Pandemic strategies with computational and structural biology against COVID-19: A retrospective

Lu, Cheng-Hua; Lin, Liang-Tzung

doi:10.1016/j.csbj.2021.11.040

Cited by 7 publications

(3 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In attempts to prevent further outbreaks and deaths from these diseases, worldwide efforts to discover rapid early-detection methods, effective antiviral drugs, and preventive vaccines have been increasing over the years [46,47]. Other than conventional approaches, novel and advantageous technologies, such as high-throughput sequencing, searchable database repositories of gene expression data, and assistance from web-based or computer-based bioinformatics tools, have been employed to elucidate underlying molecular mechanisms during viral infection and explore how different host cells respond via performing analyses of alterations in gene expressions [48][49][50][51][52][53][54].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Transcriptomic Signatures between Monkeypox-Infected Monkey and Human Cell Lines

Xuan

Yeh

et al. 2022

Journal of Immunology Research

View full text Add to dashboard Cite

Monkeypox virus (MPV) is a smallpox-like virus belonging to the genus Orthopoxvirus of the family Poxviridae. Unlike smallpox with no animal reservoir identified and patients suffering from milder symptoms with less mortality, several animals were confirmed to serve as natural hosts of MPV. The reemergence of a recently reported monkeypox epidemic outbreak in nonendemic countries has raised concerns about a global outburst. Since the underlying mechanism of animal-to-human transmission remains largely unknown, comprehensive analyses to discover principal differences in gene signatures during disease progression have become ever more critical. In this study, two MPV-infected in vitro models, including human immortal epithelial cancer (HeLa) cells and rhesus monkey (Macaca mulatta) kidney epithelial (MK2) cells, were chosen as the two subjects to identify alterations in gene expression profiles, together with co-regulated genes and pathways that are affected during monkeypox disease progression. Using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and MetaCore analyses, we discovered that elevated expression of genes associated with interleukins (ILs), G protein-coupled receptors (GPCRs), heat shock proteins (HSPs), Toll-like receptors (TLRs), and metabolic-related pathways play major roles in disease progression of both monkeypox-infected monkey MK2 and human HeLa cell lines. Interestingly, our analytical results also revealed that a cluster of differentiation 40 (CD40), plasmin, and histamine served as major regulators in the monkeypox-infected monkey MK2 cell line model, while interferons (IFNs), macrophages, and neutrophil-related signaling pathways dominated the monkeypox-infected human HeLa cell line model. Among immune pathways of interest, apart from traditional monkeypox-regulated signaling pathways such as nuclear factor- (NF-κB), mitogen-activated protein kinases (MAPKs), and tumor necrosis factors (TNFs), we also identified highly significantly expressed genes in both monkey and human models that played pivotal roles during the progression of monkeypox infection, including CXCL1, TNFAIP3, BIRC3, IL6, CCL2, ZC3H12A, IL11, CSF2, LIF, PTX3, IER3, EGR1, ADORA2A, and DUOX1, together with several epigenetic regulators, such as histone cluster family gene members, HIST1H3D, HIST1H2BJ, etc. These findings might contribute to specific underlying mechanisms related to the pathophysiology and provide suggestions regarding modes of transmission, post-infectious sequelae, and vaccine development for monkeypox in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of Transcriptomic Signatures between Monkeypox-Infected Monkey and Human Cell Lines

Xuan

Yeh

et al. 2022

Journal of Immunology Research

View full text Add to dashboard Cite

show abstract

“…Liu, Lu [84] described how computational and structural biology, as well as AI technologies, are used. Virtual drug screening and its characteristics using AI approaches were explored in their article.…”

Section: Preprocessing and Tlmentioning

confidence: 99%

Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

show abstract

“…The second part was to uncover BPs, disease biomarker networks, breast neoplasm signaling pathways, and drug target networks [61][62][63][64][65]. Moreover, a gene ontology (GO) analysis was implemented to discover the functional significance of genes with BPs, MFs, CCs, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) with p values of <0.05 indicating statistical significance [66][67][68][69][70]. Next, we used the median expression of targeted genes and then performed a differential analysis with an algorithm in the "DESeq2" package in R/Bioconductor.…”

Section: Functional Enrichment and Mirna-regulated Network Analysesmentioning

confidence: 99%

Comprehensive analysis of prognostic significance of cadherin (CDH) gene family in breast cancer

Liu

et al. 2022

Aging

View full text Add to dashboard Cite

Breast cancer is one of the leading deaths in all kinds of malignancies; therefore, it is important for early detection. At the primary tumor site, tumor cells could take on mesenchymal properties, termed the epithelialto-mesenchymal transition (EMT). This process is partly regulated by members of the cadherin (CDH) family of genes, and it is an essential step in the formation of metastases. There has been a lot of study of the roles of some of the CDH family genes in cancer; however, a holistic approach examining the roles of distinct CDH family genes in the development of breast cancer remains largely unexplored. In the present study, we used a bioinformatics approach to examine expression profiles of CDH family genes using the Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), cBioPortal, MetaCore, and Tumor IMmune Estimation Resource (TIMER) platforms. We revealed that CDH1/2/4/11/12/13 messenger (m)RNA levels are overexpressed in breast cancer cells compared to normal cells and were correlated with poor prognoses in breast cancer patients' distant metastasis-free survival. An enrichment analysis showed that high expressions of CDH1/2/4/11/12/13 were significantly correlated with cell adhesion, the extracellular matrix remodeling process, the EMT, WNT/beta-catenin, and interleukin-mediated immune responses. Collectively, CDH1/2/4/11/12/13 are thought to be potential biomarkers for breast cancer progression and metastasis. AGING Table 1. Basic characteristics of the CDH gene family.

show abstract

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

Cited by 7 publications

References 87 publications

Comparison of Transcriptomic Signatures between Monkeypox-Infected Monkey and Human Cell Lines

Comparison of Transcriptomic Signatures between Monkeypox-Infected Monkey and Human Cell Lines

Machine learning applications for COVID-19 outbreak management

Comprehensive analysis of prognostic significance of cadherin (CDH) gene family in breast cancer

Contact Info

Product

Resources

About