Exploring Host Genetic Polymorphisms Involved in SARS-CoV Infection Outcomes: Implications for Personalized Medicine in COVID-19

COVID-19: An Overview of SARS-CoV-2 Variants-The Current Vaccines and Drug Development

The world is presently in crisis facing an outbreak of a health-threatening microorganism known as COVID-19, responsible for causing uncommon viral pneumonia in humans. The virus was first reported in Wuhan, China, in early December 2019, and it quickly became a global concern due to the pandemic. Challenges in this regard have been compounded by the emergence of several variants such as B.1.1.7, B.1.351, P1, and B.1.617, which show an increase in transmission power and resistance to therapies and vaccines. Ongoing researches are focused on developing and manufacturing standard treatment strategies and effective vaccines to control the pandemic. Despite developing several vaccines such as Pfizer/BioNTech and Moderna approved by the U.S. Food and Drug Administration (FDA) and other vaccines in phase 4 clinical trials, preventive measures are mandatory to control the COVID-19 pandemic. In this review, based on the latest findings, we will discuss different types of drugs as therapeutic options and confirmed or developing vaccine candidates against SARS-CoV-2. We also discuss in detail the challenges posed by the variants and their effect on therapeutic and preventive interventions.

Host genetic polymorphisms involved in long-term symptoms of COVID-19

Host genetic polymorphisms are recognized as a critical determinant of diversity in clinical symptoms of Coronavirus disease 2019 (COVID-19). Accordingly, this study aimed to determine possible associations between single nucleotide polymorphisms (SNPs) in 37 candidate genes and clinical consequences of COVID-19 - especially long-term symptoms, Long COVID. A total of 260 COVID-19 patients, divided into mild (n=239) and severe (n=21) and further categorized based on the presence of Long COVID (no, n=211; yes, n=49), were recruited. Genotyping of selected polymorphisms in 37 genes responsible for viral entry, immune response, and inflammation was performed using MassARRAY system. Out of 37 SNPs, 9 including leucine zipper transcription factor like-1 (LZTFL1) rs10490770 C allele, LZTFL1 rs11385942 dupA allele, nicotinamide adenine dinucleotide synthetase-1 (NADSYN1) rs12785878 TT genotype, plexin A-4 (PLXNA4) rs1424597 AA genotype, LZTFL1 rs17713054 A allele, interleukin-10 (IL10) rs1800896 TC genotype and C allele, angiotensin converting enzyme-2 (ACE2) rs2285666 T allele, and plasmanylethanolamine desaturase-1 (PEDS1) rs6020298 GG genotype and G allele were significantly associated with an increased risk of developing Long COVID, whereas interleukin-10 receptor subunit beta (IL10RB) rs8178562 GG genotype was significantly associated with a reduced risk of Long COVID. Kaplan-Meier curve displayed that polymorphisms in the above genes were significantly associated with cumulative rate of Long COVID occurrence. Polymorphisms in LZTFL1 rs10490770, LZTFL1 rs11385942, LZTFL1 rs17713054, NADSYN1 rs12785878, PLXNA4 rs1424597, IL10 rs1800896, ACE2 rs2285666, PEDS1 rs6020298, and IL10RB rs8178562 appear to be genetic factors involved in development of Long COVID.

Prognostic Value of CYP1A2 (rs2069514 and rs762551) Polymorphisms in COVID-19 Patients

The aim of the study was to examine the genotype-allele determination of CYP1A2 rs2069514 and rs762551 polymorphisms in patients with mild and severe COVID-19 and to determine their effectiveness as prognostic criteria in COVID-19. The study consists of 60 patients who were hospitalized in intensive care or outpatient treatment due to COVID-19 in Istanbul NP Brain Hospital between 2020-2021. Genotyping was conducted by Real-Time PCR. Age (p<0.001); chronic disease (p=0.002); cardiovascular disease (p=0.004); respiratory distress (p<0.001); neurological disease (p=0.004); fatigue (p=0.048); loss of taste and smell (p=0.003); nausea/vomiting (p=0.026); intubated (p<0.001); ground glass image (p<0.001) and CYP1A2 genotypes (p<0.001) showed a statistically significant difference between patients with and without intensive care admission. According to multivariate logistic regression analysis, CYP1A2 *1A/*1C + *1C/*1C genotypes (OR:5.23 95% CI: 1.22-22.36; p=0.025), chronic disease (OR:4.68 95% CI:1.14-19.15; p=0.032) or patients at 65 years or older (OR:5.17, 95%CI:1.26-21.14; p=0.022) increased the risk of admission to the intensive care unit. According to our results, we strongly suggest considering the CYP1A2 rs2069514 and rs762551 polymorphisms as important predictors of Intensive Care Unit admission in patients with COVID-19, and we also suggest that genotype results will guide clinicians for the benefit and the efficiency of the treatment.

Computational network analysis of host genetic risk variants of severe COVID-19

BACKGROUND

Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks.

RESULTS

We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction.

CONCLUSIONS

This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

Genetic polymorphisms associated with susceptibility to COVID-19 disease and severity: A systematic review and meta-analysis

Although advanced age and presence of comorbidities significantly impact the variation observed in the clinical symptoms of COVID-19, it has been suggested that genetic variants may also be involved in the disease. Thus, the aim of this study was to perform a systematic review with meta-analysis of the literature to identify genetic polymorphisms that are likely to contribute to COVID-19 pathogenesis. Pubmed, Embase and GWAS Catalog repositories were systematically searched to retrieve articles that investigated associations between polymorphisms and COVID-19. For polymorphisms analyzed in 3 or more studies, pooled OR with 95% CI were calculated using random or fixed effect models in the Stata Software. Sixty-four eligible articles were included in this review. In total, 8 polymorphisms in 7 candidate genes and 74 alleles of the HLA loci were analyzed in 3 or more studies. The HLA-A*30 and CCR5 rs333Del alleles were associated with protection against COVID-19 infection, while the APOE rs429358C allele was associated with risk for this disease. Regarding COVID-19 severity, the HLA-A*33, ACE1 Ins, and TMPRSS2 rs12329760T alleles were associated with protection against severe forms, while the HLA-B*38, HLA-C*6, and ApoE rs429358C alleles were associated with risk for severe forms of COVID-19. In conclusion, polymorphisms in the ApoE, ACE1, TMPRSS2, CCR5, and HLA loci appear to be involved in the susceptibility to and/or severity of COVID-19.

Potential protective effect against SARS-CoV-2 infection by APOE rs7412 polymorphism

The pandemic burden caused by the SARS-CoV-2 coronavirus constitutes a global public health emergency. Increasing understanding about predisposing factors to infection and severity is now a priority. Genetic, metabolic, and environmental factors can play a crucial role in the course and clinical outcome of COVID-19. We aimed to investigate the putative relationship between genetic factors associated to obesity, metabolism and lifestyle, and the presence and severity of SARS-CoV-2 infection. A total of 249 volunteers (178 women and 71 men, with mean and ± SD age of 49 ± 11 years) characterized for dietary, lifestyle habits and anthropometry, were studied for presence and severity of COVID-19 infection, and genotyped for 26 genetic variants related to obesity, lipid profile, inflammation, and biorhythm patterns. A statistically significant association was found concerning a protective effect of APOE rs7412 against SARS-CoV-2 infection (p = 0.039; OR 0.216; CI 0.084, 0.557) after correction for multiple comparisons. This protective effect was also ascribed to the APOɛ2 allele (p = 0.001; OR 0.207; CI 0.0796, 0.538). The genetic variant rs7412 resulting in ApoE2, genetic determinant of lipid and lipoprotein levels, could play a significant role protecting against SARS-CoV-2 infection.

Socio-demographic, lifestyle and health characteristics as predictors of self-reported Covid-19 history among older adults: 2006-2020 Health and Retirement Study

BACKGROUND

To identify key socio-demographic, lifestyle, and health predictors of self-reported coronavirus disease 2019 (Covid-19) history, examine cardiometabolic health characteristics as predictors of self-reported Covid-19 history and compare groups with and without a history of Covid-19 on trajectories in cardiometabolic health and blood pressure measurements over time, among United States (U.S.) older adults.

METHODS

Nationally representative longitudinal data on U.S. older adults from the 2006-2020 Health and Retirement Study were analyzed using logistic and mixed-effects logistic regression models.

RESULTS

Based on logistic regression, number of household members (OR=1.26, 95% CI: 1.05, 1.52), depressive symptoms score (OR = 1.21, 95% CI: 1.04, 1.42) and number of cardiometabolic risk factors or chronic conditions ("1-2" vs "0") (OR = 0.27, 95% CI: 0.11, 0.67) were significant predictors of self-reported Covid-19 history. Based on mixed-effects logistic regression, several statistically significant predictors of Covid-19 history were identified, including female sex (OR = 3.06, 95% CI: 1.57, 5.96), other race (OR = 5.85, 95% CI: 2.37, 14.43), Hispanic ethnicity (OR = 2.66, 95% CI: 1.15, 6.17), number of household members (OR = 1.25, 95% CI: 1.10, 1.42), moderate-to-vigorous physical activity (1-4 times per month vs never) (OR = 0.38, 95% CI: 0.18, 0.78) and number of cardiometabolic risk factors or chronic conditions ("1-2" vs "0") (OR = 0.34, 95% CI: 0.19, 0.60).

CONCLUSIONS

Number of household members, depressive symptoms and number of cardiometabolic risk factors or chronic conditions may be key predictors for self-reported Covid-19 history among U.S. older adults. In-depth analyses are needed to confirm preliminary findings.

Predictors of Covid-19 level of concern among older adults from the health and retirement study

The purpose of this longitudinal study is to construct a prediction model for Covid-19 level of concern using established Covid-19 socio-demographic, lifestyle and health risk characteristics and to examine specific contributions of obesity-related cardiometabolic health characteristics as predictors of Covid-19 level of concern among a representative sample of U.S. older adults. We performed secondary analyses of existing data on 2872 2006–2020 Health and Retirement Study participants and examined 19 characteristics in relation to the outcome of interest using logistic regression and machine learning algorithms. In mixed-effects ordinal logistic regression models, a history of diabetes, stroke as well as 1–2 cardiometabolic risk factors and/or chronic conditions were associated with greater Covid-19 level of concern, after controlling for confounders. Female sex, birth cohort, minority race, Hispanic ethnicity and total wealth as well as depressive symptoms were associated with higher level of Covid-19 concern, and education was associated with lower level of Covid-19 concern in fully adjusted mixed-effects ordinal logistic regression models. The selected socio-demographic, lifestyle and health characteristics accounted for < 70% of the variability in Covid-19 level of concern based on machine learning algorithms. Independent risk factors for Covid-19 level of concern among U.S. older adults include socio-demographic characteristics and depressive symptoms. Advanced research is needed to identify relevant predictors and elucidate underlying mechanisms of observed relationships.

COVID-19 pandemic impact on trajectories in cardiometabolic health, physical activity and functioning among adults from the 2006-2020 Health and Retirement Study

Background

The aim of this study was to evaluate the impact of the COVID-19 pandemic on trajectories in cardiometabolic health, physical activity, and functioning among U.S. older adults, overall and according to selected baseline sociodemographic characteristics.

Methods

We performed secondary analyses using longitudinal data on 1,372 participants from the 2006–2020 Health and Retirement Study. Pre-post COVID-19 pandemic onset was examined in relation to body mass index (BMI), number of cardiometabolic risk factors and/or chronic conditions, physical activity, Activities of Daily Living (ADL), and Instrumental Activities of Daily Living (IADL) using mixed-effects regression models and group-based trajectory models.

Results

The COVID-19 pandemic was associated with significantly increased BMI (β = 1.39, 95% confidence interval [CI]: 0.74, 2.03). Furthermore, the odds of having at least one cardiometabolic risk factor and/or chronic disease increased pre-post COVID-19 onset (odds ratio 1.16, 95% CI: 1.00, 1.36), whereas physical functioning worsened pre-post COVID-19 onset (ADL: β = 1.11, 95% CI: 0.94, 1.28; IADL: β = 0.59, 95% CI: 0.46, 0.73). The pre-post COVID-19 period (2018–2020) showed a stable group of trajectories, with low, medium and high levels of the selected health indicators. Health disparities according to sex, race/ethnicity, educational level, work status, and total wealth are highlighted.

Conclusions

The COVID-19 pandemic onset appears to worsen cardiometabolic health and physical functioning among U.S. older adults, with clusters of individuals defined by selected sociodemographic characteristics experiencing distinct trajectories pre-post COVID-19 pandemic onset.

Role of Genetic Variants and Host Polymorphisms on COVID‐19: From Viral Entrance Mechanisms to Immunological Reactions

Coronavirus disease 2019 (COVID‐19), caused by a highly pathogenic emerging virus, is called severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Knowledge regarding the pathogenesis of this virus is in infancy; however, investigation on the pathogenic mechanisms of the SARS‐CoV‐2 is underway. In COVID‐19, one of the most remarkable characteristics is the wide range of disease manifestation and severity seen across individuals of different ethnic backgrounds and geographical locations. To effectively manage COVID‐19 in the populations, beyond SARS‐CoV‐2 detection, serological response assessment, and analytic techniques, it is critical to obtain knowledge about at‐risk individuals and comprehend the identified variations in the disease's severity in general and also in the populations' levels. Several factors can contribute to variation in disease presentation, including population density, gender and age differences, and comorbid circumstances including diabetes mellitus, hypertension, and obesity. Genetic factors presumably influence SARS‐CoV‐2 infection susceptibility. Besides this, COVID‐19 has also been linked with a higher risk of mortality in men and certain ethnic groups, revealing that host genetic characteristics may affect the individual risk of death. Also, genetic variants involved in pathologic processes, including virus entrance into cells, antiviral immunity, and inflammatory response, are not entirely understood. Regarding SARS‐CoV‐2 infection characteristics, the present review suggests that various genetic polymorphisms influence virus pathogenicity and host immunity, which might have significant implications for understanding and interpreting the matter of genetics in SARS‐CoV‐2 pathogenicity and customized integrative medical care based on population investigation.

Genetic factors presumably influence SARS‐CoV‐2 infection susceptibility.

Genetic variants were involved in the pathologic processes of SARS‐CoV‐2 infection.

Various genetic polymorphisms influence virus pathogenicity and host immunity.

Human leukocyte antigens (HLAs) may play a vital role in SARS‐CoV‐2 susceptibility.

Polymorphisms in several genes such as IL‐6, TMPRSS2, IFITM3, CD26, ACE, and DBP were associated with the COVID‐19 severity.

[Evolution of patients infected with SARS-CoV-2 according to previous metabolic status]

Introduction: coronavirus disease 2019 (COVID-19) encompasses a wide spectrum of symptoms, including respiratory, gastrointestinal, hematological, and dermatological manifestations. The virus interaction with cells located in the respiratory tract causes the release of inflammatory mediators, whose involvement could be exacerbated by co-existing obesity, diabetes, and cardiovascular events. Objectives: the objective of this research was to analyze the clinically metabolic status in patients who have suffered COVID-19 disease in order to predict the outcome. Methods: this research is a retrospective study based on a cohort of 165 consecutively admitted patients with criteria for COVID-19 pneumonia according to WHO guidelines at the Hospital Universitario Puerta de Hierro between March and April 2020. Recorded variables included demographic and epidemiological data plus diagnoses as well as morbid complications during hospitalization. The Biochemistry Unit Laboratory carried out laboratory analyses according to validated operational procedures. The statistical tests included univariate and multivariate models adjusted for baseline characteristics and clinically relevant features. Results: the most frequent comorbidity in our cohort was arterial hypertension (44.0 %), followed by dyslipidemia (32.1 %), obesity (30.9 %), and diabetes mellitus (20.0 %). The association between admission to the intensive care unit (ICU) with body mass index (BMI) in a multivariate model was statistically significant, evidencing that obese subjects (BMI ≥ 30 kg/m2) have a 19 % higher risk of requiring ICU care. The univariate model revealed a statistically significant association between obesity and ICU admission and length of hospital stay (p < 0.05). The relationship between baseline blood glucose and in-hospital mortality was also statistically significant (p = 0.03), as well as with total cholesterol and ICU admission (p = 0.007). Conclusions: obesity is related to a longer time of hospitalization and a higher rate of admissions to the ICU. Low total cholesterol levels and abnormal baseline blood glucose were risk factors for ICU requirement and in-hospital mortality. Patient categorization based on obesity could be valuable in the development of a precision medicine model within the COVID-19 pandemic.

Human genetic basis of coronavirus disease 2019

Coronavirus disease 2019 (COVID-19) caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in considerable morbidity and mortality worldwide. COVID-19 incidence, severity, and mortality rates differ greatly between populations, genders, ABO blood groups, human leukocyte antigen (HLA) genotypes, ethnic groups, and geographic backgrounds. This highly heterogeneous SARS-CoV-2 infection is multifactorial. Host genetic factors such as variants in the angiotensin-converting enzyme gene (ACE), the angiotensin-converting enzyme 2 gene (ACE2), the transmembrane protease serine 2 gene (TMPRSS2), along with HLA genotype, and ABO blood group help to explain individual susceptibility, severity, and outcomes of COVID-19. This review is focused on COVID-19 clinical and viral characteristics, pathogenesis, and genetic findings, with particular attention on genetic diversity and variants. The human genetic basis could provide scientific bases for disease prediction and targeted therapy to address the COVID-19 scourge.

Proinflammatory and Hepatic Features Related to Morbidity and Fatal Outcomes in COVID-19 Patients

OBJECTIVE

to screen putative associations between liver markers and proinflammatory-related features concerning infectious morbidity and fatal outcomes in COVID-19 patients.

METHODS

a total of 2094 COVID-19 positive patients from the COVID-DATA-SAFE-LIFES cohort (HM hospitals consortium) were classified according to median values of hepatic, inflammatory, and clinical indicators. Logistic regression models were fitted and ROC cures were generated to explain disease severity and mortality.

RESULTS

intensive care unit (ICU) assistance plus death outcomes were associated with liver dysfunction, hyperinflammation, respiratory insufficiency, and higher associated comorbidities. Four models including age, sex, neutrophils, D-dimer, oxygen saturation lower than 92%, C-reactive protein (CRP), Charlson Comorbidity Index (CCI), FIB-4 and interactions with CRP, neutrophils, and CCI explained ICU plus death variance in more than 28%. The predictive values of ROC curves were: FIB-4 (0.7339), AST/ALT ratio (0.7107), CRP (0.7003), CCI index (0.6778), neutrophils (0.6772), and platelets (0.5618) concerning ICU plus death outcomes.

CONCLUSIONS

the results of this research revealed that liver and proinflammatory features are important determinants of COVID-19 morbidity and fatal outcomes, which could improve the current understanding of the COVID-19 physiopathology as well as to facilitate the clinical management and therapy decision-making of this disease under a personalized medicine scope.