MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula

Background: Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets. Findings: Using our new analysis tool, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips® can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi. Conclusions: MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants.

PR gene families of citrus: their organ specific-biotic and abiotic inducible expression profiles based on ESTs approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Gene expression profile of primary prostate epithelial and stromal cells in response to sulforaphane or iberin exposure

BACKGROUND: Broccoli consumption has been associated with a reduced risk of prostate cancer. Isothiocyanates (ITCs) derived from glucosinolates that accumulate in broccoli are dietary compounds that may mediate these health effects. Sulforaphane (SF, 4-methylsulphinylbutyl ITC) derives from heading broccoli (calabrese) and iberin (IB, 3-methylsulphinypropyl ITC) from sprouting broccoli. While there are many studies regarding the biological activity of SF, mainly undertaken with cancerous cells, there are few studies associated with IB. METHODS: Primary epithelial and stromal cells were derived from benign prostatic hyperplasia tissue. Affymetrix U133 Plus 2.0 whole genome arrays were used to compare global gene expression between these cells, and to quantify changes in gene expression following exposure to physiologically appropriate concentrations of SF and IB. Ontology and pathway analyses were used to interpret results. Changes in expression of a subset of genes were confirmed by real-time RT-PCR. RESULTS: Global gene expression profiling identified epithelial and stromal-specific gene expression profiles. SF induced more changes in epithelial cells, whereas IB was more effective in stromal cells. Although IB and SF induced different changes in gene expression in both epithelial and stromal cells, these were associated with similar pathways, such as cell cycle and detoxification. Both ITCs increased expression of PLAGL1, a tumor suppressor gene, in stromal cells and suppressed expression of the putative tumor promoting genes IFITM1, CSPG2, and VIM in epithelial cells. CONCLUSION: These data suggest that IB and SF both alter genes associated with cancer prevention, and IB should be investigated further as a potential chemopreventative agent.

Development of an eight gene expression profile implicating human breast tumours of all grade

The goal of improving systemic treatment of breast cancers is to evolve from treating every patient with non-specific cytotoxic chemotherapy/hormonal therapy, to a more individually-tailored direct treatment. Although anatomic staging and histological grade are important prognostic factors, they often fail to predict the clinical course of this disease. This study aimed to develop a gene expression profile associated with breast cancers of differing grades. We extracted mRNA from FFPE archival breast IDC tissue samples (Grades I–III), including benign tumours. Affymetrix GeneChip� Human Genome U133 Plus 2.0 Arrays were used to determine gene expression profiles and validated by Q-PCR. IHC was used to detect the AXIN2 protein in all tissues. From the array data, an independent group t-test revealed that 178 genes were significantly (P B 0.01) differentially expressed between three grades of malignant breast tumours when compared to benign tissues. From these results, eight genes were significantly differentially expressed in more than one comparison group and are involved in processes implicated in breast cancer development and/or progression. The two most implicated candidates genes were CLD10 and ESPTI1 as their gene expression profile from the microarray analysis was replicated in Q-PCR analyses of the original tumour samples as well as in an extended population. The IHC revealed a significant association between AXIN2 protein expression and ER status. It is readily acknowledged and established that significant differences exist in gene expression between different cancer grades. Expansion of this approach may lead to an improved ability to discriminate between cancer grade and other pathological factors.

FGFR1 regulated gene-expression, cell proliferation and differentiation in the developing midbrain and hindbrain

The neuroectodermal tissue close to the midbrain hindbrain boundary (MHB) is an important secondary organizer in the developing neural tube. This so-called isthmic organizer (IsO) regulates cellular survival, patterning and proliferation in the midbrain (Mb) and rhombomere 1 (R1) of the hindbrain. Signaling molecules of the IsO, such as fibroblast growth factor 8 (FGF8) and WNT1 are expressed in distinct bands of cells around the MHB. It has been previously shown that FGF-receptor 1 (FGFR1) is required for the normal development of this brain region in the mouse embryo. In the present study, we have compared the gene expression profiles of wild-type and Fgfr1 mutant embryos. We show that the loss of Fgfr1 results in the downregulation of several genes expressed close to the MHB and in the disappearance of gene expression gradients in the midbrain and R1. Our microarray screen identified several previously uncharacterized genes which may participate in the development of midbrain R1 region. Our results also show altered neurogenesis in the midbrain and R1 of the Fgfr1 mutants. Interestingly, the neuronal progenitors in midbrain and R1 show different responses to the loss of signaling through FGFR1. As Wnt1 expression at the MHB region requires the FGF signaling pathway, WNT target genes, including Drapc1, were also identified in our screen. The microarray data analysis also suggested that the cells next to the midbrain hindbrain boundary express distinct cell cycle regulators. We showed that the cells close to the border appeared to have unique features. These cells proliferate less rapidly than the surrounding cells. Unlike the cells further away from the boundary, these cells express Fgfr1 but not the other FGF receptors. The slowly proliferating boundary cells are necessary for development of the characteristic isthmic constriction. They may also contribute to compartmentalization of this brain region.

Gene expression signatures of radiation response are specific, durable and accurate in mice and humans.

BACKGROUND: Previous work has demonstrated the potential for peripheral blood (PB) gene expression profiling for the detection of disease or environmental exposures. METHODS AND FINDINGS: We have sought to determine the impact of several variables on the PB gene expression profile of an environmental exposure, ionizing radiation, and to determine the specificity of the PB signature of radiation versus other genotoxic stresses. Neither genotype differences nor the time of PB sampling caused any lessening of the accuracy of PB signatures to predict radiation exposure, but sex difference did influence the accuracy of the prediction of radiation exposure at the lowest level (50 cGy). A PB signature of sepsis was also generated and both the PB signature of radiation and the PB signature of sepsis were found to be 100% specific at distinguishing irradiated from septic animals. We also identified human PB signatures of radiation exposure and chemotherapy treatment which distinguished irradiated patients and chemotherapy-treated individuals within a heterogeneous population with accuracies of 90% and 81%, respectively. CONCLUSIONS: We conclude that PB gene expression profiles can be identified in mice and humans that are accurate in predicting medical conditions, are specific to each condition and remain highly accurate over time.

Individual mRNA expression profiles reveal the effects of specific microRNAs

BACKGROUND: MicroRNAs (miRNAs) are oligoribonucleotides with an important role in regulation of gene expression at the level of translation. Despite imperfect target complementarity, they can also significantly reduce mRNA levels. The validity of miRNA target gene predictions is difficult to assess at the protein level. We sought, therefore, to determine whether a general lowering of predicted target gene mRNA expression by endogenous miRNAs was detectable within microarray gene expression profiles. RESULTS: The target gene sets predicted for each miRNA were mapped onto known gene expression data from a range of tissues. Whether considering mean absolute target gene expression, rank sum tests or 'ranked ratios', many miRNAs with significantly reduced target gene expression corresponded to those known to be expressed in the cognate tissue. Expression levels of miRNAs with reduced target mRNA levels were higher than those of miRNAs with no detectable effect on mRNA expression. Analysis of microarray data gathered after artificial perturbation of expression of a specific miRNA confirmed the predicted increase or decrease in influence of the altered miRNA upon mRNA levels. Strongest associations were observed with targets predicted by TargetScan. CONCLUSION: We have demonstrated that the effect of a miRNA on its target mRNAs' levels can be measured within a single gene expression profile. This emphasizes the extent of this mode of regulation in vivo and confirms that many of the predicted miRNA-mRNA interactions are correct. The success of this approach has revealed the vast potential for extracting information about miRNA function from gene expression profiles.

sscMap: An extensible Java application for connecting small-molecule drugs using gene-expression signatures

Background
Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures.

Results
This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies.

Conclusion
The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMap

A simple and robust method for connecting small-molecule drugs using gene-expression signatures

Background

Interaction of a drug or chemical with a biological system can result in a gene-expression profile or signature characteristic of the event. Using a suitably robust algorithm these signatures can potentially be used to connect molecules with similar pharmacological or toxicological properties by gene expression profile. Lamb et al first proposed the Connectivity Map [Lamb et al (2006), Science 313, 1929–1935] to make successful connections among small molecules, genes, and diseases using genomic signatures.

Results

Here we have built on the principles of the Connectivity Map to present a simpler and more robust method for the construction of reference gene-expression profiles and for the connection scoring scheme, which importantly allows the valuation of statistical significance of all the connections observed. We tested the new method with two randomly generated gene signatures and three experimentally derived gene signatures (for HDAC inhibitors, estrogens, and immunosuppressive drugs, respectively). Our testing with this method indicates that it achieves a higher level of specificity and sensitivity and so advances the original method.

Conclusion

The method presented here not only offers more principled statistical procedures for testing connections, but more importantly it provides effective safeguard against false connections at the same time achieving increased sensitivity. With its robust performance, the method has potential use in the drug development pipeline for the early recognition of pharmacological and toxicological properties in chemicals and new drug candidates, and also more broadly in other 'omics sciences.

Association of gene expression with sequential proliferation, differentiation and tumor formation in murine skin

Differential gene expression in two established initiation and promotion skin carcinogenesis models during promotion and tumor formation was determined by microarray technology with the purpose of distinguishing the genes more associated with neoplastic transformation from those linked with proliferation and differentiation. The first model utilized dimethylbenz[a]anthracene initiation and 12-O-tetradecanoylphorbol 13-acetate (TPA) promotion in the FVB/N mouse, and the second TPA promotion of the Tg.Ac mouse, which is endogenously initiated by virtue of an activated Ha-ras transgene. Comparison of gene expression profiles across the two models identified genes whose altered expression was associated with papilloma formation rather than TPA-induced proliferation and differentiation. DMBA suppressed TPA-induced differentiation which allowed identification of those genes associated more specifically with differentiation rather than proliferation. EASE (Expression Analysis Systemic Explorer) indicated a correlation between muscle-associated genes and skin differentiation, whereas genes involved with protein biosynthesis were strongly correlated with proliferation. For verification the altered expression of selected genes were confirmed by RT-PCR; Carbonic anhydrase 2, Thioredoxin 1 and Glutathione S-transferase omega 1 associated with papilloma formation and Enolase 3, Cystatin 6 and Filaggrin associated with TPA-induced proliferation and differentiation. In situ analysis located the papillomas Glutathione S-transferase omega 1 expression to the proliferating areas of the papillomas. Thus we have identified profiles of differential gene expression associated with the tumorigenesis and promotion stages for skin carcinogenesis in the mouse.

Application of connectivity mapping in predictive toxicology based on gene-expression similarity.

Connectivity mapping is the process of establishing connections between different biological states using gene-expression profiles or signatures. There are a number of applications but in toxicology the most pertinent is for understanding mechanisms of toxicity. In its essence the process involves comparing a query gene signature generated as a result of exposure of a biological system to a chemical to those in a database that have been previously derived. In the ideal situation the query gene-expression signature is characteristic of the event and will be matched to similar events in the database. Key criteria are therefore the means of choosing the signature to be matched and the means by which the match is made. In this article we explore these concepts with examples applicable to toxicology.

New prognostic markers, determined using gene expression analyses, reveal two distinct subtypes of chronic myelomonocytic leukaemia patients

Chronic myelomonocytic leukaemia (CMML) is a heterogeneous haematopoietic disorder characterized by myeloproliferative or myelodysplastic features. At present, the pathogenesis of this malignancy is not completely understood. In this study, we sought to analyse gene expression profiles of CMML in order to characterize new molecular outcome predictors. A learning set of 32 untreated CMML patients at diagnosis was available for TaqMan low-density array gene expression analysis. From 93 selected genes related to cancer and cell cycle, we built a five-gene prognostic index after multiplicity correction. Using this index, we characterized two categories of patients with distinct overall survival (94% vs. 19% for good and poor overall survival, respectively; P = 0.007) and we successfully validated its strength on an independent cohort of 21 CMML patients with Affymetrix gene expression data. We found no specific patterns of association with traditional prognostic stratification parameters in the learning cohort. However, the poor survival group strongly correlated with high-risk treated patients and transformation to acute myeloid leukaemia. We report here a new multigene prognostic index for CMML, independent of the gene expression measurement method, which could be used as a powerful tool to predict clinical outcome and help physicians to evaluate criteria for treatments.

Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy

BACKGROUND & AIMS:
Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences in patient survival times and responses to various standard-of-care cytotoxic drugs.
METHODS:
We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-fluorouracil, cisplatin, oxaliplatin) was also assessed.
RESULTS:
Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF) that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren's histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-fluorouracil and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-fluorouracil-based therapy.
CONCLUSIONS:
Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.

Single-cell gene-expression profiling reveals qualitatively distinct CD8 T cells elicited by different gene-based vaccines.

CD8 T cells play a key role in mediating protective immunity against selected pathogens after vaccination. Understanding the mechanism of this protection is dependent upon definition of the heterogeneity and complexity of cellular immune responses generated by different vaccines. Here, we identify previously unrecognized subsets of CD8 T cells based upon analysis of gene-expression patterns within single cells and show that they are differentially induced by different vaccines. Three prime-boost vector combinations encoding HIV Env stimulated antigen-specific CD8 T-cell populations of similar magnitude, phenotype, and functionality. Remarkably, however, analysis of single-cell gene-expression profiles enabled discrimination of a majority of central memory (CM) and effector memory (EM) CD8 T cells elicited by the three vaccines. Subsets of T cells could be defined based on their expression of Eomes, Cxcr3, and Ccr7, or Klrk1, Klrg1, and Ccr5 in CM and EM cells, respectively. Of CM cells elicited by DNA prime-recombinant adenoviral (rAd) boost vectors, 67% were Eomes(-) Ccr7(+) Cxcr3(-), in contrast to only 7% and 2% stimulated by rAd5-rAd5 or rAd-LCMV, respectively. Of EM cells elicited by DNA-rAd, 74% were Klrk1(-) Klrg1(-)Ccr5(-) compared with only 26% and 20% for rAd5-rAd5 or rAd5-LCMV. Definition by single-cell gene profiling of specific CM and EM CD8 T-cell subsets that are differentially induced by different gene-based vaccines will facilitate the design and evaluation of vaccines, as well as enable our understanding of mechanisms of protective immunity.

Development and application of in vivo expression technology (IVET) for analysing microbial gene expression in complex environments

Establishing the mechanisms by which microbes interact with their environment, including eukaryotic hosts, is a major challenge that is essential for the economic utilisation of microbes and their products. Techniques for determining global gene expression profiles of microbes, such as microarray analyses, are often hampered by methodological restraints, particularly the recovery of bacterial transcripts (RNA) from complex mixtures and rapid degradation of RNA. A pioneering technology that avoids this problem is In Vivo Expression Technology (IVET). IVET is a 'promoter-trapping' methodology that can be used to capture nearly all bacterial promoters (genes) upregulated during a microbe-environment interaction. IVET is especially useful because there is virtually no limit to the type of environment used (examples to date include soil, oomycete, a host plant or animal) to select for active microbial promoters. Furthermore, IVET provides a powerful method to identify genes that are often overlooked during genomic annotation, and has proven to be a flexible technology that can provide even more information than identification of gene expression profiles. A derivative of IVET, termed resolvase-IVET (RIVET), can be used to provide spatio-temporal information about environment-specific gene expression. More recently, niche-specific genes captured during an IVET screen have been exploited to identify the regulatory mechanisms controlling their expression. Overall, IVET and its various spin-offs have proven to be a valuable and robust set of tools for analysing microbial gene expression in complex environments and providing new targets for biotechnological development.