Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets

Genomic insights into mRNA COVID-19 vaccines efficacy: Linking genetic polymorphisms to waning immunity

Genetic polymorphisms have been linked to the differential waning of vaccine-induced immunity against COVID-19 following vaccination. Despite this, evidence on the mechanisms behind this waning and its implications for vaccination policy remains limited. We hypothesize that specific gene variants may modulate the development of vaccine-initiated immunity, leading to impaired immune function. This study investigates genetic determinants influencing the sustainability of immunity post-mRNA vaccination through a genome-wide association study (GWAS). Utilizing a hospital-based, test negative case-control design, we enrolled 1,119 participants from the Taiwan Precision Medicine Initiative (TPMI) cohort, all of whom completed a full mRNA COVID-19 vaccination regimen and underwent PCR testing during the Omicron outbreak. Participants were classified into breakthrough and protected groups based on PCR results. Genetic samples were analyzed using SNP arrays with rigorous quality control. Cox regression identified significant single nucleotide polymorphisms (SNPs) associated with breakthrough infections, affecting 743 genes involved in processes such as antigenic protein translation, B cell activation, and T cell function. Key genes identified include CD247, TRPV1, MYH9, CCL16, and RPTOR, which are vital for immune responses. Polygenic risk score (PRS) analysis revealed that individuals with higher PRS are at greater risk of breakthrough infections post-vaccination, demonstrating a high predictability (AUC = 0.787) in validating population. This finding confirms the significant influence of genetic variations on the durability of immune responses and vaccine effectiveness. This study highlights the importance of considering genetic polymorphisms in evaluating vaccine-induced immunity and proposes potential personalized vaccination strategies by tailoring regimens to individual genetic profiles.

Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing

Even though the use of SARS-CoV-2 vaccines during the COVID-19 pandemic showed unprecedented success in a short time, it also exposed a flaw in the current vaccine design strategy to offer broad protection against emerging variants of concern. However, developing broad-spectrum vaccines is still a challenge for immunologists. The development of universal vaccines against emerging pathogens and their variants appears to be a practical solution to mitigate the economic and physical effects of the pandemic on society. Very few reports are available to explain the basic concept of universal vaccine design and development. This review provides an overview of the innate and adaptive immune responses generated against vaccination and essential insight into immune mechanisms helpful in designing universal vaccines targeting influenza viruses and coronaviruses. In addition, the characteristics, safety, and factors affecting the efficacy of universal vaccines have been discussed. Furthermore, several advancements in methods worthy of designing universal vaccines are described, including chimeric immunogens, heterologous prime-boost vaccines, reverse vaccinology, structure-based antigen design, pan-reactive antibody vaccines, conserved neutralizing epitope-based vaccines, mosaic nanoparticle-based vaccines, etc. In addition to the several advantages, significant potential constraints, such as defocusing the immune response and subdominance, are also discussed.

Plausible Influence of HLA Class I and Class II Diversity on SARS-CoV-2 Vulnerability

Severe acute respiratory syndrome CoV-2 (SARS-CoV-2) caused the global coronavirus disease 2019 (COVID-19) pandemic, which adversely affected almost all aspects of human life and resulted in the loss of millions of lives, while affecting nearly 0.67 billion people worldwide. SARS-CoV-2 still poses a challenge to the healthcare system as there are more than 200,000 active cases of COVID-19 around the globe. Epidemiological data suggests that the magnitude of morbidity and mortality due to COVID-19 was low in a few geographical regions and was unpredictably higher in a few regions. The genetic diversity of different geographical regions might explain the sporadic prevalence of the disease. In this context, human leukocyte antigens (HLA) represent the most polymorphic gene-dense region of the human genome and serve as an excellent mini-genome model for evaluating population genetic diversity in the context of susceptibility and progression of various diseases. In this review, we highlight the plausible influence of HLA in susceptibility, severity, immune response, and designing of epitope-based vaccines for COVID-19. Further, there is a need for extensive investigations for illustration and clarification of the functional impact of HLA class I and II alleles in the pathogenesis and progression of SARS-CoV-2.

The Challenges of Vaccine Trial Participation among Underserved and Hard-to-Reach Communities: An Internal Expert Consultation of the VACCELERATE Consortium

Underserved and hard-to-reach population groups are under-represented in vaccine trials. Thus, we aimed to identify the challenges of vaccine trial participation of these groups in member countries of the VACCELERATE network. Seventeen National Coordinators (NC), each representing their respective country (15 European countries, Israel, and Turkey), completed an online survey. From 15 eligible groups, those that were more frequently declared underserved/hard-to-reach in vaccine research were ethnic minorities (76.5%), persons experiencing homelessness (70.6%), illegal workers and refugees (64.7%, each). When prioritization for education on vaccine trials was considered, ethnic groups, migrants, and immigrants (5/17, 29.4%) were the groups most frequently identified by the NC as top targets. The most prominent barriers in vaccine trial participation affecting all groups were low levels of health literacy, reluctance to participate in trials due to engagement level, and low levels of trust in vaccines/vaccinations. This study highlighted population groups considered underserved/hard-to-reach in countries contained within the European region, and the respective barriers these groups face when participating in clinical studies. Our findings aid with the design of tailored interventions (within-and across-countries of the European region) and with the development of strategies to overcome major barriers in phase 2 and phase 3 vaccine trial participation.

Associations of functional human leucocyte antigen class I groups with HIV viral load in a heterogeneous cohort

OBJECTIVE

Human leucocyte antigen (HLA) class I alleles are the main host genetic factors involved in controlling HIV-1 viral load (VL). Nevertheless, HLA diversity has proven a significant challenge in association studies. We assessed how accounting for binding affinities of HLA class I alleles to HIV-1 peptides facilitate association testing of HLA with HIV-1 VL in a heterogeneous cohort.

DESIGN

Cohort from the Strategic Timing of AntiRetroviral Treatment (START) study.

METHODS

We imputed HLA class I alleles from host genetic data (2546 HIV+ participants) and sampled immunopeptidomes from 2079 host-paired viral genomes (targeted amplicon sequencing). We predicted HLA class I binding affinities to HIV-1 and unspecific peptides, grouping alleles into functional clusters through consensus clustering. These functional HLA class I clusters were used to test associations with HIV VL.

RESULTS

We identified four clades totaling 30 HLA alleles accounting for 11.4% variability in VL. We highlight HLA-B∗57:01 and B∗57:03 as functionally similar but yet overrepresented in distinct ethnic groups, showing when combined a protective association with HIV+ VL (log, β -0.25; adj. P-value < 0.05). We further demonstrate only a slight power reduction when using unspecific immunopeptidomes, facilitating the use of the inferred functional HLA groups in other studies.

CONCLUSION

The outlined computational approach provides a robust and efficient way to incorporate HLA function and peptide diversity, aiding clinical association studies in heterogeneous cohorts. To facilitate access to the proposed methods and results we provide an interactive application for exploring data.

Serological response following COVID-19 vaccines in patients living with HIV: a dose-response meta-analysis

To quantify the pooled rate and risk ratio of seroconversion following the uncomplete, complete, or booster dose of COVID-19 vaccines in patients living with HIV. PubMed, Embase and Cochrane library were searched for eligible studies to perform a systematic review and meta-analysis based on PRIMSA guidelines. The pooled rate and risk ratio of seroconversion were assessed using the Freeman-Tukey double arcsine method and Mantel-Haenszel approach, respectively. Random-effects model was preferentially used as the primary approach to pool results across studies. A total of 50 studies involving 7160 patients living with HIV were analyzed. We demonstrated that only 75.0% (56.4% to 89.9%) patients living with HIV achieved a seroconversion after uncomplete vaccination, which improved to 89.3% (84.2% to 93.5%) after complete vaccination, and 98.4% (94.8% to 100%) after booster vaccination. The seroconversion rates were significantly lower compared to controls at all the stages, while the risk ratios for uncomplete, complete, and booster vaccination were 0.87 (0.77 to 0.99), 0.95 (0.92 to 0.98), and 0.97 (0.94 to 0.99), respectively. We concluded that vaccine doses were associated with consistently improved rates and risk ratios of seroconversion in patients living with HIV, highlighting the significance of booster vaccination for patients living with HIV.

A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta

Licensed COVID-19 vaccines ameliorate viral infection by inducing production of neutralizing antibodies that bind the SARS-CoV-2 Spike protein and inhibit viral cellular entry. However, the clinical effectiveness of these vaccines is transitory as viral variants escape antibody neutralization. Effective vaccines that solely rely upon a T cell response to combat SARS-CoV-2 infection could be transformational because they can utilize highly conserved short pan-variant peptide epitopes, but a mRNA-LNP T cell vaccine has not been shown to provide effective anti-SARS-CoV-2 prophylaxis. Here we show a mRNA-LNP vaccine (MIT-T-COVID) based on highly conserved short peptide epitopes activates CD8+ and CD4+ T cell responses that attenuate morbidity and prevent mortality in HLA-A*02:01 transgenic mice infected with SARS-CoV-2 Beta (B.1.351). We found CD8+ T cells in mice immunized with MIT-T-COVID vaccine significantly increased from 1.1% to 24.0% of total pulmonary nucleated cells prior to and at 7 days post infection (dpi), respectively, indicating dynamic recruitment of circulating specific T cells into the infected lungs. Mice immunized with MIT-T-COVID had 2.8 (2 dpi) and 3.3 (7 dpi) times more lung infiltrating CD8+ T cells than unimmunized mice. Mice immunized with MIT-T-COVID had 17.4 times more lung infiltrating CD4+ T cells than unimmunized mice (7 dpi). The undetectable specific antibody response in MIT-T-COVID-immunized mice demonstrates specific T cell responses alone can effectively attenuate the pathogenesis of SARS-CoV-2 infection. Our results suggest further study is merited for pan-variant T cell vaccines, including for individuals that cannot produce neutralizing antibodies or to help mitigate Long COVID.

Effectiveness of COVID-19 Booster on the Risk of Hospitalization Among Medicare Beneficiaries

OBJECTIVE

To determine the effectiveness of booster vaccinations on the risk of hospitalization with coronavirus disease 2019 (COVID-19) and how it varies by enrollee characteristics and interval from the initial vaccination to receipt of a booster.

PATIENTS AND METHODS

This cohort study used 100% Medicare claims from January 1, 2020, through December 31, 2021, and matched 3,940,475 individuals who received boosters to 3,940,475 controls based on week and type of original COVID-19 vaccine and demographic and clinical characteristics. We compared the association of booster vs no booster with COVID-19 hospitalization using Cox proportional hazards regression models controlling for patient characteristics. We also determined the association of time from original vaccine to booster with COVID-19 hospitalization.

RESULTS

Over a maximum of 130 days of follow-up, boosted enrollees had 8.20 (95% CI, 7.81 to 8.60) COVID-19 hospitalizations per million days vs 43.70 (95% CI, 42.79 to 44.64) for controls (81% effectiveness). Effectiveness varied by race, prior hospitalizations, and certain comorbidities, for example, leukemia/lymphoma (53% effectiveness), autoimmune disease (73%), and dementia (73%). Boosters received between 6 and 9 months after original vaccination varied between 81% and 85% effectiveness, while boosters received at 5 to 6 months (62%) or less than 5 months (58%) were less effective.

CONCLUSION

Boosters are highly effective in the Medicare population. Approximately 69,225 hospitalizations would be prevented by boosters in the 15 million individuals aged 65 years or older currently not boosted in a period similar to the September 2020 through January 2021 period studied. Boosters provided the greatest benefits if they were received between 6 and 9 months following original vaccinations. However, boosters were associated with substantial decreases in COVID-19 hospitalizations in all categories of enrollees.

Young Age, Female Sex, and No Comorbidities Are Risk Factors for Adverse Reactions after the Third Dose of BNT162b2 COVID-19 Vaccine against SARS-CoV-2: A Prospective Cohort Study in Japan

Background: This study compared the adverse events (AEs) of the second and third doses of BNT162b2, as well as investigated the impact of vaccine recipients’ background and vaccination interval on the AEs of the third dose. Methods: We conducted a questionnaire survey of AEs among health care workers at Osaka University Dental Hospital. Chi-square tests were performed to compare AEs to the administration of second and third vaccine doses. Logistic regression analyses were conducted to identify factors influencing the presence of AEs using age, sex, comorbidities, and the vaccination interval. Spearman’s rank correlation coefficient was calculated to investigate the correlation between age, vaccination interval, and severity of each AE. Results: The third dose of BNT162b2 was associated with significantly more frequent or milder AEs than the second dose. Logistic regression analyses detected significant differences in six items of AEs by age, three by sex, two by comorbidities, and zero by vaccination interval. Consistently, the risk of AEs was greater among younger persons, females, and those without comorbidities. Significant negative correlations were detected between age and vaccination interval, and between age and the severity of most AEs. Conclusions: Young, female, and having no comorbidities are risk factors for AEs after the third dose of BNT162b2, while vaccination interval is not.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by major histocompatibility complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here, we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high-throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. While the peptide datasets show broad agreement with previously described MHC-binding motifs, they additionally reveal experimentally validated computational false positives and false negatives. We therefore present this approach as able to complement current experimental datasets and computational predictions. Further, our yeast display approach underlines design considerations for epitope identification experiments and serves as a framework for examining relationships between viral conservation and MHC binding, which can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of our approach to determine peptide-MHC binding interactions in a manner that can supplement and potentially enhance current algorithm-based approaches.

Will Host Genetics Affect the Response to SARS-CoV-2 Vaccines? Historical Precedents

Recent progress in genomics and bioinformatics technologies have allowed for the emergence of immunogenomics field. This intersection of immunology and genetics has broadened our understanding of how the immune system responds to infection and vaccination. While the immunogenetic basis of the huge clinical variability in response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is currently being extensively studied, the host genetic determinants of SARS-CoV-2 vaccines remain largely unknown. Previous reports evidenced that vaccines may not protect all populations or individuals equally, due to multiple host- and vaccine-specific factors. Several studies on vaccine response to measles, rubella, hepatitis B, smallpox, and influenza highlighted the contribution of genetic mutations or polymorphisms in modulating the innate and adaptive immunity following vaccination. Specifically, genetic variants in genes encoding virus receptors, antigen presentation, cytokine production, or related to immune cells activation and differentiation could influence how an individual responds to vaccination. Although such knowledge could be utilized to generate personalized vaccine strategies to optimize the vaccine response, studies in this filed are still scarce. Here, we briefly summarize the scientific literature related to the immunogenetic determinants of vaccine-induced immunity, highlighting the possible role of host genetics in response to SARS-CoV-2 vaccines as well.

T cell responses to SARS-CoV-2 in humans and animals

SARS-CoV-2, the causative agent of COVID-19, first emerged in 2019. Antibody responses against SARS-CoV-2 have been given a lot of attention. However, the armamentarium of humoral and T cells may have differing roles in different viral infections. Though the exact role of T cells in COVID-19 remains to be elucidated, prior experience with human coronavirus has revealed an essential role of T cells in the outcomes of viral infections. Moreover, an increasing body of evidence suggests that T cells might be effective against SARS-CoV-2. This review summarizes the role of T cells in mouse CoV, human pathogenic respiratory CoV in general and SARS-CoV-2 in specific.

Impact of age, sex and medical history on adverse reactions to the first and second dose of BNT162b2 mRNA COVID-19 vaccine in Japan: a cross-sectional study

BACKGROUND

Vaccines for coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are being promoted worldwide. In this study, we analyzed the relationship between adverse reactions and the profile of vaccinated recipients.

METHODS

Vaccinated subjects who received two doses of BNT162b2 between May 17 and June 11, 2021, at Osaka University Dental Hospital were included in this study. Adverse reactions and profiles were collected by questionnaires, and the relationship between the presence of adverse reactions and the profiles of the vaccinated persons was analyzed by logistic regression analysis. The correlation between the severity of adverse reactions and age was analyzed by Spearman's rank correlation.

RESULTS

Logistic regression analysis showed that, for many kinds of adverse reactions, the incidence was significantly higher in females than in males and in younger than in older people. There was a very weak but significant negative correlation between age and the severity of many kinds of adverse reactions. The relationship between sex and the incidence of each adverse reaction was significant for injection site reactions and fatigue in the first vaccination, whereas significant relationships were found for fatigue, chills, fever, arthralgia, myalgia and headache in the second vaccination, all of which were clearly more likely to occur in females.

CONCLUSION

Adverse reactions to BNT162b2 were found to be more frequent and more intense in females and younger people in Japan, especially after the second vaccination.

The Safety and Immunogenicity of the BNT162b2 mRNA COVID-19 Vaccine in Japanese Patients after Allogeneic Stem Cell Transplantation

Patients who have undergone hematopoietic stem cell transplantation (HSCT) for hematological disease experience high mortality when infected by coronavirus disease 2019 (COVID-19). However, the safety and efficacy of the COVID-19 vaccine in HSCT patients remain to be investigated. We prospectively evaluated the safety and immunogenicity of the BNT162b2 mRNA COVID-19 vaccine (Pfizer BioNTech) in 25 Japanese allogeneic HSCT patients in comparison with 19 healthy volunteers. While anti-S1 antibody titers in almost all healthy volunteers after the second dose were higher than the cut-off value reported previously, levels in HSCT patients after the second dose were diverse. Nineteen patients (76%) had seroconversion of anti-S1 IgG. The median optical density of antibody levels in HSCT patients with low IgG levels (<600 mg/dL), steroid treatment, or low lymphocytes (<1000/μL) was significantly lower than that in the other HSCT patients. There were no serious adverse events (>Grade 3) and no new development or exacerbation of graft-versus-host disease after vaccination. We concluded that the BNT162b2 mRNA vaccine is safe and effective in Japanese allogeneic HSCT patients.

A Promising Vaccination Strategy against COVID-19 on the Horizon: Heterologous Immunization

To overcome the ongoing COVID-19 pandemic, vaccination campaigns are the highest priority of majority of countries. Limited supply and worldwide disproportionate availability issues for the approved vaccines, together with concerns about rare side-effects have recently initiated the switch to heterologous vaccination, commonly known as mixing of vaccines. The COVID-19 vaccines are highly effective in the general population. However, none of the vaccines is 100% efficacious or effective, with variants posing more challenges, resulting in breakthrough cases. This review summarizes the current knowledge of immune responses to variants of concern (VOC) and breakthrough infections. Furthermore, we discuss the scope of heterologous vaccination and future strategies to tackle the COVID-19 pandemic, including fractionation of vaccine doses and alternative route of vaccination.

Are There Global Syndemics?

In this article, we address the nature of syndemics and whether, as some have asserted, these epidemiological phenomena are global configurations. Our argument that syndemics are not global rests on recognition that they are composed of social/environment contexts, disease clusters, demographics, and biologies that vary across locations. These points are illustrated with the cases of syndemics involving COVID-19, diabetes mellitus, and HIV/AIDS. We draw on theoretical discourse from epidemiology, biology, and anthropology to present what we believe is a more accurate framework for thinking about syndemics with shared elements.

Role of artificial intelligence in peptide vaccine design against RNA viruses

RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Individual HLA-A, -B, -C, and -DRB1 Genotypes Are No Major Factors Which Determine COVID-19 Severity

HLA molecules are key restrictive elements to present intracellular antigens at the crossroads of an effective T-cell response against SARS-CoV-2. To determine the impact of the HLA genotype on the severity of SARS-CoV-2 courses, we investigated data from 6,919 infected individuals. HLA-A, -B, and -DRB1 allotypes grouped into HLA supertypes by functional or predicted structural similarities of the peptide-binding grooves did not predict COVID-19 severity. Further, we did not observe a heterozygote advantage or a benefit from HLA diplotypes with more divergent physicochemical peptide-binding properties. Finally, numbers of in silico predicted viral T-cell epitopes did not correlate with the severity of SARS-CoV-2 infections. These findings suggest that the HLA genotype is no major factor determining COVID-19 severity. Moreover, our data suggest that the spike glycoprotein alone may allow for abundant T-cell epitopes to mount robust T-cell responses not limited by the HLA genotype.

Personalized health and the coronavirus vaccines-Do individual genetics matter?

Vaccines represent preventative interventions amenable to immunogenetic prediction of how human variability will influence their safety and efficacy. The genetic polymorphism among individuals within any population can render possible that the immunity elicited by a vaccine is variable in length and strength. The same immune challenge (virus and/or vaccine) could provoke partial, complete or even failed protection for some individuals treated under the same conditions. We review genetic variants and mechanistic relationships among chemokines, chemokine receptors, interleukins, interferons, interferon receptors, toll‐like receptors, histocompatibility antigens, various immunoglobulins and major histocompatibility complex antigens. These are the targets for variation among macrophages, dendritic cells, natural killer cells, T‐ and B‐lymphocytes, and complement. The technology platforms (mRNA, viral vectors, proteins) utilized to produce vaccines against SARS‐CoV‐2 infections may each trigger genetically distinct immune reactogenic profiles. With DNA biobanking and immunoprofiling of recipients, global COVID‐19 vaccinations could launch a new era of personalized healthcare.

Influence of HLA Class II Polymorphism on Predicted Cellular Immunity Against SARS-CoV-2 at the Population and Individual Level

Development of adaptive immunity after COVID-19 and after vaccination against SARS-CoV-2 is predicated on recognition of viral peptides, presented on HLA class II molecules, by CD4+ T-cells. We capitalised on extensive high-resolution HLA data on twenty five human race/ethnic populations to investigate the role of HLA polymorphism on SARS-CoV-2 immunogenicity at the population and individual level. Within populations, we identify wide inter-individual variability in predicted peptide presentation from structural, non-structural and accessory SARS-CoV-2 proteins, according to individual HLA genotype. However, we find similar potential for anti-SARS-CoV-2 cellular immunity at the population level suggesting that HLA polymorphism is unlikely to account for observed disparities in clinical outcomes after COVID-19 among different race/ethnic groups. Our findings provide important insight on the potential role of HLA polymorphism on development of protective immunity after SARS-CoV-2 infection and after vaccination and a firm basis for further experimental studies in this field.

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

SARS-CoV-2 mutations in MHC-I-restricted epitopes evade CD8+ T cell responses

CD8⁺ T cell immunity to SARS-CoV-2 has been implicated in COVID-19 severity and virus control. Here, we identified nonsynonymous mutations in MHC-I-restricted CD8⁺ T cell epitopes after deep sequencing of 747 SARS-CoV-2 virus isolates. Mutant peptides exhibited diminished or abrogated MHC-I binding in a cell-free in vitro assay. Reduced MHC-I binding of mutant peptides was associated with decreased proliferation, IFN-γ production and cytotoxic activity of CD8⁺ T cells isolated from HLA-matched COVID-19 patients. Single cell RNA sequencing of ex vivo expanded, tetramer-sorted CD8⁺ T cells from COVID-19 patients further revealed qualitative differences in the transcriptional response to mutant peptides. Our findings highlight the capacity of SARS-CoV-2 to subvert CD8⁺ T cell surveillance through point mutations in MHC-I-restricted viral epitopes.