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.

Epithelial sodium transport and its control by aldosterone: the story of our internal environment revisited.

Transcription and translation require a high concentration of potassium across the entire tree of life. The conservation of a high intracellular potassium was an absolute requirement for the evolution of life on Earth. This was achieved by the interplay of P- and V-ATPases that can set up electrochemical gradients across the cell membrane, an energetically costly process requiring the synthesis of ATP by F-ATPases. In animals, the control of an extracellular compartment was achieved by the emergence of multicellular organisms able to produce tight epithelial barriers creating a stable extracellular milieu. Finally, the adaptation to a terrestrian environment was achieved by the evolution of distinct regulatory pathways allowing salt and water conservation. In this review we emphasize the critical and dual role of Na(+)-K(+)-ATPase in the control of the ionic composition of the extracellular fluid and the renin-angiotensin-aldosterone system (RAAS) in salt and water conservation in vertebrates. The action of aldosterone on transepithelial sodium transport by activation of the epithelial sodium channel (ENaC) at the apical membrane and that of Na(+)-K(+)-ATPase at the basolateral membrane may have evolved in lungfish before the emergence of tetrapods. Finally, we discuss the implication of RAAS in the origin of the present pandemia of hypertension and its associated cardiovascular diseases.

Innovative implementation by non-state actors in environment-related areas : towards a positive implementation gap ?

This contribution (presented in the first International Conference on Public Policy (ICPP) in Grenoble in June 2013) explores the phenomena of innovation in action ("innovative implementation"). To do so, we operationalize "innovative implementation" as a strategy by which (coalitions of) non-state actors seek to develop ad hoc solutions to address a given environmental issue, going beyond what is provided for in formal policy designs. Following an inductive research strategy, we elaborate a conceptual framework whose main advantage is to bring the actors and their coalition (in all their diversity) back in the analysis. More concretely, we state that perceiving implementation as broader 'social interaction processes' (De Boer & Bressers 2011) within which actors play strategic 'games' (Bardach 1977, Scharpf 1997) opens interesting lines of research to better account for their innovative and strategic behaviours. In a second step, we apply this framework to three strategies of innovative implementation in different contexts, and identify on this basis empirical regularities in the individual pathways related to the emergence and success (or failure) of these strategies.

Collective decision making in a heterogeneous environment: Lasius niger colonies preferentially forage at easy to learn locations

Many ants forage in complex environments and use a combination of trail pheromone information and route memory to navigate between food sources and the nest. Previous research has shown that foraging routes differ in how easily they are learned. In particular, it is easier to learn feeding locations that are reached by repeating (e.g. left-left or right-right) than alternating choices (left-right or right-left) along a route with two T-bifurcations. This raises the hypothesis that the learnability of the feeding sites may influence overall colony foraging patterns. We studied this in the mass-recruiting ant Lasius niger. We used mazes with two T-bifurcations, and allowed colonies to exploit two equidistant food sources that differed in how easily their locations were learned. In experiment 1, learnability was manipulated by using repeating versus alternating routes from nest to feeder. In experiment 2, we added visual landmarks along the route to one food source. Our results suggest that colonies preferentially exploited the feeding site that was easier to learn. This was the case even if the more difficult to learn feeding site was discovered first. Furthermore, we show that these preferences were at least partly caused by lower error rates (experiment 1) and greater foraging speeds (experiment 2) of foragers visiting the more easily learned feeder locations. Our results indicate that the learnability of feeding sites is an important factor influencing collective foraging patterns of ant colonies under more natural conditions, given that in natural environments foragers often face multiple bifurcations on their way to food sources.

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.

Prenatal Exposure to DEHP Affects Spermatogenesis and Sperm DNA Methylation in a Strain-Dependent Manner.

Di-(2-ethylhexyl)phtalate (DEHP) is a plasticizer with endocrine disrupting properties found ubiquitously in the environment and altering reproduction in rodents. Here we investigated the impact of prenatal exposure to DEHP on spermatogenesis and DNA sperm methylation in two distinct, selected, and sequenced mice strains. FVB/N and C57BL/6J mice were orally exposed to 300 mg/kg/day of DEHP from gestation day 9 to 19. Prenatal DEHP exposure significantly decreased spermatogenesis in C57BL/6J (fold-change = 0.6, p-value = 8.7*10-4), but not in FVB/N (fold-change = 1, p-value = 0.9). The number of differentially methylated regions (DMRs) by DEHP-exposure across the entire genome showed increased hyper- and decreased hypo-methylation in C57BL/6J compared to FVB/N. At the promoter level, three important subsets of genes were massively affected. Promoters of vomeronasal and olfactory receptors coding genes globally followed the same trend, more pronounced in the C57BL/6J strain, of being hyper-methylated in DEHP related conditions. In contrast, a large set of micro-RNAs were hypo-methylated, with a trend more pronounced in the FVB/N strain. We additionally analyze both the presence of functional genetic variations within genes that were associated with the detected DMRs and that could be involved in spermatogenesis, and DMRs related with the DEHP exposure that affected both strains in an opposite manner. The major finding in this study indicates that prenatal exposure to DEHP can decrease spermatogenesis in a strain-dependent manner and affects sperm DNA methylation in promoters of large sets of genes putatively involved in both sperm chemotaxis and post-transcriptional regulatory mechanisms.

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.

Sensing the light environment in plants: photoreceptors and early signaling steps.

Plants must constantly adapt to a changing light environment in order to optimize energy conversion through the process of photosynthesis and to limit photodamage. In addition, plants use light cues for timing of key developmental transitions such as initiation of reproduction (transition to flowering). Plants are equipped with a battery of photoreceptors enabling them to sense a very broad light spectrum spanning from UV-B to far-red wavelength (280-750nm). In this review we briefly describe the different families of plant photosensory receptors and the mechanisms by which they transduce environmental information to influence numerous aspects of plant growth and development throughout their life cycle.

Studying social interactions through immersive virtual environment technology: Virtues, pitfalls, and future challenges

Thegoalofthepresentreviewistoexplainhowimmersivevirtualenvironmenttechnology(IVET)canbeusedforthestudyofsocialinteractionsandhowtheuseofvirtualhumansinimmersivevirtualenvironmentscanadvanceresearchandapplicationinmanydifferentfields.Researchersstudyingindividualdifferencesinsocialinteractionsaretypicallyinterestedinkeepingthebehaviorandtheappearanceoftheinteractionpartnerconstantacrossparticipants.WithIVETresearchershavefullcontrolovertheinteractionpartners,canstandardizethemwhilestillkeepingthesimulationrealistic.Virtualsimulationsarevalid:growingevidenceshowsthatindeedstudiesconductedwithIVETcanreplicatesomewell-knownfindingsofsocialpsychology.Moreover,IVETallowsresearcherstosubtlymanipulatecharacteristicsoftheenvironment(e.g.,visualcuestoprimeparticipants)orofthesocialpartner(e.g.,his/herrace)toinvestigatetheirinfluencesonparticipants'behaviorandcognition.Furthermore,manipulationsthatwouldbedifficultorimpossibleinreallife(e.g.,changingparticipants'height)canbeeasilyobtainedwithIVET.Besidetheadvantagesfortheoreticalresearch,weexplorethemostrecenttrainingandclinicalapplicationsofIVET,itsintegrationwithothertechnologies(e.g.,socialsensing)andfuturechallengesforresearchers(e.g.,makingthecommunicationbetweenvirtualhumansandparticipantssmoother).

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.

Assessing the use of very high resolution data to predict species distribution in a mountain environment

Nowadays, Species Distribution Models (SDMs) are a widely used tool. Using different statistical approaches these models reconstruct the realized niche of a species using presence data and a set of variables, often topoclimatic. There utilization range is quite large from understanding single species requirements, to the creation of nature reserve based on species hotspots, or modeling of climate change impact, etc... Most of the time these models are using variables at a resolution of 50km x 50km or 1 km x 1 km. However in some cases these models are used with resolutions below the kilometer scale and thus called high resolution models (100 m x 100 m or 25 m x 25 m). Quite recently a new kind of data has emerged enabling precision up to lm x lm and thus allowing very high resolution modeling. However these new variables are very costly and need an important amount of time to be processed. This is especially the case when these variables are used in complex calculation like models projections over large areas. Moreover the importance of very high resolution data in SDMs has not been assessed yet and is not well understood. Some basic knowledge on what drive species presence-absences is still missing. Indeed, it is not clear whether in mountain areas like the Alps coarse topoclimatic gradients are driving species distributions or if fine scale temperature or topography are more important or if their importance can be neglected when balance to competition or stochasticity. In this thesis I investigated the importance of very high resolution data (2-5m) in species distribution models using either very high resolution topographic, climatic or edaphic variables over a 2000m elevation gradient in the Western Swiss Alps. I also investigated more local responses of these variables for a subset of species living in this area at two precise elvation belts. During this thesis I showed that high resolution data necessitates very good datasets (species and variables for the models) to produce satisfactory results. Indeed, in mountain areas, temperature is the most important factor driving species distribution and needs to be modeled at very fine resolution instead of being interpolated over large surface to produce satisfactory results. Despite the instinctive idea that topographic should be very important at high resolution, results are mitigated. However looking at the importance of variables over a large gradient buffers the importance of the variables. Indeed topographic factors have been shown to be highly important at the subalpine level but their importance decrease at lower elevations. Wether at the mountane level edaphic and land use factors are more important high resolution topographic data is more imporatant at the subalpine level. Finally the biggest improvement in the models happens when edaphic variables are added. Indeed, adding soil variables is of high importance and variables like pH are overpassing the usual topographic variables in SDMs in term of importance in the models. To conclude high resolution is very important in modeling but necessitate very good datasets. Only increasing the resolution of the usual topoclimatic predictors is not sufficient and the use of edaphic predictors has been highlighted as fundamental to produce significantly better models. This is of primary importance, especially if these models are used to reconstruct communities or as basis for biodiversity assessments. -- Ces dernières années, l'utilisation des modèles de distribution d'espèces (SDMs) a continuellement augmenté. Ces modèles utilisent différents outils statistiques afin de reconstruire la niche réalisée d'une espèce à l'aide de variables, notamment climatiques ou topographiques, et de données de présence récoltées sur le terrain. Leur utilisation couvre de nombreux domaines allant de l'étude de l'écologie d'une espèce à la reconstruction de communautés ou à l'impact du réchauffement climatique. La plupart du temps, ces modèles utilisent des occur-rences issues des bases de données mondiales à une résolution plutôt large (1 km ou même 50 km). Certaines bases de données permettent cependant de travailler à haute résolution, par conséquent de descendre en dessous de l'échelle du kilomètre et de travailler avec des résolutions de 100 m x 100 m ou de 25 m x 25 m. Récemment, une nouvelle génération de données à très haute résolution est apparue et permet de travailler à l'échelle du mètre. Les variables qui peuvent être générées sur la base de ces nouvelles données sont cependant très coûteuses et nécessitent un temps conséquent quant à leur traitement. En effet, tout calcul statistique complexe, comme des projections de distribution d'espèces sur de larges surfaces, demande des calculateurs puissants et beaucoup de temps. De plus, les facteurs régissant la distribution des espèces à fine échelle sont encore mal connus et l'importance de variables à haute résolution comme la microtopographie ou la température dans les modèles n'est pas certaine. D'autres facteurs comme la compétition ou la stochasticité naturelle pourraient avoir une influence toute aussi forte. C'est dans ce contexte que se situe mon travail de thèse. J'ai cherché à comprendre l'importance de la haute résolution dans les modèles de distribution d'espèces, que ce soit pour la température, la microtopographie ou les variables édaphiques le long d'un important gradient d'altitude dans les Préalpes vaudoises. J'ai également cherché à comprendre l'impact local de certaines variables potentiellement négligées en raison d'effets confondants le long du gradient altitudinal. Durant cette thèse, j'ai pu monter que les variables à haute résolution, qu'elles soient liées à la température ou à la microtopographie, ne permettent qu'une amélioration substantielle des modèles. Afin de distinguer une amélioration conséquente, il est nécessaire de travailler avec des jeux de données plus importants, tant au niveau des espèces que des variables utilisées. Par exemple, les couches climatiques habituellement interpolées doivent être remplacées par des couches de température modélisées à haute résolution sur la base de données de terrain. Le fait de travailler le long d'un gradient de température de 2000m rend naturellement la température très importante au niveau des modèles. L'importance de la microtopographie est négligeable par rapport à la topographie à une résolution de 25m. Cependant, lorsque l'on regarde à une échelle plus locale, la haute résolution est une variable extrêmement importante dans le milieu subalpin. À l'étage montagnard par contre, les variables liées aux sols et à l'utilisation du sol sont très importantes. Finalement, les modèles de distribution d'espèces ont été particulièrement améliorés par l'addition de variables édaphiques, principalement le pH, dont l'importance supplante ou égale les variables topographique lors de leur ajout aux modèles de distribution d'espèces habituels.

Adjusting for the environment in DEA: a comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.

Geospatial approach for defining the Wildland-Urban Interface in the Alpine environment

Problems related to fire hazard and fire management have become in recent decades one of the most relevant issues in the Wildland-Urban Interface (WUI), that is the area where human infrastructures meet or intermingle with natural vegetation. In this paper we develop a robust geospatial method for defining and mapping the WUI in the Alpine environment, where most interactions between infrastructures and wildland vegetation concern the fire ignition through human activities, whereas no significant threats exist for infrastructures due to contact with burning vegetation. We used the three Alpine Swiss cantons of Ticino, Valais and Grisons as the study area. The features representing anthropogenic infrastructures (urban or infrastructural components of the WUI) as well as forest cover related features (wildland component of the WUI) were selected from the Swiss Topographic Landscape Model (TLM3D). Georeferenced forest fire occurrences derived from the WSL Swissfire database were used to define suitable WUI interface distances. The Random Forest algorithm was applied to estimate the importance of predictor variables to fire ignition occurrence. This revealed that buildings and drivable roads are the most relevant anthropogenic components with respect to fire ignition. We consequently defined the combination of drivable roads and easily accessible (i.e. 100 m from the next drivable road) buildings as the WUI-relevant infrastructural component. For the definition of the interface (buffer) distance between WUI infrastructural and wildland components, we computed the empirical cumulative distribution functions (ECDF) of the percentage of ignition points (observed and simulated) arising at increasing distances from the selected infrastructures. The ECDF facilitates the calculation of both the distance at which a given percentage of ignition points occurred and, in turn, the amount of forest area covered at a given distance. Finally, we developed a GIS ModelBuilder routine to map the WUI for the selected buffer distance. The approach was found to be reproducible, robust (based on statistical analyses for evaluating parameters) and flexible (buffer distances depending on the targeted final area covered) so that fire managers may use it to detect WUI according to their specific priorities.

Ejaculate Characteristics Depend on Social Environment in the Horse (Equus caballus).

Sperm competition theory predicts semen characteristics to be affected by the social environment. We used the polygamous horse (Equus caballus) to experimentally study within-subject plasticity in response to different social environments. Stallions were sequentially exposed, over a period of 8 weeks each, to other stallions and then singly to mares, or vice versa (in adjacent boxes separated by grills). Ejaculates were collected to determine semen characteristics. Highest sperm numbers were found in stallions that were first exposed to other stallions and then to mares, while lowest sperm numbers were observed in stallions that had been exposed to mares but not yet to other stallions. One of three sperm velocity measures (curvilinear velocity) was consistently elevated in stallions that were first exposed to stallions and then to mares. Sperm number after exposure to mares and curvilinear sperm velocity after exposure to stallions were both positively correlated to average blood testosterone levels during the corresponding period of exposure. We conclude that ejaculate characteristics are plastic traits affected by the social environment in horses.