Evaluating the Suitability of Using Rat Models for Preclinical Efficacy and Side Effects with Inhaled Corticosteroids Nanosuspension Formulations

Inhaled corticosteroids (ICS) are often prescribed as first-line therapy for patients with asthma Despite their efficacy and improved safety profile compared with oral corticosteroids, the potential for systemic side effects continues to cause concern. In order to reduce the potential for systemic side effects, the pharmaceutical industry has begun efforts to generate new drugs with pulmonary-targeted topical efficacy. One of the major challenges of this approach is to differentiate both efficacy and side effects (pulmonary vs. systemic) in a preclinical animal model. In this study, fluticasone and ciclesonide were used as tool compounds to explore the possibility of demonstrating both efficacy and side effects in a rat model using pulmonary delivery via intratracheal (IT) instillation with nanosuspension formulations. The inhibition of neutrophil infiltration into bronchoalveolar lavage fluid (BALF) and cytokine (TNFα) production were utilized to assess pulmonary efficacy, while adrenal and thymus involution as well as plasma corticosterone suppression was measured to assess systemic side effects. Based on neutrophil infiltration and cytokine production data, the ED50s for ciclesonide and fluticasone were calculated to be 0.1 and 0.03 mg, respectively. At the ED50, the average adrenal involution was 7.6 ± 5.3% for ciclesonide versus 16.6 ± 5.1% for fluticasone, while the average thymus involution was 41.0 ± 4.3% for ciclesonide versus 59.5 ± 5.8% for fluticasone. However, the differentiation became less significant when the dose was pushed to the EDmax (0.3 mg for ciclesonide, 0.1 mg for fluticasone). Overall, the efficacy and side effect profiles of the two compounds exhibited differentiation at low to mid doses (0.03–0.1 mg ciclesonide, 0.01–0.03 mg fluticasone), while this differentiation diminished at the maximum efficacious dose (0.3 mg ciclesonide, 0.1 mg fluticasone), likely due to overdosing in this model. We conclude that the rat LPS model using IT administration of nanosuspensions of ICS is a useful tool to demonstrate pulmonary-targeted efficacy and to differentiate the side effects. However, it is only suitable at sub-maximum efficacious levels

Accurate and exact CNV identification from targeted high-throughput sequence data

<p>Abstract</p> <p>Background</p> <p>Massively parallel sequencing of barcoded DNA samples significantly increases screening efficiency for clinically important genes. Short read aligners are well suited to single nucleotide and indel detection. However, methods for CNV detection from targeted enrichment are lacking. We present a method combining coverage with map information for the identification of deletions and duplications in targeted sequence data.</p> <p>Results</p> <p>Sequencing data is first scanned for gains and losses using a comparison of normalized coverage data between samples. CNV calls are confirmed by testing for a signature of sequences that span the CNV breakpoint. With our method, CNVs can be identified regardless of whether breakpoints are within regions targeted for sequencing. For CNVs where at least one breakpoint is within targeted sequence, exact CNV breakpoints can be identified. In a test data set of 96 subjects sequenced across ~1 Mb genomic sequence using multiplexing technology, our method detected mutations as small as 31 bp, predicted quantitative copy count, and had a low false-positive rate.</p> <p>Conclusions</p> <p>Application of this method allows for identification of gains and losses in targeted sequence data, providing comprehensive mutation screening when combined with a short read aligner.</p

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

<p>Abstract</p> <p>Background</p> <p>High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.</p> <p>Results</p> <p>We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.</p> <p>Conclusions</p> <p>Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.</p

ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads

Copy number alterations are important contributors to many genetic diseases, including cancer. We present the readDepth package for R, which can detect these aberrations by measuring the depth of coverage obtained by massively parallel sequencing of the genome. In addition to achieving higher accuracy than existing packages, our tool runs much faster by utilizing multi-core architectures to parallelize the processing of these large data sets. In contrast to other published methods, readDepth does not require the sequencing of a reference sample, and uses a robust statistical model that accounts for overdispersed data. It includes a method for effectively increasing the resolution obtained from low-coverage experiments by utilizing breakpoint information from paired end sequencing to do positional refinement. We also demonstrate a method for inferring copy number using reads generated by whole-genome bisulfite sequencing, thus enabling integrative study of epigenomic and copy number alterations. Finally, we apply this tool to two genomes, showing that it performs well on genomes sequenced to both low and high coverage. The readDepth package runs on Linux and MacOSX, is released under the Apache 2.0 license, and is available at http://code.google.com/p/readdepth/

TALEN-mediated editing of the mouse Y chromosome

The functional study of Y chromosome genes has been hindered by a lack of mouse models with specific Y chromosome mutations. We used transcription activator-like effector nuclease (TALEN)-mediated gene editing in mouse embryonic stem cells (mESCs) to produce mice with targeted gene disruptions and insertions in two Y-linked genes—Sry and Uty. TALEN-mediated gene editing is a useful tool for dissecting the biology of the Y chromosome.National Institutes of Health (U.S.) (US NIH grant R01-HG000257)National Institutes of Health (U.S.) (US NIH grant R01-CA084198)National Institutes of Health (U.S.) (US NIH grant R37-HD045022)Croucher Foundation (Scholarship)Howard Hughes Medical Institute (Investigator

Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning

Retinopathy of prematurity (ROP) is a disease that affects premature infants, where abnormal growth of the retinal blood vessels can lead to blindness unless treated accordingly. Infants considered at risk of severe ROP are monitored for symptoms of plus disease, characterized by arterial tortuosity and venous dilation at the posterior pole, with a standard photographic definition. Disagreement among ROP experts in diagnosing plus disease has driven the development of computer-based methods that classify images based on hand-crafted features extracted from the vasculature. However, most of these approaches are semi-automated, which are time-consuming and subject to variability. In contrast, deep learning is a fully automated approach that has shown great promise in a wide variety of domains, including medical genetics, informatics and imaging. Convolutional neural networks (CNNs) are deep networks which learn rich representations of disease features that are highly robust to variations in acquisition and image quality. In this study, we utilized a U-Net architecture to perform vessel segmentation and then a GoogLeNet to perform disease classification. The classifier was trained on 3,000 retinal images and validated on an independent test set of patients with different observed progressions and treatments. We show that our fully automated algorithm can be used to monitor the progression of plus disease over multiple patient visits with results that are consistent with the experts’ consensus diagnosis. Future work will aim to further validate the method on larger cohorts of patients to assess its applicability within the clinic as a treatment monitoring tool

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets

Prenatal Detection of Aneuploidy and Imbalanced Chromosomal Arrangements by Massively Parallel Sequencing

Fetal chromosomal abnormalities are the most common reasons for invasive prenatal testing. Currently, G-band karyotyping and several molecular genetic methods have been established for diagnosis of chromosomal abnormalities. Although these testing methods are highly reliable, the major limitation remains restricted resolutions or can only achieve limited coverage on the human genome at one time. The massively parallel sequencing (MPS) technologies which can reach single base pair resolution allows detection of genome-wide intragenic deletions and duplication challenging karyotyping and microarrays as the tool for prenatal diagnosis. Here we reported a novel and robust MPS-based method to detect aneuploidy and imbalanced chromosomal arrangements in amniotic fluid (AF) samples. We sequenced 62 AF samples on Illumina GAIIx platform and with averagely 0.01× whole genome sequencing data we detected 13 samples with numerical chromosomal abnormalities by z-test. With up to 2× whole genome sequencing data we were able to detect microdeletion/microduplication (ranged from 1.4 Mb to 37.3 Mb of 5 samples from chorionic villus sampling (CVS) using SeqSeq algorithm. Our work demonstrated MPS is a robust and accurate approach to detect aneuploidy and imbalanced chromosomal arrangements in prenatal samples