The Biobanque québécoise de la COVID-19 (BQC19)-A cohort to prospectively study the clinical and biological determinants of COVID-19 clinical trajectories

Metabolomic pattern associated with physical sequelae in patients presenting with respiratory symptoms validates the aestivation concept in dehydrated patients

Hypertonic dehydration is associated with muscle wasting and synthesis of organic osmolytes. We recently showed a metabolic shift to amino acid production and urea cycle activation in coronavirus-2019 (COVID-19), consistent with the aestivation response. The aim of the present investigation was to validate the metabolic shift and development of long-term physical outcomes in the non-COVID cohort of the Biobanque Québécoise de la COVID-19 (BQC19). We included 824 patients from BQC19, where 571 patients had data of dehydration in the form of estimated osmolality (eOSM = 2Na + 2K + glucose + urea), and 284 patients had metabolome data and long-term follow-up. We correlated the degree of dehydration to mortality, invasive mechanical ventilation, acute kidney injury, and long-term symptoms. As found in the COVID cohort, higher eOSM correlated with a higher proportion of urea and glucose of total eOSM, and an enrichment of amino acids compared with other metabolites. Sex-stratified analysis indicated that women may show a weaker aestivation response. More severe dehydration was associated with mortality, invasive mechanical ventilation, and acute kidney injury during the acute illness. Importantly, more severe dehydration was associated with physical long-term symptoms but not mental long-term symptoms after adjustment for age, sex, and disease severity. Patients with water deficit in the form of increased eOSM tend to have more severe disease and experience more physical symptoms after an acute episode of care. This is associated with amino acid and urea production, indicating dehydration-induced muscle wasting.NEW & NOTEWORTHY We have previously shown that humans exhibit an aestivation-like response where dehydration leads to a metabolic shift to urea synthesis, which is associated with long-term weakness indicating muscle wasting. In the present study, we validate this response in a new cohort and present a deeper metabolomic analysis and pathway analysis. Finally, we present a sex-stratified analysis suggesting weaker aestivation in women. However, women show less dehydration, so the association warrants further study.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Genomic analysis of severe COVID-19 considering or not asthma comorbidity: GWAS insights from the BQC19 cohort

BACKGROUND

The severity of COVID-19 is influenced by various factors including the presence of respiratory diseases. Studies have indicated a potential relationship between asthma and COVID-19 severity.

OBJECTIVE

This study aimed to conduct a genome-wide association study (GWAS) to identify genetic and clinical variants associated with the severity of COVID-19, both among patients with and without asthma.

METHODS

We analyzed data from 2131 samples sourced from the Biobanque québécoise de la COVID-19 (BQC19), with 1499 samples from patients who tested positive for COVID-19. Among these, 1110 exhibited mild-to-moderate symptoms, 389 had severe symptoms, and 58 had asthma. We conducted a comparative analysis of clinical data from individuals in these three groups and GWAS using a logistic regression model. Phenotypic data analysis resulted in the refined covariates integrated into logistic models for genetic studies.

RESULTS

Considering a significance threshold of 1 × 10-6, seven genetic variants were associated with severe COVID-19. These variants were located proximal to five genes: sodium voltage-gated channel alpha subunit 1 (SCN10A), desmoplakin (DSP), RP1 axonemal microtubule associated (RP1), IGF like family member 1 (IGFL1), and docking protein 5 (DOK5). The GWAS comparing individuals with severe COVID-19 with asthma to those without asthma revealed four genetic variants in transmembrane protein with EGF like and two follistatin like domains 2 (TMEFF2) and huntingtin interacting protein-1 (HIP1) genes.

CONCLUSION

This study provides significant insights into the genetic profiles of patients with severe forms of the disease, whether accompanied by asthma or not. These findings enhance our comprehension of the genetic factors that affect COVID-19 severity.

KEY MESSAGES

Seven genetic variants were associated with the severe form of COVID-19; Four genetic variants were associated with the severe form of COVID-19 in individuals with comorbid asthma; These findings help define the genetic component of the severe form of COVID-19 in relation to asthma as a comorbidity.

Proteomics validate circulating GDF-15 as an independent biomarker for COVID-19 severity

Introduction

Growth differentiation factor 15 (GDF-15) was originally described as a stress-induced cytokine, and a biomarker of aging and cardiovascular diseases. We hypothesized that circulating GDF-15 would be associated with COVID-19 disease severity. Herein, we explored this hypothesis in a large cohort of COVID-19 patients.

Methods

Blood samples were collected from 926 COVID-19 adult patients and from 285 hospitalized controls from the Biobanque Québécoise de la COVID-19 (BQC19). COVID-19 severity was graded according to the WHO criteria. SOMAscan proteomics assay was performed on 50µL of plasma. ELISA were performed on 46 selected participants with left-over plasma to validate differences in plasma GDF-15 levels. Statistical analyses were conducted using GraphPad Prism 9.0 and SPSS. P values < 0.01 were considered significant.

Results

Proteomics showed that plasma GDF-15 levels were higher in COVID-19 patients compared to hospitalized controls. GDF-15 levels increased with COVID-19 severity. COVID-19 patients presenting with comorbidities including diabetes, cancer, chronic obstructive pulmonary disease (COPD) and cardiovascular disease had higher GDF-15 levels. ELISA revealed significant elevation of GDF-15 until 30 days after hospitalization. Plasma GDF-15 elevation was correlated with older age. Moreover, GDF-15 levels correlated with pro-inflammatory cytokine interleukin-6 (IL-6) and inflammation marker C-reactive protein (CRP) as well as soluble levels of its putative receptor CD48. No association was established between anti-SARS-CoV-2 IgG levels and plasma GDF-15 levels.

Conclusions

This study confirms GDF-15 as a biomarker for COVID-19 severity. Clinical evaluation of GDF-15 levels could assist identification of persons at high-risk of progressing to severe disease, thus improving patient care.

Motivation to participate and attrition factors in a COVID-19 biobank: A qualitative study

BACKGROUND

The Biobanque québécoise de la COVID-19 (Quebec Biobank for COVID-19, or BQC19) is a provincial initiative that aims to manage the longitudinal collection, storage, and sharing of biological samples and clinical data related to COVID-19. During the study, BQC19 investigators reported a high loss-to-follow-up rate. The current study aimed to explore motivational and attrition factors from the perspective of BQC19 participants and health care and research professionals.

METHODS

This was an inductive exploratory qualitative study. Using a theoretical sampling approach, a sample of BQC19 participants and professionals were invited to participate via semi-structured interviews. Topics included motivations to participate; participants' fears, doubts, and barriers to participation; and professionals' experiences with biobanking during the COVID-19 pandemic.

RESULTS

Interviews were conducted with BQC19 participants (n = 23) and professionals (n = 17) from 8 clinical data collection sites. Motivations included the contribution to science and society in crisis, self-worth, and interactions with medical professionals. Reasons for attrition included logistical barriers, negative attitudes about public health measures or genomic studies, fear of clinical settings, and a desire to move on from COVID-19. Motivations and barriers seemed to evolve over time and with COVID-19 trends and surges. Certain situations were associated with attrition, such as when patients experienced indirect verbal consent during hospitalization. Barriers related to human and material resources and containment/prevention measures limited the ability of research teams to recruit and retain participants, especially in the ever-evolving context of crisis.

CONCLUSION

The pandemic setting impacted participation and attrition, either by influencing participants' motivations and barriers or by affecting research teams' ability to recruit and retain participants. Longitudinal and/or biobanking studies in a public health crisis setting should consider these factors to limit attrition.

Canadian COVID-19 host genetics cohort replicates known severity associations

The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic contributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospitalized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and constructed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation.

Gaining Biological Insights through Supervised Data Visualization

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

A longitudinal prospective cohort study of health-related quality of life assessment in outpatient adults with post-COVID-19 conditions

Background

Few studies have reported the long-term effects of post-COVID-19 condition (PCC) on health-related quality of life (HRQoL). We aim to assess HRQoL in outpatient adults with PCC over a 2-year period.

Methods

This prospective longitudinal cohort study compared 413 PCC participants (cases) to 520 COVID-19-positive participants who recovered (controls). HRQoL was assessed with the EuroQol 5-Dimension 5-Level preference-based questionnaire (EQ-5D-5L) and fitness and frailty with the Clinical Frailty Scale (CFS) at each visit for up to 24 months.

Results

Among a total of 933 participants, 413 (42.3%) met the definition of PCC (cases) and 520 (55.7%) did not (controls). Overall, there was a significant difference in EQ-5D-5L index score from 3 months post-infection up to 18 months between cases and controls (p < 0.001). This score continued to decline up to 18 months in the PCC group only. Most impaired EQ-5D-5L dimensions at 12 months in the PCC group included pain/discomfort, anxiety/depression, and usual activities.

Conclusions

This is one of the first studies to report 2-year alterations of HRQoL in outpatients with PCC. Our study highlights the need for continued monitoring for PCC long-term consequences. Given the high proportion of PCC participants experiencing anxiety/depression problems, further studies are needed to specifically address mental health in this population.

Plasma SARS-CoV-2 RNA elimination and RAGE kinetics distinguish COVID-19 severity

Objectives

Identifying biomarkers causing differential SARS-CoV-2 infection kinetics associated with severe COVID-19 is fundamental for effective diagnostics and therapeutic planning.

Methods

In this work, we applied mathematical modelling to investigate the relationships between patient characteristics, plasma SARS-CoV-2 RNA dynamics and COVID-19 severity. Using a straightforward mathematical model of within-host viral kinetics, we estimated key model parameters from serial plasma viral RNA (vRNA) samples from 256 hospitalised COVID-19+ patients.

Results

Our model predicted that clearance rates distinguish key differences in plasma vRNA kinetics and severe COVID-19. Moreover, our analyses revealed a strong correlation between plasma vRNA kinetics and plasma receptor for advanced glycation end products (RAGE) concentrations (a plasma biomarker of lung damage), collected in parallel to plasma vRNA from patients in our cohort, suggesting that RAGE can substitute for viral plasma shedding dynamics to prospectively classify seriously ill patients.

Conclusion

Overall, our study identifies factors of COVID-19 severity, supports interventions to accelerate viral clearance and underlines the importance of mathematical modelling to better understand COVID-19.

Bacterial Biomarkers of the Oropharyngeal and Oral Cavity during SARS-CoV-2 Infection

(1) Background: Individuals with COVID-19 display different forms of disease severity and the upper respiratory tract microbiome has been suggested to play a crucial role in the development of its symptoms. (2) Methods: The present study analyzed the microbial profiles of the oral cavity and oropharynx of 182 COVID-19 patients compared to 75 unaffected individuals. The samples were obtained from gargle screening samples. 16S rRNA amplicon sequencing was applied to analyze the samples. (3) Results: The present study shows that SARS-CoV-2 infection induced significant differences in bacterial community assemblages, with Prevotella and Veillonella as biomarkers for positive-tested people and Streptococcus and Actinomyces for negative-tested people. It also suggests a state of dysbiosis on the part of the infected individuals due to significant differences in the bacterial community in favor of a microbiome richer in opportunistic pathogens. (4) Conclusions: SARS-CoV-2 infection induces dysbiosis in the upper respiratory tract. The identification of these opportunistic pathogenic biomarkers could be a new screening and prevention tool for people with prior dysbiosis.

Long-Term Consequences of COVID-19 in Predominantly Immunonaive Patients: A Canadian Prospective Population-Based Study

Background: Lingering symptoms are frequently reported after acute SARS-CoV-2 infection, a condition known as post-COVID-19 condition (PCC). The duration and severity of PCC in immunologically naïve persons remain unclear. Furthermore, the long-term consequences of these chronic symptoms on work and mental health are poorly documented. Objective: To determine the outcome, the risk factors, and the impact on work and mental health associated with post-COVID-19 symptoms. Methods: This prospective population-based study assessed acute COVID-19 symptoms and their evolution for up to nine months following infection. Individuals aged 18 years and older with COVID-19 in three Canadian regions between 1 November 2020 and 31 May 2021 were recruited. Participants completed a questionnaire that was either administered by trained student investigators over the phone or self-administered online. Results: A total of 1349 participants with a mean age of 46.6 ± 16.0 years completed the questionnaire. Participants were mostly unvaccinated at the time of their COVID-19 episode (86.9%). Six hundred and twenty-two participants (48.0%) exhibited one symptom or more, at least three months post-COVID-19. Among participants with PCC, 23.0% to 37.8% experienced fatigue at the time of survey. Moreover, 6.1% expressed psychological distress. Risk factors for PCC and fatigue included female sex (OR = 1.996), higher number of symptoms (OR = 1.292), higher severity of episode (OR = 3.831), and having a mental health condition prior to the COVID-19 episode (OR = 5.155). Conclusions: In this multicenter cohort study, almost half (47%) of the participants reported persistent symptoms >3 months after acute infection. Baseline risk factors for PCC include female sex, number and severity of symptoms during acute infection, and a previous diagnosis of mental health disorder. Having PCC negatively impacted health-related quality of life and these patients were more likely to exhibit psychological distress, as well as fatigue.

Post-COVID symptoms are associated with endotypes reflecting poor inflammatory and hemostatic modulation

Introduction

Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID.

Methods

RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development.

Results

Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification.

Discussion

This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment.

From target discovery to clinical drug development with human genetics

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

HostSeq: a Canadian whole genome sequencing and clinical data resource

HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.

Circulating proteins to predict COVID-19 severity

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.

No time for complacency: The CoVaRR-Net Biobank is an essential element of laboratory preparedness for infectious disease outbreaks

The SARS-CoV-2 pandemic highlighted the need for rapid, collaborative, and population-centric research to define health impact, develop health care policies and establish reliable diagnostic and surveillance tests. Critical for these objectives were in-depth clinical data collected in standardized fashion and large numbers of various types of human samples prior and post-viral encounter. As the pandemic evolved with the emergence of new variants of concern (VOCs), access to samples and data from infected and vaccinated individuals were needed to monitor immune durability, the possibility of increased transmissibility and virulence, and vaccine protection against new and emerging VOCs. Therefore, essential to the pandemic response is a strong laboratory and data research component, supported by effective biobanking and data sharing. Critically important to the speed of the research response is the rapid access to biobanked samples. To address critical challenges brought to light by the pandemic, the Coronavirus Variants Rapid Response Network (CoVaRR-Net), funded by the Canadian Institutes of Health Research, was established to coordinate research efforts to provide rapid evidence-based responses to emerging VOCs. The purpose of this paper is to introduce the CoVaRR-Net Biobank and define its contribution to pandemic preparedness.

SARS-CoV-2 infection outcomes associated with the Delta variant: A prospective cohort study

Background

It is well established that fully vaccinated (≥2 doses) individuals may develop COVID-19. However, little is known about the specific prevalence of post-COVID-19 conditions associated with the Delta variant or the effect of vaccination on post-acute outcomes of COVID-19. In addition, how Delta variant infection severity compares in fully vaccinated individuals versus in those unvaccinated is unknown.

Methods

This prospective single-centre observational cohort study assessed adults with SARS-CoV-2 proven infection from August 1 to November 1, 2021. Study participants were enrolled in the Biobanque Québécoise de la COVID-19. Data on demographics, comorbidities, and severity of COVID-19 were collected. Simple and multiple logistic regressions were used to identify risk factors for post-COVID-19 conditions.

Results

Among the 395 individuals who were interviewed by phone, 138 (39.4%) agreed to participate. Of the 138 participants, 62.8% were Delta-associated breakthrough infections that occurred in fully vaccinated individuals and 37.1% in unvaccinated individuals. The majority (93.5%) had a history of mild COVID-19 illness. The prevalence of Delta-variant-associated post-COVID-19 conditions was similar in both vaccinated (61.4%) and unvaccinated (51.4%) groups (p = 0.347). The number of symptoms during acute infection was an independent risk factor for post-COVID-19 conditions.

Conclusions

This study is the first to describe the incidence of Delta-associated post-COVID-19 condition. In this study, COVID-19 vaccination was not associated with decreased post-COVID-19 conditions in patients with breakthrough Delta infection. These findings have important implications for provincial services planning and underscore the need to develop alternative strategies to prevent post-COVID-19 conditions.

Cohort profile: A Québec-based plasma donor biobank to study COVID-19 immunity (PlasCoV)

PURPOSE

The long-term humoral immunity to COVID-19 is not well understood owing to the continuous emergence of new variants of concern, the evolving vaccine-induced and infection-induced immunity, and the limited duration of follow-up in previous studies. As the sole blood service in Québec (Canada), Héma-Québec established a COVID-19-focused biobank ('PlasCoV') in April 2021.

PARTICIPANTS

As of January 2022, the biobank included 86 483 plasma samples from 15 502 regular donors (age range=18-84 years, females=49.7%), for an average of 5.6 donations per donor. Nearly two-thirds (65.6%) of biobank donors made at least two donations, with many donors having provided samples prevaccination and postvaccination (3061 (19.7%)) or preinfection and postinfection (131 (0.8%)), thus allowing for longitudinal studies on vaccine-induced and infection-induced immunity.

FINDINGS TO DATE

A study that used PlasCoV samples revealed that previously infected individuals who received a single dose of the BNT162b2 COVID-19 vaccine exhibited the strongest immune response. By contrast, SARS-CoV-2-naïve individuals required two vaccine doses to produce a maximal immune response. Furthermore, the results of a four-phase seroprevalence study indicated that the antinucleocapsid (N) response wanes rapidly, so that up to one-third of previously infected donors were seronegative for anti-N.

FUTURE PLANS

Donations from individuals who consented to participate before 1 October 2022 will be collected up until 31 March 2023. This plasma biobank will facilitate the conduct of longitudinal studies on COVID-19 immunity, thus helping to provide valuable insights into the anti-SARS-CoV-2 immune response and its persistence, and the effects of vaccination and variants on the specificity of the anti-SARS-CoV-2 immune response.

Association between Circulating Amino Acids and COVID-19 Severity

The severity of the symptoms associated with COVID-19 is highly variable, and has been associated with circulating amino acids as a group of analytes in metabolomic studies. However, for each individual amino acid, there are discordant results among studies. The aims of the present study were: (i) to investigate the association between COVID-19-symptom severity and circulating amino-acid concentrations; and (ii) to assess the ability of circulating amino-acid levels to predict adverse outcomes (intensive-care-unit admission or hospital death). We studied a sample of 736 participants from the Biobanque Québécoise COVID-19. All participants tested positive for COVID-19, and the severity of symptoms was determined using the World-Health-Organization criteria. Circulating amino acids were measured by HPLC-MS/MS. We used logistic models to assess the association between circulating amino acids concentrations and the odds of presenting mild vs. severe or mild vs. moderate symptoms, as well as their accuracy in predicting adverse outcomes. Patients with severe COVID-19 symptoms were older on average, and they had a higher prevalence of obesity and type 2 diabetes. Out of 20 amino acids tested, 16 were significantly associated with disease severity, with phenylalanine (positively) and cysteine (inversely) showing the strongest associations. These associations remained significant after adjustment for age, sex and body mass index. Phenylalanine had a fair ability to predict the occurrence of adverse outcomes, similar to traditionally measured laboratory variables. A multivariate model including both circulating amino acids and clinical variables had a 90% accuracy at predicting adverse outcomes in this sample. In conclusion, patients presenting severe COVID-19 symptoms have an altered amino-acid profile, compared to those with mild or moderate symptoms.

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.

Outcomes associated with antiphospholipid antibodies in COVID-19: A prospective cohort study

Background

The significance of antiphospholipid antibodies (aPL) in COVID-19 remains uncertain.

Objectives

We determined whether aPL are associated with COVID-19 and/or thrombosis or adverse outcomes during hospitalization for COVID-19.

Methods

Symptomatic adults tested for SARS-CoV-2 for clinical reasons (March-July 2020) with either ≥1 positive polymerase chain reaction (COVID-19+) or all negative (non-COVID-19) results were recruited to a biobank collecting plasma, clinical data, and outcomes. We tested baseline plasma samples (days 0-7) of all subjects (and day-30 samples in the COVID-19+ subjects, when available) for aPL (anticardiolipin immunoglobulin [Ig]M/IgG, anti-β2-glycoprotein I IgM/IgG, antiphosphatidylserine/prothrombin IgM/IgG, and lupus anticoagulant). We compared the baseline prevalence of aPL between the COVID-19+ and non-COVID-19 subjects. Among hospitalized COVID-19+ subjects, multivariable logistic regression was used to evaluate the association of aPL (and their subtypes) with arterial or venous thromboembolic events, acute kidney injury, intensive care unit admission, mechanical ventilation, and death after adjusting for potential confounders.

Results

At baseline, 123 of 289 (43%) COVID+ subjects had ≥1 aPL versus 116 of 261 (32%) non-COVID-19 subjects (difference, 10%; 95% CI, 3%-18%). Among 89 COVID+ subjects with repeated samples, aPL persisted on day 30 in 15 of 34 (44%) subjects with baseline aPL positivity, and half of those without aPL at baseline developed one or more new aPL. In hospitalized COVID-19 subjects (n = 241), baseline aPL positivity was associated with acute kidney injury (odds ratio [OR], 1.8; 95% CI, 1.1-3.2) and mechanical ventilation (OR, 3.2; 95% CI, 1.5-6.8) but not death (OR, 1.2; 95% CI, 0.6-2.5). In secondary analyses, medium-to-high titers of anticardiolipin IgG (>40) were associated with thromboembolic events (OR, 7.3; 95% CI, 1.8-30.1).

Conclusion

In patients with COVID-19, aPL may help identify an increased risk of thrombosis and other adverse outcomes.

Prevalence of persistent symptoms at least 1 month after SARS-CoV-2 Omicron infection in adults

Background: Persistent post-COVID-19 symptoms pose an important health care burden. The Omicron variant has rapidly spread across the world and infected millions of people, largely exceeding previous variants. The potential for many of these people to develop persistent symptoms is a major public health concern. The aim of this study was to determine the prevalence and risk factors of post-COVID-19 symptoms associated with Omicron. Methods: We conducted a single-centre prospective observational study in Quebec, Canada, between December 2021 and April 2022. Participants were adults enrolled in the Biobanque Québécoise de la COVID-19 (BQC19). Cases were considered Omicron cases as more than 85% were estimated to be attributable to Omicron variant during that period. Adults with polymerase chain reaction (PCR)-confirmed COVID-19 were recruited at least 4 weeks after the onset of infection. Results: Of 1,338 individuals contacted, 290 (21.7%) participants were recruited in BQC19 during that period. Median duration between the initial PCR test and follow-up was 44 days (IQR: 31–56 days). A total of 137 (47.2%) participants reported symptoms at least 1-month post-infection. The majority (98.6%) had a history of mild COVID‐19 illness. Most common persistent symptoms included fatigue (48.2%), shortness of breath (32.6%), and cough (24.1%). Number of symptoms during acute COVID-19 (OR: 1.07, 95% CI 1.03% to 1.10%; p = 0.009) was identified as a risk factor for post-COVID-19 symptoms. Conclusions: This is the first study reporting the prevalence of post-COVID-19 symptoms associated with Omicron in Canada. These findings will have important implications for provincial services planning.

External Validation of the COVID-NoLab and COVID-SimpleLab Prognostic Tools

Our objective was to externally validate 2 simple risk scores for mortality among a mostly inpatient population with COVID-19 in Canada (588 patients for COVID-NoLab and 479 patients for COVID-SimpleLab). The mortality rates in the low-, moderate-, and high-risk groups for COVID-NoLab were 1.1%, 9.6%, and 21.2%, respectively. The mortality rates for COVID-SimpleLab were 0.0%, 9.8%, and 20.0%, respectively. These values were similar to those in the original derivation cohort. The 2 simple risk scores, now successfully externally validated, offer clinicians a reliable way to quickly identify low-risk inpatients who could potentially be managed as outpatients in the event of a bed shortage. Both are available online (https://ebell-projects.shinyapps.io/covid_nolab/ and https://ebell-projects.shinyapps.io/COVID-SimpleLab/).

The dynamic changes and sex differences of 147 immune-related proteins during acute COVID-19 in 580 individuals

INTRODUCTION

Severe COVID-19 leads to important changes in circulating immune-related proteins. To date it has been difficult to understand their temporal relationship and identify cytokines that are drivers of severe COVID-19 outcomes and underlie differences in outcomes between sexes. Here, we measured 147 immune-related proteins during acute COVID-19 to investigate these questions.

METHODS

We measured circulating protein abundances using the SOMAscan nucleic acid aptamer panel in two large independent hospital-based COVID-19 cohorts in Canada and the United States. We fit generalized additive models with cubic splines from the start of symptom onset to identify protein levels over the first 14 days of infection which were different between severe cases and controls, adjusting for age and sex. Severe cases were defined as individuals with COVID-19 requiring invasive or non-invasive mechanical respiratory support.

RESULTS

580 individuals were included in the analysis. Mean subject age was 64.3 (sd 18.1), and 47% were male. Of the 147 proteins, 69 showed a significant difference between cases and controls (p < 3.4 × 10-4). Three clusters were formed by 108 highly correlated proteins that replicated in both cohorts, making it difficult to determine which proteins have a true causal effect on severe COVID-19. Six proteins showed sex differences in levels over time, of which 3 were also associated with severe COVID-19: CCL26, IL1RL2, and IL3RA, providing insights to better understand the marked differences in outcomes by sex.

CONCLUSIONS

Severe COVID-19 is associated with large changes in 69 immune-related proteins. Further, five proteins were associated with sex differences in outcomes. These results provide direct insights into immune-related proteins that are strongly influenced by severe COVID-19 infection.

World Society for Virology first international conference: Tackling global virus epidemics

This communication summarizes the presentations given at the 1st international conference of the World Society for Virology (WSV) held virtually during 16-18 June 2021, under the theme of tackling global viral epidemics. The purpose of this biennial meeting is to foster international collaborations and address important viral epidemics in different hosts. The first day included two sessions exclusively on SARS-CoV-2 and COVID-19. The other two days included one plenary and three parallel sessions each. Last not least, 16 sessions covered 140 on-demand submitted talks. In total, 270 scientists from 49 countries attended the meeting, including 40 invited keynote speakers.

Severe COVID-19 and long COVID in a 31-year-old woman with incontinentia pigmenti: A case report

Incontinentia pigmenti is a rare genetic disease affecting the skin, microvasculature, and central nervous system, in which a hyperactive inflammatory response is observed. Due to the inflammatory phase of COVID-19 and associated cytokine storm, infection with SARS-CoV-2 in individuals with incontinentia pigmenti is a concern. Furthermore, type I interferon autoantibodies are found in life-threatening COVID-19 pneumonia and in 25% of individuals with incontinentia pigmenti. The present case report describes a 31-year-old Caucasian woman with incontinentia pigmenti and severe COVID-19. She was hospitalized for oxygen therapy, intravenous antibiotics, and corticosteroids. Eight months later, she is still symptomatic. To our knowledge, she is the first reported case of long COVID in incontinentia pigmenti. Increased autoimmunity may be implicated in both incontinentia pigmenti and long COVID. Pending evidence-based guidelines, COVID-protective measures including vaccination should be recommended to all patients with incontinentia pigmenti. Specific interferon therapy may be considered along with usual COVID treatment.

Integrated immunovirological profiling validates plasma SARS-CoV-2 RNA as an early predictor of COVID-19 mortality

Despite advances in COVID-19 management, identifying patients evolving toward death remains challenging. To identify early predictors of mortality within 60 days of symptom onset (DSO), we performed immunovirological assessments on plasma from 279 individuals. On samples collected at DSO11 in a discovery cohort, high severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral RNA (vRNA), low receptor binding domain–specific immunoglobulin G and antibody-dependent cellular cytotoxicity, and elevated cytokines and tissue injury markers were strongly associated with mortality, including in patients on mechanical ventilation. A three-variable model of vRNA, with predefined adjustment by age and sex, robustly identified patients with fatal outcome (adjusted hazard ratio for log-transformed vRNA = 3.5). This model remained robust in independent validation and confirmation cohorts. Since plasma vRNA’s predictive accuracy was maintained at earlier time points, its quantitation can help us understand disease heterogeneity and identify patients who may benefit from new therapies.