Leukocytosis and Expression of Bovine Leukemia Virus microRNAs in Cattle.

Bovine Leukemia Virus (BLV) is an established model for studying retroviral infections, in particular the infection by the human T-cell leukemia type 1 (HTLV-1) virus. Here, we quantified gene expression of several BLV-related genes: effector protein of T and NK-killer cells NK-lysin (Nklys), reverse BLV transcriptase pol, BLV receptor (blvr), and also key enzymes of the microRNA maturation, Dicer (dc1) and Argonaut (ago2). The differences in the expression of the above genes were compared between five groups: (1) BLV infected cows with high and (2) low lymphocyte count, (3) with and (4) without BLV microRNA expressions, and (5) cows without BLV infections (control group). As compared to control, infected cows with high lymphocyte count and BLV microRNA expression had significantly decreased Nklys gene expression and increased dc1 and ago2 gene expressions. Few infected animals without pol gene expression nevertheless transcribed BLV microRNA, while others with pol gene expression didn't transcribe BLV microRNA. Notably, Pol expression significantly (P < 0.05) correlated with dc1 expression. For infected animals, there were no direct correlations between the number of leukocytes and pol, Nklys, and BLV microRNA gene expressions. Blvr gene expression is typical for juvenile lymphocytes and decreases during terminal differentiation. Our data suggest that BLV infects primarily juvenile lymphocytes, which further divide into two groups. One group expresses BLV DNA and another one expressed BLV microRNA that decreases host immune response against cells, expressing BLV proteins. It is suspected that regulatory microRNAs play a significant role in the bovine leukemia infections, yet the precise mechanisms and targets of the microRNAs remain poorly defined. Vaccines that are currently in use have a low response rate. Understanding of microRNA regulatory mechanisms and targets would allow to develop more effective vaccines for retroviral infections.

Nucleotide Weight Matrices Reveal Ubiquitous Mutational Footprints of AID/APOBEC Deaminases in Human Cancer Genomes.

Cancer genomes accumulate nucleotide sequence variations that number in the tens of thousands per genome. A prominent fraction of these mutations is thought to arise as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases. These enzymes, collectively called activation induced deaminase (AID)/APOBECs, deaminate cytosines located within defined DNA sequence contexts. The resulting changes of the original C:G pair in these contexts (mutational signatures) provide indirect evidence for the participation of specific cytosine deaminases in a given cancer type. The conventional method used for the analysis of mutable motifs is the consensus approach. Here, for the first time, we have adopted the frequently used weight matrix (sequence profile) approach for the analysis of mutagenesis and provide evidence for this method being a more precise descriptor of mutations than the sequence consensus approach. We confirm that while mutational footprints of APOBEC1, APOBEC3A, APOBEC3B, and APOBEC3G are prominent in many cancers, mutable motifs characteristic of the action of the humoral immune response somatic hypermutation enzyme, AID, are the most widespread feature of somatic mutation spectra attributable to deaminases in cancer genomes. Overall, the weight matrix approach reveals that somatic mutations are significantly associated with at least one AID/APOBEC mutable motif in all studied cancers.

Evasion of regulatory phosphorylation by an alternatively spliced isoform of Musashi2.

The Musashi family of RNA binding proteins act to promote stem cell self-renewal and oppose cell differentiation predominantly through translational repression of mRNAs encoding pro-differentiation factors and inhibitors of cell cycle progression. During tissue development and repair however, Musashi repressor function must be dynamically regulated to allow cell cycle exit and differentiation. The mechanism by which Musashi repressor function is attenuated has not been fully established. Our prior work indicated that the Musashi1 isoform undergoes site-specific regulatory phosphorylation. Here, we demonstrate that the canonical Musashi2 isoform is subject to similar regulated site-specific phosphorylation, converting Musashi2 from a repressor to an activator of target mRNA translation. We have also characterized a novel alternatively spliced, truncated isoform of human Musashi2 (variant 2) that lacks the sites of regulatory phosphorylation and fails to promote translation of target mRNAs. Consistent with a role in opposing cell cycle exit and differentiation, upregulation of Musashi2 variant 2 was observed in a number of cancers and overexpression of the Musashi2 variant 2 isoform promoted cell transformation. These findings indicate that alternately spliced isoforms of the Musashi protein family possess distinct functional and regulatory properties and suggest that differential expression of Musashi isoforms may influence cell fate decisions.

Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients.

BACKGROUND: Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism. However, the analysis of such data is very demanding. In this study, we aimed to establish robust analysis procedures that can be used in clinical practice. METHODS: We studied RNA-seq data from triple-negative breast cancer patients. Specifically, we investigated the subsampling of RNA-seq data. RESULTS: The main results of our investigations are as follows: (1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices; (2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads; and (3) for an abrogated feature selection, higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values. CONCLUSIONS: Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.

Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential.

In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using competitive gene set methods, it is imperative to apply a stringent gene filtering criterion. However, even when genes are filtered appropriately, for gene expression data from chips that do not provide a genome-scale coverage of the expression values of all mRNAs, this is not enough for GSEA, GSEArot and GAGE to ensure the statistical soundness of the applied procedure. For this reason, for biomedical and clinical studies, we strongly advice not to use GSEA, GSEArot and GAGE for such data sets.

Mutational hotspots in the TP53 gene and, possibly, other tumor suppressors evolve by positive selection.

BACKGROUND: The mutation spectra of the TP53 gene and other tumor suppressors contain multiple hotspots, i.e., sites of non-random, frequent mutation in tumors and/or the germline. The origin of the hotspots remains unclear, the general view being that they represent highly mutable nucleotide contexts which likely reflect effects of different endogenous and exogenous factors shaping the mutation process in specific tissues. The origin of hotspots is of major importance because it has been suggested that mutable contexts could be used to infer mechanisms of mutagenesis contributing to tumorigenesis. RESULTS: Here we apply three independent tests, accounting for non-uniform base compositions in synonymous and non-synonymous sites, to test whether the hotspots emerge via selection or due to mutational bias. All three tests consistently indicate that the hotspots in the TP53 gene evolve, primarily, via positive selection. The results were robust to the elimination of the highly mutable CpG dinucleotides. By contrast, only one, the least conservative test reveals the signature of positive selection in BRCA1, BRCA2, and p16. Elucidation of the origin of the hotspots in these genes requires more data on somatic mutations in tumors. CONCLUSION: The results of this analysis seem to indicate that positive selection for gain-of-function in tumor suppressor genes is an important aspect of tumorigenesis, blurring the distinction between tumor suppressors and oncogenes. REVIEWERS: This article was reviewed by Sandor Pongor, Christopher Lee and Mikhail Blagosklonny.

p53 gain-of-function: tumor biology and bioinformatics come together.

p53 is typically viewed as a tumor suppressor. However, many missense somatic and germline mutations in the p53 gene cause gain-of-function whereby p53 acquires novel biochemical activities, such as the ability to transactivate transcription of new genes or to mediate new regulatory protein-protein interactions. Several recent studies show that at least some gain-of-function mutations of p53 are biologically relevant leading to a change in the tumor phenotype. Independent bioinformatic analysis of somatic mutation spectra of the p53 gene yields three lines of evidence supporting the notion that gain-of-function could be the prevalent mode of p53 evolution in tumors. (1) The hotspots in the p53 gene show signs of intensive positive selection. (2) The hotspots are located primarily in functionally important motifs of the DNA-binding domain of p53 which are highly conserved in interspecies evolution. (3) The spectra of hotspots significantly differ among various tumor types and the germline (Li-Fraumeni syndrome); in addition to the hotspots shared by the germline and some of the tumors, many are tumor-specific. The latter observation suggests an unexpected level of complexity of p53 evolution in tumors, with distinct novel function gained in different tumors.

Mutation hotspots in the p53 gene in tumors of different origin: correlation with evolutionary conservation and signs of positive selection.

We present a classification analysis of the mutation spectra of the p53 gene and construct maps of hotspots for the germline (Li-Fraumein syndrome), different types of tumors and their derived cell lines. While spectra from solid tumors share common hotspots with the germline spectrum, they also contain unique sets of somatic hotspots that are not observed in the germline. All these hotspots correspond to amino acid replacements in the DNA-binding interface of p53. The mutation spectra of lymphomas and cell lines derived from lymphomas and lung cancers contained few hotspots compared to solid tumors. Thus, the distribution of hotspots in the p53 gene appears to depend on the tumor type and cell growth conditions; this specificity is missed by the bulk hotspot analysis. A negative correlation was detected between the amino acid replacement propensity in tumors and evolutionary variability: the hotspots are located in the positions that are highly conserved in p53 and its paralogs, p63 and p73. In all the mutation spectra, substitutions leading to amino acid replacements strongly dominate over silent substitutions, indicating that functional sites evolving under strong purifying selection are subject to intensive positive selection in p53-dependent tumors. These results are compatible with the gain-of-function concept of the role of p53 in tumorigenesis.