Computational Drug Repurposing for Alzheimer's Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies

The landscape of the methodology in drug repurposing using human genomic data: a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.

Single Cell Sequencing Technology and Its Application in Alzheimer's Disease

Alzheimer's disease (AD) involves degeneration of cells in the brain. Due to insidious onset and slow progression, AD is often not diagnosed until it gets progressed to a more severe stage. The diagnosis and treatment of AD has been a challenge. In recent years, high-throughput sequencing technologies have exhibited advantages in exploring the pathogenesis of diseases. However, the types of cells of the central nervous system are complex and traditional bulk sequencing cannot reflect their heterogeneity. Single-cell sequencing technology enables study at the individual cell level and has an irreplaceable advantage in the study of complex diseases. In recent years, this field has expanded rapidly and several types of single-cell sequencing technologies have emerged, including transcriptomics, epigenomics, genomics and proteomics. This review article provides an overview of these single-cell sequencing technologies and their application in AD.

Relationship Between FERMT2, CELF1, COPI, CHRNA2, and ABCA7 Genetic Polymorphisms and Alzheimer's Disease Risk in the Southern Chinese Population

Background

Alzheimer's disease (AD) is a multi-gene inherited disease, and apolipoprotein E (APOE) ɛ4 is a strong risk factor. Other genetic factors are important but limited.

Objective

This study aimed to investigate the relationship between 17 single-nucleotide polymorphisms (SNPs) and AD in the Southern Chinese populations.

Methods

We recruited 242 AD patients and 208 controls. The SNaPshot technique was used to detect the SNPs.

Results

Adjusted for sex and age, we found rs6572869 (FERMT2), rs11604680 (CELF1), and rs1317149 (CELF1) were associated with AD risk in the dominant (rs6572869: p = 0.022, OR = 1.55; rs11604680: p = 0.007, OR = 1.68; rs1317149: p = 0.033, OR = 1.50) and overdominant models (rs6572869: p = 0.001, OR = 1.96; rs11604680: p = 0.002, OR = 1.82; rs1317149: p = 0.003, OR = 1.80). rs9898218 (COPI) was associated with AD risk in the overdominant model (p = 0.004, OR = 1.81). Further, rs2741342 (CHRNA2) was associated with AD protection in the dominant (p = 0.002, OR = 0.5) and additive models (p = 0.002, OR = 0.64). Mutations in rs10742814 (CELF1), rs11039280 (CELF1), and rs3752242 (ABCA7) contributed to AD protection. Among them, rs10742814 (CELF1), rs3752242 (ABCA7), and rs11039280 (CELF1) were more significantly associated with AD carrying APOE ɛ4, whereas rs1317149 (CELF1) showed an opposite trend. Interestingly, rs4147912 (ABCA7) and rs2516049 (HLA-DRB1) were identified to be relevant with AD carrying APOE ɛ4. Using expression quantitative trait locus analysis, we found polymorphisms in CELF1 (rs10742814 and rs11039280), ABCA7 (rs4147912), HLA-DRB1 (rs2516049), and ADGRF4 (rs1109581) correlated with their corresponding gene expression in the brain.

Conclusions

We identified four risk and four protective SNPs associated with AD in the Southern Chinese population, with different correlations between APOE ɛ4 carriers and non-carriers. rs4147912 (ABCA7) and rs2516049 (HLA-DRB1) were associated with AD carrying APOE ɛ4.

Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets

Background

Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.

Methods

We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.

Results

As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

Linagliptin and Empagliflozin Inhibit Microtubule Affinity Regulatory Kinase 4: Repurposing Anti-Diabetic Drugs in Neurodegenerative Disorders Using In Silico and In Vitro Approaches

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) are significant public health burdens. Many studies have revealed the possibility of common pathophysiology between T2DM and AD. Thus, in recent years, studies deciphering the action mechanism of anti-diabetic drugs with their future use in AD and related pathologies are on high demand. Drug repurposing is a safe and effective approach owing to its low cost and time-saving attributes. Microtubule affinity regulating kinase 4 (MARK4) is a druggable target for various diseases and is found to be linked with AD and diabetes mellitus. MARK4 plays a vital role in energy metabolism and regulation and thus serves as an irrefutable target to treat T2DM. The present study was intended to identify the potent MARK4 inhibitors among FDA-approved anti-diabetic drugs. We performed structure-based virtual screening of FDA-approved drugs to identify the top hits against MARK4. We identified five FDA-approved drugs having an appreciable affinity and specificity toward the binding pocket of MARK4. Among these identified hits, two drugs, linagliptin, and empagliflozin, favorably bind to the MARK4 binding pocket, interacting with its critical residues and thus subjected to detailed analysis. All-atom detailed molecular dynamics (MD) simulations revealed the dynamics of binding of linagliptin and empagliflozin with MARK4. Kinase assay showed significant inhibition of MARK4 kinase activity in the presence of these drugs, implying them as potent MARK4 inhibitors. In conclusion, linagliptin and empagliflozin may be promising MARK4 inhibitors, which can further be exploited as potential lead molecules against MARK4-directed neurodegenerative diseases.

Insights into Alzheimer's disease from single-cell genomic approaches

Alzheimer's disease (AD) is an age-related disease pathologically defined by the deposition of amyloid plaques and neurofibrillary tangles in the brain parenchyma. Single-cell profiling has shown that Alzheimer's dementia involves the complex interplay of virtually every major brain cell type. Here, we highlight cell-type-specific molecular perturbations in AD. We discuss how genomic information from single cells expands existing paradigms of AD pathogenesis and highlight new opportunities for therapeutic interventions.

Sex-specific transcriptional rewiring in the brain of Alzheimer’s disease patients

Sex-specific differences may contribute to Alzheimer’s disease (AD) development. AD is more prevalent in women worldwide, and female sex has been suggested as a disease risk factor. Nevertheless, the molecular mechanisms underlying sex-biased differences in AD remain poorly characterized. To this end, we analyzed the transcriptional changes in the entorhinal cortex of symptomatic and asymptomatic AD patients stratified by sex. Co-expression network analysis implemented by SWItchMiner software identified sex-specific signatures of switch genes responsible for drastic transcriptional changes in the brain of AD and asymptomatic AD individuals. Pathway analysis of the switch genes revealed that morphine addiction, retrograde endocannabinoid signaling, and autophagy are associated with both females with AD (F-AD) and males with (M-AD). In contrast, nicotine addiction, cell adhesion molecules, oxytocin signaling, adipocytokine signaling, prolactin signaling, and alcoholism are uniquely associated with M-AD. Similarly, some of the unique pathways associated with F-AD switch genes are viral myocarditis, Hippo signaling pathway, endometrial cancer, insulin signaling, and PI3K-AKT signaling. Together these results reveal that there are many sex-specific pathways that may lead to AD. Approximately 20–30% of the elderly have an accumulation of amyloid beta in the brain, but show no cognitive deficit. Asymptomatic females (F-asymAD) and males (M-asymAD) both shared dysregulation of endocytosis. In contrast, pathways uniquely associated with F-asymAD switch genes are insulin secretion, progesterone-mediated oocyte maturation, axon guidance, renal cell carcinoma, and ErbB signaling pathway. Similarly, pathways uniquely associated with M-asymAD switch genes are fluid shear stress and atherosclerosis, FcγR mediated phagocytosis, and proteoglycans in cancer. These results reveal for the first time unique pathways associated with either disease progression or cognitive resilience in asymptomatic individuals. Additionally, we identified numerous sex-specific transcription factors and potential neurotoxic chemicals that may be involved in the pathogenesis of AD. Together these results reveal likely molecular drivers of sex differences in the brain of AD patients. Future molecular studies dissecting the functional role of these switch genes in driving sex differences in AD are warranted.

Neuroimmune contributions to Alzheimer's disease: a focus on human data

The past decade has seen the convergence of a series of new insights that arose from genetic and systems analyses of Alzheimer's disease (AD) with a wealth of epidemiological data from a variety of fields; this resulted in renewed interest in immune responses as important, potentially causal components of AD. Here, we focus primarily on a review of human data which has recently yielded a set of robust, reproducible results that exist in a much larger universe of conflicting reports stemming from small studies with important limitations in their study design. Thus, we are at an important crossroads in efforts to first understand at which step of the long, multiphasic course of AD a given immune response may play a causal role and then modulate this response to slow or block the pathophysiology of AD. We have a wealth of new experimental tools, analysis methods, and capacity to sample human participants at large scale longitudinally; these resources, when coupled to a foundation of reproducible results and novel study designs, will enable us to monitor human immune function in the CNS at the level of complexity that is required while simultaneously capturing the state of the peripheral immune system. This integration of peripheral and central perturbations in immune responses results in pathologic responses in the central nervous system parenchyma where specialized cellular microenvironments composed of multiple cell subtypes respond to these immune perturbations as well as to environmental exposures, comorbidities and the impact of the advancing life course. Here, we offer an overview that seeks to illustrate the large number of interconnecting factors that ultimately yield the neuroimmune component of AD.

Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

BACKGROUND

The tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.

METHODS AND RESULTS

The stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.

CONCLUSION

These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.