Conversion of Prostate Adenocarcinoma to Small Cell Carcinoma-Like by Reprogramming.

The lineage relationship between prostate adenocarcinoma and small cell carcinoma was studied by using the LuCaP family of xenografts established from primary neoplasm to metastasis. Expression of four stem cell transcription factor (TF) genes, LIN28A, NANOG, POU5F1, SOX2, were analyzed in the LuCaP lines. These genes, when force expressed in differentiated cells, can reprogram the recipients into stem-like induced pluripotent stem (iPS) cells. Most LuCaP lines expressed POU5F1, while LuCaP 145.1, representative of small cell carcinoma, expressed all four. Through transcriptome database query, many small cell carcinoma genes were also found in stem cells. To test the hypothesis that prostate cancer progression from "differentiated" adenocarcinoma to "undifferentiated" small cell carcinoma could involve re-expression of stem cell genes, the four TF genes were transduced via lentiviral vectors into five adenocarcinoma LuCaP lines-70CR, 73CR, 86.2, 92, 105CR-as done in iPS cell reprogramming. The resultant cells from these five transductions displayed a morphology of small size and dark appearing unlike the parentals. Transcriptome analysis of LuCaP 70CR* ("*" to denote transfected progeny) revealed a unique gene expression close to that of LuCaP 145.1. In a prostate principal components analysis space based on cell-type transcriptomes, the different LuCaP transcriptome datapoints were aligned to suggest a possible ordered sequence of expression changes from the differentiated luminal-like adenocarcinoma cell types to the less differentiated, more stem-like small cell carcinoma types, and LuCaP 70CR*. Prostate cancer progression can thus be molecularly characterized by loss of differentiation with re-expression of stem cell genes. J. Cell. Physiol. 231: 2040-2047, 2016. © 2016 Wiley Periodicals, Inc.

Processing of voided urine for prostate cancer RNA biomarker analysis.

BACKGROUND: Voided urine samples have been shown to contain cells released from prostate tumors. Could good quality RNA from cells in urine be obtained from every donor for multimarker analysis? In addition, could urine donation be as simple as possible, a practical consideration for a lab test, without involving a prostate massage (as indicated for PCA3 testing), which precludes frequent collection; needing it done at a specific time of day (e.g., first or second urine); and requiring prompt processing of samples in clinics with limited molecular biology capability? METHODS: Collected urine samples were pelleted, and the RNA isolated was processed for cDNA synthesis and in vitro transcription to generate amplified sense aRNA. The resultant aRNA was rigorously analyzed for possible introduced changes. DMSO was used as a cell preservative for frozen storage of urine samples. RESULTS: Good quality aRNA was obtained for over 100 samples collected at two different institutions. The process of RNA amplification removed co-isolated DNA in some samples, which did not affect RNA amplification. Amplification did not amplify genes that were absent and produce other expression alterations. The sense aRNA could be used to generate urinary transcriptomes specific to individual patients. No chaotropic agents for RNA preservation were added to the urine samples so that the supernatant could be used for analysis of secreted protein biomarkers. The time of donation was not important since patients were seen during the entire day. DMSO was an effective cell preservative for freezing urine. CONCLUSIONS: Urinary RNA can be readily isolated and amplified for prostate cancer biomarker analysis. Individual patients had unique set of transcripts derived from their tumor.

ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics.

We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography-mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions.

A system-level model for the microbial regulatory genome.

Microbes can tailor transcriptional responses to diverse environmental challenges despite having streamlined genomes and a limited number of regulators. Here, we present data-driven models that capture the dynamic interplay of the environment and genome-encoded regulatory programs of two types of prokaryotes: Escherichia coli (a bacterium) and Halobacterium salinarum (an archaeon). The models reveal how the genome-wide distributions of cis-acting gene regulatory elements and the conditional influences of transcription factors at each of those elements encode programs for eliciting a wide array of environment-specific responses. We demonstrate how these programs partition transcriptional regulation of genes within regulons and operons to re-organize gene-gene functional associations in each environment. The models capture fitness-relevant co-regulation by different transcriptional control mechanisms acting across the entire genome, to define a generalized, system-level organizing principle for prokaryotic gene regulatory networks that goes well beyond existing paradigms of gene regulation. An online resource (http://egrin2.systemsbiology.net) has been developed to facilitate multiscale exploration of conditional gene regulation in the two prokaryotes.