Submit to Genes Review for Genes Propose a Special Issue

Journal Browser

Current Advances in Network Biology for Disease Understanding

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 38448

Share This Special Issue

Special Issue Editors

Dr. Sujoy Ghosh

E-Mail Website
Guest Editor

1. Duke-NUS Medical School, Singapore 169857, Singapore
2. National Heart Research Institute, Singapore 169609, Singapore
3. Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
Interests: integrative bioinformatics; systems genomics; obesity genetics and genomics; nutrigenomics; functional genomics

Dr. Enrico G. Petretto

E-Mail Website
Guest Editor

1. Duke-NUS Medical School, Singapore 169857, Singapore
2. Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London W12 0NN, UK
Interests: systems genetics; genomics; computational biology

Special Issue Information

Dear Colleagues,

Biological processes involve a wide range of hierarchically layered intermolecular connections, such as protein–protein interactions, protein–DNA interactions, functional gene regulatory modules, signal transduction, etc. Similar to the more widely discussed social, professional, or internet-type networks, biological interactions can also be mathematically represented as mostly scale-free, small-world networks with nodes denoting molecules, and edges denoting specific relations or interactions between them. In addition to molecular interactions, biological networks also encompass higher-level organizations, such as those involving disease relationships (disease networks) or patient sub-groups within a clinical diagnosis (patient similarity networks). Network science is currently a highly active area of biological research. This is due to the confluence of a triumvirate of factors: (i) the generation and access to explosively large volumes of “omics” data from high-throughput experiments, and large-scale patient information from electronic medical records; (ii) the intensive development of deep computational methodologies and data visualization tools for the analysis of complex networks; and (iii) the urgent need for new approaches to interrogate complex disorders to effect transformative changes in clinical care. Network biology provides a novel conceptual framework that illuminates biological phenomena and provides new paths for their manipulation.

This Special Issue is dedicated to highlighting the current frontline status of network research today through expert commentaries on all three of the factors described above. We welcome the submission of reviews, research articles, and short communications encompassing diverse aspects of network science, including computational approaches (e.g., new algorithms for network construction, network visualization, and network analysis), investigations of network behaviour in molecular- to population-level datasets, and experimental manipulation of network components for testing predicted robustness and vulnerabilities in the context of network medicine.

Dr. Sujoy Ghosh
Dr. Enrico G. Petretto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

bioinformatics
networks
interactomes
biomolecular interactions
data visualization
computational biology

Published Papers (7 papers)

Download All Papers

Research

Jump to: Review

13 pages, 1774 KiB

Open AccessArticle

Personalized Early-Warning Signals during Progression of Human Coronary Atherosclerosis by Landscape Dynamic Network Biomarker

by Jing Ge, Chenxi Song, Chengming Zhang, Xiaoping Liu, Jingzhou Chen, Kefei Dou and Luonan Chen

Genes 2020, 11(6), 676; https://doi.org/10.3390/genes11060676 - 20 Jun 2020

Cited by 7 | Viewed by 2574

Abstract

Coronary atherosclerosis is one of the major factors causing cardiovascular diseases. However, identifying the tipping point (predisease state of disease) and detecting early-warning signals of human coronary atherosclerosis for individual patients are still great challenges. The landscape dynamic network biomarkers (l-DNB) methodology is based on the theory of dynamic network biomarkers (DNBs), and can use only one-sample omics data to identify the tipping point of complex diseases, such as coronary atherosclerosis. Based on the l-DNB methodology, by using the metabolomics data of plasma of patients with coronary atherosclerosis at different stages, we accurately detected the early-warning signals of each patient. Moreover, we also discovered a group of dynamic network biomarkers (DNBs) which play key roles in driving the progression of the disease. Our study provides a new insight into the individualized early diagnosis of coronary atherosclerosis and may contribute to the development of personalized medicine. Full article

(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)

► Show Figures

Figure 1

16 pages, 2314 KiB

Open AccessArticle

A Network-Based Approach for Identification of Subtype-Specific Master Regulators in Pancreatic Ductal Adenocarcinoma

by Yuchen Zhang, Lina Zhu and Xin Wang

Genes 2020, 11(2), 155; https://doi.org/10.3390/genes11020155 - 01 Feb 2020

Cited by 7 | Viewed by 2851

Abstract

Pancreatic ductal adenocarcinoma (PDAC), the predominant subtype of pancreatic cancer, has been reported with equal mortality and incidence for decades. The lethality of PDAC is largely due to its late presentation, when surgical resection is no longer an option. Similar to other major malignancies, it is now clear that PDAC is not a single disease, posing a great challenge to precise selection of patients for optimized adjuvant therapy. A representative study found that PDAC comprises four distinct molecular subtypes: squamous, pancreatic progenitor, immunogenic, and aberrantly differentiated endocrine exocrine (ADEX). However, little is known about the molecular mechanisms underlying specific PDAC subtypes, hampering the design of novel targeted agents. In this study we performed network inference that integrates miRNA expression and gene expression profiles to dissect the miRNA regulatory mechanism specific to the most aggressive squamous subtype of PDAC. Master regulatory analysis revealed that the particular subtype of PDAC is predominantly influenced by miR-29c and miR-192. Further integrative analysis found miR-29c target genes LOXL2, ADAM12 and SERPINH1, which all showed strong association with prognosis. Furthermore, we have preliminarily revealed that the PDAC cell lines with high expression of these miRNA target genes showed significantly lower sensitivities to multiple anti-tumor drugs. Together, our integrative analysis elucidated the squamous subtype-specific regulatory mechanism, and identified master regulatory miRNAs and their downstream genes, which are potential prognostic and predictive biomarkers. Full article

(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)

► Show Figures

Figure 1

21 pages, 1131 KiB

Open AccessArticle

Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach

by Fernando M. Delgado-Chaves, Francisco Gómez-Vela, Miguel García-Torres, Federico Divina and José Luis Vázquez Noguera

Genes 2019, 10(12), 962; https://doi.org/10.3390/genes10120962 - 22 Nov 2019

Cited by 5 | Viewed by 3517

Abstract

Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes

N C K A P 1 L

and

D M D

are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers. Full article

(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)

► Show Figures

Graphical abstract

19 pages, 3937 KiB

Open AccessArticle

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

by Hongzhu Cui, Suhas Srinivasan and Dmitry Korkin

Genes 2019, 10(11), 933; https://doi.org/10.3390/genes10110933 - 15 Nov 2019

Cited by 7 | Viewed by 4362

Abstract

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers. Full article

(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)

► Show Figures

Figure 1

7 pages, 1416 KiB

Open AccessCommunication

Digital Immune Gene Expression Profiling Discriminates Allergic Rhinitis Responders from Non-Responders to Probiotic Supplementation

by Nicholas P. West, Annabelle M. Watts, Peter K. Smith, Ping Zhang, Isolde Besseling-van der Vaart, Allan W. Cripps and Amanda J. Cox

Genes 2019, 10(11), 889; https://doi.org/10.3390/genes10110889 - 04 Nov 2019

Cited by 5 | Viewed by 3180

Abstract

Probiotic supplementation for eight weeks with a multi-strain probiotic by individuals with allergic rhinitis (AR) reduced overall symptom severity, the frequency of medication use and improved quality of life. The purported mechanism of action is modulation of the immune system. This analysis examined changes in systemic and mucosal immune gene expression in a subgroup of individuals, classified as either responders or non-responders based on improvement of AR symptoms in response to the probiotic supplement. Based on established criteria of a beneficial change in the mini-rhinoconjunctivitis quality of life questionnaire (mRQLQ), individuals with AR were classified as either responders or non-responders. Systemic and mucosal immune gene expression was assessed using nCounter PanCancer Immune Profiling (Nanostring Technologies, Seattle, WA, USA) kit on blood samples and a nasal lysate. There were 414 immune genes in the blood and 312 immune genes in the mucosal samples expressed above the limit of detection. Unsupervised hierarchical clustering of immune genes separated responders from non-responders in blood and mucosal samples at baseline and after supplementation, with key T-cell immune genes differentially expressed between the groups. Striking differences in biological processes and pathways were evident in nasal mucosa but not blood in responders compared to non-responders. These findings support the use of network approaches to understand probiotic-induced changes to the immune system. Full article

(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)

► Show Figures

Figure 1

Review

Jump to: Research

32 pages, 3100 KiB

Open AccessReview

Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease

by Montgomery Blencowe, Tilan Karunanayake, Julian Wier, Neil Hsu and Xia Yang

Genes 2019, 10(12), 966; https://doi.org/10.3390/genes10120966 - 24 Nov 2019

Cited by 24 | Viewed by 14417

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics. Full article

(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)

► Show Figures

Figure 1

24 pages, 1583 KiB

Open AccessFeature PaperReview

Transcriptional Networks of Microglia in Alzheimer’s Disease and Insights into Pathogenesis

by Gabriel Chew and Enrico Petretto

Genes 2019, 10(10), 798; https://doi.org/10.3390/genes10100798 - 12 Oct 2019

Cited by 17 | Viewed by 6625

Abstract

Microglia, the main immune cells of the central nervous system, are increasingly implicated in Alzheimer’s disease (AD). Manifold transcriptomic studies in the brain have not only highlighted microglia’s role in AD pathogenesis, but also mapped crucial pathological processes and identified new therapeutic targets. An important component of many of these transcriptomic studies is the investigation of gene expression networks in AD brain, which has provided important new insights into how coordinated gene regulatory programs in microglia (and other cell types) underlie AD pathogenesis. Given the rapid technological advancements in transcriptional profiling, spanning from microarrays to single-cell RNA sequencing (scRNA-seq), tools used for mapping gene expression networks have evolved to keep pace with the unique features of each transcriptomic platform. In this article, we review the trajectory of transcriptomic network analyses in AD from brain to microglia, highlighting the corresponding methodological developments. Lastly, we discuss examples of how transcriptional network analysis provides new insights into AD mechanisms and pathogenesis. Full article

(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)

► Show Figures