The expression of tubulin cofactor A (TBCA) is regulated by a noncoding antisense Tbca RNA during testis maturation

Abstract - Recently, long noncoding RNAs have emerged as pivotal molecules for the regulation of coding genes' expression. These molecules might result from antisense transcription of functional genes originating natural antisense transcripts (NATs) or from transcriptional active pseudogenes. TBCA interacts with β-tubulin and is involved in the folding and dimerization of new tubulin heterodimers, the building blocks of microtubules. Methodology/Principal findings: We found that the mouse genome contains two structurally distinct Tbca genes located in chromosomes 13 (Tbca13) and 16 (Tbca16). Interestingly, the two Tbca genes albeit ubiquitously expressed, present differential expression during mouse testis maturation. In fact, as testis maturation progresses Tbca13 mRNA levels increase progressively, while Tbca16 mRNA levels decrease. This suggests a regulatory mechanism between the two genes and prompted us to investigate the presence of the two proteins. However, using tandem mass spectrometry we were unable to identify the TBCA16 protein in testis extracts even in those corresponding to the maturation step with the highest levels of Tbca16 transcripts. These puzzling results led us to re-analyze the expression of Tbca16. We then detected that Tbca16 transcription produces sense and natural antisense transcripts. Strikingly, the specific depletion by RNAi of these transcripts leads to an increase of Tbca13 transcript levels in a mouse spermatocyte cell line. Conclusions/Significance: Our results demonstrate that Tbca13 mRNA levels are post-transcriptionally regulated by the sense and natural antisense Tbca16 mRNA levels. We propose that this regulatory mechanism operates during spermatogenesis, a process that involves microtubule rearrangements, the assembly of specific microtubule structures and requires critical TBCA levels.

Data analytics in the cloud with flexible mapreduced workflows

Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.