Assessing the contributions of phylogenetic and environmental determinants of allergic cosensitization to fungi in humans.

BACKGROUND: Understanding how allergies to 1 environmental fungus can lead to cosensitization to related fungi is important for the clinical management of allergies. Cosensitization can be caused by monosensitization combined with antibody cross-reactivity, or by coexposures driving independent sensitizations. A pioneering study showed that patterns of IgE cosensitization among 17 fungal species mirror fungal phylogeny. This could reflect either epitope or habitat similarity. Thanks to an improved understanding of fungal phylogeny, larger serologic testing datasets, and environmental data on household fungi, we can now characterize the relationship between cosensitization, species similarity, and likely coexposure with greater precision. OBJECTIVE: To assess the degree to which IgE cosensitization in a group of 17 fungi can be attributed to species similarity or environmental coexposure. METHODS: Cosensitization patterns among 17 fungal species were estimated from a dataset of approximately 8 million serologic tests on 1.6 million patients. Linear regression of cosensitization on phylogenetic distance and imputed coexposure was performed. In addition, branch lengths for the phylogenetic tree were re-estimated on the basis of cosensitization and compared with corresponding phylogenetic branch lengths. RESULTS: Phylogenetic distance explains much of the observed cosensitization (adjusted r2 = .68, p < .001). Imputed environmental coexposures and test co-ordering patterns do not significantly predict cosensitization. Branch length comparisons between the cosensitization and phylogenetic trees identified several species as less cosensitizing than phylogenetic distance predicts. CONCLUSION: Combined evidence from clinical IgE testing data on fungi, along with phylogenetic and environmental exposure data, supports the hypothesis that cosensitization is caused primarily by monosensitization plus cross-reactivity, rather than multisensitization. A serologic test result should be interpreted as pointing to a group of related species that include the sensitizing agent rather than as uniquely identifying the agent. The identified patterns of cross-reactivity may help optimize test panel design.

Fungal allergen sensitization: Prevalence, risk factors, and geographic variation in the United States.

BACKGROUND: Many fungal species are associated with the pathogenesis of allergic disease, yet most epidemiologic studies on IgE-mediated fungal sensitization have only included a few species. OBJECTIVE: We investigated fungal allergen sensitization prevalence, risk factors, and geographic variation in the United States. METHODS: From 2014 to 2019, a total of 7,912,504 serum-specific IgE (sIgE) test results for 17 fungal species were measured in 1,651,203 patients aged 0-85 years by a US-wide clinical laboratory. Fungal sensitization prevalence, patterns, and relationship with demographic characteristics, clinical diagnoses, and geographic regions were analyzed. RESULTS: Twenty-two percent of patients were positive (sIgE > 0.10 kUA/L) to at least 1 fungal allergen; 13.7% were positive to >2 fungal allergens. Fungal species-specific positivity rates ranged 7.4-18.6% and were highest for Candida albicans (18.6%), Alternaria alternata (16.6%), Stemphylium herbarum (14.9%), and Aspergillus fumigatus (14.2%). Other fungi that were frequently tested had relatively low positivity rates (eg, Cladosporium herbarum 11.1%, Penicillium chrysogenum 10.7%). Independent risk factors for test positivity for all fungal species included male sex, teen age (highest in those aged 10-19 years), atopic dermatitis, and asthma. Fungal sensitization was generally higher in urban areas and ecoregions composed predominantly of grasslands and prairies compared to woodlands and forest, although there was greater variation in sensitization risk to different fungi in different ecoregions. CONCLUSION: Independent risk factors for fungal sensitization include male sex, teen ages, atopic dermatitis, asthma, and ecoregion.

SARS-CoV-2 spike-protein targeted serology test results and their association with subsequent COVID-19-related outcomes.

Importance: In the absence of evidence of clinical utility, the United States' Centers for Disease Control and Prevention does not currently recommend the assessment of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike-protein antibody levels. Clinicians and their patients, especially immunocompromised patients, may benefit from an adjunctive objective clinical laboratory measure of risk, using SARS-CoV-2 serology. Objective: The aim of this study is to estimate the association between SARS-CoV-2 spike-protein targeted antibody levels and clinically relevant outcomes overall and among clinically relevant subgroups, such as vaccine and immunocompetency statuses. Design: A retrospective cohort study was conducted using laboratory-based data containing SARS-CoV-2 antibody testing results, as well as medical and pharmacy claim data. SARS-CoV-2 testing was performed by two large United States-based reference clinical laboratories, Labcorp® and Quest Diagnostics, and was linked to medical insurance claims, including vaccination receipt, through the HealthVerity Marketplace. Follow-up for outcomes began after each eligible individual's first SARS-CoV-2 semiquantitative spike-protein targeted antibody test, from 16 November 2020 to 30 December 2021. Exposures: Exposure is defined as having SARS-CoV-2 spike-protein targeted antibody testing. Main outcomes and measures: Study outcomes were SARS-CoV-2 infection and a serious composite outcome (hospitalization with an associated SARS-CoV-2 infection or all-cause death). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Propensity score matching was used for confounding covariate control. Results: In total, 143,091 (73.2%) and 52,355 (26.8%) eligible individuals had detectable and non-detectable levels of SARS-CoV-2 spike-protein targeted antibodies, respectively. In the overall population, having detectable vs. non-detectable antibodies was associated with an estimated 44% relative reduction in SARS-CoV-2 subsequent infection risk (HR, 0.56; 95% CI 0.53-0.59) and an 80% relative reduction in the risk of serious composite outcomes (HR 0.20; 95% CI 0.15-0.26). Relative risk reductions were observed across subgroups, including among immunocompromised persons. Conclusion and relevance: Individuals with detectable SARS-CoV-2 spike-protein targeted antibody levels had fewer associated subsequent SARS-CoV-2 infections and serious adverse clinical outcomes. Policymakers and clinicians may find SARS-CoV-2 spike-protein targeted serology testing to be a useful adjunct in counseling patients with non-detectable antibody levels about adverse risks and reinforcing appropriate actions to mitigate such risks.

Clinical characterization of the mutational landscape of 24,639 real-world samples from patients with myeloid malignancies.

Myeloid neoplasms represent a broad spectrum of hematological disorders for which somatic mutation status in key driver genes is important for diagnosis, prognosis and treatment. Here we summarize the findings of a targeted, next generation sequencing laboratory developed test in 24,639 clinical myeloid samples. Data were analyzed comprehensively and as part of individual cohorts specific to acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and myeloproliferative neoplasms (MPN). Overall, 48,015 variants were detected, and variants were found in all 50 genes in the panel. The mean number of mutations per patient was 1.95. Mutation number increased with age (Spearman's rank correlation coefficient, ρ = 0.29, P < 0.0001) and was higher in patients with AML than MDS or MPN (Student's t-test, P < 0.0001). TET2 was the most common mutation detected (19.1% of samples; 4,695/24,639) including 7.7% (1,908/24,639) with multi-hit TET2 mutations. Mutation frequency was correlated between patients with cytopenias and MDS (Spearman's, ρ = 0.97, P < 2.2×10-16) with the MDS diagnostic gene SF3B1 being the only notable outlier. This large retrospective study shows the utility of NGS testing to inform clinical decisions during routine clinical care and highlights the mutational landscape of a broad population of myeloid patients.

Estimated SARS-CoV-2 antibody seroprevalence trends and relationship to reported case prevalence from a repeated, cross-sectional study in the 50 states and the District of Columbia, United States-October 25, 2020-February 26, 2022.

Background: Sero-surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can reveal trends and differences in subgroups and capture undetected or unreported infections that are not included in case-based surveillance systems. Methods: Cross-sectional, convenience samples of remnant sera from clinical laboratories from 51 U.S. jurisdictions were assayed for infection-induced SARS-CoV-2 antibodies biweekly from October 25, 2020, to July 11, 2021, and monthly from September 6, 2021, to February 26, 2022. Test results were analyzed for trends in infection-induced, nucleocapsid-protein seroprevalence using mixed effects models that adjusted for demographic variables and assay type. Findings: Analyses of 1,469,792 serum specimens revealed U.S. infection-induced SARS-CoV-2 seroprevalence increased from 8.0% (95% confidence interval (CI): 7.9%-8.1%) in November 2020 to 58.2% (CI: 57.4%-58.9%) in February 2022. The U.S. ratio of the change in estimated seroprevalence to the change in reported case prevalence was 2.8 (CI: 2.8-2.9) during winter 2020-2021, 2.3 (CI: 2.0-2.5) during summer 2021, and 3.1 (CI: 3.0-3.3) during winter 2021-2022. Change in seroprevalence to change in case prevalence ratios ranged from 2.6 (CI: 2.3-2.8) to 3.5 (CI: 3.3-3.7) by region in winter 2021-2022. Interpretation: Ratios of the change in seroprevalence to the change in case prevalence suggest a high proportion of infections were not detected by case-based surveillance during periods of increased transmission. The largest increases in the seroprevalence to case prevalence ratios coincided with the spread of the B.1.1.529 (Omicron) variant and with increased accessibility of home testing. Ratios varied by region and season with the highest ratios in the midwestern and southern United States during winter 2021-2022. Our results demonstrate that reported case counts did not fully capture differing underlying infection rates and demonstrate the value of sero-surveillance in understanding the full burden of infection. Levels of infection-induced antibody seroprevalence, particularly spikes during periods of increased transmission, are important to contextualize vaccine effectiveness data as the susceptibility to infection of the U.S. population changes. Funding: This work was supported by the Centers for Disease Control and Prevention, Atlanta, Georgia.

SARS-CoV-2 Convalescent Sera Binding and Neutralizing Antibody Concentrations Compared with COVID-19 Vaccine Efficacy Estimates against Symptomatic Infection.

Previous COVID-19 vaccine efficacy (VE) studies have estimated neutralizing and binding antibody concentrations that correlate with protection from symptomatic infection; how these estimates compare to those generated in response to SARS-CoV-2 infection is unclear. Here, we assessed quantitative neutralizing and binding antibody concentrations using standardized SARS-CoV-2 assays on 3,067 serum specimens collected during 27 July 2020 to 27 August 2020 from COVID-19-unvaccinated persons with detectable anti-SARS-CoV-2 antibodies. Neutralizing and binding antibody concentrations were severalfold lower in the unvaccinated study population compared to published concentrations at 28 days postvaccination. In this convenience sample, ~88% of neutralizing and ~63 to 86% of binding antibody concentrations met or exceeded concentrations associated with 70% COVID-19 VE against symptomatic infection; ~30% of neutralizing and 1 to 14% of binding antibody concentrations met or exceeded concentrations associated with 90% COVID-19 VE. Our study not only supports observations of infection-induced immunity and current recommendations for vaccination postinfection to maximize protection against COVID-19, but also provides a large data set of pre-COVID-19 vaccination anti-SARS-CoV-2 antibody concentrations that will serve as an important comparator in the current setting of vaccine-induced and hybrid immunity. As new SARS-CoV-2 variants emerge and displace circulating virus strains, we recommend that standardized binding antibody assays that include spike protein-based antigens be utilized to estimate antibody concentrations correlated with protection from COVID-19. These estimates will be helpful in informing public health guidance, such as the need for additional COVID-19 vaccine booster doses to prevent symptomatic infection. IMPORTANCE Although COVID-19 vaccine efficacy (VE) studies have estimated antibody concentrations that correlate with protection from COVID-19, how these estimates compare to those generated in response to SARS-CoV-2 infection is unclear. We assessed quantitative neutralizing and binding antibody concentrations using standardized assays on serum specimens collected from COVID-19-unvaccinated persons with detectable antibodies. We found that most unvaccinated persons with qualitative antibody evidence of prior infection had quantitative antibody concentrations that met or exceeded concentrations associated with 70% VE against COVID-19. However, only a small proportion had antibody concentrations that met or exceeded concentrations associated with 90% VE, suggesting that persons with prior COVID-19 would benefit from vaccination to maximize protective antibody concentrations against COVID-19.

Chronic Kidney Disease Testing Among At-Risk Adults in the U.S. Remains Low: Real-World Evidence From a National Laboratory Database.

OBJECTIVE: An estimated 37 million Americans have chronic kidney disease (CKD). Nearly 90% do not know about their condition because of low awareness about the importance of CKD testing and diagnosis among practitioners and people at risk for CKD. This study uses data from a national clinical laboratory to identify guideline-recommended CKD testing rates across the U.S. RESEARCH DESIGN AND METHODS: Patients with Laboratory Corporation of America Holdings (Labcorp) testing between 2013 and 2019 were defined as at risk for CKD if they had any testing ordered with diagnosis codes for diabetes and/or hypertension. Guideline-concordant CKD assessment was defined by estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (uACR) testing within the study year. RESULTS: We identified 28,295,982 at-risk patients (mean age 60.6 ± 14.8 years; 53.6% women): 16.2% had diabetes, 63.8% had hypertension, and 20.1% had both comorbidities. Of these, 80.3% did not receive guideline-concordant assessment during the study period. Furthermore, only 21.0% had uACR testing versus 89.6% with eGFR. CKD assessment occurred at least once in 28.7% of patients with diabetes, 10.5% of patients with hypertension, and 41.4% of patients with both conditions. In a state-by-state comparison, annual testing rates ranged from 5 to 30%. The nationwide rate increased modestly each year between 2013 and 2018 (from 10.7% to 15.2%). CONCLUSIONS: Despite guideline recommendations, testing for CKD with uACR and eGFR in U.S. adults with diabetes and hypertension is low in routine clinical care. These data highlight the need for strategies to improve routine CKD assessment nationwide.

A population-based analysis of the longevity of SARS-CoV-2 antibody seropositivity in the United States.

BACKGROUND: This cross-sectional study aimed to track population-based SARS-CoV-2 antibody seropositivity duration across the United States using observational data from a national clinical laboratory registry of patients tested by nucleic acid amplification (NAAT) and serologic assays. Knowledge of antibody seropositivity and its duration may help dictate post-pandemic planning. METHODS: Using assays to detect antibodies to either nucleocapsid (N) or spike (S) proteins performed on specimens from 39,086 individuals with confirmed positive COVID-19 by reverse transcription-polymerase chain reaction (RT-PCR) from March 2020 to January 2021, we analyzed nationwide seropositivity rates of IgG up to 300 days following patients' initial positive NAAT test. Linear regression identified trends in seropositivity rates and logistic regression tested positive predictability by age, sex, assay type and days post-infection. FINDINGS: Seropositivity of IgG antibodies to both SARS-CoV-2 S and N-proteins followed a linear trend reaching approximately 90% positivity at 21 days post-index. The rate of N-protein seropositivity declined at a sharper rate, decaying to 68·2% [95% CI: 63·1-70·8%] after 293 days, while S-antibody seropositivity maintained a rate of 87·8% [95% CI: 86·3-89·1%] through 300 days. In addition to antigen type and the number of days post-positive PCR, age and gender were also significant factors in seropositivity prediction, with those under 65 years of age showing a more sustained seropositivity rate. INTERPRETATION: Observational data from a national clinical laboratory, though limited by an epidemiological view of the U.S. population, offer an encouraging timeline for the development and sustainability of antibodies up to ten months from natural infection and could inform post-pandemic planning.

A customized scaffolds approach for the detection and phasing of complex variants by next-generation sequencing.

Next-generation sequencing (NGS) is widely used in genetic testing for the highly sensitive detection of single nucleotide changes and small insertions or deletions. However, detection and phasing of structural variants, especially in repetitive or homologous regions, can be problematic due to uneven read coverage or genome reference bias, resulting in false calls. To circumvent this challenge, a computational approach utilizing customized scaffolds as supplementary reference sequences for read alignment was developed, and its effectiveness demonstrated with two CBS gene variants: NM_000071.2:c.833T>C and NM_000071.2:c.[833T>C; 844_845ins68]. Variant c.833T>C is a known causative mutation for homocystinuria, but is not pathogenic when in cis with the insertion, c.844_845ins68, because of alternative splicing. Using simulated reads, the custom scaffolds method resolved all possible combinations with 100% accuracy and, based on > 60,000 clinical specimens, exceeded the performance of current approaches that only align reads to GRCh37/hg19 for the detection of c.833T>C alone or in cis with c.844_845ins68. Furthermore, analysis of two 1000 Genomes Project trios revealed that the c.[833T>C; 844_845ins68] complex variant had previously been undetected in these datasets, likely due to the alignment method used. This approach can be configured for existing workflows to detect other challenging and potentially underrepresented variants, thereby augmenting accurate variant calling in clinical NGS testing.

BRCA Share: A Collection of Clinical BRCA Gene Variants.

As next-generation sequencing increases access to human genetic variation, the challenge of determining clinical significance of variants becomes ever more acute. Germline variants in the BRCA1 and BRCA2 genes can confer substantial lifetime risk of breast and ovarian cancer. Assessment of variant pathogenicity is a vital part of clinical genetic testing for these genes. A database of clinical observations of BRCA variants is a critical resource in that process. This article describes BRCA Share™, a database created by a unique international alliance of academic centers and commercial testing laboratories. By integrating the content of the Universal Mutation Database generated by the French Unicancer Genetic Group with the testing results of two large commercial laboratories, Quest Diagnostics and Laboratory Corporation of America (LabCorp), BRCA Share™ has assembled one of the largest publicly accessible collections of BRCA variants currently available. Although access is available to academic researchers without charge, commercial participants in the project are required to pay a support fee and contribute their data. The fees fund the ongoing curation effort, as well as planned experiments to functionally characterize variants of uncertain significance. BRCA Share™ databases can therefore be considered as models of successful data sharing between private companies and the academic world.

Exploring the landscape of pathogenic genetic variation in the ExAC population database: insights of relevance to variant classification.

PURPOSE: We evaluated the Exome Aggregation Consortium (ExAC) database as a control cohort to classify variants across a diverse set of genes spanning dominant and recessively inherited disorders. METHODS: The frequency of pathogenic variants in ExAC was compared with the estimated maximal pathogenic allele frequency (MPAF), based on the disease prevalence, penetrance, inheritance, allelic and locus heterogeneity of each gene. Additionally, the observed carrier frequency and the ethnicity-specific variant distribution were compared between ExAC and the published literature. RESULTS: The carrier frequency and ethnic distribution of pathogenic variants in ExAC were concordant with reported estimates. Of 871 pathogenic/likely pathogenic variants across 19 genes, only 3 exceeded the estimated MPAF. Eighty-four percent of variants with ExAC frequencies above the estimated MPAF were classified as "benign." Additionally, 20% of the cardiac and 19% of the Lynch syndrome gene variants originally classified as "VUS" occurred with ExAC frequencies above the estimated MPAF, making these suitable for reassessment. CONCLUSIONS: The ExAC database is a useful source for variant classification and is not overrepresented for pathogenic variants in the genes evaluated. However, the mutational spectrum, pseudogenes, genetic heterogeneity, and paucity of literature should be considered in deriving meaningful classifications using ExAC.Genet Med 18 8, 850-854.

Edge-count probabilities for the identification of local protein communities and their organization.

We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher-level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function.

Predicting protein function from protein/protein interaction data: a probabilistic approach.

MOTIVATION: The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network. RESULTS: We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cerevisiae.