TOWARD DISCOVERING DISEASE-SPECIFIC GENE NETWORKS FROM ONLINE LITERATURE

AbstractRapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting new drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

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Publisher Correction: c-Maf controls immune responses by regulating disease-specific gene networks and repressing IL-2 in CD4+ T cells

Nature Immunology ◽

10.1038/s41590-019-0331-3 ◽

2019 ◽

Vol 20 (3) ◽

pp. 374-374

Author(s):

Leona Gabryšová ◽

Marisol Alvarez-Martinez ◽

Raphaëlle Luisier ◽

Luke S. Cox ◽

Jan Sodenkamp ◽

...

Keyword(s):

T Cells ◽

Immune Responses ◽

Cd4 T Cells ◽

Gene Networks ◽

Specific Gene ◽

Disease Specific

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c-Maf controls immune responses by regulating disease-specific gene networks and repressing IL-2 in CD4+ T cells

Nature Immunology ◽

10.1038/s41590-018-0083-5 ◽

2018 ◽

Vol 19 (5) ◽

pp. 497-507 ◽

Cited By ~ 52

Author(s):

Leona Gabryšová ◽

Marisol Alvarez-Martinez ◽

Raphaëlle Luisier ◽

Luke S. Cox ◽

Jan Sodenkamp ◽

...

Keyword(s):

T Cells ◽

Immune Responses ◽

Cd4 T Cells ◽

Gene Networks ◽

Specific Gene ◽

Disease Specific

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Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements

BMC Systems Biology ◽

10.1186/1752-0509-2-72 ◽

2008 ◽

Vol 2 (1) ◽

pp. 72 ◽

Cited By ~ 51

Author(s):

Wei Jiang ◽

Xia Li ◽

Shaoqi Rao ◽

Lihong Wang ◽

Lei Du ◽

...

Keyword(s):

Colon Cancer ◽

Gene Networks ◽

Interleukin 8 ◽

Specific Gene ◽

Central Elements ◽

Disease Specific

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Transcriptomic Data Analysis Reveals a Down-Expression of Galectin-8 in Schizophrenia Hippocampus

Brain Sciences ◽

10.3390/brainsci11080973 ◽

2021 ◽

Vol 11 (8) ◽

pp. 973

Author(s):

Maria Cristina Petralia ◽

Rosella Ciurleo ◽

Alessia Bramanti ◽

Placido Bramanti ◽

Andrea Saraceno ◽

...

Keyword(s):

Inflammatory Responses ◽

Clinical Manifestations ◽

Antipsychotic Agents ◽

Standards Of Care ◽

Specific Gene ◽

Contributing Factors ◽

Healthy Donors ◽

Dorsolateral Prefrontal ◽

Inflammatory Processes ◽

Disease Specific

Schizophrenia (SCZ) is a severe psychiatric disorder with several clinical manifestations that include cognitive dysfunction, decline in motivation, and psychosis. Current standards of care treatment with antipsychotic agents are often ineffective in controlling the disease, as only one-third of SCZ patients respond to medications. The mechanisms underlying the pathogenesis of SCZ remain elusive. It is believed that inflammatory processes may play a role as contributing factors to the etiology of SCZ. Galectins are a family of β-galactoside-binding lectins that contribute to the regulation of immune and inflammatory responses, and previous reports have shown their role in the maintenance of central nervous system (CNS) homeostasis and neuroinflammation. In the current study, we evaluated the expression levels of the galectin gene family in post-mortem samples of the hippocampus, associative striatum, and dorsolateral prefrontal cortex from SCZ patients. We found a significant downregulation of LGALS8 (Galectin-8) in the hippocampus of SCZ patients as compared to otherwise healthy donors. Interestingly, the reduction of LGALS8 was disease-specific, as no modulation was observed in the hippocampus from bipolar nor major depressive disorder (MDD) patients. Prediction analysis identified TBL1XR1, BRF2, and TAF7 as potential transcription factors controlling LGALS8 expression. In addition, MIR3681HG and MIR4296 were negatively correlated with LGALS8 expression, suggesting a role for epigenetics in the regulation of LGALS8 levels. On the other hand, no differences in the methylation levels of LGALS8 were observed between SCZ and matched control hippocampus. Finally, ontology analysis of the genes negatively correlated with LGALS8 expression identified an enrichment of the NGF-stimulated transcription pathway and of the oligodendrocyte differentiation pathway. Our study identified LGALS8 as a disease-specific gene, characterizing SCZ patients, that may in the future be exploited as a potential therapeutic target.

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Genome-wide analysis and expression profile of the bZIP gene family in poplar

BMC Plant Biology ◽

10.1186/s12870-021-02879-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kai Zhao ◽

Song Chen ◽

Wenjing Yao ◽

Zihan Cheng ◽

Boru Zhou ◽

...

Keyword(s):

Salt Stress ◽

Systems Biology ◽

Gene Family ◽

Stress Responses ◽

Metal Ion ◽

Gene Networks ◽

Expression Patterns ◽

Gene Set Enrichment Analysis ◽

Specific Gene ◽

Biological Processes

Abstract Background The bZIP gene family, which is widely present in plants, participates in varied biological processes including growth and development and stress responses. How do the genes regulate such biological processes? Systems biology is powerful for mechanistic understanding of gene functions. However, such studies have not yet been reported in poplar. Results In this study, we identified 86 poplar bZIP transcription factors and described their conserved domains. According to the results of phylogenetic tree, we divided these members into 12 groups with specific gene structures and motif compositions. The corresponding genes that harbor a large number of segmental duplication events are unevenly distributed on the 17 poplar chromosomes. In addition, we further examined collinearity between these genes and the related genes from six other species. Evidence from transcriptomic data indicated that the bZIP genes in poplar displayed different expression patterns in roots, stems, and leaves. Furthermore, we identified 45 bZIP genes that respond to salt stress in the three tissues. We performed co-expression analysis on the representative genes, followed by gene set enrichment analysis. The results demonstrated that tissue differentially expressed genes, especially the co-expressing genes, are mainly involved in secondary metabolic and secondary metabolite biosynthetic processes. However, salt stress responsive genes and their co-expressing genes mainly participate in the regulation of metal ion transport, and methionine biosynthetic. Conclusions Using comparative genomics and systems biology approaches, we, for the first time, systematically explore the structures and functions of the bZIP gene family in poplar. It appears that the bZIP gene family plays significant roles in regulation of poplar development and growth and salt stress responses through differential gene networks or biological processes. These findings provide the foundation for genetic breeding by engineering target regulators and corresponding gene networks into poplar lines.

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A multi-context feature learning approach to identify disease-specific gene neighborhoods

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics ◽

10.1145/3388440.3412419 ◽

2020 ◽

Author(s):

Sudhir Ghandikota ◽

Anil G. Jegga

Keyword(s):

Feature Learning ◽

Specific Gene ◽

Learning Approach ◽

Disease Specific

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An approach to infer putative disease-specific mechanisms using neighboring gene networks

Bioinformatics ◽

10.1093/bioinformatics/btx097 ◽

2017 ◽

Vol 33 (13) ◽

pp. 1987-1994 ◽

Cited By ~ 4

Author(s):

Sahar Ansari ◽

Michele Donato ◽

Nafiseh Saberian ◽

Sorin Draghici

Keyword(s):

Gene Networks ◽

Neighboring Gene ◽

Disease Specific

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Novel cancer subtyping method based on patient-specific gene regulatory network

10.1101/2021.03.24.436731 ◽

2021 ◽

Author(s):

Mai Adachi Nakazawa ◽

Yoshinori Tamada ◽

Yoshihisa Tanaka ◽

Marie Ikeguchi ◽

Kako Higashihara ◽

...

Keyword(s):

Gene Networks ◽

Regulatory Networks ◽

The Cancer Genome Atlas ◽

Patient Specific ◽

Specific Gene ◽

Omics Data ◽

Cancer Subtypes ◽

Molecular Systems ◽

Molecular Features ◽

Cancer Genome Atlas

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

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