Sensitivity and specificity of two WHO approved SARS-CoV2 antigen assays in detecting patients with SARS-CoV2 infection.

BACKGROUND: SARS-CoV-2 rapid antigen (Ag) detection kits are widely used in addition to quantitative reverse transcription PCR PCR (RT-qPCR), as they are cheaper with a rapid turnaround time. As there are many concerns regarding their sensitivity and specificity, in different settings, we evaluated two WHO approved rapid Ag kits in a large cohort of Sri Lankan individuals. METHODS: Paired nasopharangeal swabs were obtained from 4786 participants for validation of the SD-Biosensor rapid Ag assay and 3325 for the Abbott rapid Ag assay, in comparison to RT-qPCR. A short questionnaire was used to record symptoms at the time of testing, and blood samples were obtained from 2721 of them for detection of SARS-CoV-2 specific antibodies. RESULTS: The overall sensitivity of the SD-Biosensor Ag kit was 36.5% and the Abbott Ag test was 50.76%. The Abbott Ag test showed specificity of 99.4% and the SD-Biosensor Ag test 97.5%. At Ct values < 25, the sensitivity was 71.3% to 76.6% for the SD-Biosensor Ag test and 77.3% to 88.9% for the Abbott Ag test. The Ct values for all genes (RdRP, S, E and N) tested with all RT-qPCR kits were significantly lower for the positive results of the Abbott Ag test compared to the SD-Biosensor test. 209 (48.04%) individuals who had antibodies gave a positive RT-qPCR result, and antibody positivity rates were higher at Ct values > 30 (46.1 to 82.9%). 32.1% of those who gave a positive result with the SD-Biosensor Ag test and 26.3% of those who gave positive results with the Abbott Ag test had SARS-CoV-2 antibodies at the time of detection. CONCLUSIONS: Both rapid Ag tests appeared to be highly sensitive in detecting individuals at lower Ct values, in a community setting in Sri Lanka, but it will be important to further establish the relationship to infectivity.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation.

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases. IMPLICATIONS: This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Fusion crystallization reveals the behavior of both the 1TEL crystallization chaperone and the TNK1 UBA domain.

Human thirty-eight-negative kinase-1 (TNK1) is implicated in cancer progression. The TNK1-UBA domain binds polyubiquitin and plays a regulatory role in TNK1 activity and stability. Sequence analysis suggests an unusual architecture for the TNK1 UBA domain, but an experimentally-validated molecular structure is undetermined. To gain insight into TNK1 regulation, we fused the UBA domain to the 1TEL crystallization chaperone and obtained crystals diffracting as far as 1.53 Å. A 1TEL search model enabled solution of the X-ray phases. GG and GSGG linkers allowed the UBA to reproducibly find a productive binding mode against its host 1TEL polymer and to crystallize at protein concentrations as low as 0.1 mg/mL. Our studies support a mechanism of TELSAM fusion crystallization and show that TELSAM fusion crystals require fewer crystal contacts than traditional protein crystals. Modeling and experimental validation suggest the UBA domain may be selective for both the length and linkages of polyubiquitin chains.

Fusion crystallization reveals the behavior of both the 1TEL crystallization chaperone and the TNK1 UBA domain.

Human thirty-eight-negative kinase-1 (TNK1) is implicated in cancer progression. The TNK1 ubiquitin-associated (UBA) domain binds polyubiquitin and plays a regulatory role in TNK1 activity and stability. No experimentally determined molecular structure of this unusual UBA domain is available. We fused the UBA domain to the 1TEL variant of the translocation ETS leukemia protein sterile alpha motif (TELSAM) crystallization chaperone and obtained crystals diffracting as far as 1.53 Å. GG and GSGG linkers allowed the UBA to reproducibly find a productive binding mode against its host 1TEL polymer and crystallize at protein concentrations as low as 0.2 mg/mL. Our studies support a mechanism of 1TEL fusion crystallization and show that 1TEL fusion crystals require fewer crystal contacts than traditional protein crystals. Modeling and experimental validation suggest the UBA domain may be selective for both the length and linkages of polyubiquitin chains.

Molecular Epidemiology of AY.28 and AY.104 Delta Sub-lineages in Sri Lanka.

Background: The worst SARS-CoV-2 outbreak in Sri Lanka was due to the two Sri Lankan delta sub-lineages AY.28 and AY.104. We proceeded to further characterize the mutations and clinical disease severity of these two sub-lineages. Methods: 705 delta SARS-CoV-2 genomes sequenced by our laboratory from mid-May to November 2021 using Illumina and Oxford Nanopore were included in the analysis. The clinical disease severity of 440/705 individuals were further analyzed to determine if infection with either AY.28 or AY.104 was associated with more severe disease. Sub-genomic RNA (sg-RNA) expression was analyzed using periscope. Results: AY.28 was the dominant variant throughout the outbreak, accounting for 67.7% of infections during the peak of the outbreak. AY.28 had three lineage defining mutations in the spike protein: A222V (92.80%), A701S (88.06%), and A1078S (92.04%) and seven in the ORF1a: R24C, K634N, P1640L, A2994V, A3209V, V3718A, and T3750I. AY.104 was characterized by the high prevalence of T95I (90.81%) and T572L (65.01%) mutations in the spike protein and by the absence of P1640L (94.28%) in ORF1a with the presence of A1918V (98.58%) mutation. The mean sgRNA expression levels of ORF6 in AY.28 were significantly higher compared to AY.104 (p < 0.0001) and B.1.617.2 (p < 0.01). Also, ORF3a showed significantly higher sgRNA expression in AY.28 compared to AY.104 (p < 0.0001). There was no difference in the clinical disease severity or duration of hospitalization in individuals infected with these sub lineages. Conclusions: Therefore, AY.28 and AY.104 appear to have a fitness advantage over the parental delta variant (B.1.617.2), while AY.28 also had a higher expression of sg-RNA compared to other sub-lineages. The clinical implications of these should be further investigated.

Identification of Novel Candidate CD8+ T Cell Epitopes of the SARS-CoV2 with Homology to Other Seasonal Coronaviruses.

Cross-reactive T cell immunity to seasonal coronaviruses (HCoVs) may lead to immunopathology or protection during SARS-CoV2 infection. To understand the influence of cross-reactive T cell responses, we used IEDB (Immune epitope database) and NetMHCpan (ver. 4.1) to identify candidate CD8+ T cell epitopes, restricted through HLA-A and B alleles. Conservation analysis was carried out for these epitopes with HCoVs, OC43, HKU1, and NL63. 12/18 the candidate CD8+ T cell epitopes (binding score of ≥0.90), which had a high degree of homology (>75%) with the other three HCoVs were within the NSP12 and NSP13 proteins. They were predicted to be restricted through HLA-A*2402, HLA-A*201, HLA-A*206, and HLA-B alleles B*3501. Thirty-one candidate CD8+ T cell epitopes that were specific to SARS-CoV2 virus (<25% homology with other HCoVs) were predominantly identified within the structural proteins (spike, envelop, membrane, and nucleocapsid) and the NSP1, NSP2, and NSP3. They were predominantly restricted through HLA-B*3501 (6/31), HLA-B*4001 (6/31), HLA-B*4403 (7/31), and HLA-A*2402 (8/31). It would be crucial to understand T cell responses that associate with protection, and the differences in the functionality and phenotype of epitope specific T cell responses, presented through different HLA alleles common in different geographical groups, to understand disease pathogenesis.

Seroprevalence of SARS-CoV-2 Infection in the Colombo Municipality Region, Sri Lanka.

Background: As the Municipality Council area in Colombo (CMC) experienced the highest number of cases until the end of January 2021, in Sri Lanka, we carried out a serosurvey prior to initiation of the vaccination program to understand the extent of the SARS-CoV-2 outbreak. Methods: SARS-CoV-2 seropositivity was determined in 2,547 individuals between the ages of 10-86 years, by the Wantai total antibody ELISA. We also compared seroprevalence using the haemagglutination test (HAT) to evaluate its usefulness in carrying out serosurveys. Results: The overall seropositivity rate was 24.46%, while seropositivity by HAT was 18.90%. Although The SARS-CoV-2 infection detection rates by PCR were highest in the population between the ages of 20-60 years of age, there was no statistically significant difference in the seropositivity rates in different age groups. For instance, although the seropositivity rate was highest in the 10-20 age group (34.03%), the PCR positivity rate was 9.80%. Differences in the PCR positivity rates and seropositivity rates were also seen in 60-70-year-olds (8.90 vs. 30.4%) and in individuals >70 years (4.10 vs. 1.20%). The seropositivity rate of the females was 29.70% (290/976), which was significantly higher (p < 0.002) than in males 21.2% (333/1,571). Conclusions: A high seroprevalence rate (24.5%) was seen in all age groups in the CMC suggesting that a high level of transmission was seen during this time. The higher PCR positivity rates between the ages of 20-60 are likely to be due to increased testing carried out in the working population. Therefore, the PCR positivity rates, appear to underestimate the true extent of the outbreak and the age groups which were infected.