rSW-seq: Algorithm for detection of copy number alterations in deep sequencing data

Background Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy. Results We develop a method for identification of copy number alterations in a tumor genome compared to its matched control, based on application of Smith-Waterman algorithm to single-end sequencing data. In a performance test with simulated data, our algorithm shows >90% sensitivity and >90% precision in detecting a single copy number change that contains approximately 500 reads for the normal sample. With 100-bp reads, this corresponds to a ~50 kb region for 1X genome coverage of the human genome. We further refine the algorithm to develop rSW-seq, (recursive Smith-Waterman-seq) to identify alterations in a complex configuration, which are commonly observed in the human cancer genome. To validate our approach, we compare our algorithm with an existing algorithm using simulated and publicly available datasets. We also compare the sequencing-based profiles to microarray-based results. Conclusion We propose rSW-seq as an efficient method for detecting copy number changes in the tumor genome.National Institute of General Medical Sciences (U.S.) (R01 GM082798

BIC-seq: a fast algorithm for detection of copy number alterations based on high-throughput sequencing data

DNA copy number alterations (CNA), which are amplifications and deletions of certain regions in the genome, play an important role in the pathogenesis of cancer and have been shown to be associated with other diseases such as autism, schizophrenia and obesity

Sex Hormone-Binding Globulin Levels Are Inversely Associated With Nonalcoholic Fatty Liver Disease in HIV-Infected and -Uninfected Men.

BackgroundNonalcoholic fatty liver disease (NAFLD) is a leading cause of liver disease worldwide. Elevated sex hormone-binding globulin (SHBG) levels have been observed in the setting of HIV and may protect against some metabolic disorders. We aimed to investigate whether higher SHBG levels may protect against NAFLD in men with/without HIV.MethodsNAFLD was assessed using noncontrast computed tomography in 530 men in the Multicenter AIDS Cohort Study (MACS) who drank <3 alcoholic drinks/d and were uninfected with chronic hepatitis C or B (340HIV+, 190HIV-). Morning serum samples were tested for SHBG, total testosterone (TT), and adiponectin. Multivariable logistic regression was used to assess associations between HIV, SHBG, TT, adiponectin, and NAFLD.ResultsMedian SHBG was highest among HIV+/NAFLD- men and lowest among HIV-/NAFLD+ men. Adjusted for demographics, HIV, visceral adiposity, HOMA-IR, TT, and PNPLA3 genotype, higher SHBG was associated with lower odds of NAFLD (odds ratio [OR], 0.52 per doubling; 95% confidence interval [CI], 0.34-0.80). In separate multivariable models without SHBG, HIV (OR, 0.46; 95% CI, 0.26-0.79) and higher adiponectin (OR, 0.66 per doubling; 95% CI, 0.49-0.89) were associated with lower NAFLD odds, whereas TT was not significantly associated (OR, 0.74 per doubling; 95% CI, 0.53-1.04). Adjusting for SHBG attenuated the associations of HIV (OR, 0.61; 95% CI, 0.34-1.08) and adiponectin (OR, 0.74; 95% CI, 0.54-1.02) with NAFLD.ConclusionsSHBG levels were higher among HIV+ men, were independently associated with lower NAFLD, and could partially explain the associations of HIV and higher adiponectin with lower NAFLD in our cohort. These findings suggest that SHBG may protect against NAFLD, supporting further prospective and mechanistic studies

Acid activation mechanism of the influenza A M2 proton channel

The homotetrameric influenza A M2 channel (AM2) is an acid-activated proton channel responsible for the acidification of the influenza virus interior, an important step in the viral lifecycle. Four histidine residues (His37) in the center of the channel act as a pH sensor and proton selectivity filter. Despite intense study, the pH-dependent activation mechanism of the AM2 channel has to date not been completely understood at a molecular level. Herein we have used multiscale computer simulations to characterize (with explicit proton transport free energy profiles and their associated calculated conductances) the activation mechanism of AM2. All proton transfer steps involved in proton diffusion through the channel, including the protonation/deprotonation of His37, are explicitly considered using classical, quantum, and reactive molecular dynamics methods. The asymmetry of the proton transport free energy profile under high-pH conditions qualitatively explains the rectification behavior of AM2 (i.e., why the inward proton flux is allowed when the pH is low in viral exterior and high in viral interior, but outward proton flux is prohibited when the pH gradient is reversed). Also, in agreement with electrophysiological results, our simulations indicate that the C-terminal amphipathic helix does not significantly change the proton conduction mechanism in the AM2 transmembrane domain; the four transmembrane helices flanking the channel lumen alone seem to determine the proton conduction mechanism.United States. National Institutes of Health (R01-GM088204

Design, Fabrication, and Characterization of a Bifrequency Colinear Array

Ultrasound imaging with high resolution and large penetration depth has been increasingly adopted in medical diagnosis, surgery guidance, and treatment assessment. Conventional ultrasound works at a particular frequency, with a −6 dB fractional bandwidth of ~70 %, limiting the imaging resolution or depth of field. In this paper, a bi-frequency co-linear array with resonant frequencies of 8 MHz and 20 MHz was investigated to meet the requirements of resolution and penetration depth for a broad range of ultrasound imaging applications. Specifically, a 32-element bi-frequency co-linear array was designed and fabricated, followed by element characterization and real-time sectorial scan (S-scan) phantom imaging using a Verasonics system. The bi-frequency co-linear array was tested in four different modes by switching between low and high frequencies on transmit and receive. The four modes included the following: (1) transmit low, receive low, (2) transmit low, receive high, (3) transmit high, receive low, (4) transmit high, receive high. After testing, the axial and lateral resolutions of all modes were calculated and compared. The results of this study suggest that bi-frequency co-linear arrays are potential aids for wideband fundamental imaging and harmonic/sub-harmonic imaging

Systematic Identification of Synergistic Drug Pairs Targeting HIV

The systematic identification of effective drug combinations has been hindered by the unavailability of methods that can explore the large combinatorial search space of drug interactions. Here we present a multiplex screening method named MuSIC (Multiplex Screening for Interacting Compounds), which expedites the comprehensive assessment of pair-wise compound interactions. We examined ~500,000 drug pairs from 1000 FDA-approved or clinically tested drugs and identified drugs that synergize to inhibit HIV replication. Our analysis reveals an enrichment of anti-inflammatory drugs in drug combinations that synergize against HIV, indicating HIV benefits from inflammation that accompanies its infection. Multiple drug pairs identified in this study, including glucocorticoid and nitazoxanide, synergize by targeting different steps of the HIV life cycle. As inflammation accompanies HIV infection, our findings indicate that inhibiting inflammation could curb HIV propagation. MuSIC can be applied to a wide variety of disease-relevant screens to facilitate efficient identification of compound combinations

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF

Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants

Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately. Analysis of simulation data showed that this method outperforms existing methods. We apply this algorithm to low coverage whole-genome sequencing data from peripheral blood of nearly a thousand patients across eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions. We confirm known regions and discover new ones including those covering KMT2C, GOLPH3, ERBB2 and PLAG1. Analysis of colorectal cancer genomes in particular reveals novel recurrent CNVs including deletions at two chromatin-remodeling genes RERE and NPM2. This method will be useful to many researchers interested in profiling CNVs from whole-genome sequencing data