Cellular Gene Modulation of HIV-Infected CD4 T Cells in Response to Serial Treatment with the Histone Deacetylase Inhibitor Vorinostat.

Histone deacetylase inhibitors (HDACi) are the most widely studied HIV latency-reversing agents (LRAs). The HDACi suberoylanilide hydroxamic acid (vorinostat [VOR]) has been employed in several clinical HIV latency reversal studies, as well as in vitro models of HIV latency, and has been shown to effectively induce HIV RNA and protein expression. Despite these findings, response to HDACi can vary, particularly with intermittent dosing, and information is lacking on the relationship between the host transcriptional response and HIV latency reversal. Here, we report on global gene expression responses to VOR and examine the longevity of the transcriptional response in various cellular models. We found that many genes are modulated at 6 h post-VOR treatment in HCT116, Jurkat, and primary resting CD4 T cells, yet return to baseline levels after an 18-h VOR-free period. With repeat exposure to VOR in resting CD4 T cells, we found similar and consistent transcriptional changes at 6 h following each serial treatment. In addition, serial exposure in HIV-infected suppressed donor CD4 T cells showed consistent transcriptional changes after each exposure to VOR. We identified five host genes that were strongly and consistently modulated following histone deacetylase (HDAC) inhibition; three (H1F0, IRGM, and WIPI49) were upregulated, and two (PHF15 and PRDM10) were downregulated. These genes demonstrated consistent modulation in peripheral blood mononuclear cell (PBMC) samples from HIV-positive (HIV+) participants who received either single or multiple doses of 400 mg of VOR. Interestingly, the host transcriptional response did not predict induction of cell-associated HIV RNA, suggesting that other cellular factors play key roles in HIV latency reversal in vivo despite robust HDACi pharmacological activity.IMPORTANCE Histone deacetylase inhibitors are widely studied HIV latency-reversing agents (LRAs). VOR, an HDACi, induces histone acetylation and chromatin remodeling and modulates host and HIV gene expression. However, the relationship between these events is poorly defined, and clinical studies suggest diminished HIV reactivation in resting CD4 T cells with daily exposure to VOR. Our study provides evidence that VOR induces a consistent level of host cell gene transcription following intermittent exposure. In addition, in response to VOR exposure a gene signature that was conserved across single and serial exposures both in vitro and in vivo was identified, indicating that VOR can consistently and reproducibly modulate transcriptional host responses. However, as the HIV response to HDACi declines over time, other factors modulate viral reactivation in vivo despite robust HDAC activity. The identified host gene VOR biomarkers can be used for monitoring the pharmacodynamic activity of HDAC inhibitors.

Improved mass defect model for theoretical tryptic peptides.

Improvements in the mass accuracy and resolution of mass spectrometers have greatly aided mass spectrometry-based proteomics in profiling complex biological mixtures. With the use of innovative bioinformatics approaches, high mass accuracy and resolution information can be used for filtering chemical noise in mass spectral data. Using our recent algorithmic developments, we have generated the mass distributions of all theoretical tryptic peptides composed of 20 natural amino acids and with masses limited to 3.5 kDa. Peptide masses are distributed discretely, with well-defined peak clusters separated by empty or sparsely populated trough regions. Accurate models for peak centers and widths can be used to filter peptide signals from chemical noise. We modeled mass defects, the difference between monoisotopic and nominal masses, and peak centers and widths in the peptide mass distributions. We found that peak widths encompassing 95% of all peptide sequences are substantially smaller than previously thought. The result has implications for filtering out larger stretches of the mass axis. Mass defects of peptides exhibit an oscillatory behavior which is damped at high mass values. The periodicity of the oscillations is about 14 Da which is the most common difference between the masses of the 20 natural amino acids. To determine the effects of amino acid modifications on our findings, we examined the mass distributions of peptides composed of the 20 natural amino acids, oxidized Met, and phosphorylated Ser, Thr, and Tyr. We found that extension of the amino acid set by modifications increases the 95% peak width. Mass defects decrease, reflecting the fact that the average mass defect of natural amino acids is larger than that of oxidized Met. We propose that a new model for mass defects and peak widths of peptides may improve peptide identifications by filtering chemical noise in mass spectral data.

A parallel method for enumerating amino acid compositions and masses of all theoretical peptides.

BACKGROUND: Enumeration of all theoretically possible amino acid compositions is an important problem in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing. Because of the high computational complexity of this task, reported methods for peptide enumeration were restricted to cover limited mass ranges (below 2 kDa). In addition, implementation details of these methods as well as their computational performance have not been provided. The increasing availability of parallel (multi-core) computers in all fields of research makes the development of parallel methods for peptide enumeration a timely topic. RESULTS: We describe a parallel method for enumerating all amino acid compositions up to a given length. We present recursive procedures which are at the core of the method, and show that a single task of enumeration of all peptide compositions can be divided into smaller subtasks that can be executed in parallel. The computational complexity of the subtasks is compared with the computational complexity of the whole task. Pseudocodes of processes (a master and workers) that are used to execute the enumerating procedure in parallel are given. We present computational times for our method executed on a computer cluster with 12 Intel Xeon X5650 CPUs (72 cores) running Windows HPC Server. Our method has been implemented as a 32- and 64-bit Windows application using Microsoft Visual C++ and the Message Passing Interface. It is available for download at https://ispace.utmb.edu/users/rgsadygo/Proteomics/ParallelMethod. CONCLUSION: We describe implementation of a parallel method for generating mass distributions of all theoretically possible amino acid compositions.

Examining troughs in the mass distribution of all theoretically possible tryptic peptides.

This work describes the mass distribution of all theoretically possibly tryptic peptides made of 20 amino acids, up to the mass of 3 kDa, with resolution of 0.001 Da. We characterize regions between the peaks of the distribution, including gaps (forbidden zones) and low-populated areas (quiet zones). We show how the gaps shrink over the mass range and when they completely disappear. We demonstrate that peptide compositions in quiet zones are less diverse than those in the peaks of the distribution and that by eliminating certain types of unrealistic compositions the gaps in the distribution may be increased. The mass distribution is generated using a parallel implementation of a recursive procedure that enumerates all amino acid compositions. It allows us to enumerate all compositions of tryptic peptides below 3 kDa in 48 min using a computer cluster with 12 Intel Xeon X5650 CPUs (72 cores). The results of this work can be used to facilitate protein identification and mass defect labeling in mass spectrometry-based proteomics experiments.

SVM model for quality assessment of medium resolution mass spectra from 18O-water labeling experiments.

We describe a method for assessing the quality of mass spectra and improving reliability of relative ratio estimations from (18)O-water labeling experiments acquired from low resolution mass spectrometers. The mass profiles of heavy and light peptide pairs are often affected by artifacts, including coeluting contaminant species, noise signal, instrumental fluctuations in measuring ion position and abundance levels. Such artifacts distort the profiles, leading to erroneous ratio estimations, thus reducing the reliability of ratio estimations in high throughput quantification experiments. We used support vector machines (SVMs) to filter out mass spectra that deviated significantly from expected theoretical isotope distributions. We built an SVM classifier with a decision function that assigns a score to every mass profile based on such spectral features as mass accuracy, signal-to-noise ratio, and differences between experimental and theoretical isotopic distributions. The classifier was trained using a data set obtained from samples of mouse renal cortex. We then tested it on protein samples (bovine serum albumin) mixed in five different ratios of labeled and unlabeled species. We demonstrated that filtering the data using our SVM classifier results in as much as a 9-fold reduction in the coefficient of variance of peptide ratios, thus significantly improving the reliability of ratio estimations.

Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms.

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Early warning indicators for hock burn in broiler flocks.

Hock burn is a common disease of broiler chickens affecting flock welfare and farmer income. Here we use hierarchical logistic regression (HLR) models to identify risk factors for hock burn using data from 5895 flocks, collected over 3.5 years by a large UK broiler company. The results suggest that at 2 weeks of age, weight and weight density may be useful predictors of flocks at risk of a high incidence of hock burn. In contrast, stocking density at placement is not. The use of these and other variables in disease prevention add value to routinely collected management data and can assist in improving broiler welfare and farm income.

Identification of proximal biomarkers of PKC agonism and evaluation of their role in HIV reactivation.

DESIGN: The HIV latent CD4+ T cell reservoir is broadly recognized as a barrier to HIV cure. Induction of HIV expression using protein kinase C (PKC) agonists is one approach under investigation for reactivation of latently infected CD4+ T cells (Beans et al., 2013; Abreu et al., 2014; Jiang et al., 2014; Jiang and Dandekar, 2015). We proposed that an increased understanding of the molecular mechanisms of action of PKC agonists was necessary to inform on biological signaling and pharmacodynamic biomarkers. RNA sequencing (RNA Seq) was applied to identify genes and pathways modulated by PKC agonists. METHODS: Human CD4+ T cells were treated ex vivo with Phorbol 12-myristate 13-acetate, prostatin or ingenol-3-angelate. At 3 h and 24 h post-treatment, cells were harvested and RNA-Seq was performed on RNA isolated from cell lysates. The genes differentially expressed across the PKC agonists were validated by quantitative RT-PCR (qPCR). A subset of genes was evaluated for their role in HIV reactivation using siRNA and CRISPR approaches in the Jurkat latency cell model. RESULTS: Treatment of primary human CD4+ T cells with PKC agonists resulted in alterations in gene expression. qPCR of RNA Seq data confirmed upregulation of 24 genes, including CD69, Egr1, Egr2, Egr3, CSF2, DUSP5, and NR4A1. Gene knockdown of Egr1 and Egr3 resulted in reduced expression and decreased HIV reactivation in response to PKC agonist treatment, indicating a potential role for Egr family members in latency reversal. CONCLUSION: Overall, our results offer new insights into the mechanism of action of PKC agonists, biomarkers of pathway engagement, and the potential role of EGR family in HIV reactivation.

Identifying amyloid pathology-related cerebrospinal fluid biomarkers for Alzheimer's disease in a multicohort study.

INTRODUCTION: The dynamic range of cerebrospinal fluid (CSF) amyloid ß (Aß1-42) measurement does not parallel to cognitive changes in Alzheimer's disease (AD) and cognitively normal (CN) subjects across different studies. Therefore, identifying novel proteins to characterize symptomatic AD samples is important. METHODS: Proteins were profiled using a multianalyte platform by Rules Based Medicine (MAP-RBM). Due to underlying heterogeneity and unbalanced sample size, we combined subjects (344 AD and 325 CN) from three cohorts: Alzheimer's Disease Neuroimaging Initiative, Penn Center for Neurodegenerative Disease Research of the University of Pennsylvania, and Knight Alzheimer's Disease Research Center at Washington University in St. Louis. We focused on samples whose cognitive and amyloid status was consistent. We performed linear regression (accounted for age, gender, number of APOE e4 alleles, and cohort variable) to identify amyloid-related proteins for symptomatic AD subjects in this largest ever CSF-based MAP-RBM study. ANOVA and Tukey's test were used to evaluate if these proteins were related to cognitive impairment changes as measured by mini-mental state examination (MMSE). RESULTS: Seven proteins were significantly associated with Aß1-42 levels in the combined cohort (false discovery rate adjusted P < .05), of which lipoprotein a (Lp(a)), prolactin (PRL), resistin, and vascular endothelial growth factor (VEGF) have consistent direction of associations across every individual cohort. VEGF was strongly associated with MMSE scores, followed by pancreatic polypeptide and immunoglobulin A (IgA), suggesting they may be related to staging of AD. DISCUSSION: Lp(a), PRL, IgA, and tissue factor/thromboplastin have never been reported for AD diagnosis in previous individual CSF-based MAP-RBM studies. Although some of our reported analytes are related to AD pathophysiology, others' roles in symptomatic AD samples worth further explorations.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.