scholarly journals Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

2021 ◽  
Author(s):  
Elisabetta Sciacca ◽  
Anna E.A. Surace ◽  
Salvatore Alaimo ◽  
Alfredo Pulvirenti ◽  
Felice Rivellese ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Rna Sequencing ◽  
Gene Networks ◽  
Prediction Models ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Gene Interactions ◽  
Specific Gene ◽  
Rna Seq ◽  
Gene Interaction Networks

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

scholarly journals The structure balance of gene-gene networks beyond pairwise interactions

2021 ◽  
Author(s):  
Gholamreza Jafari ◽  
Nastaran Allahyari ◽  
Amir Kargaran ◽  
Ali Hosseiny
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Essential Gene ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Underlying Structure ◽  
Yeast Saccharomyces Cerevisiae ◽  
Gene Interaction Networks ◽  
Nonessential Gene ◽  
Strong Notion

Despite its high and direct impact on nearly all biological processes, the underlying structure of gene-gene interaction networks is investigated so far according to pair connections. To address this, we explore the gene interaction networks of the yeast Saccharomyces cerevisiae beyond pairwise interaction using the structural balance theory (SBT). Specifically, we ask whether essential and nonessential gene interaction networks are structurally balanced. We study triadic interactions in the weighted signed undirected gene networks and observe that balanced and unbalanced triads are over and underrepresented in both networks, thus beautifully in line with the strong notion of balance. Moreover, we note that the energy distribution of triads is significantly different in both essential and nonessential networks compared with the shuffled networks. Yet, this difference is greater in the essential network regarding the frequency as well as the energy of triads. Additionally, results demonstrate that triads in the essential gene network are more interconnected through sharing common links, while in the nonessential network they tend to be isolated. Last but not least, we investigate the contribution of all-length signed walks and its impact on the degree of balance. Our findings reveal that interestingly when considering longer cycles the nonessential gene network is more balanced compared to the essential network.

scholarly journals Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection

2019 ◽  
Vol 70 (20) ◽  
pp. 5603-5616 ◽  
Author(s):  
Tianhua He ◽  
Camilla Beate Hill ◽  
Tefera Tolera Angessa ◽  
Xiao-Qi Zhang ◽  
Kefei Chen ◽  
...  
Keyword(s):  
Flowering Time ◽  
Gene Networks ◽  
Statistical Power ◽  
Gene Interaction ◽  
High Accuracy ◽  
Association Test ◽  
Interaction Networks ◽  
Gene Set ◽  
Epistasis Analysis ◽  
Gene Interaction Networks

Using gene-set association test and epistasis analysis, this research achieved higher statistical power with potentially high accuracy, and detected significant genes and gene networks that influence flowering time in barley.

scholarly journals Personalized analysis of breast cancer using sample-specific networks

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9161
Author(s):  
Ke Zhu ◽  
Cong Pian ◽  
Qiong Xiang ◽  
Xin Liu ◽  
Yuanyuan Chen
Keyword(s):  
Breast Cancer ◽  
Expression Profiles ◽  
Single Gene ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Cancer Prognosis ◽  
Specific Gene ◽  
Validation Data ◽  
Data Set ◽  
Gene Interaction Networks

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

scholarly journals GNE: A deep learning framework for gene network inference by aggregating biological information

2018 ◽  
Author(s):  
K C Kishan ◽  
Rui Li ◽  
Feng Cui ◽  
Qi Yu ◽  
Anne R. Haake
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Network Inference ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Biological Information ◽  
Gene Interactions ◽  
Learning Framework ◽  
Gene Interaction Networks ◽  
Low Dimensional

AbstractThe topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low-dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. GNE is freely available under the GNU General Public License and can be downloaded from Github (https://github.com/kckishan/GNE)

scholarly journals Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

2016 ◽  
Author(s):  
Le Shu ◽  
Yuqi Zhao ◽  
Zeyneb Kurt ◽  
Sean Geoffrey Byars ◽  
Taru Tukiainen ◽  
...  
Keyword(s):  
Cholesterol Metabolism ◽  
Meta Analysis ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Specific Gene ◽  
Data Types ◽  
Tissue Specific ◽  
Pathway Network ◽  
Tissue Specific Gene ◽  
Gene Interaction Networks

Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

scholarly journals Importance of experimental information (metadata) for archived sequence data: case of specific gene bias due to lag time between sample harvest and RNA protection in RNA sequencing

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11875
Author(s):  
Tomoko Matsuda
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Time Course ◽  
Sequence Data ◽  
Specific Gene ◽  
Time Interval ◽  
Short Time Interval ◽  
Rna Seq ◽  
Lysis Buffer ◽  
Rna Protection

Large volumes of high-throughput sequencing data have been submitted to the Sequencing Read Archive (SRA). The lack of experimental metadata associated with the data makes reuse and understanding data quality very difficult. In the case of RNA sequencing (RNA-Seq), which reveals the presence and quantity of RNA in a biological sample at any moment, it is necessary to consider that gene expression responds over a short time interval (several seconds to a few minutes) in many organisms. Therefore, to isolate RNA that accurately reflects the transcriptome at the point of harvest, raw biological samples should be processed by freezing in liquid nitrogen, immersing in RNA stabilization reagent or lysing and homogenizing in RNA lysis buffer containing guanidine thiocyanate as soon as possible. As the number of samples handled simultaneously increases, the time until the RNA is protected can increase. Here, to evaluate the effect of different lag times in RNA protection on RNA-Seq data, we harvested CHO-S cells after 3, 5, 6, and 7 days of cultivation, added RNA lysis buffer in a time course of 15, 30, 45, and 60 min after harvest, and conducted RNA-Seq. These RNA samples showed high RNA integrity number (RIN) values indicating non-degraded RNA, and sequence data from libraries prepared with these RNA samples was of high quality according to FastQC. We observed that, at the same cultivation day, global trends of gene expression were similar across the time course of addition of RNA lysis buffer; however, the expression of some genes was significantly different between the time-course samples of the same cultivation day; most of these differentially expressed genes were related to apoptosis. We conclude that the time lag between sample harvest and RNA protection influences gene expression of specific genes. It is, therefore, necessary to know not only RIN values of RNA and the quality of the sequence data but also how the experiment was performed when acquiring RNA-Seq data from the database.

Sign in / Sign up

Export Citation Format

Share Document