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1.  Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans 
Nature immunology  2008;10(1):116-125.
A major challenge in vaccinology is to prospectively determine vaccine efficacy. Here we have used a systems biology approach to identify early gene ‘signatures’ that predicted immune responses in humans vaccinated with yellow fever vaccine YF-17D. Vaccination induced genes that regulate virus innate sensing and type I interferon production. Computational analyses identified a gene signature, including complement protein C1qB and eukaryotic translation initiation factor 2 alpha kinase 4—an orchestrator of the integrated stress response—that correlated with and predicted YF-17D CD8+ T cell responses with up to 90% accuracy in an independent, blinded trial. A distinct signature, including B cell growth factor TNFRS17, predicted the neutralizing antibody response with up to 100% accuracy. These data highlight the utility of systems biology approaches in predicting vaccine efficacy.
PMCID: PMC4049462  PMID: 19029902
2.  Memory T Cell RNA Rearrangement Programmed by Heterogeneous Nuclear Ribonucleoprotein hnRNPLL 
Immunity  2008;29(6):863-875.
Differentiation of memory cells involves DNA-sequence changes in B lymphocytes but is less clearly defined in T cells. RNA rearrangement is identified here as a key event in memory T cell differentiation by analysis of a mouse mutation that altered the proportions of naive and memory T cells and crippled the process of Ptprc exon silencing needed to generate CD45RO in memory T cells. A single substitution in a memory-induced RNA-binding protein, hnRNPLL, destabilized an RNA-recognition domain that bound with micromolar affinity to RNA containing the Ptprc exon-silencing sequence. Hnrpll mutation selectively diminished T cell accumulation in peripheral lymphoid tissues but not proliferation. Exon-array analysis of Hnrpll mutant naive and memory T cells revealed an extensive program of alternative mRNA splicing in memory T cells, coordinated by hnRNPLL. A remarkable overlap with alternative splicing in neural tissues may reflect a co-opted strategy for diversifying memory T cells.
PMCID: PMC3057111  PMID: 19100700
3.  SEQADAPT: an adaptable system for the tracking, storage and analysis of high throughput sequencing experiments 
BMC Bioinformatics  2010;11:377.
High throughput sequencing has become an increasingly important tool for biological research. However, the existing software systems for managing and processing these data have not provided the flexible infrastructure that research requires.
Existing software solutions provide static and well-established algorithms in a restrictive package. However as high throughput sequencing is a rapidly evolving field, such static approaches lack the ability to readily adopt the latest advances and techniques which are often required by researchers. We have used a loosely coupled, service-oriented infrastructure to develop SeqAdapt. This system streamlines data management and allows for rapid integration of novel algorithms. Our approach also allows computational biologists to focus on developing and applying new methods instead of writing boilerplate infrastructure code.
The system is based around the Addama service architecture and is available at our website as a demonstration web application, an installable single download and as a collection of individual customizable services.
PMCID: PMC2916924  PMID: 20630057
4.  Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes 
BMC Cancer  2009;9:452.
Prostate cancer cells in primary tumors have been typed CD10-/CD13-/CD24hi/CD26+/CD38lo/CD44-/CD104-. This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure.
CD26+ cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells.
The G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines.
Genes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.
PMCID: PMC2809079  PMID: 20021671
5.  The Prion Disease Database: a comprehensive transcriptome resource for systems biology research in prion diseases 
Prion diseases reflect conformational conversion of benign isoforms of prion protein (PrPC) to malignant PrPSc isoforms. Networks perturbed by PrPSc accumulation and their ties to pathological events are poorly understood. Time-course transcriptomic and phenotypic data in animal models are critical for understanding prion-perturbed networks in systems biology studies. Here, we present the Prion Disease Database (PDDB), the most comprehensive data resource on mouse prion diseases to date. The PDDB contains: (i) time-course mRNA measurements spanning the interval from prion inoculation through appearance of clinical signs in eight mouse strain-prion strain combinations and (ii) histoblots showing temporal PrPSc accumulation patterns in brains from each mouse–prion combination. To facilitate prion research, the PDDB also provides a suite of analytical tools for reconstructing dynamic networks via integration of temporal mRNA and interaction data and for analyzing these networks to generate hypotheses.
Database URL:
PMCID: PMC2790306  PMID: 20157484
6.  Prevalence of transcription promoters within archaeal operons and coding sequences 
Despite the knowledge of complex prokaryotic-transcription mechanisms, generalized rules, such as the simplified organization of genes into operons with well-defined promoters and terminators, have had a significant role in systems analysis of regulatory logic in both bacteria and archaea. Here, we have investigated the prevalence of alternate regulatory mechanisms through genome-wide characterization of transcript structures of ∼64% of all genes, including putative non-coding RNAs in Halobacterium salinarum NRC-1. Our integrative analysis of transcriptome dynamics and protein–DNA interaction data sets showed widespread environment-dependent modulation of operon architectures, transcription initiation and termination inside coding sequences, and extensive overlap in 3′ ends of transcripts for many convergently transcribed genes. A significant fraction of these alternate transcriptional events correlate to binding locations of 11 transcription factors and regulators (TFs) inside operons and annotated genes—events usually considered spurious or non-functional. Using experimental validation, we illustrate the prevalence of overlapping genomic signals in archaeal transcription, casting doubt on the general perception of rigid boundaries between coding sequences and regulatory elements.
PMCID: PMC2710873  PMID: 19536208
archaea; ChIP–chip; non-coding RNA; tiling array; transcription
7.  The Innate Immune Database (IIDB) 
BMC Immunology  2008;9:7.
As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site . Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens.
We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser.
We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at .
PMCID: PMC2268913  PMID: 18321385
8.  Transcriptional responses to fatty acid are coordinated by combinatorial control 
In transcriptional regulatory networks, the coincident binding of a combination of factors to regulate a gene implies the existence of complex mechanisms to control both the gene expression profile and specificity of the response. Unraveling this complexity is a major challenge to biologists. Here, a novel network topology-based clustering approach was applied to condition-specific genome-wide chromatin localization and expression data to characterize a dynamic transcriptional regulatory network responsive to the fatty acid oleate. A network of four (predicted) regulators of the response (Oaf1p, Pip2p, Adr1p and Oaf3p) was investigated. By analyzing trends in the network structure, we found that two groups of multi-input motifs form in response to oleate, each controlling distinct functional classes of genes. This functionality is contributed in part by Oaf1p, which is a component of both types of multi-input motifs and has two different regulatory activities depending on its binding context. The dynamic cooperation between Oaf1p and Pip2p appears to temporally synchronize the two different responses. Together, these data suggest a network mechanism involving dynamic combinatorial control for coordinating transcriptional responses.
PMCID: PMC1911199  PMID: 17551510
Oaf3p; oleate; peroxisome; regulatory network; stress response
9.  Prediction of phenotype and gene expression for combinations of mutations 
Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified.
PMCID: PMC1847951  PMID: 17389876
computational biology; data integration; gene expression; genetic interaction; network model
10.  SLIMarray: Lightweight software for microarray facility management 
Microarray core facilities are commonplace in biological research organizations, and need systems for accurately tracking various logistical aspects of their operation. Although these different needs could be handled separately, an integrated management system provides benefits in organization, automation and reduction in errors.
We present SLIMarray (System for Lab Information Management of Microarrays), an open source, modular database web application capable of managing microarray inventories, sample processing and usage charges. The software allows modular configuration and is well suited for further development, providing users the flexibility to adapt it to their needs. SLIMarray Lite, a version of the software that is especially easy to install and run, is also available.
SLIMarray addresses the previously unmet need for free and open source software for managing the logistics of a microarray core facility.
PMCID: PMC1636632  PMID: 17147785
11.  Identification of New Flagellar Genes of Salmonella enterica Serovar Typhimurium 
Journal of Bacteriology  2006;188(6):2233-2243.
RNA levels of flagellar genes in eight different genetic backgrounds were compared to that of the wild type by DNA microarray analysis. Cluster analysis identified new, potential flagellar genes, three putative methyl-accepting chemotaxis proteins, STM3138 (McpA), STM3152 (McpB), and STM3216(McpC), and a CheV homolog, STM2314, in Salmonella, that are not found in Escherichia coli. Isolation and characterization of Mud-lac insertions in cheV, mcpB, mcpC, and the previously uncharacterized aer locus of S. enterica serovar Typhimurium revealed them to be controlled by σ28-dependent flagellar class 3 promoters. In addition, the srfABC operon previously isolated as an SsrB-regulated operon clustered with the flagellar class 2 operon and was determined to be under FlhDC control. The previously unclassified fliB gene, encoding flagellin methylase, clustered as a class 2 gene, which was verified using reporter fusions, and the fliB transcriptional start site was identified by primer extension analysis. RNA levels of all flagellar genes were elevated in flgM or fliT null strains. RNA levels of class 3 flagellar genes were elevated in a fliS null strain, while deletion of the fliY, fliZ, or flk gene did not affect flagellar RNA levels relative to those of the wild type. The cafA (RNase G) and yhjH genes clustered with flagellar class 3 transcribed genes. Null alleles in cheV, mcpA, mcpB, mcpC, and srfB did not affect motility, while deletion of yhjH did result in reduced motility compared to that of the wild type.
PMCID: PMC1428135  PMID: 16513753
12.  SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology 
BMC Bioinformatics  2006;7:286.
The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.
SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform.
SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research.
PMCID: PMC1524999  PMID: 16756676

Results 1-12 (12)