HINCUTs in cancer: hypoxia-induced noncoding ultraconserved transcripts.

Recent data have linked hypoxia, a classic feature of the tumor microenvironment, to the function of specific microRNAs (miRNAs); however, whether hypoxia affects other types of noncoding transcripts is currently unknown. Starting from a genome-wide expression profiling, we demonstrate for the first time a functional link between oxygen deprivation and the modulation of long noncoding transcripts from ultraconserved regions, termed transcribed-ultraconserved regions (T-UCRs). Interestingly, several hypoxia-upregulated T-UCRs, henceforth named 'hypoxia-induced noncoding ultraconserved transcripts' (HINCUTs), are also overexpressed in clinical samples from colon cancer patients. We show that these T-UCRs are predominantly nuclear and that the hypoxia-inducible factor (HIF) is at least partly responsible for the induction of several members of this group. One specific HINCUT, uc.475 (or HINCUT-1) is part of a retained intron of the host protein-coding gene, O-linked N-acetylglucosamine transferase, which is overexpressed in epithelial cancer types. Consistent with the hypothesis that T-UCRs have important function in tumor formation, HINCUT-1 supports cell proliferation specifically under hypoxic conditions and may be critical for optimal O-GlcNAcylation of proteins when oxygen tension is limiting. Our data gives a first glimpse of a novel functional hypoxic network comprising protein-coding transcripts and noncoding RNAs (ncRNAs) from the T-UCRs category.

Estimation of Gene Expression at Isoform Level from mRNA-Seq Data by Bayesian Hierarchical Modeling.

mRNA-Seq is a precise and highly reproducible technique for measurement of transcripts levels and yields sequence information of a transcriptome at a single nucleotide base-level thus enabling us to determine splice junctions and alternative splicing events with high confidence. Often analysis of mRNA-Seq data does not attempt to quantify the expressions at isoform level. In this paper our objective would be use the mRNA-Seq data to infer expression at isoform level, where splicing patterns of a gene is assumed to be known. A Bayesian latent variable based modeling framework is proposed here, where the parameterization enables us to infer at various levels. For example, expression variability of an isoform across different conditions; the model parameterization also allows us to carry out two-sample comparisons, e.g., using a Bayesian t-test, in addition simple presence or absence of an isoform can also be estimated by the use of the latent variables present in the model. In this paper we would carry out inference on isoform expression under different normalization techniques, since it has been recently shown that one of the most prominent sources of variation in differential call using mRNA-Seq data is the normalization method used. The statistical framework is developed for multiple isoforms and easily extends to reads mapping to multiple genes. This could be achieved by slight conceptual modifications in definitions of what we consider as a gene and what as an exon. Additionally proposed framework can be extended by appropriate modeling of the design matrix to infer about yet unknown novel transcripts. However such attempts should be made judiciously since the input date used in the proposed model does not use reads from splice junctions.

RP58/ZNF238 directly modulates proneurogenic gene levels and is required for neuronal differentiation and brain expansion.

Although neurogenic pathways have been described in the developing neocortex, less is known about mechanisms ensuring correct neuronal differentiation thus also preventing tumor growth. We have shown that RP58 (aka zfp238 or znf238) is highly expressed in differentiating neurons, that its expression is lost or diminished in brain tumors, and that its reintroduction blocks their proliferation. Mice with loss of RP58 die at birth with neocortical defects. Using a novel conditional RP58 allele here we show that its CNS-specific loss yields a novel postnatal phenotype: microencephaly, agenesis of the corpus callosum and cerebellar hypoplasia that resembles the chr1qter deletion microcephaly syndrome in human. RP58 mutant brains maintain precursor pools but have reduced neuronal and increased glial differentiation. Well-timed downregulation of pax6, ngn2 and neuroD1 depends on RP58 mediated transcriptional repression, ngn2 and neuroD1 being direct targets. Thus, RP58 may act to favor neuronal differentiation and brain growth by coherently repressing multiple proneurogenic genes in a timely manner.

A microRNA component of the hypoxic response.

microRNAs participate in a wide variety of physiological and pathological cellular processes. Recent studies have established a link between a specific group of microRNAs and hypoxia, a key feature of the neoplastic microenvironment. A significant proportion of the hypoxia-regulated microRNAs (HRMs) are also overexpressed in human cancers, suggesting a role in tumorigenesis. Preliminary evidence suggests that they could affect important processes such as apoptosis, proliferation and angiogenesis. Several HRMs exhibit induction in response to HIF activation, thus extending its repertoire of targets beyond translated genes. In the present review, we discuss the emerging roles of HRMs in oxygen deprivation in cancer context.

ERTargetDB: an integral information resource of transcription regulation of estrogen receptor target genes.

The estrogen receptor (ER) plays an important role in several physiologic functions of both the reproductive and non-reproductive systems. Malignancies of the ER have been associated with the development of cancers, including those of the prostate and breast. Hence it has become of significant importance to characterize the transcriptional regulation of ER target genes. We have created ERTargetDB in order to integrate the previously published ER target gene information that is available in various publications and databases. This information resource provides researchers with an easy access to ER target genes and the regulatory mechanisms in the corresponding promoters. The current version contains 40 genes with experimentally verified estrogen response elements (EREs), 32 experimentally verified ERE tethering sites, 40 genes identified by the chromatin immunoprecipitation microarray, 381 genes from gene expression microarray and 2948 genes from computational prediction. ERTargetDB provides an integral information resource for direct target genes of ERs for the endocrinology research community. It should prove useful in the investigation of gene regulation and aid the development of computational tools for the prediction of ER target genes.

Computational identification of promoters and first exons in the human genome.

The identification of promoters and first exons has been one of the most difficult problems in gene-finding. We present a set of discriminant functions that can recognize structural and compositional features such as CpG islands, promoter regions and first splice-donor sites. We explain the implementation of the discriminant functions into a decision tree that constitutes a new program called FirstEF. By using different models to predict CpG-related and non-CpG-related first exons, we showed by cross-validation that the program could predict 86% of the first exons with 17% false positives. We also demonstrated the prediction accuracy of FirstEF at the genome level by applying it to the finished sequences of human chromosomes 21 and 22 as well as by comparing the predictions with the locations of the experimentally verified first exons. Finally, we present the analysis of the predicted first exons for all of the 24 chromosomes of the human genome.

Identifying the 3'-terminal exon in human DNA.

MOTIVATION: We present JTEF, a new program for finding 3' terminal exons in human DNA sequences. This program is based on quadratic discriminant analysis, a standard non-linear statistical pattern recognition method. The quadratic discriminant functions used for building the algorithm were trained on a set of 3' terminal exons of type 3tuexon (those containing the true STOP codon). RESULTS: We showed that the average predictive accuracy of JTEF is higher than the presently available best programs (GenScan and Genemark.hmm) based on a test set of 65 human DNA sequences with 121 genes. In particular JTEF performs well on larger genomic contigs containing multiple genes and significant amounts of intergenic DNA. It will become a valuable tool for genome annotation and gene functional studies. AVAILABILITY: JTEF is available free for academic users on request from ftp://cshl.org/pub/science/mzhanglab/JTEF and will be made available through the World Wide Web (http://argon.cshl.org/).

CART classification of human 5' UTR sequences.

A nonredundant database of 2312 full-length human 5'-untranslated regions (UTRs) was carefully prepared using state-of-the-art experimental and computational technologies. A comprehensive computational analysis of this data was conducted for characterizing the 5' UTR features. Classification and regression tree (CART) analysis was used to classify the data into three distinct classes. Class I consists of mRNAs that are believed to be poorly translated with long 5' UTRs filled with potential inhibitory features. Class II consists of terminal oligopyrimidine tract (TOP) mRNAs that are regulated in a growth-dependent manner, and class III consists of mRNAs with favorable 5' UTR features that may help efficient translation. The most accurate tree we found has 92.5% classification accuracy as estimated by cross validation. The classification model included the presence of TOP, a secondary structure, 5' UTR length, and the presence of upstream AUGs (uAUGs) as the most relevant variables. The present classification and characterization of the 5' UTRs provide precious information for better understanding the translational regulation of human mRNAs. Furthermore, this database and classification can help people build better computational models for predicting the 5'-terminal exon and separating the 5' UTR from the coding region.