Identification of Druggable Genes for Asthma by Integrated Genomic Network Analysis

Asthma-Genomic Advances Toward Risk Prediction

Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Integrated genomic network analysis revealed potential of a druggable target for hemorrhoid treatment

Hemorrhoids are a prevalent medical condition that necessitates effective treatment options. The current options for treatment consist of oral medications, topical applications, or surgery, yet a scarcity of highly effective drugs still exists. Genetic markers provide promising avenues for investigating the treatment of hemorrhoids, as they may reveal intricate biological mechanisms and targeted drug therapies, ultimately enhancing more precise treatment tailored to the patient. This study aims to identify new drug candidates for treating hemorrhoids through a meticulous bioinformatics approach and integrated with genomic network analysis. After extracting 21 druggable target genes using DrugBank from 293 genes connected to hemorrhoids, 87 possible drugs were selected. Three of these drugs (ketamine, methylene blue, and fulvestrant) hold potential in addressing issues associated with hemorrhoids and have been supported by clinical or preclinical studies. Eighty-four compounds present new therapeutic possibilities for managing hemorrhoids. We highlight that our findings indicate that NOX1 and NOS3 genes are promising biomarkers, with NOS3 gaining significance owing to its robust systemic functional annotations. Sapropterin, an existing drug, is closely associated with NOS3, providing a clear target for biomarker-driven interventions. This study illustrates the potential of combining genomic network analysis with bioinformatics to repurpose drugs for treating hemorrhoids. Subsequent research will explore the mechanisms for utilizing NOS3 targeting in the treatment of hemorrhoids.

Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis

Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to play a pivotal role in DM progression, but few have utilized this information geared toward drug discovery for the disease. Here, we exploited genomic variation associated with DM and integrated this with genomic and bioinformatic analyses to discover new drug candidates. We first integrated genome-wide association study (GWAS) and phenome-wide association study (PheWAS) catalogs to identify disease-associated genomic variants. Biological risk genes for DM were prioritized using strict functional annotations, further identifying candidate drug targets based on druggable genes from databases. Overall, we analyzed 1239 variants associated with DM and obtained 43 drugs that overlapped with 13 target genes (JAK2, FCGR3B, CD4, CD3D, LCK, CD2, CD3E, FCGR3A, CD3G, IFNAR1, CD247, JAK1, IFNAR2). Six drugs clinically investigated for DM, as well as eight drugs under pre-clinical investigation, are candidate drugs that could be repositioned for DM. Further studies are necessary to validate potential biomarkers for novel DM therapeutics from our findings.

Transcriptomics-driven drug repositioning for the treatment of diabetic foot ulcer

Diabetic foot ulcers (DFUs) are a common complication of diabetes and can lead to severe disability and even amputation. Despite advances in treatment, there is currently no cure for DFUs and available drugs for treatment are limited. This study aimed to identify new candidate drugs and repurpose existing drugs to treat DFUs based on transcriptomics analysis. A total of 31 differentially expressed genes (DEGs) were identified and used to prioritize the biological risk genes for DFUs. Further investigation using the database DGIdb revealed 12 druggable target genes among 50 biological DFU risk genes, corresponding to 31 drugs. Interestingly, we highlighted that two drugs (urokinase and lidocaine) are under clinical investigation for DFU and 29 drugs are potential candidates to be repurposed for DFU therapy. The top 5 potential biomarkers for DFU from our findings are IL6ST, CXCL9, IL1R1, CXCR2, and IL10. This study highlights IL1R1 as a highly promising biomarker for DFU due to its high systemic score in functional annotations, that can be targeted with an existing drug, Anakinra. Our study proposed that the integration of transcriptomic and bioinformatic-based approaches has the potential to drive drug repurposing for DFUs. Further research will further examine the mechanisms by which targeting IL1R1 can be used to treat DFU.

Comprehensive analysis of immune-related genes for classification and immune microenvironment of asthma

OBJECTIVES

To determine the effects of immune-related genes (IRGs) and immune landscape of induced sputum, and develop novel, non-invasive diagnostic molecular therapeutic targets for asthma.

METHODS

GSE76262 datasets were used to identify differentially expressed IRGs in asthma. Key IRGs were detected using a protein-protein interaction network. Receiver operating characteristic (ROC) curves were analyzed to investigate the diagnostic value of key IRGs. Gene set enrichment analysis (GSEA) was performed with WebGestalt. Single-sample gene set enrichment analysis and CIBERSORT were used to investigate the immune landscape of induced sputum.

RESULTS

A total of 75 potential IRGs were associated with asthma, most of which were involved in the NF-kappa B signaling pathway. ROC analysis showed AUC values for the hub pathway ranging from 0.676-0.767, with moderate diagnostic value for asthma. We also identified IRGs-related cytokines (TNF-α, IL-1β, IL-8 and IL-6) in 76 asthma and 91 control serum samples to further explore diagnostic efficacy, showing a cumulative AUC of 0.998 for these four related cytokines. Analysis of immune cell infiltration levels showed that follicular helper T cells, activated dendritic cells, activated mast cells and eosinophils were significantly higher and macrophages M0 and macrophages M2 were significantly reduced in sputum from patients with asthma.

CONCLUSIONS

IRGs-related cytokines and immune infiltration may contribute to the diagnosis and immune classification of asthma.

Diagnostic and Prognostic Risk Assessment of Heat Shock Protein HSPA1B rs2763979 Gene Variant in Asthma

Given the significant role the heat shock protein Hsp70 plays in modulating cellular homeostasis in several chronic inflammatory disorders, the genetic variation of the inducible HSP70 (HSPA1B) gene may impact protein expression and disease phenotype. The HSPA1B rs2763979 variant has been associated with multiple inflammatory scenarios, but no previous studies have explored its association with asthma. In this sense, this cross-sectional study enrolled 90 children with asthma and 218 age-/sex-matched healthy volunteers for rs2763979 variant genotyping by TaqMan allelic discrimination analysis. The results were investigated under several genetic models and associated with disease susceptibility and clinicolaboratory data. Overall analysis, including the 308 participants, revealed a higher C allele frequency among patients relative to controls (43.0% vs. 33%, p = 0.006). Furthermore, patients with the C variant initially had a higher risk of asthma under heterozygous (OR = 2.75, 95%CI = 1.46-5.18, p = 0.003), homozygous (OR = 3.35, 95%CI = 1.19-9.39, p = 0.008), dominant (OR = 2.83, 95%CI = 1.52-5.25, p < 0.001), and overdominant (OR = 2.12, 95%CI = 1.20-3.74, p = 0.008) models. However, after employing a 1:1 nearest propensity matching analysis, the studied variant showed only borderline significance with asthma under the dominant model in 71 matched cohorts. Interestingly, patients who carry the rs2763979 CC genotype showed favorable spirometric parameters in terms of better (mean ± SD) forced vital capacity (86.3 ± 7.4 vs. 77.7 ± 6.1 and 75.7 ± 7.2 for CT and TT, respectively, p = 0.021), forced expiratory volume in one second before bronchodilation (60.7 ± 12.9 vs. 54.9 ± 7.6 and 56.1 ± 7.5 for CT and TT, respectively, p = 0.021), and an improvement in peak expiratory flow rate after inhaled salbutamol bronchodilator (p = 0.044) relative to the counterpart genotypes. In conclusion, the HSPA1B rs2763979 variant might have prognostic utility as a genetic marker for asthma in our population. Further larger studies on different ethnicities are recommended to validate the results.

Integrated genomic analysis to identify druggable targets for pancreatic cancer

According to the National Comprehensive Cancer Network and the American Society of Clinical Oncology, the standard treatment for pancreatic cancer (PC) is gemcitabine and fluorouracil. Other chemotherapeutic agents have been widely combined. However, drug resistance remains a huge challenge, leading to the ineffectiveness of cancer therapy. Therefore, we are trying to discover new treatments for PC by utilizing genomic information to identify PC-associated genes as well as drug target genes for drug repurposing. Genomic information from a public database, the cBio Cancer Genomics Portal, was employed to retrieve the somatic mutation genes of PC. Five functional annotations were applied to prioritize the PC risk genes: Kyoto Encyclopedia of Genes and Genomes; biological process; knockout mouse; Gene List Automatically Derived For You; and Gene Expression Omnibus Dataset. DrugBank database was utilized to extract PC drug targets. To narrow down the most promising drugs for PC, CMap Touchstone analysis was applied. Finally, ClinicalTrials.gov and a literature review were used to screen the potential drugs under clinical and preclinical investigation. Here, we extracted 895 PC-associated genes according to the cBioPortal database and prioritized them by using five functional annotations; 318 genes were assigned as biological PC risk genes. Further, 216 genes were druggable according to the DrugBank database. CMap Touchstone analysis indicated 13 candidate drugs for PC. Among those 13 drugs, 8 drugs are in the clinical trials, 2 drugs were supported by the preclinical studies, and 3 drugs are with no evidence status for PC. Importantly, we found that midostaurin (targeted PRKA) and fulvestrant (targeted ESR1) are promising candidate drugs for PC treatment based on the genomic-driven drug repurposing pipelines. In short, integrated analysis using a genomic information database demonstrated the viability for drug repurposing. We proposed two drugs (midostaurin and fulvestrant) as promising drugs for PC.

Genomic variants-driven drug repurposing for tuberculosis by utilizing the established bioinformatic-based approach

A major challenge in translating genomic variants of Tuberculosis (TB) into clinical implementation is to integrate the disease-associated variants and facilitate drug discovery through the concept of genomic-driven drug repurposing. Here, we utilized two established genomic databases, namely a Genome-Wide Association Study (GWAS) and a Phenome-Wide Association Study (PheWAS) to identify the genomic variants associated with TB disease and further utilize them for drug-targeted genes. We evaluated 3.425 genomic variants associated with TB disease which overlapped with 200 TB-associated genes. To prioritize the biological TB risk genes, we devised an in-silico pipeline and leveraged an established bioinformatics method based on six functional annotations (missense mutation, cis-eQTL, biological process, cellular component, molecular function, and KEGG molecular pathway analysis). Interestingly, based on the six functional annotations that we applied, we discovered that 14 biological TB risk genes are strongly linked to the deregulation of the biological TB risk genes. Hence, we demonstrated that 12 drug target genes overlapped with 40 drugs for other indications and further suggested that the drugs may be repurposed for the treatment of TB. We highlighted that CD44, CCR5, CXCR4, and C3 are highly promising proposed TB targets since they are connected to SELP and HLA-B, which are biological TB risk genes with high systemic scores on functional annotations. In sum, the current study shed light on the genomic variants involved in TB pathogenesis as the biological TB risk genes and provided empirical evidence that the genomics of TB may contribute to drug discovery.

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The feasibility of utilizing genomic variants to facilitate drug repurposing for Tuberculosis.

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Genomic information can be effectively used for drug discovery and treatment through genomic-based therapies.

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Findings from our research support the possibility of drug repurposing for Tuberculosis based on genomic variations.

Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis

Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new drugs is to utilize old drugs for new indications, an approach known as drug repurposing. Herein, we first identified 421 MS-associated SNPs from the Genome-Wide Association Study (GWAS) catalog (p-value < 5 × 10-8), and a total of 427 risk genes associated with MS using HaploReg version 4.1 under the criterion r 2  > 0.8. MS risk genes were then prioritized using bioinformatics analysis to identify biological MS risk genes. The prioritization was performed based on six defined categories of functional annotations, namely missense mutation, cis-expression quantitative trait locus (cis-eQTL), molecular pathway analysis, protein-protein interaction (PPI), genes overlap with knockout mouse phenotype, and primary immunodeficiency (PID). A total of 144 biological MS risk genes were found and mapped into 194 genes within an expanded PPI network. According to the DrugBank and the Therapeutic Target Database, 27 genes within the list targeted by 68 new candidate drugs were identified. Importantly, the power of our approach is confirmed with the identification of a known approved drug (dimethyl fumarate) for MS. Based on additional data from ClinicalTrials.gov, eight drugs targeting eight distinct genes are prioritized with clinical evidence for MS disease treatment. Notably, CD80 and CD86 pathways are promising targets for MS drug repurposing. Using in silico drug repurposing, we identified belatacept as a promising MS drug candidate. Overall, this study emphasized the integration of functional genomic variants and bioinformatic-based approach that reveal important biological insights for MS and drive drug repurposing efforts for the treatment of this devastating disease.

Identification of Hub Genes and Potential Biomarkers for Childhood Asthma by Utilizing an Established Bioinformatic Analysis Approach

Childhood asthma represents a heterogeneous disease resulting from the interaction between genetic factors and environmental exposures. Currently, finding reliable biomarkers is necessary for the clinical management of childhood asthma. However, only a few biomarkers are being used in clinical practice in the pediatric population. In the long run, new biomarkers for asthma in children are required and would help direct therapy approaches. This study aims to identify potential childhood asthma biomarkers using a genetic-driven biomarkers approach. Herein, childhood asthma-associated Single Nucleotide Polymorphisms (SNPs) were utilized from the GWAS database to drive and facilitate the biomarker of childhood asthma. We uncovered 466 childhood asthma-associated loci by extending to proximal SNPs based on r2 > 0.8 in Asian populations and utilizing HaploReg version 4.1 to determine 393 childhood asthma risk genes. Next, the functional roles of these genes were subsequently investigated using Gene Ontology (GO) term enrichment analysis, a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and a protein−protein interaction (PPI) network. MCODE and CytoHubba are two Cytoscape plugins utilized to find biomarker genes from functional networks created using childhood asthma risk genes. Intriguingly, 10 hub genes (IL6, IL4, IL2, IL13, PTPRC, IL5, IL33, TBX21, IL2RA, and STAT6) were successfully identified and may have been identified to play a potential role in the pathogenesis of childhood asthma. Among 10 hub genes, we strongly suggest IL6 and IL4 as prospective childhood asthma biomarkers since both of these biomarkers achieved a high systemic score in Cytohubba’s MCC algorithm. In summary, this study offers a valuable genetic-driven biomarker approach to facilitate the potential biomarkers for asthma in children.

Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants

From inadequate prior antidepressants that targeted monoamine neurotransmitter systems emerged the discovery of alternative drugs for depression. For instance, drugs targeted interleukin 6 receptor (IL6R) in inflammatory system. Genomic analysis-based drug repurposing using single nucleotide polymorphism (SNP) inclined a promising method for several diseases. However, none of the diseases was depression. Thus, we aimed to identify drug repurposing candidates for depression treatment by adopting a genomic-analysis-based approach. The 5885 SNPs obtained from the machine learning approach were annotated using HaploReg v4.1. Five sets of functional annotations were applied to determine the depression risk genes. The STRING database was used to expand the target genes and identify drug candidates from the DrugBank database. We validated the findings using the ClinicalTrial.gov and PubMed databases. Seven genes were observed to be strongly associated with depression (functional annotation score = 4). Interestingly, IL6R was auspicious as a target gene according to the validation outcome. We identified 20 drugs that were undergoing preclinical studies or clinical trials for depression. In addition, we identified sarilumab and satralizumab as drugs that exhibit strong potential for use in the treatment of depression. Our findings indicate that a genomic-analysis-based approach can facilitate the discovery of drugs that can be repurposed for treating depression.

Investigational approaches for unmet need in severe asthma

INTRODUCTION

Molecular antibodies (mAb) targeting inflammatory mediators are effective in T2-high asthma. The recent approval of Tezepelumab presents a novel mAb therapeutic option to those with T2-low asthma.

AREAS COVERED

We discuss a number of clinical problems pertinent to severe asthma which are less responsive to current therapies, such as persistent airflow obstruction and airway hyperresponsiveness. We discuss selected investigational approaches, including a number of candidate therapies under investigation in two adaptive platform trials currently in progress, with particular reference to this unmet need, as well as their potential in phenotypes such as neutrophilic asthma and obese asthma, which may or may not overlap with a T2-high phenotype.

EXPERT OPINION

The application of discrete targeting approaches to T2-low molecular phenotypes, including those phenotypes in which inflammation may not arise within the airway, has yielded variable results to date. Endotypes associated with T2-low asthma are likely to be diverse but await validation. Investigational therapeutic approaches must, likewise, be diverse if the goal of remission is to become attainable for all those living with asthma.

Integrative genetics-metabolomics analysis of infant bronchiolitis-childhood asthma link: A multicenter prospective study

Background

Infants with bronchiolitis are at high risk for developing childhood asthma. While genome-wide association studies suggest common genetic susceptibilities between these conditions, the mechanisms underlying the link remain unclear.

Objective

Through integrated genetics-metabolomics analysis in this high-risk population, we sought to identify genetically driven metabolites associated with asthma development and genetic loci associated with both these metabolites and asthma susceptibility.

Methods

In a multicenter prospective cohort study of infants hospitalized for bronchiolitis, we profiled the nasopharyngeal metabolome and genotyped the whole genome at hospitalization. We identified asthma-related metabolites from 283 measured compounds and conducted metabolite quantitative trait loci (mtQTL) analyses. We further examined the mtQTL associations by testing shared genetic loci for metabolites and asthma using colocalization analysis and the concordance between the loci and known asthma-susceptibility genes.

Results

In 744 infants hospitalized with bronchiolitis, 28 metabolites (e.g., docosapentaenoate [DPA], 1,2-dioleoyl-sn-glycero-3-phosphoglycerol, sphingomyelin) were associated with asthma risk. A total of 349 loci were associated with these metabolites-161 for non-Hispanic white, 120 for non-Hispanic black, and 68 for Hispanics. Of these, there was evidence for 30 shared loci between 16 metabolites and asthma risk (colocalization posterior probability ≥0.5). The significant SNPs within loci were aligned with known asthma-susceptibility genes (e.g., ADORA1, MUC16).

Conclusion

The integrated genetics-metabolomics analysis identified genetically driven metabolites during infancy that are associated with asthma development and genetic loci associated with both these metabolites and asthma susceptibility. Identifying these metabolites and genetic loci should advance research into the functional mechanisms of the infant bronchiolitis-childhood asthma link.