Dabao Zhang

HIV-1 Transactivator of Transcription Protein Induces Mitochondrial Hyperpolarization and Synaptic Stress Leading to Apoptosis

Despite the efficacy of highly active antiretroviral therapy in reducing viral burden, neurologic disease associated with HIV-1 infection of the CNS has not decreased in prevalence. HIV-1 does not induce disease by direct infection of neurons, although extensive data suggest that intra-CNS viral burden correlates with both the severity of virally induced neurologic disease, and with the generation of neurotoxic metabolites. Many of these molecules are capable of inducing neuronal apoptosis in vitro, but neuronal apoptosis in vivo does not correlate with CNS dysfunction, thus prompting us to investigate cellular and synaptic events occurring before cell death that may contribute to HIV-1-associated neurologic disease. We now report that the HIV-1 regulatory protein transactivator of transcription protein (Tat) increased oxidative stress, ATP levels, and mitochondrial membrane potential in primary rodent cortical neurons. Additionally, a proinflammatory cellular metabolite up-regulated by Tat, platelet-activating factor, also induced oxidative stress and mitochondrial hyperpolarization in neurons, suggesting that this type of metabolic dysfunction may occur on a chronic basis during HIV-1 infection of the CNS. Tat-induced mitochondrial hyperpolarization could be blocked with a low dose of the protonophore FCCP, or the mitochondrial KATP channel antagonist, tolbutamide. Importantly, blocking the mitochondrial hyperpolarization attenuated Tat-induced neuronal apoptosis, suggesting that increased mitochondrial membrane potential may be a causal event in precipitating neuronal apoptosis in cell culture. Finally, Tat and platelet-activating factor also increased neuronal vesicular release, which may be related to increased mitochondrial bioenergetics and serve as a biomarker for early damage to neurons.

Two-Dimensional Correlation Optimized Warping Algorithm for Aligning GC×GC−MS Data

A two-dimensional (2-D) correlation optimized warping (COW) algorithm has been developed to align 2-D gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/TOF-MS) data. By partitioning raw chromatographic profiles and warping the grid points simultaneously along the first and second dimensions on the basis of applying a one-dimensional COW algorithm to characteristic vectors, nongrid points can be interpolatively warped. This 2-D algorithm was directly applied to total ion counts (TIC) chromatographic profiles of homogeneous chemical samples, i.e., samples including mostly identical compounds. For heterogeneous chemical samples, the 2-D algorithm is first applied to certain selected ion counts chromatographic profiles, and the resultant warping parameters are then used to warp the corresponding TIC chromatographic profiles. The developed 2-D COW algorithm can also be applied to align other 2-D separation images, e.g., LC x LC data, LC x GC data, GC x GC data, LC x CE data, and CE x CE data.

Proteomic analysis of peripheral leukocytes in Alzheimer's disease patients treated with divalproex sodium

The molecular profiling of peripheral tissues, including circulating leukocytes, may hold promise in the discovery of biomarkers for diagnosing and treating neurodegenerative diseases, including Alzheimers disease (AD). As a proof-of-concept, we performed a proteomics study on peripheral leukocytes from patients with AD both before and during treatment with divalproex sodium. Using two-dimensional gel electrophoresis and MALDI-TOF mass spectrometry, we identified 10 differentially expressed proteins: two up-regulated proteins, 14-3-3 protein epsilon and peroxiredoxin 2; and eight down-regulated proteins, actin-interacting protein, mitogen activated protein kinase 1, beta actin, annexin A1, glyceraldehyde 3-phosphate dehydrogenase, transforming protein RhoA, acidic leucine-rich nuclear phosphoprotein 32 family member B, and a currently unidentified protein. A subset was validated on both the transcript and protein levels in normal human peripheral blood mononuclear cell cultures treated with valproic acid. These proteins comprise a number of functional classes that may be important to the biology of AD and to the therapeutic action of valproate. These data also suggest the potential of using peripheral leukocytes to monitor pharmaceutical action for neurodegenerative diseases.

Exploring Metabolic Profile Differences between Colorectal Polyp Patients and Controls Using Seemingly Unrelated Regression

Despite the fact that colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world, the development of improved and robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC continues to be evasive. In particular, patients with colon polyps are at higher risk of developing colon cancer; however, noninvasive methods to identify these patients suffer from poor performance. In consideration of the challenges involved in identifying metabolite biomarkers in individuals with high risk for colon cancer, we have investigated NMR-based metabolite profiling in combination with numerous demographic parameters to investigate the ability of serum metabolites to differentiate polyp patients from healthy subjects. We also investigated the effect of disease risk on different groups of biologically related metabolites. A powerful statistical approach, seemingly unrelated regression (SUR), was used to model the correlated levels of metabolites in the same biological group. The metabolites were found to be significantly affected by demographic covariates such as gender, BMI, BMI(2), and smoking status. After accounting for the effects of the confounding factors, we then investigated potential of metabolites from serum to differentiate patients with polyps and age matched healthy controls. Our results showed that while only valine was slightly associated, individually, with polyp patients, a number of biologically related groups of metabolites were significantly associated with polyps. These results may explain some of the challenges and promise a novel avenue for future metabolite profiling methodologies.

Penalized orthogonal-components regression for large p small n data

Here we propose a penalized orthogonal-componentsregression (POCRE) for large p small n data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlation to the re- sponse residuals. A new penalization framework, implemented via empiri- cal Bayes thresholding, is presented to effectively identify sparse predictors of each component. POCRE is computationally efficient owing t its se- quential construction of leading sparse principal components. In addition, such construction offers other properties such as grouping highly correlated predictors and allowing for collinear or nearly collinear predictors. With multivariate responses, POCRE can construct common components and thus build up latent-variable models for large p small n data. AMS 2000 subject classifications: Primary 62J05; secondary 62H20,

Chromosome Size in Diploid Eukaryotic Species Centers on the Average Length with a Conserved Boundary

Understanding genome and chromosome evolution is important for understanding genetic inheritance and evolution. Universal events comprising DNA replication, transcription, repair, mobile genetic element transposition, chromosome rearrangements, mitosis, and meiosis underlie inheritance and variation of living organisms. Although the genome of a species as a whole is important, chromosomes are the basic units subjected to genetic events that coin evolution to a large extent. Now many complete genome sequences are available, we can address evolution and variation of individual chromosomes across species. For example, “How are the repeat and nonrepeat proportions of genetic codes distributed among different chromosomes in a multichromosome species?” “Is there a general rule behind the intuitive observation that chromosome lengths tend to be similar in a species, and if so, can we generalize any findings in chromosome content and size across different taxonomic groups?” Here, we show that chromosomes within a species do not show dramatic fluctuation in their content of mobile genetic elements as the proliferation of these elements increases from unicellular eukaryotes to vertebrates. Furthermore, we demonstrate that, notwithstanding the remarkable plasticity, there is an upper limit to chromosome-size variation in diploid eukaryotes with linear chromosomes. Strikingly, variation in chromosome size for 886 chromosomes in 68 eukaryotic genomes (including 22 human autosomes) can be viably captured by a single model, which predicts that the vast majority of the chromosomes in a species are expected to have a base pair length between 0.4035 and 1.8626 times the average chromosome length. This conserved boundary of chromosome-size variation, which prevails across a wide taxonomic range with few exceptions, indicates that cellular, molecular, and evolutionary mechanisms, possibly together, confine the chromosome lengths around a species-specific average chromosome length.

A Coefficient of Determination for Generalized Linear Models

ABSTRACT The coefficient of determination, a.k.a. R2, is well-defined in linear regression models, and measures the proportion of variation in the dependent variable explained by the predictors included in the model. To extend it for generalized linear models, we use the variance function to define the total variation of the dependent variable, as well as the remaining variation of the dependent variable after modeling the predictive effects of the independent variables. Unlike other definitions that demand complete specification of the likelihood function, our definition of R2 only needs to know the mean and variance functions, so applicable to more general quasi-models. It is consistent with the classical measure of uncertainty using variance, and reduces to the classical definition of the coefficient of determination when linear regression models are considered.

Case-control genome-wide association study of rheumatoid arthritis from Genetic Analysis Workshop 16 using penalized orthogonal-components regression-linear discriminant analysis

Currently, genome-wide association studies (GWAS) are conducted by collecting a massive number of SNPs (i.e., large p) for a relatively small number of individuals (i.e., small n) and associations are made between clinical phenotypes and genetic variation one single-nucleotide polymorphism (SNP) at a time. Univariate association approaches like this ignore the linkage disequilibrium between SNPs in regions of low recombination. This results in a low reliability of candidate gene identification. Here we propose to improve the case-control GWAS approach by implementing linear discriminant analysis (LDA) through a penalized orthogonal-components regression (POCRE), a newly developed variable selection method for large p small n data. The proposed POCRE-LDA method was applied to the Genetic Analysis Workshop 16 case-control data for rheumatoid arthritis (RA). In addition to the two regions on chromosomes 6 and 9 previously associated with RA by GWAS, we identified SNPs on chromosomes 10 and 18 as potential candidates for further investigation.

Multiplicative background correction for spotted microarrays to improve reproducibility

We propose a simple approach, the multiplicative background correction, to solve a perplexing problem in spotted microarray data analysis: correcting the foreground intensities for the background noise, especially for spots with genes that are weakly expressed or not at all. The conventional approach, the additive background correction, directly subtracts the background intensities from foreground intensities. When the foreground intensities marginally dominate the background intensities, the additive background correction provides unreliable estimates of the differential gene expression levels and usually presents M-A plots with fishtails or fans. Unreliable additive background correction makes it preferable to ignore the background noise, which may increase the number of false positives. Based on the more realistic multiplicative assumption instead of the conventional additive assumption, we propose to logarithmically transform the intensity readings before the background correction, with the logarithmic transformation symmetrizing the skewed intensity readings. This approach not only precludes the fishtails and fans in the M-A plots, but provides highly reproducible background-corrected intensities for both strongly and weakly expressed genes. The superiority of the multiplicative background correction to the additive one as well as the no background correction is justified by publicly available self-hybridization datasets.

Simultaneous genome-wide association studies of anti-cyclic citrullinated peptide in rheumatoid arthritis using penalized orthogonal-components regression

Genome-wide associations between single-nucleotide polymorphisms and clinical traits were simultaneously conducted using penalized orthogonal-components regression. This method was developed to identify the genetic variants controlling phenotypes from a massive number of candidate variants. By investigating the association between all single-nucleotide polymorphisms to the phenotype of antibodies against cyclic citrullinated peptide using the rheumatoid arthritis data provided by Genetic Analysis Workshop 16, we identified genetic regions which may contribute to the pathogenesis of rheumatoid arthritis. Bioinformatic analysis of these genomic regions showed most of them harbor protein-coding gene(s).