Honghui Wang

Endogenous intrahepatic IFNs and association with IFN-free HCV treatment outcome

BACKGROUND. Hepatitis C virus (HCV) infects approximately 170 million people worldwide and may lead to cirrhosis and hepatocellular carcinoma in chronically infected individuals. Treatment is rapidly evolving from IFN-α-based therapies to IFN-α-free regimens that consist of directly acting antiviral agents (DAAs), which demonstrate improved efficacy and tolerability in clinical trials. Virologic relapse after DAA therapy is a common cause of treatment failure; however, it is not clear why relapse occurs or whether certain individuals are more prone to recurrent viremia. METHODS. We conducted a clinical trial using the DAA sofosbuvir plus ribavirin (SOF/RBV) and performed detailed mRNA expression analysis in liver and peripheral blood from patients who achieved either a sustained virologic response (SVR) or relapsed. RESULTS. On-treatment viral clearance was accompanied by rapid downregulation of IFN-stimulated genes (ISGs) in liver and blood, regardless of treatment outcome. Analysis of paired pretreatment and end of treatment (EOT) liver biopsies from SVR patients showed that viral clearance was accompanied by decreased expression of type II and III IFNs, but unexpectedly increased expression of the type I IFN IFNA2. mRNA expression of ISGs was higher in EOT liver biopsies of patients who achieved SVR than in patients who later relapsed. CONCLUSION. These results suggest that restoration of type I intrahepatic IFN signaling by EOT may facilitate HCV eradication and prevention of relapse upon withdrawal of SOF/RBV. TRIAL REGISTRATION. ClinicalTrials.gov NCT01441180.

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.

Genome analysis of three Pneumocystis species reveals adaptation mechanisms to life exclusively in mammalian hosts

Pneumocystis jirovecii is a major cause of life-threatening pneumonia in immunosuppressed patients including transplant recipients and those with HIV/AIDS, yet surprisingly little is known about the biology of this fungal pathogen. Here we report near complete genome assemblies for three Pneumocystis species that infect humans, rats and mice. Pneumocystis genomes are highly compact relative to other fungi, with substantial reductions of ribosomal RNA genes, transporters, transcription factors and many metabolic pathways, but contain expansions of surface proteins, especially a unique and complex surface glycoprotein superfamily, as well as proteases and RNA processing proteins. Unexpectedly, the key fungal cell wall components chitin and outer chain N-mannans are absent, based on genome content and experimental validation. Our findings suggest that Pneumocystis has developed unique mechanisms of adaptation to life exclusively in mammalian hosts, including dependence on the lungs for gas and nutrients and highly efficient strategies to escape both host innate and acquired immune defenses.

A Rapid Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry-Based Method for Single-Plasmid Tracking in an Outbreak of Carbapenem-Resistant Enterobacteriaceae

ABSTRACT Carbapenem-resistant Enterobacteriaceae (CRE) have spread globally and represent a serious and growing threat to public health. Rapid methods for tracking plasmids carrying carbapenemase genes could greatly benefit infection control efforts. Here, we demonstrate that real-time, direct tracking of a single plasmid in a bacterial strain responsible for an outbreak is possible using a commercial matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) system. In this case, we retrospectively tracked the blaKPC carbapenemase gene-bearing pKpQIL plasmid responsible for a CRE outbreak that occurred at the NIH Clinical Center in 2011. An ∼11,109-Da MS peak corresponding to a gene product of the blaKPC pKpQIL plasmid was identified and characterized using a combination of proteomics and molecular techniques. This plasmid peak was present in spectra from retrospectively analyzed K. pneumoniae outbreak isolates, concordant with results from whole-genome sequencing, and absent from a diverse control set of blaKPC -negative clinical Enterobacteriaceae isolates. Notably, the gene characterized here is located adjacent to the blaKPC Tn4401 transposon on the pKpQIL plasmid. Sequence analysis demonstrates the presence of this gene in other blaKPC Tn4401-containing plasmids and suggests that this signature MS peak may be useful in tracking other plasmids conferring carbapenem resistance. Plasmid identification using this MALDI-TOF MS method was accomplished in as little as 10 min from isolated colonies and 30 min from positive (spiked) blood cultures, demonstrating the potential clinical utility for real-time plasmid tracking in an outbreak.

Effects of methylprednisolone infusion on markers of inflammation, coagulation, and angiogenesis in early acute respiratory distress syndrome.

Objective: Evaluate the effects of methylprednisolone on markers of inflammation, coagulation, and angiogenesis during early acute respiratory distress syndrome. Design: Retrospective analysis. Setting: Four intensive care units. Subjects: Seventy-nine of 91 patients with available samples enrolled in a randomized, blinded controlled trial. Interventions: Early methylprednisolone infusion (n = 55) compared with placebo (n = 24). Measurements and Main Results: Interleukin-6, tumor necrosis factor &agr;, vascular endothelial growth factor, protein C, procalcitonin, and proadrenomedullin were measured in archived plasma. Changes from baseline to day 3 and day 7 were compared between groups and in subgroups based on the precipitating cause of acute respiratory distress syndrome. Methylprednisolone therapy was associated with greater improvement in Lung Injury Score (p = .003), shorter duration of mechanical ventilation (p = .005), and lower intensive care unit mortality (p = .05) than control subjects. On days 3 and 7, methylprednisolone decreased interleukin-6 and increased protein C levels (all p < .0001) compared with control subjects. Proadrenomedullin levels were lower by day 3 with methylprednisolone treatment (p = .004). Methylprednisolone decreased interleukin-6 by days 3 and 7 in patients with pulmonary causes of acute respiratory distress syndrome but only at day 3 in those with extrapulmonary causes of acute respiratory distress syndrome. Protein C levels were increased with methylprednisolone on days 3 and 7 in patients with infectious and/or pulmonary causes of acute respiratory distress syndrome (all p < .0001) but not in patients with noninfectious or extrapulmonary causes of acute respiratory distress syndrome. Proadrenomedullin levels were decreased with methylprednisolone on day 3 in patients with infectious or extrapulmonary causes of acute respiratory distress syndrome (both p ⩽ .008) but not in noninfectious or pulmonary acute respiratory distress syndrome. Tumor necrosis factor, vascular endothelial growth factor, and procalcitonin were elevated but not differentially affected by methylprednisolone therapy. Conclusions: In early acute respiratory distress syndrome, administration of methylprednisolone was associated with improvement in important biomarkers of inflammation and coagulation and clinical outcomes. Biomarker changes varied with the precipitating cause of acute respiratory distress syndrome, suggesting that the underlying mechanisms and response to anti-inflammatory therapy may vary with the cause of acute respiratory distress syndrome.

Logical Analysis of Data (LAD) model for the early diagnosis of acute ischemic stroke

BackgroundStrokes are a leading cause of morbidity and the first cause of adult disability in the United States. Currently, no biomarkers are being used clinically to diagnose acute ischemic stroke. A diagnostic test using a blood sample from a patient would potentially be beneficial in treating the disease.ResultsA classification approach is described for differentiating between proteomic samples of stroke patients and controls, and a second novel predictive model is developed for predicting the severity of stroke as measured by the National Institutes of Health Stroke Scale (NIHSS). The models were constructed by applying the Logical Analysis of Data (LAD) methodology to the mass peak profiles of 48 stroke patients and 32 controls. The classification model was shown to have an accuracy of 75% when tested on an independent validation set of 35 stroke patients and 25 controls, while the predictive model exhibited superior performance when compared to alternative algorithms. In spite of their high accuracy, both models are extremely simple and were developed using a common set consisting of only 3 peaks.ConclusionWe have successfully identified 3 biomarkers that can detect ischemic stroke with an accuracy of 75%. The performance of the classification model on the validation set and on cross-validation does not deteriorate significantly when compared to that on the training set, indicating the robustness of the model. As in the case of the LAD classification model, the results of the predictive model validate the function constructed on our support-set for approximating the severity scores of stroke patients. The correlation and root mean absolute error of the LAD predictive model are consistently superior to those of the other algorithms used (Support vector machines, C4.5 decision trees, Logistic regression and Multilayer perceptron).

Computational Prediction Models for Early Detection of Risk of Cardiovascular Events Using Mass Spectrometry Data

Early prediction of the risk of cardiovascular events in patients with chest pain is critical in order to provide appropriate medical care for those with positive diagnosis. This paper introduces a computational methodology for predicting such events in the context of robust computerized classification using mass spectrometry data of blood samples collected from patients in emergency departments. We applied the computational theories of statistical and geostatistical linear prediction models to extract effective features of the mass spectra and a simple decision logic to classify disease and control samples for the purpose of early detection. While the statistical and geostatistical techniques provide better results than those obtained from some other methods, the geostatistical approach yields superior results in terms of sensitivity and specificity in various designs of the data set for validation, training, and testing. The proposed computational strategies are very promising for predicting major adverse cardiac events within six months.

Molecular cytogenetic characterization of a new wheat-Thinopyrum intermedium partial amphiploid resistant to powdery mildew and stripe rust.

A partial amphiploid, TE253, derived from crosses between the common wheat (Triticum aestivum L.) cultivar Yannong 15 and Thinopyrum intermedium (Host) Barkworth & D.R. Dewey was characterized by cytogenetic observations, disease resistance tests and genomic in situ hybridization (GISH). Mitotic observations showed that most plants of TE253 had 56 chromosomes, but a few had 54 or 55 chromosomes. The chromosomes in most pollen mother cells of plants with 2n = 56 formed 28 bivalents. Univalents (0.89 per cell) and tetravalents (0.087 per cell) occasionally occurred at meiotic metaphase I, showing a high degree of cytogenetic stability. After inoculation with the powdery mildew and stripe rust pathogens, Yannong 15 was highly susceptible, whereas TE253 and Th. intermedium were immune to both diseases. This indicated that the resistance of TE253 to powdery mildew and stripe rust was derived from Th. intermedium. GISH analysis using St-genomic DNA from Pseudoroegneria strigosa (M. Bieb) Á. Löve as a probe and ABD-genomic DNA from Chinese Spring wheat as a blocker demonstrated that TE253 consisted of 2 St-genome chromosomes, 8 JS-genome chromosomes, 2 SAT J chromosomes and 2 J-St translocated chromosomes. Line TE253 is a new partial amphiploid with resistance to both powdery mildew and stripe rust and can be used as a source of resistance genes in wheat improvement.

Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model

Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity. Contact: XZhou@tmhs.org

Biomarker Discovery for Risk Stratification of Cardiovascular Events using an Improved Genetic Algorithm

Detection of an optimal panel of biomarkers capable of predicting a patients risk of major adverse cardiac events (MACE) is of clinical significance. Due to the high dynamic range of the protein concentration in human blood, applying proteomics techniques for protein profiling can generate large arrays of data for development of optimized clinical biomarker panels. The objective of this study is to discover a panel of biomarkers for predicting risk of MACE in subjects reliably. The development of immunoassay can only tolerate the complexity of the prediction model with less than ten selected biomarkers. Hence, traditional optimization methods, such as genetic algorithm, cannot be used to derive a solution in such a high-dimensional space. In this paper, we propose an improved genetic algorithm with the local floating searching technique to discover a subset of biomarkers with improved prognostic values for prediction of MACE. The proposed method has been compared with standard genetic algorithm and other feature selection approaches based on the MACE prediction experiments