Differential Expression of Mitosis and Cell Cycle Regulatory Genes during Recovery from an Acute Respiratory Virus Infection

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

The Changing Landscape of Respiratory Syncytial Virus Infections

Respiratory syncytial virus (RSV), a negative-sense, enveloped RNA virus of the family Paramyxoviridae, subfamily Pneumovirinae, and genus Pneumovirus, is the single most important respiratory pathogen affecting infants and young children [...].

Tumor-promoting inflammation in lung cancer: A literature review

Tumor-promoting inflammation is an inflammation that occurs because tumor cells cause necrosis of healthy cells which releases cell contents into the environment, triggering the release of proinflammatory mediators. There are intrinsic and outside factors of tumor-promoting inflammation. Intrinsic factors are genetically related, while extrinsic factors are due to mediators and inflammatory cells. The primary inflammatory mediators in the tumorigenesis process include NF-kB, STAT3, HIF-1, and TNF-α. in contrast, the inflammatory cells that play a role are TAM, a collection of tumor-associated leukocytes. Bacteria is also one of the extrinsic factors that can cause tumors because of the chronic inflammation it causes.

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Chronic inflammation in the lung increases lung cancer risk.

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As small as inflammatory in lung risk of becoming lung cancer.

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Tumor-Promoting Inflammation is one of the mechanisms of Lung Cancer.