Pre-COVID-19 Immunity to Common Cold Human Coronaviruses Induces a Recall-Type IgG Response to SARS-CoV-2 Antigens Without Cross-Neutralisation

The capacity of pre-existing immunity to human common coronaviruses (HCoV) to cross-protect against de novo COVID-19is yet unknown. In this work, we studied the sera of 175 COVID-19 patients, 76 healthy donors and 3 intravenous immunoglobulins (IVIG) batches. We found that most COVID-19 patients developed anti-SARS-CoV-2 IgG antibodies before IgM. Moreover, the capacity of their IgGs to react to beta-HCoV, was present in the early sera of most patients before the appearance of anti-SARS-CoV-2 IgG. This implied that a recall-type antibody response was generated. In comparison, the patients that mounted an anti-SARS-COV2 IgM response, prior to IgG responses had lower titres of anti-beta-HCoV IgG antibodies. This indicated that pre-existing immunity to beta-HCoV was conducive to the generation of memory type responses to SARS-COV-2. Finally, we also found that pre-COVID-19-era sera and IVIG cross-reacted with SARS-CoV-2 antigens without neutralising SARS-CoV-2 infectivity in vitro. Put together, these results indicate that whilst pre-existing immunity to HCoV is responsible for recall-type IgG responses to SARS-CoV-2, it does not lead to cross-protection against COVID-19.

Target specific serologic analysis of COVID-19 convalescent plasma

This study compared the performance of four serology assays for Coronavirus Disease 2019 (COVID-19) and investigated whether COVID-19 disease history correlates with assay performance. Samples were tested at Northshore using the Elecsys Anti-SARS-CoV-2 (Roche Diagnostics), Access SARS-CoV-2 IgG anti-RBD (Beckman Coulter), and LIAISON SARS-CoV-2 S1/S2 IgG (DiaSorin) as well as at Genalyte using Maverick Multi-Antigen Serology Panel. The study included one hundred clinical samples collected before December 2019 and ninety-seven samples collected from convalescent plasma donors originally diagnosed with COVID-19 by PCR. COVID-19 disease history was self-reported by the plasma donors. There was no difference in specificity between the assays tested. Clinical sensitivity of these four tests was 98% (Genalyte), 96% (Roche), 92% (DiaSorin), and 87% (Beckman). The only statistically significant differences in clinical sensitivity was between the Beckman assay and both Genalyte and Roche assays. Convalescent plasma donor characteristics and disease symptoms did not correlate with false negative results from the Beckman and DiaSorin assays. All four tests showed high specificity (100%) and varying sensitivities (89-98%). No correlations between disease history and serology results were observed. The Genalyte Multiplex assay showed as good or better sensitivity to three other previously validated assays with FDA Emergency Use Authorizations.

IgA dominates the early neutralizing antibody response to SARS-CoV-2

Humoral immune responses are typically characterized by primary IgM antibody responses followed by secondary antibody responses associated with immune memory and composed of IgG, IgA, and IgE. Here, we measured acute humoral responses to SARS-CoV-2, including the frequency of antibody-secreting cells and the presence of SARS-CoV-2-specific neutralizing antibodies in the serum, saliva, and bronchoalveolar fluid of 159 patients with COVID-19. Early SARS-CoV-2-specific humoral responses were dominated by IgA antibodies. Peripheral expansion of IgA plasmablasts with mucosal homing potential was detected shortly after the onset of symptoms and peaked during the third week of the disease. The virus-specific antibody responses included IgG, IgM, and IgA, but IgA contributed to virus neutralization to a greater extent compared with IgG. Specific IgA serum concentrations decreased notably 1 month after the onset of symptoms, but neutralizing IgA remained detectable in saliva for a longer time (days 49 to 73 post-symptoms). These results represent a critical observation given the emerging information as to the types of antibodies associated with optimal protection against reinfection and whether vaccine regimens should consider targeting a potent but potentially short-lived IgA response.

The potential value of rapid, cloud-enabled onsite testing for the diagnosis of rheumatoid arthritis in the United States

AIMS

Improvements in information technology have granted the recent development of rapid, cloud-enabled, onsite laboratory testing for rheumatoid arthritis (RA). This study aims to quantify the value to payers of such technologies.

MATERIALS AND METHODS

To calculate the value of rapid, cloud-enabled, onsite laboratory testing to diagnose RA relative to traditional, centralized laboratory testing, an Excel-based decision tree model was created that simulated potential cost-savings to payers who cover routine evaluations of RA patients in the US. First, a conceptual framework was created to identify the value components of rapid, cloud-enabled onsite testing. Second, value associated with patient time savings, savings on visit fees, change in treatment costs, and QALY improvements was measured, leveraging existing literature and information from an observational study. Lastly, these value components were combined to estimate the total incremental value accruing to payers per patient-year relative to centralized laboratory testing.

RESULTS

Rapid, cloud-enabled, onsite testing is estimated to save one office and 1.81 laboratory visits during the evaluation period for the average patient. Results from an observational study found that rapid, cloud-enabled testing increased the likelihood of completing diagnostic orders from 84.5% to 97%, resulting in an increased probability of early treatment (3.5 percentage points) with disease-modifying anti-rheumatic drugs among patients eligible for treatment. The combined total value was $5,648 per evaluated patient-year. This value is primarily attributed to health benefits of early treatment ($5,048), fewer visit payments ($459), and patient time savings due to fewer office ($216) and laboratory visits ($255).

LIMITATIONS AND CONCLUSIONS

Data on the impact of rapid, cloud-enabled, onsite testing on patient health, care delivery, and clinical decision-making is scarce. More robust real-world data would confirm the validity of our model. Rapid, cloud-enabled, onsite testing has the potential to generate significant value to payers.

Deletional rearrangement in the human T-cell receptor alpha-chain locus

The antigen-specific receptor on the surface of mature T lymphocytes is a heterodimer consisting of polypeptides termed alpha and beta. In the course of characterizing human T-cell tumors with an immature (CD4-, CD8-) surface phenotype, we detected a 2-kilobase alpha-related transcript. Analysis of cDNA clones corresponding to this transcript established that a genetic element (which we call TEA, for "T early alpha") located between the alpha-chain variable- and joining-region genes had been spliced to the alpha constant region. The TEA transcript is present early in thymocyte ontogeny, and its expression declines during T-cell maturation. More important, the TEA area functions as an active site for rearrangement within the alpha gene locus. Blot hybridization of restriction enzyme-digested DNA with a TEA probe revealed a narrowly limited pattern of rearrangement in polyclonal thymic DNA, surprisingly different from the pattern expected for the mature alpha gene with its complex diversity. These DNA blots also showed that TEA is generally present in the germ-line configuration in cells expressing the gamma delta heterodimeric receptor and is deleted from mature (alpha beta-expressing) T-lymphocyte tumors and lines. Moreover, the TEA transcript lacked a long open reading frame for protein but instead possessed multiple copies of a repetitive element resembling those utilized in the heavy-chain class switch of the immunoglobulin genes. The temporal nature of the rearrangements and expression detected by TEA suggests that this recombination could mediate a transition between immature (gamma delta-expressing) T cells and mature (alpha beta-expressing) T cells.