Dongxiao Zhu

Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors

Components of multiprotein complexes are routinely determined by using proteomic approaches. However, this information lacks functional content except when new complex members are identified. To analyze quantitatively the abundance of proteins in human Mediator we used normalized spectral abundance factors generated from shotgun proteomics data sets. With this approach we define a common core of mammalian Mediator subunits shared by alternative forms that variably associate with the kinase module and RNA polymerase (pol) II. Although each version of affinity-purified Mediator contained some kinase module and RNA pol II, Mediator purified through F-Med26 contained the most RNA pol II and the least kinase module as demonstrated by the normalized spectral abundance factor approach. The distinct forms of Mediator were functionally characterized by using a transcriptional activity assay, where F-Med26 Mediator/RNA pol II was the most active. This method of protein complex visualization has important implications for the analysis of multiprotein complexes and assembly of protein interaction networks.

Stem Cell Aging Is Controlled Both Intrinsically and Extrinsically in the Drosophila Ovary

It is widely postulated that tissue aging could be, at least partially, caused by reduction of stem cell number, activity, or both. However, the mechanisms of controlling stem cell aging remain largely a mystery. Here, we use Drosophila ovarian germline stem cells (GSCs) as a model to demonstrate that age-dependent decline in the functions of stem cells and their niche contributes to overall stem cell aging. BMP signaling activity from the niche significantly decreases with age, and increasing BMP signaling can prolong GSC life span and promote their proliferation. In addition, the age-dependent E-cadherin decline in the stem cell-niche junction also contributes to stem cell aging. Finally, overexpression of SOD, an enzyme that helps eliminate free oxygen species, in either GSCs or their niche alone can prolong GSC life span and increase GSC proliferation. Therefore, this study demonstrates that stem cell aging is controlled extrinsically and intrinsically in the Drosophila ovary.

Cse4 Is Part of an Octameric Nucleosome in Budding Yeast

The budding yeast CenH3 histone variant Cse4 localizes to centromeric nucleosomes and is required for kinetochore assembly and chromosome segregation. The exact composition of centromeric Cse4-containing nucleosomes is a subject of debate. Using unbiased biochemical, cell-biological, and genetic approaches, we have tested the composition of Cse4-containing nucleosomes. Using micrococcal nuclease-treated chromatin, we find that Cse4 is associated with the histones H2A, H2B, and H4, but not H3 or the nonhistone protein Scm3. Overexpression of Cse4 rescues the lethality of a scm3 deletion, indicating that Scm3 is not essential for the formation of functional centromeric chromatin. We also find that octameric Cse4 nucleosomes can be reconstituted in vitro. Furthermore, Cse4-Cse4 dimerization occurs in vivo at the centromeric nucleosome, and this requires the predicted Cse4-Cse4 dimerization interface. Taken together, our experimental evidence supports the model that the Cse4 nucleosome is an octamer, containing two copies each of Cse4, H2A, H2B, and H4.

Structural comparison of metabolic networks in selected single cell organisms

BackgroundThere has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life.ResultsThree groups of topological properties were used in this study: network indices, degree distribution measures and motif profile measure. All of which are higher-level topological properties except for the marginal degree distribution. Metabolic networks in Archaeal species are found to be different from those in S. cerevisiae and the six Bacterial species in almost all measured higher-level topological properties. Our findings also indicate that the metabolic network in Archaeal species is similar to the exponential random network.ConclusionIf these metabolic network properties of the organisms studied can be extended to other species in their respective domains (which is likely), then the design principle(s) of Archaea are fundamentally different from those of Bacteria and Eukaryote. Furthermore, the functional mechanisms of Archaeal metabolic networks revealed in this study differentiate significantly from those of Bacterial and Eukaryotic organisms, which warrant further investigation.

Quantitative and Qualitative RNA-Seq-Based Evaluation of Epstein-Barr Virus Transcription in Type I Latency Burkitt's Lymphoma Cells

ABSTRACT RNA-seq provides a rich source of transcriptome information with high qualitative and quantitative value. Here, we provide a pipeline for Epstein-Barr virus (EBV) transcriptome analysis using RNA-seq and we apply it to two type I latency cell lines, Mutu I and Akata. This analysis revealed substantial average expression levels of many lytic genes in predominantly latent cell populations. The lytic transcripts BHLF1 and LF3 were expressed at levels greater than those for 98% of all cellular polyadenylated transcripts. Exon junction mapping accurately identified the Qp-derived EBNA1 splicing pattern, lytic gene splicing, and a complex splicing pattern within the BamHI A region.

TEAK: Topology Enrichment Analysis frameworK for detecting activated biological subpathways

To mine gene expression data sets effectively, analysis frameworks need to incorporate methods that identify intergenic relationships within enriched biologically relevant subpathways. For this purpose, we developed the Topology Enrichment Analysis frameworK (TEAK). TEAK employs a novel in-house algorithm and a tailor-made Clique Percolation Method to extract linear and nonlinear KEGG subpathways, respectively. TEAK scores subpathways using the Bayesian Information Criterion for context specific data and the Kullback-Leibler divergence for case–control data. In this article, we utilized TEAK with experimental studies to analyze microarray data sets profiling stress responses in the model eukaryote Saccharomyces cerevisiae. Using a public microarray data set, we identified via TEAK linear sphingolipid metabolic subpathways activated during the yeast response to nitrogen stress, and phenotypic analyses of the corresponding deletion strains indicated previously unreported fitness defects for the dpl1Δ and lag1Δ mutants under conditions of nitrogen limitation. In addition, we studied the yeast filamentous response to nitrogen stress by profiling changes in transcript levels upon deletion of two key filamentous growth transcription factors, FLO8 and MSS11. Via TEAK we identified a nonlinear glycerophospholipid metabolism subpathway involving the SLC1 gene, which we found via mutational analysis to be required for yeast filamentous growth.

Isoform-level microRNA-155 target prediction using RNA-seq

Computational prediction of microRNA targets remains a challenging problem. The existing rule-based, data-driven and expression profiling approaches to target prediction are mostly approached from the gene-level. The increasing availability of RNA-seq data provides a new perspective for microRNA target prediction on the isoform-level. We hypothesize that the splicing isoform is the ultimate effector in microRNA targeting and that the proposed isoform-level approach is capable of predicting non-dominant isoform targets as well as their targeting regions that are otherwise invisible to many existing approaches. To test the hypothesis, we used an iterative expectation maximization (EM) algorithm to quantify transcriptomes at the isoform-level. The performance of the EM algorithm in transcriptome quantification was examined in simulation studies using FluxSimulator. We used joint evidence from isoform-level down-regulation and seed enrichment to predict microRNA-155 targets. We validated our computational approach using results from 149 in-house performed in vitro 3′-UTR assays. We also augmented the splicing database using exon–exon junction evidence, and applied the EM algorithm to predict and quantify 1572 cell line specific novel isoforms. Combined with seed enrichment analysis, we predicted 51 novel microRNA-155 isoform targets. Our work is among the first computational studies advocating the isoform-level microRNA target prediction.

Detecting Splicing Variants in Idiopathic Pulmonary Fibrosis from Non-Differentially Expressed Genes

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease of unknown cause that lacks a proven therapy for altering its high mortality rate. Microarrays have been employed to investigate the pathogenesis of IPF, but are presented mostly at the gene-expression level due to technologic limitations. In as much as, alternative RNA splicing isoforms are increasingly identified as potential regulators of human diseases, including IPF, we propose a new approach with the capacity to detect splicing variants using RNA-seq data. We conducted a joint analysis of differential expression and differential splicing on annotated human genes and isoforms, and identified 122 non-differentially expressed genes with a high degree of “switch” between major and minor isoforms. Three cases with variant mechanisms for alternative splicing were validated using qRT-PCR, among the group of genes in which expression was not significantly changed at the gene level. We also identified 35 novel transcripts that were unique to the fibrotic lungs using exon-exon junction evidence, and selected a representative for qRT-PCR validation. The results of our study are likely to provide new insight into the pathogenesis of pulmonary fibrosis and may eventuate in new treatment targets.

A statistical framework for consolidating "sibling" probe sets for Affymetrix GeneChip data

BackgroundAffymetrix GeneChip typically contains multiple probe sets per gene, defined as sibling probe sets in this study. These probe sets may or may not behave similar across treatments. The most appropriate way of consolidating sibling probe sets suitable for analysis is an open problem. We propose the Analysis of Variance (ANOVA) framework to decide which sibling probe sets can be consolidated.ResultsThe ANOVA model allows us to separate the sibling probe sets into two types: those behave similarly across treatments and those behave differently across treatments. We found that consolidation of sibling probe sets of the former type results in large increase in the number of differentially expressed genes under various statistical criteria. The approach to selecting sibling probe sets suitable for consolidating is implemented in R language and freely available from http://research.stowers-institute.org/hul/affy/.ConclusionOur ANOVA analysis of sibling probe sets provides a statistical framework for selecting sibling probe sets for consolidation. Consolidating sibling probe sets by pooling data from each greatly improves the estimates of a gene expression level and results in identification of more biologically relevant genes. Sibling probe sets that do not qualify for consolidation may represent annotation errors or other artifacts, or may correspond to differentially processed transcripts of the same gene that require further analysis.

miRNA-mediated relationships between Cis-SNP genotypes and transcript intensities in lymphocyte cell lines.

In metazoans, miRNAs regulate gene expression primarily through binding to target sites in the 3′ UTRs (untranslated regions) of messenger RNAs (mRNAs). Cis-acting variants within, or close to, a gene are crucial in explaining the variability of gene expression measures. Single nucleotide polymorphisms (SNPs) in the 3′ UTRs of genes can affect the base-pairing between miRNAs and mRNAs, and hence disrupt existing target sites (in the reference sequence) or create novel target sites, suggesting a possible mechanism for cis regulation of gene expression. Moreover, because the alleles of different SNPs within a DNA sequence of limited length tend to be in strong linkage disequilibrium (LD), we hypothesize the variants of miRNA target sites caused by SNPs potentially function as bridges linking the documented cis-SNP markers to the expression of the associated genes. A large-scale analysis was herein performed to test this hypothesis. By systematically integrating multiple latest information sources, we found 21 significant gene-level SNP-involved miRNA-mediated post-transcriptional regulation modules (SNP-MPRMs) in the form of SNP-miRNA-mRNA triplets in lymphocyte cell lines for the CEU and YRI populations. Among the cognate genes, six including ALG8, DGKE, GNA12, KLF11, LRPAP1, and MMAB are related to multiple genetic diseases such as depressive disorder and Type-II diabetes. Furthermore, we found that ∼35% of the documented transcript intensity-related cis-SNPs (∼950) in a recent publication are identical to, or in significant linkage disequilibrium (LD) (p<0.01) with, one or multiple SNPs located in miRNA target sites. Based on these associations (or identities), 69 significant exon-level SNP-MPRMs and 12 disease genes were further determined for two populations. These results provide concrete in silico evidence for the proposed hypothesis. The discovered modules warrant additional follow-up in independent laboratory studies.