Systems Genomics Approaches for Understanding Multi-omics Data

A special issue of Biomolecules (ISSN 2218-273X).

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 17938

Special Issue Editors


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Guest Editor
Amrita School of Biotechnology, Amrita University, Kerala 690525, India
Interests: systems genomics; lncRNAs; protein interactions; known unknowns; next generation sequencing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Science, Engineering and Technology, University of Abertay, Dundee, UK
Interests: bioinformatics; systems biology

Special Issue Information

Dear Colleagues,

In the recent past, comprehensive studies on systems biology integrated approaches have yielded answers to that have allowed one to understand various biological processes. From integrating multi-layer ‘omic’ data to target identification, a host of molecular mechanisms at the systems level has been applied. With the advent of next-generation sequencing technologies, understanding complex biological systems has not only become easy but has also enabled one to ascertain biological functions at the organism level. In this process, a lot of focus has been on regulatory systems genomic approaches inherent to biomolecules, specifically, protein–protein interactions and protein–DNA interactions. This has further helped researchers to construct integrative genome-phenome approaches besides exploring non-coding RNA-protein interactions for the development of heuristic models to help strengthen experiments and therapeutic models to ascertain diseasome studies. Inferring bona fide systems genomic approaches, however, is a major bottleneck. In this proposed call, we invite authors to contribute to this research topic, focusing on (but not limited to) the following:

(a)  Sequence–structure–functional  genomics approaches to ascertain the multi-omics data

(b)  Shared pathways and mechanisms to understand diseasome and phenome-interactome networks

(c)  Non-coding RNA -protein interactions and systems networks  

(d) Machine learning-based approaches for systems biology

(e)  Gene ontology, classification, and precision medicine 

Dr. Prashanth N Suravajhala
Dr. Alexey Golstov
Guest Editors

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Keywords

  • Functional Genomics
  • Parallels between top-down and bottom-up system approaches
  • Efflux pumps and flux balance genomics
  • Non-coding RNA
  • Regulatory genomics
  • Molecular pathway approaches

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Published Papers (4 papers)

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Editorial

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3 pages, 173 KiB  
Editorial
Three Grand Challenges in High Throughput Omics Technologies
by Prashanth Suravajhala and Alexey Goltsov
Biomolecules 2022, 12(9), 1238; https://doi.org/10.3390/biom12091238 - 4 Sep 2022
Viewed by 1954
Abstract
Over the years, next-generation sequencing (NGS) and advanced bioinformatics approaches have allowed the transition of genomic assays into translational practices [...] Full article
(This article belongs to the Special Issue Systems Genomics Approaches for Understanding Multi-omics Data)

Research

Jump to: Editorial

22 pages, 1984 KiB  
Article
BabyBoom: 3-Dimensional Structure-Based Ligand and Protein Interaction Prediction by Molecular Docking
by Sameera Sastry Panchangam
Biomolecules 2022, 12(11), 1633; https://doi.org/10.3390/biom12111633 - 3 Nov 2022
Cited by 2 | Viewed by 2233
Abstract
Baby Boom (BBM) is a key transcription factor that triggers embryogenesis, enhances transformation and regeneration efficiencies, and regulates developmental pathways in plants. Triggering or activating BBM in non-model crops could overcome the bottlenecks in plant breeding. Understanding BBM’s structure is critical for functional [...] Read more.
Baby Boom (BBM) is a key transcription factor that triggers embryogenesis, enhances transformation and regeneration efficiencies, and regulates developmental pathways in plants. Triggering or activating BBM in non-model crops could overcome the bottlenecks in plant breeding. Understanding BBM’s structure is critical for functional characterization and determination of interacting partners and/or ligands. The current in silico study aimed to study BBM’s sequence and conservation across all plant proteomes, predict protein-protein and protein-ligand interactions, and perform molecular docking and molecular dynamics (MD) simulation to specifically determine the binding site amino acid residues. In addition, peptide sequences that interact with BBM have also been predicted, which provide avenues for altered functional interactions and the design of peptide mimetics that can be experimentally validated for their role in tissue culture or transformation media. This novel data could pave the way for the exploitation of BBM’s potential as the master regulator of specialized plant processes such as apomixes, haploid embryogenesis, and CRISPR/Cas9 transgenic development. Full article
(This article belongs to the Special Issue Systems Genomics Approaches for Understanding Multi-omics Data)
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20 pages, 2719 KiB  
Article
Whole Genome Sequencing of Familial Non-Medullary Thyroid Cancer Identifies Germline Alterations in MAPK/ERK and PI3K/AKT Signaling Pathways
by Aayushi Srivastava, Abhishek Kumar, Sara Giangiobbe, Elena Bonora, Kari Hemminki, Asta Försti and Obul Reddy Bandapalli
Biomolecules 2019, 9(10), 605; https://doi.org/10.3390/biom9100605 - 13 Oct 2019
Cited by 22 | Viewed by 6366
Abstract
Evidence of familial inheritance in non-medullary thyroid cancer (NMTC) has accumulated over the last few decades. However, known variants account for a very small percentage of the genetic burden. Here, we focused on the identification of common pathways and networks enriched in NMTC [...] Read more.
Evidence of familial inheritance in non-medullary thyroid cancer (NMTC) has accumulated over the last few decades. However, known variants account for a very small percentage of the genetic burden. Here, we focused on the identification of common pathways and networks enriched in NMTC families to better understand its pathogenesis with the final aim of identifying one novel high/moderate-penetrance germline predisposition variant segregating with the disease in each studied family. We performed whole genome sequencing on 23 affected and 3 unaffected family members from five NMTC-prone families and prioritized the identified variants using our Familial Cancer Variant Prioritization Pipeline (FCVPPv2). In total, 31 coding variants and 39 variants located in upstream, downstream, 5′ or 3′ untranslated regions passed FCVPPv2 filtering. Altogether, 210 genes affected by variants that passed the first three steps of the FCVPPv2 were analyzed using Ingenuity Pathway Analysis software. These genes were enriched in tumorigenic signaling pathways mediated by receptor tyrosine kinases and G-protein coupled receptors, implicating a central role of PI3K/AKT and MAPK/ERK signaling in familial NMTC. Our approach can facilitate the identification and functional validation of causal variants in each family as well as the screening and genetic counseling of other individuals at risk of developing NMTC. Full article
(This article belongs to the Special Issue Systems Genomics Approaches for Understanding Multi-omics Data)
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35 pages, 11948 KiB  
Article
Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray
by Ali Mohamed Alshabi, Basavaraj Vastrad, Ibrahim Ahmed Shaikh and Chanabasayya Vastrad
Biomolecules 2019, 9(7), 282; https://doi.org/10.3390/biom9070282 - 15 Jul 2019
Cited by 15 | Viewed by 5940
Abstract
Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential [...] Read more.
Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes—miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H), which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes—miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA. Full article
(This article belongs to the Special Issue Systems Genomics Approaches for Understanding Multi-omics Data)
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