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Using Omics Data and Systems Biology Approaches Based on Network...
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Using Omics Data and Systems Biology Approaches Based on Network Analysis to Understand Biological Systems

A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 16100

Special Issue Editors

Dr. Dario Di Silvestre

E-Mail Website
Guest Editor
Institute for Biomedical Technologies-National Research Council (ITB-CNR), Via Fratelli Cervi 93, 20090 Segrate, MI, Italy
Interests: proteomics; liquid chromatography; mass-spectrometry; computational biology methods; biomarker discovery; systems biology; protein–protein interaction network; co-expression networks
Special Issues, Collections and Topics in MDPI journals
Dr. Scardoni Giovanni

E-Mail Website
Guest Editor
1. Università degli Studi Verona, Dipartimento di Medicina, Sezione di Patologia Generale, Policlinico GB Rossi, Piazzale L.A. Scuro 10, 37134 Verona, Italy
2. Centro Interdipartimentale di Biomedicina Computazionale “CBMC”, Strada le Grazie 8. 37134, Verona, Italy
Interests: systems biology; network construction and analysis; network analysis software design and implementation
Dr. Gabriele Tosadori

E-Mail Website
Guest Editor
1. Università degli Studi Verona, Dipartimento di Medicina, Sezione di Patologia Generale, Policlinico GB Rossi, Piazzale L.A. Scuro 10, 37134 Verona, Italy
2. Centro Interdipartimentale di Biomedicina Computazionale “CBMC”, Strada le Grazie 8. 37134, Verona, Italy
Interests: systems biology; network construction and analysis
Dr. Thanh-Phuong Nguyen

E-Mail
Guest Editor
1. Megeno S.A.6A, avenue des Hauts-FourneauxL-4362 Esch-sur-Alzette, Esch- sur-Alzette, Luxembourg
2. Life Sciences Research Unit, University of Luxembourg, Esch-sur-Alzette, Luxembourg
Interests: PPI network; disease network; drug network; network analysis; systems biology; systems pharmacology; machine learning
Prof. Dr. Jonathan W. Boyd

E-Mail Website
Guest Editor
1. Director of Graduate Studies and Associate Professor, Department of Orthopaedics, West Virginia University School of Medicine, 64 Medical Center Drive, Morgantown, WV 26506, USA
2. Advisory Board, Center for Inhalational Toxicology, West Virginia University School of Medicine, Morgantown, WV, USA
3. Associate Director, Musculoskeletal Laboratory, West Virginia University School of Medicine, Morgantown, WV, USA
4. Associate Professor (joint appointment), Department of Physiology and Pharmacology, West Virginia University School of Medicine, Morgantown, WV, USA
Interests: mammalian signal transduction; toxicology; trauma; infection; inflammation; integration of multiscale ‘omics; imaging

Special Issue Information

Dear Colleagues,

During the 20th century, biological research was characterized by Reductionism, assuming that “the whole is no more than the sum of its parts” and that every biological theory could be deduced by studying the simplest components of a biological system. This way of thinking has allowed for a listing of those molecules that were mostly present in a cell and, eventually, has revealed the complexity of biological systems, as well as the limitations of Reductionism itself. Starting from the 21st century, scientists began to investigate biological systems from a holistic point of view, by assuming they consist of integrated molecular networks that communicate at multiple levels.

To address the complexity of biological systems, in recent decades, systems biology-oriented approaches have been developed. These novel approaches encompass many scientific disciplines, such as biology, mathematics, and computer science. In this scenario, -omics technologies are playing a fundamental role by providing massive amounts of data in an increasingly fast, efficient, and affordable manner. At the same time, more and more mathematical models have been developed to better integrate -omics data at a multiscale level. These models were, and currently are, developed as computational tools and algorithms that assist researchers in data handling, processing, and modeling with the goal of extracting relevant information in an unbiased way.

Although these approaches are increasingly adopted to address all kinds of biological questions, they still remain largely unexplored, and their full potential has yet to be reached. On the other hand, biologists and physicians are demonstrating their interest, and a growing number of studies are addressed through the system biology perspective. The landscape of possible applications is dominated by the use of transcriptomic data, usually visualized and analyzed by means of gene co-expression networks. A smaller number of works have relied on the combination of proteomic and metabolomic data with protein–protein interaction (PPI) network models. However, the improvement of high-throughput mass spectrometry-based technologies is increasing that number of studies, as well as the application of multi-omics integrative strategies.

Based on these premises, the aim of this Special Issue concerns those studies that take advantage of -omics technologies coupled to systems biology approaches, based on but not limited to PPI and co-expression networks. All research fields of application, ranging from medicine to plant science, are eligible. Special attention will also be given to the potential that may derive from the use of topological analysis and network models. In the same way, studies concerning computational tools and algorithms supporting biologists in data processing are welcome, too.

Dr. Dario Di Silvestre
Dr. Scardoni Giovanni
Dr. Gabriele Tosadori
Dr. Thanh-Phuong Nguyen
Prof. Dr. Jonathan W. Boyd
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. Biology 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 2700 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

  • omics data
  • systems biology
  • data integration
  • computational biology
  • network analysis
  • network topology
  • machine learning
  • biomedicine
  • pharmacotherapy personalized medicine
  • microbiology
  • infection
  • plant biology

Published Papers (5 papers)

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Research

17 pages, 1866 KiB  
Article
Presence of a Mitovirus Is Associated with Alteration of the Mitochondrial Proteome, as Revealed by Protein–Protein Interaction (PPI) and Co-Expression Network Models in Chenopodium quinoa Plants
by Dario Di Silvestre, Giulia Passignani, Rossana Rossi, Marina Ciuffo, Massimo Turina, Gianpiero Vigani and Pier Luigi Mauri
Biology 2022, 11(1), 95; https://doi.org/10.3390/biology11010095 - 8 Jan 2022
Cited by 8 | Viewed by 2461
Abstract
Plant mitoviruses belong to Mitoviridae family and consist of positive single-stranded RNA genomes replicating exclusively in host mitochondria. We previously reported the biological characterization of a replicating plant mitovirus, designated Chenopodium quinoa mitovirus 1 (CqMV1), in some Chenopodium quinoa accessions. In this study, [...] Read more.
Plant mitoviruses belong to Mitoviridae family and consist of positive single-stranded RNA genomes replicating exclusively in host mitochondria. We previously reported the biological characterization of a replicating plant mitovirus, designated Chenopodium quinoa mitovirus 1 (CqMV1), in some Chenopodium quinoa accessions. In this study, we analyzed the mitochondrial proteome from leaves of quinoa, infected and not infected by CqMV1. Furthermore, by protein–protein interaction and co-expression network models, we provided a system perspective of how CqMV1 affects mitochondrial functionality. We found that CqMV1 is associated with changes in mitochondrial protein expression in a mild but well-defined way. In quinoa-infected plants, we observed up-regulation of functional modules involved in amino acid catabolism, mitochondrial respiratory chain, proteolysis, folding/stress response and redox homeostasis. In this context, some proteins, including BCE2 (lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex), DELTA-OAT (ornithine aminotransferase) and GR-RBP2 (glycine-rich RNA-binding protein 2) were interesting because all up-regulated and network hubs in infected plants; together with other hubs, including CAT (catalase) and APX3 (L-ascorbate peroxidase 3), they play a role in stress response and redox homeostasis. These proteins could be related to the higher tolerance degree to drought we observed in CqMV1-infected plants. Although a specific causative link could not be established by our experimental approach at this stage, the results suggest a new mechanistic hypothesis that demands further in-depth functional studies. Full article
(This article belongs to the Special Issue Using Omics Data and Systems Biology Approaches Based on Network Analysis to Understand Biological Systems)
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17 pages, 2680 KiB  
Article
Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes
by Apurva Badkas, Thanh-Phuong Nguyen, Laura Caberlotto, Jochen G. Schneider, Sébastien De Landtsheer and Thomas Sauter
Biology 2021, 10(2), 107; https://doi.org/10.3390/biology10020107 - 3 Feb 2021
Cited by 2 | Viewed by 2143
Abstract
A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD [...] Read more.
A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities. Full article
(This article belongs to the Special Issue Using Omics Data and Systems Biology Approaches Based on Network Analysis to Understand Biological Systems)
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18 pages, 4394 KiB  
Article
Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy
by Jin Li, Yang Huo, Xue Wu, Enze Liu, Zhi Zeng, Zhen Tian, Kunjie Fan, Daniel Stover, Lijun Cheng and Lang Li
Biology 2020, 9(9), 278; https://doi.org/10.3390/biology9090278 - 7 Sep 2020
Cited by 8 | Viewed by 3915
Abstract
In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy [...] Read more.
In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction. Full article
(This article belongs to the Special Issue Using Omics Data and Systems Biology Approaches Based on Network Analysis to Understand Biological Systems)
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14 pages, 2400 KiB  
Article
Metabolic Responses of Carotenoid and Cordycepin Biosynthetic Pathways in Cordyceps militaris under Light-Programming Exposure through Genome-Wide Transcriptional Analysis
by Roypim Thananusak, Kobkul Laoteng, Nachon Raethong, Yu Zhang and Wanwipa Vongsangnak
Biology 2020, 9(9), 242; https://doi.org/10.3390/biology9090242 - 21 Aug 2020
Cited by 11 | Viewed by 3881
Abstract
Cordyceps militaris is currently exploited for commercial production of specialty products as its biomass constituents are enriched in bioactive compounds, such as cordycepin. The rational process development is important for economically feasible production of high quality bioproducts. Light is an abiotic factor affecting [...] Read more.
Cordyceps militaris is currently exploited for commercial production of specialty products as its biomass constituents are enriched in bioactive compounds, such as cordycepin. The rational process development is important for economically feasible production of high quality bioproducts. Light is an abiotic factor affecting the cultivation process of this entomopathogenic fungus, particularly in its carotenoid formation. To uncover the cell response to light exposure, this study aimed to systematically investigate the metabolic responses of C. militaris strain TBRC6039 using integrative genome-wide transcriptome and genome-scale metabolic network (GSMN)-driven analysis. The genome-wide transcriptome analysis showed 8747 expressed genes in the glucose and sucrose cultures grown under light-programming and dark conditions. Of them, 689 differentially expressed genes were significant in response to the light-programming exposure. Through integration with the GSMN-driven analysis using the improved network (iRT1467), the reporter metabolites, e.g., adenosine-5′-monophosphate (AMP) and 2-oxoglutarate, were identified when cultivated under the carotenoid-producing condition controlled by light-programming exposure, linking to up-regulations of the metabolic genes involved in glyoxalase system, as well as cordycepin and carotenoid biosynthesis. These results indicated that C. militaris had a metabolic control in acclimatization to light exposure through transcriptional co-regulation, which supported the cell growth and cordycepin production in addition to the accumulation of carotenoid as a photo-protective bio-pigment. This study provides a perspective in manipulating the metabolic fluxes towards the target metabolites through either genetic or physiological approaches. Full article
(This article belongs to the Special Issue Using Omics Data and Systems Biology Approaches Based on Network Analysis to Understand Biological Systems)
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20 pages, 2810 KiB  
Article
Impact of Cytokines and Phosphoproteins in Response to Chronic Joint Infection
by Nicole Prince, Julia A. Penatzer, Matthew J. Dietz and Jonathan W. Boyd
Biology 2020, 9(7), 167; https://doi.org/10.3390/biology9070167 - 16 Jul 2020
Cited by 2 | Viewed by 2418
Abstract
The early cellular response to infection has been investigated extensively, generating valuable information regarding the mediators of acute infection response. Various cytokines have been highlighted for their critical roles, and the actions of these cytokines are related to intracellular phosphorylation changes to promote [...] Read more.
The early cellular response to infection has been investigated extensively, generating valuable information regarding the mediators of acute infection response. Various cytokines have been highlighted for their critical roles, and the actions of these cytokines are related to intracellular phosphorylation changes to promote infection resolution. However, the development of chronic infections has not been thoroughly investigated. While it is known that wound healing processes are disrupted, the interactions of cytokines and phosphoproteins that contribute to this dysregulation are not well understood. To investigate these relationships, this study used a network centrality approach to assess the impact of individual cytokines and phosphoproteins during chronic inflammation and infection. Tissues were taken from patients undergoing total knee arthroplasty (TKA) and total knee revision (TKR) procedures across two tissue depths to understand which proteins are contributing most to the dysregulation observed at the joint. Notably, p-c-Jun, p-CREB, p-BAD, IL-10, IL-12p70, IL-13, and IFN-γ contributed highly to the network of proteins involved in aseptic inflammation caused by implants. Similarly, p-PTEN, IL-4, IL-10, IL-13, IFN-γ, and TNF-α appear to be central to signaling disruptions observed in septic joints. Ultimately, the network centrality approach provided insight into the altered tissue responses observed in chronic inflammation and infection. Full article
(This article belongs to the Special Issue Using Omics Data and Systems Biology Approaches Based on Network Analysis to Understand Biological Systems)
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