A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue

Moni, Mohammad Ali; Rana, Humayan Kabir; Islam, M. Babul; Ahmed, Mohammad Boshir; Xu, Haoming; Hasan, Al Mehedi; Lei, Yiming; Quinn, Julian M.W.

doi:10.1016/j.compbiomed.2019.103385

Cited by 26 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this way, the miRNAs regulate the level of protein expression by inhibiting the mRNAs levels. There is evidence indicating the high potential of miRNAs as biomarkers for complex diseases [28], [52]- [60]. Among the miRNA post-transcriptional regulators of the identified DEGs, it has been shown that mir-335-5p increases insulin resistance and suppresses pancreatic Îš cell secretion [61].…”

Section: Discussionmentioning

confidence: 99%

Network-Based Computational Approach to Identify Delineating Common Cell Pathways Influencing Type 2 Diabetes and Diseases of Bone and Joints

Moni

Islam

Rahman³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Developing type 2 diabetes (T2D) can increase patient risk of developing other common diseases and exacerbate their severity, including diseases that affect bone and joints. Such comorbidity interactions are hard to study in detail by traditional endocrinological methods. Thus, we developed tissue transcript analytical approaches to identify common pathways through which these diseases can interact. We examined RNAseq and microarray transcript datasets from studies of T2D and chronic bone and joint diseases, namely rheumatoid arthritis (RA), osteoarthritis (OA), juvenile idiopathic arthritis (JIA) and low peak bone density, a key osteoporosis (OP) determinant. These datasets contained data from affected individuals and matched controls. Differentially expressed genes (DEGs) for each condition were compared with T2D DEG. Overlapping DEGs (i.e., those common to T2D and a bone or joint condition) were subjected to gene enrichment by pathway analyses and by gene ontology methods, and the results were evaluated by using SNP-disease linkage (dbGaP) and gene-disease association (OMIM) databases that indicate gene involvement in pathologies. By examining gene targets of transcription factors (TFs) and microRNA (miRNAs), we also constructed DEG-TF and DEG-miRNA interactions networks for analysis. We identified strong candidate genes in common pathways, notably including SYK, UCP3, ROR1, PPARG, BUB1, AKT2, ADCY2 and CCR5. The DEG-TF network and DEG-miRNA interactions network analyses revealed a number of TFs (GATA2, FOXC1, USF2, YY1, E2F1, JUN, RELA, CREB1, TFAP2A, NFB1) and miRNAs (mir-335-5p, mir-16-5p, mir-26b-5p, mir-124-3p, mir-218-5p, mir-98-5p, mir-29b-3p, mir-3135b, mir-29c-3p, mir-1-1) that can regulate the identified DEGs at the transcriptional and post-transcriptional levels. Thus this data-driven approach has enabled identification and validation of regulatory factors and cell pathways by which T2D may influence bone and joint conditions, which may suggest new ways to interfere with the pathogenic processes involved.INDEX TERMS T2 diabetes, network-based approach, joint diseases, bone diseases.

show abstract

Section: Discussionmentioning

confidence: 99%

Network-Based Computational Approach to Identify Delineating Common Cell Pathways Influencing Type 2 Diabetes and Diseases of Bone and Joints

Moni

Islam

Rahman³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several statistical operations have been performed on the datasets in order to determine the DEGs. The limma (Linear Models for Microarray Analysis) R package has been used to perform statistical tests for identifying DEGs [ 37 , 42 ]. In addition, the Benjamini–Hochberg false discovery rate method is used to provide a good balance between the discovery of statistically significant genes and the limitation of false positives.…”

Section: Methodsmentioning

confidence: 99%

Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders

Nashiry¹,

Sumi²,

Shohan

et al. 2021

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn’s (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein–protein interaction network, gene regulatory network, protein–chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

show abstract

“…For Alzheimer Disease (AD) we used dataset GSE1297, a study of 9 control and 22 AD subjects using hippocampal CA1 tissue [46]; GSE4226 and GSE4229, which included studies of peripheral blood cells using normal elderly control (NEC) and AD subjects [47], [48]; GSE12685, a study of 8 control and 6 AD subjects using frontal cortex synaptoneurosome samples [49]; and GSE28146, a study of 8 control and 22 AD subjects using laser captured CA1 tissue [50]. To study Parkinson's Disease (PD) we used dataset GSE7621, a study of 9 control and 16 PD subjects from substantia nigra tissue from both male and female subjects [51]; GSE19587, a study of 5 control and 6 PD subjects using post-mortem brain tissue samples [52]; GSE20141, an analysis of 8 control and 10 PD subjects also using the post-mortem brain tissue samples [53]; GSE20333, an analysis of 6 control and 6 PD subjects using substantia nigra tissue samples; GSE22491, an analysis of 8 control and 10 PD subjects using peripheral blood samples [54]; GSE28894, a study of tissue samples from four different brain regions with control and PD subjects; GSE42966, analysis of 6 control and 9 PD subjects using substantia nigra tissue samples; and GSE54536, a study of 5 control and 5 PD subjects using peripheral blood samples [55], [56]. For the case of Amyotrophic Lateral Sclerosis (ALS) we used dataset GSE833, a study of 4 control and 7 sporadic and familial ALS subjects using post mortem spinal cord [57]; GSE4595, an analysis of 9 control and 11 sporadic ALS subjects using human motor cortex tissue samples [58]; GSE19332, a study of 7 control and 3 sporadic ALS subjects using motor neuron tissue samples [59]; GSE52672, an analysis of 10 control and 10 sporadic ALS and familial ALS subjects using whole spinal cord homogenate [60]; and GSE68605, a study of 3 control and 8 ALS subjects using motor neuron tissue samples [61].…”

Section: Methodology a Survey Of Available Datamentioning

confidence: 99%

Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities

Rahman

Peng

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Type 2 diabetes (T2D) is a chronic metabolic disorder characterised by high blood sugar and insulin insensitivity which greatly increases the risk of developing neurological diseases (NDs). The coexistence of T2D and comorbidities such as NDs can complicate or even cause the failure of standard treatments for those diseases. Comorbidities can be both causally linked and influence each other's development through genetic, molecular, environmental or lifestyle-based risk factors that they share. For T2D and NDs, such underlying common molecular mechanisms remain elusive but large amounts of molecular data accumulated on these diseases enable analytical approaches to identify their interconnected pathways. Here, we propose a framework to explore possible comorbidity interactions between a pair of diseases using a bioinformatic examination of the cellular pathways involved and explore this framework for T2D and NDs with analyses of a large number of publicly available gene expression datasets from tissues affected by these diseases. We designed a bioinformatics pipeline to analyse, utilize and combine gene expression, Gene Ontology (GO) and molecular pathway data by incorporating Gene Set Enrichment Analysis and Semantic Similarity. Our bioinformatics methodology was implemented in R, available at https://github.com/HabibUCAS/T2D_Comorbidity. We identified genes with altered expression shared by T2D and NDs as well as GOs and molecular pathways these diseases share. We also computed the proximity between T2D and neurological pathologies using these genes and GO term semantic similarity. Thus, our method has generated new insights into disease mechanisms important for both T2D and NDs that may mediate their interaction. Our bioinformatics pipeline could be applied to other co-morbidities to identify possible interactions and causal relationships between them.

show abstract

A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue

Cited by 26 publications

References 41 publications

Network-Based Computational Approach to Identify Delineating Common Cell Pathways Influencing Type 2 Diabetes and Diseases of Bone and Joints

Network-Based Computational Approach to Identify Delineating Common Cell Pathways Influencing Type 2 Diabetes and Diseases of Bone and Joints

Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders

Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities

Contact Info

Product

Resources

About