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Biology
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Multi-Omics Co-expression Network Analysis
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Multi-Omics Co-expression Network Analysis

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 7808

Special Issue Editor

Dr. Thomas Ulas

E-Mail Website
Guest Editor
Platform for Single Cell Genomics and Epigenomics (PRECISE), University of Bonn and Deutsches Zentrum für Neurodegenerative Erkrankungen e.V., 53127 Bonn, Germany
Interests: computational biology; bioinformatics; innate immunity; neonate immunity; R; bulk and single-cell transcriptomics co-expression network analysis, data integration

Special Issue Information

Dear Colleagues,

Independent high-throughput omic studies (e.g., genomics, epigenomics, transcriptomics, proteomics, and metagenomics) that aimed to understand a specific problem, such as human disease, have been successfully applied to a large extent in the last decade. In transcriptomics, statistical analysis methods are used to explore biological functions which contribute to the discovery of multiple pathways and the understanding of gene–disease relationships. These studies create a large amount of data, which may be used to answer even broader questions than originally planned by carefully integrating them using suitable mathematical frameworks. To fully comprehend the complex interactions that occur in cellular processes, approaches that can also capture the relationships between the genes involved are very beneficial. Co-expression networks have been utilized as a method to describe and study gene relationships in order to overcome this problem. Furthermore, co-expression networks can be utilized to integrate different omics datasets.

We invite original research and short communications on tools that enable the combination of co-expression network analysis with at least one other omic layer, including an application demonstrating the benefits of your approach, as well as review articles on topics related to "Multi-Omics Co-Expression Network Analysis" to be submitted to this Special Issue. We look forward to receiving your contributions.

Dr. Thomas Ulas
Guest Editor

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

  • multi-omics
  • co-expression network analysis
  • transcriptomics
  • data integration
  • bioinformatics
  • network visualization
  • network comparison

Published Papers (5 papers)

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Research

20 pages, 3356 KiB  
Article
Multiomics Picture of Obesity in Young Adults
by Olga I. Kiseleva, Mikhail A. Pyatnitskiy, Viktoriia A. Arzumanian, Ilya Y. Kurbatov, Valery V. Ilinsky, Ekaterina V. Ilgisonis, Oksana A. Plotnikova, Khaider K. Sharafetdinov, Victor A. Tutelyan, Dmitry B. Nikityuk, Elena A. Ponomarenko and Ekaterina V. Poverennaya
Biology 2024, 13(4), 272; https://doi.org/10.3390/biology13040272 - 18 Apr 2024
Viewed by 192
Abstract
Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their [...] Read more.
Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC–MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC–MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions. Full article
(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)
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16 pages, 4625 KiB  
Article
Integrative Multi-Omics Analysis Identifies Transmembrane p24 Trafficking Protein 1 (TMED1) as a Potential Prognostic Marker in Colorectal Cancer
by Xin Guo, Wei Zhou, Jinmei Jin, Jiayi Lin, Weidong Zhang, Lijun Zhang and Xin Luan
Biology 2024, 13(2), 83; https://doi.org/10.3390/biology13020083 - 29 Jan 2024
Viewed by 1296
Abstract
Several TMED protein family members are overexpressed in malignant tumors and associated with tumor progression. TMED1 belongs to the TMED protein family and is involved in protein vesicular trafficking. However, the expression level and biological role of TMED1 in colorectal cancer (CRC) have [...] Read more.
Several TMED protein family members are overexpressed in malignant tumors and associated with tumor progression. TMED1 belongs to the TMED protein family and is involved in protein vesicular trafficking. However, the expression level and biological role of TMED1 in colorectal cancer (CRC) have yet to be fully elucidated. In this study, the integration of patient survival and multi-omics data (immunohistochemical staining, transcriptomics, and proteomics) revealed that the highly expressed TMED1 was related to the poor prognosis in CRC. Crystal violet staining indicated the cell growth was reduced after knocking down TMED1. Moreover, the flow cytometry results showed that TMED1 knockdown could increase cell apoptosis. The expression of TMED1 was positively correlated with other TMED family members (TMED2, TMED4, TMED9, and TMED10) in CRC, and the protein–protein interaction network suggested its potential impact on immune regulation. Furthermore, TMED1 expression was positively associated with the infiltration levels of regulatory T cells (Tregs), cancer-associated fibroblasts (CAFs), and endothelial cells and negatively correlated with the infiltration levels of CD4+ T cells, CD8+ T cells, and B cells. At last, the CTRP and GDSC datasets on the GSCA platform were used to analyze the relationship between TMED1 expression and drug sensitivity (IC50). The result found that the elevation of TMED1 was positively correlated with IC50 and implied it could increase the drug resistance of cancer cells. This research revealed that TMED1 is a novel prognostic biomarker in CRC and provided a valuable strategy for analyzing potential therapeutic targets of malignant tumors. Full article
(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)
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14 pages, 2158 KiB  
Article
Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification
by Amira Al-Aamri, Andrzej S. Kudlicki, Maher Maalouf, Kamal Taha and Dirar Homouz
Biology 2023, 12(4), 518; https://doi.org/10.3390/biology12040518 - 29 Mar 2023
Cited by 1 | Viewed by 1702
Abstract
Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating [...] Read more.
Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score. Full article
(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)
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16 pages, 3284 KiB  
Article
Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34
by Ekaterina V. Poverennaya, Mikhail A. Pyatnitskiy, Georgii V. Dolgalev, Viktoria A. Arzumanian, Olga I. Kiseleva, Ilya Yu. Kurbatov, Leonid K. Kurbatov, Igor V. Vakhrushev, Daniil D. Romashin, Yan S. Kim and Elena A. Ponomarenko
Biology 2023, 12(2), 198; https://doi.org/10.3390/biology12020198 - 28 Jan 2023
Cited by 3 | Viewed by 1830
Abstract
Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack [...] Read more.
Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34’s cellular functions. Full article
(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)
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19 pages, 4883 KiB  
Article
Integrative Analysis of Transcriptome and Metabolome Reveals Molecular Responses in Eriocheir sinensis with Hepatopancreatic Necrosis Disease
by Ming Zhan, Lujie Wen, Mengru Zhu, Jie Gong, Changjun Xi, Haibo Wen, Gangchun Xu and Huaishun Shen
Biology 2022, 11(9), 1267; https://doi.org/10.3390/biology11091267 - 26 Aug 2022
Cited by 1 | Viewed by 1670
Abstract
Hepatopancreatic necrosis disease (HPND) is a highly lethal disease that first emerged in 2015 in Jiangsu Province, China. So far, most researchers believe that this disease is caused by abiotic factors. However, its true pathogenic mechanism remains unknown. In this study, the effects [...] Read more.
Hepatopancreatic necrosis disease (HPND) is a highly lethal disease that first emerged in 2015 in Jiangsu Province, China. So far, most researchers believe that this disease is caused by abiotic factors. However, its true pathogenic mechanism remains unknown. In this study, the effects of HPND on the metabolism and other biological indicators of the Chinese mitten crab (Eriocheir sinensis) were evaluated by integrating transcriptomics and metabolomics. Our findings demonstrate that the innate immunity, antioxidant activity, detoxification ability, and nervous system of the diseased crabs were affected. Additionally, metabolic pathways such as lipid metabolism, nucleotide metabolism, and protein metabolism were dysregulated, and energy production was slightly increased. Moreover, the IL-17 signaling pathway was activated and high levels of autophagy and apoptosis occurred in diseased crabs, which may be related to hepatopancreas damage. The abnormal mitochondrial function and possible anaerobic metabolism observed in our study suggested that functional hypoxia may be involved in HPND progression. Furthermore, the activities of carboxylesterase and acetylcholinesterase were significantly inhibited, indicating that the diseased crabs were likely stressed by pesticides such as pyrethroids. Collectively, our findings provide new insights into the molecular mechanisms altered in diseased crabs, as well as the etiology and pathogenic mechanisms of HPND. Full article
(This article belongs to the Special Issue Multi-Omics Co-expression Network Analysis)
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