Biology subjects

Islam, M. B.

Publications and source records attributed to Islam, M. B..

4 recordsLinked to original sources

bioRxiv2018

Genetic Effects of Welding Fumes to the development ofRespiratory System Diseases

BackgroundThe welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WFs may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood.\n\nMethodsWe developed a quantitative framework to identify gene expression effects of WFs that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (disea-some) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology.\n\nResultsWe observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEGs analysis, with disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC.\n\nConclusionsOur network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

bioinformatics↗

bioRxiv2018

A Network based Approach to Identify the Genetic Influence Caused by Associated Factors and Disorders for the Parkinsons Disease Progression

Actual causes of Parkinsons disease (PD) are still unknown. In any case, a better comprehension of genetic and ecological influences to the PD and their interaction will assist physicians and patients to evaluate individual hazard for the PD, and definitely, there will be a possibility to find a way to reduce the progression of the PD. We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors for the PD. In this study, we analyzed gene expression microarray data from the PD, ageing (AG), severe alcohol consumption (AC), type II diabetes (T2D), high body fat (HBF), hypercholesterolemia (HC), high dietary fat (HDF), red meat dietary (RMD), sedentary lifestyle (SL), smoking (SM), and control datasets. We have developed genetic associations of various factors with the PD based on the neighborhood-based benchmarking and multilayer network topology.\n\nWe identified 1343 significantly dysregulated genes in the PD patients compared to the healthy control, where we have 779 genes down regulated and 544 genes up regulated. 69 genes were highly expressed in both for the PD and alcohol consumption whereas the number of shared genes for the PD and the type II diabetes is 51. However, the PD shared 45, 43 and 42 significantly expressed genes with the ageing, high dietary fat and high body fat respectively. The PD shared less than 40 significant transcripts with other factors. Ontological and pathway analyses have identified significant gene ontology and molecular pathways that enhance our understanding of the fundamental molecular procedure of the PD progression. Therapeutic targets of the PD could be developed using these identified target genes, ontologies and pathways. Our formulated methodologies demonstrate a network-based approach to understand the disease mechanism and the causative reason of the PD, and the identification for therapeutic targets of the PD.

neuroscience↗

bioRxiv2018

Network-based identification of genetic factors in Ageing, lifestyle and Type 2 Diabetes that Influence in the progression of Alzheimer’s disease

MotivationAlzheimers disease (AD) is currently incurable and the causative risk factors are still poorly understood, which impedes development of effective prevention and treatment strategies. We propose a network-based quantitative framework to reveal details of the complex interaction between the various genetic contributors to AD susceptibility. We analyzed gene expression microarray data from tissues affected by AD, advanced ageing, high alcohol consumption, type II diabetes, high body fat, high dietary fat, obesity, high dietary red meat intake, sedentary lifestyle, smoking, and control datasets. We developed genetic associations and diseasome networks for these factors and AD using the neighborhood-based benchmarking and multilayer network topology approaches.\n\nResultsThe study identified 484 genes differentially expressed between AD and controls. Among these, 27 genes showed elevated expression both in individuals in AD and in smoker datasets; similarly 21 were observed in AD and type II diabetes datasets and 12 for AD and sedentary lifestyle datsets. However, AD shared less than ten such elevated expression genes with other factors examined. 3 genes, namely HLA-DRB4, IGH and IGHA2 showed increased expression among the AD, type II diabetes and alcohol consumption datasets; 2 genes, IGHD and IGHG1, were commonly up-regulated among the AD, type II diabetes, alcohol consumption and sedentary lifestyle datasets. Protein-protein interaction networks identified 10 hub genes: CREBBP, PRKCB, ITGB1, GAD1, GNB5, PPP3CA, CABP1, SMARCA4, SNAP25 and GRIA1. Ontological and pathway analyses genes, including Online Mendelian Inheritance in Man (OMIM) and dbGaP databases were used for gold benchmark gene-disease associations to validate the significance of these putative target genes of AD progression.\n\nConclusionOur network-based methodologies have uncovered molecular pathways that may influence AD development, suggesting novel mechanisms that contribute to AD risk and which may form the basis of new therapeutic and diagnostic approaches.\n\nContactmohammad.moni@sydney.edu.au

molecular biology↗

bioRxiv2018

Early Detection of Neurological Dysfunction Using Blood Cell Transcript Profiles

Identification of genes whose regulation of expression is similar in both brain and blood cells could enable monitoring of significant neurological traits and disorders by analysis of blood samples. We thus employed transcriptional analysis of pathologically affected tissues, using agnostic approaches to identify overlapping gene functions and integrating this transcriptomic information with expression quantitative trait loci (eQTL) data. Here, we estimate the correlation of genetic expression in the top-associated cis-eQTLs of brain tissue and blood cells in Parkinsons (PD). We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors in PD, a neurodegenerative disease. We examined gene expression microarray and RNA-Seq datasets from human brain and blood tissues from PD-affected and control individuals. Differentially expressed genes (DEG) were identified for both brain and blood cells to determine common DEG overlaps. Based on neighborhood-based benchmarking and multilayer network topology aproaches we then developed genetic associations of factors with PD. Overlapping DEG sets underwent gene enrichment using pathway analysis and gene ontology methods, which identified candidate common genes and pathways. We identified 12 significantly dysregulated genes shared by brain and blood cells, which were validated using dbGaP (gene SNP-disease linkage) database for gold-standard benchmarking of their significance in disease processes. Ontological and pathway analyses identified significant gene ontology and molecular pathways that indicate PD progression. In sum, we found possible novel links between pathological processes in brain and blood cells by examining cell path-way commonalities, corroborating these associations using well validated datasets. This demonstrates that for brain-related pathologies combining gene expression analysis and blood cell cis-eQTL is a potentially powerful analytical approach. Thus, our methodologies facilitate data-driven approaches that can advance knowledge of disease mechanisms and may enable prediction of neurological dysfunction using blood cell transcript profiling.

bioinformatics↗