Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology

A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease

Huntington disease (HD) is a degenerative brain disease caused by the expansion of CAG (cytosine-adenine-guanine) repeats, which is inherited as a dominant trait and progressively worsens over time possessing threat. Although HD is monogenetic, the specific pathophysiology and biomarkers are yet unknown specifically, also, complex to diagnose at an early stage, and identification is restricted in accuracy and precision. This study combined bioinformatics analysis and network-based system biology approaches to discover the biomarker, pathways, and drug targets related to molecular mechanism of HD etiology. The gene expression profile data sets GSE64810 and GSE95343 were analyzed to predict the molecular markers in HD where 162 mutual differentially expressed genes (DEGs) were detected. Ten hub genes among them (DUSP1, NKX2-5, GLI1, KLF4, SCNN1B, NPHS1, SGK2, PITX2, S100A4, and MSX1) were identified from protein-protein interaction (PPI) network which were mostly expressed as down-regulated. Following that, transcription factors (TFs)-DEGs interactions (FOXC1, GATA2, etc), TF-microRNA (miRNA) interactions (hsa-miR-340, hsa-miR-34a, etc), protein-drug interactions, and disorders associated with DEGs were predicted. Furthermore, we used gene set enrichment analysis (GSEA) to emphasize relevant gene ontology terms (eg, TF activity, sequence-specific DNA binding) linked to DEGs in HD. Disease interactions revealed the diseases that are linked to HD, and the prospective small drug molecules like cytarabine and arsenite was predicted against HD. This study reveals molecular biomarkers at the RNA and protein levels that may be beneficial to improve the understanding of molecular mechanisms, early diagnosis, as well as prospective pharmacologic targets for designing beneficial HD treatment.

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

Proteomic and Bioinformatic Tools to Identify Potential Hub Proteins in the Audiogenic Seizure-Prone Hamster GASH/Sal

The GASH/Sal (Genetic Audiogenic Seizure Hamster, Salamanca) is a model of audiogenic seizures with the epileptogenic focus localized in the inferior colliculus (IC). The sound-induced seizures exhibit a short latency (7–9 s), which implies innate protein disturbances in the IC as a basis for seizure susceptibility and generation. Here, we aim to study the protein profile in the GASH/Sal IC in comparison to controls. Protein samples from the IC were processed for enzymatic digestion and then analyzed by mass spectrometry in Data-Independent Acquisition mode. After identifying the proteins using the UniProt database, we selected those with differential expression and performed ontological analyses, as well as gene-protein interaction studies using bioinformatics tools. We identified 5254 proteins; among them, 184 were differentially expressed proteins (DEPs), with 126 upregulated and 58 downregulated proteins, and 10 of the DEPs directly related to epilepsy. Moreover, 12 and 7 proteins were uniquely found in the GASH/Sal or the control. The results indicated a protein profile alteration in the epileptogenic nucleus that might underlie the inborn occurring audiogenic seizures in the GASH/Sal model. In summary, this study supports the use of bioinformatics methods in proteomics to delve into the relationship between molecular-level protein mechanisms and the pathobiology of rodent models of audiogenic seizures.

Bioinformatics and System Biology Techniques to Determine Biomolecular Signatures and Pathways of Prion Disorder

Prion disorder (PD) is caused by misfolding and the formation of clumps of proteins in the brain, notably Prion proteins resulting in a steady decrease in brain function. Early detection of PD is difficult due to its unpredictable nature, and diagnosis is limited regarding specificity and sensitivity. Considering the uncertainties, the current study used network-based integrative system biology approaches to reveal promising molecular biomarkers and therapeutic targets for PD. In this study, brain transcriptomics gene expression microarray datasets (GSE160208 and GSE124571) of human PD were evaluated and 35 differentially expressed genes (DEGs) were identified. By employing network-based protein-protein interaction (PPI) analysis on these DEGs, 10 central hub proteins, including SPP1, FKBP5, HPRT1, CDKN1A, BAG3, HSPB1, SYK, TNFRSF1A, PTPN6, and CD44, were identified. Employing bioinformatics approaches, a variety of transcription factors (EGR1, SSRP1, POLR2A, TARDP, and NR2F1) and miRNAs (hsa-mir-8485, hsa-mir-148b-3p, hsa-mir-4295, hsa-mir-26b-5p, and hsa-mir-16-5p) were predicted. EGR1 was found as the most imperative transcription factor (TF), and hsa-mir-16-5p and hsa-mir-148b-3p were found as the most crucial miRNAs targeted in PD. Finally, resveratrol and hypochlorous acid were predicted as possible therapeutic drugs for PD. This study could be helpful in better understanding of molecular systems and prospective pharmacological targets for developing effective PD treatments.