Single Cell Omics and Integrative Biology: Selected Papers from ICBCB 2022

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 10632

Special Issue Editor

Special Issue Information

Dear Colleagues,

Bioinformatics and Computational Biology has become an important part of many areas of biology. The era of bioinformatics emerges accompanied by the development and application of various omics technologies, from genomics to the latest single cell omics. Huge amount of omics data and numerous analysis tools have been generated, which brings more challenges. How to carry out integrative bioinformatics research, including the integration of data, methods, even tools and so on? How to conduct integrative biology studies on biological problems in health, agriculture and ecology, etc. based on integrated omics data and bioinformatics methods and technologies? This special issue is dedicated to advances in multiple omics and integrative biology, especially single cell scale omics, integrative bioinformatics and its interdisciplinary disciplines, promote the convergence of biological data and big data integration and analysis of functional genomics, transcriptomics, RNomics, proteomics and metabolomics, and accelerate the integration and analysis of biological research in health, resources and other fields.

The 10th International Conference on Bioinformatics and Computational Biology (ICBCB 2022) will be held in Hangzhou, China from 13 to 15 May 2022. ICBCB conference series will be held annually to provide an interactive forum for presentation and discussion on bioinformatics and computational biology. As one of the partners of ICBCB 2022, Biology, is an international, peer-reviewed, quick-refereeing open access journal of Biological Science published by MDPI online, aims to build a platform through this special issue to demonstrate the application of bioinformatics and computational biology approaches to a variety of biological problems in the life sciences. Selected papers presented at the conference and included in the conference proceedings are invited to be submitted as extended versions to this Special Issue.

Prof. Dr. Ming Chen
Guest Editor

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Keywords

  • single cell omics
  • scRNA-seq
  • integrative biology
  • computational biology
  • bioinformatics

Published Papers (4 papers)

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Research

23 pages, 37954 KiB  
Article
Principal Component and Structural Element Analysis Provide Insights into the Evolutionary Divergence of Conotoxins
by Akira Kio V. Kikuchi and Lemmuel L. Tayo
Biology 2023, 12(1), 20; https://doi.org/10.3390/biology12010020 - 22 Dec 2022
Viewed by 1597
Abstract
Predatory cone snails (Conus) developed a sophisticated neuropharmacological mechanism to capture prey, escape against other predators, and deter competitors. Their venom’s remarkable specificity for various ion channels and receptors is an evolutionary feat attributable to the venom’s variety of peptide components [...] Read more.
Predatory cone snails (Conus) developed a sophisticated neuropharmacological mechanism to capture prey, escape against other predators, and deter competitors. Their venom’s remarkable specificity for various ion channels and receptors is an evolutionary feat attributable to the venom’s variety of peptide components (conotoxins). However, what caused conotoxin divergence remains unclear and may be related to the role of prey shift. Principal component analysis revealed clustering events within diet subgroups indicating peptide sequence similarity patterns based on the prey they subdue. Molecular analyses using multiple sequence alignment and structural element analysis were conducted to observe the events at the molecular level that caused the subgrouping. Three distinct subgroups were identified. Results showed homologous regions and conserved residues within diet subgroups but divergent between other groups. We specified that these structural elements caused subgrouping in alpha conotoxins that may play a role in function specificity. In each diet subgroup, amino acid character, length of intervening amino acids between cysteine residues, and polypeptide length influenced subgrouping. This study provides molecular insights into the role of prey shift, specifically diet preference, in conotoxin divergence. Full article
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18 pages, 1522 KiB  
Article
Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19
by Ryan Christian Mailem and Lemmuel L. Tayo
Biology 2022, 11(12), 1827; https://doi.org/10.3390/biology11121827 - 15 Dec 2022
Cited by 6 | Viewed by 2169
Abstract
SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little [...] Read more.
SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study. Full article
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11 pages, 13892 KiB  
Article
Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction
by Zhe Liu, Weihao Pan, Weihao Li, Xuyang Zhen, Jisheng Liang, Wenxiang Cai, Fei Xu, Kai Yuan and Guan Ning Lin
Biology 2022, 11(10), 1454; https://doi.org/10.3390/biology11101454 - 03 Oct 2022
Cited by 1 | Viewed by 2105
Abstract
Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep [...] Read more.
Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations. Full article
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15 pages, 3283 KiB  
Article
Mendelian Randomization and GWAS Meta Analysis Revealed the Risk-Increasing Effect of Schizophrenia on Cancers
by Kai Yuan, Weichen Song, Zhe Liu, Guan Ning Lin and Shunying Yu
Biology 2022, 11(9), 1345; https://doi.org/10.3390/biology11091345 - 12 Sep 2022
Cited by 7 | Viewed by 3443
Abstract
The causal relationship between cancer and Schizophrenia (SCZ) remains controversial. Some researchers have found that SCZ is a cancer-preventive factor in cohort studies or meta-analyses, whereas others have found the opposite. To understand more about this issue, we used two-sample Mendelian randomization (2SMR) [...] Read more.
The causal relationship between cancer and Schizophrenia (SCZ) remains controversial. Some researchers have found that SCZ is a cancer-preventive factor in cohort studies or meta-analyses, whereas others have found the opposite. To understand more about this issue, we used two-sample Mendelian randomization (2SMR) on available GWAS summary results to evaluate potential genetic connections between SCZ and 13 cancers. We discovered that the genetic susceptibility to schizophrenia lead to an increasing risk of breast cancer (odds ratio [OR] per log-odds increase in schizophrenia risk: 1.049, 95% confidence interval [CI]:1.023–1.075; p = 0.00012; FDR = 0.0017), ovarian cancer (OR, 1.326; 95% CI, 1.267–1.387; p = 0.0007; FDR = 0.0045), and thyroid cancer (OR, 1.575; 95% CI, 1.048–2.365; p = 0.0285; FDR = 0.123). Secondly, we performed a meta-analysis based on the GWAS summary statistics of SCZ and the three significant cancers. Next, we associated genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, we identified 114 shared loci and 437 shared genes in three groups, respectively. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification. In addition, we noticed that SCZ would affect the level of thyroid-stimulating hormone (OR, 1.095; 95% CI, 1.006–1.191; p = 0.0354; FDR = 0.177), which may further affect the level of estrogen and the risk of the above three cancers. In conclusion, our findings under the 2SMR assumption provide crucial insights into the risk-increasing effect of SCZ on three cancers’ risk. Furthermore, these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid SCZ and cancers. Full article
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