Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs.

AIM: Heart disease is recognized as a consequence of dysregulation of cardiac gene regulatory networks. Previously, unappreciated components of such networks are the long non-coding RNAs (lncRNAs). Their roles in the heart remain to be elucidated. Thus, this study aimed to systematically characterize the cardiac long non-coding transcriptome post-myocardial infarction and to elucidate their potential roles in cardiac homoeostasis. METHODS AND RESULTS: We annotated the mouse transcriptome after myocardial infarction via RNA sequencing and ab initio transcript reconstruction, and integrated genome-wide approaches to associate specific lncRNAs with developmental processes and physiological parameters. Expression of specific lncRNAs strongly correlated with defined parameters of cardiac dimensions and function. Using chromatin maps to infer lncRNA function, we identified many with potential roles in cardiogenesis and pathological remodelling. The vast majority was associated with active cardiac-specific enhancers. Importantly, oligonucleotide-mediated knockdown implicated novel lncRNAs in controlling expression of key regulatory proteins involved in cardiogenesis. Finally, we identified hundreds of human orthologues and demonstrate that particular candidates were differentially modulated in human heart disease. CONCLUSION: These findings reveal hundreds of novel heart-specific lncRNAs with unique regulatory and functional characteristics relevant to maladaptive remodelling, cardiac function and possibly cardiac regeneration. This new class of molecules represents potential therapeutic targets for cardiac disease. Furthermore, their exquisite correlation with cardiac physiology renders them attractive candidate biomarkers to be used in the clinic.

Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps

The role of competition for light among plants has long been recognized at local scales, but its potential importance for plant species' distribution at larger spatial scales has largely been ignored. Tree cover acts as a modulator of local abiotic conditions, notably by reducing light availability below the canopy and thus the performance of species that are not adapted to low-light conditions. However, this local effect may propagate to coarser spatial grains. Using 6,935 vegetation plots located across the European Alps, we fit Generalized Linear Models (GLM) for the distribution of 960 herbs and shrubs species to assess the effect of tree cover at both plot and landscape grain sizes (~ 10-m and 1-km, respectively). We ran four models with different combinations of variables (climate, soil and tree cover) for each species at both spatial grains. We used partial regressions to evaluate the independent effects of plot- and landscape-scale tree cover on plant communities. Finally, the effects on species' elevational range limits were assessed by simulating a removal experiment comparing the species' distribution under high and low tree cover. Accounting for tree cover improved model performance, with shade-tolerant species increasing their probability of presence at high tree cover whereas shade-intolerant species showed the opposite pattern. The tree cover effect occurred consistently at both plot and landscape spatial grains, albeit strongest at the former. Importantly, tree cover at the two grain sizes had partially independent effects on plot-scale plant communities, suggesting that the effects may be transmitted to coarser grains through meta-community dynamics. At high tree cover, shade-intolerant species exhibited elevational range contractions, especially at their upper limit, whereas shade-tolerant species showed elevational range expansions at both limits. Our findings suggest that the range shifts for herb and shrub species may be modulated by tree cover dynamics.

Tissue-Specific Evolution of Protein Coding Genes in Human and Mouse.

Protein-coding genes evolve at different rates, and the influence of different parameters, from gene size to expression level, has been extensively studied. While in yeast gene expression level is the major causal factor of gene evolutionary rate, the situation is more complex in animals. Here we investigate these relations further, especially taking in account gene expression in different organs as well as indirect correlations between parameters. We used RNA-seq data from two large datasets, covering 22 mouse tissues and 27 human tissues. Over all tissues, evolutionary rate only correlates weakly with levels and breadth of expression. The strongest explanatory factors of purifying selection are GC content, expression in many developmental stages, and expression in brain tissues. While the main component of evolutionary rate is purifying selection, we also find tissue-specific patterns for sites under neutral evolution and for positive selection. We observe fast evolution of genes expressed in testis, but also in other tissues, notably liver, which are explained by weak purifying selection rather than by positive selection.

Coarse-graining and thermodynamics in far-from-equilibrium systems

Lying at the core of statistical physics is the need to reduce the number of degrees of freedom in a system. Coarse-graining is a frequently-used procedure to bridge molecular modeling with experiments. In equilibrium systems, this task can be readily performed; however in systems outside equilibrium, a possible lack of equilibration of the eliminated degrees of freedom may lead to incomplete or even misleading descriptions. Here, we present some examples showing how an improper coarse-graining procedure may result in linear approaches to nonlinear processes, miscalculations of activation rates and violations of the fluctuation-dissipation theorem.

Phase mask selection in wavefront coding systems: an adaptive approach

A method for optimizing the strength of a parametric phase mask for a wavefront coding imaging system is presented. The method is based on an optimization process that minimizes a proposed merit function. The goal is to achieve modulation transfer function invariance while quantitatively maintaining nal image delity. A parametric lter that copes with the noise present in the captured images is used to obtain the nal images, and this lter is optimized. The whole process results in optimum phase mask strength and optimal parameters for the restoration lter. The results for a particular optical system are presented and tested experimentally in the labo- ratory. The experimental results show good agreement with the simulations, indicating that the procedure is useful.

Involvement of long non-coding RNAs in beta cell failure at the onset of type 1 diabetes in NOD mice.

AIMS/HYPOTHESIS: Exposure of pancreatic beta cells to cytokines released by islet-infiltrating immune cells induces alterations in gene expression, leading to impaired insulin secretion and apoptosis in the initial phases of type 1 diabetes. Long non-coding RNAs (lncRNAs) are a new class of transcripts participating in the development of many diseases. As little is known about their role in insulin-secreting cells, this study aimed to evaluate their contribution to beta cell dysfunction. METHODS: The expression of lncRNAs was determined by microarray in the MIN6 beta cell line exposed to proinflammatory cytokines. The changes induced by cytokines were further assessed by real-time PCR in islets of control and NOD mice. The involvement of selected lncRNAs modified by cytokines was assessed after their overexpression in MIN6 cells and primary islet cells. RESULTS: MIN6 cells were found to express a large number of lncRNAs, many of which were modified by cytokine treatment. The changes in the level of selected lncRNAs were confirmed in mouse islets and an increase in these lncRNAs was also seen in prediabetic NOD mice. Overexpression of these lncRNAs in MIN6 and mouse islet cells, either alone or in combination with cytokines, favoured beta cell apoptosis without affecting insulin production or secretion. Furthermore, overexpression of lncRNA-1 promoted nuclear translocation of nuclear factor of κ light polypeptide gene enhancer in B cells 1 (NF-κB). CONCLUSIONS/INTERPRETATION: Our study shows that lncRNAs are modulated during the development of type 1 diabetes in NOD mice, and that their overexpression sensitises beta cells to apoptosis, probably contributing to their failure during the initial phases of the disease.

Comprehensive Genomic Characterization of Long Non-coding RNAs across Human Cancers.

The discovery of long non-coding RNA (lncRNA) has dramatically altered our understanding of cancer. Here, we describe a comprehensive analysis of lncRNA alterations at transcriptional, genomic, and epigenetic levels in 5,037 human tumor specimens across 13 cancer types from The Cancer Genome Atlas. Our results suggest that the expression and dysregulation of lncRNAs are highly cancer type specific compared with protein-coding genes. Using the integrative data generated by this analysis, we present a clinically guided small interfering RNA screening strategy and a co-expression analysis approach to identify cancer driver lncRNAs and predict their functions. This provides a resource for investigating lncRNAs in cancer and lays the groundwork for the development of new diagnostics and treatments.

A feed-forward spiking model of shape-coding by IT cells

The ability to recognize a shape is linked to figure-ground (FG) organization. Cell preferences appear to be correlated across contrast-polarity reversals and mirror reversals of polygon displays, but not so much across FG reversals. Here we present a network structure which explains both shape-coding by simulated IT cells and suppression of responses to FG reversed stimuli. In our model FG segregation is achieved before shape discrimination, which is itself evidenced by the difference in spiking onsets of a pair of output cells. The studied example also includes feature extraction and illustrates a classification of binary images depending on the dominance of vertical or horizontal borders.

Multimodal stimulus coding by a gustatory sensory neuron in Drosophila larvae.

Accurate perception of taste information is crucial for animal survival. In adult Drosophila, gustatory receptor neurons (GRNs) perceive chemical stimuli of one specific gustatory modality associated with a stereotyped behavioural response, such as aversion or attraction. We show that GRNs of Drosophila larvae employ a surprisingly different mode of gustatory information coding. Using a novel method for calcium imaging in the larval gustatory system, we identify a multimodal GRN that responds to chemicals of different taste modalities with opposing valence, such as sweet sucrose and bitter denatonium, reliant on different sensory receptors. This multimodal neuron is essential for bitter compound avoidance, and its artificial activation is sufficient to mediate aversion. However, the neuron is also essential for the integration of taste blends. Our findings support a model for taste coding in larvae, in which distinct receptor proteins mediate different responses within the same, multimodal GRN.

An Engineering Solution for using Coarse Meshes in the Simulation of Delamination with Cohesive Zone Models

This paper presents a methodology to determine the parameters used in the simulation of delamination in composite materials using decohesion finite elements. A closed-form expression is developed to define the stiffness of the cohesive layer. A novel procedure that allows the use of coarser meshes of decohesion elements in large-scale computations is proposed. The procedure ensures that the energy dissipated by the fracture process is correctly computed. It is shown that coarse-meshed models defined using the approach proposed here yield the same results as the models with finer meshes normally used in the simulation of fracture processes

Investigating the spontaneous formation of SDS micelle in aqueous solution using a coarse-grained force field

A 1µs Molecular Dynamic simulation was performed with a realistic model system of Sodium Dodecyl Sulfate (SDS) micelles in aqueous solution, comprising of 360 DS-, 360 Na+ and 90000 water particles. After 300 ns three different micellar shapes and sizes 41, 68 and 95 monomers, were observed. The process led to stabilization in the total number of SDS clusters and an increase in the micellar radius to 2.23 nm, in agreement with experimental results. An important conclusion, is be aware that simulations employed in one aggregate, should be considered as a constraint. Size and shape distribution must be analyzed.