Whole-Genome Sequencing of Individuals from a Founder Population Identifies Candidate Genes for Asthma

Asthma is a complex genetic disease caused by a combination of genetic and environmental risk factors. We sought to test classes of genetic variants largely missed by genome-wide association studies (GWAS), including copy number variants (CNVs) and low-frequency variants, by performing whole-genome sequencing (WGS) on 16 individuals from asthma-enriched and asthma-depleted families. The samples were obtained from an extended 13-generation Hutterite pedigree with reduced genetic heterogeneity due to a small founding gene pool and reduced environmental heterogeneity as a result of a communal lifestyle. We sequenced each individual to an average depth of 13-fold, generated a comprehensive catalog of genetic variants, and tested the most severe mutations for association with asthma. We identified and validated 1960 CNVs, 19 nonsense or splice-site single nucleotide variants (SNVs), and 18 insertions or deletions that were out of frame. As follow-up, we performed targeted sequencing of 16 genes in 837 cases and 540 controls of Puerto Rican ancestry and found that controls carry a significantly higher burden of mutations in IL27RA (2.0% of controls; 0.23% of cases; nominal p = 0.004; Bonferroni p = 0.21). We also genotyped 593 CNVs in 1199 Hutterite individuals. We identified a nominally significant association (p = 0.03; Odds ratio (OR) = 3.13) between a 6 kbp deletion in an intron of NEDD4L and increased risk of asthma. We genotyped this deletion in an additional 4787 non-Hutterite individuals (nominal p = 0.056; OR = 1.69). NEDD4L is expressed in bronchial epithelial cells, and conditional knockout of this gene in the lung in mice leads to severe inflammation and mucus accumulation. Our study represents one of the early instances of applying WGS to complex disease with a large environmental component and demonstrates how WGS can identify risk variants, including CNVs and low-frequency variants, largely untested in GWAS

Independent Risk Factors for Urinary Tract Infection and for Subsequent Bacteremia or Acute Cellular Rejection

BackgroundUrinary tract infection (UTI) is a frequent, serious complication in kidney allograft recipients.MethodsWe reviewed the records of 1166 kidney allograft recipients who received their allografts at our institution between January 2005 and December 2010 and determined the incidence of UTI during the first 3 months after transplantation (early UTI). We used Cox proportional hazards models to determine the risk factors for early UTI and whether early UTI was an independent risk factor for subsequent bacteremia or acute cellular rejection (ACR).ResultsUTI, defined as 10 or more bacterial colony-forming units/mL urine, developed in 247 (21%) of the 1166 recipients. Independent risk factors for the first episode of UTI were female gender (hazard ratio [HR], 2.9; 95% confidence intervals [CI], 2.2-3.7; P<0.001), prolonged use of Foley catheter (HR, 3.9; 95% CI, 2.8-5.4; P <0.001), ureteral stent (HR, 1.4; 95% CI, 1.1-1.8; P=0.01), age (HR, 1.1; 95% CI, 1.0-1.2; P=0.03), and delayed graft function (HR, 1.4; 95% CI, 1.0-1.9; P=0.06). Trimethoprim/sulfamethoxazole prophylaxis was associated with a reduced risk of UTI (HR, 0.6; 95% CI, 0.3-0.9; P=0.02). UTI was an independent risk factor for subsequent bacteremia (HR, 2.4; 95% CI, 1.2-4.8; P=0.01). Untreated UTI, but not treated UTI, was associated with an increased risk of ACR (HR, 2.8; 95% CI, 1.3-6.2; P=0.01).ConclusionsFemale gender, prolonged use of Foley catheter, ureteral stent, age, and delayed graft function are independent risk factors for early UTI. UTI is independently associated with the development of bacteremia, and untreated UTI is associated with subsequent ACR

Biome representational in silico karyotyping

Metagenomic characterization of complex biomes remains challenging. Here we describe a modification of digital karyotyping—biome representational in silico karyotyping (BRISK)—as a general technique for analyzing a defined representation of all DNA present in a sample. BRISK utilizes a Type IIB DNA restriction enzyme to create a defined representation of 27-mer DNAs in a sample. Massively parallel sequencing of this representation allows for construction of high-resolution karyotypes and identification of multiple species within a biome. Application to normal human tissue demonstrated linear recovery of tags by chromosome. We apply this technique to the biome of the oral mucosa and find that greater than 25% of recovered DNA is nonhuman. DNA from 41 microbial species could be identified from oral mucosa of two subjects. Of recovered nonhuman sequences, fewer than 30% are currently annotated. We characterized seven prevalent unknown sequences by chromosome walking and find these represent novel microbial sequences including two likely derived from novel phage genomes. Application of BRISK to archival tissue from a nasopharyngeal carcinoma resulted in identification of Epstein-Barr virus infection. These results suggest that BRISK is a powerful technique for the analysis of complex microbiomes and potentially for pathogen discovery

Correction: A Year of Infection in the Intensive Care Unit: Prospective Whole Genome Sequencing of Bacterial Clinical Isolates Reveals Cryptic Transmissions and Novel Microbiota

<p>Correction: A Year of Infection in the Intensive Care Unit: Prospective Whole Genome Sequencing of Bacterial Clinical Isolates Reveals Cryptic Transmissions and Novel Microbiota</p

A Year of Infection in the Intensive Care Unit: Prospective Whole Genome Sequencing of Bacterial Clinical Isolates Reveals Cryptic Transmissions and Novel Microbiota

<div><p>Bacterial whole genome sequencing holds promise as a disruptive technology in clinical microbiology, but it has not yet been applied systematically or comprehensively within a clinical context. Here, over the course of one year, we performed prospective collection and whole genome sequencing of nearly all bacterial isolates obtained from a tertiary care hospital’s intensive care units (ICUs). This unbiased collection of 1,229 bacterial genomes from 391 patients enables detailed exploration of several features of clinical pathogens. A sizable fraction of isolates identified as clinically relevant corresponded to previously undescribed species: 12% of isolates assigned a species-level classification by conventional methods actually qualified as distinct, novel genomospecies on the basis of genomic similarity. Pan-genome analysis of the most frequently encountered pathogens in the collection revealed substantial variation in pan-genome size (1,420 to 20,432 genes) and the rate of gene discovery (1 to 152 genes per isolate sequenced). Surprisingly, although potential nosocomial transmission of actively surveilled pathogens was rare, 8.7% of isolates belonged to genomically related clonal lineages that were present among multiple patients, usually with overlapping hospital admissions, and were associated with clinically significant infection in 62% of patients from which they were recovered. Multi-patient clonal lineages were particularly evident in the neonatal care unit, where seven separate <i>Staphylococcus epidermidis</i> clonal lineages were identified, including one lineage associated with bacteremia in 5/9 neonates. Our study highlights key differences in the information made available by conventional microbiological practices versus whole genome sequencing, and motivates the further integration of microbial genome sequencing into routine clinical care.</p></div

Clustering of sequenced isolates by genomic similarity.

<p>(<b>A</b>) Network diagram of all 1,229 sequenced isolates that could be assigned to one of 78 clusters on the basis of pairwise ANIb. Each node represents an individual isolate, and is colored black if robustly matching a previously reported genome (≥ 95% ANIb) or white if corresponding to a novel genomospecies. Nodes connected by a visible edge indicate pairwise ANIb values ≥ 95%. Edges connecting isolates within the same cluster are colored according to that cluster, edges connecting isolates that match multiple clusters are grey. Clusters are labeled according to the most detailed taxonomic classification given to isolates during conventional identification. (<b>B</b>) Network diagram of 419 isolates corresponding to novel genomospecies, assigned to 53 clusters on the basis of pairwise ANIb. Clusters are labeled as in (A). For both panels, the length of edges between nodes is not informative or proportional to ANIb values, and consequently neither is the placement of specific nodes or groups within the graph. The amount of connectivity among nodes indicates the basis of their inclusion with respect to specific groups.</p