Gene Expression Profiling and Association Studies Implicate the Neuregulin Signaling Pathway in Behçet's Disease Susceptibility

Behçet's disease (BD) is a complex disease with genetic and environmental risk factors implicated in its etiology; however, its pathophysiology is poorly understood. To decipher BD's genetic underpinnings, we combined gene expression profiling with pathway analysis and association studies. We compared the gene expression profiles in peripheral blood mononuclear cells (PBMCs) of 15 patients and 14 matched controls using Affymetrix microarrays and found that the neuregulin signaling pathway was over-represented among the differentially expressed genes. The Epiregulin (EREG), Amphiregulin (AREG), and Neuregulin-1 (NRG1) genes of this pathway stand out as they are also among the top differentially expressed genes. Twelve haplotype tagging SNPs at the EREG-AREG locus and 15 SNPs in NRG1 found associated in at least one published BD genome-wide association study were tested for association with BD in a dataset of 976 Iranian patients and 839 controls. We found a novel association with BD for the rs6845297 SNP located downstream of EREG, and replicated three associations at NRG1 (rs4489285, rs383632, and rs1462891). Multifactor dimensionality reduction analysis indicated the existence of epistatic interactions between EREG and NRG1 variants. EREG-AREG and NRG1, which are members of the epidermal growth factor (EGF) family, seem to modulate BD susceptibility through main effects and gene–gene interactions. These association findings support a role for the EGF/ErbB signaling pathway inBD pathogenesis that warrants further investigation and highlight the importance of combining genetic and genomic approaches to dissect the genetic architecture of complex diseases.

Reprogramming energy metabolism and inducing angiogenesis : co-expression of monocarboxylate transporters with VEGF family members in cervical adenocarcinomas

Reprogramming energy metabolism and inducing angiogenesis: co-expression of monocarboxylate transporters with VEGF family members in cervical adenocarcinomas.

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275

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.

EuroDia: a beta-cell gene expression resource.

Type 2 diabetes mellitus (T2DM) is a major disease affecting nearly 280 million people worldwide. Whilst the pathophysiological mechanisms leading to disease are poorly understood, dysfunction of the insulin-producing pancreatic beta-cells is key event for disease development. Monitoring the gene expression profiles of pancreatic beta-cells under several genetic or chemical perturbations has shed light on genes and pathways involved in T2DM. The EuroDia database has been established to build a unique collection of gene expression measurements performed on beta-cells of three organisms, namely human, mouse and rat. The Gene Expression Data Analysis Interface (GEDAI) has been developed to support this database. The quality of each dataset is assessed by a series of quality control procedures to detect putative hybridization outliers. The system integrates a web interface to several standard analysis functions from R/Bioconductor to identify differentially expressed genes and pathways. It also allows the combination of multiple experiments performed on different array platforms of the same technology. The design of this system enables each user to rapidly design a custom analysis pipeline and thus produce their own list of genes and pathways. Raw and normalized data can be downloaded for each experiment. The flexible engine of this database (GEDAI) is currently used to handle gene expression data from several laboratory-run projects dealing with different organisms and platforms. Database URL: http://eurodia.vital-it.ch.

Messenger RNA expression of transporter and ion channel genes in undifferentiated and differentiated Caco-2 cells compared to human intestines.

PURPOSE: The purpose of this work was to study the influence of cell differentiation on the mRNA expression of transporters and channels in Caco-2 cells and to assess Caco-2 cells as a model for carrier-mediated drug transport in the intestines. METHOD: Gene mRNA expression was measured using a custom-designed microarray chip with 750 deoxyoligonucleotide probes (70mers). Each oligomer was printed four times on poly-lysine-coated glass slides. Expression profiles were expressed as ratio values between fluorescence intensities of Cy3 and Cy5 dye-labeled cDNA derived from poly(A) + RNA samples of Caco-2 cells and total RNA of human intestines. RESULTS: Significant differences in the mRNA expression profile of transporters and channels were observed upon differentiation of Caco-2 cells from 5 days to 2 weeks in culture, including changes for MAT8, S-protein, and Nramp2. Comparing Caco-2 cells of different passage number revealed few changes in mRNAs except for GLUT3, which was down-regulated 2.4-fold within 13 passage numbers. Caco-2 cells had a similar expression profile when either cultured in flasks or on filters but differed more strongly from human small and large intestine, regardless of the differentiation state of Caco-2 cells. Expression of several genes highly transcribed in small or large intestines differed fourfold or more in Caco-2 cells. CONCLUSIONS: Although Caco-2 cells have proven a suitable model for studying carrier-mediated transport in human intestines, the expression of specific transporter and ion channel genes may differ substantially.

Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes.

The development of targeted treatment strategies adapted to individual patients requires identification of the different tumor classes according to their biology and prognosis. We focus here on the molecular aspects underlying these differences, in terms of sets of genes that control pathogenesis of the different subtypes of astrocytic glioma. By performing cDNA-array analysis of 53 patient biopsies, comprising low-grade astrocytoma, secondary glioblastoma (respective recurrent high-grade tumors), and newly diagnosed primary glioblastoma, we demonstrate that human gliomas can be differentiated according to their gene expression. We found that low-grade astrocytoma have the most specific and similar expression profiles, whereas primary glioblastoma exhibit much larger variation between tumors. Secondary glioblastoma display features of both other groups. We identified several sets of genes with relatively highly correlated expression within groups that: (a). can be associated with specific biological functions; and (b). effectively differentiate tumor class. One prominent gene cluster discriminating primary versus nonprimary glioblastoma comprises mostly genes involved in angiogenesis, including VEGF fms-related tyrosine kinase 1 but also IGFBP2, that has not yet been directly linked to angiogenesis. In situ hybridization demonstrating coexpression of IGFBP2 and VEGF in pseudopalisading cells surrounding tumor necrosis provided further evidence for a possible involvement of IGFBP2 in angiogenesis. The separating groups of genes were found by the unsupervised coupled two-way clustering method, and their classification power was validated by a supervised construction of a nearly perfect glioma classifier.

Modular analysis of gene expression data with R.

SUMMARY: Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called 'modules'. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different 'resolutions' of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY: http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch

Small nucleolar RNA expression profiling identifies potential prognostic markers in peripheral T-cell lymphoma.

Peripheral T-cell lymphoma (PTCL) is a rare, heterogeneous type of non-Hodgkin lymphoma (NHL) that, in general, is associated with a poor clinical outcome. Therefore, a current major challenge is the discovery of new prognostic tools for this disease. In the present study, a cohort of 122 patients with PTCL was collected from a multicentric T-cell lymphoma consortium (TENOMIC). We analyzed the expression of 80 small nucleolar RNAs (snoRNAs) using high-throughput quantitative PCR. We demonstrate that snoRNA expression analysis may be useful in both the diagnosis of some subtypes of PTCL and the prognostication of both PTCL-not otherwise specified (PTCL-NOS; n = 26) and angio-immunoblastic T-cell lymphoma (AITL; n = 46) patients treated with chemotherapy. Like miRNAs, snoRNAs are globally down-regulated in tumor cells compared with their normal counterparts. In the present study, the snoRNA signature was robust enough to differentiate anaplastic large cell lymphoma (n = 32) from other PTCLs. For PTCL-NOS and AITL, we obtained 2 distinct prognostic signatures with a reduced set of 3 genes. Of particular interest was the prognostic value of HBII-239 snoRNA, which was significantly over-expressed in cases of AITL and PTCL-NOS that had favorable outcomes. Our results suggest that snoRNA expression profiles may have a diagnostic and prognostic significance for PTCL, offering new tools for patient care and follow-up.

Genome-wide mRNA expression correlates of viral control in CD4+ T-cells from HIV-1-infected individuals.

There is great interindividual variability in HIV-1 viral setpoint after seroconversion, some of which is known to be due to genetic differences among infected individuals. Here, our focus is on determining, genome-wide, the contribution of variable gene expression to viral control, and to relate it to genomic DNA polymorphism. RNA was extracted from purified CD4+ T-cells from 137 HIV-1 seroconverters, 16 elite controllers, and 3 healthy blood donors. Expression levels of more than 48,000 mRNA transcripts were assessed by the Human-6 v3 Expression BeadChips (Illumina). Genome-wide SNP data was generated from genomic DNA using the HumanHap550 Genotyping BeadChip (Illumina). We observed two distinct profiles with 260 genes differentially expressed depending on HIV-1 viral load. There was significant upregulation of expression of interferon stimulated genes with increasing viral load, including genes of the intrinsic antiretroviral defense. Upon successful antiretroviral treatment, the transcriptome profile of previously viremic individuals reverted to a pattern comparable to that of elite controllers and of uninfected individuals. Genome-wide evaluation of cis-acting SNPs identified genetic variants modulating expression of 190 genes. Those were compared to the genes whose expression was found associated with viral load: expression of one interferon stimulated gene, OAS1, was found to be regulated by a SNP (rs3177979, p = 4.9E-12); however, we could not detect an independent association of the SNP with viral setpoint. Thus, this study represents an attempt to integrate genome-wide SNP signals with genome-wide expression profiles in the search for biological correlates of HIV-1 control. It underscores the paradox of the association between increasing levels of viral load and greater expression of antiviral defense pathways. It also shows that elite controllers do not have a fully distinctive mRNA expression pattern in CD4+ T cells. Overall, changes in global RNA expression reflect responses to viral replication rather than a mechanism that might explain viral control.

Effects of ploidy and sex-locus genotype on gene expression patterns in the fire ant Solenopsis invicta.

Males in many animal species differ greatly from females in morphology, physiology and behaviour. Ants, bees and wasps have a haplodiploid mechanism of sex determination whereby unfertilized eggs become males while fertilized eggs become females. However, many species also have a low frequency of diploid males, which are thought to develop from diploid eggs when individuals are homozygous at one or more sex determination loci. Diploid males are morphologically similar to haploids, though often larger and typically sterile. To determine how ploidy level and sex-locus genotype affect gene expression during development, we compared expression patterns between diploid males, haploid males and females (queens) at three developmental timepoints in Solenopsis invicta. In pupae, gene expression profiles of diploid males were very different from those of haploid males but nearly identical to those of queens. An unexpected shift in expression patterns emerged soon after adult eclosion, with diploid male patterns diverging from those of queens to resemble those of haploid males, a pattern retained in older adults. The finding that ploidy level effects on early gene expression override sex effects (including genes implicated in sperm production and pheromone production/perception) may explain diploid male sterility and lack of worker discrimination against them during development.

Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.

BACKGROUND: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profiles of breast cancers and whether such profiles could be used to improve histologic grading. METHODS: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. RESULTS: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P < .001, log-rank test). CONCLUSIONS: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value.

Expression profiling of rectal tumors defines response to neoadjuvant treatment related genes.

To date, no effective method exists that predicts the response to preoperative chemoradiation (CRT) in locally advanced rectal cancer (LARC). Nevertheless, identification of patients who have a higher likelihood of responding to preoperative CRT could be crucial in decreasing treatment morbidity and avoiding expensive and time-consuming treatments. The aim of this study was to identify signatures or molecular markers related to response to pre-operative CRT in LARC. We analyzed the gene expression profiles of 26 pre-treatment biopsies of LARC (10 responders and 16 non-responders) without metastasis using Human WG CodeLink microarray platform. Two hundred and fifty seven genes were differentially over-expressed in the responder patient subgroup. Ingenuity Pathway Analysis revealed a significant ratio of differentially expressed genes related to cancer, cellular growth and proliferation pathways, and c-Myc network. We demonstrated that high Gng4, c-Myc, Pola1, and Rrm1 mRNA expression levels was a significant prognostic factor for response to treatment in LARC patients (p<0.05). Using this gene set, we were able to establish a new model for predicting the response to CRT in rectal cancer with a sensitivity of 60% and 100% specificity. Our results reflect the value of gene expression profiling to gain insight about the molecular pathways involved in the response to treatment of LARC patients. These findings could be clinically relevant and support the use of mRNA levels when aiming to identify patients who respond to CRT therapy.

Glucose-dependent insulinotropic polypeptide (GIP) and its receptor (GIPR): cellular localization, lesion-affected expression, and impaired regenerative axonal growth.

Glucose-dependent insulinotropic polypeptide (GIP) was initially described to be rapidly regulated by endocrine cells in response to nutrient ingestion, with stimulatory effects on insulin synthesis and release. Previously, we demonstrated a significant up-regulation of GIP mRNA in the rat subiculum after fornix injury. To gain more insight into the lesion-induced expression of GIP and its receptor (GIPR), expression profiles of the mRNAs were studied after rat sciatic nerve crush injury in 1) affected lumbar dorsal root ganglia (DRG), 2) spinal cord segments, and 3) proximal and distal nerve fragments by means of quantitative RT-PCR. Our results clearly identified lesion-induced as well as tissue type-specific mRNA regulation of GIP and its receptor. Furthermore, comprehensive immunohistochemical stainings not only confirmed and exceeded the previous observation of neuronal GIP expression but also revealed corresponding GIPR expression, implying putative modulatory functions of GIP/GIPR signaling in adult neurons. In complement, we also observed expression of GIP and its receptor in myelinating Schwann cells and oligodendrocytes. Polarized localization of GIPR in the abaxonal Schwann cell membranes, plasma membrane-associated GIPR expression of satellite cells, and ependymal GIPR expression strongly suggests complex cell type-specific functions of GIP and GIPR in the adult nervous system that are presumably mediated by autocrine and paracrine interactions, respectively. Notably, in vivo analyses with GIPR-deficient mice suggest a critical role of GIP/GIPR signal transduction in promoting spontaneous recovery after nerve crush, insofar as traumatic injury of GIPR-deficient mouse sciatic nerve revealed impaired axonal regeneration compared with wild-type mice.

Cross-species and cross-platform gene expression studies with the Bioconductor-compliant R package 'annotationTools'.

Background: The variety of DNA microarray formats and datasets presently available offers an unprecedented opportunity to perform insightful comparisons of heterogeneous data. Cross-species studies, in particular, have the power of identifying conserved, functionally important molecular processes. Validation of discoveries can now often be performed in readily available public data which frequently requires cross-platform studies.Cross-platform and cross-species analyses require matching probes on different microarray formats. This can be achieved using the information in microarray annotations and additional molecular biology databases, such as orthology databases. Although annotations and other biological information are stored using modern database models ( e. g. relational), they are very often distributed and shared as tables in text files, i.e. flat file databases. This common flat database format thus provides a simple and robust solution to flexibly integrate various sources of information and a basis for the combined analysis of heterogeneous gene expression profiles.Results: We provide annotationTools, a Bioconductor-compliant R package to annotate microarray experiments and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file databases. First, annotationTools contains a specialized set of functions for mining this widely used database format in a systematic manner. It thus offers a straightforward solution for annotating microarray experiments. Second, building on these basic functions and relying on the combination of information from several databases, it provides tools to easily perform cross-species analyses of gene expression data.Here, we present two example applications of annotationTools that are of direct relevance for the analysis of heterogeneous gene expression profiles, namely a cross-platform mapping of probes and a cross-species mapping of orthologous probes using different orthology databases. We also show how to perform an explorative comparison of disease-related transcriptional changes in human patients and in a genetic mouse model.Conclusion: The R package annotationTools provides a simple solution to handle microarray annotation and orthology tables, as well as other flat molecular biology databases. Thereby, it allows easy integration and analysis of heterogeneous microarray experiments across different technological platforms or species.