Esophageal Microbiome in Healthy Children and Esophageal Eosinophilia.

OBJECTIVES: There is limited knowledge about the role of esophageal microbiome in pediatric esophageal eosinophilia (EE). We aimed to characterize the esophageal microbiome in pediatric patients with and without EE. METHODS: In the present prospective study, esophageal mucosal biopsies were obtained from 41 children. Of these, 22 had normal esophageal mucosal biopsies ("healthy"), 6 children had reflux esophagitis (RE), 4 had proton pump inhibitor (PPi)-responsive esophageal eosinophilia (PPi-REE), and 9 had eosinophilic esophagitis (EoE). The microbiome composition was analyzed using 16S rRNA gene sequencing. The age median (range) in years for the healthy, RE, PPi-REE, and EoE group were 10 (1.5-18), 6 (2-15), 6.5 (5-15), and 9 (1.5-17), respectively. RESULTS: The bacterial phylum Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria were the most predominant. The Epsilonproteobacteria, Betaproteobacteria, Flavobacteria, Fusobacteria, and Sphingobacteria class were underrepresented across groups. The Vibrionales was predominant in healthy and EoE group but lower in RE and PPi-REE groups. The genus Streptococcus, Rahnella, and Leptotrichia explained 29.65% of the variation in the data with an additional 10.86% variation in the data was explained by Microbacterium, Prevotella, and Vibrio genus. The healthy group had a higher diversity and richness index compared to other groups, but this was not statistically different. CONCLUSIONS: The pediatric esophagus has an abundant and diverse microbiome, both in the healthy and diseased states. The healthy group had a higher, but not significantly different, diversity and richness index compared to other groups.

SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data.

BACKGROUND: Recently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision. RESULTS: We have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools. CONCLUSIONS: The DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.

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 .

The Genome of C57BL/6J "Eve", the Mother of the Laboratory Mouse Genome Reference Strain.

Isogenic laboratory mouse strains enhance reproducibility because individual animals are genetically identical. For the most widely used isogenic strain, C57BL/6, there exists a wealth of genetic, phenotypic, and genomic data, including a high-quality reference genome (GRCm38.p6). Now 20 years after the first release of the mouse reference genome, C57BL/6J mice are at least 26 inbreeding generations removed from GRCm38 and the strain is now maintained with periodic reintroduction of cryorecovered mice derived from a single breeder pair, aptly named Adam and Eve. To provide an update to the mouse reference genome that more accurately represents the genome of today's C57BL/6J mice, we took advantage of long read, short read, and optical mapping technologies to generate a de novo assembly of the C57BL/6J Eve genome (B6Eve). Using these data, we have addressed recurring variants observed in previous mouse genomic studies. We have also identified structural variations, closed gaps in the mouse reference assembly, and revealed previously unannotated coding sequences. This B6Eve assembly explains discrepant observations that have been associated with GRCm38-based analyses, and will inform a reference genome that is more representative of the C57BL/6J mice that are in use today.

A Hypermorphic Nfkbid Allele Contributes to Impaired Thymic Deletion of Autoreactive Diabetogenic CD8+ T Cells in NOD Mice.

In both NOD mice and humans, the development of type 1 diabetes (T1D) is dependent in part on autoreactive CD8+ T cells recognizing pancreatic ß cell peptides presented by often quite common MHC class I variants. Studies in NOD mice previously revealed that the common H2-Kd and/or H2-Db class I molecules expressed by this strain aberrantly lose the ability to mediate the thymic deletion of pathogenic CD8+ T cell responses through interactions with T1D susceptibility genes outside the MHC. A gene(s) mapping to proximal chromosome 7 was previously shown to be an important contributor to the failure of the common class I molecules expressed by NOD mice to mediate the normal thymic negative selection of diabetogenic CD8+ T cells. Using an inducible model of thymic negative selection and mRNA transcript analyses, we initially identified an elevated Nfkbid expression variant as a likely NOD-proximal chromosome 7 region gene contributing to impaired thymic deletion of diabetogenic CD8+ T cells. CRISPR/Cas9-mediated genetic attenuation of Nfkbid expression in NOD mice resulted in improved negative selection of autoreactive diabetogenic AI4 and NY8.3 CD8+ T cells. These results indicated that allelic variants of Nfkbid contribute to the efficiency of intrathymic deletion of diabetogenic CD8+ T cells. However, although enhancing thymic deletion of pathogenic CD8+ T cells, ablating Nfkbid expression surprisingly accelerated T1D onset that was associated with numeric decreases in both regulatory T and B lymphocytes in NOD mice.

Genomes of the Mouse Collaborative Cross.

The Collaborative Cross (CC) is a multiparent panel of recombinant inbred (RI) mouse strains derived from eight founder laboratory strains. RI panels are popular because of their long-term genetic stability, which enhances reproducibility and integration of data collected across time and conditions. Characterization of their genomes can be a community effort, reducing the burden on individual users. Here we present the genomes of the CC strains using two complementary approaches as a resource to improve power and interpretation of genetic experiments. Our study also provides a cautionary tale regarding the limitations imposed by such basic biological processes as mutation and selection. A distinct advantage of inbred panels is that genotyping only needs to be performed on the panel, not on each individual mouse. The initial CC genome data were haplotype reconstructions based on dense genotyping of the most recent common ancestors (MRCAs) of each strain followed by imputation from the genome sequence of the corresponding founder inbred strain. The MRCA resource captured segregating regions in strains that were not fully inbred, but it had limited resolution in the transition regions between founder haplotypes, and there was uncertainty about founder assignment in regions of limited diversity. Here we report the whole genome sequence of 69 CC strains generated by paired-end short reads at 30× coverage of a single male per strain. Sequencing leads to a substantial improvement in the fine structure and completeness of the genomes of the CC. Both MRCAs and sequenced samples show a significant reduction in the genome-wide haplotype frequencies from two wild-derived strains, CAST/EiJ and PWK/PhJ. In addition, analysis of the evolution of the patterns of heterozygosity indicates that selection against three wild-derived founder strains played a significant role in shaping the genomes of the CC. The sequencing resource provides the first description of tens of thousands of new genetic variants introduced by mutation and drift in the CC genomes. We estimate that new SNP mutations are accumulating in each CC strain at a rate of 2.4 ± 0.4 per gigabase per generation. The fixation of new mutations by genetic drift has introduced thousands of new variants into the CC strains. The majority of these mutations are novel compared to currently sequenced laboratory stocks and wild mice, and some are predicted to alter gene function. Approximately one-third of the CC inbred strains have acquired large deletions (>10 kb) many of which overlap known coding genes and functional elements. The sequence of these mice is a critical resource to CC users, increases threefold the number of mouse inbred strain genomes available publicly, and provides insight into the effect of mutation and drift on common resources.