A genome wide dosage suppressor network reveals genomic robustness.

Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures.

SEQADAPT: an adaptable system for the tracking, storage and analysis of high throughput sequencing experiments.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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 approximately 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.

Circulating microRNAs, potential biomarkers for drug-induced liver injury.

Drug-induced liver injury is a frequent side effect of many drugs, constitutes a significant threat to patient health and has an enormous economic impact on health care expenditures. Numerous efforts have been made to identify reliable and predictive markers to detect the early signs of drug-induced injury to the liver, one of the most vulnerable organs in the body. These studies have, however, not delivered any more informative candidates than the serum aminotransferase markers that have been available for approximately 30 years. Using acetaminophen overdose-induced liver injury in the mouse as a model system, we have observed highly significant differences in the spectrum and levels of microRNAs in both liver tissues and in plasma between control and overdosed animals. Based on our survey of microRNA expression among normal tissues, some of the microRNAs, like messenger RNAs, display restricted tissue distributions. A number of elevated circulating microRNAs in plasma collected from acetaminophen-overdosed animals are highly expressed in the liver. We have demonstrated that specific microRNA species, such as mir-122 and mir-192, both are enriched in the liver tissue and exhibit dose- and exposure duration-dependent changes in the plasma that parallel serum aminotransferase levels and the histopathology of liver degeneration, but their changes can be detected significantly earlier. These findings suggest the potential of using specific circulating microRNAs as sensitive and informative biomarkers for drug-induced liver injury.

Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans.

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.

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 (PrP(C)) to malignant PrP(Sc) isoforms. Networks perturbed by PrP(Sc) 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 PrP(Sc) 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:http://prion.systemsbiology.net.

Memory T cell RNA rearrangement programmed by heterogeneous nuclear ribonucleoprotein hnRNPLL.

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.

The Innate Immune Database (IIDB).

BACKGROUND: 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 http://www.innateImmunity-systemsbiology.org. 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. DESCRIPTION: 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. CONCLUSION: 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 http://db.systemsbiology.net/IIDB.

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.

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.

SLIMarray: lightweight software for microarray facility management.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: SLIMarray addresses the previously unmet need for free and open source software for managing the logistics of a microarray core facility.

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research.

Identification of new flagellar genes of Salmonella enterica serovar Typhimurium.

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 sigma28-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.

Gene expression profiles in prostate cancer: association with patient subgroups and tumour differentiation.

Prostate carcinoma is the most common cancer of western men and is a markedly heterogeneous disease. The aim of this study was to identify signatures of differentially expressed genes in prostate cancer using DNA microarray technology, evaluating expression profiles in matched pairs of benign and malignant tissue. Samples were collected from 33 radical prostatectomies, and 52 specimens were included, representing 29 histologically verified primary tumours, 19 paired samples of malignant and benign tissue, and 4 non-paired benign tissue samples. Microarray analysis was performed using an expanded sequence verified set of 40,000 human cDNA clones, revealing several genes with significant differences between malignant and benign tissue, including recently reported genes like alpha-methylacyl-CoA racemase (AMACR) and hepsin, as well as genes relevant for tumour development and progression. Leave out cross validation (LOCV) test correctly predicted tumour or benign tissue in 47 (90.3%) out of 52 cases, significantly better than cross validation tests using randomly permuted tissue labels. Unsupervised clustering analysis revealed 3 distinct patient clusters significantly associated with Gleason score, and high grade tumours (Gleason score >/=7) accumulated in cluster 1 (C1). Gene expression profiles correctly predicted 100% of tumour samples segregating to C1, as also validated by LOCV. Gene expression profiles were analysed in filtered and floored datasets with similar results, and a pair-wise design was also tested. Gene expression profiles provided tumour clusters linked to differentiation, and revealed novel markers relevant for molecular classification, grading and therapy of prostate cancer.

Analysis of acute myelogenous leukemia: preparation of samples for genomic and proteomic analyses.

During the last decade, several large clinical studies have demonstrated that analysis of chromosomal abnormalities is an essential basis for therapeutic decisions in patients with acute myelogenous leukemia (AML), and cytogenetic studies should now be regarded as mandatory both for routine treatment and as a part of clinical investigations in AML. However, new techniques for detailed genetic characterization and analysis of gene expression as well as protein modulation will become important in the further classification of AML subsets and the development of risk-adapted therapeutic strategies. In this context, we emphasize the importance of population-based clinical studies as a basis for future therapeutic guidelines. Such studies will then require the inclusion of patients at small clinical centers without specialized hematological research laboratories. To document a high and uniform quality of the laboratory investigations, it will be necessary to collect material for later analysis in selected laboratories. In this article, we describe current methods for collection of biological samples that can be used for later preparation of DNA, RNA, and proteins. With the use of gradient-separated AML cells, it should be possible to establish the necessary techniques for collection and handling of biological samples even at smaller centers, and complete collections from all included patients should then be possible even in population-based clinical studies.