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Background: Severe COVID-19 and obesity are characterized by higher inflammation. We aimed to examine early inflammatory patterns in people with (Ob) and without (NOb) obesity and COVID-19 and how they relate to COVID-19 disease severity Methods: Ob (BMI >30 Kg/m2) and NOb with COVID-19 matched for age, sex and WHO disease severity provided blood early after diagnosis. Immunoassays measured 57 plasma biomarkers reflecting innate immune and endothelial activation, systemic inflammation, coagulation, metabolism and microbial translocation (Fig 1). Between-group differences were assessed by Mann- Whitney. Associations between subsequent maximal COVID-19 severity (mild vs moderate/severe/critical) and biomarkers were explored by logistic regression adjusted for age, sex, hypertension (HTN) and diabetes (DM). Data are median pg/mL [IQR] or n [%] unless stated Results: Of 100 subjects (50 Ob and 50 Nob) presenting between April 2020 and March 2021, characteristics (Ob vs Nob) included: age 65 [23-91] vs 65 [21-95];female sex 27 (48%) vs 28 (56%);BMI 33.7 [30.0-71.8] vs 23.3 [15.3-25.9];disease severity mild 22 [48%] vs 23 [46%], moderate 15 [30%] vs 13 [26%], severe 6 [12%] vs 7 [14%];HTN 30 (60%) vs 17 (34%);DM 19 [38%] vs 6 [12%];days from symptom onset 7 [2-17] vs 8 [1-15];vaccinated 3 (6%) vs 0 (0%). Compared to NOb, Ob had higher IFN-alpha (1.8 [0.6;11] vs 0.9 [0.1;4.7]), CRP (10 mAU/mL [9.6;10.2] vs 9.7 [7.2;10]), IL-1RA (197 [122;399] vs 138 [88;253]), IL-4 (288 AU/mL [161;424] vs 205 [82;333]), vWF (252 [166;383] vs 163 [96;318]), Zonulin (114 ng/mL [77;131] vs 57 [18;106]), Resistin (956 [569;1153] vs 727 [712;1525]), Leptin (3482 [1513;5738] vs 848 [249;2114]), and lower Adiponectin (1.12 mg/L [0.09;1.5] vs 1.5 [1.18;1.93]), all p< 0.05. In both groups higher, proinflammatory IL-18 and lower levels of antiinflammatory CCL22 and IL-5 were associated with higher odds of disease severity, and lower E-selectin with higher disease severity only in Ob. However, in NOb higher type 3 interferons (IL-28A), macrophage activation (sCD163, CCL3) and vascular inflammation markers (ICAM-1, VCAM-1), along with higher S100B, GM-CSF and leptin were also associated with disease severity, a pattern not observed in Ob (Fig 1) Conclusion(s): Although Ob had higher overall levels of inflammation than NOb, few biomarkers predicted subsequent COVID-19 severity in Ob. These differential inflammatory patterns suggest dysregulated immune responses in Ob with COVID-19. (Figure Presented).
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Background: Better understanding of host inflammatory changes that precede development of severe COVID-19 could improve delivery of available antiviral and immunomodulatory therapies, and provide insights for the development of new therapies. Method(s): In plasma from individuals with COVID-19, sampled <=10 days from symptom onset from the All-Ireland Infectious Diseases Cohort study, we measured 61 biomarkers, including markers of innate immune and T cell activation, coagulation, tissue repair, lung injury, and immune regulation. We used principal component analysis (PCA) and k-means clustering to derive biomarker clusters, and univariate and multivariate ordinal logistic regression to explore association between cluster membership and maximal disease severity, adjusting for risk factors for severe COVID-19, including age, sex, ethnicity, BMI, hypertension and diabetes. Result(s): From March 2020-April 2021, we included 312 individuals, (median (IQR) age 62 (48-77) years, 7 (4-9) days from symptom onset, 54% male) in the analysis. PCA and clustering derived 4 clusters. Compared to cluster 1, clusters 2-4 were significantly older and of higher BMI but there were no significant differences in sex or ethnicity. Cluster 1 had low levels of inflammation, cluster 2 had higher levels of markers of tissue repair and endothelial activation (EGF, VEGF, PDGF, TGFalpha, serpin E1 and p-selectin). Cluster 3 and 4 were both characterised by higher overall inflammation, but compared to cluster 4, cluster 3 had downregulation of growth factors, markers of endothelial activation, and immune regulation (IL10, PDL1), but higher alveolar epithelial injury markers (RAGE, ST2). In univariate analysis, compared to cluster 1, cluster 3 had the highest odds of severe disease (OR (95% CI) 9.02 (4.62-18.31), followed by cluster 4: 5.59 (2.75-11.72) then cluster 2: 4.5 (2.38-8.81), all p < 0.05). Cluster 3 remained most strongly associated with severe disease in fully adjusted analyses;cluster 3: OR(95% CI) 5.99 (2.69-13.35), cluster 2: 3.14 (1.54-6.42), cluster 4: 3.13 (1.36-7.19), all p< 0.05). Conclusion(s): Distinct early inflammatory profiles predicted maximal disease severity independent of known risk factors for severe COVID-19. A cluster characterised by downregulation of growth factor and endothelial markers and early evidence of alveolar injury was associated with highest risk of developing severe COVID19. Whether this reflects a dysregulated inflammatory response that could improve targeted treatment requires further study. Heatmap of biomarker derived clusters and forest plot of association between clusters and disease severity. A: Heatmap demonstrating differences in biomarkers between clusters B: Forest plot demonstrating odds ratio of specific clusters for progressing to moderate or severe disease (reference Cluster 1), calculated using ordinal logistic regression. Odds ratio (95% CI) presented as unadjusted and fully adjusted (for age, sex, ethnicity, BMI, hypertension, diabetes, immunosuppression, smoking and baseline anticoagulant use). Maximal disease severity graded per the WHO severity scale.
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Background. Which components of the immune response to SARS-CoV-2 vaccination best protect against subsequent infection remains unclear. We explored SARS-CoV-2 specific antibody and B-cell responses post 3rd dose vaccine and their relationship to subsequent SARS-CoV-2 infection. Methods. In a multicentre prospective cohort, adult subjects provided samples before and 14 days (d14) post 3rd dose vaccine with Pfizer-BioNTech 162b2. At 18-22 weeks post vaccine, subjects self-reported SARS-CoV-2 infection (confirmed by PCR or antigen test). We used electrochemiluminescence assays to quantify antibodies to SARS-CoV-2 spike subunit 1 (S1), subunit 2 (S2) and receptor-binding domain (RBD) in plasma (reported inWHOIU/mL). In a subset of subjects, we assessed SARS-CoV-2 specific differentiated B-cell (plasma cell) and memory B-cell responses from peripheral blood mononuclear cells. Unstimulated plasma cells, and memory B cells stimulated with R848 and IL2, were seeded on plates coated with RBD or full Spike antigen and antigen-specific responses measured by ELISpot (Mabtech ELISpot, Sweden). We compared between group differences by Wilcoxon signed rank or Mann-Whitney tests. Data are median [IQR] unless specified. Results. Of 133 subjects (age 43 [32-50], 81.2% female (table 1), 77 subjects in the B-cell subgroup (table 2)), 47 (35.3%) reported SARS-CoV-2 infection post 3rd vaccine. Antibody titres, plasma cell and memory B-cell responses all increased significantly at d14 post 3rd vaccine (Table 1 & 2, all P< 0.001). Although d14 antibody titres did not differ in those with and without subsequent infection (table 1), those reporting subsequent infection had significantly lower d14 RBD-specific plasma cells and a lower proportion of RBD-specific memory B-cells (Figure 1a-b, both P< 0.05). Similar results were observed with full-spike-specific memory B-cell responses (Figure 1d). The differences persisted when the non-infected group was restricted only to those reporting a confirmed close contact (n=26). Conclusion. Infection following 3rd dose vaccine is associated with lower d14 circulating and memory B cell responses, but not antibody titres, suggesting B-cell responses better predict protection against subsequent SARS-CoV-2 infection.
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Background. Quantifying neutralising capacity of circulating SARS-COV-2 antibodies is critical in evaluating protective humoral immune responses generated postinfection/post-vaccination. Here we describe a novel medium-throughput flow cytometry based micro-neutralisation assay to evaluate Neutralising Antibody (NAb) responses against live SARS-CoV-2 Wild Type (D641G) and Variants of Concern (VoC) in convalescent/vaccinated populations. Methods. Micro-Neutralisation assay (Micro-NT) was performed in 96-well plates using clinical isolate 2019-nCoV/Italy-INMI1, D641G (SARS-CoV-2/human/ IRL/AIIDV1446/2020) and/or VOCs Beta (SARS-CoV-2/human/IRL/AIIDV1752/ 2021) and Omicron (SARS-Cov-2/human/IRL/AIIDV2326/2021). Plasma samples (All Ireland Infectious Diseases (AIID) Cohort) were serially diluted (8 points, halflog) from 1/20 and pre-incubated with SARS-CoV-2 (1h, 37degreeC). Virus-plasma mixture were added onto VERO E6/VERO-E6 TMPRSS2 cells for 18h. Percentage infected cells was analysed by automated flow cytometry following trypsinisation,fixation and SARS-CoV-2 Nucleoprotein intracellular staining. Half-maximal Neutralisation Titres (NT50) was determined using four-parameter logistic regression. Our assay was compared to Plaque Reduction Neutralisation Test (PRNT) and validated against WHO anti-SARS-CoV-2 Immunoglobulin Standards. Results. Using WHO Standards with low, medium or high anti-SARS-CoV-2 IgG, both Micro-NT and PRNT achieved comparable NT50 values (Table 1). Micro-NT was found to be highly reproducible (inter-assay CV of 11.39%). Screening 190 convalescent samples and 11 COVID-19 naive controls (AIID cohort) we achieved an assay sensitivity of 90% and specificity of 81%. We demonstrated that Micro-NT has broad dynamic range differentiating NT50s < 1/20 to > 1/5000 (Figure 1). We could also characterise immune-escape VoC, observing up to 10-fold reduction in NT50 against Beta (Figure 2). Table 1: NT50s of Low, Medium and High Titre Anti-SARS-CoV-2 IgG Standards measured against Live SARS-CoV-2 using PRNT and Micro-NT Neutralising Capacity of low, medium and high-titre anti-SARS-CoV-2 IgG (WHO, International Standards) against live SARS-CoV-2 (2019-nCoV/Italy-INMI1) measured using PRNT and Micro-NT Assays on Vero E6 cells, as well as the potency of NAbs in each sample in International Units (IU/ml) as determined by the WHO. Figure 1: Dynamic Range of Micro-NT Micro-NT has a broad Dynamic Range, distinguishing low (A), medium (B) and high (C) neutralising plasma samples against live SARS-CoV-2 (2019-nCoV/Italy-INMI1) from a cohort of COVID-19 convalescent individuals (AIID cohort), as well as negative samples from COVID-19 naive samples (D). Graphs show 3 representative samples of each NT50 range. (E) shows the population distribution of 190 Convalescent plasma samples as measured by Micro-NT on Vero E6 cells. Figure 2: Reduced Neutralisation Capacities measured against SARS-CoV-2 VoC using Micro-NT Low (A), Medium (B) and High (C) anti-SARS-CoV-2 IgG (WHO Standards) show different neutralising capacities against WT (D614G) SARS-CoV-2 and variants Beta and Omicron, measured using Micro-NT on Vero-E6-TMPRSS2 cells. Conclusion. Our flow-cytometry-based Micro-NT is a robust and reliable assay to quantify NAb titres, an important evaluation endpoint in clinical trials. It has higher throughput (96 well format versus 12 well) and reduced infection time (18h vs 48-96h) compared to the gold standard PRNT.
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Background. A wide array of assays to detect the serologic response to SARS-CoV-2 have been developed since the emergence of the pandemic. The majority of these are either qualitative or semi-quantitative, detect antibodies against one antigenic target, and are not adaptable to new antigens. Methods. We developed a new, multiplex immunoassay to detect antibodies against the receptor binding domain, S1 and S2 spike subunits and nucleocapsid (N) antigens of SARS-CoV-2 (the CEPHR SARS-CoV-2 Serology Assay). This assay uses electrochemiluminescence technology which allows for a broad dynamic range, and a linker format which allows for the addition of new antigenic targets. We tested this assay on a series of biobanked samples and validated its performance against the Abbott SARS-CoV-2 IgG and Abbott SARS-CoV-2 IgG II assays, and the MesoScale Diagnostics V-PLEX SARS-CoV-2 Panel 2 Kit. Results. Participant demographics are shown in Table 1. The mean (standard deviation (SD)) intra-assay (within plate) coefficient of variation (CV) of 80 plasma samples run on the same plate was 3.9% (2.9) for N, 3.8% (6.2) for RBD, 3.8% (5.9) for S1 and 3.9% (5.3) for S2. The mean (SD) inter-assay CV derived from 5 samples run across 3 days by two different operators was 11% (6.5) for N, 13% (5.7) for RBD, 14% (8.9) for S1 and 13% (5.1) for S2. In the convalescent group (n=193), overall sensitivity for each assay was;RBD 82% (95% CI 76-87), S1 86% (81-91%), S2 88% (83 - 92%) and N 72% (64 - 78%). Sensitivity improved when analysis included only individuals who were sampled more than 14 days from onset of symptoms (n=166), RBD 87% (81 - 95%), S1 91% (85 - 95%), S2 91% (85 - 95%) but not for the N-target (73% (66-80%)). In vaccinated individuals (n = 58), 100% (94-100%) had both detectable RBD and S1 antibodies. Overall specificity was 96% (87-99%) for RBD, 90% (78-97%) for S1, 94% (84-99%) for S2 and 90% (78-97%) for N. There was excellent correlation between the Abbott IgG II and both CEPHR anti-RBD IgG (rho 0.91) and CEPHR anti-S1 IgG (rho 0.9, both p < 0.001, Figure 1.) and the V-PLEX full spike and both CEPHR RBD IgG (rho 0.83) and S1 IgG (rho 0.82, both p < 0.001, Figure 4). Conclusion. The CEPHR SARS-CoV-2 Serology Assay is a robust, customisable, multiplex serologic assay for the detection of several different IgG specific to SARS-CoV-2.
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Background. Long COVID is a heterogenous condition. We previously demonstrated distinct phenotypes of long COVID, but the impact of later waves caused by SARS-CoV-2 variants on long COVID presentations has not been described. Methods. We selected individuals with ongoing symptoms > 4 weeks from PCR-confirmed COVID-19 in a multicentre, prospective cohort study. We used multiple correspondence analysis and hierarchical clustering on self-reported symptoms to identify symptom clusters, in individuals recruited during two periods;cohort 1 from March 2020 to April 2021, and cohort 2 from April 2021 to March 2022. We explored differences in symptoms by mapping acute infection to one of four COVID-19 waves in Ireland (table 1) as well as vaccination status, and used Chi2 test to compare symptoms frequencies. Results. Demographics are shown in Table 2. Cluster analysis of each cohort demonstrated 3 distinct clusters in both cohorts, which shared similar clinical characteristics;a musculoskeletal/pain symptom cluster, a cardiorespiratory cluster and a third less symptomatic cluster (Figure 1). While symptoms within clusters were similar across both periods, in the cardiorespiratory cluster, the frequency of palpitations decreased (56% vs 16%) and cough increased (14% vs 45%) between reporting periods (both P< 0.01). Furthermore, a greater proportion of palpitations were reported in those with COVID-19 from waves 1 and 2 (35% and 28%) compared to 3 and 4 (both 12%, P< 0.001), and a greater proportion of chest pain in waves 1, 2 and 4 compared to wave 3. There were no differences in other symptoms (Table 3). Additionally there were significantly less palpitations reported in those vaccinated at the time of review (17% vs 31% P=0.002), but not chest pain (30% vs 39% P=0.13). In multivariate analysis, infection in wave 3 and 4 but not vaccination status remained significantly associated with lower reported palpitations (OR (95% CI) 0.28 (0.13-0.97) and 0.5 (0.06-0.87) for waves 3 and 4, both P< 0.05), and wave 3 infection remained independently associated with lower reported chest pain (OR 0.3 (0.25-0.7)). Conclusion. Three symptom clusters define long COVID across the two cohorts, but characteristics of the cardiorespiratory phenotype have evolved over time with evolution of SARS-CoV-2 variants. (Table Presented).
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Background: Although presence of SARS-CoV-2 neutralising antibodies can provide protection against development of COVID-19, how reflective circulating anti-SARS-CoV-2 antibody levels are of underlying neutralising capacity, and whether a threshold exists to predict sufficient neutralising capacity remains unclear. Methods: In plasma from individuals with PCR-confirmed COVID-19 recruited to the All Ireland Infectious Diseases Cohort Study, we measured IgG concentrations against RBD, Spike protein sub-unit 1 and 2 (S1, S2) and Nucleocapsid (NC) using multiplex electrochemiluminescence (normalised to World Health Organisation reference serum as IU/mL). Neutralising capacity was measured against live SARS-CoV-2 virus (clinical isolate 2019-nCoV/Italy-INMI1) by determining the maximum plasma dilution required to maintain 50% inhibition of infection of Vero E6 cells (50% Neutralisation Titre (NT50)), by flow cytometry-based micro-neutralisation assay. Given that the Beta SARS-CoV-2 variant of concern (VOC) reduces neutralising activity up to six fold, we estimated a NT50 of 1:1000 against wild type SARS-CoV-2 would maintain neutralising activity against VOC. We used Spearman correlation and linear regression to model relationships between NT50 and IgG concentrations. Data are presented as median (IQR) unless specified. Results: In 190 individuals (age 50 (40-64) years, 55% female, time from symptom onset 98 (35-179) days), NT50 most highly correlated with anti-RBD IgG (Rho 0.81 p<0.001, Fig 1a) compared with other IgG classes (S1;Rho 0.8, S2;0.73, NC;0.72, all p<0.001). Median RBD titre was 246 (71-662) but trended lower over time, with a median of 319 (61-1012) IU/ml at 0-90 days, 244 (86-523) IU/ml at 90-180 days and 157 (80-364) IU/ml at >180 days post symptom onset respectively (p=0.08, Fig 1b). RBD IgG titres of 476 IU/ml corresponded to a NT50 of 1:1000. Overall, RBD ≥476 IU/ml predicted NT50 ≥1:1000 with a sensitivity of 77 (95% CI 65-87)% and specificity 89 (95% CI 82-93)%. This improved in an analysis restricted to convalescent samples (>30 days post symptom onset, n=148), with a sensitivity 88% (95% CI 74-96%) and specificity 90% (95%CI 82-95%) respectively. Conclusion: In convalescent plasma, RBD IgG titres ≥476IU/mL is sensitive and specific for predicting robust underlying neutralising capacity. Further research is required to validate these findings in other cohorts and confirm these thresholds in post-vaccinated individuals.
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Background: Coronavirus disease 2019 (COVID19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has varied clinical presentations from mild subclinical to severe disease with high mortality. Our aim was to determine whether examining immune-related gene expression early in infection could predict progression to severe disease. Methods: In subjects of the All Ireland Infectious Diseases Cohort study, we analysed expression of 579 genes with the NanoString nCounter Immunology panel in peripheral blood mononuclear cells in those with confirmed SARS-CoV-2 infection collected within 5 days of symptom onset and matched SARS-CoV-2 negative controls with respiratory infection. Subsequent maximum COVID19 disease severity was classified as mild or severe. Read counts were normalized using panel housekeeping genes. Expression changes in severity groups were estimated against control baseline. Results: Between April and July of 2020, we recruited 120 subjects, 62 with COVID19 and 58 controls, with average age 59 y.o. (IQR 34-88), 66% males and 69% Caucasian ethnicity. Maximal disease severity was used to separate COVID19 cases into mild (n=31) and severe (n=31). We identified 20 significantly deregulated genes between those with COVID19 and controls (;log2 fold;>0.5, p<0.05, Benjamin-Yekutieli p-adjustment). Function of 12 of these genes related to cytokine signaling, 9 upregulated genes to type I interferon signaling (MX1, IRF7, IFITM1, IFI35, STAT2, IRF4, PML, BST2, STAT1), while 7 downregulated genes mapped to innate immune function (IRF7, ICAM2, SERPING1, IFI16, BST2, FCER1A, PTK2). Expression in the severe group showed downregulation of FCER1A (innate immunity regulation), IL1B and TNF (inflammatory cytokines), and PTGS2 (inflammatory mediator) and greater upregulation of TNFSF4 (cytokine signaling) and PTK2 (innate immunity). Mild cases presented higher upregulation of IFIT2 (type I interferon signaling). Conclusion: Observed early downregulation of regulators and mediators of inflammation in those who developed severe COVID19, suggested dysregulation of inflammation. Specifically, IFIT2 upregulation in mild cases and FCER1A downregulation in severe cases, points to early differences in host responses centered on deregulation of the interferon and inflammation responses. Whether these patterns reflect delayed interferon involvement in pathways to control the infection and contribute to pathological inflammation and cytokine storms observed in severe COVID19 requires further research.
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Since the coronavirus pandemic began in the United States in early 2020, employers, legal practitioners, federal and state agencies, and the courts have wrangled with whether and in what circumstances workers impacted by COVID-19 (COVID) may have legal protections at work. Because the virus is novel, case law and other legal authorities are scarce. However, these questions are likely to persist well into the future as the virus continues to evolve and the pandemic rages on. This Article explores whether and in what circumstances courts in the Tenth Circuit are likely to treat COVID as a “disability” under the Americans with Disabilities Act of 1990 (ADA),1 thereby affording workers impacted by the illness some legal protections at work. Analogizing to judicial treatment of Human Immunodeficincy Virus (HIV) infections in the employment context, this Article argues that, despite the often temporary nature of COVID illness, there are some circumstances in which courts are likely to deem the illness a disability under the ADA. This Article also explores whether workers who are not ill themselves, but who are associated with a person suffering from COVID, may enjoy some legal protections at work. Finally, this Article examines whether employers may be prohibited from retaliating against workers who engage in protected activity for COVID-related issues. © 2022 Denver University Law Review. All rights reserved.
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Background: Although reports suggest that most individuals with COVID-19 infection develop detectable antibodies post infection, the kinetics, durability, and relative differences between IgM and IgG responses remain poorly understood beyond the first few weeks after symptom onset. Methods: Within a large, well-phenotyped, diverse, prospective cohort of subjects with and without SARS-CoV-2 PCR-confirmed infection and historical controls derived from cohorts with high prevalence of viral coinfections and samples taken during prior flu seasons, we measured SARS-CoV-2 serological responses (both IgG and IgM) using three commercially available assays. We calculated sensitivity and specificity, relationship with disease severity and mapped the kinetics of antibody seropositivity and antibody levels over time using generalised additive models. Results: We analysed 1,001 samples (327 confirmed SARS-CoV-2, of whom 30% developed severe disease) from 752 subjects spanning a period of 90 days from symptom onset. Overall sensitivity was lower (44.1-47.1%) early (<10 days) after symptom onset but increased to >80% after 10 days. IgM positivity increased earlier than IgG-targeted assay but positivity peaked between day 32 and 38 post onset of symptoms and declined thereafter, a dynamic that was confirmed when antibody levels were analysed and was more rapid with IgM. Early (<10 days) IgM but not IgG levels were significantly higher in those who subsequently developed severe disease (signal / cut-off 4.20 (0.75-17.93) versus 1.07 (0.21-5.46), P=0.048). Conclusion: This study suggests that post-infectious antibody responses in those with confirmed COVID-19 infection begin to decline relatively early post infection and suggests a potential role for higher IgM levels early in infection predicting subsequent disease severity.