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

Bridging data management platforms and visualization tools to enable ad-hoc and smart analytics in life sciences

Core facilities have to offer technologies that best serve the needs of their users and provide them a competitive advantage in research. They have to set up and maintain instruments in the range of ten to a hundred, which produce large amounts of data and serve thousands of active projects and customers. Particular emphasis has to be given to the reproducibility of the results. More and more, the entire process from building the research hypothesis, conducting the experiments, doing the measurements, through the data explorations and analysis is solely driven by very few experts in various scientific fields. Still, the ability to perform the entire data exploration in real-time on a personal computer is often hampered by the heterogeneity of software, the data structure formats of the output, and the enormous data sizes. These impact the design and architecture of the implemented software stack. At the Functional Genomics Center Zurich (FGCZ), a joint state-of-the-art research and training facility of ETH Zurich and the University of Zurich, we have developed the B-Fabric system, which has served for more than a decade, an entire life sciences community with fundamental data science support. In this paper, we sketch how such a system can be used to glue together data (including metadata), computing infrastructures (clusters and clouds), and visualization software to support instant data exploration and visual analysis. We illustrate our in-daily life implemented approach using visualization applications of mass spectrometry data.

Systematic Discovery of Archaeal Transcription Factor Functions in Regulatory Networks through Quantitative Phenotyping Analysis

To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.

Gene regulatory networks (GRNs) are critical for dynamic transcriptional responses to environmental stress. However, the mechanisms by which GRN regulation adjusts physiology to enable stress survival remain unclear. Here we investigate the functions of transcription factors (TFs) within the global GRN of the stress-tolerant archaeal microorganism Halobacterium salinarum. We measured growth phenotypes of a panel of TF deletion mutants in high temporal resolution under heat shock, oxidative stress, and low-salinity conditions. To quantitate the noncanonical functional forms of the growth trajectories observed for these mutants, we developed a novel modeling framework based on Gaussian process regression and functional analysis of variance (FANOVA). We employ unique statistical tests to determine the significance of differential growth relative to the growth of the control strain. This analysis recapitulated known TF functions, revealed novel functions, and identified surprising secondary functions for characterized TFs. Strikingly, we observed that the majority of the TFs studied were required for growth under multiple stress conditions, pinpointing regulatory connections between the conditions tested. Correlations between quantitative phenotype trajectories of mutants are predictive of TF-TF connections within the GRN. These phenotypes are strongly concordant with predictions from statistical GRN models inferred from gene expression data alone. With genome-wide and targeted data sets, we provide detailed functional validation of novel TFs required for extreme oxidative stress and heat shock survival. Together, results presented in this study suggest that many TFs function under multiple conditions, thereby revealing high interconnectivity within the GRN and identifying the specific TFs required for communication between networks responding to disparate stressors.

IMPORTANCE To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.

Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features.

Systems biology approaches to defining transcription regulatory networks in halophilic archaea

To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.

Evolution of context dependent regulation by expansion of feast/famine regulatory proteins

Background

Expansion of transcription factors is believed to have played a crucial role in evolution of all organisms by enabling them to deal with dynamic environments and colonize new environments. We investigated how the expansion of the Feast/Famine Regulatory Protein (FFRP) or Lrp-like proteins into an eight-member family in Halobacterium salinarum NRC-1 has aided in niche-adaptation of this archaeon to a complex and dynamically changing hypersaline environment.

Results

We mapped genome-wide binding locations for all eight FFRPs, investigated their preference for binding different effector molecules, and identified the contexts in which they act by analyzing transcriptional responses across 35 growth conditions that mimic different environmental and nutritional conditions this organism is likely to encounter in the wild. Integrative analysis of these data constructed an FFRP regulatory network with conditionally active states that reveal how interrelated variations in DNA-binding domains, effector-molecule preferences, and binding sites in target gene promoters have tuned the functions of each FFRP to the environments in which they act. We demonstrate how conditional regulation of similar genes by two FFRPs, AsnC (an activator) and VNG1237C (a repressor), have striking environment-specific fitness consequences for oxidative stress management and growth, respectively.

Conclusions

This study provides a systems perspective into the evolutionary process by which gene duplication within a transcription factor family contributes to environment-specific adaptation of an organism.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0122-2) contains supplementary material, which is available to authorized users.

A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen

Methanogens catalyze the critical methane-producing step (called methanogenesis) in the anaerobic decomposition of organic matter. Here, we present the first predictive model of global gene regulation of methanogenesis in a hydrogenotrophic methanogen, Methanococcus maripaludis. We generated a comprehensive list of genes (protein-coding and noncoding) for M. maripaludis through integrated analysis of the transcriptome structure and a newly constructed Peptide Atlas. The environment and gene-regulatory influence network (EGRIN) model of the strain was constructed from a compendium of transcriptome data that was collected over 58 different steady-state and time-course experiments that were performed in chemostats or batch cultures under a spectrum of environmental perturbations that modulated methanogenesis. Analyses of the EGRIN model have revealed novel components of methanogenesis that included at least three additional protein-coding genes of previously unknown function as well as one noncoding RNA. We discovered that at least five regulatory mechanisms act in a combinatorial scheme to intercoordinate key steps of methanogenesis with different processes such as motility, ATP biosynthesis, and carbon assimilation. Through a combination of genetic and environmental perturbation experiments we have validated the EGRIN-predicted role of two novel transcription factors in the regulation of phosphate-dependent repression of formate dehydrogenase—a key enzyme in the methanogenesis pathway. The EGRIN model demonstrates regulatory affiliations within methanogenesis as well as between methanogenesis and other cellular functions.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Reprogramming of prostate cancer-associated stromal cells to embryonic stem-like

BACKGROUND

CD90(+) prostate cancer-associated (CP) stromal cells represent a diseased cell type found only in tumor tissue. They differ from their normal counterpart in gene expression and inductive signaling. Genetic reprogramming by induced pluripotent stem (iPS) cell technology can effectively change adult cells into stem-like cells through wholesale alteration of the gene expression program. This technology might be used to 'erase' the abnormal gene expression of diseased cells. The resultant iPS cells would no longer express the disease phenotype, and behave like stem cells.

METHODS

CP stromal cells, isolated from tumor tissue of a surgically resected prostate by anti-CD90-mediated sorting and cultured in vitro, were transfected with in vitro packaged lentiviral expression vectors containing stem cell transcription factor genes POU5F1, LIN28, NANOG, and SOX2.

RESULTS

Alkaline phosphatase-positive iPS cells were obtained in about 3 weeks post-transfection at a frequency of 10(-4) . Their colony morphology was indistinguishable from that of human embryonic stem (ES) cells. Transcriptome analysis showed a virtually complete match in gene expression between the iPS and ES cells.

CONCLUSIONS

Genes of CP stromal cells could be fully inactivated by genetic reprogramming. As a consequence, the disease phenotype was 'cured'.

Bladder expression of CD cell surface antigens and cell-type-specific transcriptomes

Many cell types have no known functional attributes. In the bladder and prostate, basal epithelial and stromal cells appear similar in cytomorphology and share several cell surface markers. Their total gene expression (transcriptome) should provide a clear measure of the extent to which they are alike functionally. Since urologic stromal cells are known to mediate organ-specific tissue formation, these cells in cancers might exhibit aberrant gene expression affecting their function. For transcriptomes, cluster designation (CD) antigens have been identified for cell sorting. The sorted cell populations can be analyzed by DNA microarrays. Various bladder cell types have unique complements of CD molecules. CD9(+) urothelial, CD104(+) basal and CD13(+) stromal cells of the lamina propria were therefore analyzed, as were CD9(+) cancer and CD13(+) cancer-associated stromal cells. The transcriptome datasets were compared by principal components analysis for relatedness between cell types; those with similarity in gene expression indicated similar function. Although bladder and prostate basal cells shared CD markers such as CD104, CD44 and CD49f, they differed in overall gene expression. Basal cells also lacked stem cell gene expression. The bladder luminal and stromal transcriptomes were distinct from their prostate counterparts. In bladder cancer, not only the urothelial but also the stromal cells showed gene expression alteration. The cancer process in both might thus involve defective stromal signaling. These cell-type transcriptomes provide a means to monitor in vitro models in which various CD-isolated cell types can be combined to study bladder differentiation and bladder tumor development based on cell-cell interaction.

openBIS: a flexible framework for managing and analyzing complex data in biology research

Background

Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.

Results

We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies.

Conclusions

openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

EAF2 loss enhances angiogenic effects of Von Hippel-Lindau heterozygosity on the murine liver and prostate

Von Hippel-Lindau (VHL) disease results from the inactivation of the VHL gene and is characterized by highly vascular tumors. A consequence of VHL loss is the stabilization of hypoxia-inducible factor (HIF) alpha subunits and increased expression of HIF target genes, which include pro-angiogenic growth factors such as vascular endothelial growth factor (VEGF). In mice, homozygous deletion of VHL is embryonic lethal due to vascular abnormalities in the placenta; and, VHL^+/− mice develop proliferative vascular lesions in several major organs, most prominently the liver. Loss of ELL-associated factor (EAF2) in murine models has also been shown to induce increased vascular density in the liver as well as the prostate. Previously, EAF2 was determined to be a binding partner of VHL and loss of EAF2 induced a reduction in pVHL levels and an increase in hypoxia induced factor 1α (HIF1α) levels in vitro. Here we characterized the cooperative effects of VHL- and EAF2-deficiency on angiogenesis in the liver and prostate of male mice. VHL deficiency consistently increased the incidence of hepatic vascular lesions across three mouse strains. These vascular lesions where characterized by an increase in microvessel density, and staining intensity of VHL target proteins HIF1α and VEGF. EAF2^−/−VHL^+/− mice had increased incidence of proliferative hepatic vascular lesions (4/4) compared to VHL^+/− (10/18) and EAF2^−/− (0/5) mice. Prostates of EAF2^−/−VHL^+/− mice also displayed an increase in microvessel density, as well as stromal inflammation and prostatic intraepithelial neoplasia. These results suggest that cooperation of VHL and EAF2 may be critical for angiogenic regulation of the liver and prostate, and concurrent loss of these two tumor suppressors may result in a pro-angiogenic phenotype.

Lineage relationship of prostate cancer cell types based on gene expression

Background

Prostate tumor heterogeneity is a major factor in disease management. Heterogeneity could be due to multiple cancer cell types with distinct gene expression. Of clinical importance is the so-called cancer stem cell type. Cell type-specific transcriptomes are used to examine lineage relationship among cancer cell types and their expression similarity to normal cell types including stem/progenitor cells.

Methods

Transcriptomes were determined by Affymetrix DNA array analysis for the following cell types. Putative prostate progenitor cell populations were characterized and isolated by expression of the membrane transporter ABCG2. Stem cells were represented by embryonic stem and embryonal carcinoma cells. The cancer cell types were Gleason pattern 3 (glandular histomorphology) and pattern 4 (aglandular) sorted from primary tumors, cultured prostate cancer cell lines originally established from metastatic lesions, xenografts LuCaP 35 (adenocarcinoma phenotype) and LuCaP 49 (neuroendocrine/small cell carcinoma) grown in mice. No detectable gene expression differences were detected among serial passages of the LuCaP xenografts.

Results

Based on transcriptomes, the different cancer cell types could be clustered into a luminal-like grouping and a non-luminal-like (also not basal-like) grouping. The non-luminal-like types showed expression more similar to that of stem/progenitor cells than the luminal-like types. However, none showed expression of stem cell genes known to maintain stemness.

Conclusions

Non-luminal-like types are all representatives of aggressive disease, and this could be attributed to the similarity in overall gene expression to stem and progenitor cell types.

Embryonal carcinoma cell induction of miRNA and mRNA changes in co-cultured prostate stromal fibromuscular cells

The prostate stromal mesenchyme controls organ-specific development. In cancer, the stromal compartment shows altered gene expression compared to non-cancer. The lineage relationship between cancer-associated stromal cells and normal tissue stromal cells is not known. Nor is the cause underlying the expression difference. Previously, the embryonal carcinoma (EC) cell line, NCCIT, was used by us to study the stromal induction property. In the current study, stromal cells from non-cancer (NP) and cancer (CP) were isolated from tissue specimens and co-cultured with NCCIT cells in a trans-well format to preclude heterotypic cell contact. After 3 days, the stromal cells were analyzed by gene arrays for microRNA (miRNA) and mRNA expression. In co-culture, NCCIT cells were found to alter the miRNA and mRNA expression of NP stromal cells to one like that of CP stromal cells. In contrast, NCCIT had no significant effect on the gene expression of CP stromal cells. We conclude that the gene expression changes in stromal cells can be induced by diffusible factors synthesized by EC cells, and suggest that cancer-associated stromal cells represent a more primitive or less differentiated stromal cell type.

LabKey Server: an open source platform for scientific data integration, analysis and collaboration

BACKGROUND

Broad-based collaborations are becoming increasingly common among disease researchers. For example, the Global HIV Enterprise has united cross-disciplinary consortia to speed progress towards HIV vaccines through coordinated research across the boundaries of institutions, continents and specialties. New, end-to-end software tools for data and specimen management are necessary to achieve the ambitious goals of such alliances. These tools must enable researchers to organize and integrate heterogeneous data early in the discovery process, standardize processes, gain new insights into pooled data and collaborate securely.

RESULTS

To meet these needs, we enhanced the LabKey Server platform, formerly known as CPAS. This freely available, open source software is maintained by professional engineers who use commercially proven practices for software development and maintenance. Recent enhancements support: (i) Submitting specimens requests across collaborating organizations (ii) Graphically defining new experimental data types, metadata and wizards for data collection (iii) Transitioning experimental results from a multiplicity of spreadsheets to custom tables in a shared database (iv) Securely organizing, integrating, analyzing, visualizing and sharing diverse data types, from clinical records to specimens to complex assays (v) Interacting dynamically with external data sources (vi) Tracking study participants and cohorts over time (vii) Developing custom interfaces using client libraries (viii) Authoring custom visualizations in a built-in R scripting environment. Diverse research organizations have adopted and adapted LabKey Server, including consortia within the Global HIV Enterprise. Atlas is an installation of LabKey Server that has been tailored to serve these consortia. It is in production use and demonstrates the core capabilities of LabKey Server. Atlas now has over 2,800 active user accounts originating from approximately 36 countries and 350 organizations. It tracks roughly 27,000 assay runs, 860,000 specimen vials and 1,300,000 vial transfers.

CONCLUSIONS

Sharing data, analysis tools and infrastructure can speed the efforts of large research consortia by enhancing efficiency and enabling new insights. The Atlas installation of LabKey Server demonstrates the utility of the LabKey platform for collaborative research. Stable, supported builds of LabKey Server are freely available for download at http://www.labkey.org. Documentation and source code are available under the Apache License 2.0.

Setting the standards for signal transduction research

Major advances in high-throughput technology platforms, coupled with increasingly sophisticated computational methods for systematic data analysis, have provided scientists with tools to better understand the complexity of signaling networks. In this era of massive and diverse data collection, standardization efforts that streamline data gathering, analysis, storage, and sharing are becoming a necessity. Here, we give an overview of current technologies to study signal transduction. We argue that along with the opportunities the new technologies open, their heterogeneous nature poses critical challenges for data handling that are further increased when data are to be integrated in mathematical models. Efficient standardization through markup languages and data annotation is a sine qua non condition for a systems-level analysis of signaling processes. It remains to be seen the extent to which and the speed at which the emerging standardization efforts will be embraced by the signaling community.

Differential Inductive Signaling of CD90 Prostate Cancer-Associated Fibroblasts Compared to Normal Tissue Stromal Mesenchyme Cells

Prostate carcinomas are surrounded by a layer of stromal fibroblastic cells that are characterized by increased expression of CD90. These CD90⁺ cancer-associated stromal fibroblastic cells differ in gene expression from their normal counterpart, CD49a⁺CD90^lo stromal smooth muscle cells; and were postulated to represent a less differentiated cell type with altered inductive properties. CD90⁺ stromal cells were isolated from tumor tissue specimens and co-cultured with the pluripotent embryonal carcinoma cell line NCCIT in order to elucidate the impact of tumor-associated stroma on stem cells, and the ‘cancer stem cell.’ Transcriptome analysis identified a notable decreased induction of smooth muscle and prostate stromal genes such as PENK, BMP2 and ChGn compared to previously determined NCCIT response to normal prostate stromal cell induction. CD90⁺ stromal cell secreted factors induced an increased expression of CD90 and differential induction of genes involved in extracellular matrix remodeling and the RECK pathway in NCCIT. These results suggest that, compared to normal tissue stromal cells, signaling from cancer-associated stromal cells has a markedly different effect on stem cells as represented by NCCIT. Given that stromal cells are important in directing organ-specific differentiation, stromal cells in tumors appear to be defective in this function, which may contribute to abnormal differentiation found in diseases such as cancer.

Software pipeline and data analysis for MS/MS proteomics: the trans-proteomic pipeline

The LC-MS/MS shotgun proteomics workflow is widely used to identify and quantify sample peptides and proteins. The technique, however, presents a number of challenges for large-scale use, including the diverse raw data file formats output by mass spectrometers, the large false positive rate among peptide assignments to MS/MS spectra, and the loss of connectivity between identified peptides and the sample proteins that gave rise to them. Here we describe the Trans-Proteomic Pipeline, a freely available open source software suite that provides uniform analysis of LC-MS/MS data from raw data to quantified sample proteins. In a straightforward manner, users can extract MS/MS information from raw data of many instrument formats, submit them to search engines for peptide identification, validate the results to remove false hits, combine together results of multiple search engines, infer sample proteins that gave rise to the identified peptides, and perform quantitation at the peptide and protein levels.

Two transcription factors are necessary for iron homeostasis in a salt-dwelling archaeon

Because iron toxicity and deficiency are equally life threatening, maintaining intracellular iron levels within a narrow optimal range is critical for nearly all known organisms. However, regulatory mechanisms that establish homeostasis are not well understood in organisms that dwell in environments at the extremes of pH, temperature, and salinity. Under conditions of limited iron, the extremophile Halobacterium salinarum, a salt-loving archaeon, mounts a specific response to scavenge iron for growth. We have identified and characterized the role of two transcription factors (TFs), Idr1 and Idr2, in regulating this important response. An integrated systems analysis of TF knockout gene expression profiles and genome-wide binding locations in the presence and absence of iron has revealed that these TFs operate collaboratively to maintain iron homeostasis. In the presence of iron, Idr1 and Idr2 bind near each other at 24 loci in the genome, where they are both required to repress some genes. By contrast, Idr1 and Idr2 are both necessary to activate other genes in a putative a feed forward loop. Even at loci bound independently, the two TFs target different genes with similar functions in iron homeostasis. We discuss conserved and unique features of the Idr1-Idr2 system in the context of similar systems in organisms from other domains of life.

MRMaid-DB: a repository of published SRM transitions

Selected reaction monitoring (SRM) is a technique that applies tandem mass spectrometry to quantify specific proteins of biological interest. The key to SRM is finding the best peptide-to-product ion transitions to monitor. The MRMaid database (MRMaid-DB) is a new online database for capturing SRM transitions from published research papers to save practitioners time when searching for transitions that have been previously validated. It contains all the information needed to reproduce the transitions, such as information on the sample matrix, HPLC, and MS instrumentation used, and also includes details of the manuscript of origin. Transitions are submitted using simple Web-based data entry forms, meaning researchers have a simple way to increase access to their transitions, and in turn, may increase the citations for their research papers. MRMaid-DB is free to use, via the Web at www.mrmaid-db.info .

A guided tour of the Trans-Proteomic Pipeline

The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein-level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.

The PeptideAtlas Project

PeptideAtlas is a multi-species compendium of peptides observed with tandem mass spectrometry methods. Raw mass spectrometer output files are collected from the community and reprocessed through a uniform analysis and validation pipeline that continues to advance. The results are loaded into a database and the information derived from the raw data is returned to the community via several web-based data exploration tools. The PeptideAtlas resource is useful for experiment planning, improving genome annotation, and other data mining projects. PeptideAtlas has become especially useful for planning targeted proteomics experiments.

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.

BASE--2nd generation software for microarray data management and analysis

BACKGROUND

Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2.

RESULTS

The new BASE presented in this report is a comprehensive annotable local microarray data repository and analysis application providing researchers with an efficient information management and analysis tool. The information management system tracks all material from biosource, via sample and through extraction and labelling to raw data and analysis. All items in BASE can be annotated and the annotations can be used as experimental factors in downstream analysis. BASE stores all microarray experiment related data regardless if analysis tools for specific techniques or data formats are readily available. The BASE team is committed to continue improving and extending BASE to make it usable for even more experimental setups and techniques, and we encourage other groups to target their specific needs leveraging on the infrastructure provided by BASE.

CONCLUSION

BASE is a comprehensive management application for information, data, and analysis of microarray experiments, available as free open source software at http://base.thep.lu.se under the terms of the GPLv3 license.

Gene expression down-regulation in CD90+ prostate tumor-associated stromal cells involves potential organ-specific genes

Background

The prostate stroma is a key mediator of epithelial differentiation and development, and potentially plays a role in the initiation and progression of prostate cancer. The tumor-associated stroma is marked by increased expression of CD90/THY1. Isolation and characterization of these stromal cells could provide valuable insight into the biology of the tumor microenvironment.

Methods

Prostate CD90⁺ stromal fibromuscular cells from tumor specimens were isolated by cell-sorting and analyzed by DNA microarray. Dataset analysis was used to compare gene expression between histologically normal and tumor-associated stromal cells. For comparison, stromal cells were also isolated and analyzed from the urinary bladder.

Results

The tumor-associated stromal cells were found to have decreased expression of genes involved in smooth muscle differentiation, and those detected in prostate but not bladder. Other differential expression between the stromal cell types included that of the CXC-chemokine genes.

Conclusion

CD90⁺ prostate tumor-associated stromal cells differed from their normal counterpart in expression of multiple genes, some of which are potentially involved in organ development.

Prediction and experimental validation of novel STAT3 target genes in human cancer cells

The comprehensive identification of functional transcription factor binding sites (TFBSs) is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs.

Temporal expression profiling of the effects of secreted factors from prostate stromal cells on embryonal carcinoma stem cells

BACKGROUND

There is a growing body of evidence indicating that epigenetic influences originating from stromal cells in the immediate microenvironment may play a role in carcinogenesis. Determining the molecular mechanisms involved in stromal-stem cell interaction could provide critical insight into prostate development and disease progression, particularly with regard to their relationship to and influence on the putative cancer stem cell.

METHODS

Prostate and bladder stromal cells prepared from tissue specimens were co-cultured with the pluripotent embryonal carcinoma cell line NCCIT. Transcriptome analysis was used to characterize NCCIT cell response to prostate or bladder signaling.

RESULTS

A systems approach demonstrated that prostate stromal cells were capable of inducing gene expression changes in NCCIT through secreted factors. Induction led to a loss of embryonic stem cell markers, with concurrent up-regulation of many genes characteristic of stromal mesenchyme cells as well as some of epithelial and cancer stem cells. Bladder stromal signaling produced gene expression changes different from those of prostate signaling.

CONCLUSIONS

This study indicates that paracrine stromal cell signaling can affect cancer stem cell response in an organ-specific manner and may provide insight for future development of treatment strategies such as differentiation therapy.

Systems biology of innate immunity

Systems biology is the comprehensive and quantitative analysis of the interactions between all of the components of biological systems over time. Systems biology involves an iterative cycle, in which emerging biological problems drive the development of new technologies and computational tools. These technologies and tools then open new frontiers that revolutionize biology. Innate immunity is well suited for systems analysis, because the relevant cells can be isolated in various functional states and their interactions can be reconstituted in a biologically meaningful manner. Application of the tools of systems biology to the innate immune system will enable comprehensive analysis of the complex interactions that maintain the difficult balance between host defense and inflammatory disease. In this review, we discuss innate immunity in the context of the systems biology concepts, emergence, robustness, and modularity, and we describe emerging technologies we are applying in our systems-level analyses. These technologies include genomics, proteomics, computational analysis, forward genetics screens, and analyses that link human genetic polymorphisms to disease resistance.

A single transcription factor regulates evolutionarily diverse but functionally linked metabolic pathways in response to nutrient availability

During evolution, enzyme-coding genes are acquired and/or replaced through lateral gene transfer and compiled into metabolic pathways. Gene regulatory networks evolve to fine tune biochemical fluxes through such metabolic pathways, enabling organisms to acclimate to nutrient fluctuations in a competitive environment. Here, we demonstrate that a single TrmB family transcription factor in Halobacterium salinarum NRC-1 globally coordinates functionally linked enzymes of diverse phylogeny in response to changes in carbon source availability. Specifically, during nutritional limitation, TrmB binds a cis-regulatory element to activate or repress 113 promoters of genes encoding enzymes in diverse metabolic pathways. By this mechanism, TrmB coordinates the expression of glycolysis, TCA cycle, and amino-acid biosynthesis pathways with the biosynthesis of their cognate cofactors (e.g. purine and thiamine). Notably, the TrmB-regulated metabolic network includes enzyme-coding genes that are uniquely archaeal as well as those that are conserved across all three domains of life. Simultaneous analysis of metabolic and gene regulatory network architectures suggests an ongoing process of co-evolution in which TrmB integrates the expression of metabolic enzyme-coding genes of diverse origins.

MIMAS 3.0 is a Multiomics Information Management and Annotation System

Background

DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types.

Description

MIMAS was originally conceived as a simple, convenient and local Microarray Information Management and Annotation System focused on GeneChips for expression profiling studies. MIMAS 3.0 enables users to manage data from high-density oligonucleotide SNP Chips, expression arrays (both 3'UTR and tiling) and promoter arrays, BeadArrays as well as UHTS data using MIAME-compliant standardized vocabulary. Importantly, researchers can export data in MAGE-TAB format and upload them to the EBI's ArrayExpress certified data repository using a one-step procedure.

Conclusion

We have vastly extended the capability of the system such that it processes the data output of six types of GeneChips (Affymetrix), two different BeadArrays for mRNA and miRNA (Illumina) and the Genome Analyzer (a popular Ultra-High Throughput DNA Sequencer, Illumina), without compromising on its flexibility and user-friendliness. MIMAS, appropriately renamed into Multiomics Information Management and Annotation System, is currently used by scientists working in approximately 50 academic laboratories and genomics platforms in Switzerland and France. MIMAS 3.0 is freely available via .

Systems biology approaches and pathway tools for investigating cardiovascular disease

Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine how they evolve with temporal and environmental changes. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies, such as inflammatory-related diseases, herein exemplified by atherosclerosis. In this paper, the initial studies in this discipline are reviewed and examined within the context of the development of the field. In addition, several different software tools are briefly described and a novel application for the KEGG database suite called KegArray is presented. This tool is designed for mapping the results of high-throughput omics studies, including transcriptomics, proteomics and metabolomics data, onto interactive KEGG metabolic pathways. The utility of KegArray is demonstrated using a combined transcriptomics and lipidomics dataset from a published study designed to examine the potential of cholesterol in the diet to influence the inflammatory component in the development of atherosclerosis. These data were mapped onto the KEGG PATHWAY database, with a low cholesterol diet affecting 60 distinct biochemical pathways and a high cholesterol exposure affecting 76 biochemical pathways. A total of 77 pathways were differentially affected between low and high cholesterol diets. The KEGG pathways "Biosynthesis of unsaturated fatty acids" and "Sphingolipid metabolism" evidenced multiple changes in gene/lipid levels between low and high cholesterol treatment, and are discussed in detail. Taken together, this paper provides a brief introduction to systems biology and the applications of pathway mapping to the study of cardiovascular disease, as well as a summary of available tools. Current limitations and future visions of this emerging field are discussed, with the conclusion that combining knowledge from biological pathways and high-throughput omics data will move clinical medicine one step further to individualize medical diagnosis and treatment.

MiMiR--an integrated platform for microarray data sharing, mining and analysis

Background

Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data.

Results

A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package.

Conclusion

The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies.

Systems biology driven software design for the research enterprise

Background

In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.

Results

We illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.

Conclusion

By using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.

PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows

A crucial part of a successful systems biology experiment is an assay that provides reliable, quantitative measurements for each of the components in the system being studied. For proteomics to be a key part of such studies, it must deliver accurate quantification of all the components in the system for each tested perturbation without any gaps in the data. This will require a new approach to proteomics that is based on emerging targeted quantitative mass spectrometry techniques. The PeptideAtlas Project comprises a growing, publicly accessible database of peptides identified in many tandem mass spectrometry proteomics studies and software tools that allow the building of PeptideAtlas, as well as its use by the research community. Here, we describe the PeptideAtlas Project, its contents and components, and show how together they provide a unique platform to select and validate mass spectrometry targets, thereby allowing the next revolution in proteomics.

Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics

Data processing is a central and critical component of a successful proteomics experiment, and is often the most time-consuming step. There have been considerable advances in the field of proteomics informatics in the past 5 years, spurred mainly by free and open-source software tools. Along with the gains afforded by new software, the benefits of making raw data and processed results freely available to the community in data repositories are finally in evidence. In this review, we provide an overview of the general analysis approaches, software tools, and repositories that are enabling successful proteomics research via tandem mass spectrometry.

The urologic epithelial stem cell database (UESC) - a web tool for cell type-specific gene expression and immunohistochemistry images of the prostate and bladder

BACKGROUND

Public databases are crucial for analysis of high-dimensional gene and protein expression data. The Urologic Epithelial Stem Cells (UESC) database http://scgap.systemsbiology.net/ is a public database that contains gene and protein information for the major cell types of the prostate, prostate cancer cell lines, and a cancer cell type isolated from a primary tumor. Similarly, such information is available for urinary bladder cell types.

DESCRIPTION

Two major data types were archived in the database, protein abundance localization data from immunohistochemistry images, and transcript abundance data principally from DNA microarray analysis. Data results were organized in modules that were made to operate independently but built upon a core functionality. Gene array data and immunostaining images for human and mouse prostate and bladder were made available for interrogation. Data analysis capabilities include: (1) CD (cluster designation) cell surface protein data. For each cluster designation molecule, a data summary allows easy retrieval of images (at multiple magnifications). (2) Microarray data. Single gene or batch search can be initiated with Affymetrix Probeset ID, Gene Name, or Accession Number together with options of coalescing probesets and/or replicates.

CONCLUSION

Databases are invaluable for biomedical research, and their utility depends on data quality and user friendliness. UESC provides for database queries and tools to examine cell type-specific gene expression (normal vs. cancer), whereas most other databases contain only whole tissue expression datasets. The UESC database provides a valuable tool in the analysis of differential gene expression in prostate cancer genes in cancer progression.

The anatomy of microbial cell state transitions in response to oxygen

Adjustment of physiology in response to changes in oxygen availability is critical for the survival of all organisms. However, the chronology of events and the regulatory processes that determine how and when changes in environmental oxygen tension result in an appropriate cellular response is not well understood at a systems level. Therefore, transcriptome, proteome, ATP, and growth changes were analyzed in a halophilic archaeon to generate a temporal model that describes the cellular events that drive the transition between the organism's two opposing cell states of anoxic quiescence and aerobic growth. According to this model, upon oxygen influx, an initial burst of protein synthesis precedes ATP and transcription induction, rapidly driving the cell out of anoxic quiescence, culminating in the resumption of growth. This model also suggests that quiescent cells appear to remain actively poised for energy production from a variety of different sources. Dynamic temporal analysis of relationships between transcription and translation of key genes suggests several important mechanisms for cellular sustenance under anoxia as well as specific instances of post-transcriptional regulation.

Advances in proteomic workflows for systems biology

Mass spectrometry, specifically the analysis of complex peptide mixtures by liquid chromatography and tandem mass spectrometry (shotgun proteomics) has been at the centre of proteomics research for the past decade. To overcome some of the fundamental limitations of the approach, including its limited sensitivity and high degree of redundancy, new proteomic workflows are being developed. Among these, targeting methods in which specific peptides are selectively isolated, identified and quantified are particularly promising. Here we summarize recent incremental advances in shotgun proteomic methods and outline emerging targeted workflows. The development of the target-driven approaches with their ability to detect and quantify identical, non-redundant sets of proteins in multiple repeat analyses will be crucially important for the application of proteomics to biomarker discovery and validation, and to systems biology research.

Public proteomic MS repositories and pipelines: available tools and biological applications

As proteomic MS has increased in throughput, so has the demand to catalogue the increasing number of peptides and proteins observed by the community using this technique. As in other 'omics' fields, this brings obvious scientific benefits such as sharing of results and prevention of unnecessary repetition, but also provides technical insights, such as the ability to compare proteome coverage between different laboratories, or between different proteomic platforms. Journals are also moving towards mandating that proteomics data be submitted to public repositories upon publication. In response to these demands, several web-based repositories have been established to store protein and peptide identifications derived from MS data, and a similar number of peptide identification software pipelines have emerged to deliver identifications to these repositories. This paper reviews the latest developments in public domain peptide and protein identification databases and describes the analysis pipelines that feed them. Recent applications of the tools to pertinent biological problems are examined, and through comparing and contrasting the capabilities of each system, the issues facing research users of web-based repositories are explored. Future developments and mechanisms to enhance system functionality and user-interfacing opportunities are also suggested.

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.

Computational prediction of proteotypic peptides for quantitative proteomics

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

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.