Early SARS-CoV-2 Reinfections Involving the Same or Different Genomic Lineages, Spain.

Centers for Disease Control and Prevention guidelines consider SARS-CoV-2 reinfection when sequential COVID-19 episodes occur >90 days apart. However, genomic diversity acquired over recent COVID-19 waves could mean previous infection provides insufficient cross-protection. We used genomic analysis to assess the percentage of early reinfections in a sample of 26 patients with 2 COVID-19 episodes separated by 20-45 days. Among sampled patients, 11 (42%) had reinfections involving different SARS-CoV-2 variants or subvariants. Another 4 cases were probable reinfections; 3 involved different strains from the same lineage or sublineage. Host genomic analysis confirmed the 2 sequential specimens belonged to the same patient. Among all reinfections, 36.4% involved non-Omicron, then Omicron lineages. Early reinfections showed no specific clinical patterns; 45% were among unvaccinated or incompletely vaccinated persons, 27% were among persons <18 years of age, and 64% of patients had no risk factors. Time between sequential positive SARS-CoV-2 PCRs to consider reinfection should be re-evaluated.

A mutation responsible for impaired detection by the Xpert SARS-CoV-2 assay independently emerged in different lineages during the SARS-CoV-2 pandemic.

BACKGROUND: COVID-19 diagnosis lies on the detection of SARS-CoV-2 on nasopharyngeal specimens by RT-PCR. The Xpert-Xpress SARS-CoV-2 assay provides results in less than one hour from specimen reception, which makes it suitable for clinical/epidemiological circumstances that require faster responses. The analysis of a COVID-19 outbreak suspected in the neonatology ward from our institution showed that the Ct values obtained for the targeted genes in the Xpert assay were markedly different within each specimen (N Ct value > 20 cycles above the E Ct value). RESULTS: We identified the mutation C29200T in the N gene as responsible for an impairment in the N gene amplification by performing whole genome sequencing of the specimens involved in the outbreak (Omicron variant). Subsequently, a retrospective analysis of all specimens sequenced in our institution allowed us to identify the same SNP as responsible for similar impairments in another 12 cases (42% of the total cases reported in the literature). Finally, we found that the same SNP emerged in five different lineages independently, throughout almost all the COVID-19 pandemic. CONCLUSIONS: We demonstrated for the first time the impact of this SNP on the Xpert assay, when harbored by new Omicron variants. We extend our observation period throughout almost all the COVID-19 pandemic, offering the most updated observations of this phenomenon, including sequences from the seventh pandemic wave, until now absent in the reports related to this issue. Continuous monitoring of emerging SNPs that could affect the performance of the most commonly used diagnostic tests, is required to redesign the tests to restore their correct performance.

Mining genomic repositories to further our knowledge of the extent of SARS-CoV-2 co-infections.

Recombination events between Delta and Omicron SARS-CoV-2 lineages highlight the need for co-infection research. Existing studies focus on late-phase co-infections, with few examining earlier pandemic stages. This new study aims to globally identify and characterize co-infections using a bioinformatic pipeline to analyse genomic data from diverse locations and pandemic phases. Among 26988 high-quality SARS-CoV-2 isolates from 11 diverse project databases, we identified 141 potential co-infection cases (0.52%), surpassing previous prevalence estimates. These co-infections were observed throughout the pandemic timeline, with an increase noted after the emergence of the Omicron variant. Co-infections involving the Omicron variant were the most prevalent, potentially influenced by the high level of diversity within this lineage and its impact on the viral landscape. Additionally, we found co-infections involving the pre-Alpha/Alpha lineages, which have been rarely described, raising possibilities of contributing to new lineage emergence through recombination events. The analysis revealed co-infection cases involving both different and the same lineages/sublineages. Our study showcases the potential of our pipeline to leverage valuable information stored in global sequence repositories, advancing our understanding of SARS-CoV-2 co-infections. The prevalence of co-infections highlights the importance of monitoring viral diversity and its potential implications on disease dynamics. Integrating clinical data with genomic findings can further shed light on the clinical implications and outcomes of co-infections.

Systematic genomic analysis of SARS-CoV-2 co-infections throughout the pandemic and segregation of the strains involved.

BACKGROUND: SARS-CoV-2 recombinants involving the divergent Delta and Omicron lineages have been described, and one of them, "Kraken" (XBB.1.5), has recently been a matter of concern. Recombination requires the coexistence of two SARS-CoV-2 strains in the same individual. Only a limited number of studies have focused on the identification of co-infections and are restricted to co-infections involving the Delta/Omicron lineages. METHODS: We performed a systematic identification of SARS-CoV-2 co-infections throughout the pandemic (7609 different patients sequenced), not biassed towards the involvement of highly divergent lineages. Through a comprehensive set of validations based on the distribution of allelic frequencies, phylogenetic consistency, re-sequencing, host genetic analysis and contextual epidemiological analysis, these co-infections were robustly assigned. RESULTS: Fourteen (0.18%) co-infections with ≥ 8 heterozygous calls (8-85 HZs) were identified. Co-infections were identified throughout the pandemic and involved an equal proportion of strains from different lineages/sublineages (including pre-Alpha variants, Delta and Omicron) or strains from the same lineage. Co-infected cases were mainly unvaccinated, with mild or asymptomatic clinical presentation, and most were at risk of overexposure associated with the healthcare environment. Strain segregation enabled integration of sequences to clarify nosocomial outbreaks where analysis had been impaired due to co-infection. CONCLUSIONS: Co-infection cases were identified throughout the pandemic, not just in the time periods when highly divergent lineages were co-circulating. Co-infections involving different lineages or strains from the same lineage were occurring in the same proportion. Most cases were mild, did not require medical assistance and were not vaccinated, and a large proportion were associated with the hospital environment.

Rapid Identification of Relevant Microbial Strains by Identifying Multiple Marker Single Nucleotide Polymorphisms via Amplicon Sequencing: Epidemic Monkeypox Virus as a Proof of Concept.

Despite the proven value of applying genomic data for epidemiological purposes, commonly used high-throughput sequencing formats are not adapted to the response times required to intervene and finally control outbreaks. In this study, we propose a fast alternative to whole-genome sequencing (WGS) to track relevant microbiological strains: nanopore sequencing of multiple amplicons including strain marker single nucleotide polymorphisms (SNPs). As a proof a concept, we evaluated the performance of our approach to offer a rapid response to the most recent public health global alarm, the monkeypox virus (MPXV) global outbreak. Through a multisequence alignment, a list of 42 SNPs were extracted as signature makers for this outbreak. Twenty primer pairs were designed to amplify in a multiplex PCR the regions including 22 of these SNPs. Amplicon pools were sequenced in a MinION device, and SNPs were called in real time by an in-house bioinformatic pipeline. A total of 120 specimens (95 MPXV-PCR positive, Ct values from 14 to 39) were selected. In 67.37% of the positive subset, all 22 SNPs were called. After excluding low viral load specimens, in 92% of samples ≥11 outbreak SNPs were called. No false positives were observed in any of the 25 negative specimens. The total turnaround time required for this strategy was 5 hours, and the cost per sample was 14 euros. Nanopore sequencing of multiple amplicons harboring signature SNPs escapes the targeting limitations of strain-specific PCRs and offers a powerful alternative to systematic WGS, paving the way to real-time genomic epidemiology and making immediate intervention possible to finally optimize transmission control. IMPORTANCE Nanopore sequencing of multiple amplicons harboring signature single nucleotide polymorphisms (SNPs) escapes the targeting limitations of strain-specific PCRs and offers a powerful alternative to systematic whole-genome analysis, paving the way to real-time genomic epidemiology and making immediate intervention possible to finally optimize transmission control.

Identifying well-folded de novo proteins in the new era of accurate structure prediction.

Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing.