Nfkbid Overexpression in Nonobese Diabetic Mice Elicits Complete Type 1 Diabetes Resistance in Part Associated with Enhanced Thymic Deletion of Pathogenic CD8 T Cells and Increased Numbers and Activity of Regulatory T Cells.

Type 1 diabetes (T1D) in both humans and NOD mice is caused by T cell-mediated autoimmune destruction of pancreatic ß cells. Increased frequency or activity of autoreactive T cells and failures of regulatory T cells (Tregs) to control these pathogenic effectors have both been implicated in T1D etiology. Due to the expression of MHC class I molecules on ß cells, CD8 T cells represent the ultimate effector population mediating T1D. Developing autoreactive CD8 T cells normally undergo extensive thymic negative selection, but this process is impaired in NOD mice and also likely T1D patients. Previous studies identified an allelic variant of Nfkbid, a NF-κB signal modulator, as a gene strongly contributing to defective thymic deletion of autoreactive CD8 T cells in NOD mice. These previous studies found ablation of Nfkbid in NOD mice using the clustered regularly interspaced short palindromic repeats system resulted in greater thymic deletion of pathogenic CD8 AI4 and NY8.3 TCR transgenic T cells but an unexpected acceleration of T1D onset. This acceleration was associated with reductions in the frequency of peripheral Tregs. In this article, we report transgenic overexpression of Nfkbid in NOD mice also paradoxically results in enhanced thymic deletion of autoreactive CD8 AI4 T cells. However, transgenic elevation of Nfkbid expression also increased the frequency and functional capacity of peripheral Tregs, in part contributing to the induction of complete T1D resistance. Thus, future identification of a pharmaceutical means to enhance Nfkbid expression might ultimately provide an effective T1D intervention approach.

Development and validation of the JAX Cancer Treatment Profile™ for detection of clinically actionable mutations in solid tumors.

BACKGROUND: The continued development of targeted therapeutics for cancer treatment has required the concomitant development of more expansive methods for the molecular profiling of the patient's tumor. We describe the validation of the JAX Cancer Treatment Profile™ (JAX-CTP™), a next generation sequencing (NGS)-based molecular diagnostic assay that detects actionable mutations in solid tumors to inform the selection of targeted therapeutics for cancer treatment. METHODS: NGS libraries are generated from DNA extracted from formalin fixed paraffin embedded tumors. Using hybrid capture, the genes of interest are enriched and sequenced on the Illumina HiSeq 2500 or MiSeq sequencers followed by variant detection and functional and clinical annotation for the generation of a clinical report. RESULTS: The JAX-CTP™ detects actionable variants, in the form of single nucleotide variations and small insertions and deletions (≤50 bp) in 190 genes in specimens with a neoplastic cell content of ≥10%. The JAX-CTP™ is also validated for the detection of clinically actionable gene amplifications. CONCLUSIONS: There is a lack of consensus in the molecular diagnostics field on the best method for the validation of NGS-based assays in oncology, thus the importance of communicating methods, as contained in this report. The growing number of targeted therapeutics and the complexity of the tumor genome necessitate continued development and refinement of advanced assays for tumor profiling to enable precision cancer treatment.

ISA-TAB-Nano: a specification for sharing nanomaterial research data in spreadsheet-based format.

BACKGROUND AND MOTIVATION: The high-throughput genomics communities have been successfully using standardized spreadsheet-based formats to capture and share data within labs and among public repositories. The nanomedicine community has yet to adopt similar standards to share the diverse and multi-dimensional types of data (including metadata) pertaining to the description and characterization of nanomaterials. Owing to the lack of standardization in representing and sharing nanomaterial data, most of the data currently shared via publications and data resources are incomplete, poorly-integrated, and not suitable for meaningful interpretation and re-use of the data. Specifically, in its current state, data cannot be effectively utilized for the development of predictive models that will inform the rational design of nanomaterials. RESULTS: We have developed a specification called ISA-TAB-Nano, which comprises four spreadsheet-based file formats for representing and integrating various types of nanomaterial data. Three file formats (Investigation, Study, and Assay files) have been adapted from the established ISA-TAB specification; while the Material file format was developed de novo to more readily describe the complexity of nanomaterials and associated small molecules. In this paper, we have discussed the main features of each file format and how to use them for sharing nanomaterial descriptions and assay metadata. CONCLUSION: The ISA-TAB-Nano file formats provide a general and flexible framework to record and integrate nanomaterial descriptions, assay data (metadata and endpoint measurements) and protocol information. Like ISA-TAB, ISA-TAB-Nano supports the use of ontology terms to promote standardized descriptions and to facilitate search and integration of the data. The ISA-TAB-Nano specification has been submitted as an ASTM work item to obtain community feedback and to provide a nanotechnology data-sharing standard for public development and adoption.

Glucose oxidation-dependent survival of activated B cells provides a putative novel therapeutic target for lupus treatment.

Aberrant metabolic demand is observed in immune/inflammatory disorders, yet the role in pathogenesis remains unclear. Here, we discover that in lupus, activated B cells, including germinal center B (GCB) cells, have remarkably high glycolytic requirement for survival over T cell populations, as demonstrated by increased metabolic activity in lupus-activated B cells compared to immunization-induced cells. The augmented reliance on glucose oxidation makes GCB cells vulnerable to mitochondrial ROS-induced oxidative stress and apoptosis. Short-term glycolysis inhibition selectively reduces pathogenic activated B in lupus-prone mice, extending their lifespan, without affecting T follicular helper cells. Particularly, BCMA-expressing GCB cells rely heavily on glucose oxidation. Depleting BCMA-expressing activated B cells with APRIL-based CAR-T cells significantly prolongs the lifespan of mice with severe autoimmune disease. These results reveal that glycolysis-dependent activated B and GCB cells, especially those expressing BCMA, are potentially key lupus mediators, and could be targeted to improve disease outcomes.

The caBIG® Life Science Business Architecture Model.

MOTIVATION: Business Architecture Models (BAMs) describe what a business does, who performs the activities, where and when activities are performed, how activities are accomplished and which data are present. The purpose of a BAM is to provide a common resource for understanding business functions and requirements and to guide software development. The cancer Biomedical Informatics Grid (caBIG®) Life Science BAM (LS BAM) provides a shared understanding of the vocabulary, goals and processes that are common in the business of LS research. RESULTS: LS BAM 1.1 includes 90 goals and 61 people and groups within Use Case and Activity Unified Modeling Language (UML) Diagrams. Here we report on the model's current release, LS BAM 1.1, its utility and usage, and plans for future use and continuing development for future releases. AVAILABILITY AND IMPLEMENTATION: The LS BAM is freely available as UML, PDF and HTML (https://wiki.nci.nih.gov/x/OFNyAQ).

A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer.

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research. SIGNIFICANCE: Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

Draft Genome Sequences of 27 Northern Maine Clinical Isolates.

We report the draft genome sequences of 27 common pathogens collected from a northern Maine hospital in 2017. These were sequenced in order to determine temporal and biogeographical patterns of antibiotic gene distribution. A total of 908 antibiotic resistance genes, 848 insertion sequence elements, and 57 plasmids were identified.

Genes adapt to outsmart gene-targeting strategies in mutant mouse strains by skipping exons to reinitiate transcription and translation.

BACKGROUND: Gene disruption in mouse embryonic stem cells or zygotes is a conventional genetics approach to identify gene function in vivo. However, because different gene disruption strategies use different mechanisms to disrupt genes, the strategies can result in diverse phenotypes in the resulting mouse model. To determine whether different gene disruption strategies affect the phenotype of resulting mutant mice, we characterized Rhbdf1 mouse mutant strains generated by three commonly used strategies-definitive-null, targeted knockout (KO)-first, and CRISPR/Cas9. RESULTS: We find that Rhbdf1 responds differently to distinct KO strategies, for example, by skipping exons and reinitiating translation to potentially yield gain-of-function alleles rather than the expected null or severe hypomorphic alleles. Our analysis also revealed that at least 4% of mice generated using the KO-first strategy show conflicting phenotypes. CONCLUSIONS: Exon skipping is a widespread phenomenon occurring across the genome. These findings have significant implications for the application of genome editing in both basic research and clinical practice.

The caBIG terminology review process.

The National Cancer Institute (NCI) is developing an integrated biomedical informatics infrastructure, the cancer Biomedical Informatics Grid (caBIG), to support collaboration within the cancer research community. A key part of the caBIG architecture is the establishment of terminology standards for representing data. In order to evaluate the suitability of existing controlled terminologies, the caBIG Vocabulary and Data Elements Workspace (VCDE WS) working group has developed a set of criteria that serve to assess a terminology's structure, content, documentation, and editorial process. This paper describes the evolution of these criteria and the results of their use in evaluating four standard terminologies: the Gene Ontology (GO), the NCI Thesaurus (NCIt), the Common Terminology for Adverse Events (known as CTCAE), and the laboratory portion of the Logical Objects, Identifiers, Names and Codes (LOINC). The resulting caBIG criteria are presented as a matrix that may be applicable to any terminology standardization effort.

Enhancing the efficacy of glycolytic blockade in cancer cells via RAD51 inhibition.

Targeting the early steps of the glycolysis pathway in cancers is a well-established therapeutic strategy; however, the doses required to elicit a therapeutic effect on the cancer can be toxic to the patient. Consequently, numerous preclinical and clinical studies have combined glycolytic blockade with other therapies. However, most of these other therapies do not specifically target cancer cells, and thus adversely affect normal tissue. Here we first show that a diverse number of cancer models - spontaneous, patient-derived xenografted tumor samples, and xenografted human cancer cells - can be efficiently targeted by 2-deoxy-D-Glucose (2DG), a well-known glycolytic inhibitor. Next, we tested the cancer-cell specificity of a therapeutic compound using the MEC1 cell line, a chronic lymphocytic leukemia (CLL) cell line that expresses activation induced cytidine deaminase (AID). We show that MEC1 cells, are susceptible to 4,4'-Diisothiocyano-2,2'-stilbenedisulfonic acid (DIDS), a specific RAD51 inhibitor. We then combine 2DG and DIDS, each at a lower dose and demonstrate that this combination is more efficacious than fludarabine, the current standard- of- care treatment for CLL. This suggests that the therapeutic blockade of glycolysis together with the therapeutic inhibition of RAD51-dependent homologous recombination can be a potentially beneficial combination for targeting AID positive cancer cells with minimal adverse effects on normal tissue. Implications: Combination therapy targeting glycolysis and specific RAD51 function shows increased efficacy as compared to standard of care treatments in leukemias.

Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines.

BACKGROUND: Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison. RESULTS: We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). CONCLUSIONS: The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows .

Life sciences domain analysis model.

OBJECTIVE: Meaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research. MATERIALS AND METHODS: The content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types. RESULTS: The LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research. DISCUSSION: The LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts. CONCLUSIONS: The LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science.