Biology subjects

Quinn, J. M. W.

Publications and source records attributed to Quinn, J. M. W..

5 recordsLinked to original sources

bioRxiv2020

Osteocyte Transcriptome Mapping Identifies a Molecular Landscape Controlling Skeletal Homeostasis and Susceptibility to Skeletal Disease

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this program is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders (P=2.4x10-22) and are associated with the polygenic diseases osteoporosis (P=1.8x10-13) and osteoarthritis (P=1.6x10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease.

systems biology↗

bioRxiv2019

Protein Expression Patterns in ovarian cancer cells Associated with Monofunctional Platinums Treatment

Platinum drugs cisplatin and carboplatin, given in combination with paclitaxel, constitute the standard chemotherapy against ovarian cancer (OC). Oc chemoresistance is a major obstacle to effective treatment, but knowledge of the mechanisms that underlie it remains incomplete. We thus sought to discover key proteins associated with platinum resistance by comparing A2780 OC cells with A2780cisR cells (resistant cells derived from the A2780 line) to identify proteins with markedly altered expression levels in the resistant cells. We also determined which proteins in these cells had altered expression in response to treatment with either designed monofunctional platinum alone or a combination with cisplatin with selected phytochemical therapeutic agents.\n\nWe thus performed proteomic analysis using 2D-gel electrophoresis A2780 and A2780cisR to identify proteins with differential expression; these were eluted and analysed by mass spectrometry to identify them. A total of 122 proteins were found to be differentially expressed between A2780 and A2780cisR cell lines in the absence of any drug treatment. Among them, levels of 27 proteins in A2780cisR cell line were further altered (up-or down-regulated) in response to one or more of the drug treatments. We then investigated primary OC tissue RNA expression levels (compared to l ovarian tissue) of genes coding for these candidate 27 proteins using publically available datasets (The Cancer Genome Atlas). We assessed how expression of these genes in OC tissue associates with patient survival using Cox Proportional Hazard (PH) regression models to determine relative risk of death associated with each factor. Our Cox PH regression-based machine learning method confirmed a significant relationship of mortality with altered expression of ARHGDIA, CCT6A and HISTIH4F genes. This indicated that these genes affect OC patient survival, i.e., provided mechanistic evidence, in addition to that of the clinical traits, that these genes may be critical mediators of the processes that underlie OC progression and mortality.\n\nThus, we identified differentially expressed proteins that are implicated in platinum-based chemotherapy resistance mechanisms which may serve as resistance biomarkers. These drug resistance associated proteins may also serve as potential OC therapeutic targets whose blockade may enhance the effectiveness of platinum based drugs.

cancer biology↗

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 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

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↗