scholarly journals MasterPATH: network analysis of functional genomics screening data

2018 ◽  
Author(s):  
Natalia Rubanova ◽  
Anna Polesskaya ◽  
Anna Campalans ◽  
Guillaume Pinna ◽  
Jeremie Kropp ◽  
...  
Keyword(s):  
Network Analysis ◽  
Functional Genomics ◽  
Biological Process ◽  
Transcriptome Profiling ◽  
Repair Process ◽  
Biological Processes ◽  
Analysis Method ◽  
Loss Of Function ◽  
Gene Functions ◽  
High Throughput Manner

AbstractFunctional genomics employs several experimental techniques to investigate gene functions. These techniques such as loss-of-function screening and transcriptome profiling performed in a high-throughput manner give as result a list of genes involved in the biological process of interest. There exist several computational methods for analysis and interpretation of the list. The most widespread methods aim at investigation of biological processes significantly represented in the list or at extracting significantly represented subnetworks. Here we present a new exploratory network analysis method that employs the shortest path approach and centrality measure to uncover members of active molecular pathways leading to the studied phenotype based on the results of functional genomics screening data. We present the method and we demonstrate what data can be retrieved by its application to the terminal muscle differentiation miRNA loss-of-function screening and transcriptomic profiling data and to the ‘druggable’ loss-of-function RNAi screening data of the DNA repair process.

scholarly journals MasterPATH: network analysis of functional genomics screening data

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Natalia Rubanova ◽  
Guillaume Pinna ◽  
Jeremie Kropp ◽  
Anna Campalans ◽  
Juan Pablo Radicella ◽  
...  
Keyword(s):  
Dna Damage ◽  
Functional Genomics ◽  
Oxidative Dna Damage ◽  
Transcriptome Profiling ◽  
Molecular Pathways ◽  
Biological Processes ◽  
Loss Of Function ◽  
Damage Recognition ◽  
Dna Damage Recognition

Abstract Background Functional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks. Results Here we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks – HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers – the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition. Conclusion The current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.

scholarly journals Heparan Sulfate Deficiency in Cartilage: Enhanced BMP-Sensitivity, Proteoglycan Production and an Anti-Apoptotic Expression Signature after Loading

2021 ◽  
Vol 22 (7) ◽  
pp. 3726
Author(s):  
Matthias Gerstner ◽  
Ann-Christine Severmann ◽  
Safak Chasan ◽  
Andrea Vortkamp ◽  
Wiltrud Richter
Keyword(s):  
Heparan Sulfate ◽  
Pain Perception ◽  
Biological Process ◽  
Transcriptome Profiling ◽  
Mrna Levels ◽  
Unconfined Compression ◽  
Expression Signature ◽  
Hypomorphic Allele ◽  
Underlying Mechanisms ◽  
Engineered Cartilage

Osteoarthritis (OA) represents one major cause of disability worldwide still evading efficient pharmacological or cellular therapies. Severe degeneration of extracellular cartilage matrix precedes the loss of mobility and disabling pain perception in affected joints. Recent studies showed that a reduced heparan sulfate (HS) content protects cartilage from degradation in OA-animal models of joint destabilization but the underlying mechanisms remained unclear. We aimed to clarify whether low HS-content alters the mechano-response of chondrocytes and to uncover pathways relevant for HS-related chondro-protection in response to loading. Tissue-engineered cartilage with HS-deficiency was generated from rib chondrocytes of mice carrying a hypomorphic allele of Exostosin 1 (Ext1), one of the main HS-synthesizing enzymes, and wildtype (WT) littermate controls. Engineered cartilage matured for 2 weeks was exposed to cyclic unconfined compression in a bioreactor. The molecular loading response was determined by transcriptome profiling, bioinformatic data processing, and qPCR. HS-deficient chondrocytes expressed 3–6% of WT Ext1-mRNA levels. Both groups similarly raised Sox9, Col2a1, and Acan levels during maturation. However, HS-deficient chondrocytes synthesized and deposited 50% more GAG/DNA. TGFβ and FGF2-sensitivity of Ext1gt/gt chondrocytes was similar to WT cells but their response to BMP-stimulation was enhanced. Loading induced similar activation of mechano-sensitive ERK and P38-signaling in WT and HS-reduced chondrocytes. Transcriptome analysis reflected regulation of cell migration as major load-induced biological process with similar stimulation of common (Fosl1, Itgα5, Timp1, and Ngf) as well as novel mechano-regulated genes (Inhba and Dhrs9). Remarkably, only Ext1-hypomorphic cartilage responded to loading by an expression signature of negative regulation of apoptosis with pro-apoptotic Bnip3 being selectively down-regulated. HS-deficiency enhanced BMP-sensitivity, GAG-production and fostered an anti-apoptotic expression signature after loading, all of which may protect cartilage from load-induced erosion.

scholarly journals Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Erica Ponzi ◽  
Magne Thoresen ◽  
Therese Haugdahl Nøst ◽  
Kajsa Møllersen
Keyword(s):  
Lung Cancer ◽  
Biological Process ◽  
Case Control ◽  
Integrative Analysis ◽  
Data Sources ◽  
Omics Data ◽  
Biological Processes ◽  
Blood Samples ◽  
Model Predictions

Abstract Background Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific (“individual”) patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as “shared” or “joint”. In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case–control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case–control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. Results Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. Conclusions In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

Abstract 211: Myocardial Hypertrophy and Circulating RNAs

2017 ◽  
Vol 121 (suppl_1) ◽  
Author(s):  
Ravi V Shah ◽  
Olivia Ziegler ◽  
Kahraman Tanriverdi ◽  
Jian Rong ◽  
Martin Larson ◽  
...  
Keyword(s):  
Biological Process ◽  
Molecular Level ◽  
Myocardial Hypertrophy ◽  
Clinical Phenotype ◽  
Left Ventricular ◽  
Loss Of Function ◽  
Heart Study ◽  
Non Coding Rnas

While increased left ventricular mass (LVM) is strongly associated with incident heart failure (HF), events during transition from increased LVM to HF remain unclear. Extracellular non-coding RNAs (ex-RNAs) have been implicated in cardiac hypertrophy, though whether these ex-RNAs reflect important pathways in HF in humans is underexplored. In >2,000 individuals with concomitant M-mode echocardiography and ex-RNA measurements in the Framingham Heart Study, we found that lower circulating concentrations of three ex-RNAs—miR-20a-5p, miR-106b-5p, miR-17-5p—were associated with (1) greater LVM (+ one other pre-clinical phenotype, e.g., left atrial dimension or LVEDV) and (2) greater incident HF risk over a median follow-up 7.7 years ( Fig. A ). These 3 miRNAs were members of a tight cluster, regulating 883 mRNAs in common, associated with “hypertension” (OMIM) and biological process relevant to HF, including TGF-β signaling. We observed an increase in myocardial expression of these miRNAs during different phases of hypertrophy/HF development ( Fig. C, D ). Using gain and loss of function in vitro , our preliminary results suggest up-regulation of cardiomyocyte miR-106b expression abrogates expression of pathologic hypertrophy markers (ANP and BNP) during phenylephrine treatment, consistent with in silico results suggesting broad connections between miR-106b targets and natriuretic peptide signaling ( Fig. B, E-F ). These results provide translational evidence that circulating miRNAs associated with hypertrophy in patients may be protective in the transition from hypertrophy to HF at the molecular level.

Role Network Analysis of Team Interactions and Individual Activities: Application to VTE Prophylaxis

2017 ◽  
Vol 61 (1) ◽  
pp. 896-900 ◽  
Author(s):  
Ann Schoofs Hundt ◽  
Pascale Carayon ◽  
Yushi Yang ◽  
Jason Stamm ◽  
Vaibhav Agrawal ◽  
...  
Keyword(s):  
Network Analysis ◽  
Flow Model ◽  
Visual Representations ◽  
Hospitalized Patients ◽  
Healthcare Team ◽  
Analysis Method ◽  
Team Members ◽  
Vte Prophylaxis ◽  
Team Interactions ◽  
Clinical Interactions

In this paper, we describe the role network analysis method to capture and visually convey healthcare team members’ clinical interactions as well as individual activities performed in light of VTE prophylaxis management for hospitalized patients. Our visual representations expand on the role network analysis work of Pasmore (1988) and flow model of Beyer and Holtzblatt (1998) and offer a deeper sociotechnical representation of the work of healthcare team members.

Sign in / Sign up

Export Citation Format

Share Document