scholarly journals Discovering Biomarkers and Pathways Shared by Alzheimer’s Disease and Ischemic Stroke to Identify Novel Therapeutic Targets

Medicina ◽  
2019 ◽  
Vol 55 (5) ◽  
pp. 191 ◽  
Author(s):  
Md. Rezanur Rahman ◽  
Tania Islam ◽  
Md. Shahjaman ◽  
Toyfiquz Zaman ◽  
Hossain Md. Faruquee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Interaction Analysis ◽  
Mapk Signaling ◽  
Protein Protein Interactions ◽  
Microarray Gene Expression ◽  
Pathway Analyses

Background and objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms. Materials and Methods: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD. Results: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein–protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found. Conclusions: Protein–drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.

scholarly journals Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

2020 ◽  
Vol 17 (4) ◽  
pp. 1373 ◽  
Author(s):  
Md. Ali Hossain ◽  
Tania Akter Asa ◽  
Md. Mijanur Rahman ◽  
Shahadat Uddin ◽  
Ahmed A. Moustafa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Gene Expression Omnibus ◽  
Thyroid Adenoma ◽  
Hub Genes ◽  
Microarray Gene Expression ◽  
Genetic Profiling ◽  
Follicular Thyroid Adenoma ◽  
Pathway Analyses

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

scholarly journals Serum Glycoproteomics and Identification of Potential Mechanisms Underlying Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Naphatthakarn Kerdsaeng ◽  
Sittiruk Roytrakul ◽  
Suwannee Chanprasertyothin ◽  
Piangporn Charernwat ◽  
Sirintorn Chansirikarnjana ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Signaling Pathway ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Wnt Signaling Pathway ◽  
Cytokine Signaling ◽  
Control Group ◽  
Protein Protein Interactions ◽  
Liquid Chromatography Tandem Mass

Objectives. This study compares glycoproteomes in Thai Alzheimer’s disease (AD) patients with those of cognitively normal individuals. Methods. Study participants included outpatients with clinically diagnosed AD ( N = 136 ) and healthy controls without cognitive impairment ( N = 183 ). Blood samples were collected from all participants for biochemical analysis and for Apolipoprotein   E (APOE) genotyping by real-time TaqMan PCR assays. Comparative serum glycoproteomic profiling by liquid chromatography-tandem mass spectrometry was then performed to identify differentially abundant proteins with functional relevance. Results. Statistical differences in age, educational level, and APOE ɛ3/ɛ4 and ɛ4/ɛ4 haplotype frequencies were found between the AD and control groups. The frequency of the APOE ɛ4 allele was significantly higher in the AD group than in the control group. In total, 871 glycoproteins were identified, including 266 and 259 unique proteins in control and AD groups, respectively. There were 49 and 297 upregulated and downregulated glycoproteins, respectively, in AD samples compared with the controls. Unique AD glycoproteins were associated with numerous pathways, including Alzheimer’s disease-presenilin pathway (16.6%), inflammation pathway mediated by chemokine and cytokine signaling (9.2%), Wnt signaling pathway (8.2%), and apoptosis signaling pathway (6.7%). Conclusion. Functions and pathways associated with protein-protein interactions were identified in AD. Significant changes in these proteins can indicate the molecular mechanisms involved in the pathogenesis of AD, and they have the potential to serve as AD biomarkers. Such findings could allow us to better understand AD pathology.

scholarly journals Shared Blood Transcriptomic Signatures between Alzheimer’s Disease and Diabetes Mellitus

Biomedicines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Taesic Lee ◽  
Hyunju Lee
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Expression Patterns ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Gene Modules ◽  
Gene Regulatory ◽  
The Brain

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

scholarly journals Network Protein Interaction in Parkinson's Disease and Periodontitis Interplay: A Bioinformatic Analysis

2020 ◽  
Author(s):  
João Botelho ◽  
Paulo Mascarenhas ◽  
José João Mendes ◽  
Vanessa Machado
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Interaction Analysis ◽  
Bioinformatic Analysis ◽  
Comprehensive Analysis ◽  
Protein Network ◽  
Protein Protein Interactions ◽  
Network Interaction ◽  
Genes Encoding

Recent studies supported a clinical association between Parkinson’s Disease (PD) and periodontitis. Hence, investigating possible protein interactions between these two conditions is of interest. In this study, we conducted a protein-protein network interaction analysis with recognized genes encoding proteins for PD and periodontitis. Genes of interest were collected via GWAS database. Then, we conducted a protein interaction analysis using STRING database, with a highest confidence cut-off of 0.9. Our protein network casted a comprehensive analysis of potential protein-protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted giving the limitations of this approach.

Targeting Post-Translational Remodeling of Ryanodine Receptor: A New Track for Alzheimer's Disease Therapy?

2020 ◽  
Vol 17 (4) ◽  
pp. 313-323 ◽  
Author(s):  
Mounia Chami ◽  
Frédéric Checler
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Complex Structure ◽  
Ryanodine Receptors ◽  
Therapeutic Window ◽  
Signaling Cascade ◽  
Macromolecular Complex ◽  
Macromolecular Complexes

Pathologic calcium (Ca2+) signaling linked to Alzheimer’s Disease (AD) involves the intracellular Ca2+ release channels/ryanodine receptors (RyRs). RyRs are macromolecular complexes where the protein-protein interactions between RyRs and several regulatory proteins impact the channel function. Pharmacological and genetic approaches link the destabilization of RyRs macromolecular complexes to several human pathologies including brain disorders. In this review, we discuss our recent data, which demonstrated that enhanced neuronal RyR2-mediated Ca2+ leak in AD is associated with posttranslational modifications (hyperphosphorylation, oxidation, and nitrosylation) leading to RyR2 macromolecular complex remodeling, and dissociation of the stabilizing protein Calstabin2 from the channel. We describe RyR macromolecular complex structure and discuss the molecular mechanisms and signaling cascade underlying neuronal RyR2 remodeling in AD. We provide evidence linking RyR2 dysfunction with β-adrenergic signaling cascade that is altered in AD. RyR2 remodeling in AD leads to histopathological lesions, alteration of synaptic plasticity, learning and memory deficits. Targeting RyR macromolecular complex remodeling should be considered as a new therapeutic window to treat/or prevent AD setting and/or progression.

scholarly journals Gibbs Free Energy, a Thermodynamic Measure of Protein–Protein Interactions, Correlates with Neurologic Disability

2021 ◽  
Vol 1 (3) ◽  
pp. 201-210
Author(s):  
Michael Keegan ◽  
Hava T. Siegelmann ◽  
Edward A. Rietman ◽  
Giannoula Lakka Klement ◽  
Jack A. Tuszynski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Free Energy ◽  
Gibbs Free Energy ◽  
Protein Interactions ◽  
Neurological Diseases ◽  
Pearson Correlation ◽  
Brain Network ◽  
Network Organization ◽  
Protein Protein Interactions

Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, have begun to change the paradigms of neurodegenerative disorders. In this paper, we explore the use of thermodynamics for protein–protein network interactions in Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy. To assess the validity of using network interactions in neurological diseases, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores. We found a linear correlation (Pearson correlation of −0.828) between disease disability (a clinically validated measurement of a person’s functional status) and Gibbs free energy (a thermodynamic measure of protein–protein interactions). In other words, we found an inverse relationship between disease disability and thermodynamic energy. Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer’s disease could potentially be prevented by therapeutically correcting the changes in Gibbs free energy.

scholarly journals Bioinformatics and Multi-omics Approach to Identify Comorbidities with Application in Schizophrenia with Psychiatric Disorders

2021 ◽  
pp. 35-47
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Protein Interactions ◽  
Depressive Disorders ◽  
Expression Patterns ◽  
Mapk Signaling ◽  
Protein Protein Interactions ◽  
Obsessive Compulsive ◽  
Psychiatric Comorbidities ◽  
Compulsive Disorder

Schizophrenia (SCZ) is a major psychiatric disorder and often presents with psychiatric comorbidities. But, the interactions or links between the pathogenesis of SCZ and comorbidities are not known. In this study, we aimed to develop an integrated multi-omics approach based on gene expression, gene ontology, pathways, protein-protein interactions data that help clinical researchers to assess the links between SCZ and major psychiatric pathologies. We compared the transcriptomic alterations between diseases and controls and observed significant perturbed gene expression patterns i.e. differentially expressed (DEGs) shared among SCZ and major depressive disorders, obsessive-compulsive disorder, alcoholism, eating disorder. We observed deregulated expression of three DEGs, namely, HAPLN1, CNDP1, SLC12A2 in SCZ and pathologies, which were common among the selected pathologies suggesting the selected disorders are comorbidities of SCZ. The pathways including FoxO signaling pathway, MAPK signaling pathway, transcriptional misregulation in cancer, cellular senescence, cell cycle, PI3-Akt signaling pathway, TNF signaling pathway, and TGF-beta signaling pathway altered by the shared SCZ and psychiatric comorbidities also identified. The present study revealed biomolecules (DEGs), ontologies, and cellular pathways of the etiopathogenetic mechanisms of SCZ and psychiatric comorbidities.

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