Using Haplotype-Based Artificial Intelligence to Evaluate SARS-CoV-2 Novel Variants and Mutations.

Importance: Earlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emerging novel variants based on variant-specific mutation haplotypes and, in turn, be associated with enhanced implementation of risk-stratified public health prevention strategies. Objective: To develop a haplotype-based artificial intelligence (HAI) model for identifying novel variants, including mixture variants (MVs) of known variants and new variants with novel mutations. Design, Setting, and Participants: This cross-sectional study used serially observed viral genomic sequences globally (prior to March 14, 2022) to train and validate the HAI model and used it to identify variants arising from a prospective set of viruses from March 15 to May 18, 2022. Main Outcomes and Measures: Viral sequences, collection dates, and locations were subjected to statistical learning analysis to estimate variant-specific core mutations and haplotype frequencies, which were then used to construct an HAI model to identify novel variants. Results: Through training on more than 5 million viral sequences, an HAI model was built, and its identification performance was validated on an independent validation set of more than 5 million viruses. Its identification performance was assessed on a prospective set of 344 901 viruses. In addition to achieving an accuracy of 92.8% (95% CI within 0.1%), the HAI model identified 4 Omicron MVs (Omicron-Alpha, Omicron-Delta, Omicron-Epsilon, and Omicron-Zeta), 2 Delta MVs (Delta-Kappa and Delta-Zeta), and 1 Alpha-Epsilon MV, among which Omicron-Epsilon MVs were most frequent (609/657 MVs [92.7%]). Furthermore, the HAI model found that 1699 Omicron viruses had unidentifiable variants given that these variants acquired novel mutations. Lastly, 524 variant-unassigned and variant-unidentifiable viruses carried 16 novel mutations, 8 of which were increasing in prevalence percentages as of May 2022. Conclusions and Relevance: In this cross-sectional study, an HAI model found SARS-COV-2 viruses with MV or novel mutations in the global population, which may require closer examination and monitoring. These results suggest that HAI may complement phylogenic variant assignment, providing additional insights into emerging novel variants in the population.

Rapidly identifying new coronavirus mutations of potential concern in the Omicron variant using an unsupervised learning strategy.

Extensive mutations in the Omicron spike protein appear to accelerate the transmission of SARS-CoV-2, and rapid infections increase the odds that additional mutants will emerge. To build an investigative framework, we have applied an unsupervised machine learning approach to 4296 Omicron viral genomes collected and deposited to GISAID as of December 14, 2021, and have identified a core haplotype of 28 polymutants (A67V, T95I, G339D, R346K, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S, Q498R, N501Y, Y505H, T547K, D614G, H655Y, N679K, P681H, N764K, K796Y, N856K, Q954H, N69K, L981F) in the spike protein and a separate core haplotype of 17 polymutants in non-spike genes: (K38, A1892) in nsp3, T492 in nsp4, (P132, V247, T280, S284) in 3C-like proteinase, I189 in nsp6, P323 in RNA-dependent RNA polymerase, I42 in Exonuclease, T9 in envelope protein, (D3, Q19, A63) in membrane glycoprotein, and (P13, R203, G204) in nucleocapsid phosphoprotein. Using these core haplotypes as reference, we have identified four newly emerging polymutants (R346, A701, I1081, N1192) in the spike protein (p value = 9.37*10-4, 1.0*10-15, 4.76*10-7 and 1.56*10-4, respectively), and five additional polymutants in non-spike genes (D343G in nucleocapsid phosphoprotein, V1069I in nsp3, V94A in nsp4, F694Y in the RNA-dependent RNA polymerase and L106L/F of ORF3a) that exhibit significant increasing trajectories (all p values < 1.0*10-15). In the absence of relevant clinical data for these newly emerging mutations, it is important to monitor them closely. Two emerging mutations may be of particular concern: the N1192S mutation in spike protein locates in an extremely highly conserved region of all human coronaviruses that is integral to the viral fusion process, and the F694Y mutation in the RNA polymerase may induce conformational changes that could impact remdesivir binding.

Clinical Performance Characteristics of the Swift Normalase Amplicon Panel for Sensitive Recovery of Severe Acute Respiratory Syndrome Coronavirus 2 Genomes.

Amplicon-based sequencing methods are central in characterizing the diversity, transmission, and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but need to be rigorously assessed for clinical utility. Herein, we validated the Swift Biosciences' SARS-CoV-2 Swift Normalase Amplicon Panels using remnant clinical specimens. High-quality genomes meeting our established library and sequence quality criteria were recovered from positive specimens, with 95% limit of detection of 40.08 SARS-CoV-2 copies/PCR. Breadth of genome recovery was evaluated across a range of CT values (11.3 to 36.7; median, 21.6). Of 428 positive samples, 413 (96.5%) generated genomes with <10% unknown bases, with a mean genome coverage of 13,545× ± SD 8382×. No genomes were recovered from PCR-negative specimens (n = 30) or from specimens positive for non-SARS-CoV-2 respiratory viruses (n = 20). Compared with whole-genome shotgun metagenomic sequencing (n = 14) or Sanger sequencing for the spike gene (n = 11), pairwise identity between consensus sequences was 100% in all cases, with highly concordant allele frequencies (R2 = 0.99) between Swift and shotgun libraries. When samples from different clades were mixed at varying ratios, expected variants were detected even in 1:99 mixtures. When deployed as a clinical test, 268 tests were performed in the first 23 weeks, with a median turnaround time of 11 days, ordered primarily for outbreak investigations and infection control.

Using time-weighted average change from baseline of SARS-CoV-2 viral load to assess impact of hydroxychloroquine as postexposure prophylaxis and early treatment for COVID-19.

Two randomized controlled trials demonstrated no clinical benefit of hydroxychloroquine (HCQ) for either postexposure prophylaxis or early treatment of SARS-CoV-2 infection. Using data from these studies, we calculated the time-weighted average change from baseline SARS-CoV-2 viral load and demonstrated that HCQ did not affect viral clearance.

SIRT5 is a proviral factor that interacts with SARS-CoV-2 Nsp14 protein.

SARS-CoV-2 non-structural protein Nsp14 is a highly conserved enzyme necessary for viral replication. Nsp14 forms a stable complex with non-structural protein Nsp10 and exhibits exoribonuclease and N7-methyltransferase activities. Protein-interactome studies identified human sirtuin 5 (SIRT5) as a putative binding partner of Nsp14. SIRT5 is an NAD-dependent protein deacylase critical for cellular metabolism that removes succinyl and malonyl groups from lysine residues. Here we investigated the nature of this interaction and the role of SIRT5 during SARS-CoV-2 infection. We showed that SIRT5 interacts with Nsp14, but not with Nsp10, suggesting that SIRT5 and Nsp10 are parts of separate complexes. We found that SIRT5 catalytic domain is necessary for the interaction with Nsp14, but that Nsp14 does not appear to be directly deacylated by SIRT5. Furthermore, knock-out of SIRT5 or treatment with specific SIRT5 inhibitors reduced SARS-CoV-2 viral levels in cell-culture experiments. SIRT5 knock-out cells expressed higher basal levels of innate immunity markers and mounted a stronger antiviral response, independently of the Mitochondrial Antiviral Signaling Protein MAVS. Our results indicate that SIRT5 is a proviral factor necessary for efficient viral replication, which opens novel avenues for therapeutic interventions.

Application of Statistical Learning to Identify Omicron Mutations in SARS-CoV-2 Viral Genome Sequence Data From Populations in Africa and the United States.

Importance: With timely collection of SARS-CoV-2 viral genome sequences, it is important to apply efficient data analytics to detect emerging variants at the earliest time. Objective: To evaluate the application of a statistical learning strategy (SLS) to improve early detection of novel SARS-CoV-2 variants using viral sequence data from global surveillance. Design, Setting, and Participants: This case series applied an SLS to viral genomic sequence data collected from 63 686 individuals in Africa and 531 827 individuals in the United States with SARS-CoV-2. Data were collected from January 1, 2020, to December 28, 2021. Main Outcomes and Measures: The outcome was an indicator of Omicron variant derived from viral sequences. Centering on a temporally collected outcome, the SLS used the generalized additive model to estimate locally averaged Omicron caseload percentages (OCPs) over time to characterize Omicron expansion and to estimate when OCP exceeded 10%, 25%, 50%, and 75% of the caseload. Additionally, an unsupervised learning technique was applied to visualize Omicron expansions, and temporal and spatial distributions of Omicron cases were investigated. Results: In total, there were 2698 cases of Omicron in Africa and 12 141 in the United States. The SLS found that Omicron was detectable in South Africa as early as December 31, 2020. With 10% OCP as a threshold, it may have been possible to declare Omicron a variant of concern as early as November 4, 2021, in South Africa. In the United States, the application of SLS suggested that the first case was detectable on November 21, 2021. Conclusions and Relevance: The application of SLS demonstrates how the Omicron variant may have emerged and expanded in Africa and the United States. Earlier detection could help the global effort in disease prevention and control. To optimize early detection, efficient data analytics, such as SLS, could assist in the rapid identification of new variants as soon as they emerge, with or without lineages designated, using viral sequence data from global surveillance.

Narrow transmission bottlenecks and limited within-host viral diversity during a SARS-CoV-2 outbreak on a fishing boat.

The long-term evolution of viruses is ultimately due to viral mutants that arise within infected individuals and transmit to other individuals. Here, we use deep sequencing to investigate the transmission of viral genetic variation among individuals during a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that infected the vast majority of crew members on a fishing boat. We deep-sequenced nasal swabs to characterize the within-host viral population of infected crew members, using experimental duplicates and strict computational filters to ensure accurate variant calling. We find that within-host viral diversity is low in infected crew members. The mutations that did fix in some crew members during the outbreak are not observed at detectable frequencies in any of the sampled crew members in which they are not fixed, suggesting that viral evolution involves occasional fixation of low-frequency mutations during transmission rather than persistent maintenance of within-host viral diversity. Overall, our results show that strong transmission bottlenecks dominate viral evolution even during a superspreading event with a very high attack rate.

Molecular Analysis of SARS-CoV-2 Lineages in Armenia.

The sequencing of SARS-CoV-2 provides essential information on viral evolution, transmission, and epidemiology. In this paper, we performed the whole-genome sequencing of SARS-CoV-2 using nanopore and Illumina sequencing to describe the circulation of the virus lineages in Armenia. The analysis of 145 full genomes identified six clades (19A, 20A, 20B, 20I, 21J, and 21K) and considerable intra-clade PANGO lineage diversity. Phylodynamic and transmission analysis allowed to attribute specific clades as well as infer their importation routes. Thus, the first two waves of positive case increase were caused by the 20B clade, the third peak caused by the 20I (Alpha), while the last two peaks were caused by the 21J (Delta) and 21K (Omicron) variants. The functional analyses of mutations in sequences largely affected epitopes associated with protective HLA loci and did not cause the loss of the signal in PCR tests targeting ORF1ab and N genes as confirmed by RT-PCR. We also compared the performance of nanopore and Illumina short-read sequencing and showed the utility of nanopore sequencing as an efficient and affordable alternative for large-scale molecular epidemiology research. Thus, our paper describes new data on the genomic diversity of SARS-CoV-2 variants in Armenia in the global context of the virus molecular genomic surveillance.

Rapid and accurate identification of SARS-CoV-2 Omicron variants using droplet digital PCR (RT-ddPCR).

BACKGROUND: Some mutations in the receptor binding domain of the SARS-CoV-2 Spike protein are associated with increased transmission or substantial reductions in vaccine efficacy, including in recently described Omicron subvariants. The changing frequencies of these mutations combined with their differing susceptibility to available therapies have posed significant problems for clinicians and public health professionals. OBJECTIVE: To develop an assay capable of rapidly and accurately identifying variants including Omicron in clinical specimens to enable case tracking and/or selection of appropriate clinical treatment. STUDY DESIGN: Using three duplex RT-ddPCR reactions targeting four amino acids, we tested 419 positive clinical specimens from February to December 2021 during a period of rapidly shifting variant prevalences and compared genotyping results to genome sequences for each sample, determining the sensitivity and specificity of the assay for each variant. RESULTS: Mutation determinations for 99.7% of detected samples agree with NGS data for those samples, and are accurate despite wide variation in RNA concentration and potential confounding factors like transport medium, presence of additional respiratory viruses, and additional mutations in primer and probe sequences. The assay accurately identified the first 15 Omicron variants in our laboratory including the first Omicron in Washington State and discriminated against S-gene dropout Delta specimen. CONCLUSION: We describe an accurate, precise, and specific RT-ddPCR assay for variant detection that remains robust despite being designed prior the emergence of Delta and Omicron variants. The assay can quickly identify mutations in current and past SARS-CoV-2 variants, and can be adapted to future mutations.

Molecular Analysis of SARS-CoV-2 Lineages in Armenia

The sequencing of SARS-CoV-2 provides essential information on viral evolution, transmission, and epidemiology. In this paper, we performed the whole-genome sequencing of SARS-CoV-2 using nanopore and Illumina sequencing to describe the circulation of the virus lineages in Armenia. The analysis of 145 full genomes identified six clades (19A, 20A, 20B, 20I, 21J, and 21K) and considerable intra-clade PANGO lineage diversity. Phylodynamic and transmission analysis allowed to attribute specific clades as well as infer their importation routes. Thus, the first two waves of positive case increase were caused by the 20B clade, the third peak caused by the 20I (Alpha), while the last two peaks were caused by the 21J (Delta) and 21K (Omicron) variants. The functional analyses of mutations in sequences largely affected epitopes associated with protective HLA loci and did not cause the loss of the signal in PCR tests targeting ORF1ab and N genes as confirmed by RT-PCR. We also compared the performance of nanopore and Illumina short-read sequencing and showed the utility of nanopore sequencing as an efficient and affordable alternative for large-scale molecular epidemiology research. Thus, our paper describes new data on the genomic diversity of SARS-CoV-2 variants in Armenia in the global context of the virus molecular genomic surveillance.

Self-Assessed Severity as a Determinant of Coronavirus Disease 2019 Symptom Specificity: A Longitudinal Cohort Study.

Coronavirus disease 2019 symptom definitions rarely include symptom severity. We collected daily nasal swab samples and symptom diaries from contacts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case patients. Requiring ≥1 moderate or severe symptom reduced sensitivity to predict SARS-CoV-2 shedding from 60.0% (95% confidence interval [CI], 52.9%-66.7%) to 31.5% (95% CI, 25.7%- 38.0%) but increased specificity from 77.5% (95% CI, 75.3%-79.5%) to 93.8% (95% CI, 92.7%-94.8%).

Respiratory viruses in hematopoietic cell transplant candidates: impact of preexisting lower tract disease on outcomes.

Pretransplant respiratory virus infections (RVIs) have been shown to negatively affect hematopoietic cell transplantation (HCT) outcomes. The impact of and need for delay of HCT for pretransplant infection with human rhinovirus (HRV) or endemic human coronavirus (HCoV; 229E, OC43, NL63, and HKU1) remain controversial. We analyzed the impact of symptomatic RVI within ≤90 days before HCT on overall mortality, posttransplant lower respiratory tract disease (LRD), and days alive and out of hospital (DAOH) by day 100 post-HCT in multivariable models. Among 1,643 adult HCT recipients (58% allogeneic recipients), 704 (43%) were tested for RVI before HCT, and 307 (44%) tested positive. HRV was most commonly detected (56%). Forty-five (15%) of 307 HCT recipients had LRD with the same virus early after HCT. Pretransplant upper respiratory tract infection (URI) with influenza, respiratory syncytial virus, adenovirus, human metapneumovirus, parainfluenza virus, HRV, or endemic HCoV was not associated with increased overall mortality or fewer DAOH. However, in allogeneic recipients who received myeloablative conditioning, LRD due to any respiratory virus, including HRV alone, was associated with increased overall mortality (adjusted hazard ratio, 10.8 [95% confidence interval, 3.29-35.1] for HRV and 3.21 [95% confidence interval, 1.15-9.01] for all other viruses). HRV LRD was also associated with fewer DAOH. Thus, the presence of LRD due to common respiratory viruses, including HRV, before myeloablative allogeneic HCT was associated with increased mortality and hospitalization. Pretransplant URI due to HRV and endemic HCoV was not associated with these outcomes. Improved management strategies for pretransplant LRD are warranted.

Detection and Kinetics of Subgenomic Severe Acute Respiratory Syndrome Coronavirus 2 RNA Viral Load in Longitudinal Diagnostic RNA-Positive Samples.

While detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by diagnostic reverse-transcription polymerase chain reaction (RT-PCR) is highly sensitive for viral RNA, the nucleic acid amplification of subgenomic RNAs (sgRNAs) that are the product of viral replication may more accurately identify replication. We characterized the diagnostic RNA and sgRNA detection by RT-PCR from nasal swab samples collected daily by participants in postexposure prophylaxis or treatment studies for SARS-CoV-2. Among 1932 RT-PCR-positive swab samples with sgRNA tests, 40% (767) had detectable sgRNA. Above a diagnostic RNA viral load (VL) threshold of 5.1 log10 copies/mL, 96% of samples had detectable sgRNA with VLs that followed a linear trend. The trajectories of diagnostic RNA and sgRNA VLs differed, with 80% peaking on the same day but duration of sgRNA detection being shorter (8 vs 14 days). With a large sample of daily swab samples we provide comparative sgRNA kinetics and a diagnostic RNA threshold that correlates with replicating virus independent of symptoms or duration of illness.

Variants of Concern Are Overrepresented Among Postvaccination Breakthrough Infections of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Washington State.

Across 20 vaccine breakthrough cases detected at our institution, all 20 (100%) infections were due to variants of concern (VOCs) and had a median Ct of 20.2 (IQR, 17.1-23.3). When compared with 5174 contemporaneous samples sequenced in our laboratory, VOCs were significantly enriched among breakthrough infections (P < .05).

Evaluation of Commercially Available High-Throughput SARS-CoV-2 Serologic Assays for Serosurveillance and Related Applications.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serosurveys can estimate cumulative incidence for monitoring epidemics, requiring assessment of serologic assays to inform testing algorithm development and interpretation of results. We conducted a multilaboratory evaluation of 21 commercial high-throughput SARS-CoV-2 serologic assays using blinded panels of 1,000 highly characterized specimens. Assays demonstrated a range of sensitivities (96%-63%), specificities (99%-96%), and precision (intraclass correlation coefficient 0.55-0.99). Durability of antibody detection was dependent on antigen and immunoglobulin targets; antispike and total Ig assays demonstrated more stable longitudinal reactivity than antinucleocapsid and IgG assays. Assays with high sensitivity, specificity, and durable antibody detection are ideal for serosurveillance, but assays demonstrating waning reactivity are appropriate for other applications, including correlation with neutralizing activity and detection of anamnestic boosting by reinfections. Assay performance must be evaluated in context of intended use, particularly in the context of widespread vaccination and circulation of SARS-CoV-2 variants.

Mutations in viral nucleocapsid protein and endoRNase are discovered to associate with COVID19 hospitalization risk

SARS-CoV-2 is spreading worldwide with continuously evolving variants, some of which occur in the Spike protein and appear to increase viral transmissibility. However, variants that cause severe COVID-19 or lead to other breakthroughs have not been well characterized. To discover such viral variants, we assembled a cohort of 683 COVID-19 patients;388 inpatients (“cases”) and 295 outpatients (“controls”) from April to August 2020 using electronically captured COVID test request forms and sequenced their viral genomes. To improve the analytical power, we accessed 7137 viral sequences in Washington State to filter out viral single nucleotide variants (SNVs) that did not have significant expansions over the collection period. Applying this filter led to the identification of 53 SNVs that were statistically significant, of which 13 SNVs each had 3 or more variant copies in the discovery cohort. Correlating these selected SNVs with case/control status, eight SNVs were found to significantly associate with inpatient status (q-values < 0.01). Using temporal synchrony, we identified a four SNV-haplotype (t19839-g28881-g28882-g28883) that was significantly associated with case/control status (Fisher’s exact p = 2.84 × 10–11). This haplotype appeared in April 2020, peaked in June, and persisted into January 2021. The association was replicated (OR = 5.46, p-value = 4.71 × 10−12) in an independent cohort of 964 COVID-19 patients (June 1, 2020 to March 31, 2021). The haplotype included a synonymous change N73N in endoRNase, and three non-synonymous changes coding residues R203K, R203S and G204R in the nucleocapsid protein. This discovery points to the potential functional role of the nucleocapsid protein in triggering “cytokine storms” and severe COVID-19 that led to hospitalization. The study further emphasizes a need for tracking and analyzing viral sequences in correlations with clinical status.

Measuring infectious SARS-CoV-2 in clinical samples reveals a higher viral titer:RNA ratio for Delta and Epsilon vs. Alpha variants.

Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants pose a challenge to controlling the COVID-19 pandemic. Previous studies indicate that clinical samples collected from individuals infected with the Delta variant may contain higher levels of RNA than previous variants, but the relationship between levels of viral RNA and infectious virus for individual variants is unknown. We measured infectious viral titer (using a microfocus-forming assay) and total and subgenomic viral RNA levels (using RT-PCR) in a set of 162 clinical samples containing SARS-CoV-2 Alpha, Delta, and Epsilon variants that were collected in identical swab kits from outpatient test sites and processed soon after collection. We observed a high degree of variation in the relationship between viral titers and RNA levels. Despite this, the overall infectivity differed among the three variants. Both Delta and Epsilon had significantly higher infectivity than Alpha, as measured by the number of infectious units per quantity of viral E gene RNA (5.9- and 3.0-fold increase; P < 0.0001, P = 0.014, respectively) or subgenomic E RNA (14.3- and 6.9-fold increase; P < 0.0001, P = 0.004, respectively). In addition to higher viral RNA levels reported for the Delta variant, the infectivity (amount of replication competent virus per viral genome copy) may be increased compared to Alpha. Measuring the relationship between live virus and viral RNA is an important step in assessing the infectivity of novel SARS-CoV-2 variants. An increase in the infectivity for Delta may further explain increased spread, suggesting a need for increased measures to prevent viral transmission.

An international, interlaboratory ring trial confirms the feasibility of an extraction-less "direct" RT-qPCR method for reliable detection of SARS-CoV-2 RNA in clinical samples.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is used worldwide to test and trace the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). "Extraction-less" or "direct" real time-reverse transcription polymerase chain reaction (RT-PCR) is a transparent and accessible qualitative method for SARS-CoV-2 detection from nasopharyngeal or oral pharyngeal samples with the potential to generate actionable data more quickly, at a lower cost, and with fewer experimental resources than full RT-qPCR. This study engaged 10 global testing sites, including laboratories currently experiencing testing limitations due to reagent or equipment shortages, in an international interlaboratory ring trial. Participating laboratories were provided a common protocol, common reagents, aliquots of identical pooled clinical samples, and purified nucleic acids and used their existing in-house equipment. We observed 100% concordance across laboratories in the correct identification of all positive and negative samples, with highly similar cycle threshold values. The test also performed well when applied to locally collected patient nasopharyngeal samples, provided the viral transport media did not contain charcoal or guanidine, both of which appeared to potently inhibit the RT-PCR reaction. Our results suggest that direct RT-PCR assay methods can be clearly translated across sites utilizing readily available equipment and expertise and are thus a feasible option for more efficient COVID-19 coronavirus disease testing as demanded by the continuing pandemic.

Trajectory of Viral RNA Load Among Persons With Incident SARS-CoV-2 G614 Infection (Wuhan Strain) in Association With COVID-19 Symptom Onset and Severity.

Importance: The SARS-CoV-2 viral trajectory has not been well characterized in incident infections. These data are needed to inform natural history, prevention practices, and therapeutic development. Objective: To characterize early SARS-CoV-2 viral RNA load (hereafter referred to as viral load) in individuals with incident infections in association with COVID-19 symptom onset and severity. Design, Setting, and Participants: This prospective cohort study was a secondary data analysis of a remotely conducted study that enrolled 829 asymptomatic community-based participants recently exposed (<96 hours) to persons with SARS-CoV-2 from 41 US states from March 31 to August 21, 2020. Two cohorts were studied: (1) participants who were SARS-CoV-2 negative at baseline and tested positive during study follow-up, and (2) participants who had 2 or more positive swabs during follow-up, regardless of the initial (baseline) swab result. Participants collected daily midturbinate swab samples for SARS-CoV-2 RNA detection and maintained symptom diaries for 14 days. Exposure: Laboratory-confirmed SARS-CoV-2 infection. Main Outcomes and Measures: The observed SARS-CoV-2 viral load among incident infections was summarized, and piecewise linear mixed-effects models were used to estimate the characteristics of viral trajectories in association with COVID-19 symptom onset and severity. Results: A total of 97 participants (55 women [57%]; median age, 37 years [IQR, 27-52 years]) developed incident infections during follow-up. Forty-two participants (43%) had viral shedding for 1 day (median peak viral load cycle threshold [Ct] value, 38.5 [95% CI, 38.3-39.0]), 18 (19%) for 2 to 6 days (median Ct value, 36.7 [95% CI, 30.2-38.1]), and 31 (32%) for 7 days or more (median Ct value, 18.3 [95% CI, 17.4-22.0]). The cycle threshold value has an inverse association with viral load. Six participants (6%) had 1 to 6 days of viral shedding with censored duration. The peak mean (SD) viral load was observed on day 3 of shedding (Ct value, 33.8 [95% CI, 31.9-35.6]). Based on the statistical models fitted to 129 participants (60 men [47%]; median age, 38 years [IQR, 25-54 years]) with 2 or more SARS-CoV-2-positive swab samples, persons reporting moderate or severe symptoms tended to have a higher peak mean viral load than those who were asymptomatic (Ct value, 23.3 [95% CI, 22.6-24.0] vs 30.7 [95% CI, 29.8-31.4]). Mild symptoms generally started within 1 day of peak viral load, and moderate or severe symptoms 2 days after peak viral load. All 535 sequenced samples detected the G614 variant (Wuhan strain). Conclusions and Relevance: This cohort study suggests that having incident SARS-CoV-2 G614 infection was associated with a rapid viral load peak followed by slower decay. COVID-19 symptom onset generally coincided with peak viral load, which correlated positively with symptom severity. This longitudinal evaluation of the SARS-CoV-2 G614 with frequent molecular testing serves as a reference for comparing emergent viral lineages to inform clinical trial designs and public health strategies to contain the spread of the virus.

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.