Multiple early factors anticipate post-acute COVID-19 sequelae.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged.

BACKGROUND: For accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded coverage, and 32% have to pay out-of pocket. Those with no insurance or public coverage find dental care more unaffordable compared to those with private insurance. To support the development of more comprehensive publicly funded dental care programs, it is important to understand the socio-demographic attributes of all those, who find dental care unaffordable. METHODS: This study is a secondary analysis of the data collected from Ontarians during the latest available cycle of the Canadian Community Health Survey (2017-18), a cross-sectional survey that collects information on health status, health care utilization, and health determinants for the Canadian population. First, bivariate analysis was conducted to determine the characteristics of Ontarians who lack dental insurance. Afterwards, we employed machine learning (ML) to analyze data and identify risk indicators for not having private dental insurance. Specifically, we trained several supervised ML models and utilized Shapley additive explanations (SHAP) to determine the relative feature importance for not having private dental insurance from the best ML model [the gradient boosting (GBM)]. RESULTS: Approximately one-third of Ontarians do not have private insurance coverage for dental care. Individuals with an income below $20,000, those unemployed or working part-time, seniors aged above 70, and those unable to afford to have their own housing are more at risk of not having private dental insurance, leading to financial barriers in accessing dental care. CONCLUSION: In the future, government-funded programs can incorporate these identified risk indicators when determining eligible populations for publicly funded dental programs. Understanding these attributes is critical for developing targeted and effective interventions, ensuring equitable access to dental care for Canadians.

Monkeypox Virus Detection in Different Clinical Specimen Types.

A global monkeypox outbreak began in May 2022. Limited data exist on specimen type performance in associated molecular diagnostics. Consequently, a diverse range of specimen sources were collected in the initial weeks of the outbreak in Ontario, Canada. Our clinical evaluation identified skin lesions as the optimal diagnostic specimen source.

Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak.

The 2014-2015 highly pathogenic avian influenza (HPAI) H5NX outbreak represents the largest and most expensive HPAI outbreak in the United States to date. Despite extensive traditional and molecular epidemiological studies, factors associated with the spread of HPAI among midwestern poultry premises remain unclear. To better understand the dynamics of this outbreak, 182 full genome HPAI H5N2 sequences isolated from commercial layer chicken and turkey production premises were analyzed using evolutionary models able to accommodate epidemiological and geographic information. Epidemiological compartmental models embedded in a phylogenetic framework provided evidence that poultry type acted as a barrier to the transmission of virus among midwestern poultry farms. Furthermore, after initial introduction, the propagation of HPAI cases was self-sustainable within the commercial poultry industries. Discrete trait diffusion models indicated that within state viral transitions occurred more frequently than inter-state transitions. Distance and sample size were very strongly supported as associated with viral transition between county groups (Bayes Factor > 30.0). Together these findings indicate that the different types of midwestern poultry industries were not a single homogenous population, but rather, the outbreak was shaped by poultry industries and geographic factors.

The human immune system recognizes neopeptides derived from mitochondrial DNA deletions.

Mutations in mitochondrial (mt) DNA accumulate with age and can result in the generation of neopeptides. Immune surveillance of such neopeptides may allow suboptimal mitochondria to be eliminated, thereby avoiding mt-related diseases, but may also contribute to autoimmunity in susceptible individuals. To date, the direct recognition of neo-mtpeptides by the adaptive immune system has not been demonstrated. In this study we used bioinformatics approaches to predict MHC binding of neopeptides identified from known deletions in mtDNA. Six such peptides were confirmed experimentally to bind to HLA-A*02. Pre-existing human CD4(+) and CD8(+) T cells from healthy donors were shown to recognize and respond to these neopeptides. One remarkably promiscuous immunodominant peptide (P9) could be presented by diverse MHC molecules to CD4(+) and/or CD8(+) T cells from 75% of the healthy donors tested. The common soil microbe, Bacillus pumilus, encodes a 9-mer that differs by one amino acid from P9. Similarly, the ATP synthase F0 subunit 6 from normal human mitochondria encodes a 9-mer with a single amino acid difference from P9 with 89% homology to P9. T cells expanded from human PBMCs using the B. pumilus or self-mt peptide bound to P9/HLA-A2 tetramers, arguing for cross-reactivity between T cells with specificity for self and foreign homologs of the altered mt peptide. These findings provide proof of principal that the immune system can recognize peptides arising from spontaneous somatic mutations and that such responses might be primed by foreign peptides and/or be cross-reactive with self.

Human metapneumovirus prevalence and molecular epidemiology in respiratory outbreaks in Ontario, Canada.

Human metapneumovirus (hMPV) has been identified previously as a cause of respiratory outbreaks in adults, including the elderly. The objective of this study was to document respiratory outbreaks that were caused by hMPV in Ontario, Canada and to identify the various circulating genotypes during April 2009-February 2012. The majority of the outbreaks that were part of this study were in adults (>65 years). Total nucleic acid extraction was done on 123 residual anonymized clinical specimens from 51 different respiratory outbreaks. Specimens were subjected to PCR amplification and Sanger sequencing targeting the F and G genes of hMPV. Phylogenetic analysis was performed to identify genotypes. HMPV accounted for 195 (8.5%) of 2,292 respiratory outbreaks. Genotype A2b was most prevalent, detected in 28 (54.9%) of 51 typed hMPV-positive outbreaks. The genotype A2b2 that was described recently was also identified. In earlier reports, subtype A1 was reported in Canada which was absent in the specimens typed in this study. This shift in genotype may be significant in terms of disease severity, and for any future vaccine considerations. Regular testing for hMPV should be done as part of outbreak investigation.

Temporal changes in respiratory adenovirus serotypes circulating in the greater Toronto area, Ontario, during December 2008 to April 2010.

BACKGROUND: Certain adenovirus serotypes cause severe infections, especially in children. It is important to monitor temporal changes in serotypes causing clinical disease. The objective of this study was to document circulating respiratory adenovirus serotypes by sequencing adenovirus culture isolates from the Greater Toronto Area, Ontario, during December 2008 to April 2010. METHODS: Nucleic acid extraction was performed on 90 respiratory tract adenovirus culture isolates. PCR amplification was conducted with primers targeting the adenovirus hexon gene hypervariable region 7. Sanger sequencing and phylogenetic analyses were performed to determine serotype identities. RESULTS: Among 90 clinical respiratory isolates sequenced, eight different serotypes were identified. Serotype 3 (34, 38%), serotype 2 (30, 30%), and serotype 1 (14, 16%) isolates were most common; serotypes 5, 6, 11, 17 and 21 were also observed. Seventeen (50%) of the 34 HAdV-3 isolates were identified between December 2008 and February 2009, while none were identified from December 2009 to February 2010. Between December 2008 and April 2009, the two most common serotypes were HAdV-3 and HAdV-2, detected in 18 (53%) and 8 (24%) of the 34 cultures isolates, respectively. However, from December 2009 to April 2010, there was an increase in HAdV-2, which became the most prevalent serotype, detected in 10 (50%) of the 20 isolates identified (p = 0.05). CONCLUSIONS: There was a gradual shift in prevailing adenovirus serotypes during the 17 month study period, from predominantly HAdV-3 to HAdV-2. If an adenovirus vaccine were to be broadly implemented, multiple serotypes should be included.

Developing a temperature-driven map of the basic reproductive number of the emerging tick vector of Lyme disease Ixodes scapularis in Canada.

A mechanistic model of the tick vector of Lyme disease, Ixodes scapularis, was adapted to a deterministic structure. Using temperature normals smoothed by Fourier analysis to generate seasonal temperature-driven development rates and host biting rates, and a next generation matrix approach, the model was used to obtain values for the basic reproduction number (R(0)) for I. scapularis at locations in southern Canada where the tick is established and emerging. The R(0) at Long Point, Point Pelee and Chatham sites where I. scapularis are established, was estimated at 1.5, 3.19 and 3.65, respectively. The threshold temperature conditions for tick population survival (R(0)=1) were shown to be the same as those identified using the mechanistic model (2800-3100 cumulative annual degree days >0°C), and a map of R(0) for I. scapularis, the first such map for an arthropod vector, was drawn for Canada east of the Rocky Mountains. This map supports current risk assessments for Lyme disease risk emergence in Canada. Sensitivity analysis identified host abundance, tick development rates and summer temperatures as highly influential variables in the model, which is consistent with our current knowledge of the biology of this tick. The development of a deterministic model for I. scapularis that is capable of providing values for R(0) is a key step in our evolving ability to develop tools for assessment of Lyme disease risk emergence and for development of public health policies on surveillance, prevention and control.

Influenza H3N2 variant viruses with pandemic potential: preventing catastrophe in remote and isolated Canadian communities.

OBJECTIVE: To evaluate the impact of age-specific cross-reactive antibody protection levels on the outcomes of a pandemic outbreak of new variants of H3N2 influenza A viruses (H3N2v). METHODS: We calibrated a previously validated agent-based model of human-to-human transmission of influenza viruses to project the outcomes of various protection levels in a remote and isolated Canadian community, when demographics are drawn from the Statistics Canada census data. We then compared the outcomes with a scenario in which demographic variables were shifted to resemble an urban structure. This comparative evaluation was conducted using in-silico computer simulations, where the epidemiological data were drawn from relevant estimates in published literature. RESULTS: Simulations, using estimates of transmissibility for the 2009 H1N1 pandemic strain in the study population, show that the epidemic size is primarily affected by the cross-reactive protection levels of young children. A lower number of secondary infections at the early stages of an outbreak does not necessarily correspond to a lower epidemic size. CONCLUSIONS: Demographic variables could play a significant role in determining the outcomes of an outbreak. The findings strongly suggest that, when an H3N2v-specific vaccine becomes available, children below the age of 17 should be prioritized for vaccination. This prioritization is essential in population settings with a low average age, including aboriginal communities in northern latitudes.

T cell memory to evolutionarily conserved and shared hemagglutinin epitopes of H1N1 viruses: a pilot scale study.

BACKGROUND: The 2009 pandemic influenza was milder than expected. Based on the apparent lack of pre-existing cross-protective antibodies to the A (H1N1)pdm09 strain, it was hypothesized that pre-existing CD4+ T cellular immunity provided the crucial immunity that led to an attenuation of disease severity. We carried out a pilot scale study by conducting in silico and in vitro T cellular assays in healthy population, to evaluate the pre-existing immunity to A (H1N1)pdm09 strain. METHODS: Large-scale epitope prediction analysis was done by examining the NCBI available (H1N1) HA proteins. NetMHCIIpan, an eptiope prediction tool was used to identify the putative and shared CD4+ T cell epitopes between seasonal H1N1 and A (H1N1)pdm09 strains. To identify the immunogenicity of these putative epitopes, human IFN-γ-ELISPOT assays were conducted using the peripheral blood mononuclear cells from fourteen healthy human donors. All donors were screened for the HLA-DRB1 alleles. RESULTS: Epitope-specific CD4+ T cellular memory responses (IFN-γ) were generated to highly conserved HA epitopes from majority of the donors (93%). Higher magnitude of the CD4+ T cell responses was observed in the older adults. The study identified two HA2 immunodominant CD4+ T cell epitopes, of which one was found to be novel. CONCLUSIONS: The current study provides a compelling evidence of HA epitope specific CD4+ T cellular memory towards A (H1N1)pdm09 strain. These well-characterized epitopes could recruit alternative immunological pathways to overcome the challenge of annual seasonal flu vaccine escape.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R 0 ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R 0 values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R 0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R 0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R 0 estimation and surveillance-based R 0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

Stabilised DNA secondary structures with increasing transcription localise hypermutable bases for somatic hypermutation in IGHV3-23.

Somatic hypermutation (SHM) mediated by activation-induced cytidine deaminase (AID) is a transcription-coupled mechanism most responsible for generating high affinity antibodies. An issue remaining enigmatic in SHM is how AID is preferentially targeted during transcription to hypermutable bases in its substrates (WRC motifs) on both DNA strands. AID targets only single stranded DNA. By modelling the dynamical behaviour of IGHV3-23 DNA, a commonly used human variable gene segment, we observed that hypermutable bases on the non-transcribed strand are paired whereas those on transcribed strand are mostly unpaired. Hypermutable bases (both paired and unpaired) are made accessible to AID in stabilised secondary structures formed with increasing transcription levels. This observation provides a rationale for the hypermutable bases on both the strands of DNA being targeted to a similar extent despite having differences in unpairedness. We propose that increasing transcription and RNAP II stalling resulting in the formation and stabilisation of stem-loop structures with AID hotspots in negatively supercoiled region can localise the hypermutable bases of both strands of DNA, to AID-mediated SHM.

The role of cellular immunity in influenza H1N1 population dynamics.

BACKGROUND: Pre-existing cellular immunity has been recognized as one of the key factors in determining the outcome of influenza infection by reducing the likelihood of clinical disease and mitigates illness. Whether, and to what extent, the effect of this self-protective mechanism can be captured in the population dynamics of an influenza epidemic has not been addressed. METHODS: We applied previous findings regarding T-cell cross-reactivity between the 2009 pandemic H1N1 strain and seasonal H1N1 strains to investigate the possible changes in the magnitude and peak time of the epidemic. Continuous Monte-Carlo Markov Chain (MCMC) model was employed to simulate the role of pre-existing immunity on the dynamical behavior of epidemic peak. RESULTS: From the MCMC model simulations, we observed that, as the size of subpopulation with partially effective pre-existing immunity increases, the mean magnitude of the epidemic peak decreases, while the mean time to reach the peak increases. However, the corresponding ranges of these variations are relatively small. CONCLUSIONS: Our study concludes that the effective role of pre-existing immunity in alleviating disease outcomes (e.g., hospitalization) of novel influenza virus remains largely undetectable in population dynamics of an epidemic. The model outcome suggests that rapid clinical investigations on T-cell assays remain crucial for determining the protection level conferred by pre-existing cellular responses in the face of an emerging influenza virus.

Angiotensin-Converting Enzyme (ACE) Inhibitors May Moderate COVID-19 Hyperinflammatory Response: An Observational Study with Deep Immunophenotyping.

Background: Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin-II receptor blockers (ARB), the most commonly prescribed antihypertensive medications, counter renin-angiotensin-aldosterone system (RAAS) activation via induction of angiotensin-converting enzyme 2 (ACE2) expression. Considering that ACE2 is the functional receptor for SARS-CoV-2 entry into host cells, the association of ACEi and ARB with COVID-19 outcomes needs thorough evaluation. Methods: We conducted retrospective analyses using both unmatched and propensity score (PS)-matched cohorts on electronic health records (EHRs) to assess the impact of RAAS inhibitors on the risk of receiving invasive mechanical ventilation (IMV) and 30-day mortality among hospitalized COVID-19 patients. Additionally, we investigated the immune cell gene expression profiles of hospitalized COVID-19 patients with prior use of antihypertensive treatments from an observational prospective cohort. Results: The retrospective analysis revealed that there was no increased risk associated with either ACEi or ARB use. In fact, the use of ACEi showed decreased risk for mortality. Survival analyses using PS-matched cohorts suggested no significant relationship between RAAS inhibitors with a hospital stay and in-hospital mortality compared to non-RAAS medications and patients not on antihypertensive medications. From the analysis of gene expression profiles, we observed a noticeable up-regulation in the expression of 1L1R2 (an anti-inflammatory receptor) and RETN (an immunosuppressive marker) genes in monocytes among prior users of ACE inhibitors. Conclusion: Overall, the findings do not support the discontinuation of ACEi or ARB treatment and suggest that ACEi may moderate the COVID-19 hyperinflammatory response.

Risk factors for severe COVID-19 differ by age for hospitalized adults.

Risk stratification for hospitalized adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments. This retrospective study analyzed data from 6906 hospitalized adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.81 (95% CI 0.71-0.91) and models for age ≥ 50 years reached AUROC 0.82 (95% CI 0.77-0.86). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients including age, BMI, vital signs, and laboratory results. In addition, for hospitalized patients, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.

Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19.

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

Altered spectrum of somatic hypermutation in common variable immunodeficiency disease characteristic of defective repair of mutations.

Pathogenic common variable immunodeficiency diseases (CVID) are genetic, usually inherited diseases for which a limited number of genetic defects have been implicated. As CVID presents with a wide range of clinical characteristics, there are likely diverse and for the most part unidentified genetic causes. In some individuals, defects in somatic hypermutation (SHM) have been suggested as the underlying cause of CVID. To address the mechanisms of SHM defects in CVID, we conducted a comprehensive mutational analysis of immunoglobulin heavy chain sequences from CVID patients. We identified several remarkably specific alterations in the spectra of SHM in comparison to healthy individuals. We provide evidence that some CVID cases are associated with defective repair of AID-induced mutations by the DNA mismatch repair (MMR) machinery. Our findings together with reports of increased chromosomal radiosensitivity and associated lymphoproliferative disorders amongst CVID patients, suggest that altered DNA damage repair may be a cause of CVID.

Angiotensin II receptor I auto-antibodies following SARS-CoV-2 infection.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with endothelial activation and coagulopathy, which may be related to pre-existing or infection-induced pro-thrombotic autoantibodies such as those targeting angiotensin II type I receptor (AT1R-Ab). METHODS: We compared prevalence and levels of AT1R-Ab in COVID-19 cases with mild or severe disease to age and sex matched negative controls utilizing multivariate logistic and quantile regression adjusted for comorbidities including hypertension, diabetes, and heart disease. RESULTS: There were trends toward increased prevalence (50% vs. 33%, p = 0.1) and level of AT1R-Ab (median 9.8 vs. 6.1 U/mL, p = 0.06) in all cases versus controls. When considered by COVID-19 disease severity, there was a trend toward increased prevalence of AT1R-Ab (55% vs. 31%, p = 0.07), as well as significantly higher AT1R-Ab levels (median 10.7 vs. 5.9 U/mL, p = 0.03) amongst individuals with mild COVID-19 versus matched controls. In contrast, the prevalence (42% vs. 37%, p = 0.9) and level (both medians 6.7 U/mL, p = 0.9) of AT1R-Ab amongst those with severe COVID-19 did not differ from matched controls. CONCLUSIONS: These findings support an association between COVID-19 and AT1R-Ab, emphasizing that vascular pathology may be present in individuals with mild COVID-19 as well as those with severe disease.

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories.

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 + and CD4 + T cells, and cytotoxic CD4 + T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.