Novel Algorithms Reveal Streptococcal Transcriptomes and Clues about Undefined Genes

Bacteria–host interactions are dynamic processes, and understanding transcriptional responses that directly or indirectly regulate the expression of genes involved in initial infection stages would illuminate the molecular events that result in host colonization. We used oligonucleotide microarrays to monitor (in vitro) differential gene expression in group A streptococci during pharyngeal cell adherence, the first overt infection stage. We present neighbor clustering, a new computational method for further analyzing bacterial microarray data that combines two informative characteristics of bacterial genes that share common function or regulation: (1) similar gene expression profiles (i.e., co-expression); and (2) physical proximity of genes on the chromosome. This method identifies statistically significant clusters of co-expressed gene neighbors that potentially share common function or regulation by coupling statistically analyzed gene expression profiles with the chromosomal position of genes. We applied this method to our own data and to those of others, and we show that it identified a greater number of differentially expressed genes, facilitating the reconstruction of more multimeric proteins and complete metabolic pathways than would have been possible without its application. We assessed the biological significance of two identified genes by assaying deletion mutants for adherence in vitro and show that neighbor clustering indeed provides biologically relevant data. Neighbor clustering provides a more comprehensive view of the molecular responses of streptococci during pharyngeal cell adherence

Identifying mRNA targets of microRNA dysregulated in cancer: with application to clear cell Renal Cell Carcinoma

BACKGROUND. MicroRNA regulate mRNA levels in a tissue specific way, either by inducing degradation of the transcript or by inhibiting translation or transcription. Putative mRNA targets of microRNA identified from seed sequence matches are available in many databases. However, such matches have a high false positive rate and cannot identify tissue specificity of regulation. RESULTS. We describe a simple method to identify direct mRNA targets of microRNA dysregulated in cancers from expression level measurements in patient matched tumor/normal samples. The word "direct" is used here in a strict sense to: a) represent mRNA which have an exact seed sequence match to the microRNA in their 3'UTR, b) the seed sequence match is strictly conserved across mouse, human, rat and dog genomes, c) the mRNA and microRNA expression levels can distinguish tumor from normal with high significance and d) the microRNA/mRNA expression levels are strongly and significantly anti-correlated in tumor and/or normal samples. We apply and validate the method using clear cell Renal Cell Carcinoma (ccRCC) and matched normal kidney samples, limiting our analysis to mRNA targets which undergo degradation of the mRNA transcript because of a perfect seed sequence match. Dysregulated microRNA and mRNA are first identified by comparing their expression levels in tumor vs normal samples. Putative dysregulated microRNA/mRNA pairs are identified from these using seed sequence matches, requiring that the seed sequence be conserved in human/dog/rat/mouse genomes. These are further pruned by requiring a strong anti-correlation signature in tumor and/or normal samples. The method revealed many new regulations in ccRCC. For instance, loss of miR-149, miR-200c and mir-141 causes gain of function of oncogenes (KCNMA1, LOX), VEGFA and SEMA6A respectively and increased levels of miR-142-3p, miR-185, mir-34a, miR-224, miR-21 cause loss of function of tumor suppressors LRRC2, PTPN13, SFRP1, ERBB4, and (SLC12A1, TCF21) respectively. We also found strong anti-correlation between VEGFA and the miR-200 family of microRNA: miR-200a*, 200b, 200c and miR-141. Several identified microRNA/mRNA pairs were validated on an independent set of matched ccRCC/normal samples. The regulation of SEMA6A by miR-141 was verified by a transfection assay. CONCLUSIONS. We describe a simple and reliable method to identify direct gene targets of microRNA in any cancer. The constraints we impose (strong dysregulation signature for microRNA and mRNA levels between tumor/normal samples, evolutionary conservation of seed sequence and strong anti-correlation of expression levels) remove spurious matches and identify a subset of robust, tissue specific, functional mRNA targets of dysregulated microRNA.Cancer Institute of New Jersy; New Jersey Commission for Cacner Research; Lineberger Comprehensive Cancer Center Tissue Procurement and Genomics Core Facility; Crawford Fun

Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips

Interlaboratory comparison of microarray data, even when using the same platform, imposes several challenges to scientists. RNA quality, RNA labeling efficiency, hybridization procedures and data-mining tools can all contribute variations in each laboratory. In Affymetrix GeneChips, about 11–20 different 25-mer oligonucleotides are used to measure the level of each transcript. Here, we report that ‘labeling extension values (LEVs)’, which are correlation coefficients between probe intensities and probe positions, are highly correlated with the gene expression levels (GEVs) on eukayotic Affymetrix microarray data. By analyzing LEVs and GEVs in the publicly available 2414 cel files of 20 Affymetrix microarray types covering 13 species, we found that correlations between LEVs and GEVs only exist in eukaryotic RNAs, but not in prokaryotic ones. Surprisingly, Affymetrix results of the same specimens that were analyzed in different laboratories could be clearly differentiated only by LEVs, leading to the identification of ‘laboratory signatures’. In the examined dataset, GSE10797, filtering out high-LEV genes did not compromise the discovery of biological processes that are constructed by differentially expressed genes. In conclusion, LEVs provide a new filtering parameter for microarray analysis of gene expression and it may improve the inter- and intralaboratory comparability of Affymetrix GeneChips data

Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability

Clinical progression of colorectal cancers (CRC) may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence.Studies were performed on normal mucosa, adenoma, and carcinoma samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data was generated using two normalization algorithms: MAS5.0 and GCRMA with least-variant set (LVS). The data was evaluated using pair-wise comparisons and data decomposition into singular value decomposition (SVD) modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Expressional profiles obtained in 105 samples of whole tissue sections were used to establish oncogenic signaling alterations in progression of CRC, while those representing 40 microdissected specimens were used to select differences in KEGG pathways between epithelium and mucosa. Based on a consensus of the results obtained by two normalization algorithms, and two probe set sorting criteria, we identified 14 and 17 KEGG signaling and metabolic pathways that are significantly altered between normal and tumor samples and between benign and malignant tumors, respectively. Several of them were also selected from the raw microarray data of 2 recently published studies (GSE4183 and GSE8671).Although the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results

Identification of regulatory targets of tissue-specific transcription factors: application to retina-specific gene regulation

Identification of tissue-specific gene regulatory networks can yield insights into the molecular basis of a tissue's development, function and pathology. Here, we present a computational approach designed to identify potential regulatory target genes of photoreceptor cell-specific transcription factors (TFs). The approach is based on the hypothesis that genes related to the retina in terms of expression, disease and/or function are more likely to be the targets of retina-specific TFs than other genes. A list of genes that are preferentially expressed in retina was obtained by integrating expressed sequence tag, SAGE and microarray datasets. The regulatory targets of retina-specific TFs are enriched in this set of retina-related genes. A Bayesian approach was employed to integrate information about binding site location relative to a gene's transcription start site. Our method was applied to three retina-specific TFs, CRX, NRL and NR2E3, and a number of potential targets were predicted. To experimentally assess the validity of the bioinformatic predictions, mobility shift, transient transfection and chromatin immunoprecipitation assays were performed with five predicted CRX targets, and the results were suggestive of CRX regulation in 5/5, 3/5 and 4/5 cases, respectively. Together, these experiments strongly suggest that RP1, GUCY2D, ABCA4 are novel targets of CRX