Increased Receptor Affinity and Reduced Recognition by Specific Antibodies Contribute to Immune Escape of SARS-CoV-2 Variant Omicron

Automated antigen assays display a high heterogeneity for the detection of SARS-CoV-2 variants of concern, including several Omicron sublineages

Diagnostic tests for direct pathogen detection have been instrumental to contain the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic. Automated, quantitative, laboratory-based nucleocapsid antigen (Ag) tests for SARS-CoV-2 have been launched alongside nucleic acid-based test systems and point-of-care (POC) lateral-flow Ag tests. Here, we evaluated four commercial Ag tests on automated platforms for the detection of different sublineages of the SARS-CoV-2 Omicron variant of concern (VoC) (B.1.1.529) in comparison with "non-Omicron" VoCs. A total of 203 Omicron PCR-positive respiratory swabs (53 BA.1, 48 BA.2, 23 BQ.1, 39 XBB.1.5 and 40 other subvariants) from the period February to March 2022 and from March 2023 were examined. In addition, tissue culture-expanded clinical isolates of Delta (B.1.617.2), Omicron-BA.1, -BF.7, -BN.1 and -BQ.1 were studied. These results were compared to previously reported data from 107 clinical "non-Omicron" samples from the end of the second pandemic wave (February to March 2021) as well as cell culture-derived samples of wildtype (wt) EU-1 (B.1.177), Alpha VoC (B.1.1.7) and Beta VoC (B.1.351)). All four commercial Ag tests were able to detect at least 90.9% of Omicron-containing samples with high viral loads (Ct < 25). The rates of true-positive test results for BA.1/BA.2-positive samples with intermediate viral loads (Ct 25-30) ranged between 6.7% and 100.0%, while they dropped to 0 to 15.4% for samples with low Ct values (> 30). This heterogeneity was reflected also by the tests' 50%-limit of detection (LoD50) values ranging from 44,444 to 1,866,900 Geq/ml. Respiratory samples containing Omicron-BQ.1/XBB.1.5 or other Omicron subvariants that emerged in 2023 were detected with enormous heterogeneity (0 to 100%) for the intermediate and low viral load ranges with LoD50 values between 23,019 and 1,152,048 Geq/ml. In contrast, detection of "non-Omicron" samples was more sensitive, scoring positive in 35 to 100% for the intermediate and 1.3 to 32.9% of cases for the low viral loads, respectively, corresponding to LoD50 values ranging from 6181 to 749,792 Geq/ml. All four assays detected cell culture-expanded VoCs Alpha, Beta, Delta and Omicron subvariants carrying up to six amino acid mutations in the nucleocapsid protein with sensitivities comparable to the non-VoC EU-1. Overall, automated quantitative SARS-CoV-2 Ag assays are not more sensitive than standard rapid antigen tests used in POC settings and show a high heterogeneity in performance for VoC recognition. The best of these automated Ag tests may have the potential to complement nucleic acid-based assays for SARS-CoV-2 diagnostics in settings not primarily focused on the protection of vulnerable groups. In light of the constant emergence of new Omicron subvariants and recombinants, most recently the XBB lineage, these tests' performance must be regularly re-evaluated, especially when new VoCs carry mutations in the nucleocapsid protein or immunological and clinical parameters change.

A novel ACE2 decoy for both neutralization of SARS-CoV-2 variants and killing of infected cells

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to millions of infections and deaths worldwide. As this virus evolves rapidly, there is a high need for treatment options that can win the race against new emerging variants of concern. Here, we describe a novel immunotherapeutic drug based on the SARS-CoV-2 entry receptor ACE2 and provide experimental evidence that it cannot only be used for (i) neutralization of SARS-CoV-2 in vitro and in SARS-CoV-2-infected animal models but also for (ii) clearance of virus-infected cells. For the latter purpose, we equipped the ACE2 decoy with an epitope tag. Thereby, we converted it to an adapter molecule, which we successfully applied in the modular platforms UniMAB and UniCAR for retargeting of either unmodified or universal chimeric antigen receptor-modified immune effector cells. Our results pave the way for a clinical application of this novel ACE2 decoy, which will clearly improve COVID-19 treatment.

Correlating the differences in the receptor binding domain of SARS-CoV-2 spike variants on their interactions with human ACE2 receptor

Spike glycoprotein of SARS-CoV-2 variants plays a critical role in infection and transmission through its interaction with human angiotensin converting enzyme 2 (hACE2) receptors. Prior findings using molecular docking and biomolecular studies reported varied findings on the difference in the interactions among the spike variants with the hACE2 receptors. Hence, it is a prerequisite to understand these interactions in a more precise manner. To this end, firstly, we performed ELISA with trimeric spike glycoproteins of SARS-CoV-2 variants including Wuhan Hu-1(Wild), Delta, C.1.2 and Omicron. Further, to study the interactions in a more specific manner by mimicking the natural infection, we developed hACE2 receptors expressing HEK-293T cell line, evaluated their binding efficiencies and competitive binding of spike variants with D614G spike pseudotyped virus. In line with the existing findings, we observed that Omicron had higher binding efficiency compared to Delta in both ELISA and Cellular models. Intriguingly, we found that cellular models could differentiate the subtle differences between the closely related C.1.2 and Delta in their binding to hACE2 receptors. Our study using the cellular model provides a precise method to evaluate the binding interactions between spike sub-lineages to hACE2 receptors.

A computationally designed ACE2 decoy has broad efficacy against SARS-CoV-2 omicron variants and related viruses in vitro and in vivo

SARS-CoV-2, especially B.1.1.529/omicron and its sublineages, continues to mutate to evade monoclonal antibodies and antibodies elicited by vaccination. Affinity-enhanced soluble ACE2 (sACE2) is an alternative strategy that works by binding the SARS-CoV-2 S protein, acting as a 'decoy' to block the interaction between the S and human ACE2. Using a computational design strategy, we designed an affinity-enhanced ACE2 decoy, FLIF, that exhibited tight binding to SARS-CoV-2 delta and omicron variants. Our computationally calculated absolute binding free energies (ABFE) between sACE2:SARS-CoV-2 S proteins and their variants showed excellent agreement to binding experiments. FLIF displayed robust therapeutic utility against a broad range of SARS-CoV-2 variants and sarbecoviruses, and neutralized omicron BA.5 in vitro and in vivo. Furthermore, we directly compared the in vivo therapeutic efficacy of wild-type ACE2 (non-affinity enhanced ACE2) against FLIF. A few wild-type sACE2 decoys have shown to be effective against early circulating variants such as Wuhan in vivo. Our data suggest that moving forward, affinity-enhanced ACE2 decoys like FLIF may be required to combat evolving SARS-CoV-2 variants. The approach described herein emphasizes how computational methods have become sufficiently accurate for the design of therapeutics against viral protein targets. Affinity-enhanced ACE2 decoys remain highly effective at neutralizing omicron subvariants.

Vaccine effectiveness against transmission of alpha, delta and omicron SARS-COV-2-infection, Belgian contact tracing, 2021-2022

OBJECTIVES

Vaccine effectiveness against transmission (VET) of SARS-CoV-2-infection can be estimated from secondary attack rates observed during contact tracing. We estimated VET, the vaccine-effect on infectiousness of the index case and susceptibility of the high-risk exposure contact (HREC).

METHODS

We fitted RT-PCR-test results from HREC to immunity status (vaccine schedule, prior infection, time since last immunity-conferring event), age, sex, calendar week of sampling, household, background positivity rate and dominant VOC using a multilevel Bayesian regression-model. We included Belgian data collected between January 2021 and January 2022.

RESULTS

For primary BNT162b2-vaccination we estimated initial VET at 96% (95%CI 95-97) against Alpha, 87% (95%CI 84-88) against Delta and 31% (95%CI 25-37) against Omicron. Initial VET of booster-vaccination (mRNA primary and booster-vaccination) was 87% (95%CI 86-89) against Delta and 68% (95%CI 65-70) against Omicron. The VET-estimate against Delta and Omicron decreased to 71% (95%CI 64-78) and 55% (95%CI 46-62) respectively, 150-200 days after booster-vaccination. Hybrid immunity, defined as vaccination and documented prior infection, was associated with durable and higher or comparable (by number of antigen exposures) protection against transmission.

CONCLUSIONS

While we observed VOC-specific immune-escape, especially by Omicron, and waning over time since immunization, vaccination remained associated with a reduced risk of SARS-CoV-2-transmission.

Clinical characteristics analysis of COVID-19 patients from the first significant community outbreak by SARS-CoV-2 variant B.1.1.7 in Taiwan as experienced from a single northern medical center

BACKGROUND/PURPOSE

Clinical characteristics of patients in the first community outbreak of coronavirus disease 2019 (COVID-19) by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant B.1.1.7 in Taiwan have not been characterized.

METHODS

SARS-CoV-2 positive specimens from inpatients between May 7 and June 15 in 2021were screen for SARS-CoV-2 B.1.1.7 lineage by VirSNiP assay. Clinical characteristics were reviewed and compared with those from Feb 1 to April 30, 2020 and from Jan 1 to March 31, 2022.

RESULTS

One hundred forty-one inpatients from May 7 to June 15, 2021 infected with SARS-CoV-2 B.1.1.7 lineage were included. The major presenting symptoms were fever (88.7%) and cough (59.6%). Incidence of relevant complications including pulmonary embolism, simultaneous infections with bacteria, virus, and fungi were 0.7%, 12.8%, 13.5%, and 2.1%, respectively. Old age, high Charlson comorbidity index, short of breath, and initial critical illness were independently associated with 28-day mortality (all p < 0.05). In comparison to COVID-19 inpatients from Feb 1 to April 30, 2020, patients from the outbreak by SARS-CoV-2 B.1.1.7 lineage were older, more severe in disease condition, higher mortality but less obvious initial presenting symptoms. After implementation of nationwide vaccination campaign in the next half year of 2021, COVID-19 inpatients from Jan 1 to March 31 in 2022 indicated less severe diseases than those infected with SARS-CoV-2 B.1.1.7 lineage.

CONCLUSION

COVID-19 inpatients by SARS-CoV-2 variant B.1.1.7 with old age, multiple comorbidities, and more severe disease conditions were associated with increased mortality. Vaccination for this vulnerable populations may be helpful.

Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning

Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports. Fortuitously, a subset of GISAID, the global SARS-CoV-2 sequence repository, includes "patient status" metadata that can indicate whether a sequence record is associated with mild or severe disease. While GISAID lacks data on comorbidities relevant to severity, such as obesity and chronic disease, it does include metadata for age and sex to use as additional attributes in modeling. With these caveats, previous efforts have demonstrated that genotype-patient status models can be fit to GISAID data, particularly when country-of-origin is used as an additional feature. But are these models robust and biologically meaningful? This paper shows that, in fact, temporal and geographic biases in sequences submitted to GISAID, as well as the evolving pandemic response, particularly reduction in severe disease due to vaccination, create complex issues for model development and interpretation. This paper poses a potential solution: efficient mixed effects machine learning using GPBoost, treating country as a random effect group. Training and validation using temporally split GISAID data and emerging Omicron variants demonstrates that GPBoost models are more predictive of the impact of spike protein mutations on patient outcomes than fixed effect XGBoost, LightGBM, random forests, and elastic net logistic regression models.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and humoral responses against different variants of concern in domestic pet animals and stray cats from North-Eastern Spain

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease 2019 (COVID-19) pandemic in humans, is able to infect several domestic, captive and wildlife animal species. Since reverse zoonotic transmission to pets has been demonstrated, it is crucial to determine their role in the epidemiology of the disease to prevent further spillover events and major spread of SARS-CoV-2. In the present study, we determined the presence of virus and the seroprevalence to SARS-CoV-2, as well as the levels of neutralizing antibodies (nAbs) against several variants of concern (VOCs) in pets (cats, dogs and ferrets) and stray cats from North-Eastern of Spain. We confirmed that cats and dogs can be infected by different VOCs of SARS-CoV-2 and, together with ferrets, are able to develop nAbs against the ancestral (B.1), Alpha (B.1.1.7), Beta (B.1.315), Delta (B.1.617.2) and Omicron (BA.1) variants, with lower titres against the latest in dogs and cats, but not in ferrets. Although the prevalence of active SARS-CoV-2 infection measured as direct viral RNA detection was low (0.3%), presence of nAbs in pets living in COVID-19-positive households was relatively high (close to 25% in cats, 10% in dogs and 40% in ferrets). It is essential to continue monitoring SARS-CoV-2 infections in these animals due to their frequent contact with human populations, and we cannot discard the probability of a higher animal susceptibility to new potential SARS-CoV-2 VOCs.

Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.