Dataset Information

Altered Expression of a Unique Set of Genes Reveals Complex Etiology of Schizophrenia.

ABSTRACT: Background: The etiology of schizophrenia is extensively debated, and multiple factors have been contended to be involved. A panoramic view of the contributing factors in a genome-wide study can be an effective strategy to provide a comprehensive understanding of its causality. Materials and Methods: GSE53987 dataset downloaded from GEO-database, which comprised mRNA expression data of post-mortem brain tissue across three regions from control (C) and age-matched subjects (T) of schizophrenia (N = Hippocampus [HIP]: C-15, T-18, Prefrontal cortex [PFC]: C-15, T-19, Associative striatum [STR]: C-18, T-18). Bio-conductor-affy-package used to compute mRNA expression, and further t-test applied to investigate differential gene expression. The analysis of the derived genes performed using the PANTHER Classification System and NCBI database. Further, a protein interactome analysis of the derived gene set was performed using STRING v10 database (https://string-db.org/) Results: A set of 40 genes showed significantly altered (p < 0.01) expression across all three brain regions. The analyses unraveled genes implicated in biological processes and events, and molecular pathways relating basic neuronal functions. Conclusions: The aberrant expression of genes maintaining basic cell machinery explains compromised neuronal processing in SCZ.

SUBMITTER: Kumar A

PROVIDER: S-EPMC6920214 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Altered Expression of a Unique Set of Genes Reveals Complex Etiology of Schizophrenia.

Kumar Ashutosh A Pareek Vikas V Singh Himanshu N HN Faiq Muneeb A MA Narayan Ravi K RK Raza Khursheed K Kumar Pavan P

Frontiers in psychiatry 20191212

<b>Background:</b> The etiology of schizophrenia is extensively debated, and multiple factors have been contended to be involved. A panoramic view of the contributing factors in a genome-wide study can be an effective strategy to provide a comprehensive understanding of its causality. <b>Materials and Methods:</b> GSE53987 dataset downloaded from GEO-database, which comprised mRNA expression data of post-mortem brain tissue across three regions from control (C) and age-matched subjects (T) of sc ...[more]

PMID: 31920755

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Project description:Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon's entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ?80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder.

Project description:Impairments in social cognition and interactions are core psychopathologies in schizophrenia, often manifesting as an inability to appropriately relate to the intentions and feelings of others. Neuroimaging has helped to demarcate the dynamics of two distinct functional connectivity circuits underlying the social-affective processes related to mentalization (known as Theory of Mind, ToM) and somatic-affiliation (known as Embodied Simulation, ES). While evidence points to abnormal activation patterns within these networks among those suffering from schizophrenia, it is yet unclear however, if these patients exhibit this abnormal functional connectivity in the context of social-affective experiences. The current fMRI study, investigated functional connectivity dynamics within ToM and ES networks as subjects experienced evolving cinematic portrayals of fear. During scanning, schizophrenia patients and healthy controls passively watched a cinematic scene in which a mother and her son face various threatening events. Participants then provided a continuous and retrospective report of their fear intensity during a second viewing outside the scanner. Using network cohesion index (NCI) analysis, we examined modulations of ES-related and ToM-related functional connectivity dynamics and their relation to symptom severity and the continuous emotional ratings of the induced cinematic fear. Compared to patients, healthy controls showed higher ES-NCI and marginally lower ToM-NCI during emotional peaks. Cross-correlation analysis revealed an intriguing dynamic between NCI and the inter-group difference of reported fear. Schizophrenia patients rated their fear as lower relative to healthy controls, shortly after exhibiting lower ES connectivity. This increased difference in rating was also followed by higher ToM connectivity among schizophrenia patients. The clinical relevance of these findings is further highlighted by the following two results: (a) ToM-NCI was found to have a strong correlation with the severity of general symptoms during one of the two main emotional peaks (Spearman R?=?0.77); and (b) k-mean clustering demonstrated that the networks' NCI dynamic during the social-affective context reliably differentiated between patients and controls. Together, these findings point to a possible neural marker for abnormal social-affective processing in schizophrenia, manifested as the disturbed balance between two functional networks involved in social-affective affiliation. This in turn suggests that exaggerated mentalization over somatic-affiliative processing, in response to another's' distress may underlie social-affective deficits in schizophrenia.

Project description:AimsPresent a novel machine learning computational strategy to predict the neuroprotection potential of nicotine analogs acting over the behavior of unpaired signaling pathways in Parkinson's disease.BackgroundDopaminergic replacement has been used for Parkinson's Disease (PD) treatment with positive effects on motor symptomatology but low progression and prevention effects. Epidemiological studies have shown that nicotine consumption decreases PD prevalence through neuroprotective mechanisms activation associated with the overstimulation of signaling pathways (SP) such as PI3K/AKT through nicotinic acetylcholine receptors (e.g α7 nAChRs) and over-expression of anti-apoptotic genes such as Bcl-2. Nicotine analogs with similar neuroprotective activity but decreased secondary effects remain as a promissory field.ObjectiveThe objective of this study is to develop an interdisciplinary computational strategy predicting the neuroprotective activity of a series of 8 novel nicotine analogs over Parkinson's disease.MethodsWe present a computational strategy integrating structural bioinformatics, SP manual reconstruction, and deep learning to predict the potential neuroprotective activity of 8 novel nicotine analogs over the behavior of PI3K/AKT. We performed a protein-ligand analysis between nicotine analogs and α7 nAChRs receptor using geometrical conformers, physicochemical characterization of the analogs and developed manually curated neuroprotective datasets to analyze their potential activity. Additionally, we developed a predictive machine-learning model for neuroprotection in PD through the integration of Markov Chain Monte-Carlo transition matrix for the 2 SP with synthetic training datasets of the physicochemical properties and structural dataset.ResultsOur model was able to predict the potential neuroprotective activity of seven new nicotine analogs based on the binomial Bcl-2 response regulated by the activation of PI3K/AKT.ConclusionHereby, we present a robust novel strategy to assess the neuroprotective potential of biomolecules based on SP architecture. Our theoretical strategy can be further applied to the study of new treatments related to SP deregulation and may ultimately offer new opportunities for therapeutic interventions in neurodegenerative diseases.

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Altered Expression of a Unique Set of Genes Reveals Complex Etiology of Schizophrenia.

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Altered Expression of a Unique Set of Genes Reveals Complex Etiology of Schizophrenia.

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