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Biomolecules
Special Issues
Systems Genomics Approaches for Understanding Multi-omics Data
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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 18289

Special Issue Editors

Dr. Prashanth N Suravajhala

E-Mail Website
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
Dr. Alexey Golstov

E-Mail Website
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

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. Biomolecules 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

  • 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

Jump to: Research

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 2009
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 2322
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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Figure 1

Figure 1
<p>Fragments of BBM and its interacting partners that showed pLDDT &gt; 90 on AlphaFold server used further for docking as (<b>A</b>) receptor and (<b>B</b>) ligands.</p> Full article ">Figure 2
<p>Phylogenetic analysis of BBM. Protein sequences were aligned by Clustal W, and the mid-point rooted phylogenetic tree was constructed using iTOL by the Neighborhood Joining (NJ) method. Based on the phylogenetic relationships, different subgroups were marked with different colors.</p> Full article ">Figure 3
<p>(<b>A</b>) Full-length BBM protein with ligand interactions at predicted active sites. (<b>B</b>) BBM-ligand interactions with focus on Zn (magenta ball), CLA (grey balls), STD, and MET1.</p> Full article ">Figure 4
<p>BBM PPI. (<b>a</b>) Predicted interactions from STRING. (<b>b</b>) Predicted interactions from BioGRID.</p> Full article ">Figure 5
<p>List of the residues for the interaction partners of BBM that showed ∆ SASA (solvent accessible surface area) &gt; 0 in the best-docked complexes (<b>a</b>) BBM frag1-LEC2, (<b>b</b>) BBM frag1-WUS, (<b>c</b>) BBM frag1-LEC1, (<b>d</b>) BBM frag1-AGLI5 frag1, (<b>e</b>) BBM frag1-AGLI5 frag2, (<b>f</b>) BBM frag2-LEC2, (<b>g</b>) BBM frag2-WUS, (<b>h</b>) BBM frag2-LEC1, (<b>i</b>) BBM frag2-AGLI5 frag1, (<b>j</b>) BBM frag2-AGLI5 frag2.</p> Full article ">Figure 6
<p>Best 3D conformation of selected 7 peptides (P1–P7) modeled using APPTEST server. Peptides are shown from top to bottom as N-terminal to C-terminal.</p> Full article ">Figure 7
<p>Best docked pose of (<b>a</b>) P1-BBM1 (<b>b</b>) P2-BBM1 (dock10) (<b>c</b>) P2-BBM1 (dock89) (<b>d</b>) P3-BBM2 (<b>e</b>) P4-BBM1 (<b>f</b>) P5-BBM1 (<b>g</b>) P6-BBM1 and (<b>h</b>) P7-BBM2 complexes.</p> Full article ">Figure 8
<p>(<b>a</b>) Root mean square deviation (RMSD) for peptides P2 (dock10) and P4 was calculated from the protein-peptide complex. (<b>b</b>) Root mean square deviation (RMSD) for protein BBM Fragment 1 calculated from the protein-peptide complex. Figures are generated using the xmgrace tool of Linux.</p> Full article ">Figure 9
<p>(<b>a</b>) Root mean square fluctuation (RMSF) for peptide in protein-peptide complexes with P2 (dock10) and P4. (<b>b</b>) Root mean square fluctuation (RMSF) for residue of BBM Fragment 1. Figures are generated using xmgrace tool of linux.</p> Full article ">Figure 10
<p>Binding site residues of BBM Fragment 1 protein with the peptide P2 (dock10). Interactions are calculated and figures are generated using LigPlot + 2.2.5.</p> Full article ">Figure 11
<p>Binding site residues of BBM Fragment 1 protein with the peptide P4.</p> Full article ">
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 6463
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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Figure 1

Figure 1
<p>Pedigrees of the five non-medullary thyroid cancer (NMTC)-prone families analyzed in this study.</p> Full article ">Figure 2
<p>Summary of the familial cancer variant prioritization pipeline version 2 (FCVPPv2).</p> Full article ">Figure 3
<p>Top 18 overlapping canonical pathways visualized as a network, which shows each pathway as a single “node” colored proportionally to the Fisher’s Exact Test p-value, where brighter red indicates higher significance. Nodes marked with asterisk (*) belong to the group of GPCR and RTK mediated pathways.</p> Full article ">Figure 4
<p>The top three molecular networks identified by Ingenuity Pathway Analysis (IPA): (<b>a</b>) Network 1. Protein Synthesis, Cardiovascular System Development and Function, Cellular Assembly and Organization; (<b>b</b>) Network 2. Cell Morphology, Cellular Assembly and Organization, Cellular Development and (<b>c</b>) Network 3. Endocrine System Disorders, Metabolic Disease, Organismal Injury and Abnormalities. Genes from our input-data that were prioritized based on pedigree segregation and PHRED-like CADD scores are shown in peach. Our top coding and non-coding candidates are highlighted in dark orange. Interactions of central genes of the network are highlighted in blue.</p> Full article ">Figure 5
<p>Proposed model for the most important molecular mechanisms in FNMTC. Genes from our input-data are highlighted in orange and genes corresponding to variants prioritized using the FCVPPv2 are highlighted in red.</p> Full article ">
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 6030
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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Figure 1

Figure 1
<p>The workflow representing the methodology and the major outcome of the study. BRCA—breast cancer, GO—gene ontology, miRNA—MicroRNA, TF—transcription factor, DEGs—differential expressed genes.</p> Full article ">Figure 2
<p>Box plots of the gene expression data before (<b>A</b>) and after (<b>B</b>) normalization. The horizontal axis represents the sample symbol, and the vertical axis represents the gene expression values. The black line in the box plot represents the median value of gene expression. (A1, A2, A3 = T47D (wild type genotype + estradiol); B1, B2, B3 = T47D (wild type genotype + none); C1, C2, C3 = T47D (wild type genotype + tamoxifen); D1, D2, D3 = T47D (overexpressing Split Ends (SPEN) + estradiol); E1, E2, E3 = T47D (overexpressing Split Ends (SPEN) + none); F1, F2, F3 = T47D (overexpressing Split Ends (SPEN) + tamoxifen)).</p> Full article ">Figure 3
<p>Volcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected.</p> Full article ">Figure 4
<p>Heat map of up-regulated differentially expressed genes. The legend on the top left indicates log fold change of genes. (A1, A2, A3 = T47D (wild type genotype + estradiol); B1, B2, B3 = T47D (wild type genotype + none); C1, C2, C3 = T47D (wild type genotype + tamoxifen); D1, D2, D3 = T47D (overexpressing Split Ends (SPEN) + estradiol); E1, E2, E3 = T47D (overexpressing Split Ends (SPEN) + none); F1, F2, F3 = T47D (overexpressing Split Ends (SPEN) + tamoxifen)).</p> Full article ">Figure 5
<p>Heat map of down-regulated differentially expressed genes. The legend on the top left indicates log fold change of genes. (A1, A2, A3 = T47D (wild type genotype + estradiol); B1, B2, B3 = T47D (wild type genotype + none); C1, C2, C3 = T47D (wild type genotype + tamoxifen); D1, D2, D3 = T47D (overexpressing Split Ends (SPEN) + estradiol); E1, E2, E3 = T47D (overexpressing Split Ends (SPEN) + none); F1, F2, F3 = T47D (overexpressing Split Ends (SPEN) + tamoxifen)).</p> Full article ">Figure 6
<p>Protein-protein interaction network of differentially expressed genes (DEGs). Green nodes denote up-regulated genes.</p> Full article ">Figure 7
<p>Node degree distribution. (<b>A</b>) Up-regulated genes; (<b>B</b>) Down-regulated genes.</p> Full article ">Figure 8
<p>Regression diagrams for up-regulated genes (<b>A</b>) Betweenness centrality; (<b>B</b>) Stress centrality; (<b>C</b>) Closeness centrality; (<b>D</b>) Clustering coefficient.</p> Full article ">Figure 9
<p>Protein-protein interaction network of differentially expressed genes (DEGs). Orange nodes denote down-regulated genes.</p> Full article ">Figure 10
<p>Regression diagrams for down-regulated genes (<b>A</b>) Betweenness centrality; (<b>B</b>) Stress centrality; (<b>C</b>) Closeness centrality; (<b>D</b>) Clustering coefficient.</p> Full article ">Figure 11
<p>Modules in protein-protein interaction (PPI) network. The green nodes denote the up-regulated genes.</p> Full article ">Figure 12
<p>Modules in protein-protein interaction (PPI) network. The orange nodes denote the down-regulated genes.</p> Full article ">Figure 13
<p>The network of up-regulated differential expressed genes (DEGs) and their related miRNAs. The green circle nodes are the up-regulated DEGs, and blue diamond nodes are the miRNAs.</p> Full article ">Figure 14
<p>The network of down-regulated differential expressed genes (DEGs) and their related miRNAs. The orange-red circle nodes are the down-regulated DEGs, and blue diamond nodes are the miRNAs.</p> Full article ">Figure 15
<p>The network of up-regulated differential expressed genes (DEGs) and their related transcription factors (TFs). (Lavender triangles—TFs, and green circles—target up-regulated genes).</p> Full article ">Figure 16
<p>The network of down-regulated differential expressed genes (DEGs) and their related transcription factors (TFs). (Blue triangles—TFs, and pink circles—target down-regulated genes).</p> Full article ">Figure 17
<p>Kaplan-Meier survival curves using The Cancer Genome Atlas (TCGA) data validate the prognostic value of genes having favorable overall survival in BRCA (Green—low expression; Red—high expression).</p> Full article ">Figure 18
<p>Kaplan-Meier survival curves using The Cancer Genome Atlas (TCGA) data validate the prognostic value of genes having worse overall survival in BRCA (Green—low expression; Red—high expression).</p> Full article ">Figure 19
<p>Box plots of hub genes (<span class="html-italic">BRCA1</span>, <span class="html-italic">HIST1H3B</span>, <span class="html-italic">MAPK6</span>, <span class="html-italic">NDRG1</span>, and <span class="html-italic">PCNA</span>). Red—high-risk; Green—low-risk.</p> Full article ">Figure 20
<p>Box plots of hub genes (<span class="html-italic">FLNA</span>, <span class="html-italic">FLNB</span>, <span class="html-italic">HSPA5</span>, <span class="html-italic">MAP1LC3B</span>, and <span class="html-italic">TUBB2B</span>). Red—high-risk; Green—low-risk.</p> Full article ">Figure 21
<p>Validation of the hub genes using the Human Protein Atlas (HPA) database.</p> Full article ">Figure 22
<p>A visual summary, which displays genetic alteration of the ten hub genes in The Cancer Genome Atlas-Breast cancer (TCGA-BRCA) patients.</p> Full article ">
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