and knockout along with a cell permeable BK channel blocker, we show that mitoBK modulates overall cellular and mitochondrial energy production, and mediates the metabolic rewiring referred to as the 'Warburg effect', thereby promoting BC cell proliferation in the presence and absence of oxygen. Additionally, we detect mitoBK and BK transcripts in low or high abundance, respectively, in clinical BC specimens. Together, our results emphasize, that targeting mitoBK could represent a treatment strategy for selected BC patients in future.
Topics: Humans; Animals; Mice; Breast Neoplasms; Cell Line, Tumor; Cell Proliferation; Mitochondria; Large-Conductance Calcium-Activated Potassium Channel alpha Subunits; Mitochondrial Membranes; Female; Energy Metabolism
PubMed: 38808578
DOI: 10.7554/eLife.92511 -
The Pharmacogenomics Journal May 2024Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of...
Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.
Topics: Humans; Pharmacogenetics; Pharmacogenomic Variants; Precision Medicine; Algorithms; Computational Biology
PubMed: 38802404
DOI: 10.1038/s41397-024-00338-x -
Cellular and Molecular Neurobiology May 2024Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management...
Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP ( https://www.ncbi.nlm.nih.gov/snp/ ), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein-protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene-miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein-Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, L-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein-Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.
Topics: Humans; Pain, Postoperative; Precision Medicine; Analgesics, Opioid; Pharmacogenetics; Pain Management; Computer Simulation; Spine
PubMed: 38801645
DOI: 10.1007/s10571-024-01466-5