Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020–March 2021) Using a Statistical Learning Strategy

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

Mitophagy in neurological disorders

Selective autophagy is an evolutionarily conserved mechanism that removes excess protein aggregates and damaged intracellular components. Most eukaryotic cells, including neurons, rely on proficient mitophagy responses to fine-tune the mitochondrial number and preserve energy metabolism. In some circumstances (such as the presence of pathogenic protein oligopolymers and protein mutations), dysfunctional mitophagy leads to nerve degeneration, with age-dependent intracellular accumulation of protein aggregates and dysfunctional organelles, leading to neurodegenerative disease. However, when pathogenic protein oligopolymers, protein mutations, stress, or injury are present, mitophagy prevents the accumulation of damaged mitochondria. Accordingly, mitophagy mediates neuroprotective effects in some forms of neurodegenerative disease (e.g., Alzheimer's disease, Parkinson’s disease, Huntington's disease, and Amyotrophic lateral sclerosis) and acute brain damage (e.g., stroke, hypoxic–ischemic brain injury, epilepsy, and traumatic brain injury). The complex interplay between mitophagy and neurological disorders suggests that targeting mitophagy might be applicable for the treatment of neurodegenerative diseases and acute brain injury. However, due to the complexity of the mitophagy mechanism, mitophagy can be both harmful and beneficial, and future efforts should focus on maximizing its benefits. Here, we discuss the impact of mitophagy on neurological disorders, emphasizing the contrast between the positive and negative effects of mitophagy.

Molecular and Serological Characterization of the SARS-CoV-2 Delta Variant in Bangladesh in 2021

Novel SARS-CoV-2 variants are emerging at an alarming rate. The delta variant and other variants of concern (VoC) carry spike (S)-protein mutations, which have the potential to evade protective immunity, to trigger break-through infections after COVID-19 vaccination, and to propagate future waves of COVID-19 pandemic. To identify SARS CoV-2 variants in Bangladesh, patients who are RT-PCR-positive for COVID-19 infections in Dhaka were screened by a RT-PCR melting curve analysis for spike protein mutations. To assess the anti-SARS CoV-2 antibody responses, the levels of the anti-S -proteins IgA and IgG and the anti-N-protein IgG were measured by ELISA. Of a total of 36 RT-PCR positive samples (75%), 27 were identified as delta variants, with one carrying an additional Q677H mutation and two with single nucleotide substitutions at position 23029 (compared to Wuhan-Hu-1 reference NC 045512) in the genome sequence. Three (8.3%) were identified as beta variants, two (5.5%) were identified as alpha variants, three (8.3%) were identified as having a B.1.1.318 lineage, and one sample was identified as an eta variant (B.1.525) carrying an additional V687L mutation. The trend of higher viral load (lower Cp values) among delta variants than in the alpha and beta variants was of borderline statistical significance (p = 0.045). Prospective studies with larger Bangladeshi cohorts are warranted to confirm the emergence of S-protein mutations and their association with antibody response in natural infection and potential breakthrough in vaccinated subjects.

Association between prognostic factors and the outcomes of patients infected with SARS-CoV-2 harboring multiple spike protein mutations

The outcome of SARS-CoV-2 infection is determined by multiple factors, including the viral, host genetics, age, and comorbidities. This study investigated the association between prognostic factors and disease outcomes of patients infected by SARS-CoV-2 with multiple S protein mutations. Fifty-one COVID-19 patients were recruited in this study. Whole-genome sequencing of 170 full-genomes of SARS-CoV-2 was conducted with the Illumina MiSeq sequencer. Most patients (47%) had mild symptoms of COVID-19 followed by moderate (19.6%), no symptoms (13.7%), severe (4%), and critical (2%). Mortality was found in 13.7% of the COVID-19 patients. There was a significant difference between the age of hospitalized patients (53.4 ± 18 years) and the age of non-hospitalized patients (34.6 ± 19) (p = 0.001). The patients’ hospitalization was strongly associated with hypertension, diabetes, and anticoagulant and were strongly significant with the OR of 17 (95% CI 2–144; p = 0.001), 4.47 (95% CI 1.07–18.58; p = 0.039), and 27.97 (95% CI 1.54–507.13; p = 0.02), respectively; while the patients’ mortality was significantly correlated with patients’ age, anticoagulant, steroid, and diabetes, with OR of 8.44 (95% CI 1.5–47.49; p = 0.016), 46.8 (95% CI 4.63–472.77; p = 0.001), 15.75 (95% CI 2–123.86; p = 0.009), and 8.5 (95% CI 1.43–50.66; p = 0.019), respectively. This study found the clade: L (2%), GH (84.3%), GR (11.7%), and O (2%). Besides the D614G mutation, we found L5F (18.8%), V213A (18.8%), and S689R (8.3%). No significant association between multiple S protein mutations and the patients’ hospitalization or mortality. Multivariate analysis revealed that hypertension and anticoagulant were the significant factors influencing the hospitalization and mortality of patients with COVID-19 with an OR of 17.06 (95% CI 2.02–144.36; p = 0.009) and 46.8 (95% CI 4.63–472.77; p = 0.001), respectively. Moreover, the multiple S protein mutations almost reached a strong association with patients’ hospitalization (p = 0.07). We concluded that hypertension and anticoagulant therapy have a significant impact on COVID-19 outcomes. This study also suggests that multiple S protein mutations may impact the COVID-19 outcomes. This further emphasized the significance of monitoring SARS-CoV-2 variants through genomic surveillance, particularly those that may impact the COVID-19 outcomes.

Sensitivity of two SARS-CoV-2 variants with spike protein mutations to neutralising antibodies

SARS-CoV-2 infections elicit a humoral immune response capable of neutralising the virus. However, multiple variants have emerged with mutations in the spike protein amongst others, the key target of neutralising antibodies. We evaluated the neutralising efficacy of 89 serum samples from patients, infected with SARS-CoV-2 in the beginning of 2020, against two virus variants isolated from acutely infected patients and harbouring spike protein mutations. One isolate was assigned to lineage B.1.351 (MUC-IMB-B.1.351) whilst the other (MUC-484) was isolated from an immunocompromised patient, sharing some but not all mutations with B.1.351 and representing a transitional variant. Both variants showed a significant reduction in neutralisation sensitivity compared to wild-type SARS-CoV-2 with MUC-IMB-B.1.351 being almost completely resistant to neutralisation. The observed reduction in neutralising activity of wild-type-specific antibodies against both variants suggests that individual mutations in the spike protein are sufficient to confer a potent escape from the humoral immune response. In addition, the effect of escape mutations seems to accumulate, so that more heavily mutated variants show a greater loss of sensitivity to neutralisation up to complete insensitivity as observed for MUC-IMB-B.1.351. From a clinical point of view, this might affect the efficacy of (monoclonal) antibody treatment of patients with prolonged infections as well as patients infected with variants other than the donor. At the same, this could also negatively influence the efficacy of current vaccines (as they are based on wild-type spike protein) emphasising the need to thoroughly surveil the emergence and distribution of variants and adapt vaccines and therapeutics accordingly.