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

Integrated bioinformatics approaches to investigate alterations in transcriptomic profiles of monkeypox infected human cell line model

BACKGROUND

The recent re-emergence of the monkeypox (mpox) epidemic in nonendemic regions has raised concerns regarding a potential global outbreak. The mpox virus (MPV) is a smallpox-like virus belonging to the genus Orthopoxvirus (family: Poxviridae). Although studies suggest that MPV infection suppresses the Toll-like receptor-3- and tumor necrosis factor-α-related signaling pathways, whether MPV regulates other immune-related pathways remains unclear.

METHODS

In this study, two distinct temporal patterns were used for establishing an MPV-infected human immortal epithelial cancer cell line (HeLa). These two durations 2 and 12 h of incubation were selected to identify the coregulated genes and pathways affected by MPV infection.

RESULTS

The use of the Gene Ontology framework, Kyoto Encyclopedia of Genes and Genome database, and MetaCore software yielded valuable insights. Specifically, various pathways were found to be enriched in HeLa cells infected with MPV for 2 and 12 h. These pathways included Notch, CD40, CD95, hypoxia-inducible factor-1-α, interleukin (IL)- 1, IL-6, phosphoinositide 3-kinase, nuclear factor-κB, mitogen-activated protein kinase, and oxidative stress-induced signalling pathways. Clusters and pathways of metabolism and viral replication cycles were significantly associated with the 2-hour infection group. This association was identified based on the regulation of genes such as HSPG2, RHPN2, MYL1, ASPHD2, CA9, VIPR1, SNX12, MGC2752, SLC25A1, PEX19, and AREG. Furthermore, clusters and pathways related to immunity and cell movement were found to be associated with the 12-hour infection group. This association was identified based on the regulation of genes such as C1orf21, C19orf48, HRK, IL8, GULP1, SCAND2, ATP5C1, FEZ1, SGSH, TACC2, CYP4X1, MMP1, CPB1, P2RY13, WDR27, PRPF4, and ENDOD1.

CONCLUSIONS

This study can improve our understanding of the mechanisms underlying the pathophysiology and post-infection sequelae of mpox. Our findings provide valuable insights into the various modes of MPV infection.

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

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 epithelial-to-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.

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

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.

Machine learning applications for COVID-19 outbreak management

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.