Biology subjects

Moni, M. A.

Publications and source records attributed to Moni, M. A..

13 recordsLinked to original sources

bioRxiv2018

Deciphering the molecular pathways of apoptosis using leaf extract of Basella alba against Ehrlichs Ascites Carcinoma (EAC) cell line in Swiss albino mice model

A series of programmed cell death that plays a vital role in the exclusion of abnormal cells without any ruin into the surrounding neighboring cells is called Apoptosis. Generally, it occurs in multi-cellular organisms through an orderly and autonomously processes that is controlled by proper function of various genes. In the current studies, cell apoptosis in EAC cells treated with different fractions (BLP-01, BLP-02 and BLP-03) of leaf extract of B. alba was detected by conducting several bio-assays such as cell growth inhibition, fluorescence and optical microscopy, DNA fragmentation and PCR amplification etc. The results of these experiments indicate that the plant extracts are able to inhibit cell growth significantly where mor- phological features of apoptosis were appeared under both fluorescence and optical microscope. The PCR amplification results showed that the leaf extracts of B. alba were able to cause EAC cell apoptosis in both the extrinsic and intrinsic pathway. An excellent figure of fragmented DNA was found in DNA fragmentation assay when the gel was observed under UV light which confirms the cell apoptosis. The current findings suggest that the samples of this experiment occupy fascinating competence to conduct cell apoptosis and become an ideal resource for cancer research as well as drugs development for cancer treatment.

molecular biology↗

bioRxiv2018

Genetic Effects of Welding Fumes on the progression ofNeurodegenerative Diseases

BackgroundWelding exposes different types of fumes, gases and radiant energy that can be potentially dangerous for unsafe welders health. Welding fumes (WFs) are a significant problem among all those exposed. WFs are a complex mixture of metallic oxides, silicates and fluorides that may result in different health effects. If a welder inhales such fumes in large quantities over a long period of time, there is a risk of various neurodegenerative diseases (NDGDs) development.\n\nMethodsWe developed quantitative frameworks to identify the genetic relationship of WFs and NDGDs. We analyzed Gene Expression microarray data from WFs exposed tissues and NDGDs including Parkinsons disease (PD), Alzheimers disease (AD), Lou Gehrigs disease (LGD), Epilepsy disease (ED), Multiple Sclerosis disease (MSD) datasets. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological path- ways and protein-protein interaction sub-network using multilayer network topology and neighborhood-based benchmarking.\n\nResultsWe observed that WFs shares 18, 16, 13, 19 and 19 differentially expressed genes with PD, AD, LGD, ED and MSD respectively. Gene expression dysregulation along with relationship networks, pathways and ontologic analysis showed that WFs are responsible for the progression of PD, AD, LGD, ED and MSD neurodegenerative diseases.\n\nConclusionOur developed network-based approach to analysis and investigate the genetic effects of welding fumes on PD, AD, LGD, ED and MSD neurodegenerative diseases could be helpful to understand the causal influences of WF exposure for the progression of the NDGDs.

bioinformatics↗

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

Mining Significant Features of Diabetes Mellitus Applying Decision Trees: A Case Study In Bangladesh

Diabetes is a chronic condition which is associated with an abnormally high level of sugar in the blood. It is a lifelong disease that causes harmful effects in human life. The goal of this research is to predict the severity of diabetes and find out significant features of it. In this work, we gathered diabetes patients records from Noakhali Diabetes Association, Noakhali, Bangladesh. Thus, We preprocessed our raw dataset by replacing and removing missing and wrong records respectively. Thus, CDT, J48, NBTree and REPtree decision tree based classification techniques were used to analyze this dataset. After this analysis, we evaluated classification outcomes of these decision tree classifiers and found the best decision tree model from them. In this work, CDT unpruned tree shows highest accuracy, precision, recall, f-measure, second highest AUROC and lowest RMSE than other models. Then, we extracted possible rules and significant features from this model and plasma glucose, plasma glucose 2hr after glucose and HDL-cholesterol have been found as the most significant features to predict the severity of Diabetes Mellitus. We hope this work will be beneficial to build a predictive system and complementary tool for diabetes treatment in future.

bioengineering↗

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

Genetic effect of type 2 Diabetes to the progression of Neurological Diseases

Neurological Diseases (NDs) are progressive disorder often advances with age and comorbidities of Type 2 diabetes (T2D). Epidemiological, clinical and neuropathological evidence advocate that patients with T2D are at an increased risk of getting NDs. However, it is very little known how T2D affects the risk and severity of NDs.\n\nTo tackle these problems, we employed a transcriptional analysis of affected tissues using agnostic approaches to identify overlapping cellular functions. In this study, we examined gene expression microarray human datasets along with control and disease-affected individuals. Differentially expressed genes (DEG) were identified for both T2D and NDs that includes Alzheimer Disease (AD), Parkinson Disease (PD), Amyotrophic Lateral Sclerosis (ALS), Epilepsy Disease (ED), Huntington Disease (HD), Cerebral Palsy (CP) and Multiple Sclerosis Disease (MSD).\n\nWe have developed genetic association and diseasome network of T2D and NDs based on the neighborhood-based benchmarking and multilayer network topology approaches. Overlapping DEG sets go through protein-protein interaction for hub protein identification and gene enrichment using pathway analysis and gene ontology methods that enhance our understanding of the fundamental molecular procedure of NDs progression.\n\nGene expression analysis platforms have been extensively used to investigate altered pathways and to identify potential biomarkers and drug targets. Finally, we validated our identified biomarkers using the gold benchmark datasets which identified the corresponding relationship of T2D and NDs. Therapeutic targets aimed at attenuating identified altered pathway could ameliorate neurological dysfunction in a T2D patient.

bioinformatics↗

bioRxiv2018

Network-based genetic profiling, and therapeutic target identification of Thyroid Cancer

Pathogenic mechanisms that underlie malignant follicular thyroid carcinoma (FTC) development are poorly understood. To identify key genes and pathways driving malignant behaviour we employed a system biology-based integrative analyses comparing FTC transcriptomes with a similar but benign lesion, follicular thyroid adenoma (FTA). We identified differentially expressed genes (DEGs) in microarray gene expression datasets (n=52) of FTCs and FTA tissues. Pathway analyses of DEGs using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources revealed significant pathways, and pathway hub genes using protein-protein interactions (PPI). We identified 598 DEGs (relative to FTAs) in FTCs and 12 significant pathways with altered expression in FTC. 10 GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression. PPI analysis identified 12 potential hub genes based on degree and betweenness centrality. Moreover, 10 transcription factors (TFs) were identified that may underlie DEG expression as well as a number of microRNA (miRNAs). Thus, we identified DEGs, pathways, TFs and miRNAs that reflect molecular mechanisms differing between FTC and benign FTA. These may constitute biomarkers that distinguish these lesions and, given the similarities and common origin of the lesions, they may also be indicators of malignant progression potential.

bioinformatics↗

bioRxiv2018

Blood-based molecular biomarker signatures in Alzheimer’s disease: Insights from systems biomedicine analyses

Background and objectivesAlzheimers disease (AD) is the progressive neurodegenerative disease characterized by dementia, but no peripheral biomarkers available yet that can detect the AD. This study aimed to identify systems biomarker signatures in the AD through integrative analyses.\n\nMaterials and MethodsWe used two microarray transcriptomics datasets of blood from AD patients to identify differentially expressed genes (DEGs). Geneset and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factor interactions, DEGs-MicroRNAs interactions, protein-drug interactions, and protein subcellular localizations analyses were done on common DEGs.\n\nResultsTotal 25 DEGs were detected between the two datasets. Integration of DEGs with biomolecular networks revealed hub proteins (TUBB, ATF3, NOL6, UQCRC1, SND1, CASP2, BTF3, INPP5K, VCAM1, and CSTF1), TFs (FOXC1, ZNF3, GEMIN7, and SMG9), miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-4270, and mir-4441). The analyses revealed candidate blood based biomarkers in the AD. We evaluated the histone modifications of the identified biomolecules. The hub genes and transcription factors (TFs) revealed that they possess several histone modification sites associated with Alzheimers disease. The protein-drug interactions revealed 10 candidate drugs consisting of antineoplastic (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), dermatologicals (Podofilox), and immunosuppressive agents (Colchicine) that may target the candidate systems biomarkers. The subcellular localization analysis revealed the interactions of the DEGs range from nucleus to plasma membrane through cytosol.\n\nConclusionsThis study presents blood based systems molecular biomarker signatures at RNA and protein levels which might be useful as peripheral biomarkers in the AD. The candidate drugs, histone modification sites, and subcellular localizations will be useful in future drug design in the AD.

systems biology↗

bioRxiv2018

Common molecular biomarker signatures in blood and brain of Alzheimers disease

BackgroundAlzheimers disease (AD) is a progressive neurodegenerative disease characterized by memory loss and confusion. Neuroimaging and cerebrospinal fluid-based early detection is limited in sensitivity and specificity as well as by cost. Therefore, detecting AD from blood cell analysis could improve early diagnosis and treatment of the disease. The present study aimed to identify blood cell transcripts that reflect brain expression levels of factors linked to AD progression.\n\nMethodsWe analyzed blood cell and brain microarray gene expression datasets from NCBI-GEO for AD association and expression in blood and brain. We also used eQTL and epigenetics data to identify AD-related genes that were regulated similarly in blood and brain.\n\nResultsWe identified 9 differentially expressed genes (DEG; AD versus controls) common to blood cells and brain (CNBD1, SUCLG2-AS1, CCDC65, PDE4D, MTMR1, C3, SLC6A15, LINC01806, and FRG1JP) and 18 genes (HSD17B1, GAS5, RPS5, VKORC1, GLE1, WDR1, RPL12, MORN1, RAD52, SDR39U1, NPHP4, MT1E, SORD, LINC00638, MCM3AP-AS1, GSDMD, RPS9, and GNL2) that were commonly dysregulated between AD blood and brain tissues using SNP and cis-eQTL data. This data revealed significant neurodegeneration-associated molecular pathways in the ribosomal and complement systems. Integration of these different analyses revealed dys-regulation of hub transcription factors (SREBF2, NR1H2, NR1H3, PRDM1, XBP1) and microRNAs (miR-518e, miR-518a-3p, miR-518b, miR-518c, miR-518d-3p and miR-518f) in AD. Several significant histone modification sites in DEGs were also identified.\n\nConclusionWe have identified new putative links between pathological processes in brain and transcripts in blood cells in AD subjects that may enable the use of blood to diagnose and monitor AD onset and progression.

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↗

bioRxiv2018

Bioinformatics Approach to Identify Diseasome and Comorbidities Effect of Mitochondrial Dysfunctions on the Progression of Neurological Disorders

Mitochondrial dysfunction can cause various neurological diseases. We therefore developed a quantitative framework to explore how mitochondrial dysfunction may influence the progression of Alzheimers, Parkinsons, Huntingtons and Lou Gehrigs diseases and cerebral palsy through analysis of genes showing altered expression in these conditions. We sought insights about the gene profiles of mitochondrial and associated neurological diseases by investigating gene-disease networks, KEGG pathways, gene ontologies and protein-protein interaction network. Gene disease networks were constructed to connect shared genes which are commonly found between the neurological diseases and Mitochondrial Dysfunction. We also generated KEGG pathways and gene ontologies to explore functional enrichment among them, and protein-protein interaction networks to identify the shared protein groups of these diseases. Finally, we verified our biomarkers using gold benchmark databases (e.g., OMIM and dbGaP) which identified effective reasons of it. Our network-based methodologies are useful to investigate disease mechanisms, predictions for comorbidities and identified distinct similarities among different neurological disorders for mitochondrial dysfunction.

bioinformatics↗

bioRxiv2018

Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality

Ovarian cancer (OC) is a common cause of death from cancer among women worldwide, so there is a pressing need to identify factors influencing mortality. Much OC patient clinical data is now publically accessible (including patient age, cancer site stage and subtype), as are large datasets of OC gene transcription profiles. These have enabled studies correlating OC patient survival with clinical variables and with gene expression but it is not well understood how these two aspects interact to influence mortality. To study this we integrated clinical and tissue transcriptome data from the same patients available from the Broad Institute Cancer Genome Atlas (TCGA) portal. We investigated OC mRNA expression levels (relative to normal patient tissue) of 26 genes already strongly implicated in OC, assessed how their expression in OC tissue predicts patient survival then employed Cox Proportional Hazard regression models to analyse both clinical factors and transcriptomic information to determine relative risk of death associated with each factor. Multivariate analysis of combined data (clinical and gene mRNA expression) found age, ovary tumour site and cancer stage IB significantly correlated with patient survival. Univariate analysis also confirmed significant differences in patient survival time when altered transcription levels of KLK6, CD36, MEF2C and SCGB2A1 were evident, while multivariate analysis that considered the 26 genes simultaneously revealed a significant relationship of mortality with KLK6, CD36 and E2F1 genes. However, analysis that considered all 26 genes with clinical variables together identified WFDC2, E2F1, BRCA1, KLK6, SCGB2A1 and SLPI genes as independently related to mortality in OC. This indicated that the latter genes affect OC patient survival, i.e., provided mechanistic and predictive information in addition to that of the clinical traits and provide strong evidence that these genes are critical markers of processes that underlie OC progression and mortality.

bioinformatics↗