Search

Introduction: The use of antibody titers against SARS-CoV-2, as a method of estimating subsequent infection following infection or vaccination, is unclear. Here, we investigate whether specific levels of antibodies, as markers of adaptive immunity, can serve to estimate the risk of symptomatic SARS-CoV-2 (re-) infection., Methods: In this real-world study, laboratory data from individuals tested for SARS-CoV-2 antibodies under routine clinical conditions were linked through tokenization to a United States medical insurance claims database to determine the risk of symptomatic/severe SARS-CoV-2 infection outcomes. Antibody titer levels were determined using the Elecsys ® Anti-SARS-CoV-2 S assay. Study outcomes included the first symptomatic SARS-CoV-2 infection (per ICD-10 diagnostic codes, occurring ≥ 7 days post-antibody titer test), and severe SARS-CoV-2 infection, characterized by adverse outcomes including hospitalization, intensive care unit admission, intubation, mechanical ventilation, or death within 30 days of infection. All outcomes were assessed for 12 months following antibody measurement. Hazard ratios of subsequent symptomatic and severe infections were estimated using Cox regression with inverse probability weighting., Results: Of 268,844 individuals with antibody data (April 2021-June 2022), those with levels ≥ 0.8 to < 1,000 U/mL had a 42% reduced risk of symptomatic infection within 12 months, compared with < 0.8 U/mL (HR = 0.58, 95% CI [0.55, 0.61]). The risk decreased by 53% (HR = 0.47, 95% CI [0.45, 0.49]) with ≥ 1000 to < 2500 U/mL and by 62% (HR = 0.38 [0.36, 0.39]) for ≥ 2500 U/mL. Risk of severe SARS-CoV-2 outcomes was also reduced. Subgroup analyses showed a consistent association between antibody levels and infection risk, by immune status and age. Clinically meaningful thresholds of antibody titers varied between Delta and Omicron infections., Conclusion: Higher antibody titer levels indicated reduced risk of developing symptomatic or severe COVID-19. Titers of ≥ 2500 U/mL indicated a 62-87% reduced infection risk. The quantitative determination of antibody titers allowed scaling of the correlate of risk to new variants., Competing Interests: Declarations. Conflict of interest: Dr Yue Jin is an employee of Roche Molecular Systems Inc. and a Roche stockholder; Dr Fei Yang is currently an employee of Phillip Morris International but was an employee of Roche Information Solutions Ltd at the time the study was conducted. Phillip Morris International has not been involved in the study or publication; Dr Christopher M. Rank is an employee of Roche Diagnostics GmbH and holds stocks/shares in F. Hofmann-La Roche Ltd; Dr Stanley Letovsky is a Labcorp employee and participates in employee stock plans; Dr Peter Ramge is an employee of Roche Diagnostics International Ltd. and a Roche stockholder; Dr Simon Jochum is an employee of Roche Diagnostics GmbH. The Elecsys Anti-SARS-CoV-2 S assay is approved under an Emergency Use Authorization in the US. ELECSYS is a trademark of Roche. All other product names and trademarks are the property of their respective owners. Ethical Approval: As a non-interventional study based on secondary data use, this study did not require informed consent or institutional/ethical review board approval., (© 2024. The Author(s).)

The COVID-19 pandemic brought forth an urgent need for widespread genomic surveillance for rapid detection and monitoring of emerging SARS-CoV-2 variants. It necessitated design, development, and deployment of a nationwide infrastructure designed for sequestration, consolidation, and characterization of patient samples that disseminates de-identified information to public authorities in tight turnaround times. Here, we describe our development of such an infrastructure, which sequenced 594,832 high coverage SARS-CoV-2 genomes from isolates we collected in the United States (U.S.) from March 13th 2020 to July 3rd 2023. Our sequencing protocol ('Virseq') utilizes wet and dry lab procedures to generate mutation-resistant sequencing of the entire SARS-CoV-2 genome, capturing all major lineages. We also characterize 379 clinically relevant SARS-CoV-2 multi-strain co-infections and ensure robust detection of emerging lineages via simulation. The modular infrastructure, sequencing, and analysis capabilities we describe support the U.S. Centers for Disease Control and Prevention national surveillance program and serve as a model for rapid response to emerging pandemics at a national scale., (© 2024. The Author(s).)

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 r 2  = .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., Competing Interests: Disclosures Dr Letovsky, Mr Robinson, Dr Valcour, and Dr Sullivan are employed by Labcorp, which performs commercial allergy testing. Dr Kwong is currently a consultant and independent contractor for Thermo-Fisher Scientific. Dr Liu reports personal fees from Phadia ThermoFisher, grants and nonfinancial support from ResMed Propeller Health, nonfinancial support from Revenio, grants and personal fees from Avillion, and personal fees from Labcorp, all outside the submitted work., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The author would like to declare that Financial support was provided by Labcorp Burlington, Burlington, NC and the study was solely undertaken by employees of Labcorp®., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare that this study received funding from Labcorp and Quest Diagnostics. The funders had the following involvement in the study: the data acquisition from HealthVerity and the independent data analysis and support for the manuscript by Aetion., (Copyright © 2023 Kaufman, Letovsky, Meyer, Gillim, Assimon, Kabelac, Kroner, Reynolds and Eisenberg.)

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., Competing Interests: BioReference Laboratories, Inc., ICF Inc., Laboratory Corporation of America Holdings, and Quest Diagnostics, Inc. were awarded federal contracts from the U.S. Centers for Disease Control and Prevention (CDC) for the execution of this project. No other disclosures were reported.

Despite a growing amount of data around the kinetics and durability of the antibody response induced by vaccination and previous infection, there is little understanding of whether or not a given quantitative level of antibodies correlates to protection against SARS-CoV-2 infection or reinfection. In this study, we examine SARS-CoV-2 anti-spike receptor binding domain (RBD) antibody titers and subsequent SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) tests in a large cohort of US-based patients. We analyzed antibody test results in a cohort of 22,204 individuals, 6.8% (n = 1,509) of whom eventually tested positive for SARS-CoV-2 RNA, suggesting infection or reinfection. Kaplan-Meier curves were plotted to understand the effect of various levels of anti-spike RBD antibody titers (classified into discrete ranges) on subsequent RT-PCR positivity rates. Statistical analyses included fitting a Cox proportional hazards model to estimate the age-, sex- and exposure-adjusted hazard ratios for S antibody titer, using zip-code positivity rates by week as a proxy for COVID-19 exposure. It was found that the best models of the temporally associated infection risk were those based on log antibody titer level (HR = 0.836 (p < 0.05)). When titers were binned, the hazard ratio associated with antibody titer >250 Binding Antibody Units (BAU) was 0.27 (p < 0.05, 95% CI [0.18, 0.41]), while the hazard ratio associated with previous infection was 0.20 (p < 0.05, 95% CI [0.10, 0.39]). Fisher exact odds ratio (OR) for Ab titers <250 BAU showed OR = 2.84 (p < 0.05; 95% CI: [2.30, 3.53]) for predicting the outcome of a subsequent PCR test. Antibody titer levels correlate with protection against subsequent SARS-CoV-2 infection or reinfection when examining a cohort of real-world patients who had the spike RBD antibody assay performed., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors.)

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.

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., (© 2021 by the American Diabetes Association.)

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., Competing Interests: None., (© 2021 The Author(s).)

By analyzing COVID-19 sequential COVID-19 test results of patients across the United States, we herein attempt to quantify some of the observations we've made around long-term infection (and false-positive rates), as well as provide observations on the uncertainty of sampling variability and other dynamics of COVID-19 infection in the United States. Retrospective cohort study of a registry of RT-PCR testing results for all patients tested at any of the reference labs operated by Labcorp ® including both positive, negative, and inconclusive results, from March 1, 2020 to January 28, 2021, including patients from all 50 states and outlying US territories. The study included 22 million patients with RT-PCR qualitative test results for SARS-CoV-2, of which 3.9 million had more than one test at Labcorp. We observed a minuscule <0.1% basal positive rate for follow up tests >115 days, which could account for false positives, long-haulers, and/or reinfection but is indistinguishable in the data. In observing repeat-testing, for patients who have a second test after a first RT-PCR, 30% across the cohort tested negative on the second test. For patients who test positive first and subsequently negative within 96 h (40% of positive test results), 18% of tests will subsequently test positive within another 96-h span. For those who first test negative and then positive within 96 h (2.3% of negative tests), 56% will test negative after a third and subsequent 96-h period. The sudden changes in RT-PCR test results for SARS-CoV-2 from this large cohort study suggest that negative test results during active infection or exposure can change rapidly within just days or hours. We also demonstrate that there does not appear to be a basal false positive rate among patients who test positive >115 days after their first RT-PCR positive test while failing to observe any evidence of widespread reinfection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All authors are employees of Labcorp., (Copyright © 2021 Sullivan, Alfego, Poirier, Williams, Adcock and Letovsky.)

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&#95;000071.2:c.833T>C and NM&#95;000071.2:c.[833T>C; 844&#95;845ins68]. Variant c.833T>C is a known causative mutation for homocystinuria, but is not pathogenic when in cis with the insertion, c.844&#95;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&#95;845ins68. Furthermore, analysis of two 1000 Genomes Project trios revealed that the c.[833T>C; 844&#95;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.

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.

Competing Interests: The authors have declared that no competing interests exist.

RNA Seq provides unparalleled levels of information about the transcriptome including precise expression levels over a wide dynamic range. It is essential to understand how technical variation impacts the quality and interpretability of results, how potential errors could be introduced by the protocol, how the source of RNA affects transcript detection, and how all of these variations can impact the conclusions drawn. Multiple human RNA samples were used to assess RNA fragmentation, RNA fractionation, cDNA synthesis, and single versus multiple tag counting. Though protocols employing polyA RNA selection generate the highest number of non-ribosomal reads and the most precise measurements for coding transcripts, such protocols were found to detect only a fraction of the non-ribosomal RNA in human cells. PolyA RNA excludes thousands of annotated and even more unannotated transcripts, resulting in an incomplete view of the transcriptome. Ribosomal-depleted RNA provides a more cost-effective method for generating complete transcriptome coverage. Expression measurements using single tag counting provided advantages for assessing gene expression and for detecting short RNAs relative to multi-read protocols. Detection of short RNAs was also hampered by RNA fragmentation. Thus, this work will help researchers choose from among a range of options when analyzing gene expression, each with its own advantages and disadvantages.

Satellite repeats in heterochromatin are transcribed into noncoding RNAs that have been linked to gene silencing and maintenance of chromosomal integrity. Using digital gene expression analysis, we showed that these transcripts are greatly overexpressed in mouse and human epithelial cancers. In 8 of 10 mouse pancreatic ductal adenocarcinomas (PDACs), pericentromeric satellites accounted for a mean 12% (range 1 to 50%) of all cellular transcripts, a mean 40-fold increase over that in normal tissue. In 15 of 15 human PDACs, alpha satellite transcripts were most abundant and HSATII transcripts were highly specific for cancer. Similar patterns were observed in cancers of the lung, kidney, ovary, colon, and prostate. Derepression of satellite transcripts correlated with overexpression of the long interspersed nuclear element 1 (LINE-1) retrotransposon and with aberrant expression of neuroendocrine-associated genes proximal to LINE-1 insertions. The overexpression of satellite transcripts in cancer may reflect global alterations in heterochromatin silencing and could potentially be useful as a biomarker for cancer detection.

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

The recent transition in gene expression analysis technology to ultra high-throughput cDNA sequencing provides a means for higher quantitation sensitivity across a wider dynamic range than previously possible. Sensitivity of detection is mostly a function of the sheer number of sequence reads generated. Typically, RNA is converted to cDNA using random hexamers and the cDNA is subsequently sequenced (RNA-Seq). With this approach, higher read numbers are generated for long transcripts as compared to short ones. This length bias necessitates the generation of very high read numbers to achieve sensitive quantitation of short, low-expressed genes. To eliminate this length bias, we have developed an ultra high-throughput sequencing approach where only a single read is generated for each transcript molecule (single-molecule sequencing Digital Gene Expression (smsDGE)). So, for example, equivalent quantitation accuracy of the yeast transcriptome can be achieved by smsDGE using only 25% of the reads that would be required using RNA-Seq. For sample preparation, RNA is first reverse-transcribed into single-stranded cDNA using oligo-dT as a primer. A poly-A tail is then added to the 3' ends of cDNA to facilitate the hybridization of the sample to the Helicos(®) single-molecule sequencing Flow-Cell to which a poly dT oligo serves as the substrate for subsequent sequencing by synthesis. No PCR, sample-size selection, or ligation steps are required, thus avoiding possible biases that may be introduced by such manipulations. Each tailed cDNA sample is injected into one of 50 flow-cell channels and sequenced on the Helicos(®) Genetic Analysis System. Thus, 50 samples are sequenced simultaneously generating 10-20 million sequence reads on average for each sample channel. The sequence reads can then be aligned to the reference of choice such as the transcriptome, for quantitation of known transcripts, or the genome for novel transcript discovery. This chapter provides a summary of the methods required for smsDGE.

The rapid adoption of high-throughput next generation sequence data in biological research is presenting a major challenge for sequence alignment tools—specifically, the efficient alignment of vast amounts of short reads to large references in the presence of differences arising from sequencing errors and biological sequence variations. To address this challenge, we developed a short read aligner for high-throughput sequencer data that is tolerant of errors or mutations of all types—namely, substitutions, deletions, and insertions. The aligner utilizes a multi-stage approach in which template-based indexing is used to identify candidate regions for alignment with dynamic programming. A template is a pair of gapped seeds, with one used with the read and one used with the reference. In this article, we focus on the development of template families that yield error-tolerant indexing up to a given error-budget. A general algorithm for finding those families is presented, and a recursive construction that creates families with higher error tolerance from ones with a lower error tolerance is developed.

We present single-molecule sequencing digital gene expression (smsDGE), a high-throughput, amplification-free method for accurate quantification of the full range of cellular polyadenylated RNA transcripts using a Helicos Genetic Analysis system. smsDGE involves a reverse-transcription and polyA-tailing sample preparation procedure followed by sequencing that generates a single read per transcript. We applied smsDGE to the transcriptome of Saccharomyces cerevisiae strain DBY746, using 6 of the available 50 channels in a single sequencing run, yielding on average 12 million aligned reads per channel. Using spiked-in RNA, accurate quantitative measurements were obtained over four orders of magnitude. High correlation was demonstrated across independent flow-cell channels, instrument runs and sample preparations. Transcript counting in smsDGE is highly efficient due to the representation of each transcript molecule by a single read. This efficiency, coupled with the high throughput enabled by the single-molecule sequencing platform, provides an alternative method for expression profiling.

Background: The traditional approach to studying complex biological networks is based on the identification of interactions between internal components of signaling or metabolic pathways. By comparison, little is known about interactions between higher order biological systems, such as biological pathways and processes. We propose a methodology for gleaning patterns of interactions between biological processes by analyzing protein-protein interactions, transcriptional co-expression and genetic interactions. At the heart of the methodology are the concept of Linked Processes and the resultant network of biological processes, the Process Linkage Network (PLN)., Results: We construct, catalogue, and analyze different types of PLNs derived from different data sources and different species. When applied to the Gene Ontology, many of the resulting links connect processes that are distant from each other in the hierarchy, even though the connection makes eminent sense biologically. Some others, however, carry an element of surprise and may reflect mechanisms that are unique to the organism under investigation. In this aspect our method complements the link structure between processes inherent in the Gene Ontology, which by its very nature is species-independent. As a practical application of the linkage of processes we demonstrate that it can be effectively used in protein function prediction, having the power to increase both the coverage and the accuracy of predictions, when carefully integrated into prediction methods., Conclusions: Our approach constitutes a promising new direction towards understanding the higher levels of organization of the cell as a system which should help current efforts to re-engineer ontologies and improve our ability to predict which proteins are involved in specific biological processes.

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., ((c) 2005 Wiley-Liss, Inc.)

We have sequenced the genome of a second Drosophila species, Drosophila pseudoobscura, and compared this to the genome sequence of Drosophila melanogaster, a primary model organism. Throughout evolution the vast majority of Drosophila genes have remained on the same chromosome arm, but within each arm gene order has been extensively reshuffled, leading to a minimum of 921 syntenic blocks shared between the species. A repetitive sequence is found in the D. pseudoobscura genome at many junctions between adjacent syntenic blocks. Analysis of this novel repetitive element family suggests that recombination between offset elements may have given rise to many paracentric inversions, thereby contributing to the shuffling of gene order in the D. pseudoobscura lineage. Based on sequence similarity and synteny, 10,516 putative orthologs have been identified as a core gene set conserved over 25-55 million years (Myr) since the pseudoobscura/melanogaster divergence. Genes expressed in the testes had higher amino acid sequence divergence than the genome-wide average, consistent with the rapid evolution of sex-specific proteins. Cis-regulatory sequences are more conserved than random and nearby sequences between the species--but the difference is slight, suggesting that the evolution of cis-regulatory elements is flexible. Overall, a pattern of repeat-mediated chromosomal rearrangement, and high coadaptation of both male genes and cis-regulatory sequences emerges as important themes of genome divergence between these species of Drosophila.

The advent of high-throughput biology has catalyzed a remarkable improvement in our ability to identify new genes. A large fraction of newly discovered genes have an unknown functional role, particularly when they are specific to a particular lineage or organism. These genes, currently labeled "hypothetical," might support important biological cell functions and could potentially serve as targets for medical, diagnostic, or pharmacogenomic studies. An important challenge to the scientific community is to associate these newly predicted genes with a biological function that can be validated by experimental screens. In the absence of sequence or structural homology to known genes, we must rely on advanced biotechnological methods, such as DNA chips and protein-protein interaction screens as well as computational techniques to assign putative functions to these genes. In this article, we propose an effective methodology for combining biological evidence obtained in several high-throughput experimental screens and integrating this evidence in a way that provides consistent functional assignments to hypothetical genes. We use the visualization method of propagation diagrams to illustrate the flow of functional evidence that supports the functional assignments produced by the algorithm. Our results contain a number of predictions and furnish strong evidence that integration of functional information is indeed a promising direction for improving the accuracy and robustness of functional genomics.

Background and Purpose: Lesion-deficit-based structure-function analysis has traditionally been empirical and nonquantitative. Our purpose was to establish a new brain image database (BRAID) that allows the statistical correlation of brain functional measures with anatomic lesions revealed by clinical brain images., Methods: Data on 303 participants in the MR Feasibility Study of the Cardiovascular Health Study were tested for lesion/deficit correlations. Functional data were derived from a limited neurologic examination performed at the time of the MR examination. Image data included 3D lesion descriptions derived from the MR examinations by hand segmentation. MR images were normalized in-plane using local, linear Talairach normalization. A database was implemented to support spatial data structures and associated geometric and statistical operations. The database stored the segmented lesions, patient functional scores, and several anatomic atlases. Lesion-deficit association was sought by contingency testing (chi2-test) for every possible combination of each neurologic variable and each labeled atlas structure. Significant associations that confirmed accepted lesion-deficit relationships were sought., Results: Two-hundred thirty-five infarctlike lesions in 117 subjects were viewed collectively after mapping into Talairach cartesian coordinates. Anatomic structures most strongly correlated with neurologic deficits tended to be situated in anatomically appropriate areas. For example, infarctlike lesions associated with visual field defects were correlated with structures in contralateral occipital structures, including the optic radiations and occipital gyri., Conclusion: Known lesion-deficit correlations can be established by a database using a standard coordinate system for representing spatial data and incorporating functional and structural data together with appropriate query mechanisms. Improvements and further applications of this methodology may provide a powerful technique for uncovering new structure-function relationships.

Objective: A new technique for analyzing the morphology of the corpus callosum is presented, and it is applied to a group of elderly subjects., Materials and Methods: The proposed approach normalizes subject data into the Talairach space using an elastic deformation transformation. The properties of this transformation are used as a quantitative description of the callosal shape with respect to the Talairach atlas, which is treated as a standard. In particular, a deformation function measures the enlargement/shrinkage associated with this elastic deformation. Intersubject comparisons are made by comparing deformation functions., Results: This technique was applied to eight male and eight female subjects. Based on the average deformation functions of each group, the posterior region of the female corpus callosum was found to be larger than its corresponding region in the males. The average callosal shape of each group was also found, demonstrating visually the callosal shape differences between the two groups in this sample., Conclusion: The proposed methodology utilizes the full resolution of the data, rather than relying on global descriptions such as area measurements. The application of this methodology to an elderly group indicated sex-related differences in the callosal shape and size.

Version 6.0 of the Human Genome Data Base introduces a number of significant improvements over previous releases of GDB. The most important of these are revised data representations for genes and genomic maps and a new curatorial model for the database. GDB 6.0 is the first major genomic database to provide read/write access directly to the scientific community, including capabilities for third-party annotation. The revised database can represent all major categories of genetic and physical maps, along with the underlying order and distance information used to construct them. The improved representation permits more sophisticated map queries to be posed and supports the graphical display of maps. In addition the new GDB has a richer model for gene information, better suited for supporting cross-references to databases describing gene function, structure, products, expression and associated phenotypes.

The exponentially increasing volume of scientific information is threatening to overwhelm the budgets and storage capacities of the world's academic libraries. At the same time, new information technologies are poised to revolutionize the publication infrastructure of science. In evaluating these new technologies we must pay attention to their scalability properties: how well will they perform as the amount of information they are handling increases by orders of magnitude? This article identifies some scalability criteria and evaluates some of the current information technologies with respect to them. It ends by suggesting future directions for these technologies.

The contents of the E. coli Genetic Stock Center database and the availability in electronic form of the subset of information most relevant to sequence databases are described. The database uses the long-standing Stock Center records (developed and curated by Dr B.J.Bachmann) in describing genotypes of mutant derivatives of E.coli K-12 in terms of alleles, structural mutations, mating type, and plasmids as well as the derivation, names and originators of the strain, and references. The database includes descriptions of mutations, mutation properties, genes, gene properties, and gene products, with EC number identifiers for enzymes. Sequence information is not included, but entries refer to sequence database accession numbers for sequenced regions. A gene is described as a subtype of a more general category of chromosome interval called Site. Since sites are used to describe any chromosomal interval, mapping information is associated with sites. Alleles are described as mutations of those sites and they are not primary map objects, but inherit map position information from the corresponding site description. The database design is intended to preserve richness of detail where it is known and uncertainty of measurements or information as it occurs in order to represent the stock center records as accurately as possible.

Gene mapping assigns chromosomal coordinates to genetic loci based on analysis of fragmentary ordering and metric data. In assembling genetic maps, geneticists use rules of inference to derive new facts about order and distance between loci from experimentally derived conclusions about order and distance. They construct comprehensive maps by merging related sets of data and resolving conflicts between them. In this article we describe software that formalizes and automates some of these rules of inference to yield a useful map construction utility called CPROP.