Metabolomic Signature as a Predictor of Liver Disease Events in Patients With HIV/HCV Coinfection.

BACKGROUND: Advanced liver disease due to hepatitis C virus (HCV) is a leading cause of human immunodeficiency virus (HIV)-related morbidity and mortality. There remains a need to develop noninvasive predictors of clinical outcomes in persons with HIV/HCV coinfection. METHODS: We conducted a nested case-control study in 126 patients with HIV/HCV and utilized multiple quantitative metabolomic assays to identify a prognostic profile that predicts end-stage liver disease (ESLD) events including ascites, hepatic encephalopathy, hepatocellular carcinoma, esophageal variceal bleed, and spontaneous bacterial peritonitis. Each analyte class was included in predictive modeling, and area under the receiver operator characteristic curves (AUC) and accuracy were determined. RESULTS: The baseline model including demographic and clinical data had an AUC of 0.79. Three models (baseline plus amino acids, lipid metabolites, or all combined metabolites) had very good accuracy (AUC, 0.84-0.89) in differentiating patients at risk of developing an ESLD complication up to 2 years in advance. The all combined metabolites model had sensitivity 0.70, specificity 0.85, positive likelihood ratio 4.78, and negative likelihood ratio 0.35. CONCLUSIONS: We report that quantification of a novel set of metabolites may allow earlier identification of patients with HIV/HCV who have the greatest risk of developing ESLD clinical events.

Association of decision support for hospital discharge disposition with outcomes.

OBJECTIVES: To assess the association of a clinical decision support (CDS) algorithm for hospital discharge disposition with spending, readmissions, and postdischarge emergency department (ED) use. STUDY DESIGN: A retrospective study in a cohort of fee-for-service Medicare patients 65 years or older linked to a database of patients receiving CDS. METHODS: We evaluated (1) patients whose discharge disposition was concordant with the CDS recommendation versus those whose disposition was not and (2) patients receiving CDS for discharge disposition versus those not receiving CDS, regardless of concordance. Outcomes were spending over a 90-day episode, 90-day readmissions, and postdischarge ED utilization not associated with a readmission. RESULTS: Analysis of concordant versus discordant cases showed decreased spending for concordant cases ($860 savings; 95% CI, $162-$1558; P = .016), a decrease in readmissions (adjusted odds ratio [OR], 0.920; 95% CI, 0.850-0.995; P = .038), and no change in rate of postdischarge ED use (adjusted OR, 0.990; 95% CI, 0.882-1.110; P = .858). Analysis of patients receiving CDS versus not receiving CDS showed no significant difference in spending ($221 savings; 95% CI, -$115 to $557; P = .198), ED use (adjusted OR, 0.959; 95% CI, 0.908-1.012; P = .128), or readmission rate (adjusted OR, 1.004; 95% CI, 0.966-1.043; P = .840). CONCLUSIONS: Following the recommendation of a CDS algorithm for hospital discharge disposition was associated with lower spending, fewer readmissions, and no change in ED use over a 90-day episode of care.

Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations.

PURPOSE: In the interferon era of hepatitis C virus (HCV) therapies, genotype/subtype, cirrhosis, prior treatment failure, sex, and race predicted relapse. Our objective is to validate a targeted proteomics platform of 17 peptides to predict sustained virologic response (SVR). EXPERIMENTAL DESIGN: Stored plasma from three, open-label, trials of HIV/HCV-coinfected subjects receiving interferon-containing regimens is identified. LC-MS/MS is used to quantitate the peptides directly from plasma, and IL28B genotyping is completed using stored peripheral blood mononuclear cells (PBMC). A logistic regression model is built to analyze the probability of SVR using responders and nonresponders to interferon-based regimens. RESULTS: The cohort (N = 35) is predominantly black (51.4%), male (86%), and with median age 48 years. Most patients achieve SVR (54%). Using multivariable models, it is verified that three human corticosteroid binding globulin (CBG) peptides are predictive of SVR in patients with the unfavorable IL28B genotypes (CT/TT). The model performs better than IL28B alone, with an area under the curve of 0.870. CONCLUSIONS AND CLINICAL RELEVANCE: In HIV/HCV-coinfected patients, three human CBG peptides that accurately predict treatment response with interferon-based therapy are identified. This study suggests that a stepwise approach combining a genetic predictor followed by targeted proteomics can improve the accuracy of clinical decision-making.

Cannabinoid exposure and altered DNA methylation in rat and human sperm.

Little is known about the reproductive effects of paternal cannabis exposure. We evaluated associations between cannabis or tetrahydrocannabinol (THC) exposure and altered DNA methylation in sperm from humans and rats, respectively. DNA methylation, measured by reduced representation bisulfite sequencing, differed in the sperm of human users from non-users by at least 10% at 3,979 CpG sites. Pathway analyses indicated Hippo Signaling and Pathways in Cancer as enriched with altered genes (Bonferroni p < 0.02). These same two pathways were also enriched with genes having altered methylation in sperm from THC-exposed versus vehicle-exposed rats (p < 0.01). Data validity is supported by significant correlations between THC exposure levels in humans and methylation for 177 genes, and substantial overlap in THC target genes in rat sperm (this study) and genes previously reported as having altered methylation in the brain of rat offspring born to parents both exposed to THC during adolescence. In humans, cannabis use was also associated with significantly lower sperm concentration. Findings point to possible pre-conception paternal reproductive risks associated with cannabis use.

Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer's disease.

Sex differences in Alzheimer's disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer's and followed up to 10 years in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aß42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having "high" or "low" likelihood for MCI due to Alzheimer's. Data were analyzed using mixed-effects models incorporating all follow-ups. The worsening from baseline in Alzheimer's Disease Assessment Scale-Cognitive score (mean, SD) (9 ± 12) in subjects with high likelihood of MCI due to Alzheimer's was markedly greater than that in subjects with low likelihood (1 ± 6, p < 0.0001). Among MCI due to AD subjects, the mean worsening in cognitive score was significantly greater in females (11.58 ± 14) than in males (6.87 ± 11, p = 0.006). Our findings highlight the need to further investigate these findings in other populations and develop sex specific timelines for Alzheimer's disease progression.

Improved risk stratification of patients with atrial fibrillation: an integrated GARFIELD-AF tool for the prediction of mortality, stroke and bleed in patients with and without anticoagulation.

OBJECTIVES: To provide an accurate, web-based tool for stratifying patients with atrial fibrillation to facilitate decisions on the potential benefits/risks of anticoagulation, based on mortality, stroke and bleeding risks. DESIGN: The new tool was developed, using stepwise regression, for all and then applied to lower risk patients. C-statistics were compared with CHA2DS2-VASc using 30-fold cross-validation to control for overfitting. External validation was undertaken in an independent dataset, Outcome Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF). PARTICIPANTS: Data from 39 898 patients enrolled in the prospective GARFIELD-AF registry provided the basis for deriving and validating an integrated risk tool to predict stroke risk, mortality and bleeding risk. RESULTS: The discriminatory value of the GARFIELD-AF risk model was superior to CHA2DS2-VASc for patients with or without anticoagulation. C-statistics (95% CI) for all-cause mortality, ischaemic stroke/systemic embolism and haemorrhagic stroke/major bleeding (treated patients) were: 0.77 (0.76 to 0.78), 0.69 (0.67 to 0.71) and 0.66 (0.62 to 0.69), respectively, for the GARFIELD-AF risk models, and 0.66 (0.64-0.67), 0.64 (0.61-0.66) and 0.64 (0.61-0.68), respectively, for CHA2DS2-VASc (or HAS-BLED for bleeding). In very low to low risk patients (CHA2DS2-VASc 0 or 1 (men) and 1 or 2 (women)), the CHA2DS2-VASc and HAS-BLED (for bleeding) scores offered weak discriminatory value for mortality, stroke/systemic embolism and major bleeding. C-statistics for the GARFIELD-AF risk tool were 0.69 (0.64 to 0.75), 0.65 (0.56 to 0.73) and 0.60 (0.47 to 0.73) for each end point, respectively, versus 0.50 (0.45 to 0.55), 0.59 (0.50 to 0.67) and 0.55 (0.53 to 0.56) for CHA2DS2-VASc (or HAS-BLED for bleeding). Upon validation in the ORBIT-AF population, C-statistics showed that the GARFIELD-AF risk tool was effective for predicting 1-year all-cause mortality using the full and simplified model for all-cause mortality: C-statistics 0.75 (0.73 to 0.77) and 0.75 (0.73 to 0.77), respectively, and for predicting for any stroke or systemic embolism over 1 year, C-statistics 0.68 (0.62 to 0.74). CONCLUSIONS: Performance of the GARFIELD-AF risk tool was superior to CHA2DS2-VASc in predicting stroke and mortality and superior to HAS-BLED for bleeding, overall and in lower risk patients. The GARFIELD-AF tool has the potential for incorporation in routine electronic systems, and for the first time, permits simultaneous evaluation of ischaemic stroke, mortality and bleeding risks. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier for GARFIELD-AF (NCT01090362) and for ORBIT-AF (NCT01165710).

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System.

HMG-CoA reductase inhibitors (or "statins") are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR "factors". A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins' cardiovascular benefits.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Metabolic network failures in Alzheimer's disease: A biochemical road map.

INTRODUCTION: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. METHODS: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. RESULTS: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aß1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. DISCUSSION: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection.

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression. These include proteins involved in acute inflammatory response, innate immune response, and the complement cascade. These data provide insights into the nature of the biological response to viral infection of the upper respiratory tract, and the proteins that are dysregulated by viral infection form the basis of signature that accurately classifies the infected state. Verification of this signature using targeted mass spectrometry in independent cohorts of subjects challenged with influenza or rhinovirus demonstrates that it performs with high accuracy (0.8623 AUROC, 75% TPR, 97.46% TNR). With further development as a clinical diagnostic, this signature may have utility in rapid screening for emerging infections, avoidance of inappropriate antibacterial therapy, and more rapid implementation of appropriate therapeutic and public health strategies.

Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus.

OBJECTIVE: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts. MATERIALS AND METHODS: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample. RESULTS: The sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements. DISCUSSION AND CONCLUSIONS: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.

Gene Expression Profiles Link Respiratory Viral Infection, Platelet Response to Aspirin, and Acute Myocardial Infarction.

BACKGROUND: Influenza infection is associated with myocardial infarction (MI), suggesting that respiratory viral infection may induce biologic pathways that contribute to MI. We tested the hypotheses that 1) a validated blood gene expression signature of respiratory viral infection (viral GES) was associated with MI and 2) respiratory viral exposure changes levels of a validated platelet gene expression signature (platelet GES) of platelet function in response to aspirin that is associated with MI. METHODS: A previously defined viral GES was projected into blood RNA data from 594 patients undergoing elective cardiac catheterization and used to classify patients as having evidence of viral infection or not and tested for association with acute MI using logistic regression. A previously defined platelet GES was projected into blood RNA data from 81 healthy subjects before and after exposure to four respiratory viruses: Respiratory Syncytial Virus (RSV) (n=20), Human Rhinovirus (HRV) (n=20), Influenza A virus subtype H1N1 (H1N1) (n=24), Influenza A Virus subtype H3N2 (H3N2) (n=17). We tested for the change in platelet GES with viral exposure using linear mixed-effects regression and by symptom status. RESULTS: In the catheterization cohort, 32 patients had evidence of viral infection based upon the viral GES, of which 25% (8/32) had MI versus 12.2% (69/567) among those without evidence of viral infection (OR 2.3; CI [1.03-5.5], p=0.04). In the infection cohorts, only H1N1 exposure increased platelet GES over time (time course p-value = 1e-04). CONCLUSIONS: A viral GES of non-specific, respiratory viral infection was associated with acute MI; 18% of the top 49 genes in the viral GES are involved with hemostasis and/or platelet aggregation. Separately, H1N1 exposure, but not exposure to other respiratory viruses, increased a platelet GES previously shown to be associated with MI. Together, these results highlight specific genes and pathways that link viral infection, platelet activation, and MI especially in the case of H1N1 influenza infection.

Comparing reference-based RNA-Seq mapping methods for non-human primate data.

BACKGROUND: The application of next-generation sequencing technology to gene expression quantification analysis, namely, RNA-Sequencing, has transformed the way in which gene expression studies are conducted and analyzed. These advances are of particular interest to researchers studying organisms with missing or incomplete genomes, as the need for knowledge of sequence information is overcome. De novo assembly methods have gained widespread acceptance in the RNA-Seq community for organisms with no true reference genome or transcriptome. While such methods have tremendous utility, computational cost is still a significant challenge for organisms with large and complex genomes. RESULTS: In this manuscript, we present a comparison of four reference-based mapping methods for non-human primate data. We utilize TopHat2 and GSNAP for mapping to the human genome, and Bowtie2 and Stampy for mapping to the human genome and transcriptome for a total of six mapping approaches. For each of these methods, we explore mapping rates and locations, number of detected genes, correlations between computed expression values, and the utility of the resulting data for differential expression analysis. CONCLUSIONS: We show that reference-based mapping methods indeed have utility in RNA-Seq analysis of mammalian data with no true reference, and the details of mapping methods should be carefully considered when doing so. Critical algorithm features include short seed sequences, the allowance of mismatches, and the allowance of gapped alignments in addition to splice junction gaps. Such features facilitate sensitive alignment of non-human primate RNA-Seq data to a human reference.

Multiplex protein analysis to determine fibrosis stage and progression in patients with chronic hepatitis C.

BACKGROUND & AIMS: Noninvasive tests cannot differentiate between adjacent stages of fibrosis, which limits assessment of disease progression and regression during therapy. We investigated whether levels of cytokines and extracellular matrix proteins in serum and biopsy samples can be used to determine actual stage of liver fibrosis in patients with chronic hepatitis C (CHC) and in prognosis. METHODS: We collected data from 383 treatment-naive patients with CHC from the Duke Hepatology Clinical Research Database and Biorepository, from 2006 through 2009, for use in the training set. Serum samples were obtained from 100 individuals without CHC (controls). We selected 37 serum biomarkers for customized array analysis by using the SearchLight multiplex sandwich enzyme-linked immunosorbent assay. Data from 434 treatment-naive patients with CHC, which were obtained from the Trent HCV cohort, were used in the validation analysis. Multivariable modeling, marker selection, and validation included randomForest and Obuchowski measures, with independent comparison with FibroSURE. RESULTS: Four serum markers (levels of hyaluronic acid, vascular cell adhesion molecule 1, alpha-2 macroglobulin, and retinol-binding protein 4) and age associated with fibrosis stage (F0-1, F2-3, or F4); these had Obuchowski measures of 0.85-0.89, with misclassification rates of 38% and 29% in training and validation sets, compared with 50% for the FibroSURE test. In the training set, area under the curve values for the multiplex markers were similar to those from the FibroSURE test: stages F0 vs F1 (0.51 vs 0.53), F1 vs F2 (0.60 vs 0.59), F2 vs F3 (0.69 vs 0.72), and F3 vs F4 (0.51 vs 0.52). Area under the curve values were similar in the validation cohort. In longitudinal analyses of 133 paired biopsies, 9 markers (level of alanine aminotransferase, γ-glutamyltransferase, hyaluronic acid, intracellular adhesion molecule 1, interleukin 4, CXCL10, CXCL9, and vascular cell adhesion molecule 1) were associated with change in the histologic activity index (P values ranging from .000 to .049), and 4 (granulocyte-macrophage colony-stimulating factor, interleukin 12, interleukin 2, and matrix metalloproteinase 13) were associated with a change in fibrosis stage (P values ranging from .001 to .042). CONCLUSIONS: We identified serum biomarkers that can be measured by multiplex enzyme-linked immunosorbent assay to determine levels of fibrosis in patients with CHC, although misclassification is frequent and results are comparable with those from the FibroSURE test. Changes in protein levels in biopsy samples were associated with progression of fibrosis in patients.

Development of a novel preclinical model of pneumococcal pneumonia in nonhuman primates.

Pneumococcal pneumonia is a leading cause of bacterial infection and death worldwide. Current diagnostic tests for detecting Streptococcus pneumoniae can be unreliable and can mislead clinical decision-making and treatment. To address this concern, we developed a preclinical model of pneumococcal pneumonia in nonhuman primates useful for identifying novel biomarkers, diagnostic tests, and therapies for human S. pneumoniae infection. Adult colony-bred baboons (n = 15) were infected with escalating doses of S. pneumoniae (Serotype 19A-7). We characterized the pathophysiological and serological profiles of healthy and infected animals over 7 days. Pneumonia was prospectively defined by the presence of three criteria: (1) change in white blood cell count, (2) isolation of S. pneumoniae from bronchoalveolar lavage fluid (BALF) or blood, and (3) concurrent signs/symptoms of infection. Animals given 10(9) CFU consistently met our definition and developed a phenotype of tachypnea, tachycardia, fever, hypoxemia, and radiographic lobar infiltrates at 48 hours. BALF and plasma cytokines, including granulocyte colony-stimulating factor, IL-6, IL-10, and IL-1ra, peaked at 24 to 48 hours. At necropsy, there was lobar consolidation with frequent pleural involvement. Lung histopathology showed alveolar edema and macrophage influx in areas of organizing pneumonia. Hierarchical clustering of peripheral blood RNA data at 48 hours correctly identified animals with and without pneumonia. Dose-dependent inoculation of baboons with S. pneumoniae produces a host response ranging from spontaneous clearance (10(6) CFU) to severe pneumonia (10(9) CFU). Selected BALF and plasma cytokine levels and RNA profiles were associated with severe pneumonia and may provide clinically useful parameters after validation.

Automated, quantitative analysis of histopathological staining in nuclei.

Technological advances have allowed the generation of high-throughput imaging of tissue sections. However, the analysis of these samples is typically still performed manually by one or multiple pathologists. We present a novel statistical model for the automated, quantitative analysis of these images. Our approach requires minimal tuning and allows recapitulation of estimates of staining strength in the nuclei of tumor cells as estimated by the gold standard. Besides, it compares favorably to other quantitative approaches available in the public domain.

A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information.

BACKGROUND: The goal of many proteomics experiments is to determine the abundance of proteins in biological samples, and the variation thereof in various physiological conditions. High-throughput quantitative proteomics, specifically label-free LC-MS/MS, allows rapid measurement of thousands of proteins, enabling large-scale studies of various biological systems. Prior to analyzing these information-rich datasets, raw data must undergo several computational processing steps. We present a method to address one of the essential steps in proteomics data processing--the matching of peptide measurements across samples. RESULTS: We describe a novel method for label-free proteomics data alignment with the ability to incorporate previously unused aspects of the data, particularly ion mobility drift times and product ion information. We compare the results of our alignment method to PEPPeR and OpenMS, and compare alignment accuracy achieved by different versions of our method utilizing various data characteristics. Our method results in increased match recall rates and similar or improved mismatch rates compared to PEPPeR and OpenMS feature-based alignment. We also show that the inclusion of drift time and product ion information results in higher recall rates and more confident matches, without increases in error rates. CONCLUSIONS: Based on the results presented here, we argue that the incorporation of ion mobility drift time and product ion information are worthy pursuits. Alignment methods should be flexible enough to utilize all available data, particularly with recent advancements in experimental separation methods.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.

A network of substrates of the E3 ubiquitin ligases MDM2 and HUWE1 control apoptosis independently of p53.

In the intrinsic pathway of apoptosis, cell-damaging signals promote the release of cytochrome c from mitochondria, triggering activation of the Apaf-1 and caspase-9 apoptosome. The ubiquitin E3 ligase MDM2 decreases the stability of the proapoptotic factor p53. We show that it also coordinated apoptotic events in a p53-independent manner by ubiquitylating the apoptosome activator CAS and the ubiquitin E3 ligase HUWE1. HUWE1 ubiquitylates the antiapoptotic factor Mcl-1, and we found that HUWE1 also ubiquitylated PP5 (protein phosphatase 5), which indirectly inhibited apoptosome activation. Breast cancers that are positive for the tyrosine receptor kinase HER2 (human epidermal growth factor receptor 2) tend to be highly aggressive. In HER2-positive breast cancer cells treated with the HER2 tyrosine kinase inhibitor lapatinib, MDM2 was degraded and HUWE1 was stabilized. In contrast, in breast cancer cells that acquired resistance to lapatinib, the abundance of MDM2 was not decreased and HUWE1 was degraded, which inhibited apoptosis, regardless of p53 status. MDM2 inhibition overcame lapatinib resistance in cells with either wild-type or mutant p53 and in xenograft models. These findings demonstrate broader, p53-independent roles for MDM2 and HUWE1 in apoptosis and specifically suggest the potential for therapy directed against MDM2 to overcome lapatinib resistance.