ABSTRACT
SARS-CoV-2 mutational variants evade humoral immune responses elicited by vaccines and current monoclonal antibody (mAb) therapies. Novel antibody-based treatments will thus need to exhibit broad neutralization against different variants. Bispecific antibodies (bsAbs) combine the specificities of two distinct antibodies into one antibody taking advantage of the avidity, synergy and cooperativity provided by targeting two different epitopes. Here we used controlled Fab-arm exchange (cFAE), a versatile and straightforward method, to produce bsAbs that neutralize SARS-CoV and SARS-CoV-2 variants, including Omicron and its subvariants, by combining potent SARS-CoV-2-specific neutralizing antibodies with broader but less potent antibodies that also neutralize SARS-CoV. We demonstrate that the parental IgGs rely on avidity for their neutralizing activity by comparing their potency to bsAbs containing one irrelevant "dead" Fab arm. We used single particle mass photometry to measure formation of antibody:spike complexes, and determined that bsAbs increase binding stoichiometry compared to corresponding cocktails, without a loss of binding affinity. The heterogeneous binding pattern of bsAbs to spike (S), observed by negative-stain electron microscopy and mass photometry provided evidence for both intra- and inter-spike crosslinking. This study highlights the utility of cross-neutralizing antibodies for designing bivalent or multivalent agents to provide a robust activity against circulating variants, as well as future SARS-like coronaviruses.
ABSTRACT
BackgroundSurveillance data in high-income countries have reported more frequent SARS-CoV-2 diagnoses in ethnic minority groups. We examined the cumulative incidence of SARS-CoV-2 and its determinants in six ethnic groups in Amsterdam, the Netherlands. MethodsWe analyzed participants enrolled in the population-based HELIUS cohort, who were tested for SARS-CoV-2-specific antibodies and answered COVID-19-related questions between June 24-October 9, 2020 (after the first wave) and November 23, 2020-March 31, 2021 (during the second wave). We modeled SARS-CoV-2 incidence from January 1, 2020-March 31, 2021 using Markov models adjusted for age and sex. We compared incidence between ethnic groups over time and identified determinants of incident infection within ethnic groups. Findings2,497 participants were tested after the first wave; 2,083 (83{middle dot}4%) were tested during the second wave. Median age at first visit was 54 years (interquartile range=44-61); 56{middle dot}6% were female. Compared to Dutch-origin participants (15{middle dot}9%), cumulative SARS-CoV-2 incidence was higher in participants of South-Asian Surinamese (25{middle dot}0%; adjusted hazard ratio [aHR]=1{middle dot}66;95%CI=1{middle dot}16-2{middle dot}40), African Surinamese (28{middle dot}9%;aHR=1{middle dot}97;95%CI=1{middle dot}37-2{middle dot}83), Turkish (37{middle dot}0%;aHR=2{middle dot}67;95%CI=1{middle dot}89-3{middle dot}78), Moroccan (41{middle dot}9%;aHR=3{middle dot}13;95%CI=2{middle dot}22-4{middle dot}42), and Ghanaian (64{middle dot}6%;aHR=6{middle dot}00;95%CI=4{middle dot}33-8{middle dot}30) origin. Compared to those of Dutch origin, differences in incidence became wider during the second versus first wave for all ethnic minority groups (all p for interaction<0.05), except Ghanaians. Having household members with suspected SARS-CoV-2 infection, larger household size, and low health literacy were common determinants of SARS-CoV-2 incidence across groups. InterpretationSARS-CoV-2 incidence was higher in the largest ethnic minority groups of Amsterdam, particularly during the second wave. Prevention measures, including vaccination, should be encouraged in these groups. FundingZonMw, Public Health Service of Amsterdam, Dutch Heart Foundation, European Union, European Fund for the Integration of non-EU immigrants.
ABSTRACT
BackgroundEthnic minorities have higher rates of SARS-CoV-2 diagnoses, but little is known about ethnic differences in past exposure. We aimed to determine whether prevalence and determinants of SARS-CoV-2 exposure varied between six ethnic groups in Amsterdam, the Netherlands. MethodsParticipants aged 25-79 years enrolled in a population-based prospective cohort were randomly selected within ethnic groups and invited to test for SARS-CoV-2-specific antibodies and answer COVID-19 related questions. We estimated prevalence and determinants of SARS-CoV-2 exposure within ethnic groups using survey-weighted logistic regression adjusting for age, sex and calendar time. ResultsBetween June 24-October 9, 2020, we included 2497 participants. Adjusted SARS-CoV-2 seroprevalence was comparable between ethnic-Dutch (25/498; 5.5%, 95%CI=3.2-7.9), South-Asian Surinamese (22/451; 4.8%, 95%CI=2.1-7.5), African Surinamese (22/400; 8.2%, 95%CI=3.0-13.4), Turkish (30/408; 7.8%, 95%CI=4.3-11.2) and Moroccan (32/391; 7.0%, 95%CI=4.0-9.9) participants, but higher among Ghanaians (95/327; 26.5%, 95%CI=18.7-34.4). 57.1% of SARS-CoV-2-positive participants did not suspect or were unsure of being infected, which was lowest in African Surinamese (18.2%) and highest in Ghanaians (90.5%). Determinants of SARS-CoV-2 exposure varied across ethnic groups, while the most common determinant was having a household member suspected of infection. In Ghanaians, seropositivity was associated with older age, larger household sizes, living with small children, leaving home to work and attending religious services. ConclusionsNo remarkable differences in SARS-CoV-2 seroprevalence were observed between the largest ethnic groups in Amsterdam after the first wave of infections. The higher infection seroprevalence observed among Ghanaians, which passed mostly unnoticed, warrants wider prevention efforts and opportunities for non-symptom-based testing.
ABSTRACT
BACKGROUNDIt is unclear how, when and where health care workers (HCW) working in hospitals are infected with SARS-CoV-2. METHODSProspective cohort study comprising 4-weekly measurement of SARS-CoV-2 specific antibodies and questionnaires from March to June 2020. We compared SARS-CoV-2 incidence between HCW working in Covid-19 patient care, HCW working in non-Covid-19 patient care and HCW not in patient care. Phylogenetic analyses of SARS-CoV-2 samples from patients and HCW were performed to identify potential transmission clusters. RESULTSWe included 801 HCW: 439 in the Covid-19 patient care group, 164 in the non-Covid-19 patient care group and 198 in the no patient care group. SARS-CoV-2 incidence was highest in HCW working in Covid-19 patient care (13.2%), as compared with HCW in non-Covid-19 patient care (6.7%, hazard ratio [HR] 2.2, 95% confidence interval [CI] 1.2 to 4.3) and in HCW not working in patient care (3.6%, HR 3.9, 95% CI 1.8 to 8.6). Within the group of HCW caring for Covid-19 patients, SARS-CoV-2 cumulative incidence was highest in HCW working on Covid-19 wards (25.7%), as compared with HCW working on intensive care units (7.1%, HR 3.6, 95% CI 1.9 to 6.9), and HCW working in the emergency room (8.0%, HR 3.3, 95% CI 1.5 to 7.1). Phylogenetic analyses on Covid-19 wards identified multiple potential HCW-to-HCW transmission clusters while no patient-to-HCW transmission clusters were identified. CONCLUSIONSHCW working on Covid-19 wards are at increased risk for nosocomial SARS-CoV-2 infection, with an important role for HCW-to-HCW transmission. (Funded by the Netherlands Organization for Health Research and Development ZonMw & the Corona Research Fund Amsterdam UMC; Netherlands Trial Register number NL8645)
