RNA interference-mediated knockdown of CD49e (α5 integrin chain) in human thymic epithelial cells modulates the expression of multiple genes and decreases thymocyte adhesion

Abstract Background The thymus is a central lymphoid organ, in which bone marrow-derived T cell precursors undergo a complex process of maturation. Developing thymocytes interact with thymic microenvironment in a defined spatial order. A component of thymic microenvironment, the thymic epithelial cells, is crucial for the maturation of T-lymphocytes through cell-cell contact, cell matrix interactions and secretory of cytokines/chemokines. There is evidence that extracellular matrix molecules play a fundamental role in guiding differentiating thymocytes in both cortical and medullary regions of the thymic lobules. The interaction between the integrin α5β1 (CD49e/CD29; VLA-5) and fibronectin is relevant for thymocyte adhesion and migration within the thymic tissue. Our previous results have shown that adhesion of thymocytes to cultured TEC line is enhanced in the presence of fibronectin, and can be blocked with anti-VLA-5 antibody. Results Herein, we studied the role of CD49e expressed by the human thymic epithelium. For this purpose we knocked down the CD49e by means of RNA interference. This procedure resulted in the modulation of more than 100 genes, some of them coding for other proteins also involved in adhesion of thymocytes; others related to signaling pathways triggered after integrin activation, or even involved in the control of F-actin stress fiber formation. Functionally, we demonstrated that disruption of VLA-5 in human TEC by CD49e-siRNA-induced gene knockdown decreased the ability of TEC to promote thymocyte adhesion. Such a decrease comprised all CD4/CD8-defined thymocyte subsets. Conclusion Conceptually, our findings unravel the complexity of gene regulation, as regards key genes involved in the heterocellular cell adhesion between developing thymocytes and the major component of the thymic microenvironment, an interaction that is a mandatory event for proper intrathymic T cell differentiation.

Trypanosoma cruzi mitochondrial maxicircles display species- and strain-specific variation and a conserved element in the non-coding region

Abstract Background The mitochondrial DNA of kinetoplastid flagellates is distinctive in the eukaryotic world due to its massive size, complex form and large sequence content. Comprised of catenated maxicircles that contain rRNA and protein-coding genes and thousands of heterogeneous minicircles encoding small guide RNAs, the kinetoplast network has evolved along with an extreme form of mRNA processing in the form of uridine insertion and deletion RNA editing. Many maxicircle-encoded mRNAs cannot be translated without this post-transcriptional sequence modification. Results We present the complete sequence and annotation of the Trypanosoma cruzi maxicircles for the CL Brener and Esmeraldo strains. Gene order is syntenic with Trypanosoma brucei and Leishmania tarentolae maxicircles. The non-coding components have strain-specific repetitive regions and a variable region that is unique for each strain with the exception of a conserved sequence element that may serve as an origin of replication, but shows no sequence identity with L. tarentolae or T. brucei. Alternative assemblies of the variable region demonstrate intra-strain heterogeneity of the maxicircle population. The extent of mRNA editing required for particular genes approximates that seen in T. brucei. Extensively edited genes were more divergent among the genera than non-edited and rRNA genes. Esmeraldo contains a unique 236-bp deletion that removes the 5'-ends of ND4 and CR4 and the intergenic region. Esmeraldo shows additional insertions and deletions outside of areas edited in other species in ND5, MURF1, and MURF2, while CL Brener has a distinct insertion in MURF2. Conclusion The CL Brener and Esmeraldo maxicircles represent two of three previously defined maxicircle clades and promise utility as taxonomic markers. Restoration of the disrupted reading frames might be accomplished by strain-specific RNA editing. Elements in the non-coding region may be important for replication, transcription, and anchoring of the maxicircle within the kinetoplast network.

Androgen responsive intronic non-coding RNAs

Abstract Background Transcription of large numbers of non-coding RNAs originating from intronic regions of human genes has been recently reported, but mechanisms governing their biosynthesis and biological functions are largely unknown. In this work, we evaluated the existence of a common mechanism of transcription regulation shared by protein-coding mRNAs and intronic RNAs by measuring the effect of androgen on the transcriptional profile of a prostate cancer cell line. Results Using a custom-built cDNA microarray enriched in intronic transcribed sequences, we found 39 intronic non-coding RNAs for which levels were significantly regulated by androgen exposure. Orientation-specific reverse transcription-PCR indicated that 10 of the 13 were transcribed in the antisense direction. These transcripts are long (0.5–5 kb), unspliced and apparently do not code for proteins. Interestingly, we found that the relative levels of androgen-regulated intronic transcripts could be correlated with the levels of the corresponding protein-coding gene (asGAS6 and asDNAJC3) or with the alternative usage of exons (asKDELR2 and asITGA6) in the corresponding protein-coding transcripts. Binding of the androgen receptor to a putative regulatory region upstream from asMYO5A, an androgen-regulated antisense intronic transcript, was confirmed by chromatin immunoprecipitation. Conclusion Altogether, these results indicate that at least a fraction of naturally transcribed intronic non-coding RNAs may be regulated by common physiological signals such as hormones, and further corroborate the notion that the intronic complement of the transcriptome play functional roles in the human gene-expression program.

Identification of protein-coding and non-coding RNA expression profiles in CD34+ and in stromal cells in refractory anemia with ringed sideroblasts

Abstract Background Myelodysplastic syndromes (MDS) are a group of clonal hematological disorders characterized by ineffective hematopoiesis with morphological evidence of marrow cell dysplasia resulting in peripheral blood cytopenia. Microarray technology has permitted a refined high-throughput mapping of the transcriptional activity in the human genome. Non-coding RNAs (ncRNAs) transcribed from intronic regions of genes are involved in a number of processes related to post-transcriptional control of gene expression, and in the regulation of exon-skipping and intron retention. Characterization of ncRNAs in progenitor cells and stromal cells of MDS patients could be strategic for understanding gene expression regulation in this disease. Methods In this study, gene expression profiles of CD34+ cells of 4 patients with MDS of refractory anemia with ringed sideroblasts (RARS) subgroup and stromal cells of 3 patients with MDS-RARS were compared with healthy individuals using 44 k combined intron-exon oligoarrays, which included probes for exons of protein-coding genes, and for non-coding RNAs transcribed from intronic regions in either the sense or antisense strands. Real-time RT-PCR was performed to confirm the expression levels of selected transcripts. Results In CD34+ cells of MDS-RARS patients, 216 genes were significantly differentially expressed (q-value ≤ 0.01) in comparison to healthy individuals, of which 65 (30%) were non-coding transcripts. In stromal cells of MDS-RARS, 12 genes were significantly differentially expressed (q-value ≤ 0.05) in comparison to healthy individuals, of which 3 (25%) were non-coding transcripts. Conclusions These results demonstrated, for the first time, the differential ncRNA expression profile between MDS-RARS and healthy individuals, in CD34+ cells and stromal cells, suggesting that ncRNAs may play an important role during the development of myelodysplastic syndromes.

Neural coding tools, based on Information Theory, applied to discrete time series: from electrophysiology to neuroethology

This work is supported by Brazilian agencies Fapesp, CAPES and CNPq

The complexity of narrative interferes in the use of conjunctions in children with specific language impairment

PURPOSE: To verify the use of conjunctions in narratives, and to investigate the influence of stimuli's complexity over the type of conjunctions used by children with specific language impairment (SLI) and children with typical language development. METHODS: Participants were 40 children (20 with typical language development and 20 with SLI) with ages between 7 and 10 years, paired by age range. Fifteen stories with increasing of complexity were used to obtain the narratives; stories were classified into mechanical, behavioral and intentional, and each of them was represented by four scenes. Narratives were analyzed according to occurrence and classification of conjunctions. RESULTS: Both groups used more coordinative than subordinate conjunctions, with significant decrease in the use of conjunctions in the discourse of SLI children. The use of conjunctions varied according to the type of narrative: for coordinative conjunctions, both groups differed only between intentional and behavioral narratives, with higher occurrence in behavioral ones; for subordinate conjunctions, typically developing children's performance did not show differences between narratives, while SLI children presented fewer occurrences in intentional narratives, which was different from other narratives. CONCLUSION: Both groups used more coordinative than subordinate conjunctions; however, typically developing children presented more conjunctions than SLI children. The production of children with SLI was influenced by stimulus, since more complex narratives has less use of subordinate conjunctions.

Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provade a very Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that interferences can be performed in time linear in the number of nodes if there is a single observed node. Because our proof is construtive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynominal-time algorithm for SQPn. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.