Drug-gene Interactions Research Articles

Potential alternative drug treatment for bone giant cell tumor.

Background: Bone giant cell tumor (BGCT) is one of the world's major disease types of locally aggressive bone tumors. In recent years, denosumab treatment has been introduced before curettage surgery. However, the current therapeutic was practical only sometimes, given the local recurrence effects after discontinuation of denosumab. Due to the complex nature of BGCT, this study aims to use bioinformatics to identify potential genes and drugs associated with BGCT. Methods: The genes that integrate BGCT and fracture healing were determined by text mining. The gene was obtained from the pubmed2ensembl website. We filtered out common genes for the function, and signal pathway enrichment analyses were implemented. The protein-protein interaction (PPI) networks and the hub genes were screened by MCODE built-in Cytoscape software. Lastly, the confirmed genes were queried in the Drug Gene Interaction Database to determine potential genes and drugs. Results: Our study finally identified 123 common specific genes in bone giant cell tumors and fracture healing text mining concepts. The GO enrichment analysis finally analyzed 115 characteristic genes in BP, CC, and MF. We selected 10 KEGG pathways and identified 68 characteristic genes. We performed protein-protein interaction analysis (PPI) on 68 selected genes and finally identified seven central genes. In this study, these seven genes were substituted into drug-gene interactions, and there were 15 antineoplastic drugs, 1 anti-involving drug, and 1 anti-influenza drug. Conclusion: The 7 genes (including ANGPT2, COL1A1, COL1A2, CTSK, FGFR1, NTRK2, and PDGFB) and 17 drugs, which have not been used in BGCT, but 6 of them approved by the FDA for other diseases, could be potential genes and drugs, respectively, to improve BGCT treatment. In addition, the correlation study and analysis of potential drugs through genes provide great opportunities to promote the repositioning of drugs and the study of pharmacology in the pharmaceutical industry.

Congruence rates for pharmacogenomic noninterruptive alerts.

Meaningful clinical decision support (CDS) recommendations are vital for implementation of pharmacogenomics (PGx) into routine clinical care. PGx CDS alerts include interruptive and noninterruptive alerts. The objective of this study was to evaluate provider ordering behavior after noninterruptive alerts are displayed. A retrospective manual chart review was conducted from the time of noninterruptive alert implementation to the time of data analysis to determine congruence with CDS recommendations. The congruence rate for noninterruptive alerts was 89.8% across all drug-gene interactions. The drug-gene interaction with the most alerts for analysis included metoclopramide (n = 138). The high rate of medication order congruence after noninterruptive alerts were deployed suggests this modality may be appropriate for PGx CDS as a method for best practice adherence.

Analysis of immunogenic cell death in atherosclerosis based on scRNA-seq and bulk RNA-seq data.

Regulated cell death plays a very important role in atherosclerosis (AS). Despite a large number of studies, there is a lack of literature on immunogenic cell death (ICD) in AS. Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define involved cells and determine their transcriptomic characteristics. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, CIBERSORT, ESTIMATE and ssGSEA (Gene Set Enrichment Analysis), consensus clustering analysis, random forest (RF), Decision Curve Analysis (DCA), and the Drug-Gene Interaction and DrugBank databases were applied for bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO). mDCs and CTLs correlated obviously with AS occurrence and development (k2(mDCs)=48.333, P<0.001; k2(CTL)=130.56, P<0.001). In total, 21 differentially expressed genes were obtained for the bulk transcriptome; KEGG enrichment analysis results were similar to those for differentially expressed genes in endothelial cells. Eleven genes with a gene importance score>1.5 were obtained in the training set and validated in the test set, resulting in 8 differentially expressed genes for ICD. A model to predict occurrence of AS and 56 drugs that may be used to treat AS were obtained with these 8 genes. Immunogenic cell death occurs mainly in endothelial cells in AS. ICD maintains chronic inflammation in AS and plays a crucial role in its occurrence and development. ICD related genes may become drug-targeted genes for AS treatment.

INVESTIGATING NEW THERAPEUTIC OPPORTUNITIES FOR HYPERTENSION THROUGH TRANSCRIPTOMIC AND PROTEOMIC ANALYSIS OF THE KIDNEY RENIN-ANGIOTENSIN SYSTEM

Objective: We systematically characterised the expression of all renin-angiotensin system (RAS) genes in the human kidney and searched in silico for new pharmacological therapeutic opportunities to modify RAS. Design and method: We compiled data from open access resources to characterise all components of the RAS pathway. We used transcriptomic profiles of 73 human kidney tissues from the Genotype-Tissue Expression Project, integrated genome-wide DNA genotypes and RNA-sequencing-derived transcriptome profiles of 478 human kidneys from Human Kidney Tissue Resource and, kidney tissue proteomics from 72 samples recruited by Clinical Proteomic Tumour Analysis Consortium. Additionally, we used an independent single-cell RNA sequencing data and gene expression datasets curated from the Kidney Precision Medicine Project (KPMP) and Enrichr (web-based enrichment analysis tool). Lastly, we integrated the Connectivity Map and Drug Gene Interaction Database to identify drug repurposing opportunities for treatment of hypertension targeting the RAS genes. Results: We identified 37 genes encoding components of the RAS pathway with measurable expression in human kidney tissue. 15% of these gene-pairs demonstrated a positive (r&gt;0.5) correlation in kidney expression most consistent with a functionally integrated network of interacting molecules. Angiotensinogen (AGT), the key component of the pathway - showed the highest level of mRNA expression in the liver, and we demonstrated that its hepatic mRNA expression correlates most strongly with its mRNA expression in the kidney (r = 0.71, P = 2.60e-06). Expression levels of mRNA for 92% of RAS genes correlated positively with their protein abundance in the kidney, where renin had the strongest positive correlation (r = 0.64, P&lt;0.01). Enrichr and KPMP revealed that RAS gene expression was enriched in mesangial and epithelial cell types. Existing genetic evidence for correlation between glutamyl aminopeptidase (ENPEP) and systolic blood pressure might relate to its capacity to generate angiotensin III acting on dilator angiotensin II type 2 receptors. This raises potential repurposing opportunities for ENPEP inhibitors like firibastat and tosedostat for the treatment of hypertension. Conclusions: Collectively, this study confirmed synchronized expression of RAS genes in the kidney at both mRNA and protein levels and identified new therapeutic opportunities for treatment of hypertension, through drug repurposing.

Identification of genes encoding targets associated with adverse events in multiple myeloma.

1556 Background: Despite novel therapeutic options, understanding the toxicity profile of anti multiple myeloma (MM) drug regimens (AMDR) is vital. Data suggests adverse event (AE) profile is orchestrated by certain associated genes. The purpose of this study is to explore the interplay of genes, regimens, AEs and provide genetic evidence for a linkage between regimens and the presence of AEs. Methods: We examined a group of AMDRs retrieved from the FDA Adverse Event Reporting System. Drug-associated genes were obtained from the Drug Gene Interaction Database, DGIdb. Then, genes were imported to the Online Mendelian Inheritance in Man (OMIM) to search for the existing clinical synopses (features) in the peer reviewed biomedical material, or phenotypes when no data was available. Clinical synopses were then linked to MedDRA. Gene gene interaction was also determined through the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We then constructed a detailed large MM pharmacogenetic network to illustrate the linkage among drugs, genes and MM via various expert integrated resources. AMDR responses were then assessed at the DNA and protein levels by modularizing genetic causation into different scenarios i) a direct drug effect on gene expression; ii) the secondary or tertiary effect of drug(s) on subsequent drug metabolism or disposition altering gene expression pathways; iii) modulation of another biological network that disturbs drug effect on gene expression resulting in exacerbating AE occurrence. Results: We found multiple genes including CTLA4, NFKB1, PSMB8, CCND1 and HLAA were complicit/associated with different AEs including cardiac or hepatobiliary disorders or other serious events. Our results suggested the expression of CTLA4 was influenced by multiple AMDRs from combinations of bortezomib (V), lenalidomide (R), melphalan (M), or dexamethasone (d). When AMDRs involve these agents, the occurrence of pericardial disorders due to the treatment exhibited high confidence. We also found carfilzomib (K) combinations (Kd or KRd) were associated with PSMB8 which was likely to be the genetic factor concomitant with heart failures, cardiac arrhythmias, or hepatobiliary toxicities. Furthermore, our findings suggested NFKB1, thalidomide associated gene interacting with PSMB8, may present the same toxicity events in hepatobiliary disorders. Conclusions: Understanding the genomic basis of specific drug-AE combinations could reveal molecular mechanisms underlying drug-gene-AE associations and recognize the causality of AEs. When considering alternative agents in MM, our study offers insights for predicting treatment responses such as PSMB8 which could potentially cause an off target effect of K on cardiomyocyte 20S beyond MM cells. Our plan is to integrate this novel computational biology approach with translational medicine to further investigate our observation.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy.

Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN. Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated. A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine-cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb). This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN.

Integration single-cell and bulk RNA-sequencing data to reveal senescence gene expression profiles in heart failure

BackgroundHeart failure (HF) represents one of healthcare's biggest challenges. Although rarely noticed, aging is a crucial risk factor for cardiovascular disease. Our study aims to reveal aging's role in HF by integrating single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing databases. MethodsWe collected HF heart sample data from the Gene Expression Omnibus database and senescence gene data from CellAge. The FindCluster () package was used for cell cluster analysis. Differentially expressed genes (DEG) were identified operating the FindMarkers function. Cell activity score calculation was performed using the AUCell package. UpSetR plotted the intersection between DEGs of active cell types, bulk data DEGs, and genes associated with aging. Using the DGIdb database gene-drug interaction data, we search for potential targeted therapeutics based on common senescence genes. ResultsThe scRNA-seq data revealed myocardial heterogeneity in HF tissues. A series of crucial common senescence genes were found. The senescence gene expression profile hints at an intriguing connection between monocytes and HF. After analyzing the DEGs in the bulk dataset, the DEGs in scRNA-seq, the DEGs in each active cell type, and senescence genes, we identified ten genes as common senescence genes present in HF. Correlation analysis of transcriptomics, proteomics, and ceRNA was performed to provide ideas for future studies individually. Moreover, we discovered that common senescence genes and potential therapeutic drugs interact among different cell types. Further research is needed on the expression pattern of senescence genes and molecular regulation in HF. ConclusionsIn summary, we identified the functional significance of the senescence gene in HF using integrated data. It is possible that this more profound understanding of how senescence contributes to the development of HF will aid in unraveling the mechanisms that promote the disease and provide hints for developing therapeutics.

Identifying and analyzing the key genes shared by papillary thyroid carcinoma and Hashimoto's thyroiditis using bioinformatics methods.

Hashimoto's thyroiditis (HT) is a chronic autoimmune disease that poses a risk factor for papillary thyroid carcinoma (PTC). The present study aimed to identify the key genes shared by HT and PTC for advancing the current understanding of their shared pathogenesis and molecular mechanisms. HT- and PTC-related datasets (GSE138198 and GSE33630, respectively) were retrieved from the Gene Expression Omnibus (GEO) database. Genes significantly related to the PTC phenotype were identified using weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) were identified between PTC and healthy samples from GSE33630, and between HT and normal samples from GSE138198. Subsequently, functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Transcription factors and miRNAs regulating the common genes in PTC and HT were forecasted using the Harmonizome and miRWalk databases, respectively, and drugs targeting these genes were investigated using the Drug-Gene Interaction Database (DGIdb). The key genes in both GSE138198 and GSE33630 were further identified via Receiver Operating Characteristic (ROC) analysis. The expression of key genes was verified in external validation set and clinical samples using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). In total, 690 and 1945 DEGs were associated with PTC and HT, respectively; of these, 56 were shared and exhibited excellent predictive accuracy in the GSE138198 and GSE33630 cohorts. Notably, four genes, Alcohol Dehydrogenase 1B (ADH1B), Active BCR-related (ABR), alpha-1 antitrypsin (SERPINA1), and lysophosphatidic acid receptor 5 (LPAR5) were recognized as key genes shared by HT and PTC. Subsequently, EGR1 was identified as a common transcription factor regulating ABR, SERPINA1, and LPAR5 expression. These findings were confirmed using qRT-PCR and immunohistochemical analysis. Four (ADH1B, ABR, SERPINA1, and LPAR5) out of 56 common genes exhibited diagnostic potential in HT and PTC. Notably, this study, for the first time, defined the close relationship between ABR and HT/PTC progression. Overall, this study provides a basis for understanding the shared pathogenesis and underlying molecular mechanisms of HT and PTC, which might help improve patient diagnosis and prognosis.

Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH.

Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes. One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals. Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAFAfr]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9×10-9); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAFAfr=0.0536), was associated with a reduced response to metformin (p=2.4×10-8), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increase in HbA1c of 0.08% and non-carriers had an HbA1c increase of 0.01% after 1 year of treatment (p=3.3×10-3). We also identified associations between type 2 diabetes-associated variants and glycaemic response, including the type 2 diabetes-protective C allele of rs703972 near ZMIZ1 and increased levels of active glucagon-like peptide 1 (GLP-1) (p=1.6×10-5), supporting the role of alterations in incretin levels in type 2 diabetes pathophysiology. We present a well-phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common glucose-lowering medications and provide insight into mechanisms of action of type 2 diabetes-related variation. The complete summary statistics from this study are available at the Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org ) and the GWAS Catalog ( www.ebi.ac.uk/gwas/ , accession IDs: GCST90269867 to GCST90269899).

Veterans Health Administration: Implementation of pharmacogenomic clinical decision support with statin medications and the SLCO1B1 gene as an exemplar.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To describe the implementation of clinical decision support tools for alerting prescribers of actionable drug-gene interactions in the Veterans Health Administration (VHA). Drug-gene interactions have been the focus of clinicians for years. Interactions between SCLO1B1 genotype and statin medications are of particular interest as these can inform risk for statin-associated muscle symptoms (SAMS). VHA identified approximately 500,000 new users of statin medications prescribed in VHA in fiscal year 2021, some of whom could benefit from pharmacogenomic testing for the SCLO1B1 gene. In 2019, VHA implemented the Pharmacogenomic Testing for Veterans (PHASER) program to offer panel-based, preemptive pharmacogenomic testing and interpretation. The PHASER panel includes SLCO1B1, and VHA utilized Clinical Pharmacogenomics Implementation Consortium statin guidelines to build its clinical decision support tools. The program's overarching goal is to reduce the risk of adverse drug reactions such as SAMS and improve medication efficacy by alerting practitioners of actionable drug-gene interactions. We describe the development and implementation of decision support for the SLCO1B1 gene as an example of the approach being used for the nearly 40 drug-gene interactions screened for by the panel. The VHA PHASER program identifies and addresses drug-gene interactions as an application of precision medicine to reduce veterans' risks for adverse events. The PHASER program's implementation of statin pharmacogenomics utilizes a patient's SCLO1B1 phenotype to alert providers of the risk for SAMS with the statin being prescribed and how to lower that risk through a lower dose or alternative statin selection. The PHASER program may help reduce the number of veterans who experience SAMS and may improve their adherence to statin medications.

Identification and comprehensive analysis of ferroptosis-related genes as potential biomarkers for the diagnosis and treatment of proliferative diabetic retinopathy by bioinformatics methods

Diabetic retinopathy (DR) is the most common retinal vascular disease. Proliferative DR (PDR) is the aggressive stage of DR with angiogenesis as a pathological hallmark, which is the main cause of blindness. There is growing evidence that ferroptosis plays a vital role in diabetics as well as its complications including DR. However, the potential functions and mechanisms of ferroptosis have not been completely elucidated in PDR. The ferroptosis-related differentially expressed genes (FRDEGs) were identified in GSE60436 and GSE94019. Then we constructed a protein-protein interaction (PPI) network and screened ferroptosis-related hub genes (FRHGs). The GO functional annotation and the KEGG pathway enrichment analyses of FRHGs were performed. The miRNet and miRTarbase databases were applied to construct the ferroptosis-related mRNA-miRNA-lncRNA network, and the Drug-Gene Interaction Database (DGIdb) was used for predicting potential therapeutic drugs. Finally, we identified 21 upregulated and 9 downregulated FRDEGs, among which 10 key target genes (P53, TXN, PTEN, SLC2A1, HMOX1, PRKAA1, ATG7, HIF1A, TGFBR1, and IL1B) were recognized with enriched functions, mainly relating to responses to oxidative stress and hypoxia in biological processes of PDR. HIF-1, FoxO and MAPK signalling may be the main pathways that influence ferroptosis in PDR. Moreover, a mRNA-miRNA-lncRNA network was constructed based on the 10 FRHGs and their co-expressed miRNAs. Finally, potential drugs targeting 10 FRHGs for PDR were predicted. Results of the receiver operator characteristic (ROC) curve indicated, with high predictive accuracy in two testing datasets (AUC>0.8), that ATG7, TGFB1, TP53, HMOX1 and ILB1 had the potential to be biomarkers of PDR.

CXCL8 as a Potential Biomarker for Crohn's Disease is Associated with Immune Infiltration and RNA Methylation.

TRP channels have an important role in regulating the function of gastrointestinal epithelial cells. The aim of this study was to investigate the molecular mechanisms of genes associated with TRP channels in Crohn's disease (CD) by bioinformatics approach and to identify potential key biomarkers. In our study, we identified TRP channel-related differentially expressed genes (DEGs) based on the GSE95095 dataset and the TRP channel-related gene set from the GeneCards database. Hub genes (CXCL8, HIF1A, NGF, JUN, IL1A) were identified by the PPI network and validated by the external GSE52746 dataset. Immune infiltration analysis revealed that CXCL8 was significantly correlated with B cells memory, NK cells activated, Mast cells resting, Mast cells activated, and Neutrophils. GSEA of CXCL8 results showed inositol phosphate metabolism, RNA polymerase, propanoate metabolism, MAPK signaling pathway, base excision repair, and Calcium signaling pathway. In addition, we constructed a lncRNA-miRNA-mRNA ceRNA network and a drug-gene interaction network. Finally, we performed in vitro experiments to verify that LPS induced CXCL8 expression in HT-29 cells and that knockdown of CXCL8 inhibited the inflammatory stimulatory effects of LPS. This study reveals that CXCL8 plays an important role in the pathogenesis of Crohn's disease and is expected to be a novel biomarker.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Proliferative Vitreoretinopathy.

Proliferative vitreoretinopathy (PVR) is the dreaded cause of failure following retinal detachment repair; however, no cures or preventative therapies exist to date. The purpose of this study was to use bioinformatics tools to identify drugs or compounds that interact with biomarkers and pathways involved in PVR pathogenesis that could be eligible for further testing for the prevention and treatment of PVR. We queried PubMed to compile a comprehensive list of genes described in PVR to date from human studies, animal models, and genomic studies found in the National Center for Biotechnology Information database. Gene enrichment analysis was performed using ToppGene on PVR-related genes against drug-gene interaction databases to construct a pharmacome and estimate the statistical significance of overrepresented compounds. Compounds with no clinical indications were filtered out from the resulting drug lists. Our query identified 34 unique genes associated with PVR. Out of 77,146 candidate drugs or compounds in the drug databases, our analysis revealed multiple drugs and compounds that have significant interactions with genes involved in PVR, including antiproliferatives, corticosteroids, cardiovascular agents, antioxidants, statins, and micronutrients. Top compounds, including curcumin, statins, and cardiovascular agents such as carvedilol and enalapril, have well-established safety profiles and potentially could be readily repurposed for PVR. Other significant compounds such as prednisone and methotrexate have shown promising results in ongoing clinical trials for PVR. This bioinformatics approach of studying drug-gene interactions can identify drugs that may affect genes and pathways implicated in PVR. Predicted bioinformatics studies require further validation by preclinical or clinical studies; however, this unbiased approach could identify potential candidates among existing drugs and compounds that could be repurposed for PVR and guide future investigations. Novel repurposable drug therapies for PVR can be found using advanced bioinformatics models.

Gene expression profiling reveals a role of immune system and inflammation in innate and stress-induced anxiety-like behavior.

Anxiety is an evolutionarily conserved response that is essential for survival. Pathological anxiety, however, is a maladaptive response to nonthreatening situations and greatly affects quality of life. The recent COVID-19 pandemic has increased the prevalence of anxiety symptoms and highlighted the urge to identify the molecular events that initiate pathological anxiety. To this aim, we investigated the extent of similarity of brain region-specific gene expression patterns associated with innate and stress-induced anxiety-like behavior. We compared the cortico-frontal (FCx) and hippocampal (Hpc) gene expression patterns of five inbred mouse strains with high or low levels of innate anxiety-like behavior with gene expression patterns of mice subjected to chronic social defeat stress. We found significantly large overlap of the Hpc but small overlap of the FCx gene expression patterns in innate and stress-induced anxiety, that however, converged onto common inflammation and immune system canonical pathways. Comparing the gene expression data with drug-gene interaction datasets revealed drug candidates, including medrysone, simvastatin, captopril, and sulpiride, that produced gene expression changes opposite to those observed in innate or stress-induced anxiety-like behavior. Together, our data provide a comprehensive overview of FCx and Hpc gene expression differences between innate and stress-induced anxiety and support the role of inflammation and immune system in anxiety-like behavior.

Characterization of pharmacogenomic variants in a Brazilian admixed cohort of elderly individuals based on whole-genome sequencing data.

Introduction: Research in the field of pharmacogenomics (PGx) aims to identify genetic variants that modulate response to drugs, through alterations in their pharmacokinetics (PK) or pharmacodynamics (PD). The distribution of PGx variants differs considerably among populations, and whole-genome sequencing (WGS) plays a major role as a comprehensive approach to detect both common and rare variants. This study evaluated the frequency of PGx markers in the context of the Brazilian population, using data from a population-based admixed cohort from Sao Paulo, Brazil, which includes variants from WGS of 1,171 unrelated, elderly individuals. Methods: The Stargazer tool was used to call star alleles and structural variants (SVs) from 38 pharmacogenes. Clinically relevant variants were investigated, and the predicted drug response phenotype was analyzed in combination with the medication record to assess individuals potentially at high-risk of gene-drug interaction. Results: In total, 352 unique star alleles or haplotypes were observed, of which 255 and 199 had a frequency < 0.05 and < 0.01, respectively. For star alleles with frequency > 5% (n = 97), decreased, loss-of-function and unknown function accounted for 13.4%, 8.2% and 27.8% of alleles or haplotypes, respectively. Structural variants (SVs) were identified in 35 genes for at least one individual, and occurred with frequencies >5% for CYP2D6, CYP2A6, GSTM1, and UGT2B17. Overall 98.0% of the individuals carried at least one high risk genotype-predicted phenotype in pharmacogenes with PharmGKB level of evidence 1A for drug interaction. The Electronic Health Record (EHR) Priority Result Notation and the cohort medication registry were combined to assess high-risk gene-drug interactions. In general, 42.0% of the cohort used at least one PharmGKB evidence level 1A drug, and 18.9% of individuals who used PharmGKB evidence level 1A drugs had a genotype-predicted phenotype of high-risk gene-drug interaction. Conclusion: This study described the applicability of next-generation sequencing (NGS) techniques for translating PGx variants into clinically relevant phenotypes on a large scale in the Brazilian population and explores the feasibility of systematic adoption of PGx testing in Brazil.

A Review of Biomarkers in Delirium Superimposed on Dementia (DSD) and Their Clinical Application to Personalized Treatment and Management.

Delirium superimposed on dementia (DSD) occurs when patients with pre-existing dementia develop delirium. This complication causes patients to become impaired, posing safety concerns for both hospital staff and patients. Furthermore, there is an increased risk of worsening functional disability and death. Despite medical advances, DSD provides both diagnostic and therapeutic challenges to providers. Identifying at-risk patients and providing personalized medicine and patient care can decrease disease burden in a time-efficient manner. This review delves into bioinformatics-based studies of DSD in order to design and implement a personalized medicine-based approach. Our findings suggest alternative medical treatment methods based on gene-gene interactions, gene-microRNA (miRNA) interactions, gene-drug interactions, and pharmacogenetic variants involved in dementia and psychiatric disorders. We identify 17 genes commonly associated with both dementia and delirium including apolipoprotein E(ApoE), brain-derived neurotrophic factor (BDNF), catechol-O-methyltransferase (COMT), butyrylcholinesterase (BChE), acetylcholinesterase(AChE), DNA methyltransferase 1 (DNMT1), prion protein (PrP), tumor necrosis factor (TNF), serine palmitoyltransferase long chain base subunit 1 (SPTLC1), microtubule-associated protein tau (MAPT), alpha-synuclein (αS), superoxide dismutase 1(SOD1), amyloid beta precursor protein (APP), neurofilament light (NFL), neurofilament heavy, 5-hydroxytryptamine receptor 2A (HTR2A), and serpin family A member 3 (ERAP3). In addition, we identify six main genes that form an inner concentric model, as well as their associated miRNA. The FDA-approved medications that were found to be effective against the six main genes were identified. Furthermore, the PharmGKB database was used to identify variants of these six genes in order to suggest future treatment options. We also looked at previous research and evidence on biomarkers that could be used to detect DSD. According to research, there are three types of biomarkers that can be used depending on the stage of delirium. The pathological mechanisms underlying delirium are also discussed. This review will identify treatment and diagnostic options for personalized DSD management.

Optimized whole-genome CRISPR interference screens identify ARID1A-dependent growth regulators in human induced pluripotent stem cells.

Perturbing expression is a powerful way to understand the role of individual genes, but can be challenging in important models. CRISPR-Cas screens in human induced pluripotent stem cells (iPSCs) are of limited efficiency due to DNA break-induced stress, while the less stressful silencing with an inactive Cas9 has been considered less effective so far. Here, we developed the dCas9-KRAB-MeCP2 fusion protein for screening in iPSCs from multiple donors. We found silencing in a 200bp window around the transcription start site in polyclonal pools to be as effective as using wild-type Cas9 for identifying essential genes, but with much reduced cell numbers. Whole-genome screens to identify ARID1A-dependent dosage sensitivity revealed the PSMB2 gene, and enrichment of proteasome genes among the hits. This selective dependency was replicated with a proteasome inhibitor, indicating a targetable drug-gene interaction. Many more plausible targets in challenging cell models can be efficiently identified with our approach.

Effects of Genetic Polymorphisms of Drug Metabolizing Enzymes and co-Medications on Tamoxifen Metabolism in Black South African Women with Breast Cancer.

Clinical outcomes of tamoxifen (TAM) treatment show wide interindividual variability. Comedications and genetic polymorphisms of enzymes involved in TAM metabolism contributes to this variability. Drug-drug and drug-gene interactions have seldom been studied in African Black populations. We evaluated the effects of commonly co-administered medicines on TAM pharmacokinetics in a cohort of 229 South African Black female patients with hormone-receptor positive breast cancer. We also investigated the pharmacokinetic effects of genetic polymorphism in enzymes involved in TAM metabolism, including the variants CYP2D6*17 and *29, which have been mainly reported in people of African descent. TAM and its major metabolites, N-desmethyltamoxifen (NDM), 4-OH-tamoxifen, and endoxifen (ENDO), were quantified in plasma using the liquid chromatography-mass spectrometry. The GenoPharm open array was used to genotype CYP2D6, CYP3A5, CYP3A4, CYP2B6, CYP2C9, and CYP2C19. Results showed that CYP2D6 diplotype and CYP2D6 phenotype significantly affected endoxifen concentration (P < 0.001 and P < 0.001). CYP2D6*17 and CYP2D6*29 significantly reduced the metabolism of NDM to ENDO. Antiretroviral therapy had a significant effect on NDM levels and the TAM/NDM and NDM/ENDO metabolic ratios but did not result in significant effects on ENDO levels. In conclusion, CYP2D6 polymorphisms affected endoxifen concentration and the variants CYP2D6*17 and CYP2D6*29 significantly contributed to low exposure levels of ENDO. This study also suggests a low risk of drug-drug interaction in patients with breast cancer on TAM.

FKBP5 blockade may provide a new horizon for the treatment of stress-associated disorders: An in-silico study.

We searched for, from the FDA (Food and Drug Administration-USA)-approved drugs, inhibitors of FKBP5 with tolerable adverse effect profiles (eg, mild headache, sedation, etc.) and with the ability to cross the blood brain barrier (BBB), using bio-informatics tools (in-silico). This may pave the road for designing clinical trials of such drugs in patients with functional seizures (FS) and other stress-associated disorders. Several databases were used to find all the approved drugs that potentially have interactions with FKBP51 protein [ie, CTD gene-chemical interaction section of FKBP51 protein of Harmonizome of Mayaanlab, DrugCenteral database, PDID (Protein Drug Interaction Database), DGIdb (the Drug Gene Interaction database)]. Other databases were also searched [eg, clinicaltrials.gov; DRUGBANK (the FASTA format of the FKBP51 protein was imported to the target sequencing section of the database to find the associated drugs), and the STITCH database (to find the related chemical interaction molecules)]. After a comprehensive search of the designated databases, 28 unique and approved drugs were identified. Fluticasone propionate and Mifepristone and Ponatinib, Mirtazapine, Clozapine, Enzalutamide, Sertraline, Prednisolone, Fluoxetine, Dexamethasone, Clomipramine, Duloxetine, Citalopram, Chlorpromazine, Nefazodone, and Escitalopram are inhibitors of FKBP5 and have BBB permeability. While the current in-silico repurposing study could identify potential drugs (that are already approved and are widely available) for designing clinical trials in patients with stress-associated disorders (eg, FS), any future clinical trial should consider the pharmacological profile of the desired drug and also the characteristics and comorbidities of the patients in order to foster a success.

Crosstalk between oxidative stress and neutrophil response in early ischemic stroke: a comprehensive transcriptome analysis.

Ischemic stroke (IS) is the second leading cause of mortality worldwide, continuing to be a serious health concern. It is well known that oxidative stress and neutrophil response play vital roles in the pathophysiology of early IS. However, the complex interactions and critical genes associated with them have not been fully understood. Two datasets (GSE37587 and GSE16561) from the Gene Expression Omnibus database were extracted and integrated as the discovery dataset. Subsequent GSVA and WGCNA approaches were used to investigate IS-specific oxidative stress-related genes (ISOSGS). Then, we explored IS-specific neutrophil-associated genes (ISNGS) using CIBERSORT analysis. Next, the protein-protein interaction network was established to ascertain candidate critical genes related with oxidative stress and neutrophil response. Furthermore, these candidate genes were validated using GSE58294 dataset and our clinical samples by RT-qPCR method. Finally, functional annotation, diagnostic capability evaluation and drug-gene interactions were performed by using GSEA analysis, ROC curves and DGIDB database. In our analysis of discovery dataset, 155 genes were determined as ISOSGS and 559 genes were defined as ISNGS. Afterward, 9 candidate genes were identified through the intersection of ISOSGS and ISNGS, PPI network construction, and filtration by degree algorithm. Then, six real critical genes, including STAT3, MMP9, AQP9, SELL, FPR1, and IRAK3, passed the validation using the GSE58294 dataset and our clinical samples. Further functional annotation analysis indicated these critical genes were associated with neutrophil response, especially neutrophil extracellular trap. Meanwhile, they had a good diagnostic performance. Lastly, 53 potential drugs targeting these genes were predicted by DGIDB database. We identified 6 critical genes, STAT3, FPR1, AQP9, SELL, MMP9 and IRAK3, related to oxidative stress and neutrophil response in early IS, which may provide new insights into understanding the pathophysiological mechanism of IS. We hope our analysis could help develop novel diagnostic biomarkers and therapeutic strategies for IS.