Detecting transcriptionally active regions using genomic tiling arrays

We have developed a method for interpreting genomic tiling array data, implemented as the program TranscriptionDetector. Probed loci expressed above background are identified by combining replicates in a way that makes minimal assumptions about the data. We performed medium-resolution Anopheles gambiae tiling array experiments and found extensive transcription of both coding and non-coding regions. Our method also showed improved detection of transcriptional units when applied to high-density tiling array data for ten human chromosomes

Glutamine supports pancreatic cancer growth through a Kras-regulated metabolic pathway

Cancer cells exhibit metabolic dependencies that distinguish them from their normal counterparts1. Among these addictions is an increased utilization of the amino acid glutamine (Gln) to fuel anabolic processes2. Indeed, the spectrum of Gln-dependent tumors and the mechanisms whereby Gln supports cancer metabolism remain areas of active investigation. Here we report the identification of a non-canonical pathway of Gln utilization in human pancreatic ductal adenocarcinoma (PDAC) cells that is required for tumor growth. While most cells utilize glutamate dehydrogenase (GLUD1) to convert Gln-derived glutamate (Glu) into α-ketoglutarate in the mitochondria to fuel the tricarboxylic acid (TCA) cycle, PDAC relies on a distinct pathway to fuel the TCA cycle such that Gln-derived aspartate is transported into the cytoplasm where it can be converted into oxaloacetate (OAA) by aspartate transaminase (GOT1). Subsequently, this OAA is converted into malate and then pyruvate, ostensibly increasing the NADPH/NADP+ ratio which can potentially maintain the cellular redox state. Importantly, PDAC cells are strongly dependent on this series of reactions, as Gln deprivation or genetic inhibition of any enzyme in this pathway leads to an increase in reactive oxygen species and a reduction in reduced glutathione. Moreover, knockdown of any component enzyme in this series of reactions also results in a pronounced suppression of PDAC growth in vitro and in vivo. Furthermore, we establish that the reprogramming of Gln metabolism is mediated by oncogenic Kras, the signature genetic alteration in PDAC, via the transcriptional upregulation and repression of key metabolic enzymes in this pathway. The essentiality of this pathway in PDAC and the fact that it is dispensable in normal cells may provide novel therapeutic approaches to treat these refractory tumors

Genomic analysis of estrogen cascade reveals histone variant H2A.Z associated with breast cancer progression

We demonstrate an integrated approach to the study of a transcriptional regulatory cascade involved in the progression of breast cancer and we identify a protein associated with disease progression. Using chromatin immunoprecipitation and genome tiling arrays, whole genome mapping of transcription factor-binding sites was combined with gene expression profiling to identify genes involved in the proliferative response to estrogen (E2). Using RNA interference, selected ERα and c-MYC gene targets were knocked down to identify mediators of E2-stimulated cell proliferation. Tissue microarray screening revealed that high expression of an epigenetic factor, the E2-inducible histone variant H2A.Z, is significantly associated with lymph node metastasis and decreased breast cancer survival. Detection of H2A.Z levels independently increased the prognostic power of biomarkers currently in clinical use. This integrated approach has accelerated the identification of a molecule linked to breast cancer progression, has implications for diagnostic and therapeutic interventions, and can be applied to a wide range of cancers

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. SIGNIFICANCE: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880.publishedVersionPeer reviewe

DBSubLoc: database of protein subcellular localization

We have built a protein subcellular localization annotation database, the DBSubLoc database, which is available at http://www.bioinfo.tsinghua.edu.cn/dbsubloc.html. Annotations were taken from primary protein databases, model organism genome projects and literature texts, and then were analyzed to dig out the subcellular localization features of the proteins. The proteins are also classified into different categories. Based on sequence alignment, non-redundant subsets of the database have been built, which may provide useful information for subcellular localization prediction. The database now contains >60 000 protein sequences including ∼30 000 protein sequences in the non-redundant data sets. Online download, search and Blast tools are also available