A novel and accurate full-length HTT mouse model for Huntington's disease.

Here, we report the generation and characterization of a novel Huntington's disease (HD) mouse model BAC226Q by using a bacterial artificial chromosome (BAC) system, expressing full-length human HTT with ~226 CAG-CAA repeats and containing endogenous human HTT promoter and regulatory elements. BAC226Q recapitulated a full-spectrum of age-dependent and progressive HD-like phenotypes without unwanted and erroneous phenotypes. BAC226Q mice developed normally, and gradually exhibited HD-like psychiatric and cognitive phenotypes at 2 months. From 3 to 4 months, BAC226Q mice showed robust progressive motor deficits. At 11 months, BAC226Q mice showed significant reduced life span, gradual weight loss and exhibited neuropathology including significant brain atrophy specific to striatum and cortex, striatal neuronal death, widespread huntingtin inclusions, and reactive pathology. Therefore, the novel BAC226Q mouse accurately recapitulating robust, age-dependent, progressive HD-like phenotypes will be a valuable tool for studying disease mechanisms, identifying biomarkers, and testing gene-targeting therapeutic approaches for HD.

Rapid, Phase-free Detection of Long Identity-by-Descent Segments Enables Effective Relationship Classification.

Identity-by-descent (IBD) segments are a useful tool for applications ranging from demographic inference to relationship classification, but most detection methods rely on phasing information and therefore require substantial computation time. As genetic datasets grow, methods for inferring IBD segments that scale well will be critical. We developed IBIS, an IBD detector that locates long regions of allele sharing between unphased individuals, and benchmarked it with Refined IBD, GERMLINE, and TRUFFLE on 3,000 simulated individuals. Phasing these with Beagle 5 takes 4.3 CPU days, followed by either Refined IBD or GERMLINE segment detection in 2.9 or 1.1 h, respectively. By comparison, IBIS finishes in 6.8 min or 7.8 min with IBD2 functionality enabled: speedups of 805-946× including phasing time. TRUFFLE takes 2.6 h, corresponding to IBIS speedups of 20.2-23.3×. IBIS is also accurate, inferring ≥7 cM IBD segments at quality comparable to Refined IBD and GERMLINE. With these segments, IBIS classifies first through third degree relatives in real Mexican American samples at rates meeting or exceeding other methods tested and identifies fourth through sixth degree pairs at rates within 0.0%-2.0% of the top method. While allele frequency-based approaches that do not detect segments can infer relationship degrees faster than IBIS, the fastest are biased in admixed samples, with KING inferring 30.8% fewer fifth degree Mexican American relatives correctly compared with IBIS. Finally, we ran IBIS on chromosome 2 of the UK Biobank dataset and estimate its runtime on the autosomes to be 3.3 days parallelized across 128 cores.

Ambient Pollution-related Reprogramming of the Human Small Airway Epithelial Transcriptome.

RATIONALE: Epidemiologic studies have demonstrated that exposure to particulate matter ambient pollution has adverse effects on lung health, exacerbated by cigarette smoking. Particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5) is among the most harmful urban pollutants and is closely linked to respiratory disease. OBJECTIVES: Based on the knowledge that the small airway epithelium (SAE) plays a central role in the pathogenesis of smoking-related lung disease, we hypothesized that elevated PM2.5 levels are associated with dysregulation of SAE gene expression, which may contribute to the development of respiratory disease. METHODS: From 2009 to 2012, healthy nonsmoker (n = 29) and smoker (n = 129) residents of New York City underwent bronchoscopy with SAE brushing (2.6 ± 1.3 samples/subject; total of 405 samples). SAE gene expression was assessed by Affymetrix HG-U133 Plus 2.0 microarray. New York City PM2.5 levels (Environmental Protection Agency data) were averaged for the 30 days before bronchoscopy. A linear mixed model was used to assess PM2.5-related gene dysregulation accounting for multiple clinical and methodologic variables. MEASUREMENTS AND MAIN RESULTS: Thirty-day mean PM2.5 levels varied from 6.2 to 18 µg/m3. In nonsmokers, there was no dysregulation of SAE gene expression associated with ambient PM2.5 levels. In marked contrast, n = 219 genes were significantly dysregulated in association with PM2.5 levels in the SAE of smokers. Many of these genes relate to cell growth and transcription regulation. Interestingly, 11% of genes were mitochondria associated. CONCLUSIONS: PM2.5 exposure contributes to significant dysregulation of the SAE transcriptome of smokers, linking pollution and airway epithelial biology in the risk of development of respiratory disease in susceptible individuals.

Inferring Identical-by-Descent Sharing of Sample Ancestors Promotes High-Resolution Relative Detection.

As genetic datasets increase in size, the fraction of samples with one or more close relatives grows rapidly, resulting in sets of mutually related individuals. We present DRUID-deep relatedness utilizing identity by descent-a method that works by inferring the identical-by-descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses an approach to identify the aunts/uncles of two or more siblings, recovering 92.2% of real aunts/uncles with zero false positives. In real and simulated data, DRUID correctly infers up to 10.5% more relatives than PADRE when using data from two sets of distantly related siblings, and 10.7%-31.3% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43.3%-58.3% of tenth degree relatives in this way compared to 79.6%-96.7% using DRUID.

Ontogeny and Biology of Human Small Airway Epithelial Club Cells.

RATIONALE: Little is known about human club cells, dome-shaped cells with dense cytoplasmic granules and microvilli that represent the major secretory cells of the human small airways (at least sixth-generation bronchi). OBJECTIVES: To define the ontogeny and biology of the human small airway epithelium club cell. METHODS: The small airway epithelium was sampled from the normal human lung by bronchoscopy and brushing. Single-cell transcriptome analysis and air-liquid interface culture were used to assess club cell ontogeny and biology. MEASUREMENTS AND MAIN RESULTS: We identified the club cell population by unbiased clustering using single-cell transcriptome sequencing. Principal component gradient analysis uncovered an ontologic link between KRT5 (keratin 5)+ basal cells and SCGB1A1 (secretoglobin family 1A member 1)+ club cells, a hypothesis verified by demonstrating in vitro that a pure population of human KRT5+ SCGB1A1- small airway epithelial basal cells differentiate into SCGB1A1+KRT5- club cells on air-liquid interface culture. Using SCGB1A1 as the marker of club cells, the single-cell analysis identified novel roles for these cells in host defense, xenobiotic metabolism, antiprotease, physical barrier function, monogenic lung disorders, and receptors for human viruses. CONCLUSIONS: These observations provide novel insights into the molecular phenotype and biologic functions of the human club cell population and identify basal cells as the human progenitor cells for club cells.

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.