A SARS-CoV-2 variant of concern triggers Fc effector function with increased cross-reactivity

SARS-CoV-2 variants of concern (VOCs) exhibit escape from neutralizing antibodies, causing concern about vaccine effectiveness. However, while non-neutralizing cytotoxic functions of antibodies are associated with decreased disease severity and vaccine protection, Fc effector function escape from VOCs is poorly defined. Furthermore, whether VOCs trigger Fc functions with altered specificity, as has been reported for neutralization, is unknown. Here, we demonstrate that the Beta VOC partially evades Fc effector activity in individuals infected with the original (D614G) variant. However, not all functions are equivalently affected, suggesting differential targeting by antibodies mediating distinct Fc functions. Furthermore, Beta infection triggered responses with significantly improved Fc cross-reactivity against global VOCs compared to either D614G infected or Ad26.COV2.S vaccinated individuals. This suggests that, as for neutralization, the infecting spike sequence impacts Fc effector function. These data have important implications for vaccine strategies that incorporate VOCs, suggesting these may induce broader Fc effector responses.

Coronaviruses with a SARS-CoV-2-like receptor-binding domain allowing ACE2-mediated entry into human cells isolated from bats of Indochinese peninsula

The animal reservoir of SARS-CoV-2 is unknown despite reports of various SARS-CoV-2-related viruses in Asian Rhinolophus bats, including the closest virus from R. affinis, RaTG13. Several studies have suggested the involvement of pangolin coronaviruses in SARS-CoV-2 emergence. SARS-CoV-2 presents a mosaic genome, to which different progenitors contribute. The spike sequence determines the binding affinity and accessibility of its receptor-binding domain (RBD) to the cellular angiotensin-converting enzyme 2 (ACE2) receptor and is responsible for host range. SARS-CoV-2 progenitor bat viruses genetically close to SARS-CoV-2 and able to enter human cells through a human ACE2 pathway have not yet been identified, though they would be key in understanding the origin of the epidemics. Here we show that such viruses indeed circulate in cave bats living in the limestone karstic terrain in North Laos, within the Indochinese peninsula. We found that the RBDs of these viruses differ from that of SARS-CoV-2 by only one or two residues, bind as efficiently to the hACE2 protein as the SARS-CoV-2 Wuhan strain isolated in early human cases, and mediate hACE2-dependent entry into human cells, which is inhibited by antibodies neutralizing SARS-CoV-2. None of these bat viruses harbors a furin cleavage site in the spike. Our findings therefore indicate that bat-borne SARS-CoV-2-like viruses potentially infectious for humans circulate in Rhinolophus spp. in the Indochinese peninsula.

Previous Infection Combined with Vaccination Produces Neutralizing Antibodies With Potency Against SARS-CoV-2 Variants

SARS-CoV-2 continues to evolve in humans. Spike protein mutations increase transmission and potentially evade antibodies raised against the original sequence used in current vaccines. Our evaluation of serum neutralizing activity in both persons soon after SARS-CoV-2 infection (in April 2020 or earlier) or vaccination without prior infection confirmed that common spike mutations can reduce antibody antiviral activity. However, when the persons with prior infection were subsequently vaccinated, their antibodies attained an apparent biologic ceiling of neutralizing potency against all tested variants, equivalent to the original spike sequence. These findings indicate that additional antigenic exposure further improves antibody efficacy against variants.

Pango lineage designation and assignment using SARS-CoV-2 spike gene nucleotide sequences

More than 2 million SARS-CoV-2 genome sequences have been generated and shared since the start of the COVID-19 pandemic and constitute a vital information source that informs outbreak control, disease surveillance, and public health policy. The Pango dynamic nomenclature is a popular system for classifying and naming genetically-distinct lineages of SARS-CoV-2, including variants of concern, and is based on the analysis of complete or near-complete virus genomes. However, for several reasons, nucleotide sequences may be generated that cover only the spike gene of SARS-CoV-2. It is therefore important to understand how much information about Pango lineage status is contained in spike-only nucleotide sequences. Here we explore how Pango lineages might be reliably designated and assigned to spike-only nucleotide sequences. We survey the genetic diversity of such sequences, and investigate the information they contain about Pango lineage status. Although many lineages, including the main variants of concern, can be identified clearly using spike-only sequences, some spike-only sequences are shared among tens or hundreds of Pango lineages. To facilitate the classification of SARS- CoV-2 lineages using subgenomic sequences we introduce the notion of designating such sequences to a lineage set, which represents the range of Pango lineages that are consistent with the observed mutations in a given spike sequence. These data provide a foundation for the development of software tools that can assign newly-generated spike nucleotide sequences to Pango lineage sets.

SARS-CoV-2 variants of concern exhibit reduced sensitivity to live-virus neutralization in sera from CoronaVac vaccinees and naturally infected COVID-19 patients

Recent surges in SARS-CoV-2 variants of concern (VOCs) call for the need to evaluate levels of vaccine- and infection- induced SARS-CoV-2 neutralizing antibodies (NAbs). CoronaVac (Sinovac Biotech, Beijing, China) is currently being used for mass vaccination in Thailand as well as other low-income countries. Three VOCs currently circulating within Thailand include the B.1.1.7 (Alpha), B.1.351 (Beta), and B.1.617.2 (Delta) strains. We assessed NAb potency against the prototypic strain containing the original spike sequence (WT) compared to that against the 3 VOCs using sera derived from a cohort of healthcare workers who received a full 2-dose regimen of CoronaVac. Sera from two other cohorts consisting of COVID-19 patients who had been hospitalized in 2020 and 2021 were evaluated for comparison. We found that, despite equally robust production of S1-RBD-binding IgG and 100% seropositivity, sera from both CoronaVac vaccinees and naturally infected individuals had significantly reduced neutralizing capacity against all 3 VOCs compared to WT. Strikingly, NAb titers against Alpha and Beta were comparable, but Delta appears to be significantly more refractory to NAbs in all groups. Our results may help inform on CoronaVac NAb-inducing capacity, which is a proxy for vaccine efficacy, in the context of the WT strain and 3 VOCs. Our results also have critical implications for public health decisionmakers who may need to maintain efficient mitigation strategies amid a potentially high risk for infection with VOCs even in those who have been previously infected.

T-cell and antibody immunity after COVID-19 mRNA vaccines in healthy and immunocompromised subjects-An exploratory study.

Background: In recent studies, up to half of immunocompromised (IC) subject populations fail to develop antibodies after COVID-19 vaccination. Purpose and Methods: Here, we explore whether T-cells which respond to the spike (S) antigenic sequence and its less conserved S1, and the conserved S2 component are present in serial samples before and after each dose of mRNA1273 or BNT162b2 vaccines in 20 healthy immunocompetent subjects. Single samples from 7 vaccinated IC subjects were also tested. Simultaneously, we measured IgG antibodies to the receptor binding domain (RBD) of S1, and anti-S IgG, and frequencies of monocytic CD14+HLA-DR- (M-MDSC) and polymorphonuclear CD14-CD15+CD11b+ (PMN-MDSC) myeloid-derived suppressor cells. Results: In healthy subjects, S1-, S2-, and S-reactive CD4 and CD8 T-cell frequencies showed a numeric but not statistically significant decrease after the first vaccine dose and were accompanied by increased MDSC frequencies (p<0.05). After the second dose, S2- and S-reactive CD4 and CD8 cells and MDSC approached pre-vaccination levels. In healthy subjects, a) S1-reactive CD8 frequencies were significantly higher after the second dose compared with pre-vaccination levels (p=0.015), b) anti-RBD and anti-S IgG were present in all after the second dose. Among seven IC subjects, anti-RBD and anti-S IgG were absent in 4 and 3 subjects, respectively. S1-reactive CD8 cells were identified in 2 of 4 anti-RBD negative subjects. S-reactive CD4 or CD8 cells were identified in all three anti-S negative subjects. Conclusions: In healthy immunocompetent subjects, mRNA vaccines induce antibodies to the spike antigenic sequences and augment CD8 cells reactive to the S1 spike sequence, which is more specific for the SARS-CoV-2 virus. In this exploratory cohort of vaccinated immunocompromised subjects, S1-reactive CD8 cells can be detected in some who are negative for RBD antibody, and S-reactive T-cells are present in all who are negative for spike antibody.

nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift

The recent “multi-neuronal spike sequence detector” (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological learning. Unfortunately, the range of problems to which this topology can be applied is limited because of the low cardinality of the parallel spike trains that it can process, and the lack of a visualization mechanism to understand its internal operation. We present here the nMNSD structure, which is a generalization of the MNSD to any number of inputs. The mathematical framework of the structure is introduced, together with the “trapezoid method,” that is a reduced method to analyze the recognition mechanism operated by the nMNSD in response to a specific input parallel spike train. We apply the nMNSD to a classification problem previously faced with the classical MNSD from the same authors, showing the new possibilities the nMNSD opens, with associated improvement in classification performances. Finally, we benchmark the nMNSD on the classification of static inputs (MNIST database) obtaining state-of-the-art accuracies together with advantageous aspects in terms of time- and energy-efficiency if compared to similar classification methods.

Spiking Neural Network Learning Models for Spike Sequence Learning and Data Classification

Supervised learning in Spiking Neural Network (SNN) is a hotbed for researchers due to the advantages temporal coded networks provide over that of rate-coded networks with respect to efficiency in information processing and transfer rates. Supervised learning in rate-coded networks though well established, it is difficult to directly apply such models to SNN due to difference in information coding schemes. In this paper, we seek to exploit the advantages of spiking neural networks for spike sequence learning in order to establish two (2) models; batch and sequential learning models for solving data classification tasks. The models are built using the least squares approach leveraging on its approximation abilities. The first set of experiments are on spikesequence learning in which an extensive evaluation of the model is performed using different inputoutput firing rates and learning periods. Results from these experiments show that the proposed model for spike sequence learning produced better performance than some existing models derived for spike sequence learning, particularly, at higher learning periods. The proposed models for data classification are also tested on some selected benchmark datasets most of which had imbalance class distributions and also on real-world road condition datasets for anomaly classification collected by the authors as part of a larger study. While the proposed models generalised very well to all datasets including those with the class imbalance problem where F1and Recall values above 0.90 were recorded, some well-know machine learning algorithms applied to the datasets yielded lower F1 and Recall values and in some cases recorded 0.0 Recall.