Search

SUMMARY Despite the well-accepted view that chronic inflammation contributes to the pathogenesis of Duchenne muscular dystrophy (DMD), the function and regulation of eosinophils remain an unclear facet of type II innate immunity in dystrophic muscle. We report the observation that group 2 innate lymphoid cells (ILC2s) are present in skeletal muscle and are the principal regulators of muscle eosinophils during muscular dystrophy. Eosinophils were elevated in DMD patients and dystrophic mice along with interleukin (IL)-5, a major eosinophil survival factor that was predominantly expressed by muscle ILC2s. We also find that IL-33 was upregulated in dystrophic muscle and was predominantly produced by fibrogenic/adipogenic progenitors (FAPs). Exogenous IL-33 and IL-2 complex (IL-2c) expanded muscle ILC2s and eosinophils, decreased the cross-sectional area (CSA) of regenerating myofibers, and increased the expression of genes associated with muscle fibrosis. The deletion of ILC2s in dystrophic mice mitigated muscle eosinophilia and impaired the induction of IL-5 and fibrosis-associated genes. Our findings highlight a FAP/ILC2/eosinophil axis that promotes type II innate immunity, which influences the balance between regenerative and fibrotic responses during muscular dystrophy., In brief Immune cells that comprise type II innate immunity coalesce to regulate tissue repair and fibrosis. Kastenschmidt et al. report that ILC2s reside in skeletal muscle, are activated in muscular dystrophy, and promote muscle eosinophilia. Stromal progenitors expressed IL-33, which expanded ILC2s and promoted a transcriptional program associated with muscle fibrosis., Graphical Abstract

Rare genetic variants are the major cause of Mendelian disorders, yet only half of described genetic diseases have been causally linked to a gene. In addition, the total number of rare genetic diseases is projected to be far greater than that of those already described. Whole-genome sequencing of patients with subsequent genetic and functional analysis is a powerful way to describe these gene anomalies. However, this approach results in tens to hundreds of candidate disease-causative genes, and the identification of additional individuals suffering from the same disorder can be difficult because of rarity and phenotypic heterogeneity. We describe a genetic network–based method to rank candidate genes identified in family-based sequencing studies, termed phenotype informed network (PIN) ranking. Furthermore, we present a case study as an extension of the PIN ranking method in which disease symptoms drive the network ranking and identification of the disease-causative gene. We demonstrate, through simulation, that our method is capable of identifying the correct disease-causative gene in a majority of cases. PIN-rank is available at https://genomics.scripps.edu/pinrank/ . We have developed a method to prioritize candidate disease-causative genes based on symptoms that would be useful for both the prioritization of candidates and the identification of additional subjects. Genet Med 17 11, 859–865.

There is considerable debate about the most efficient way to interrogate rare coding variants in association studies. The options include direct genotyping of specific known coding variants in genes or, alternatively, sequencing across the entire exome to capture known as well as novel variants. Each strategy has advantages and disadvantages, but the availability of cost-efficient exome arrays has made the former appealing. Here we consider the utility of a direct genotyping chip, the Illumina HumanExome array (HE), by evaluating its content based on: 1. functionality; and 2. amenability to imputation. We explored these issues by genotyping a large, ethnically diverse cohort on the HumanOmniExpressExome array (HOEE) which combines the HE with content from the GWAS array (HOE). We find that the use of the HE is likely to be a cost-effective way of expanding GWAS, but does have some drawbacks that deserve consideration when planning studies. © 2014 Elsevier B.V.

There have been a number of recent successes in the use of whole genome sequencing and sophisticated bioinformatics techniques to identify pathogenic DNA sequence variants responsible for individual idiopathic congenital conditions. However, the success of this identification process is heavily influenced by the ancestry or genetic background of a patient with an idiopathic condition. This is so because potential pathogenic variants in a patient's genome must be contrasted with variants in a reference set of genomes made up of other individuals' genomes of the same ancestry as the patient. We explored the effect of ignoring the ancestries of both an individual patient and the individuals used to construct reference genomes. We pursued this exploration in two major steps. We first considered variation in the per-genome number and rates of likely functional derived (i.e., non-ancestral, based on the chimp genome) single nucleotide variants and small indels in 52 individual whole human genomes sampled from 10 different global populations. We took advantage of a suite of computational and bioinformatics techniques to predict the functional effect of over 24 million genomic variants, both coding and non-coding, across these genomes. We found that the typical human genome harbors ∼5.5-6.1 million total derived variants, of which ∼12,000 are likely to have a functional effect (∼5000 coding and ∼7000 non-coding). We also found that the rates of functional genotypes per the total number of genotypes in individual whole genomes differ dramatically between human populations. We then created tables showing how the use of comparator or reference genome panels comprised of genomes from individuals that do not have the same ancestral background as a patient can negatively impact pathogenic variant identification. Our results have important implications for clinical sequencing initiatives.

Interpretation of human genomes is a major challenge. We present the Scripps Genome ADVISER (SG-ADVISER) suite, which aims to fill the gap between data generation and genome interpretation by performing holistic, in-depth, annotations and functional predictions on all variant types and effects. The SG-ADVISER suite includes a de-identification tool, a variant annotation web-server, and a user interface for inheritance and annotation-based filtration. SG-ADVISER allows users with no bioinformatics expertise to manipulate large volumes of variant data with ease – without the need to download large reference databases, install software, or use a command line interface. SG-ADVISER is freely available at genomics.scripps.edu/ADVISER.