Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza

Pressured cytotoxic T cell epitope strength among SARS-CoV-2 variants correlates with COVID-19 severity

Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we probe the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that the recognition of pressured epitopes from the parent strain Wuhan-Hu-1 correlates with COVID-19 severity. We also identify and rank list HLA-alleles and epitopes that offer protectivity against severe disease in infected individuals. Finally, we shortlist a set of 6 pressured and protective epitopes that represent regions in the viral proteome that are under high immune pressure across SARS-CoV-2 variants. Identification of such epitopes, defined by the distribution of HLA-genotypes among members of a population, could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens.

Demographic and Clinical Presentation of Hospitalised Patients with SARS-CoV-2 During the First Omicron Wave

Introduction: The objectives of this retrospective study were to describe clinical presentations and mortality outcomes of hospitalised patients with the COVID-19 Omicron variant within two acute district general hospitals, and to evaluate demographic factors associated with these presentations and mortality. Methods: Data was obtained over a month in 2021–2022 from multi-ethnic patients who were hospitalised and detected to have severe acute respiratory syndrome coronavirus 2 Omicron infection. Details included socio-demographic characteristics, vaccination, and mortality. Patients were subdivided into three groups: Group 1 were admitted with true COVID-19 pneumonitis, Group 2 had incidental COVID-19 on admission screening, and Group 3 were negative on admission but developed COVID-19 over 7 days post-admission. Results: Of 553 patients, only 24.1% (133/553) were in Group 1, 58.2% (322/553) in Group 2, and 17.7% (98/553) in Group 3. Patients in Group 1 and Group 3 were significantly older than those in Group 2 (p<0.001). Thirty percent of patients from Black, Asian, and minority ethnic backgrounds had COVID-19 pneumonitis compared with 19% of those with White ethnicity (p=0.002). Twenty percent of patients were admitted within nonmedical specialties, i.e., surgical specialties, paediatrics, and obstetrics. Of 36 requiring critical care, 21 were in Group 1. Of those patients, 20/21 (95%) were unvaccinated and seven of the 21 who died were all unvaccinated (100%). Common COVID-19 presentations included delirium, falls, seizures, chronic obstructive pulmonary disease, and antenatal problems. Overall, 13.7% (76/553) patients died and 4.7% (26/553) were directly attributable to COVID-19. Conclusions: This large, multi-ethnic study has described clinical presentations and mortality of hospitalised patients with Omicron. It has determined socio-demographic factors associated with these presentations, including ethnicity and vaccination rates. The study provides useful information for future COVID-19 studies examining outcomes and presentations of Omicron and future COVID-19 variants.

In-silico study of influence of HLA heterogeneity on CTL responses across ethnicities to SARS-CoV-2

Differences in outcome to COVID-19 infection in different individuals is largely attributed to genetic heterogeneity leading to differential immune responses across individuals and populations. HLA is one such genetic factor that varies across individuals leading to differences in how T-cell responses are triggered against SARS-CoV-2, directly influencing disease susceptibility. HLA alleles that influence COVID-19 outcome, by virtue of epitope binding and presentation, have been identified in cohorts worldwide. However, the heterogeneity in HLA distribution across ethnic groups limits the generality of such association. In this study, we address this limitation by comparing the recognition of CTL epitopes across HLA genotypes and ethnic groups. Using HLA allele frequency data for ethnic groups from Allele Frequency Net Database (AFND), we construct synthetic populations for each ethnic group and show that CTL epitope strength varies across HLA genotypes and populations. We also observe that HLA genotypes, in certain cases, can have high CTL epitope strengths in the absence of top-responsive HLA alleles. Finally, we show that the theoretical estimate of responsiveness and hence protection offered by a HLA allele is bound to vary across ethnic groups, due to the influence of other HLA alleles within the HLA genotype on CTL epitope recognition. This emphasizes the need for studying HLA-disease associations at the genotype level rather than at a single allele level.

Pandemics, past and present: The role of biological anthropology in interdisciplinary pandemic studies

Biological anthropologists are ideally suited for the study of pandemics given their strengths in human biology, health, culture, and behavior, yet pandemics have historically not been a major focus of research. The COVID‐19 pandemic has reinforced the need to understand pandemic causes and unequal consequences at multiple levels. Insights from past pandemics can strengthen the knowledge base and inform the study of current and future pandemics through an anthropological lens. In this paper, we discuss the distinctive social and epidemiological features of pandemics, as well as the ways in which biological anthropologists have previously studied infectious diseases, epidemics, and pandemics. We then review interdisciplinary research on three pandemics–1918 influenza, 2009 influenza, and COVID‐19–focusing on persistent social inequalities in morbidity and mortality related to sex and gender; race, ethnicity, and Indigeneity; and pre‐existing health and disability. Following this review of the current state of pandemic research on these topics, we conclude with a discussion of ways biological anthropologists can contribute to this field moving forward. Biological anthropologists can add rich historical and cross‐cultural depth to the study of pandemics, provide insights into the biosocial complexities of pandemics using the theory of syndemics, investigate the social and health impacts of stress and stigma, and address important methodological and ethical issues. As COVID‐19 is unlikely to be the last global pandemic, stronger involvement of biological anthropology in pandemic studies and public health policy and research is vital.

Factors Influencing Frequency of Pediatric Clinically Distinguishable Influenza: A 2 Season Case-Control Study

Background:

Little is known about the individual differences in susceptibility to, or lifetime frequency of clinically distinguishable influenza in children.

Methods:

Rapid enzyme linked immunoassay-confirmed influenza pediatric cases (n = 96) in season 1 (2017-2018) were compared to age-matched (mean 7.7 years) controls (n = 171) with no evidence of influenza in season 1. The 2 cohorts were again studied in season 2 (2018-2019) for influenza outcomes and influences. Medical records, questionnaires, and interviews were used to determine past influenza disease and vaccine histories.

Results:

After season 2, known lifetime influenza illnesses per year of age averaged 22.6% in cases and 5.6% in controls, with 62% of controls still having never experienced known influenza. Having had prior influenza was marginally significant as a risk for season 1 influenza in cases versus controls (P = .055), yet a significant risk factor in controls for season 2 (P = .018). Influenza vaccine rates were significantly higher in controls than in cases for season 1, with a greater female vaccine benefit. Lack of previous influenza had greater calculated effectiveness (52%) than vaccination (17%-26%) in escaping season 2 influenza. Lifetime rates of vaccination did not correlate with lifetime rates of known influenza in either cohort.

Conclusions:

Lifetime clinically distinguishable influenza rates varied among children, with many escaping it for years even without being immunized against it. Findings of less than expected clinical influenza, no correlation between vaccination frequency and disease frequency, sex differences, and an association between past clinical influenza and current risk, point to innate differences in individual influenza experiences.

Pharmacometric analysis of seasonal influenza epidemics and the effect of vaccination using sentinel surveillance data

The identification of influenza epidemics and assessment of the efficacy of vaccination against this infection are major challenges for the implementation of effective public health strategies, such as vaccination programs. In this study, we developed a new pharmacometric model to evaluate the efficacy of vaccination based on infection surveillance data from the 2010/2011 to 2018/2019 influenza seasons in Japan. A novel susceptible‐infected‐removed plus vaccination model, based on an indirect response structure with the effect of vaccination, was applied to describe seasonal influenza epidemics using a preseasonal collection of data regarding serological H₁ antibody titer positivity and the fraction of virus strains. Using this model, we evaluated K_in (a parameter describing the transmission rate of symptomatic influenza infection) for different age groups. Furthermore, we defined a new parameter (prevention factor) showing the efficacy of vaccination against each viral strain and in different age groups. We found that the prevention factor of vaccination against influenza varied among age groups. Notably, children aged 5–14 years showed the highest K_in value during the 10 influenza seasons and the greatest preventive effect of vaccination (prevention factor = 70.8%). The propagation of influenza epidemics varies in different age groups. Children aged 5–14 years most likely play a leading role in the transmission of influenza. Prioritized vaccination in this age group may be the most effective strategy for reducing the prevalence of influenza in the community.

An overview of some potential immunotherapeutic options against COVID-19

After the advent of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in the late 2019, the resulting severe and pernicious syndrome (COVID-19) immediately was deployed all around the world. To date, despite relentless efforts to control the disease by drug repurposing, there is no approved specific therapy for COVID-19. Given the role of innate and acquired immune components in the control and elimination of viral infections and inflammatory mutilations during SARS-CoV2 pathogenesis, immunotherapeutic strategies appear to be beneficent. Passive immunotherapies such as convalescent plasma, which has received much attention especially in severe cases, as well as suppressing inflammatory cytokines, interferon administration, inhibition of kinases and complement cascade, virus neutralization with key engineered products, cell-based therapies, immunomodulators and anti-inflammatory drugs are among the key immunotherapeutic approaches to deal with COVID-19, which is discussed in this review. Also, details of leading COVID-19 vaccine candidates as the most potent immunotherapy have been provided. However, despite salient improvements, there is still a lack of completely assured vaccines for universal application. Therefore, adopting proper immunotherapies according to the cytokine pattern and involved immune responses, alongside engineered biologics specially ACE2-Fc to curb SARS-CoV2 infection until achieving a tailored vaccine is probably the best strategy to better manage this pandemic. Therefore, gaining knowledge about the mechanism of action, potential targets, as well as the effectiveness of immune-based approaches to confront COVID-19 in the form of a well-ordered review study is highly momentous.

Prevalence and socio-demographic factors of SARS-CoV-2 antibody in multi-ethnic healthcare workers

INTRODUCTION

Healthcare workers are particularly susceptible to developing COVID-19 owing to close and frequent contact with COVID-19 patients. This cross-sectional study aimed to describe prevalence of SARS-CoV-2 antibodies among healthcare workers within a hospital trust and examine factors associated with increased prevalence of this antibody.

METHODS

Data was obtained over a 4-week period in 2020 from a cross-sectional prospective survey of healthcare workers serving a multi-ethnic inner-city population who had immunoglobulin G SARS-CoV-2 antibody test. Anonymised socio-demographic data about staff were cross-referenced with these tests.

RESULTS

Of 7,013 staff, 6,212 (89%) undertook the antibody test during this period. Overall detection rate was 26% (1,584/6,212). Univariate analyses revealed no differences in prevalence in terms of gender or age. Compared with white staff members (18%), rates were higher in black (38%) and Asian (27%) members (p<0.001). The rates in general wards (43%) were higher compared with other areas; in emergency medicine and intensive care, prevalence was 23% (p<0.001). Regarding professional groups, prevalence was highest among nursing and allied clinical services (28%), less in doctors (23%) and lower in non-clinical staff (19%).

DISCUSSION

This large study has described prevalence of recent exposure to SARS-CoV-2 infection among healthcare workers and determined associations including ethnicity, professional groups and geographical areas within healthcare settings. This information will be useful in future COVID-19 studies examining the role of antibody testing both in general populations and in healthcare settings.

No impact of cancer and plague-relevant FPR1 polymorphisms on COVID-19

Formyl peptide receptor 1 (FPR1) is a pattern-recognition receptor that detects bacterial as well as endogenous danger-associated molecular patterns to trigger innate immune responses by myeloid cells. A single nucleotide polymorphism, rs867228 (allelic frequency 19–20%), in the gene coding for FPR1 accelerates the manifestation of multiple carcinomas, likely due to reduced anticancer immunosurveillance secondary to a defect in antigen presentation by dendritic cells. Another polymorphism in FPR1, rs5030880 (allelic frequency 12–13%), has been involved in the resistance to plague, correlating with the fact that FPR1 is the receptor for Yersinia pestis. Driven by the reported preclinical effects of FPR1 on lung inflammation and fibrosis, we investigated whether rs867228 or rs5030880 would affect the severity of coronavirus disease-19 (COVID-19). Data obtained on patients from two different hospitals in Paris refute the hypothesis that rs867228 or rs5030880 would affect the severity of COVID-19.

The Good, The Bad and The Ugly: A Mathematical Model Investigates the Differing Outcomes Among CoVID-19 Patients

The disease caused by SARS-CoV-2—CoVID-19—is a global pandemic that has brought severe changes worldwide. Approximately 80% of the infected patients are largely asymptomatic or have mild symptoms such as fever or cough, while rest of the patients display varying degrees of severity of symptoms, with an average mortality rate of 3–4%. Severe symptoms such as pneumonia and acute respiratory distress syndrome may be caused by tissue damage, which is mostly due to aggravated and unresolved innate and adaptive immune response, often resulting from a cytokine storm.

Cytokine storm: A sudden acute increase in circulating levels of different inflammation causing cytokines including IL-6, IL-1, etc.

Here, we discuss how an intricate interplay among infected cells and cells of innate and adaptive immune system can lead to such diverse clinicopathological outcomes. Particularly, we discuss how the emergent nonlinear dynamics of interaction among the components of adaptive and immune system components and virally infected cells can drive different disease severity. Such minimalistic yet rigorous mathematical modeling approaches are helpful in explaining how various co-morbidity risk factors, such as age and obesity, can aggravate the severity of CoVID-19 in patients. Furthermore, such approaches can elucidate how a fine-tuned balance of infected cell killing and resolution of inflammation can lead to infection clearance, while disruptions can drive different severe phenotypes. These results can help further in a rational selection of drug combinations that can effectively balance viral clearance and minimize tissue damage.

The macroecology of the COVID-19 pandemic in the Anthropocene

Severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease 2019 (COVID-19), has expanded rapidly throughout the world. Thus, it is important to understand how global factors linked with the functioning of the Anthropocene are responsible for the COVID-19 outbreak. We tested hypotheses that the number of COVID-19 cases, number of deaths and growth rate of recorded infections: (1) are positively associated with population density as well as (2) proportion of the human population living in urban areas as a proxies of interpersonal contact rate, (3) age of the population in a given country as an indication of that population's susceptibility to COVID-19; (4) net migration rate and (5) number of tourists as proxies of infection pressure, and negatively associated with (5) gross domestic product which is a proxy of health care quality. Data at the country level were compiled from publicly available databases and analysed with gradient boosting regression trees after controlling for confounding factors (e.g. geographic location). We found a positive association between the number of COVID-19 cases in a given country and gross domestic product, number of tourists, and geographic longitude. The number of deaths was positively associated with gross domestic product, number of tourists in a country, and geographic longitude. The effects of gross domestic product and number of tourists were non-linear, with clear thresholds above which the number of COVID-19 cases and deaths increased rapidly. The growth rate of COVID-19 cases was positively linked to the number of tourists and gross domestic product. The growth rate of COVID-19 cases was negatively associated with the mean age of the population and geographic longitude. Growth was slower in less urbanised countries. This study demonstrates that the characteristics of the human population and high mobility, but not population density, may help explain the global spread of the virus. In addition, geography, possibly via climate, may play a role in the pandemic. The unexpected positive and strong association between gross domestic product and number of cases, deaths, and growth rate suggests that COVID-19 may be a new civilisation disease affecting rich economies.

Detecting HLA-infectious disease associations for multi-strain pathogens

Human Leukocyte Antigen (HLA) molecules play a vital role helping our immune system to detect the presence of pathogens. Previous work to try and ascertain which HLA alleles offer advantages against particular pathogens has generated inconsistent results. We have constructed an epidemiological model to understand why this may occur. The model captures the epidemiology of a multi strain pathogen for which the host's ability to generate immunological memory responses to particular strains depends on that host's HLA genotype. We find that an HLA allele's ability to protect against infection, as measured in a case control study, depends on the population frequency of that HLA allele. Furthermore, our capability to detect associations between HLA alleles and infection with a multi strain pathogen may be affected by the properties of the pathogen itself (i.e R₀ and length of infectious period). Both host and pathogen genetics must be considered in order to identify true HLA associations. However, in the absence of detailed pathogen genetic information, a negative correlation between the frequency of an HLA type and its apparent protectiveness against disease caused by multi strain pathogen is a strong indication that the HLA type in question is well adapted to a subset of strains of that pathogen.

Coinfections by noninteracting pathogens are not independent and require new tests of interaction

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts can be obtained by simply multiplying the individual prevalences. However, even simple epidemiological models show this to be untrue. This study develops new tests for interaction between pathogens that account for this surprising lack of statistical independence.