Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy

Abstract

Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.

Keywords: Artificial intelligence; COVID-19; Molecular biomarkers; Multiomics; SARS-CoV-2.

Fig. 1

Relationship between IL-6 and COVID-19 disease severity. IL-6 levels are reported to be higher in severe COVID-19 infections than non-severe patients in studies mentioned below. Figure 1 is a compiled reanalysis of IL-6 data from 7 studies which consistently showed increased levels of IL-6 in severe cases than non-severe ones (Chen et al. ; Gao et al. , Liu et al. , ; Yang et al. ; Qin et al. ; Fu et al. ; Thevarajan et al. ; Wu et al. ; Wang et al. 2020)

Fig. 2

Multi-biomarker profiling of severe and non-severe COVID-19 patients. Figure represents the levels of multiple biomarkers as observed in severe and non-severe COVID-19 patients. Cells mediating immune reactions including stimulatory T cells, helper T cells, NK cells, T cells and B cells are higher in non-severe cases indicating events of immune reactions. On the other hand, biomarkers such as C-reactive proteins, serum ferritin, neutrophils are all higher in severe COVID-19 patients. These individual biomarkers alone may not be conclusive of the disease, but their initial screening may enable better understanding of the disease severity for efficient patient stratification

Fig. 3

A retrospective investigation of chest CT findings to assist in COVID-19 patient stratification of severe and non-severe disease pattern. The chest CT findings presented in the figure shows 2 vital findings. The first major finding is that the prevalence of COVID-19 patients exhibiting conditions like ground glass opacities, lung consolidation, fibrosis and air bronchogram were higher during disease progression which dropped upon recovery phase of the disease. The 2nd major finding is that all the parameters (ground glass opacities, lung consolidation, fibrosis and air bronchogram) were higher in patients with severe disease pattern than non-severe disease pattern. From these evidences, an initial screening of COVID-19 patients using chest CT could be a beneficial strategy towards patient stratification

Fig. 4

Applications of multiomics in COVID-19 investigation. The figure depicts various applications of multiomics strategies (e.g., genomics, transcriptomics) such as disease susceptibility/risk prediction, disease diagnosis and prognosis, drug response and monitoring. Thus, such strategies could collectively render assistance in the overwhelming healthcare needs during pandemics and beyond