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Investigating Virological, Immunological, and Pathological Avenues to Identify Potential Targets for Developing COVID-19 Treatment and Prevention Strategies.

ABSTRACT: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging coronavirus causing respiratory disease commonly known as COVID-19. This novel coronavirus transmits from human to human and has caused profound morbidity and mortality worldwide leading to the ongoing pandemic. Moreover, disease severity differs considerably from individual to individual. Investigating the virology of COVID-19 and immunological pathways underlying its clinical manifestations will enable the identification and design of effective vaccines and potential therapies. In this review, we explore COVID-19 virology, the contribution of the immune system (innate and adaptive) during infection and control of the virus. Finally, we highlight vaccine development and implications of immune system modulation for potential therapeutic interventions to design better therapeutic strategies to guide future cure.

SUBMITTER: Mahmood Z

PROVIDER: S-EPMC7563267 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Investigating Virological, Immunological, and Pathological Avenues to Identify Potential Targets for Developing COVID-19 Treatment and Prevention Strategies.

Mahmood Zafar Z Alrefai Hani H Hetta Helal F HF A Kader Hidaya H Munawar Nayla N Abdul Rahman Sheikh S Elshaer Shereen S Batiha Gaber Ei-Saber GE Muhammad Khalid K

Vaccines 20200806 3

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging coronavirus causing respiratory disease commonly known as COVID-19. This novel coronavirus transmits from human to human and has caused profound morbidity and mortality worldwide leading to the ongoing pandemic. Moreover, disease severity differs considerably from individual to individual. Investigating the virology of COVID-19 and immunological pathways underlying its clinical manifestations will enable the identificati ...[more]

PMID: 32781571

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Project description:Aims/hypothesisThe aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.MethodsWe applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study.ResultsNine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors.Conclusions/interpretationThis study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.

Project description:Type 1 diabetes (T1D) is caused by an autoimmune process which culminates in the destruction of insulin-producing beta cells in the pancreas. It is widely believed that a complex and multifactorial interplay between genetic and environmental factors, such as viruses, play a crucial role in the development of the disease. Research over the past few decades has shown that there is not one single viral culprit, nor one single genetic pathway, causing the disease. Rather, viral infections, most notably enteroviruses (EV), appear to accelerate the autoimmune process leading to T1D and are often seen as a precipitator of clinical diagnosis. In support of this hypothesis, the use of anti-viral drugs has recently shown efficacy in preserving beta cell function after onset of diabetes. In this review, we will discuss the various pathways that viral infections utilize to accelerate the development of T1D. There are three key mechanisms linking viral infections to beta-cell death: One is modulated by the direct infection of islets by viruses, resulting in their impaired function, another occurs in a more indirect fashion, by modulating the immune system, and the third is caused by heightened stress on the beta-cell by interferon-mediated increase of insulin resistance. The first two aspects are surprisingly difficult to study, in the case of the former, because there are still many questions about how viruses might persist for longer time periods. In the latter, indirect/immune case, viruses might impact immunity as a hit-and-run scenario, meaning that many or all direct viral footprints quickly vanish, while changes imprinted upon the immune system and the anti-islet autoimmune response persist. Given the fact that viruses are often associated with the precipitation of clinical autoimmunity, there are concerns regarding the impact of the recent global coronavirus-2019 (COVID-19) pandemic on the development of autoimmune disease. The long-term effects of COVID-19 infection on T1D will therefore be discussed, including the increased development of new cases of T1D. Understanding the interplay between viral infections and autoimmunity is crucial for advancing our knowledge in this field and developing targeted therapeutic interventions. In this review we will examine the intricate relationship between viral infections and autoimmunity and discuss potential considerations for prevention and treatment strategies.

Dataset Information

Investigating Virological, Immunological, and Pathological Avenues to Identify Potential Targets for Developing COVID-19 Treatment and Prevention Strategies.

Publications

Investigating Virological, Immunological, and Pathological Avenues to Identify Potential Targets for Developing COVID-19 Treatment and Prevention Strategies.

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