Search

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, there are a limited number of effective treatments. A variety of drugs that have been approved for other diseases are being tested for the treatment of COVID-19, and thus far only remdesevir, dexamethasone, baricitinib, tofacitinib, tocilizumab, and sarilumab have been recommended by the National Institutes of Health (NIH) COVID-19 Treatment Guidelines Panel for the therapeutic management of hospitalized adults with COVID-19. Using a disease biology modeling approach, we constructed a protein-protein interactome network based on COVID-19- associated genes/proteins described in research literature together with known protein-protein interactions in epithelial cells. Phenotype and disease enrichment analysis of the COVID-19 disease biology model demonstrated strong statistical enrichments consistent with patients’ clinical presentation. The model was used to interrogate host biological response induced by SARS-CoV-2 and identify COVID-19 drug treatment candidates that may inform on drugs currently being evaluated or provide insight into possible targets for potential new therapeutic agents. We focused on cancer drugs as they are often used to control inflammation, inhibit cell division, and modulate the host microenvironment to control the disease. From the top 30 COVID-19 drug candidates, twelve have a role as an antineoplastic agent, seven of which are approved for human use. Altogether, nearly 40% of the drugs identified by our model have been identified by others for COVID-19 clinical trials. Disease biology modeling incorporating disease-associated genes/proteins discussed in the research literature together with known molecular interactions in relevant cell types is a useful method to better understand disease biology and identify potentially effective therapeutic interventions.

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.

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.

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.

7022 Background: 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 from 17,181 clinical samples analyzed by a targeted next generation sequencing (NGS) laboratory developed test. Samples were analyzed comprehensively and as part of individual cohorts specific to acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and myeloproliferative neoplasms (MPN). Methods: Whole blood or bone marrow samples from patients with cause-for-testing for hematological symptoms were submitted for analysis by a referring clinician. DNA was extracted and assayed by a targeted, NGS panel to detect and report single nucleotide variants and small indels within 50 genes associated with myeloid malignancies and sequenced on Illumina DNA sequencers (Illumina, San Diego, CA). Results were reviewed, orthogonally confirmed unless previously validated, and reported by clinical laboratory directors. Disease status or symptoms were taken from test requisitions for each patient. Results: Overall, 34,581 Tier I, II, or III somatic variants were detected. The mean number of variants reported per patient was 1.98. This number increased with age (p < 0.001) and was highest in patients with AML. Mutations in genes associated with FDA-approved therapies ( FLT3, IDH1, IDH2, KIT, and JAK2) were observed in 22.3% (3836) patients while 28.0% of samples had mutations considered diagnostically relevant by the WHO. Clinical trials are in progress for therapies targeting TP53, NPM1, and CEBPA mutations and were observed in 12.5% (2144) of samples. FLT3, NPM1 and RUNX1 mutation which are diagnostic for AML were found in 29.0% (646 of 2225) of AML samples. Pair-wise mutation analyses found 21 mutually exclusive pairs including between genes associated with RNA splicing ( SF3B1, SRSF2 and U2AF1; OR < 0.29, p < 0.001) suggesting possible candidates for targeted therapy. The clinically favorable co-mutation of NPM1 with FLT3 internal tandem duplicate was significantly enriched (OR = 7.4, p < 0.001) in the AML population. Co-occurrence of ASXL1 with RUNX1 mutations which independently confer adverse risk was also enriched in AML (OR = 3.9, p < 0.001). Some patients (1021; 6.3%) were tested at multiple time-points with 48.8% showing loss or gain of a mutation between sample dates, potentially the result of tumor evolution and/or therapeutic intervention. Conclusions: This study shows that parallel testing of multiple genes in addition to the canonical driver mutations encompasses the mutations contributing to the etiology of these diseases. Despite the breadth of different mutations observed for myeloid neoplasms, consistent patterns are routinely observed that can help the clinician tailor the treatment and chart the progression of these diseases for each patient.

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.

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.

Pablo Picasso, when first told about computers, famously quipped “Computers are useless. They can only give you answers.” Indeed, the majority of effort in the first half-century of computational research has focused on methods for producing answers. Incredible progress has been achieved in computational modeling, simulation and optimization, across domains as diverse as astrophysics, climate studies, biomedicine, architecture, and chess. However, the use of computers to pose new questions, or prioritize existing ones, has thus far been quite limited.Picasso’s comment highlights the point that good questions can sometimes be more elusive than good answers. The history of science offers numerous examples of the impact of good questions. Paul Erdős, the wandering monk of mathematical graph theory, offered small prizes for anyone who could prove conjectures he identified as important (1). The prizes varied in cash amounts based on the perceived complexity of the problem posed by Erdős.Posing technical questions and allocating resources to answer them has taken on a new guise in the Internet age. The X-Prize foundation (http://www.xprize.org/) offers multi-million dollar bounties for grand technological goals, including goals for sequencing genomes or space exploration. Several companies provide portals where customers can place cash bounties on educational, scientific or technological challenges, while potential problem solvers can compete to produce the best solutions for these problems. Amazon’s Turk site (https://www.mturk.com/mturk/welcome) links people requesting performance of intellectual tasks to people willing to work on them for a fee. Such crowd-sourcing systems create markets of questions and answers, and can help allocate resources and capabilities efficiently.This paradigm suggests a number of interesting questions for scientific research. In a resource limited environment, can funds and research capacity be allocated more efficiently? Can knowledge demand provide an alternative or complementary mechanism to traditional investigator-initiated research grants?The fathers of Artificial Intelligence (AI) and Herbert Simon in particular envisioned the application of AI to Scientific Discovery in different forms and styles (focusing on physics). We follow on these early dreams and describe a novel approach aimed at remodeling of the biomedical research infrastructure and catalyze gene function determination. We aim to start a bold discussion of new ideas aimed towards increasing the efficiency of the allocation of research capacities, reproducibility, provenance tracking, removing redundancy and catalyzing knowledge gain with each experiment. In particular, we describe a tractable computational framework and infrastructure that can help researchers assess the potential information gain of millions of experiments before conducting them. The utility of experiments in this case is modeled as the predictive knowledge (formalized as information) to be gained as a result of performing the experiment. The experimentalist would then be empowered to select experiments that maximized information gain if they wished, recognizing that there are frequently other considerations, such as a specific technological or medical utility, that might over-ride the priority of maximizing information gain. The conceptual approach we develop is general, and here we apply it to the study of gene function.

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. 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. 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. 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.

© 2013 Brian P. et al., Prior to the “genomic era,” when the acquisition of DNA sequence involved significant labor and expense, the sequencing of genes was strongly linked to the experimental characterization of their products. Sequencing at that time directly resulted from the need to understand an experimentally determined phenotype or biochemical activity. Now that DNA sequencing has become orders of magnitude faster and less expensive, focus has shifted to sequencing entire genomes. Since biochemistry and genetics have not, by and large, enjoyed the same improvement of scale, public sequence repositories now predominantly contain putative protein sequences for which there is no direct experimental evidence of function. Computational approaches attempt to leverage evidence associated with the ever-smaller fraction of experimentally analyzed proteins to predict function for these putative proteins. Maximizing our understanding of function over the universe of proteins in toto requires not only robust computational methods of inference but also a judicious allocation of experimental resources, focusing on proteins whose experimental characterization will maximize the number and accuracy of follow-on predictions., COMBREX is funded by a GO grant from the National Institute of General Medical Sciences (NIGMS) (1RC2GM092602-01).

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.

COTRANS is a program for analyzing cotransduction data. It calculates distances from pairwise cotransduction frequencies, computes crossovers required to obtain each observed recombinant class, and applies rules to draw conclusions about order. The rules are based on the correlation between the frequency of the classes and the number of required crossovers for each possible ordering compatible with the distance calculations. The procedure emulates a geneticist's stepwise analysis of the data by first calculating distances, then looking for obvious three-point ordering conclusions, and finally proceeding to a complete crossover analysis. It reports results from each step of the analysis and an overall conclusion. COTRANS provides significant gains in speed and convenience over hand analysis, particularly for multipoint crosses with several recombinant classes.

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.

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.

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. Availability: Source code available upon request. Results available athttp://genomics10.bu.edu/netmark Contact: sletovsky@aol.com Keywords: protein–protein interaction, protein function prediction, gene ontology, Markov Random fields *To whom correspondence should be addressed.

A description is given of CPU, a program analysis tool that converts programs into formal specifications. CPU takes as input a program plus a knowledge base of programming plans and finds instances of plans in the code. A technique called transformational analysis is used in which plans that are recognized in the code are replaced by descriptions of their goals. Both procedural plans and data-structuring plans can be recognized. The result of a transformational analysis is a hierarchical derivation of the program, where the topmost layer constitutes a formal specification for the input program, the bottommost layer is the original code, and the intermediate layers denote plans that were recognized in the program. This derivation can be used to generate summaries of the code and to answer questions about it. >

The Genome Database (GDB, http://www.gdb.org ) is a public repository of data on human genes, clones, STSs, polymorphisms and maps. GDB entries are highly cross-linked to each other, to literature citations and to entries in other databases, including the sequence databases, OMIM, and the Mouse Genome Database. Mapping data from large genome centers and smaller mapping efforts are added to GDB on an ongoing basis. The database can be searched by a variety of methods, ranging from keyword searches to complex queries. Major functionality extensions in the last year include the ongoing computation of integrated human genome maps, called Comprehensive Maps, and the use of those maps to support positional queries and graphic displays. The capabilities of the GDB map viewer (Mapview) have been extended to include map printing and the graphical display of ad hoc query results. The HUGO Nomenclature Committee continues to curate the proposed and official gene symbols and related data in collaboration with GDB. As genome research shifts its emphasis from mapping to sequencing and functional analysis, the scope of the GDB schema is being extended. We are in the process of adding representations of gene function and expression, and improving our representation of human polymorphism and mutation.

We present an approach for analyzing the morphology of anatomical structures of the brain, which uses an elastic transformation to normalize brain images into a reference space. The properties of this transformation are used as a quantitative description of the size and shape of brain structures; inter-subject comparisons are made by comparing the transformations themselves.

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.