Alzheimer's disease master regulators analysis: search for potential molecular targets and drug repositioning candidates

A Novel Oxo-Palmatine Derivative 2q as Potent Reversal Agents Against Alzheimer's Disease

Palmatine (PAL), as an active ingredient in traditional Chinese medicine, had been demonstrated efficacy in ameliorating the manifestations of AD. Our research group has previously designed and synthesized the novel oxo-PAL derivative 2q and found that it has exhibited notable neuroprotective properties. However, compound 2q therapeutic impact on AD remains uncertain. In the current investigation, our findings demonstrated that compound 2q displayed significant anti-AβOs activity in vitro by using xCELLigence analysis, and showed a high likelihood of crossing the blood-brain barrier. Furthermore, administration of compound 2q yielded a notable amelioration in Aβ accumulation and hyperphosphorylation of Tau in 3×Tg mice. Additionally, it was observed that compound 2q potentially enhanced the pathological characteristics of AD by targeting Potassium/Sodium Hyperpolarization-Activated Cyclic Nucleotide-Gated Channel 2 (HCN2). In conclusion, compound 2q emerged as a promising candidate for AD treatment, as it effectively restored AD-associated pathological impairments. Furthermore, it has been identified as a potential target of HCN2, thereby offering novel avenues for the development of treatments for AD.

NEAT1 Promotes Valproic Acid-Induced Autism Spectrum Disorder by Recruiting YY1 to Regulate UBE3A Transcription

Evidence suggests that long non-coding RNAs (lncRNAs) play a significant role in autism. Herein, we explored the functional role and possible molecular mechanisms of NEAT1 in valproic acid (VPA)-induced autism spectrum disorder (ASD). A VPA-induced ASD rat model was constructed, and a series of behavioral tests were performed to examine motor coordination and learning-memory abilities. qRT-PCR and western blot assays were used to evaluate target gene expression levels. Loss-and-gain-of-function assays were conducted to explore the functional role of NEAT1 in ASD development. Furthermore, a combination of mechanistic experiments and bioinformatic tools was used to assess the relationship and regulatory role of the NEAT1-YY1-UBE3A axis in ASD cellular processes. Results showed that VPA exposure induced autism-like developmental delays and behavioral abnormalities in the VPA-induced ASD rat model. We found that NEAT1 was elevated in rat hippocampal tissues after VPA exposure. NEAT1 promoted VPA-induced autism-like behaviors and mitigated apoptosis, oxidative stress, and inflammation in VPA-induced ASD rats. Notably, NEAT1 knockdown improved autism-related behaviors and ameliorated hippocampal neuronal damage. Mechanistically, it was observed that NEAT1 recruited the transcription factor YY1 to regulate UBE3A expression. Additionally, in vitro experiments further confirmed that NEAT1 knockdown mitigated hippocampal neuronal damage, oxidative stress, and inflammation through the YY1/UBE3A axis. In conclusion, our study demonstrates that NEAT1 is highly expressed in ASD, and its inhibition prominently suppresses hippocampal neuronal injury and oxidative stress through the YY1/UBE3A axis, thereby alleviating ASD development. This provides a new direction for ASD-targeted therapy.

Exploration of the shared gene signatures and comorbidity mechanisms of primary aldosteronism and atrial fibrillation

Background

Primary aldosteronism (PA) is a prevalent cause of endocrine hypertension characterized by an excess of aldosterone that can induce proinflammatory, prooxidant and profibrotic effects on the heart. Emerging evidence indicates a heightened incidence of atrial fibrillation (AF) in patients with PA, suggesting a significant association between the two conditions. However, the underlying mechanisms remain unclear. The purpose of this study was to investigate the molecular networks associated with the development of both PA and AF.

Methods

Datasets were obtained from the Gene Expression Omnibus database. Hub genes were identified by enrichment and protein-protein interaction analysis. These hub genes were subsequently validated via two independent external datasets: GSE60042 (PA microarray dataset) and GSE41177 (AF microarray dataset). Following the identification of shared genes, quantitative real-time polymerase chain reaction (qPCR) was employed to verify the reliability of the dataset and to further confirm the presence of shared genes in clinical samples.

Results

The results of the common gene analysis revealed that immune and inflammatory responses may be shared features in the pathophysiology of PA and AF. One hub gene, specifically tumor necrosis factor superfamily member 10 (TNFSF10), was identified through various analyses and subsequently validated via qPCR. Compared with that in healthy controls, the expression level of TNFSF10 was lower in PA patients with AF.

Conclusion

Our findings indicate that TNFSF10 may play a role in the pathophysiology of AF complications associated with PA conditions, suggesting that it could serve as a potential target for the diagnosis or treatment of PA patients complicated with AF.

NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

Motivation

Transcription factor (TF) coordination plays a key role in gene regulation via direct and/or indirect protein-protein interactions (PPIs) and co-binding to regulatory elements on DNA. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF-TF coordination and target gene (TG) regulation of various cell types remains unclear.

Results

To address this, we introduce our innovative computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization, using prior knowledge of PPIs among TFs, to analyze single-cell gene expression data, uncovering cell-type coordinating TFs and identifying revolutionary TF-TG candidate regulatory network links. NetREm's performance is validated using simulation studies and benchmarked across several datasets in humans, mice, yeast. Further, we showcase NetREm's ability to prioritize valid novel human TF-TF coordination links in 9 peripheral blood mononuclear and 42 immune cell sub-types. We apply NetREm to examine cell-type networks in central and peripheral nerve systems (e.g. neuronal, glial, Schwann cells) and in Alzheimer's disease versus Controls. Top predictions are validated with experimental data from rat, mouse, and human models. Additional functional genomics data helps link genetic variants to our TF-TG regulatory and TF-TF coordination networks.

Availability and implementation

https://github.com/SaniyaKhullar/NetREm.

pyPAGE: A framework for Addressing biases in gene-set enrichment analysis-A case study on Alzheimer's disease

Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease.

NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

Transcription factor (TF) coordination plays a key role in target gene (TG) regulation via protein-protein interactions (PPIs) and DNA co-binding to regulatory elements. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and TG regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization using prior knowledge of direct and/or indirect PPIs among TFs to analyze single-cell gene expression data. We test NetREm by simulation data and benchmark its performance in 4 real-world applications that have gold standard TF-TG networks available: mouse (mESCs) and simulated human (hESCs) embryonic stem (ESCs), human hematopoietic stem (HSCs), and mouse dendritic (mDCs) cells. Further, we use NetREm to prioritize valid novel TF-TF coordination links in human Peripheral Blood Mononuclear cell (PBMC) sub-types. We apply NetREm to analyze various cell types in both central (CNS) and peripheral (PNS) nerve system (NS) (e.g. neuronal, glial, Schwann cells (SCs)) as well as in Alzheimers disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-TG candidate links. We validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat/mouse models and compare results with additional functional genomic data, including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants (single nucleotide polymorphisms (SNPs)) to TF coordination.

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.

Phosphoproteome Microarray Analysis of Extracellular Particles as a Tool to Explore Novel Biomarker Candidates for Alzheimer's Disease

Phosphorylation plays a key role in Alzheimer's disease (AD) pathogenesis, impacting distinct processes such as amyloid-beta (Aβ) peptide production and tau phosphorylation. Impaired phosphorylation events contribute to senile plaques and neurofibrillary tangles' formation, two major histopathological hallmarks of AD. Blood-derived extracellular particles (bdEP) can represent a disease-related source of phosphobiomarker candidates, and hence, in this pilot study, bdEP of Control and AD cases were analyzed by a targeted phosphoproteomics approach using a high-density microarray that featured at least 1145 pan-specific and 913 phosphosite-specific antibodies. This approach, innovatively applied to bdEP, allowed the identification of 150 proteins whose expression levels and/or phosphorylation patterns were significantly altered across AD cases. Gene Ontology enrichment and Reactome pathway analysis unraveled potentially relevant molecular targets and disease-associated pathways, and protein-protein interaction networks were constructed to highlight key targets. The discriminatory value of both the total proteome and the phosphoproteome was evaluated by univariate and multivariate approaches. This pilot experiment supports that bdEP are enriched in phosphotargets relevant in an AD context, holding value as peripheral biomarker candidates for disease diagnosis.

Cross-species comparative hippocampal transcriptomics in Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial pathology, with most cases having a sporadic origin. Recently, knock-in (KI) mouse models, such as the novel humanized amyloid-β (hAβ)-KI, have been developed to better resemble sporadic human AD.

METHODS

Here, we compared hippocampal publicly available transcriptomic profiles of transgenic (5xFAD and APP/PS1) and KI (hAβ-KI) mouse models with early- (EOAD) and late- (LOAD) onset AD patients.

RESULTS

The three mouse models presented more Gene Ontology biological processes terms and enriched signaling pathways in common with LOAD than with EOAD individuals. Experimental validation of consistently dysregulated genes revealed five altered in mice (SLC11A1, S100A6, CD14, CD33, and C1QB) and three in humans (S100A6, SLC11A1, and KCNK). Finally, we identified 17 transcription factors potentially acting as master regulators of AD.

CONCLUSION

Our cross-species analyses revealed that the three mouse models presented a remarkable similarity to LOAD, with the hAβ-KI being the more specific one.

From zinc homeostasis to disease progression: Unveiling the neurodegenerative puzzle

Zinc is a crucial trace element in the human body, playing a role in various physiological processes such as oxidative stress, neurotransmission, protein synthesis, and DNA repair. The zinc transporters (ZnTs) family members are responsible for exporting intracellular zinc, while Zrt- and Irt-like proteins (ZIPs) are involved in importing extracellular zinc. These processes are essential for maintaining cellular zinc homeostasis. Imbalances in zinc metabolism have been linked to the development of neurodegenerative diseases. Disruptions in zinc levels can impact the survival and activity of neurons, thereby contributing to the progression of neurodegenerative diseases through mechanisms like cell apoptosis regulation, protein phase separation, ferroptosis, oxidative stress, and neuroinflammation. Therefore, conducting a systematic review of the regulatory network of zinc and investigating the relationship between zinc dysmetabolism and neurodegenerative diseases can enhance our understanding of the pathogenesis of these diseases. Additionally, it may offer new insights and approaches for the treatment of neurodegenerative diseases.

Identifying pyroptosis- and inflammation-related genes in intracranial aneurysms based on bioinformatics analysis

BACKGROUND

Intracranial aneurysm (IA) is the most common cerebrovascular disease, and subarachnoid hemorrhage caused by its rupture can seriously impede nerve function. Pyroptosis is an inflammatory mode of cell death whose underlying mechanisms involving the occurrence and rupture of IAs remain unclear. In this study, using bioinformatics analysis, we identified the potential pyroptosis-related genes (PRGs) and performed their inflammatory response mechanisms in IAs.

METHODS

The mRNA expression matrix of the IA tissue was obtained from the Gene Expression Omnibus database, and 51 PRGs were obtained from previous articles collected from PubMed. The differentially expressed PRGs (DEPRGs) were performed using R software. Subsequently, we performed enrichment analysis, constructed a protein-protein interaction network, performed weighted gene coexpression network analysis (WGCNA) and external validation using another dataset, and identified a correlation between hub genes and immune cell infiltration. Finally, the expression and tissue distribution of these hub genes in IA tissues were detected using Western blotting and immunohistochemical (IHC) staining.

RESULTS

In total, 12 DEPRGs associated with IA were identified in our analysis, which included 11 up-regulated and one down-regulated genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the DEPRGs were mostly enriched in the NOD-like receptor signaling pathway, interleukin-1 beta production, and the inflammasome complex. Three hub genes, NLRP3, IL1B and IL18, were identified using Cytoscape software and the WGCNA correlation module, and external validation revealed statistically significant differences between the expression of these hub genes in the ruptured and unruptured aneurysm groups (p < 0.05). Furthermore, all AUC values were > 0.75. Immune cell infiltration analysis suggested that the hub genes are related to CD8 T cell, macrophages and mast cells. Finally, IHC staining revealed that the protein levels of these hub genes were higher in ruptured and unruptured IA tissues than in normal tissues (p < 0.05).

CONCLUSION

The results of bioinformatics analysis showed that pyroptosis is closely related to the formation and rupture of IA, and identified three potential hub genes involved in the pyroptosis and infiltration ofcells. Our findings may improve the understanding of the mechanisms underlying pyroptosis in IA.

Discovery and validation of PURA as a transcription target of 20(S)-protopanaxadiol: Implications for the treatment of cognitive dysfunction

Background

20(S)-protopanaxadiol (PPD), a ginsenoside metabolite, has prominent benefits for the central nervous system, especially in improving learning and memory. However, its transcriptional targets in brain tissue remain unknown.

Methods

In this study, we first used mass spectrometry-based drug affinity responsive target stability (DARTS) to identify the potential proteins of ginsenosides and intersected them with the transcription factor library. Second, the transcription factor PURA was confirmed as a target of PPD by biolayer interferometry (BLI) and molecular docking. Next, the effect of PPD on the transcriptional levels of target genes of PURA in brain tissues was determined by qRT-PCR. Finally, bioinformatics analysis was used to analyze the potential biological features of these target proteins.

Results

The results showed three overlapping transcription factors between the proteomics of DARTS and transcription factor library. BLI analysis further showed that PPD had a higher direct interaction with PURA than parent ginsenosides. Subsequently, BLI kinetic analysis, molecular docking, and mutations in key amino acids of PURA indicated that PPD specifically bound to PURA. The results of qRT-PCR showed that PPD could increase the transcription levels of PURA target genes in brain. Finally, bioinformatics analysis showed that these target proteins were involved in learning and memory function.

Conclusion

The above-mentioned findings indicate that PURA is a transcription target of PPD in brain, and PPD upregulate the transcription levels of target genes related to cognitive dysfunction by binding PURA, which could provide a chemical and biological basis for the study of treating cognitive impairment by targeting PURA.

The potential roles of ATF family in the treatment of Alzheimer's disease

Activating transcription factors, ATFs, is a family of transcription factors that activate gene expression and transcription by recognizing and combining the cAMP response element binding proteins (CREB). It is present in various viruses as a cellular gene promoter. ATFs is involved in regulating the mammalian gene expression that is associated with various cell physiological processes. Therefore, ATFs play an important role in maintaining the intracellular homeostasis. ATF2 and ATF3 is mostly involved in mediating stress responses. ATF4 regulates the oxidative metabolism, which is associated with the survival of cells. ATF5 is presumed to regulate apoptosis, and ATF6 is involved in the regulation of endoplasmic reticulum stress (ERS). ATFs is actively studied in oncology. At present, there has been an increasing amount of research on ATFs for the treatment of neurological diseases. Here, we have focused on the different types of ATFs and their association with Alzheimer's disease (AD). The level of expression of different ATFs have a significant difference in AD patients when compared to healthy control. Recent studies have suggested that ATFs are implicated in the pathogenesis of AD, such as neuronal repair, maintenance of synaptic activity, maintenance of cell survival, inhibition of apoptosis, and regulation of stress responses. In this review, the potential role of ATFs for the treatment of AD has been highlighted. In addition, we have systematically reviewed the progress of research on ATFs in AD. This review will provide a basic and innovative understanding on the pathogenesis and treatment of AD.

Newer modalities in the management of Alzheimer's dementia along with the role of aducanumab and lecanemab in the treatment of its refractory cases

Alzheimer's disease (AD) is a common neurological condition characterized by a gradual and progressive decline in memory, language, emotion, and cognition. It mainly affects elderly people. Due to the effects of AD, pharmaceutical medications and anticholinesterases have been vigorously promoted and approved by the FDA as a form of AD therapy. However, it was progressively found that these drugs did not address the underlying causes of AD pathogenesis; rather, they focused on the symptoms in order to enhance patients' cognitive outcomes. Consequently, a hunt for superior disease-modifying options is launched. Designing new therapeutic agents requires a thorough understanding of the neuroprotective processes and varied functions carried out by certain genes, and antibodies. In this comprehensive review article, we give an overview of the history of Alzheimer's disease, the significance of the blood-brain barrier in determining the scope of treatment options, as well as the advantages and disadvantages of the current therapeutic treatment options for stem cell therapy, immunotherapy, regenerative therapy, and improved Alzheimer's disease care and diagnosis. We have also included a discussion on the potential role of aducanumab and Lecanemab as a cutting-edge therapy in refractory Alzheimer's disease patients. Lecanemab has been recently approved by the FDA for the treatment of Alzheimer's disease.

Development of the brain ventricular system from a comparative perspective

The brain ventricular system (BVS) consists of brain ventricles and channels filled with cerebrospinal fluid (CSF). Disturbance of CSF flow has been linked to scoliosis and neurodegenerative diseases, including hydrocephalus. This could be due to defects of CSF production by the choroid plexus or impaired CSF movement over the ependyma dependent on motile cilia. Most vertebrates have horizontal body posture. They retain additional evolutionary innovations assisting CSF flow, such as the Reissner fiber. The causes of hydrocephalus have been studied using animal models including rodents (mice, rats, hamsters) and zebrafish. However, the horizontal body posture reduces the effect of gravity on CSF flow, which limits the use of mammalian models for scoliosis. In contrast, fish swim against the current and experience a forward-to-backward mechanical force akin to that caused by gravity in humans. This explains the increased popularity of the zebrafish model for studies of scoliosis. "Slit-ventricle" syndrome is another side of the spectrum of BVS anomalies. It develops because of insufficient inflation of the BVS. Recent advances in zebrafish functional genetics have revealed genes that could regulate the development of the BVS and CSF circulation. This review will describe the BVS of zebrafish, a typical teleost, and vertebrates in general, in comparative perspective. It will illustrate the usefulness of the zebrafish model for developmental studies of the choroid plexus (CP), CSF flow and the BVS.

A comparative study of COVID-19 transcriptional signatures between clinical samples and preclinical cell models in the search for disease master regulators and drug repositioning candidates

Coronavirus disease 2019 (COVID-19) is an acute viral disease with millions of cases worldwide. Although the number of daily new cases and deaths has been dropping, there is still a need for therapeutic alternatives to deal with severe cases. A promising strategy to prospect new therapeutic candidates is to investigate the regulatory mechanisms involved in COVID-19 progression using integrated transcriptomics approaches. In this work, we aimed to identify COVID-19 Master Regulators (MRs) using a series of publicly available gene expression datasets of lung tissue from patients which developed the severe form of the disease. We were able to identify a set of six potential COVID-19 MRs related to its severe form, namely TAL1, TEAD4, EPAS1, ATOH8, ERG, and ARNTL2. In addition, using the Connectivity Map drug repositioning approach, we identified 52 different drugs which could be used to revert the disease signature, thus being candidates for the design of novel clinical treatments. Furthermore, we compared the identified signature and drugs with the ones obtained from the analysis of nasopharyngeal swab samples from infected patients and preclinical cell models. This comparison showed significant similarities between them, although also revealing some limitations on the overlap between clinical and preclinical data in COVID-19, highlighting the need for careful selection of the best model for each disease stage.

Bioinformatic Analysis of Human Cumulus Cells to Unravel Cellular's Processes that Could Be Used to Establish Oocyte Quality Biomarkers with Clinical Application

Metadata analysis of public microarray datasets using bioinformatics tools has been successfully used in several biomedical fields in the search for biomarkers. In reproductive science, there is an urgent need for the establishment of oocyte quality biomarkers that could be used in the clinical environment to increase the chances of successful outcomes in treatment cycles. Adaptive cellular processes observed in cumulus oophorus cells reflect the conditions of the follicular microenvironment and may thus bring relevant information of oocyte's conditions. Here we analyzed human cumulus cells gene expression datasets in search of predictors of oocyte quality, a strategy which uncovered several cellular processes positively and negatively associated with embryo development and pregnancy potential. Secondly, the expression levels of genes that were present in the majority of processes observed were validated in house with clinical samples. Our data confirmed the association of the selected biomarkers with blastocyst formation and pregnancy potential rates, independently of patients' clinical characteristics such as diagnosis, age, BMI, and stimulation protocol applied. This study shows that bioinformatic analysis of cellular processes can be successfully used to elucidate possible oocyte quality biomarkers. Our data reinforces the need to consider clinical characteristics of patients when selecting relevant biomarkers to be used in the clinical environment and suggests a combination of positive (PTGS2) and negative (CYPB1) quality biomarkers as a robust strategy for a complementary oocyte selection tool, potentially increasing assisted reproduction success rates. Also, GPX4 expression as pregnancy potential biomarker is indicated here as a possibility for further investigations.

Bioinformatics Analysis of Protein Homologues of Magnetotactic Bacteria Magnetosome Island Proteins in Human Proteome

Background. The number of biogenic magnetic nanoparticles (BMN), present in human organs and tissues in the form of magnetite (ferrimagnetic iron oxide), increases in oncological and neurodegenerative diseases. Therefore, the study of homologues of BMN biomineralization proteins (mam-proteins) of magnetotaxis bacteria (MTB) in human proteome is relevant task. This concern is due primarily to the expediency of establishing patterns of changes in the expression of these proteins and searching for correlations with oncological and neurodegenerative diseases. Objective. We are aimed to conduct the bioinformatic analysis of homologues of MTB mam-proteins in humans and to determine the patterns of changes in the expression of these proteins, as well as to search for their connections with the specified diseases. This will allow to identify the main candidate proteins (among the known homologues of MTB mam-proteins in humans) for experimental verification of their participation in the genetically programmed mechanism of BMN biosynthesis in humans. Methods. The methods of comparative genomics were used, in particular the BLAST (Basic Local Alignment Search Tool) program of the NCBI database. Database tools were also used: NCBI Conserved Domain Search, The Cancer Genome Atlas database, Ensembl database. Results. The bioinformatic analysis of 16 homologues of MTB mam-proteins in humans was carried out, namely: PEX5, ANAPC7, CDC23, CDC27 and SGTA – homologues of MamA in MTB; SLC30A4, SLC30A9, SLC39A3 and SLC39A4 – homologs of MamB and MamM in MTB; HTRA1, HTRA2, HTRA3 and HTRA4 – MamO and MamE homologues in MTB; SCRIB, PDZK1 and PDZD3 – MamE homologues in MTB. Using pairwise alignments, the degree of homology between the mam-proteins of the MTB magnetosome island and the corresponding human proteins was determined, conserved domains and their functions were determined, changes in their expression levels in cancer and normal conditions were determined by analyzing the relevant databases, and the metabolic pathways to which the data proteins are involved were analysed. The analysis of the obtained data allowed to assume the presence of the main homologues of the MTB mam-proteins of the magnetosome island in humans, which cause an increase in the level of BMN in oncological and neurodegenerative diseases, namely: an increase in the expression level of the proteins PEX5, ANAPC7 (homologs of MamA), SLC39A3, SLC39A4 (homologs of MamB and MamM), HTRA4 (MamO and MamE homolog) and SCRIB (MamE homolog). Conclusions. The obtained data allow us to assume that the proteins PEX5, ANAPC7, SGTA, SLC39A3, SLC39A4, HTRA4 and SCRIB are the main homologues of the MTB mam-proteins in humans and cause an increase in the level of BMN in oncological and neurodegenerative diseases.

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

Targeting Microglia in Alzheimer’s Disease: From Molecular Mechanisms to Potential Therapeutic Targets for Small Molecules

Alzheimer’s disease (AD) is a common, progressive, and devastating neurodegenerative disorder that mainly affects the elderly. Microglial dysregulation, amyloid-beta (Aβ) plaques, and intracellular neurofibrillary tangles play crucial roles in the pathogenesis of AD. In the brain, microglia play roles as immune cells to provide protection against virus injuries and diseases. They have significant contributions in the development of the brain, cognition, homeostasis of the brain, and plasticity. Multiple studies have confirmed that uncontrolled microglial function can result in impaired microglial mitophagy, induced Aβ accumulation and tau pathology, and a chronic neuroinflammatory environment. In the brain, most of the genes that are associated with AD risk are highly expressed by microglia. Although it was initially regarded that microglia reaction is incidental and induced by dystrophic neurites and Aβ plaques. Nonetheless, it has been reported by genome-wide association studies that most of the risk loci for AD are located in genes that are occasionally uniquely and highly expressed in microglia. This finding further suggests that microglia play significant roles in early AD stages and they be targeted for the development of novel therapeutics. In this review, we have summarized the molecular pathogenesis of AD, microglial activities in the adult brain, the role of microglia in the aging brain, and the role of microglia in AD. We have also particularly focused on the significance of targeting microglia for the treatment of AD.

Functional and Phenotypic Diversity of Microglia: Implication for Microglia-Based Therapies for Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and is closely associated with the accumulation of β-amyloid (Aβ) and neurofibrillary tangles (NFTs). Apart from Aβ and NFT pathologies, AD patients also exhibit a widespread microglial activation in various brain regions with elevated production of pro-inflammatory cytokines, a phenomenon known as neuroinflammation. In healthy central nervous system, microglia adopt ramified, “surveying” phenotype with compact cell bodies and elongated processes. In AD, the presence of pathogenic proteins such as extracellular Aβ plaques and hyperphosphorylated tau, induce the transformation of ramified microglia into amoeboid microglia. Ameboid microglia are highly phagocytic immune cells and actively secrete a cascade of pro-inflammatory cytokines and chemokines. However, the phagocytic ability of microglia gradually declines with age, and thus the clearance of pathogenic proteins becomes highly ineffective, leading to the accumulation of Aβ plaques and hyperphosphorylated tau in the aging brain. The accumulation of pathogenic proteins further augments the neuroinflammatory responses and sustains the activation of microglia. The excessive production of pro-inflammatory cytokines induces a massive loss of functional synapses and neurons, further worsening the disease condition of AD. More recently, the identification of a subset of microglia by transcriptomic studies, namely disease-associated microglia (DAM), the progressive transition from homeostatic microglia to DAM is TREM2-dependent and the homeostatic microglia gradually acquire the state of DAM during the disease progression of AD. Recent in-depth transcriptomic analysis identifies ApoE and Trem2 from microglia as the major risk factors for AD pathogenesis. In this review, we summarize current understandings of the functional roles of age-dependent microglial activation and neuroinflammation in the pathogenesis of AD. To this end, the exponential growth in transcriptomic data provides a solid foundation for in silico drug screening and gains further insight into the development of microglia-based therapeutic interventions for AD.

Parkin as a Molecular Bridge Linking Alzheimer’s and Parkinson’s Diseases?

Alzheimer’s (AD) and Parkinson’s (PD) diseases are two distinct age-related pathologies that are characterized by various common dysfunctions. They are referred to as proteinopathies characterized by ubiquitinated protein accumulation and aggregation. This accumulation is mainly due to altered lysosomal and proteasomal clearing processes and is generally accompanied by ER stress disturbance, autophagic and mitophagic defects, mitochondrial structure and function alterations and enhanced neuronal cell death. Genetic approaches aimed at identifying molecular triggers responsible for familial forms of AD or PD have helped to understand the etiology of their sporadic counterparts. It appears that several proteins thought to contribute to one of these pathologies are also likely to contribute to the other. One such protein is parkin (PK). Here, we will briefly describe anatomical lesions and genetic advances linked to AD and PD as well as the main cellular processes commonly affected in these pathologies. Further, we will focus on current studies suggesting that PK could well participate in AD and thereby act as a molecular bridge between these two pathologies. In particular, we will focus on the transcription factor function of PK and its newly described transcriptional targets that are directly related to AD- and PD-linked cellular defects.

Identification of Binding Proteins for TSC22D1 Family Proteins Using Mass Spectrometry

TSC-22 (TGF-β stimulated clone-22) has been reported to induce differentiation, growth inhibition, and apoptosis in various cells. TSC-22 is a member of a family in which many proteins are produced from four different family genes. TSC-22 (corresponding to TSC22D1-2) is composed of 144 amino acids translated from a short variant mRNA of the TSC22D1 gene. In this study, we attempted to determine the intracellular localizations of the TSC22D1 family proteins (TSC22D1-1, TSC-22 (TSC22D1-2), and TSC22(86) (TSC22D1-3)) and identify the binding proteins for TSC22D1 family proteins by mass spectrometry. We determined that TSC22D1-1 was mostly localized in the nucleus, TSC-22 (TSC22D1-2) was localized in the cytoplasm, mainly in the mitochondria and translocated from the cytoplasm to the nucleus after DNA damage, and TSC22(86) (TSC22D1-3) was localized in both the cytoplasm and nucleus. We identified multiple candidates of binding proteins for TSC22D1 family proteins in in vitro pull-down assays and in vivo binding assays. Histone H1 bound to TSC-22 (TSC22D1-2) or TSC22(86) (TSC22D1-3) in the nucleus. Guanine nucleotide-binding protein-like 3 (GNL3), which is also known as nucleostemin, bound to TSC-22 (TSC22D1-2) in the nucleus. Further investigation of the interaction of the candidate binding proteins with TSC22D1 family proteins would clarify the biological roles of TSC22D1 family proteins in several cell systems.

Reverse Engineering of the Pediatric Sepsis Regulatory Network and Identification of Master Regulators

Sepsis remains a leading cause of death in ICUs all over the world, with pediatric sepsis accounting for a high percentage of mortality in pediatric ICUs. Its complexity makes it difficult to establish a consensus on genetic biomarkers and therapeutic targets. A promising strategy is to investigate the regulatory mechanisms involved in sepsis progression, but there are few studies regarding gene regulation in sepsis. This work aimed to reconstruct the sepsis regulatory network and identify transcription factors (TFs) driving transcriptional states, which we refer to here as master regulators. We used public gene expression datasets to infer the co-expression network associated with sepsis in a retrospective study. We identified a set of 15 TFs as potential master regulators of pediatric sepsis, which were divided into two main clusters. The first cluster corresponded to TFs with decreased activity in pediatric sepsis, and GATA3 and RORA, as well as other TFs previously implicated in the context of inflammatory response. The second cluster corresponded to TFs with increased activity in pediatric sepsis and was composed of TRIM25, RFX2, and MEF2A, genes not previously described as acting in a coordinated way in pediatric sepsis. Altogether, these results show how a subset of master regulators TF can drive pathological transcriptional states, with implications for sepsis biology and treatment.

Nuclear dynamics and stress responses in Alzheimer's disease

In response to extracellular and intracellular stressors, the nucleus and nuclear compartments undergo distinct molecular changes to maintain cell homeostasis. In the context of Alzheimer’s disease, misfolded proteins and various cellular stressors lead to profound structural and molecular changes at the nucleus. This review summarizes recent research on nuclear alterations in AD development, from the nuclear envelope changes to chromatin and epigenetic regulation and then to common nuclear stress responses. Finally, we provide our thoughts on the importance of understanding cell-type-specific changes and identifying upstream causal events in AD pathogenesis and highlight novel sequencing and gene perturbation technologies to address those challenges.

Recent Trends in the Management of Alzheimer's Disease: Current Therapeutic Options and Drug Repurposing Approaches

Alzheimer's disease is one of the most progressive forms of dementia, ultimately leading to death in aged populations. The major hallmarks of Alzheimer's disease include deposition of extracellular amyloid senile plaques and intracellular neurofibrillary tangles in brain neuronal cells. Although there are classical therapeutic options available for the treatment of the diseases, however, they provide only a symptomatic relief and do not modify the molecular pathophysiological course of the disease. Recent research advances in Alzheimer's disease have highlighted the potential role of anti-amyloid, anti-tau, and anti-inflammatory therapies. However, these therapies are still in different phases of pre-clinical/clinical development. In addition, drug repositioning/repurposing is another interesting and promising approach to explore rationalized options for the treatment of Alzheimer's disease. This review discusses the different aspects of the pathophysiological mechanism involved in the progression of Alzheimer's disease along with the limitations of current therapies. Furthermore, this review also highlights emerging investigational drugs along with recent drug repurposing approaches for Alzheimer's disease.

Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children

Identification of exact causative genes is important for in silico drug repositioning based on drug-gene-disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains three steps: network-specific core gene (NCG) identification, potential therapeutic drug repositioning, and candidate drug validation. First, through the analysis of transcriptome data for 178 brain tissues, gene network analysis identified 365 NCGs in 18 coexpression modules that were significantly correlated with ASD. Second, we evaluated two proposed drug repositioning methods. In one novel approach (dtGSEA), we used the NCGs to probe drug-gene interaction data and identified 35 candidate drugs. In another approach, we compared NCG expression patterns with drug-induced transcriptome data from the Connectivity Map database and found 46 candidate drugs. Third, we validated the candidate drugs using an in-house mental diseases and compounds knowledge graph (MCKG) that contained 7509 compounds, 505 mental diseases, and 123,890 edges. We found a total of 42 candidate drugs that were associated with mental illness, among which 10 drugs (baclofen, sulpiride, estradiol, entinostat, everolimus, fluvoxamine, curcumin, calcitriol, metronidazole, and zinc) were postulated to be associated with ASD. This study proposes a powerful network-based drug repositioning framework and also provides candidate drugs as well as potential drug targets for the subsequent development of ASD therapeutic drugs.

Decreased MEF2A Expression Regulated by Its Enhancer Methylation Inhibits Autophagy and May Play an Important Role in the Progression of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by amyloid plaques and neurofibrillary tangles which significantly affects people's life quality. Recently, AD has been found to be closely related to autophagy. The aim of this study was to identify autophagy-related genes associated with the pathogenesis of AD from multiple types of microarray and sequencing datasets using bioinformatics methods and to investigate their role in the pathogenesis of AD in order to identify novel strategies to prevent and treat AD. Our results showed that the autophagy-related genes were significantly downregulated in AD and correlated with the pathological progression. Furthermore, enrichment analysis showed that these autophagy-related genes were regulated by the transcription factor myocyte enhancer factor 2A (MEF2A), which had been confirmed using si-MEF2A. Moreover, the single-cell sequencing data suggested that MEF2A was highly expressed in microglia. Methylation microarray analysis showed that the methylation level of the enhancer region of MEF2A in AD was significantly increased. In conclusion, our results suggest that AD related to the increased methylation level of MEF2A enhancer reduces the expression of MEF2A and downregulates the expression of autophagy-related genes which are closely associated with AD pathogenesis, thereby inhibiting autophagy.

Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

Melatonin and Melatonergic Influence on Neuronal Transcription Factors: Implications for the Development of Novel Therapies for Neurodegenerative Disorders

Melatonin is a multifunctional signalling molecule that is secreted by the mammalian pineal gland, and also found in a number of organisms including plants and bacteria. Research has continued to uncover an ever-increasing number of processes in which melatonin is known to play crucial roles in mammals. Amongst these functions is its contribution to cell multiplication, differentiation and survival in the brain. Experimental studies show that melatonin can achieve these functions by influencing transcription factors which control neuronal and glial gene expression. Since neuronal survival and differentiation are processes that are important determinants of the pathogenesis, course and outcome of neurodegenerative disorders; the known and potential influences of melatonin on neuronal and glial transcription factors are worthy of constant examination. In this review, relevant scientific literature on the role of melatonin in preventing or altering the course and outcome of neurodegenerative disorders, by focusing on melatonin's influence on transcription factors is examined. A number of transcription factors whose functions can be influenced by melatonin in neurodegenerative disease models have also been highlighted. Finally, the therapeutic implications of melatonin's influences have also been discussed and the potential limitations to its applications have been highlighted.

Spectrum of thyroid dysfunction and dementia: a dose-response meta-analysis of 344,248 individuals from cohort studies

Thyroid hormone, as a modifiable risk factor for dementia, promotes neurocognitive function and regulates metabolic processes. Various studies have defined different thyroid-stimulating hormone cutoffs, but the safest thyroid-stimulating hormone concentration was absent. A dose-response meta-analysis describing the overall functional relation and identifying exposure intervals associated with a higher or lower disease risk is thus desirable. Therefore, our current analysis was conducted to understand the influence of thyroid dysfunction on dementia risk. We searched PubMed, Embase, and Web of Science before May 1, 2020 for human studies published in English. Studies were considered for inclusion if they used a cohort study design to measure the risk of dementia in different thyroid function status groups, diagnosed thyroid functional status and all-cause dementia, included participants aged >18 years, and provided quantitative measures of data. The analysis contained 17 articles with 344,248 individuals with a 7.8-year mean follow-up. Ten studies with 329,287 participants indicated that only subclinical hyperthyroidism was associated with an increased risk of dementia. In contrast, subclinical hypothyroidism, clinical hyperthyroidism, and clinical hypothyroidism did not affect dementia. In the dose-response meta-analysis with 46,417 samples from 11 studies, the association of thyroid-stimulating hormone with the risk of dementia exhibited a U-shaped curve. Our study indicated that subclinical hyperthyroidism was associated with the risk of dementia and the thyroid-stimulating hormone concentration at around 1.55-1.60 mU/L as the optimum range for the risk of dementia.

A machine learning approach to unmask novel gene signatures and prediction of Alzheimer's disease within different brain regions

Alzheimer's disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unraveled the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.

Gauging the role and impact of drug interactions and repurposing in neurodegenerative disorders

Neurodegenerative diseases (ND) are of vast origin which are characterized by gradual progressive loss of neurons in the brain region. ND can be classified according to the clinical symptoms present (e.g. Cognitive decline, hyperkinetic, and hypokinetic movements disorder) or by the pathological protein deposited (e.g., Amyloid, tau, Alpha-synuclein, TDP-43). Alzheimer's disease preceded by Parkinson's is the most prevalent form of ND world-wide. Multiple factors like aging, genetic mutations, environmental factors, gut microbiota, blood-brain barrier microvascular complication, etc. may increase the predisposition towards ND. Genetic mutation is a major contributor in increasing the susceptibility towards ND, the concept of one disease-one gene is obsolete and now multiple genes are considered to be involved in causing one particular disease. Also, the involvement of multiple pathological mechanisms like oxidative stress, neuroinflammation, mitochondrial dysfunction, etc. contributes to the complexity and makes them difficult to be treated by traditional mono-targeted ligands. In this aspect, the Poly-pharmacological drug approach which targets multiple pathological pathways at the same time provides the best way to treat such complex networked CNS diseases. In this review, we have provided an overview of ND and their pathological origin, along with a brief description of various genes associated with multiple diseases like Alzheimer's, Parkinson's, Multiple sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), Huntington's and a comprehensive detail about the Poly-pharmacology approach (MTDLs and Fixed-dose combinations) along with their merits over the traditional single-targeted drug is provided. This review also provides insights into current repurposing strategies along with its regulatory considerations.

The CSRNP Gene Family Serves as a Prognostic Biomarker in Clear Cell Renal Cell Carcinoma

The cysteine-serine-rich nuclear protein (CSRNP) family has prognostic value for various cancers. However, the association between this proteins and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to determine the prognostic value of the CSRNP family for patients with ccRCC. Therefore, the gene expression profiling interactive analysis database was used to analyze the mRNA expression of CSRNP family members (CSRNPs) in relation with survival. Combined and independent prognostic values of CSRNPs were evaluated using SurvExpress and multivariate Cox regression analyses, respectively. Potential signaling pathways impacted by CSRNPs were evaluated using Metascape. Associations between the CSRNP family and immunocyte infiltration were determined from single-sample gene set enrichment analysis. Both cBioPortal and MethSurv were used to explore whether genomic and epidemic alterations might influence prognosis. We found that when both CSRNP1 and CSRNP3 had a low expression, patients with ccRCC had a worse overall survival (OS). Therefore, a prognostic signature was constructed as follows: risk score = -0.224 × exp_{mRNA of CSRNP1} + 0.820 × exp_{mRNA of CSRNP2} - 1.428 × exp_{mRNA of CSRNP3} . We found that OS was worse in patients from the high- than from the low-risk groups (AUC = 0.69). Moreover, this signature was an independent predictor after adjusting for clinical features. Functional enrichment analysis positively associated CSRNPs with the acute inflammatory response and humoral immune response pathways. This was validated by correlating each CSRNP with 28 types of immunocytes in tumor and normal tissues. A higher expression of CSRNP1 and CSRNP3 was associated with a better prognosis in both the high- and low-mutant burden groups. Cg19538674, cg07772537, and cg07811002 of CSRNP1, CSRNP2, and CSRNP3, respectively, were the predominant DNA methylation sites affecting OS. The CSRNP gene family signature may serve as a prognostic biomarker for predicting OS in patients with ccRCC. The association between CSRNPs and immune infiltration might offer future clinical treatment options.

Drug Repurposing to Treat Glucocorticoid Resistance in Asthma

Corticosteroid resistance causes significant morbidity in asthma, and drug repurposing may identify timely and cost-effective adjunctive treatments for corticosteroid resistance. In 95 subjects from the Childhood Asthma Management Program (CAMP) and 19 subjects from the Severe Asthma Research Program (SARP), corticosteroid response was measured by the change in percent predicted forced expiratory volume in one second (FEV₁). In each cohort, differential gene expression analysis was performed comparing poor (resistant) responders, defined as those with zero to negative change in FEV₁, to good responders, followed by Connectivity Map (CMap) analysis to identify inversely associated (i.e., negatively connected) drugs that reversed the gene expression profile of poor responders to resemble that of good responders. Mean connectivity scores weighted by sample size were calculated. The top five drug compound candidates underwent in vitro validation in NF-κB-based luciferase reporter A549 cells stimulated by IL-1β ± dexamethasone. In CAMP and SARP, 134 and 178 respective genes were differentially expressed in poor responders. CMap analysis identified 46 compounds in common across both cohorts with connectivity scores < −50. γ-linolenic acid, ampicillin, exemestane, brinzolamide, and INCA-6 were selected for functional validation. γ-linolenic acid, brinzolamide, and INCA-6 significantly reduced IL-1β induced luciferase activity and potentiated the anti-inflammatory effect of dexamethasone in A549/NF-κB-luc reporter cells. These results demonstrate how existing drugs, including γ-linolenic acid, brinzolamide, and INCA-6, may be repurposed to improve corticosteroid response in asthmatics.

Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.

Computational Identification of Master Regulators Influencing Trypanotolerance in Cattle

African Animal Trypanosomiasis (AAT) is transmitted by the tsetse fly which carries pathogenic trypanosomes in its saliva, thus causing debilitating infection to livestock health. As the disease advances, a multistage progression process is observed based on the progressive clinical signs displayed in the host’s body. Investigation of genes expressed with regular monotonic patterns (known as Monotonically Expressed Genes (MEGs)) and of their master regulators can provide important clue for the understanding of the molecular mechanisms underlying the AAT disease. For this purpose, we analysed MEGs for three tissues (liver, spleen and lymph node) of two cattle breeds, namely trypanosusceptible Boran and trypanotolerant N’Dama. Our analysis revealed cattle breed-specific master regulators which are highly related to distinguish the genetic programs in both cattle breeds. Especially the master regulators MYC and DBP found in this study, seem to influence the immune responses strongly, thereby susceptibility and trypanotolerance of Boran and N’Dama respectively. Furthermore, our pathway analysis also bolsters the crucial roles of these master regulators. Taken together, our findings provide novel insights into breed-specific master regulators which orchestrate the regulatory cascades influencing the level of trypanotolerance in cattle breeds and thus could be promising drug targets for future therapeutic interventions.

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.

Prediction of hub genes of Alzheimer's disease using a protein interaction network and functional enrichment analysis

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals’ memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.

Parkinson's Disease Master Regulators on Substantia Nigra and Frontal Cortex and Their Use for Drug Repositioning

Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases. Available evidences support the view of PD as a complex disease, being the outcome of interactions between genetic and environmental factors. In face of diagnosis and therapy challenges, and the elusive PD etiology, the use of alternative methodological approaches for the elucidation of the disease pathophysiological mechanisms and proposal of novel potential therapeutic interventions has become increasingly necessary. In the present study, we first reconstructed the transcriptional regulatory networks (TN), centered on transcription factors (TF), of two brain regions affected in PD, the substantia nigra pars compacta (SNc) and the frontal cortex (FCtx). Then, we used case-control studies data from these regions to identify TFs working as master regulators (MR) of the disease, based on region-specific TNs. Twenty-nine regulatory units enriched with differentially expressed genes were identified for the SNc, and twenty for the FCtx, all of which were considered MR candidates for PD. Three consensus MR candidates were found for SNc and FCtx, namely ATF2, SLC30A9, and ZFP69B. In order to search for novel potential therapeutic interventions, we used these consensus MR candidate signatures as input to the Connectivity Map (CMap), a computational drug repositioning webtool. This analysis resulted in the identification of four drugs that reverse the expression pattern of all three MR consensus simultaneously, benperidol, harmaline, tubocurarine chloride, and vorinostat, thus suggested as novel potential PD therapeutic interventions.

Drug Development for Central Nervous System Diseases Using In vitro Blood-brain Barrier Models and Drug Repositioning

An important goal of biomedical research is to translate basic research findings into practical clinical implementation. Despite the advances in the technology used in drug discovery, the development of drugs for central nervous system diseases remains challenging. The failure rate for new drugs targeting important central nervous system diseases is high compared to most other areas of drug discovery. The main reason for the failure is the poor penetration efficacy across the blood-brain barrier. The blood-brain barrier represents the bottleneck in central nervous system drug development and is the most important factor limiting the future growth of neurotherapeutics. Meanwhile, drug repositioning has been becoming increasingly popular and it seems a promising field in central nervous system drug development. In vitro blood-brain barrier models with high predictability are expected for drug development and drug repositioning. In this review, the recent progress of in vitro BBB models and the drug repositioning for central nervous system diseases will be discussed.

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.

Master regulator genes and their impact on major diseases

Master regulator genes (MRGs) have become a hot topic in recent decades. They not only affect the development of tissue and organ systems but also play a role in other signal pathways by regulating additional MRGs. Because a MRG can regulate the concurrent expression of several genes, its mutation often leads to major diseases. Moreover, the occurrence of many tumors and cardiovascular and nervous system diseases are closely related to MRG changes. With the development in omics technology, an increasing amount of investigations will be directed toward MRGs because their regulation involves all aspects of an organism’s development. This review focuses on the definition and classification of MRGs as well as their influence on disease regulation.

Role of Forkhead Transcription Factors of the O Class (FoxO) in Development and Progression of Alzheimer's Disease

In the Central Nervous System (CNS), a specific loss of focal neurons leads to mental and neurological disorders like dementia, Alzheimer's Disease (AD), Huntington's disease, Parkinson's disease, etc. AD is a neurological degenerative disorder, which is progressive and irreversible in nature and is the widely recognized reason for dementia in the geriatric populace. It affects 10% of people above the age of 65 and is the fourth driving reason for death in the United States. Numerous evidence suggests that the neuronal compartment is not the only genesis of AD, but transcription factors also hold significant importance in the occurrence and advancement of the disease. It is the need of the time to find the novel molecular targets and new techniques for treating or slowing down the progression of neurological disorders, especially AD. In this article, we summarised a conceivable association between transcriptional factors and their defensive measures against neurodegeneration and AD. The mammalian forkhead transcription factors of the class O (FoxO) illustrate one of the potential objectives for the development of new methodologies against AD and other neurocognitive disorders. The presence of FoxO is easily noticeable in the "cognitive centers" of the brain, specifically in the amygdala, hippocampus, and the nucleus accumbens. FoxO proteins are the prominent and necessary factors in memory formation and cognitive functions. FoxO also assumes a pertinent role in the protection of multiple cells in the brain by controlling the involving mechanism of autophagy and apoptosis and also modulates the process of phosphorylation of the targeted protein, thus FoxO must be a putative target in the mitigation of AD. This review features the role of FoxO as an important biomarker and potential new targets for the treatment of AD.

Integrated DNA methylation analysis reveals a potential role for ANKRD30B in Williams syndrome

Williams syndrome (WS) is a rare genetic disorder, caused by a microdeletion at the 7q11.23 region. WS exhibits a wide spectrum of features including hypersociability, which contrasts with social deficits typically associated with autism spectrum disorders. The phenotypic variability in WS likely involves epigenetic modifications; however, the nature of these events remains unclear. To better understand the role of epigenetics in WS phenotypes, we integrated DNA methylation and gene expression profiles in blood from patients with WS and controls. From these studies, 380 differentially methylated positions (DMPs), located throughout the genome, were identified. Systems-level analysis revealed multiple co-methylation modules linked to intermediate phenotypes of WS, with the top-scoring module related to neurogenesis and development of the central nervous system. Notably, ANKRD30B, a promising hub gene, was significantly hypermethylated in blood and downregulated in brain tissue from individuals with WS. Most CpG sites of ANKRD30B in blood were significantly correlated with brain regions. Furthermore, analyses of gene regulatory networks (GRNs) yielded master regulator transcription factors associated with WS. Taken together, this systems-level approach highlights the role of epigenetics in WS, and provides a possible explanation for the complex phenotypes observed in patients with WS.

Metformin Improves Learning and Memory in the SAMP8 Mouse Model of Alzheimer's Disease

Metformin is used for the treatment of insulin resistant diabetes. Diabetics are at an increased risk of developing dementia. Recent epidemiological studies suggest that metformin treatment prevents cognitive decline in diabetics. A pilot clinical study found cognitive improvement with metformin in patients with mild cognitive impairment (MCI). Preclinical studies suggest metformin reduces Alzheimer-like pathology in mouse models of Alzheimer's disease (AD). In the current study, we used 11-month-old SAMP8 mice. Mice were given daily injections of metformin at 20 mg/kg/sc or 200 mg/kg/sc for eight weeks. After four weeks, mice were tested in T-maze footshock avoidance, object recognition, and Barnes maze. At the end of the study, brain tissue was collected for analysis of PKC (PKCζ, PKCι, PKCα, PKCγ, PKCɛ), GSK-3β, pGSK-3βser9, pGSK-3βtyr216, pTau404, and APP. Metformin improved both acquisition and retention in SAMP8 mice in T-maze footshock avoidance, retention in novel object recognition, and acquisition in the Barnes maze. Biochemical analysis indicated that metformin increased both atypical and conventional forms of PKC; PKCζ, and PKCα at 20 mg/kg. Metformin significantly increased pGSK-3βser9 at 200 mg/kg, and decreased Aβ at 20 mg/kg and pTau404 and APPc99 at both 20 mg/kg and 200 mg/kg. There were no differences in blood glucose levels between the aged vehicle and metformin treated mice. Metformin improved learning and memory in the SAMP8 mouse model of spontaneous onset AD. Biochemical analysis indicates that metformin improved memory by decreasing APPc99 and pTau. The current study lends support to the therapeutic potential of metformin for AD.

D-serine Ameliorates Motor and Cognitive Impairments in β-amyloid 1-42 Injected Mice by Inhibiting JNK Signaling Pathway

The senile plaque formed by β-amyloid (Aβ) deposition in the brain is one of the main pathological features of Alzheimer's disease (AD), and the c-Jun N-terminal kinase (JNK) signaling pathway plays an important role in the pathogenesis of AD. This study aimed to investigate that D-serine may ameliorate motor and cognitive impairment in Aβ injected mice by inhibiting JNK signaling pathway. Firstly, Kunming mice were injected intrahippocampally with Aβ_1-42 to build AD model. The mice were injected intraperitoneally with saline, D-serine, D-amino acid oxidase (DAAO), and Sodium benzoate (BE) for 10 consecutive days, respectively. Subsequently, the motor and cognitive functions of mice were detected by behavioral tests. The silver staining and immunohistochemical methods were used to detect the distributions of Aβ in the hippocampus of mice. ¹⁸F-2-Fluro-D-deoxy-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) scans were performed to detected glucose metabolism of Aβ_1-42 induced lesions. The expressions of relative JNK factors were detected by immunohistochemistry and Western blot methods. These results showed that Aβ severely impaired the motor and memory abilities of mice. The expressions of glial fibrillary acidic protein (GFAP), tumor necrosis factor (TNF-α), N-methyl-D-aspartate receptor 1 (NMDAR1), phospho-JNK (p-JNK), p-c-Jun and activating transcription factor 2 (ATF2) increased significantly. After D-serine treatment, the abilities of movement and memory of mice were improved, and the clearance rate of Aβ was accelerated. The expressions of GFAP, TNF-α, NMDAR1, p-JNK, p-c-Jun and ATF2 decreased significantly. DAAO and BE were administered to further validate these results. Therefore, this study showed that D-serine could alleviate the cognitive impairment of Aβ_1-42 injected mice by inhibiting JNK signaling pathway. These results provide more evidences for the effect of D-serine on AD and relevant mechanism to treat AD.

Meta-analysis of mouse transcriptomic studies supports a context-dependent astrocyte reaction in acute CNS injury versus neurodegeneration

Background

Neuronal damage in acute CNS injuries and chronic neurodegenerative diseases is invariably accompanied by an astrocyte reaction in both mice and humans. However, whether and how the nature of the CNS insult—acute versus chronic—influences the astrocyte response, and whether astrocyte transcriptomic changes in these mouse models faithfully recapitulate the astrocyte reaction in human diseases remains to be elucidated. We hypothesized that astrocytes set off different transcriptomic programs in response to acute versus chronic insults, besides a shared “pan-injury” signature common to both types of conditions, and investigated the presence of these mouse astrocyte signatures in transcriptomic studies from human neurodegenerative diseases.

Methods

We performed a meta-analysis of 15 published astrocyte transcriptomic datasets from mouse models of acute injury (n = 6) and chronic neurodegeneration (n = 9) and identified pan-injury, acute, and chronic signatures, with both upregulated (UP) and downregulated (DOWN) genes. Next, we investigated these signatures in 7 transcriptomic datasets from various human neurodegenerative diseases.

Results

In mouse models, the number of UP/DOWN genes per signature was 64/21 for pan-injury and 109/79 for acute injury, whereas only 13/27 for chronic neurodegeneration. The pan-injury-UP signature was represented by the classic cytoskeletal hallmarks of astrocyte reaction (Gfap and Vim), plus extracellular matrix (i.e., Cd44, Lgals1, Lgals3, Timp1), and immune response (i.e., C3, Serping1, Fas, Stat1, Stat2, Stat3). The acute injury-UP signature was enriched in protein synthesis and degradation (both ubiquitin-proteasome and autophagy systems), intracellular trafficking, and anti-oxidant defense genes, whereas the acute injury-DOWN signature included genes that regulate chromatin structure and transcriptional activity, many of which are transcriptional repressors. The chronic neurodegeneration-UP signature was further enriched in astrocyte-secreted extracellular matrix proteins (Lama4, Cyr61, Thbs4), while the DOWN signature included relevant genes such as Agl (glycogenolysis), S1pr1 (immune modulation), and Sod2 (anti-oxidant). Only the pan-injury-UP mouse signature was clearly present in some human neurodegenerative transcriptomic datasets.

Conclusions

Acute and chronic CNS injuries lead to distinct astrocyte gene expression programs beyond their common astrocyte reaction signature. However, caution should be taken when extrapolating astrocyte transcriptomic findings from mouse models to human diseases.

A new computational drug repurposing method using established disease-drug pair knowledge

MOTIVATION

Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.

RESULTS

We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing.

AVAILABILITY AND IMPLEMENTATION

The R scripts are available on demand from the authors.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.