Vincent VanBuren

Transcriptome analysis of mouse stem cells and early embryos.

Understanding and harnessing cellular potency are fundamental in biology and are also critical to the future therapeutic use of stem cells. Transcriptome analysis of these pluripotent cells is a first step towards such goals. Starting with sources that include oocytes, blastocysts, and embryonic and adult stem cells, we obtained 249,200 high-quality EST sequences and clustered them with public sequences to produce an index of approximately 30,000 total mouse genes that includes 977 previously unidentified genes. Analysis of gene expression levels by EST frequency identifies genes that characterize preimplantation embryos, embryonic stem cells, and adult stem cells, thus providing potential markers as well as clues to the functional features of these cells. Principal component analysis identified a set of 88 genes whose average expression levels decrease from oocytes to blastocysts, stem cells, postimplantation embryos, and finally to newborn tissues. This can be a first step towards a possible definition of a molecular scale of cellular potency. The sequences and cDNA clones recovered in this work provide a comprehensive resource for genes functioning in early mouse embryos and stem cells. The nonrestricted community access to the resource can accelerate a wide range of research, particularly in reproductive and regenerative medicine.

Estimates of lateral and longitudinal bond energies within the microtubule lattice

We developed a stochastic model of microtubule (MT) assembly dynamics that estimates tubulin–tubulin bond energies, mechanical energy stored in the lattice dimers, and the size of the tubulin-GTP cap at MT tips. First, a simple assembly/disassembly state model was used to screen possible combinations of lateral bond energy (ΔGLat) and longitudinal bond energy (ΔGLong) plus the free energy of immobilizing a dimer in the MT lattice (ΔGS) for rates of MT growth and shortening measured experimentally. This analysis predicts ΔGLat in the range of −3.2 to −5.7 kBT and ΔGLong plus ΔGS in the range of −6.8 to −9.4 kBT. Based on these estimates, the energy of conformational stress for a single tubulin-GDP dimer in the lattice is 2.1–2.5 kBT. Second, we studied how tubulin-GTP cap size fluctuates with different hydrolysis rules and show that a mechanism of directly coupling subunit addition to hydrolysis fails to support MT growth, whereas a finite hydrolysis rate allows growth. By adding rules to mimic the mechanical constraints present at the MT tip, the model generates tubulin-GTP caps similar in size to experimental estimates. Finally, by combining assembly/disassembly and cap dynamics, we generate MT dynamic instability with rates and transition frequencies similar to those measured experimentally. Our model serves as a platform to examine GTP-cap dynamics and allows predictions of how MT-associated proteins and other effectors alter the energetics of MT assembly.

Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray

The ability to quantitatively measure the expression of all genes in a given tissue or cell with a single assay is an exciting promise of gene-expression profiling technology. An in situ-synthesized 60-mer oligonucleotide microarray designed to detect transcripts from all mouse genes was validated, as well as a set of exogenous RNA controls derived from the yeast genome (made freely available without restriction), which allow quantitative estimation of absolute endogenous transcript abundance.

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data

BackgroundAlthough expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult.ResultsSTARNET2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. STARNET2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBIs Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new HEATSEEKER module.ConclusionSTARNET2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a STARNET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at http://vanburenlab.medicine.tamhsc.edu/starnet2.html, and does not require user registration.

Characteristics of Adult Flatfoot in the United States

Many factors have been suggested to cause flatfoot deformity. The purpose of this study was to identify risk factors for flatfoot deformity, which itself can be a causative factor for other foot and ankle pathologies. The National Health Interview Survey (Podiatry Supplement) from 1990 was analyzed to determine associations of various demographic factors and other foot and ankle pathologies with self-reported flatfoot deformity. We found statistically significant (P <or= .05) associations of flatfoot with age, male gender, BMI, white-collar occupation, veteran status, bunion, hammertoe, calluses, arthritis, and poor health. Treatment and prevention of flatfoot may have an effect on an individuals overall health and occurrence of other foot and ankle pathologies.

A visual data mining tool that facilitates reconstruction of transcription regulatory networks.

Background Although the use of microarray technology has seen exponential growth, analysis of microarray data remains a challenge to many investigators. One difficulty lies in the interpretation of a list of differentially expressed genes, or in how to plan new experiments given that knowledge. Clustering methods can be used to identify groups of genes with similar expression patterns, and genes with unknown function can be provisionally annotated based on the concept of “guilt by association”, where function is tentatively inferred from the known functions of genes with similar expression patterns. These methods frequently suffer from two limitations: (1) visualization usually only gives access to group membership, rather than specific information about nearest neighbors, and (2) the resolution or quality of the relationships are not easily inferred. Methodology/Principal Findings We have addressed these issues by improving the precision of similarity detection over that of a single experiment and by creating a tool to visualize tractable association networks: we (1) performed meta-analysis computation of correlation coefficients for all gene pairs in a heterogeneous data set collected from 2,145 publicly available micorarray samples in mouse, (2) filtered the resulting distribution of over 130 million correlation coefficients to build new, more tractable distributions from the strongest correlations, and (3) designed and implemented a new Web based tool (StarNet, http://vanburenlab.medicine.tamhsc.edu/starnet.html) for visualization of sub-networks of the correlation coefficients built according to user specified parameters. Conclusions/Significance Correlations were calculated across a heterogeneous collection of publicly available microarray data. Users can access this analysis using a new freely available Web-based application for visualizing tractable correlation networks that are flexibly specified by the user. This new resource enables rapid hypothesis development for transcription regulatory relationships.

Discovering Disease-specific Biomarker Genes for Cancer Diagnosis and Prognosis

The large amounts of microarray data provide us a great opportunity to identify gene expression profiles (GEPs) in different tissues or disease states. Disease-specific biomarker genes likely share GEPs that are distinct in disease samples as compared with normal samples. The similarity of the GEPs may be evaluated by Pearson Correlation Coefficient (PCC) and the distinctness of GEPs may be assessed by Kolmogorov-Smirnov distance (KSD). In this study, we used the PCC and KSD metrics for GEPs to identify disease-specific (cancer-specific) biomarkers. We first analyzed and compared GEPs using microarray datasets for smoking and lung cancer. We found that the number of genes with highly different GEPs between comparing groups in smoking dataset was much larger than that in lung cancer dataset; this observation was further verified when we compared GEPs in smoking dataset with prostate cancer datasets. Moreover, our Gene Ontology analysis revealed that the top ranked biomarker candidate genes for prostate cancer were highly enriched in molecular function categories such as ‘cytoskeletal protein binding’ and biological process categories such as ‘muscle contraction’. Finally, we used two genes, ACTC1 (encoding an actin subunit) and HPN (encoding hepsin), to demonstrate the feasibility of diagnosing and monitoring prostate cancer using the expression intensity histograms of marker genes. In summary, our results suggested that this approach might prove promising and powerful for diagnosing and monitoring the patients who come to the clinic for screening or evaluation of a disease state including cancer.

Essential role of Cdc42 in cardiomyocyte proliferation and cell-cell adhesion during heart development

Cdc42 is a member of the Rho GTPase family and functions as a molecular switch in regulating cell migration, proliferation, differentiation and survival. However, the role of Cdc42 in heart development remains largely unknown. To determine the function of Cdc42 in heart formation, we have generated a Cdc42 cardiomyocyte knockout (CCKO) mouse line by crossing Cdc42 flox mice with myosin light chain (MLC) 2a-Cre mice. The inactivation of Cdc42 in embryonic cardiomyocytes induced lethality after embryonic day 12.5. Histological analysis of CCKO embryos showed cardiac developmental defects that included thin ventricular walls and ventricular septum defects. Microarray and real-time PCR data also revealed that the expression level of p21 was significantly increased and cyclin B1 was dramatically decreased, suggesting that Cdc42 is required for cardiomyocyte proliferation. Phosphorylated Histone H3 staining confirmed that the inactivation of Cdc42 inhibited cardiomyocytes proliferation. In addition, transmission electron microscope studies showed disorganized sarcomere structure and disruption of cell-cell contact among cardiomyocytes in CCKO hearts. Accordingly, we found that the distribution of N-cadherin/β-Catenin in CCKO cardiomyocytes was impaired. Taken together, our data indicate that Cdc42 is essential for cardiomyocyte proliferation, sarcomere organization and cell-cell adhesion during heart development.

A Provisional Gene Regulatory Atlas for Mouse Heart Development

Congenital Heart Disease (CHD) is one of the most common birth defects. Elucidating the molecular mechanisms underlying normal cardiac development is an important step towards early identification of abnormalities during the developmental program and towards the creation of early intervention strategies. We developed a novel computational strategy for leveraging high-content data sets, including a large selection of microarray data associated with mouse cardiac development, mouse genome sequence, ChIP-seq data of selected mouse transcription factors and Y2H data of mouse protein-protein interactions, to infer the active transcriptional regulatory network of mouse cardiac development. We identified phase-specific expression activity for 765 overlapping gene co-expression modules that were defined for obtained cardiac lineage microarray data. For each co-expression module, we identified the phase of cardiac development where gene expression for that module was higher than other phases. Co-expression modules were found to be consistent with biological pathway knowledge in Wikipathways, and met expectations for enrichment of pathways involved in heart lineage development. Over 359,000 transcription factor-target relationships were inferred by analyzing the promoter sequences within each gene module for overrepresentation against the JASPAR database of Transcription Factor Binding Site (TFBS) motifs. The provisional regulatory network will provide a framework of studying the genetic basis of CHD.

Prevalence of Podiatric Medical Problems in Veterans versus Nonveterans

BACKGROUND Lower-extremity pathologic abnormalities have been common in military recruits for many years. Many of these conditions can become chronic and persist even after retiring from military service. We hypothesized that certain foot abnormalities are more prevalent in veterans versus nonveterans. The purpose of this study was to evaluate what foot and ankle disorders are associated with veteran status while controlling for other demographic factors. METHODS The National Health Interview Survey (Podiatry Supplement) from 1990 was used for this secondary data analysis. The data were divided into veterans and nonveterans, and the prevalence of podiatric medical problems, including callus, flatfoot deformity, bunion deformity, hammer toe deformity, arthritis, and sprain, was evaluated for each group. RESULTS Flatfoot deformity and arthritis were significantly more prevalent in veterans versus nonveterans in the United States. Bunion deformity was significantly more prevalent in male veterans than in male nonveterans. Male veterans were less likely than male nonveterans to have sprains, and female veterans were more likely than their nonveteran counterparts to have sprains. CONCLUSIONS These results may help us understand the potential risk factors for podiatric medical problems and may be used for formulating prevention programs.