Optimization of miRNA-seq data preprocessing.

The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

Multi-objective Non-intrusive Hearing-aid Speech Assessment Model

Without the need for a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. While deep learning models have been used to develop non-intrusive speech assessment methods with promising results, there is limited research on hearing-impaired subjects. This study proposes a multi-objective non-intrusive hearing-aid speech assessment model, called HASA-Net Large, which predicts speech quality and intelligibility scores based on input speech signals and specified hearing-loss patterns. Our experiments showed the utilization of pre-trained SSL models leads to a significant boost in speech quality and intelligibility predictions compared to using spectrograms as input. Additionally, we examined three distinct fine-tuning approaches that resulted in further performance improvements. Furthermore, we demonstrated that incorporating SSL models resulted in greater transferability to OOD dataset. Finally, this study introduces HASA-Net Large, which is a non-invasive approach for evaluating speech quality and intelligibility. HASA-Net Large utilizes raw waveforms and hearing-loss patterns to accurately predict speech quality and intelligibility levels for individuals with normal and impaired hearing and demonstrates superior prediction performance and transferability

Hepatocellular carcinoma survival in uninsured and underinsured patients.

BACKGROUND: The incidence of hepatitis C virus (HCV) and hepatocellular carcinoma (HCC) is increasing. The purpose of this study is to establish baseline survival in a medically-underserved population and to evaluate the effect of HCV seropositivity on our patient population. MATERIALS AND METHODS: We reviewed clinicopathologic parameters from a prospective tumor registry and medical records from the Harris County Hospital District (HCHD). Outcomes were compared using Kaplan-Meier survival analysis and log-rank tests. RESULTS: A total of 298 HCC patients were identified. The median survival for the entire cohort was 3.4 mo. There was no difference in survival between the HCV seropositive and the HCV seronegative groups (3.6 mo versus 2.6 mo, P = 0.7). Patients with a survival \u3c1 mo had a significant increase in\u3eαfetoprotein (AFP), international normalized ratio (INR), model for end-stage liver disease (MELD) score, and total bilirubin and decrease in albumin compared with patients with a survival ≥ 1 mo. CONCLUSIONS: Survival for HCC patients in the HCHD is extremely poor compared with an anticipated median survival of 7 mo reported in other studies. HCV seropositive patients have no survival advantage over HCV seronegative patients. Poorer liver function at diagnosis appears to be related to shorter survival. Further analysis into variables contributing to decreased survival is needed

Inhibition of EGFR Signaling: All Mutations Are Not Created Equal

Gazdar and Minna discuss the context and implications of a research article that examines the transformation potential and response to inhibitors of specific EGFR mutations found in lung cancer

An endogenous inhibitor of nitric oxide synthase regulates endothelial adhesiveness for monocytes

AbstractOBJECTIVESWe sought to determine whether asymmetric dimethylarginine (ADMA) inhibits nitric oxide (NO) elaboration in cultured human endothelial cells and whether this is associated with the activation of oxidant-sensitive signaling mediating endothelial adhesiveness for monocytes.BACKGROUNDEndothelial NO elaboration is impaired in hypercholesterolemia and atherosclerosis, which may be due to elevated concentrations of ADMA, an endogenous inhibitor of NO synthase.METHODSHuman umbilical vein endothelial cells (ECV 304) and human monocytoid cells (THP-1) were studied in a functional binding assay. Nitric oxide and superoxide anion (O2−) were measured by chemiluminescence; ADMA by high pressure liquid chromatography; monocyte chemotactic protein-1 (MCP-1) by ELISA and NF-κB by electromobility gel shift assay.RESULTSIncubation of endothelial cells with ADMA (0.1 μM to 100 μM) inhibited NO formation, which was reversed by coincubation with L-arginine (1 mM). The biologically inactive stereoisomer symmetric dimethylarginine did not inhibit NO release. Asymmetric dimethylarginine (10 μM) or native low-density lipoprotein cholesterol (100 mg/dL) increased endothelial O2− to the same degree. Asymmetric dimethylarginine also stimulated MCP-1 formation by endothelial cells. This effect was paralleled by activation of the redox-sensitive transcription factor NF-κB. Preincubation of endothelial cells with ADMA increased the adhesiveness of endothelial cells for THP-1 cells in a concentration-dependent manner. Asymmetric dimethylarginine-induced monocyte binding was diminished by L-arginine or by a neutralizing anti-MCP-1 antibody.CONCLUSIONSWe concluded that the endogenous NO synthase inhibitor ADMA is synthesized in human endothelial cells. Asymmetric dimethylarginine increases endothelial oxidative stress and potentiates monocyte binding. Asymmetric dimethylarginine may be an endogenous proatherogenic molecule

LuxS Coexpression Enhances Yields of Recombinant Proteins in Escherichia coli in Part through Posttranscriptional Control of GroEL

Cell-to-cell communication, or quorum sensing (QS), enables cell density-dependent regulation of bacterial gene expression which can be exploited for the autonomous-signal-guided expression of recombinant proteins (C. Y. Tsao, S. Hooshangi, H. C. Wu, J. J. Valdes, and W. E. Bentley, Metab. Eng. 12:291-297, 2010). Earlier observations that the metabolic potential of Escherichia coli is conveyed via the QS signaling molecule autoinducer-2 (AI-2) suggested that the capacity for protein synthesis could also be affected by AI-2 signaling (M. P. DeLisa, J. J. Valdes, and W. E. Bentley, J. Bacteriol. 183:2918-2928, 2001). In this work, we found that simply adding conditioned medium containing high levels of AI-2 at the same time as inducing the synthesis of recombinant proteins doubled the yield of active product. We have hypothesized that AI-2 signaling “conditions” cells as a natural consequence of cell-to-cell communication and that this could tweak the signal transduction cascade to alter the protein synthesis landscape. We inserted luxS (AI-2 synthase) into vectors which cosynthesized proteins of interest (organophosphorus hydrolase [OPH], chloramphenicol acetyltransferase [CAT], or UV-variant green fluorescent protein [GFPuv]) and evaluated the protein expression in luxS-deficient hosts. In this way, we altered the level of luxS in the cells in order to “tune” the synthesis of AI-2. We found conditions in which the protein yield was dramatically increased. Further studies demonstrated coincident upregulation of the chaperone GroEL, which may have facilitated higher yields and is shown for the first time to be positively regulated at the posttranscriptional level by AI-2. This report is the first to demonstrate that the protein synthesis capacity of E. coli can be altered by rewiring quorum sensing circuitry