Katherine W. Herbst

Most microbiome studies to date rely on sequencing short amplicons of the 16S rRNA gene on Illumina’s platforms. Because of the short read length, sequences often can be identified reliably only to the family or genus levels. Long-read sequencing with whole-length 16S rRNA sequencing can improve taxonomic resolution but often only to the species level. StrainID is an alternative approach that amplifies a large segment of the ribosomal operon, including the entire 16S rRNA gene, internal transcribed spacer, and a portion of the 23S rRNA gene. This longer amplicon is designed to allow ribotype-level classification. Although studies have demonstrated the utility of StrainID for several sample types, a direct comparison of StrainID to alternative approaches has not been done for saliva. Here, we compared the performance of StrainID to short-read amplicons with saliva samples as well as a synthetic mock DNA community. Short reads were amplified with primer pairs targeting the V1-V3 region of the 16S rRNA gene and were classified with several different taxonomic databases. We found that StrainID outperformed short reads not only in identifying amplicon sequence variants to the species level but also in demonstrating a key benefit with phylogenetic-based beta-diversity tests. Our results further build on establishing StrainID as a powerful method and specifically for its use with saliva samples. IMPORTANCE: The interpretation of microbiome composition studies is highly dependent on the methodologies chosen during experimental design, which affects factors such as resolution, throughput, cost, and accuracy. StrainID is an approach that can improve resolution while maintaining high-throughput and similar costs to short-read sequencing. The salivary microbiome represents a diverse community of microbes with links to a variety of health conditions and disease states. Closely related strains of bacteria can have drastically different effects on their host. Establishing StrainID as a valid approach for studying the salivary microbiome opens avenues for research that improve upon alternative methods by increasing sensitivity and accuracy compared to traditional short-read approaches.

Respiratory syncytial virus (RSV) remains an important cause of lower respiratory tract infections in young children, producing mild to life-threatening disease. Although rapid viral evolution through genetic drift is well established, the structural and functional impacts of specific pathoadaptive mutations linked to enhanced virulence are poorly defined. We investigated these relationships by isolating RSV from available nasal swabs of five hospitalized infants during the 2022–2023 winter season and conducting comparative viral genomic analysis. Severity of disease was evaluated using a validated clinical scoring system. Whole-genome sequencing followed by reference-guided assembly and structural modeling revealed distinct amino acid polymorphisms correlating with disease severity. Phylogenetic analysis placed all isolates within the RSV-A GA2.3.5 G clade. Isolates from mild moderate and severe cases clustered in A.D.1.5 and A.D.1.8 subclades. Nineteen amino acid differences were associated with clinical severity and isolates from moderate or severe cases replicated more rapidly in vitro than mild isolates. Computational glycosylation predictions indicated an increasing number of glycosylation sites in the G protein corresponding with greater disease severity. Together, these data suggest that specific pathoadaptive mutations may contribute to enhanced viral replication and severity, and are relevant for future surveillance efforts and the development of immune-based strategies targeting virulence-associated residues.

Most microbiome studies to date rely on sequencing short amplicons of the 16S rRNA gene on Illumina’s platforms. Because of the short read length, sequences often can be identified reliably only to the family or genus levels. Long read sequencing with whole-length 16S rRNA sequencing can improve taxonomic resolution, but often only to the species level. StrainID is an alternative approach that amplifies a large segment of the ribosomal operon, including the entire 16S rRNA gene, internal transcribed spacer, and a portion of the 23S rRNA gene. This longer amplicon is designed to allow ribotype-level classification. Although studies have demonstrated the utility of StrainID for several sample types, it has not yet been validated for saliva. Here, we compared the performance of StrainID to short read amplicons with saliva samples as well as a synthetic mock DNA community and human and mouse fecal samples. Short reads were amplified with primer pairs appropriate for the corresponding sample type, and were classified with two different taxonomic databases. For both saliva and fecal samples, we found that StrainID performed similarly to short reads overall and demonstrated a key benefit with phylogenetic-based beta diversity tests and taxonomic classification. Our results further build on establishing StrainID as a valid method and specifically validate its use with saliva samples. Importance: The interpretation of microbiome composition studies is highly dependent on the methodologies chosen during experimental design, which affects factors such as resolution, throughput, cost, and accuracy. StrainID is an approach that can improve resolution while maintaining high-throughput and similar costs to short-read sequencing. The salivary microbiome represents a diverse community of microbes with links to a variety of health conditions and disease states. Closely related strains of bacteria can have drastically different effects on their host. Establishing StrainID as a valid approach for studying the salivary microbiome opens avenues for research that improve upon alternative methods by increasing sensitivity and accuracy compared to traditional short read approaches.

The development of new point-of-care diagnostic testing tools for the detection of infectious diseases such as COVID-19 are a key aspect of clinical care and research. Accurate predictive classification methods are required to correctly identify and treat patients. Here, the onset of multisystem inflammatory syndrome in children (MIS-C), a more serious form of COVID-19, was predicted in a pediatric population using a set of multivariate immunological biomarker expression values. A first-stage bivariate detection of statistically significant biomarkers was obtained from a chosen set of standard cytokines and chemokine biomarkers considered relevant to COVID-19-related infection and disease. To incorporate the observed correlation structure among the resulting set of significant biomarkers, dimension reduction was then applied in the form of principal components. A second-stage logistic regression model using a small number of the principal component variables provided a highly predictive classification model for MIS-C. The resulting model was shown to compare favorably with an artificial neural network-based predictive model.

Multisystem Inflammatory Syndrome in Children (MIS-C) is a potentially life-threatening complication of COVID-19. The pathophysiological mechanisms leading to severe disease are poorly understood. This study leveraged clinical samples from a well-characterized cohort of children hospitalized with COVID-19 or MIS-C to compare immune-mediated biomarkers. Our objective was to identify selected immune molecules that could explain, in part, why certain SARS-CoV-2-infected children developed MIS-C. We hypothesized that type-2 helper T cell-mediated inflammation can elicit autoantibodies, which may account for some of the differences observed between the moderate–severe COVID-19 (COVID⁺) and MIS-C cohort. We enumerated blood leukocytes and measured levels of selected serum cytokines, chemokines, antibodies to COVID-19 antigens, and autoantibodies in children presenting to an academic medical center in Connecticut, United States. The neutrophil/lymphocyte and eosinophil/lymphocyte ratios were significantly higher in those in the MIS-C versus COVID⁺ cohort. IgM and IgA, but not IgG antibodies to SARS-CoV-2 receptor binding domain were significantly higher in the MIS-C cohort than the COVID⁺ cohort. The serum levels of certain type-2 cytokines (interleukin (IL)-4, IL-5, IL-6, IL-8, IL-10, IL-13, and IL-33) were significantly higher in children with MIS-C compared to the COVID⁺ and SARS-CoV-2-negative cohorts. IgG autoantibodies to brain antigens and pentraxin were higher in children with MIS-C compared to SARS-CoV-19-negative controls, and children with MIS-C had higher levels of IgG anti-contactin-associated protein-like 2 (caspr2) compared to the COVID⁺ and SARS-CoV-19-negative controls. We speculate that autoimmune responses in certain COVID-19 patients may induce pathophysiological changes that lead to MIS-C. The triggers of autoimmunity and factors accounting for type-2 inflammation require further investigation.

There is minimal knowledge regarding the durability of neutralization capacity and level of binding antibody generated against the highly transmissible circulating Omicron subvariants following SARS-CoV-2 infection in children with acute COVID-19 and those diagnosed with multisystem inflammatory syndrome in children (MIS-C) in the absence of vaccination. In this study, SARS-CoV-2 neutralization titers against the ancestral strain (WA1) and Omicron sublineages were evaluated in unvaccinated children admitted for COVID-19 (n = 32) and MIS-C (n = 32) at the time of hospitalization (baseline) and at six to eight weeks post-discharge (follow-up) between 1 April 2020, and 1 September 2022. In addition, antibody binding to the spike receptor binding domain (RBD) from WA1, BA.1, BA.2.75, and BA.4/BA.5 was determined using surface plasmon resonance (SPR). At baseline, the children with MIS-C demonstrated two-fold to three-fold higher binding and neutralizing antibodies against ancestral WA1 compared to those with COVID-19. Importantly, in children with COVID-19, the virus neutralization titers against the Omicron subvariants at six to eight weeks post-discharge reached the same level as those with MIS-C had at baseline but were higher than titers at 6–8 weeks post-discharge for MIS-C cases. Cross-neutralization capacity against recently emerged Omicron BQ.1, BQ.1.1, and XBB.1 variants was very low in children with either COVID-19 or MIS-C at all time points. These findings about post-infection immunity in children with either COVID-19 or MIS-C suggest the need for vaccinations in children with prior COVID-19 or MIS-C to provide effective protection from emerging and circulating SARS-CoV-2 variants.