Re-thinking Alzheimer's disease therapeutic targets using gene-based tests

Reimagining old drugs with new tricks: Mechanisms, strategies and notable success stories in drug repurposing for neurological diseases

Recent evolution in drug repurposing has brought new anticipation, especially in the conflict against neurodegenerative diseases (NDDs). The traditional approach to developing novel drugs for these complex disorders is laborious, time-consuming, and often abortive. However, drug reprofiling which is the implementation of illuminating novel therapeutic applications of existing approved drugs, has shown potential as a promising strategy to accelerate the hunt for therapeutics. The advancement of computational approaches and artificial intelligence has expedited drug repurposing. These progressive technologies have enabled scientists to analyse extensive datasets and predict potential drug-disease interactions. By prospecting into the existing pharmacological knowledge, scientists can recognise potential therapeutic candidates for reprofiling, saving precious time and resources. Preclinical models have also played a pivotal role in this field, confirming the effectiveness and mechanisms of action of repurposed drugs. Several studies have occurred in recent years, including the discovery of available drugs that demonstrate significant protective effects in NDDs, relieve debilitating symptoms, or slow down the progression of the disease. These findings highlight the potential of repurposed drugs to change the landscape of NDD treatment. Here, we present an overview of recent developments and major advances in drug repurposing intending to provide an in-depth analysis of traditional drug discovery and the strategies, approaches and technologies that have contributed to drug repositioning. In addition, this chapter attempts to highlight successful case studies of drug repositioning in various therapeutic areas related to NDDs and explore the clinical trials, challenges and limitations faced by researchers in the field. Finally, the importance of drug repositioning in drug discovery and development and its potential to address discontented medical needs is also highlighted.

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.

Identification of genetic loci shared between Alzheimer's disease and hypertension

Alzheimer's disease (AD) and high blood pressure (BP) are prevalent age-related diseases with significant unexplained heritability. A thorough analysis of genetic pleiotropy between AD and BP will lay a foundation for the study of the associated molecular mechanisms, leading to a better understanding of the development of each phenotype. We used the conditional false discovery rate (cFDR) method to identify novel genetic loci associated with both AD and BP. The cFDR approach improves the effective sample size for association testing by combining GWAS summary statistics for correlated phenotypes. We identified 50 pleiotropic SNPs for AD and BP, 7 of which are novel and have not previously been reported to be associated with either AD or BP. The novel SNPs located at STK3 are particularly noteworthy, as this gene may influence AD risk via the Hippo signaling network, which regulates cell death. Bayesian colocalization analysis demonstrated that although AD and BP are associated, they do not appear to share the same causal variants. We further performed two sample Mendelian randomization analysis, but could not detect a causal effect of BP on AD. Despite the inability to establish a causal link between AD and BP, our findings report some potential novel pleiotropic loci that may influence disease susceptibility. In summary, we identified 7 SNPs that annotate to 4 novel genes which have not previously been reported to be associated with AD nor with BP and discuss the possible role of one of these genes, STK3 in the Hippo signaling network.

Modifiable Innate Biology within the Gut–Brain Axis for Alzheimer’s Disease

Alzheimer’s disease (AD) is a prototypical inflammation-associated loss of cognitive function, with approximately 90% of the AD burden associated with invading myeloid cells controlling the function of the resident microglia. This indicates that the immune microenvironment has a pivotal role in the pathogenesis of the disease. Multiple peripheral stimuli, conditioned by complex and varied interactions between signals that stem at the intestinal level and neuroimmune processes, are involved in the progression and severity of AD. Conceivably, the targeting of critical innate immune signals and cells is achievable, influencing immune and metabolic health within the gut–brain axis. Considerable progress has been made, modulating many different metabolic and immune alterations that can drive AD development. However, non-pharmacological strategies targeting immunometabolic processes affecting neuroinflammation in AD treatment remain general and, at this point, are applied to all patients regardless of disease features. Despite these possibilities, improved knowledge of the relative contribution of the different innate immune cells and molecules comprising the chronically inflamed brain network to AD pathogenesis, and elucidation of the network hierarchy, are needed for planning potent preventive and/or therapeutic interventions. Moreover, an integrative perspective addressing transdisciplinary fields can significantly contribute to molecular pathological epidemiology, improving the health and quality of life of AD patients. This review is intended to gather modifiable immunometabolic processes based on their importance in the prevention and management of AD.

Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer’s disease

Background

Recent research has investigated the connection between Diabetes Mellitus (DM) and Alzheimer’s Disease (AD). Insulin resistance plays a crucial role in this interaction. Studies have focused on dysregulated proteins to disrupt this connection. Non-coding RNAs (ncRNAs), on the other hand, play an important role in the development of many diseases. They encode the majority of the human genome and regulate gene expression through a variety of mechanisms. Consequently, identifying significant ncRNAs and utilizing them as biomarkers could facilitate the early detection of this cross-talk. On the other hand, computational-based methods may help to understand the possible relationships between different molecules and conduct future wet laboratory experiments.

Materials and methods

In this study, we retrieved Genome-Wide Association Study (GWAS, 2008) results from the United Kingdom Biobank database using the keywords “Alzheimer’s” and “Diabetes Mellitus.” After excluding low confidence variants, statistical analysis was performed, and adjusted p-values were determined. Using the Linkage Disequilibrium method, 127 significant shared Single Nucleotide Polymorphism (SNP) were chosen and the SNP-SNP interaction network was built. From this network, dense subgraphs were extracted as signatures. By mapping each signature to the reference genome, genes associated with the selected SNPs were retrieved. Then, protein-microRNA (miRNA) and miRNA-long non-coding RNA (lncRNA) bipartite networks were built and significant ncRNAs were extracted. After the validation process, by applying the scoring function, the final protein-miRNA-lncRNA tripartite network was constructed, and significant miRNAs and lncRNAs were identified.

Results

Hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p, the four most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1, the three most important lncRNAs, and their interacting proteins in the final tripartite network, have been proposed as new candidate biomarkers in the cross-talk between DM and AD. The literature review also validates the obtained ncRNAs. In addition, miRNA/lncRNA pairs; hsa-miR-124-3p/KCNQ1OT1, hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST, all expressed in the brain, and their interacting proteins in our final network are suggested for future research investigation.

Conclusion

This study identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11 lncRNAs involved in the cross-talk between DM and AD. Different network analysis and scoring function suggested the most significant miRNAs and lncRNAs as potential candidate biomarkers for wet laboratory experiments. Considering these candidate biomarkers may help in the early detection of DM and AD co-occurrence.

Cognitive training for elderly patients with early Alzheimer’s disease in the Qinghai-Tibet Plateau: A pilot study

BACKGROUND

Alzheimer’s disease (AD) influences the social and economic quality of life of older adult patients and their families.

AIM

To explore the efficacy of cognitive training in clinical nursing for patients with early AD residing in the plateau area.

METHODS

This pilot study was conducted in patients with early AD treated in the Geriatric Department of the Qinghai Provincial People’s Hospital between August 2019 and March 2021. The patients were divided into a cognitive training group and a conventional nursing group using the random number table method. Patients in the conventional nursing group received conventional nursing, whereas the patients in the cognitive training group received the new nursing intervention. The mini-mental state examination (MMSE) and activities of daily living (ADL) scales were used to compare the cognitive ability and daily activities, respectively, between the two groups before and after the intervention.

RESULTS

Sixty patients were enrolled in this study, with 30 patients in the cognitive training group and conventional nursing group, respectively. The MMSE and ADL scores were significantly higher in the cognitive training group than in the conventional nursing group after the intervention (MMSE: 25.11 ± 2.02 vs 22.26 ± 1. 23, P = 0.032; ADL: 68.72 ± 4.86 vs 60.16 ± 2.27, P = 0.018).

CONCLUSION

The application of cognitive training in clinical nursing for patients with early AD could improve both their cognitive ability and ADL. This method could be applied in clinical practice to manage cognitive dysfunction in patients with early AD.

Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several neurological diseases, including AD, for a better comprehension of genetic mechanisms of the disease. In this study, we investigated differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets of hippocampus of AD patients. The identified DEGs were submitted to Weighted correlation network analysis (WGCNA) and ClueGo to explore genes with a higher degree centrality and to comprehend their biological role. Subsequently, MCODE was used to identify subnetworks of interconnected DEGs. Our study found 40 down-regulated genes and 36 up-regulated genes as consensus DEGs. Analysis of the co-expression network revealed ACOT7, ATP8A2, CDC42, GAD1, GOT1, INA, NCALD, and WWTR1 to be genes with a higher degree centrality. ClueGO revealed the pathways that were mainly enriched, such as clathrin coat assembly, synaptic vesicle endocytosis, and DNA damage response signal transduction by p53 class mediator. In addition, we found a subnetwork of 12 interconnected genes (AMPH, CA10, CALY, NEFL, SNAP25, SNAP91, SNCB, STMN2, SV2B, SYN2, SYT1, and SYT13). Only CA10 and CALY are targets of known drugs while the others could be potential novel drug targets.

Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease

BACKGROUND

Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.

METHODS

To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.

RESULTS

Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.

CONCLUSIONS

In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Hypoglycemia, Vascular Disease and Cognitive Dysfunction in Diabetes: Insights from Text Mining-Based Reconstruction and Bioinformatics Analysis of the Gene Networks

Hypoglycemia has been recognized as a risk factor for diabetic vascular complications and cognitive decline, but the molecular mechanisms of the effect of hypoglycemia on target organs are not fully understood. In this work, gene networks of hypoglycemia and cardiovascular disease, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cognitive decline, and Alzheimer's disease were reconstructed using ANDSystem, a text-mining-based tool. The gene network of hypoglycemia included 141 genes and 2467 interactions. Enrichment analysis of Gene Ontology (GO) biological processes showed that the regulation of insulin secretion, glucose homeostasis, apoptosis, nitric oxide biosynthesis, and cell signaling are significantly enriched for hypoglycemia. Among the network hubs, INS, IL6, LEP, TNF, IL1B, EGFR, and FOS had the highest betweenness centrality, while GPR142, MBOAT4, SLC5A4, IGFBP6, PPY, G6PC1, SLC2A2, GYS2, GCGR, and AQP7 demonstrated the highest cross-talk specificity. Hypoglycemia-related genes were overrepresented in the gene networks of diabetic complications and comorbidity; moreover, 14 genes were mutual for all studied disorders. Eleven GO biological processes (glucose homeostasis, nitric oxide biosynthesis, smooth muscle cell proliferation, ERK1 and ERK2 cascade, etc.) were overrepresented in all reconstructed networks. The obtained results expand our understanding of the molecular mechanisms underlying the deteriorating effects of hypoglycemia in diabetes-associated vascular disease and cognitive dysfunction.

Antitumour drugs targeting tau R3 VQIVYK and Cys322 prevent seeding of endogenous tau aggregates by exogenous seeds

Emerging experimental evidence suggests tau pathology spreads between neuroanatomically connected brain regions in a prion-like manner in Alzheimer's disease (AD). Tau seeding, the ability of prion-like tau to recruit and misfold naïve tau to generate new seeds, is detected early in human AD brains before the development of major tau pathology. Many antitumour drugs have been reported to confer protection against neurodegeneration, supporting the repurposing of approved and experimental or investigational oncology drugs for AD therapy. In this study, we evaluated whether antitumour drugs that abrogate the generation of seed-competent aggregates of tau Repeat 3 (R3) domain peptides can prevent tau seeding and toxicity in Tau-RD P301S FRET Biosensor cells and Caenorhabditis elegans. We demonstrate that drugs that interact with the N-terminal VQIVYK or the C-terminal region housing the Cys322 prevent R3 dimerisation, abolishing the generation of prion-like R3 seeds. Preformed R3 seeds (fibrils) capped with, or R3 seeds formed in the presence of VQIVYK- or Cys322-targeting drugs have a reduced potency to cause aggregation of naïve tau in biosensor cells and protect worms from aggregate toxicity. These findings indicate that VQIVYK- or Cys322-targeting drugs may act as prophylactic agents against tau seeding.

Evolution of genetic testing supports precision medicine for caring Alzheimer's disease patients

Genetic testing for Alzheimer's disease offers a molecular diagnosis to patients and their relatives and provides information on personal risk, reproductive choices, clinical trial eligibility, and treatment options. In the past, molecular testing was limited to detecting single variations in single genes. Currently, with the advent of next-generation sequencing, simultaneous analysis of more than 100 genes using the same DNA sample is possible. This approach allows the determination of gene mutations, genetic risk factors, genotypes at many pharmacogenomic loci, and the determination of a polygenic risk scores for stratification of risk. This article reviews the diagnostic genetic testing of Alzheimer's disease, from the first molecular approaches to recent advances in NGS, focusing on a precision medicine approach.

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

Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.

Quantification of Legionella pneumophila by qPCR and culture in tap water with different concentrations of residual disinfectants and heterotrophic bacteria

Legionellosis prevalence is increasing in the United States. This disease is caused primarily by the bacterium Legionella pneumophila found in water and transmitted by aerosol inhalation. This pathogen has a slow growth rate and can "hide" in amoeba, making it difficult to monitor by the traditional culture method on selective media. Tap water samples (n = 358) collected across the United States were tested for L. pneumophila by both culture and quantitative Polymerase Chain Reaction (qPCR). The presence of other bacteria was quantified by heterotrophic plate counts (HPC). Residual disinfectant concentrations (free chlorine or monochloramine) were measured in all samples. Legionella pneumophila had the highest prevalence and concentration in the chlorinated water samples that had a free‑chlorine value of less than 0.2 mg Cl₂/L. In total, 24% (87/358) of the samples were positive for L. pneumophila either by qPCR or 3% (11/358) were positive by culture. In chloramine-treated samples, L. pneumophila was detected by qPCR in 21% (31/148) and 1% (2/148) by culture, despite a high monochloramine residual >1 mg Cl₂/L. Despite the presence of a high disinfectant residual (>1 mg Cl₂/L), HPC counts were substantial. This study indicates that both culture and qPCR methods have limitations when predicting a potential risk for disease associated with L. pneumophila in tap water. Measuring disinfectant residuals and quantifying HPC in water samples may be useful adjunct parameters for reducing Legionellosis' risk from public water supplies at high-risk locations.

A Study to Decipher the Potential Effects of Butylphthalide against Central Nervous System Diseases Based on Network Pharmacology and Molecular Docking Integration Strategy

Background

Butylphthalide (NBP), approved by the China National Medical Products Administration (NMPA) for the treatment of ischemic stroke (IS), showed pleiotropic potentials against central nervous system (CNS) diseases, including neuroprotection and cognitive deficits improvement. However, the effects and corresponding modes of action were not fully explored. This study was designed to investigate the potential of NBP against IS-associated CNS diseases based on network pharmacology (NP) and molecular docking (MD).

Methods

IS was inputted as the index disease to retrieve the “associated diseases” in DisGeNET. Three-database-based IS genes were obtained and integrated (DisGeNET, Malacards, and OMIM). Then, IS-associated genes were identified by combining these genes. Meanwhile, PubMed references and online databases were applied to identify NBP target genes. The IS-related disease-disease association (DDA) network and NBP-disease regulation network were constructed and analyzed in Cytoscape. In silico MD and references were used to validate the binding affinity of NBP with critical targets and the potential of NBP against certain IS-related CNS disease regulation.

Results

175 NBP target genes were obtained, while 312 IS-related disease genes were identified. 36 NBP target genes were predicted to be associated with IS-related CNS diseases, including Alzheimer's disease (AD), epilepsy, major depressive disorder (MDD), amyotrophic lateral sclerosis (ALS), and dementia. Six target genes (i.e., GRIN1, PTGIS, PTGES, ADRA1A, CDK5, and SULT1E1) indicating disease specificity index (DSI) >0.5 showed certain to good degree binding affinity with NBP, ranging from −9.2 to −6.7 kcal/mol. And the binding modes may be mainly related to hydrogen bonds and hydrophobic “bonds.” Further literature validations inferred that these critical NBP targets had a tight association with AD, epilepsy, ALS, and depression.

Conclusions

Our study proposed a drug-target-disease integrated method to predict the drug repurposing potentials to associated diseases by application of NP and MD, which could be an attractive alternative to facilitate the development of CNS disease therapies. NBP may be promising and showed potentials to be repurposed for treatments for AD, epilepsy, ALS, and depression, and further investigations are warranted to be carefully designed and conducted.

Chemotherapy and the Risk of Alzheimer's Disease in Colorectal Cancer Survivors: Evidence From the Medicare System

PURPOSE

Evidence on the nature of the relationship between patients receiving chemotherapy as an essential part of guideline-concordant cancer care and the onset of Alzheimer's Disease (AD) and other adverse cognitive outcomes has been mixed. Biological mechanisms were proposed to support both a potentially beneficial and an adverse role. To explore the relationship between chemotherapy and onset of AD and other neurocognitive disorders (ND) in colorectal cancer survivors.

METHODS

We conducted a retrospective cohort study of 135,834 individuals older than 65 years diagnosed with colorectal cancer between 1998 and 2007, using SEER-Medicare data. A proportional hazards model was used before and after the use of inverse probability weighting to account for populational differences between the chemotherapy and nonchemotherapy groups. Weights were normalized to the total sample size.

RESULTS

After inverse probability weighting, chemotherapy was associated with decreased AD risk (hazard ratio [HR]: 0.791; 95% CI: 0.758 to 0.824) and lower risk for the majority of other ND including AD-related diseases (HR: 0.823; CI: 0.802 to 0.844), dementia (permanent mental disorder) (HR: 0.807; CI: 0.782 to 0.832), and dementia (senile) (HR: 0.772; CI: 0.745 to 0.801). The only adverse effect to remain significant was cerebral degeneration (excluding AD) (HR: 1.067; CI: 1.033 to 1.102). The effects for AD remained after treatment was stratified by chemotherapy agent type and remained significant for up to 6 years past diagnosis.

CONCLUSION

Chemotherapy use in colorectal cancer survivors demonstrated an association with reduced risk for AD and other ND.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Herpes simplex virus and Alzheimer's disease: a Mendelian randomization study

This study assessed if any herpes simplex virus (HSV) infection was a genetically valid target for late-onset Alzheimer's disease (AD) using 2-sample Mendelian randomization. We applied strong (p-value <5×10^-6) and independent (r² < 0.05) genetic variants for any HSV infection (n = 450,581) to genome wide association studies of cognitive function (n = 300,486), and late-onset AD (n = 455,258) to obtain estimates. Genetically predicted log odds of any HSV infection was not associated with cognitive function (mean difference 0.0004 per any HSV infection, 95% confidence interval (CI) -0.001 to 0.001), or late-onset AD (odds ratio (OR) 0.999, 95% CI 0.998-1.001). Different genetic variant selections produced similar results. Any HSV infection does not appear to be a genetically valid target of intervention in late-onset AD, suggesting a rethink of the relevance of any HSV infection to late-onset AD.

An update on potential links between type 2 diabetes mellitus and Alzheimer's disease

Alzheimer's disease (AD) and type 2 diabetes (T2D) major feature is insulin resistance. Brain and peripheral insulin resistance lead to hyperglycemia, which contributes to the development of T2D-linked comorbidities, such as obesity and dyslipidemia. Individuals with hyperglycemia in AD present with neuronal loss, formation of plaques and tangles and reduced neurogenesis. Inflammation seems to play an essential role in the development of insulin resistance in AD and T2D. We conducted a literature review about the links between AD and T2D. Alterations in glucose metabolism result from changes in the expression of the insulin receptor substrates 1 and 2 (IRS-1 and IRS-2), and seem to be mediated by several inflammatory pathways being present in both pathologies. Although there are some similarities in the insulin resistance of AD and T2D, brain and peripheral insulin resistance also have their discrete features. Failure to activate IRS-1 is the hallmark of AD, while inhibition of IRS-2 is the main feature in T2D. Inflammation mediates the alterations in glucose metabolism in AD and T2D. Targeting inflammation and insulin receptors may be a successful strategy to prevent and ameliorate T2D and AD symptoms.

Perspectives for New and More Efficient Multifunctional Ligands for Alzheimer's Disease Therapy

Despite tremendous research efforts at every level, globally, there is still a lack of effective drugs for the treatment of Alzheimer's disease (AD). The biochemical mechanisms of this devastating neurodegenerative disease are not yet clearly understood. This review analyses the relevance of multiple ligands in drug discovery for AD as a versatile toolbox for a polypharmacological approach to AD. Herein, we highlight major targets associated with AD, ranging from acetylcholine esterase (AChE), beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), glycogen synthase kinase 3 beta (GSK-3β), N-methyl-d-aspartate (NMDA) receptor, monoamine oxidases (MAOs), metal ions in the brain, 5-hydroxytryptamine (5-HT) receptors, the third subtype of histamine receptor (H3 receptor), to phosphodiesterases (PDEs), along with a summary of their respective relationship to the disease network. In addition, a multitarget strategy for AD is presented, based on reported milestones in this area and the recent progress that has been achieved with multitargeted-directed ligands (MTDLs). Finally, the latest publications referencing the enlarged panel of new biological targets for AD related to the microglia are highlighted. However, the question of how to find meaningful combinations of targets for an MTDLs approach remains unanswered.

De novo identification of toxicants that cause irreparable damage to parasitic nematode intestinal cells

Efforts to identify new drugs for therapeutic and preventive treatments against parasitic nematodes have gained increasing interest with expanding pathogen omics databases and drug databases from which new anthelmintic compounds might be identified. Here, a novel approach focused on integrating a pan-Nematoda multi-omics data targeted to a specific nematode organ system (the intestinal tract) with evidence-based filtering and chemogenomic screening was undertaken. Based on de novo computational target prioritization of the 3,564 conserved intestine genes in A. suum, exocytosis was identified as a high priority pathway, and predicted inhibitors of exocytosis were tested using the large roundworm (Ascaris suum larval stages), a filarial worm (Brugia pahangi adult and L3), a whipworm (Trichuris muris adult), and the non-parasitic nematode Caenorhabditis elegans. 10 of 13 inhibitors were found to cause rapid immotility in A. suum L3 larvae, and five inhibitors were effective against the three phylogenetically diverse parasitic nematode species, indicating potential for a broad spectrum anthelmintics. Several distinct pathologic phenotypes were resolved related to molting, motility, or intestinal cell and tissue damage using conventional and novel histologic methods. Pathologic profiles characteristic for each inhibitor will guide future research to uncover mechanisms of the anthelmintic effects and improve on drug designs. This progress firmly validates the focus on intestinal cell biology as a useful resource to develop novel anthelmintic strategies.

Meta-Analysis of Gene Expression Changes in the Blood of Patients with Mild Cognitive Impairment and Alzheimer's Disease Dementia

Background: Dementia is a major public health concern affecting approximately 47 million people worldwide. Mild cognitive impairment (MCI) is one form of dementia that affects an individual’s memory with or without affecting their daily life. Alzheimer’s disease dementia (ADD) is a more severe form of dementia that usually affects elderly individuals. It remains unclear whether MCI is a distinct disorder from or an early stage of ADD. Methods: Gene expression data from blood were analyzed to identify potential biomarkers that may be useful for distinguishing between these two forms of dementia. Results: A meta-analysis revealed 91 genes dysregulated in individuals with MCI and 387 genes dysregulated in ADD. Pathway analysis identified seven pathways shared between MCI and ADD and nine ADD-specific pathways. Fifteen transcription factors were associated with MCI and ADD, whereas seven transcription factors were specific for ADD. Mir-335-5p was specific for ADD, suggesting that it may be useful as a biomarker. Diseases that are associated with MCI and ADD included developmental delays, cognition impairment, and movement disorders. Conclusion: These results provide a better molecular understanding of peripheral changes that occur in MCI and ADD patients and may be useful in the identification of diagnostic and prognostic biomarkers.