Downregulated Expression of Ly-6-ThB on Developing T Cells Marks CD4+CD8+ Subset Undergoing Selection in the Thymus

Interaction of TCRs on CD4+CD8+ immature T cell with MHC-peptide complexes on stromal cells is required for positive and negative selection in the thymus. Identification and characterization of a subpopulation of CD4+CD8+ thymocytes undergoing selection in the thymus will aid in understanding the mechanisms underlying lineage commitment and thymic selection. Herein, we describe the expression of Ly-6 ThB on developing thymocytes. The majority of CD4+CD8+ thymocytes express Ly-6 ThB at high levels. Its expression is downregulated in a subset of CD4+CD8+ thymocytes as well as in mature CD4+CD8- and CD4-CD8+ T cells. More importantly, interaction of TCR/coreceptor with the self-MHC-peptide contributes to the downregulation of ThB expression on developing thymocytes. These findings indicate that downregulation of ThB on CD4+CD8+ thymocytes identifies a unique subset (CD4+CD8+ThBneg–low) of thymocytes that has received the initial signals for thymic selection but have not yet downregulated the CD4 and CD8 cell surface expression. In addition, these results also indicate that a high frequency (Ÿ20–40%) of CD4+CD8+ immature thymocytes receive these initial signals during thymic selection

The Hymenoptera Genome Database

The Hymenoptera Genome Database (HGD) is an informatics resource supporting genomics of hymenopteran insect species. This relational database implements open-source software and components providing access to curated data contributed by an extensive, active research community. HGD includes the genome sequences and annotation data of honey bee _Apis mellifera_ and its pathogens ("http://BeeBase.org":BeeBase.org) the parasitoid wasp _Nasonia vitripennis_ ("http://NasoniaBase.org":NasoniaBase.org) and a portal to the genomes of six species of ants. Together, these species cover approximately 200 MY in the phylogeny of Hymenoptera, allowing to leverage genetic, genome sequence, and gene expression data, as well as the biological knowledge of related model organisms. The availability of resources across an order greatly facilitates comparative genomics and enhances our understanding of the biology of agriculturally important Hymenoptera species through genomics. HGD has supported research contributions from an extensive community from almost 80 institutions in 14 countries. Community annotation efforts are made possible thanks to a remote connection to a Chado database by Apollo Genome Annotation client software. Curated data at HGD includes predicted and annotated gene sets supported with evidence tracks such as ESTs/cDNAs, small RNA sequences and GC composition domains. Data at HGD can be queried using genome browsers and / or BLAST/PSI-BLAST servers, and it may also be downloaded to perform local searches. We encourage the public to access and contribute data to HGD at "http://HymenopteraGenome.org":HymenopteraGenome.org.

This poster contains material included in an article accepted for publication in Nucl. Acids Res.©: 2011. The Database Issue. Published by Oxford University Press

MGMT enrichment and second gene co-expression in hematopoietic progenitor cells using separate or dual-gene lentiviral vectors

AbstractThe DNA repair gene O6-methylguanine-DNA methyltransferase (MGMT) allows efficient in vivo enrichment of transduced hematopoietic stem cells (HSC). Thus, linking this selection strategy to therapeutic gene expression offers the potential to reconstitute diseased hematopoietic tissue with gene-corrected cells. However, different dual-gene expression vector strategies are limited by poor expression of one or both transgenes. To evaluate different co-expression strategies in the context of MGMT-mediated HSC enrichment, we compared selection and expression efficacies in cells cotransduced with separate single-gene MGMT and GFP lentivectors to those obtained with dual-gene vectors employing either encephalomyocarditis virus (EMCV) internal ribosome entry site (IRES) or foot and mouth disease virus (FMDV) 2A elements for co-expression strategies. Each strategy was evaluated in vitro and in vivo using equivalent multiplicities of infection (MOI) to transduce 5-fluorouracil (5-FU) or Lin−Sca-1+c-kit+ (LSK)-enriched murine bone marrow cells (BMCs). The highest dual-gene expression (MGMT+GFP+) percentages were obtained with the FMDV-2A dual-gene vector, but half of the resulting gene products existed as fusion proteins. Following selection, dual-gene expression percentages in single-gene vector cotransduced and dual-gene vector transduced populations were similar. Equivalent MGMT expression levels were obtained with each strategy, but GFP expression levels derived from the IRES dual-gene vector were significantly lower. In mice, vector-insertion averages were similar among cells enriched after dual-gene vectors and those cotransduced with single-gene vectors. These data demonstrate the limitations and advantages of each strategy in the context of MGMT-mediated selection, and may provide insights into vector design with respect to a particular therapeutic gene or hematologic defect

Creating a honey bee consensus gene set

BACKGROUND: We wished to produce a single reference gene set for honey bee (Apis mellifera). Our motivation was twofold. First, we wished to obtain an improved set of gene models with increased coverage of known genes, while maintaining gene model quality. Second, we wished to provide a single official gene list that the research community could further utilize for consistent and comparable analyses and functional annotation. RESULTS: We created a consensus gene set for honey bee (Apis mellifera) using GLEAN, a new algorithm that uses latent class analysis to automatically combine disparate gene prediction evidence in the absence of known genes. The consensus gene models had increased representation of honey bee genes without sacrificing quality compared with any one of the input gene predictions. When compared with manually annotated gold standards, the consensus set of gene models was similar or superior in quality to each of the input sets. CONCLUSION: Most eukaryotic genome projects produce multiple gene sets because of the variety of gene prediction programs. Each of the gene prediction programs has strengths and weaknesses, and so the multiplicity of gene sets offers users a more comprehensive collection of genes to use than is available from a single program. On the other hand, the availability of multiple gene sets is also a cause for uncertainty among users as regards which set they should use. GLEAN proved to be an effective method to combine gene lists into a single reference set

Computational and transcriptional evidence for microRNAs in the honey bee genome

A total of 68 non-redundant candidate honey bee miRNAs were identified computationally; several of them appear to have previously unrecognized orthologs in the Drosophila genome. Several miRNAs showed caste- or age-related differences in transcript abundance and are likely to be involved in regulating honey bee development

Microbial nitrogen limitation in the mammalian large intestine

Resource limitation is a fundamental factor governing the composition and function of ecological communities. However, the role of resource supply in structuring the intestinal microbiome has not been established and represents a challenge for mammals that rely on microbial symbionts for digestion: too little supply might starve the microbiome while too much might starve the host. We present evidence that microbiota occupy a habitat that is limited in total nitrogen supply within the large intestines of 30 mammal species. Lowering dietary protein levels in mice reduced their faecal concentrations of bacteria. A gradient of stoichiometry along the length of the gut was consistent with the hypothesis that intestinal nitrogen limitation results from host absorption of dietary nutrients. Nitrogen availability is also likely to be shaped by host-microbe interactions: levels of host-secreted nitrogen were altered in germ-free mice and when bacterial loads were reduced via experimental antibiotic treatment. Single-cell spectrometry revealed that members of the phylum Bacteroidetes consumed nitrogen in the large intestine more readily than other commensal taxa did. Our findings support a model where nitrogen limitation arises from preferential host use of dietary nutrients. We speculate that this resource limitation could enable hosts to regulate microbial communities in the large intestine. Commensal microbiota may have adapted to nitrogen-limited settings, suggesting one reason why excess dietary protein has been associated with degraded gut-microbial ecosystems

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics

Bovine Genome Database: integrated tools for genome annotation and discovery

The Bovine Genome Database (BGD; http://BovineGenome.org) strives to improve annotation of the bovine genome and to integrate the genome sequence with other genomics data. BGD includes GBrowse genome browsers, the Apollo Annotation Editor, a quantitative trait loci (QTL) viewer, BLAST databases and gene pages. Genome browsers, available for both scaffold and chromosome coordinate systems, display the bovine Official Gene Set (OGS), RefSeq and Ensembl gene models, non-coding RNA, repeats, pseudogenes, single-nucleotide polymorphism, markers, QTL and alignments to complementary DNAs, ESTs and protein homologs. The Bovine QTL viewer is connected to the BGD Chromosome GBrowse, allowing for the identification of candidate genes underlying QTL. The Apollo Annotation Editor connects directly to the BGD Chado database to provide researchers with remote access to gene evidence in a graphical interface that allows editing and creating new gene models. Researchers may upload their annotations to the BGD server for review and integration into the subsequent release of the OGS. Gene pages display information for individual OGS gene models, including gene structure, transcript variants, functional descriptions, gene symbols, Gene Ontology terms, annotator comments and links to National Center for Biotechnology Information and Ensembl. Each gene page is linked to a wiki page to allow input from the research community

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s pa- rameters and data-related modeling choices are also both crucial and challenging