A key F27I substitution within HCDR1 facilitates the rapid maturation of P2C-1F11-like neutralizing antibodies in a SARS-CoV-2-infected donor.

Although thousands of anti-SARS-CoV-2 monoclonal neutralizing antibodies (nAbs) have been identified and well characterized, some crucial events in the development of these nAbs during viral infection remain unclear. Using deep sequencing, we explore the dynamics of antibody repertoire in a SARS-CoV-2-infected donor, from whom the potent and broad nAb P2C-1F11 (the parent version of Brii-196) was previously isolated. Further analysis shows a rapid clonal expansion of some SARS-CoV-2-specific antibodies in early infection. Longitudinal tracing of P2C-1F11 lineage antibodies reveals that these elite nAbs were rare. Using sequence alignment, structure modeling, and bioactivity analysis based on site-mutated assay, we demonstrate that a key substitution F27I in heavy chain contributes significantly to the maturation of P2C-1F11-like antibodies. Overall, our findings elucidate the developmental process and maturation pathway of P2C-1F11, providing some important information for the design of novel immunogens to elicit more potent nAbs against SARS-CoV-2 infection.

SCIGA: Software for large-scale, single-cell immunoglobulin repertoire analysis.

BACKGROUND: B-cell immunoglobulin repertoires with paired heavy and light chain can be determined by means of 10X single-cell V(D)J sequencing. Precise and quick analysis of 10X single-cell immunoglobulin repertoires remains a challenge owing to the high diversity of immunoglobulin repertoires and a lack of specialized software that can analyze such diverse data. FINDINGS: In this study, specialized software for 10X single-cell immunoglobulin repertoire analysis was developed. SCIGA (Single-Cell Immunoglobulin Repertoire Analysis) is an easy-to-use pipeline that performs read trimming, immunoglobulin sequence assembly and annotation, heavy and light chain pairing, statistical analysis, visualization, and multiple sample integration analysis, which is all achieved by using a 1-line command. Then SCIGA was used to profile the single-cell immunoglobulin repertoires of 9 patients with coronavirus disease 2019 (COVID-19). Four neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified from these repertoires. CONCLUSIONS: SCIGA provides a complete and quick analysis for 10X single-cell V(D)J sequencing datasets. It can help researchers to interpret B-cell immunoglobulin repertoires with paired heavy and light chain.

Early Viral Clearance and Antibody Kinetics of COVID-19 Among Asymptomatic Carriers.

Asymptomatic carriers contribute to the spread of Coronavirus Disease 2019 (COVID-19), but their clinical characteristics, viral kinetics, and antibody responses remain unclear. A total of 56 COVID-19 patients without symptoms at admission and 19 age-matched symptomatic patients were enrolled. RNA of SARS-CoV-2 was tested using transcriptase quantitative PCR, and the total antibodies (Ab), IgG, IgA, and IgM against the SARS-CoV-2 were tested using Chemiluminescence Microparticle Immuno Assay. Among 56 patients without symptoms at admission, 33 cases displayed symptoms and 23 remained asymptomatic throughout the follow-up period. 43.8% of the asymptomatic carriers were children and none of the asymptomatic cases had recognizable changes in C-reactive protein or interleukin-6, except one 64-year-old patient. The initial threshold cycle value of nasopharyngeal SARS-CoV-2 in asymptomatic carriers was similar to that in pre-symptomatic and symptomatic patients, but the positive viral nucleic acid detection period of asymptomatic carriers (9.63 days) was shorter than pre-symptomatic patients (13.6 days). There were no obvious differences in the seropositive conversion rate of total Ab, IgG, and IgA among the three groups, though the rates of IgM varied largely. The average peak IgG and IgM COI of asymptomatic cases was 3.5 and 0.8, respectively, which is also lower than those in symptomatic patients with peaked IgG and IgM COI of 4.5 and 2.4 (p < 0.05). Young COVID-19 patients seem to be asymptomatic cases with early clearance of SARS-CoV-2 and low levels of IgM generation but high total Ab, IgG, and IgA. Our findings provide empirical information for viral clearance and antibody kinetics of asymptomatic COVID-19 patients.

Clinical characteristics of recovered COVID-19 patients with re-detectable positive RNA test.

BACKGROUND: The characteristics, significance and potential cause of positive SARS-CoV-2 diagnoses in recovered coronavirus disease 2019 (COVID-19) patients post discharge (re-detectable positive, RP) remained elusive. METHODS: A total of 262 COVID-19 patients discharged from January 23 to February 25, 2020 were enrolled into this study. RP and non-RP (NRP) patients were grouped according to disease severity, and the characterization at re-admission was analyzed. SARS-CoV-2 RNA and plasma antibody levels were measured, and all patients were followed up for at least 14 days, with a cutoff date of March 10, 2020. RESULTS: A total of 14.5% of RP patients were detected. These patients were characterized as young and displayed mild and moderate conditions compared to NRP patients while no severe patients were RP. RP patients displayed fewer symptoms but similar plasma antibody levels during their hospitalization compared to NRP patients. Upon hospital readmission, these patients showed no obvious symptoms or disease progression. All 21 close contacts of RP patients were tested negative for viral RNA and showed no suspicious symptoms. Eighteen out of 24 of RNA-negative samples detected by the commercial kit were tested positive for viral RNA using a hyper-sensitive method, suggesting that these patients were potential carriers of the virus after recovery from COVID-19. CONCLUSIONS: Our results indicated that young patients, with a mild diagnosis of COVID-19 are more likely to display RP status after discharge. These patients show no obvious symptoms or disease progression upon re-admission. More sensitive RNA detection methods are required to monitor these patients. Our findings provide information and evidence for the management of convalescent COVID-19 patients.