Genome-Wide Gene Expression Profiling of Fertilization Competent Mycelium in Opposite Mating Types in the Heterothallic Fungus Podospora anserina

are the major regulators of fertilization, and this study presents a genome-wide view of their target genes and analyzes their target gene regulation. strains. Of the 167 genes identified, 32 genes were selected for deletion, which resulted in the identification of two genes essential for the sexual cycle. Interspecies comparisons of mating-type target genes revealed significant numbers of orthologous pairs, although transcriptional profiles were not conserved between species.This study represents the first comprehensive genome-wide analysis of mating-type direct and indirect target genes in a heterothallic filamentous fungus. Mating-type transcription factors have many more target genes than are found in yeasts and exert a much greater diversity of regulatory actions on target genes, most of which are not directly related to mating

A Functional and Regulatory Network Associated with PIP Expression in Human Breast Cancer

BACKGROUND: The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes. PRINCIPAL FINDINGS: Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP-] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters. CONCLUSIONS: Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator

Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces

While it is universally accepted that intact RNA constitutes the best representation of the steady-state of transcription, there is no gold standard to define RNA quality prior to gene expression analysis. In this report, we evaluated the reliability of conventional methods for RNA quality assessment including UV spectroscopy and 28S:18S area ratios, and demonstrated their inconsistency. We then used two new freely available classifiers, the Degradometer and RIN systems, to produce user-independent RNA quality metrics, based on analysis of microcapillary electrophoresis traces. Both provided highly informative and valuable data and the results were found highly correlated, while the RIN system gave more reliable data. The relevance of the RNA quality metrics for assessment of gene expression differences was tested by Q-PCR, revealing a significant decline of the relative expression of genes in RNA samples of disparate quality, while samples of similar, even poor integrity were found highly comparable. We discuss the consequences of these observations to minimize artifactual detection of false positive and negative differential expression due to RNA integrity differences, and propose a scheme for the development of a standard operational procedure, with optional registration of RNA integrity metrics in public repositories of gene expression data

A general framework for optimization of probes for gene expression microarray and its application to the fungus Podospora anserina

<p>Abstract</p> <p>Background</p> <p>The development of new microarray technologies makes custom long oligonucleotide arrays affordable for many experimental applications, notably gene expression analyses. Reliable results depend on probe design quality and selection. Probe design strategy should cope with the limited accuracy of <it>de novo </it>gene prediction programs, and annotation up-dating. We present a novel <it>in silico </it>procedure which addresses these issues and includes experimental screening, as an empirical approach is the best strategy to identify optimal probes in the <it>in silico </it>outcome.</p> <p>Findings</p> <p>We used four criteria for <it>in silico </it>probe selection: cross-hybridization, hairpin stability, probe location relative to coding sequence end and intron position. This latter criterion is critical when exon-intron gene structure predictions for intron-rich genes are inaccurate. For each coding sequence (CDS), we selected a sub-set of four probes. These probes were included in a test microarray, which was used to evaluate the hybridization behavior of each probe. The best probe for each CDS was selected according to three experimental criteria: signal-to-noise ratio, signal reproducibility, and representative signal intensities. This procedure was applied for the development of a gene expression Agilent platform for the filamentous fungus <it>Podospora anserina </it>and the selection of a single 60-mer probe for each of the 10,556 <it>P. anserina </it>CDS.</p> <p>Conclusions</p> <p>A reliable gene expression microarray version based on the Agilent 44K platform was developed with four spot replicates of each probe to increase statistical significance of analysis.</p

Deciphering cellular states of innate tumor drug responses

BACKGROUND: The molecular mechanisms underlying innate tumor drug resistance, a major obstacle to successful cancer therapy, remain poorly understood. In colorectal cancer (CRC), molecular studies have focused on drug-selected tumor cell lines or individual candidate genes using samples derived from patients already treated with drugs, so that very little data are available prior to drug treatment. RESULTS: Transcriptional profiles of clinical samples collected from CRC patients prior to their exposure to a combined chemotherapy of folinic acid, 5-fluorouracil and irinotecan were established using microarrays. Vigilant experimental design, power simulations and robust statistics were used to restrain the rates of false negative and false positive hybridizations, allowing successful discrimination between drug resistance and sensitivity states with restricted sampling. A list of 679 genes was established that intrinsically differentiates, for the first time prior to drug exposure, subsequently diagnosed chemo-sensitive and resistant patients. Independent biological validation performed through quantitative PCR confirmed the expression pattern on two additional patients. Careful annotation of interconnected functional networks provided a unique representation of the cellular states underlying drug responses. CONCLUSION: Molecular interaction networks are described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses. These broad-spectrum cellular signatures represent a starting point from which by-pass chemotherapy schemes, targeting simultaneously several of the molecular mechanisms involved, may be developed for critical therapeutic intervention in CRC patients. The demonstrated power of this research strategy makes it generally applicable to other physiological and pathological situations

DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma

Epigenetic deregulation has emerged as a driver in human malignancies. There is no clear understanding of the epigenetic alterations in hepatocellular carcinoma (HCC) and of the potential role of DNA methylation markers as prognostic biomarkers. Analysis of tumor tissue from 304 patients with HCC treated with surgical resection allowed us to generate a methylation-based prognostic signature using a training-validation scheme. Methylome profiling was done with the Illumina HumanMethylation450 array (Illumina, Inc., San Diego, CA), which covers 96% of known cytosine-phosphate-guanine (CpG) islands and 485,000 CpG, and transcriptome profiling was performed with Affymetrix Human Genome U219 Plate (Affymetrix, Inc., Santa Clara, CA) and miRNA Chip 2.0. Random survival forests enabled us to generate a methylation signature based on 36 methylation probes. We computed a risk score of mortality for each individual that accurately discriminated patient survival both in the training (221 patients; 47% hepatitis C-related HCC) and validation sets (n = 83; 47% alcohol-related HCC). This signature correlated with known predictors of poor outcome and retained independent prognostic capacity of survival along with multinodularity and platelet count. The subset of patients identified by this signature was enriched in the molecular subclass of proliferation with progenitor cell features. The study confirmed a high prevalence of genes known to be deregulated by aberrant methylation in HCC (e.g., Ras association [RalGDS/AF-6] domain family member 1, insulin-like growth factor 2, and adenomatous polyposis coli) and other solid tumors (e.g., NOTCH3) and describes potential candidate epidrivers (e.g., septin 9 and ephrin B2). Conclusions: A validated signature of 36 DNA methylation markers accurately predicts poor survival in patients with HCC. Patients with this methylation profile harbor messenger RNA-based signatures indicating tumors with progenitor cell features

The Human Anatomic Gene Expression Library (H-ANGEL), the H-Inv integrative display of human gene expression across disparate technologies and platforms

The Human Anatomic Gene Expression Library (H-ANGEL) is a resource for information concerning the anatomical distribution and expression of human gene transcripts. The tool contains protein expression data from multiple platforms that has been associated with both manually annotated full-length cDNAs from H-InvDB and RefSeq sequences. Of the H-Inv predicted genes, 18 897 have associated expression data generated by at least one platform. H-ANGEL utilizes categorized mRNA expression data from both publicly available and proprietary sources. It incorporates data generated by three types of methods from seven different platforms. The data are provided to the user in the form of a web-based viewer with numerous query options. H-ANGEL is updated with each new release of cDNA and genome sequence build. In future editions, we will incorporate the capability for expression data updates from existing and new platforms. H-ANGEL is accessible at http://www.jbirc.aist.go.jp/hinv/h-angel/

Unique genomic profile of fibrolamellar hepatocellular carcinoma

BACKGROUND & AIMS: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer that develops in children and young adults without cirrhosis. Little is known about its pathogenesis, and it can be treated only with surgery. We performed an integrative genomic analysis of a large series of patients with FLC to identify associated genetic factors. METHODS: By using 78 clinically annotated FLC samples, we performed whole-transcriptome (n = 58), single-nucleotide polymorphism array (n = 41), and next-generation sequencing (n = 48) analyses; we also assessed the prevalence of the DNAJB1-PRKACA fusion transcript associated with this cancer (n = 73). We performed class discovery using non-negative matrix factorization, and functional annotation using gene-set enrichment analyses, nearest template prediction, ingenuity pathway analyses, and immunohistochemistry. The genomic identification of significant targets in a cancer algorithm was used to identify chromosomal aberrations, MuTect and VarScan2 were used to identify somatic mutations, and the random survival forest was used to determine patient prognoses. Findings were validated in an independent cohort. RESULTS: Unsupervised gene expression clustering showed 3 robust molecular classes of tumors: the proliferation class (51% of samples) had altered expression of genes that regulate proliferation and mammalian target of rapamycin signaling activation; the inflammation class (26% of samples) had altered expression of genes that regulate inflammation and cytokine enriched production; and the unannotated class (23% of samples) had a gene expression signature that was not associated previously with liver tumors. Expression of genes that regulate neuroendocrine function, as well as histologic markers of cholangiocytes and hepatocytes, were detected in all 3 classes. FLCs had few copy number variations; the most frequent were focal amplification at 8q24.3 (in 12.5% of samples), and deletions at 19p13 (in 28% of samples) and 22q13.32 (in 25% of samples). The DNAJB1-PRKACA fusion transcript was detected in 79% of samples. FLC samples also contained mutations in cancer-related genes such as BRCA2 (in 4.2% of samples), which are uncommon in liver neoplasms. However, FLCs did not contain mutations most commonly detected in liver cancers. We identified an 8-gene signature that predicted survival of patients with FLC. CONCLUSIONS: In a genomic analysis of 78 FLC samples, we identified 3 classes based on gene expression profiles. FLCs contain mutations and chromosomal aberrations not previously associated with liver cancer, and almost 80% contain the DNAJB1-PRKACA fusion transcript. By using this information, we identified a gene signature that is associated with patient survival time