Understanding the genetic determinant of severity in viral diseases: a case of SARS-Cov-2 infection

Dynamic modeling of antibody repertoire reshaping in response to viral infections

For decades, research has largely focused on the generation of high-affinity, antigen-specific antibodies during viral infections. This emphasis has made it challenging for immunologists to systematically evaluate the mechanisms initiating humoral immunity in specific immune responses. In this study, we employ ordinary differential equations (ODE) to investigate the dynamic reshaping of the entire antibody repertoire in response to viral infections. Our findings demonstrate that the host's antibody atlas undergoes significant restructuring during these infections by the selective expansion of antibody pools with strong binding activity. The simulation results indicate that the ELISA (Enzyme-Linked Immunosorbent Assay) outcomes do not directly reflect the levels of specific neutralizing antibodies, but rather represent a quantitative response of the reshaped antibody repertoire following infection. Our model transcends traditional theories of immune memory, providing an explanation for the sustained presence of specific antibodies in the human body in long term. Additionally, our model extends to explore the mechanistic basis of the original antigenic sin, providing practical applications of our framework. One important application of this model is that it indicates that antibodies with a faster forward binding rate are more effective in preventing and treating associated viral infections compared to those with higher binding affinity.

Association between angiotensin-converting enzyme-2 gene polymorphism (rs2106809) with severity and outcome of COVID-19 infection

PURPOSE

Genetic factors play important role in the severity of the COVID-19 infection since SARS-CoV-2 binds to the ACE2 receptor on the surface of host cells. ACE2 polymorphisms that may influence the expression of ACE2 can alter patients' susceptibility to COVID-19 infection or increase the severity of the disease. This study aimed to investigate the association between ACE2 rs2106809 polymorphism and the severity of the COVID-19 infection.

METHODS

In this cross-sectional study, ACE2 rs2106809 polymorphism was assessed in 142 COVID-19 patients. The disease was confirmed according to clinical symptoms, imaging, and laboratory findings. The severity of the disease was graded as severe versus non-severe based on the CDC. Genomic DNA was extracted from the whole blood and PCR- RFLP was performed to genotype the ACE2-rs2106809 with specific primers and Taq1 restriction enzyme.

RESULTS

G/G genotype was significantly associated with COVID-19 severity (44.4% in severe vs. 17.5% in non-severe, OR: 4.1; 95%CI: 1.8-9.5, p = 0.0007). Patients with the G/G genotype need more mechanical ventilation (p = 0.021). ACE2 expression in patients carrying the A/G genotype was higher in the severe compared to the non-severe form of the disease (2.99 ± 0.99 vs. 2.21 ± 1.1), but it was not statistically significant (p = 0.9).

CONCLUSION

The G allele and G/G genotype of ACE2 rs2106809 is associated with more severe COVID-19 and adverse disease outcomes.

Association of programmed cell death 1 (PD-1) gene polymorphism (rs10204525) with COVID-19 severity and mortality: A case-control study in the Iranian population

BACKGROUND

Programmed cell death 1 (PD-1), as a negative immune regulator, regulates the activation of T cells and maintains the immune system's homeostasis. Previous studies suggest that the effective immune response against COVID-19 contributes to the outcome of the disease. The present study aims to evaluate whether the PD-1 rs10204525 polymorphism is associated with PDCD-1 expression and COVID-19 severity and mortality in the Iranian population.

METHODS

The PD-1 rs10204525 was genotyped in 810 COVID-19 patients and 164 healthy individuals as a control group using Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Moreover, we assessed the expression of PDCD-1 in peripheral blood nuclear cells by real-time PCR.

RESULTS

Regarding disease severity and mortality, no significant differences were detected between study groups in alleles and genotypes frequency distribution under different inheritance models. We found that the expression of PDCD-1 was significantly lower in COVID-19 patients with AG and GG genotypes than in the control group. Regarding disease severity, mRNA levels of PDCD-1 were significantly lower in moderate and critical patients carrying AG genotype than in control (P = 0.005 and P = 0.002, respectively) and mild (P = 0.014 and P = 0.005, respectively) individuals. Additionally, the severe and critical patients with GG genotype displayed a significantly lower level of PDCD-1 compared with the control (P = 0.002 and P < 0.001, respectively), mild (P = 0.004 and P < 0.001, respectively), and moderate (P = 0.014 and P < 0.001, respectively) ones. Regarding disease mortality, the expression of PDCD-1 was significantly lower in non-survivor COVID-19 patients with GG genotype than in survivors.

CONCLUSION

Considering the lack of significant differences in PDCD-1 expression in different genotypes in the control group, lower expression of PDCD-1 in COVID-19 patients carrying the G allele suggests the impact of this single-nucleotide polymorphism on the transcriptional activity of PD-1.

COVID-19: impact on Public Health and hypothesis-driven investigations on genetic susceptibility and severity

COVID-19 is a new complex multisystem disease caused by the novel coronavirus SARS-CoV-2. In slightly over 2 years, it infected nearly 500 million and killed 6 million human beings worldwide, causing an unprecedented coronavirus pandemic. Currently, the international scientific community is engaged in elucidating the molecular mechanisms of the pathophysiology of SARS-CoV-2 infection as a basis of scientific developments for the future control of COVID-19. Global exome and genome analysis efforts work to define the human genetics of protective immunity to SARS-CoV-2 infection. Here, we review the current knowledge regarding the SARS-CoV-2 infection, the implications of COVID-19 to Public Health and discuss genotype to phenotype association approaches that could be exploited through the selection of candidate genes to identify the genetic determinants of severe COVID-19.

ACE2 and TMPRSS2 SNPs as Determinants of Susceptibility to, and Severity of, a COVID-19 Infection

Background: Genetic risk factors may be related to the infectivity and severity of SARS-CoV-2 infection. Angiotensin-converting enzyme 2 (ACE2) and host transmembrane serine protease (TMPRSS2) have key role in viral cell entrance and priming. Methods: This case-control study on 147 healthy controls and 299 COVID-19 patients identified potential determinants and risk factors, including gene polymorphism involved in the severity (mild, moderate, severe) of COVID-19 disease defined by CORAD radiological criteria. Results: The ACE2 s2285666 and TMPRSS2 rs12329760 SNPs were significantly linked with COVID-19 disease severity, as were certain co-morbidities (hypertension, heart disease) and laboratory parameters. Both SNPs were amongst the highest predictors of disease severity: TMPRSS2 rs12329760 CT + TT [odds ratio (95% CI) 17.6 (5.1-61.10), ACE2 rs2285666 CT + TT 9.9 (3.2-30.9), both p < 0.001]. There was an increase in the expression of genotype frequencies of ACE2 rs2285666 and TMPRSS2 rs1232976 (TT), (CT + TT), and (T) allele in severe COVID-19 group compared to control and mild groups. Disease severity was also linked to elevated CRP, ferritin and D-dimer, and lower lymphocytes and platelet count (all p < 0.001). Conclusion: ACE2 rs2285666 and TMPRSS2 rs12329760 SNPs, in addition to lymphocyte count, CRP, D-dimers, ferritin, and hypertension, are predictors of COVID-19 disease severity.

Hypotheses and facts for genetic factors related to severe COVID-19

Genome-wide association analysis allows the identification of potential candidate genes involved in the development of severe coronavirus disease 2019 (COVID-19). Hence, it seems that genetics matters here, as well. Nevertheless, the virus's nature, including its RNA structure, determines the rate of mutations leading to new viral strains with all epidemiological and clinical consequences. Given these observations, we herein comment on the current hypotheses about the possible role of the genes in association with COVID-19 severity. We discuss some of the major candidate genes that have been identified as potential genetic factors associated with the COVID-19 severity and infection susceptibility: HLA, ABO, ACE2, TLR7, ApoE, TYK2, OAS, DPP9, IFNAR2, CCR2, etc. Further study of genes and genetic variants will be of great benefit for the prevention and assessment of the individual risk and disease severity in different populations. These scientific data will serve as a basis for the development of clinically applicable diagnostic and prognostic tests for patients at high risk of COVID-19.

Routine Hematological Parameters May Be Predictors of COVID-19 Severity

To date, coronavirus disease 2019 (COVID-19) has affected over 100 million people globally. COVID-19 can present with a variety of different symptoms leading to manifestation of disease ranging from mild cases to a life-threatening condition requiring critical care-level support. At present, a rapid prediction of disease severity and critical care requirement in COVID-19 patients, in early stages of disease, remains an unmet challenge. Therefore, we assessed whether parameters from a routine clinical hematology workup, at the time of hospital admission, can be valuable predictors of COVID-19 severity and the requirement for critical care. Hematological data from the day of hospital admission (day of positive COVID-19 test) for patients with severe COVID-19 disease (requiring critical care during illness) and patients with non-severe disease (not requiring critical care) were acquired. The data were amalgamated and cleaned and modeling was performed. Using a decision tree model, we demonstrated that routine clinical hematology parameters are important predictors of COVID-19 severity. This proof-of-concept study shows that a combination of activated partial thromboplastin time, white cell count-to-neutrophil ratio, and platelet count can predict subsequent severity of COVID-19 with high sensitivity and specificity (area under ROC 0.9956) at the time of the patient's hospital admission. These data, pending further validation, indicate that a decision tree model with hematological parameters could potentially form the basis for a rapid risk stratification tool that predicts COVID-19 severity in hospitalized patients.