About the Talk
Presenters
Dilara Uzuner
Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer
Primary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.
Ecehan Abdik
Systematic investigation of mouse models of Parkinson’s disease by transcriptome mapping on a brain-specific genome-scale metabolic network
Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model organisms for neurodegenerative diseases, a brain-specific metabolic network model of mouse had not yet been reconstructed. Here we reconstructed the first brain-specific metabolic network model of mouse, iBrain674-Mm, by a homology-based approach, which consisted of 992 reactions controlled by 674 genes and distributed over 48 pathways. We validated the newly reconstructed network model by showing that it predicts healthy resting-state metabolic phenotypes of mouse brain compatible with literature. We later used iBrain674-Mm to interpret various experimental mouse models of Parkinson’s Disease (PD) at the transcriptome level, predicting altered metabolite productions via a constraint-based modelling biomarker prediction method called TIMBR.
Hatice Büşra Lüleci
iMAT application as an integration method in Alzheimer’s disease in order to predict reaction activity
Alzheimer’s disease (AD) is the most common cause of dementia. There is increasing evidence of a possible link between the incidence and progression of AD and metabolic dysfunction. Determining the changes in the activity of metabolic pathways is a major interest in the treatment of AD. Mapping sample-based gene expression levels using the Integrative Metabolic Analysis Tool (iMAT) optimization algorithm on Human-GEM led to personalized metabolic networks. Each personalized metabolic network for healthy and disease cases has a different number of reactions and genes, revealing the inherent heterogeneity of control and AD samples and justifying the personalized approach. Reactions in each model were converted to binary vectors and analyzed by Fisher’s Exact test. Based on these calculations, significantly changed reactions and pathways were detected.
Müberra Fatma Cesur
Network-based metabolism-centered screening of potential drug targets in Klebsiella pneumoniae at genome scale
Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of healthcare-associated infections. We used a genome-scale metabolic network (GMN) of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. We performed bacterial growth simulations within different host-mimicking media and selected targets based on property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. This is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through constraint-based analysis of its GMN in conjunction with several bioinformatic approaches.
Date: April 8th, 2022 — 14:00 (GMT+3)
Language: English