The evolution of alternative splicing patterns and regulatory mechanisms

Alternative splicing produces multiple isoforms from the same gene, thus increasing the number of transcripts of the species. Alternative splicing is a virtually ubiquitous mechanism in eukaryotes, for example more than 90% of protein-coding genes in human are alternatively spliced. Recent evolutionary studies showed that alternative splicing is a fast evolving and highly species- specific mechanism. The rapid evolution of alternative splicing was considered as a contribution to the phenotypic diversity between species. However, the function of many isoforms produced by alternative splicing remains unclear and they might be the result of noisy splicing. Thus, the functional relevance of alternative splicing and the evolutionary mechanisms of its rapid divergence among species are still poorly understood. During my thesis, I performed a large-scale analysis of the regulatory mechanisms that drive the rapid evolution of alternative splicing. To study the evolution of alternative splicing regulatory mechanisms, I used an extensive RNA-sequencing dataset comprising 12 tetrapod species (human, chimpanzee and bonobo, gorilla, orangutan, macaque, marmoset, mouse, opossum, platypus, chicken and frog) and 8 tissues (cerebellum, brain, heart, kidney, liver, testis, placenta and ovary). To identify the catalogue of alternative splicing eis-acting regulatory elements in the different tetrapod species, I used a previously defined computational approach. This approach is a statistical analysis of exons/introns and splice sites composition and relies on a principle of compensation between splice sites strength and the presence of additional regulators. With an evolutionary comparative analysis of the exonic eis-acting regulators, I showed that these regulatory elements are generally shared among primates and more conserved than non-regulatory elements. In addition, I showed that the usage of these regulatory elements is also more conserved than expected by chance. In addition to the identification of species- specific eis-acting regulators, these results may explain the rapid evolution of alternative splicing. I also developed a new approach based on evolutionary sequence changes and corresponding alternative splicing changes to identify potential splicing eis-acting regulators in primates. The identification of lineage-specific substitutions and corresponding lineage-specific alternative splicing changes, allowed me to annotate the genomic sequences that might have played a role in the alternative splicing pattern differences among primates. Finally, I showed that the identified splicing eis-acting regulator datasets are enriched in human disease-causing mutations, thus confirming their biological relevance.

Clinical evolution after a non-reactive hypothermic EEG following cardiac arrest.

BACKGROUND: Lack of electroencephalography (EEG) background reactivity during therapeutic hypothermia (TH) has been associated with poor outcome in post-anoxic comatose patients. However, decision on intensive care withdrawal is based on normothermic (NT) evaluations. This study aims at exploring whether patients showing recovery of EEG reactivity in NT after a non-reactive EEG in TH differ from those remaining non-reactive. METHODS: Patients with non-reactive EEG during TH were identified from our prospective registry of consecutive comatose adults admitted after successful resuscitation from CA between April 2009 and June 2014. Variables including neurological examination, serum neuron-specific enolase (NSE), procalcitonin, and EEG features were compared regarding impact on functional outcome at 3 months. RESULTS: Seventy-two of 197 patients (37 %) had a non-reactive EEG background during TH with thirteen (18 %) evolving towards reactivity in NT. Compared to those remaining non-reactive (n = 59), they showed significantly better recovery of brainstem reflexes (p < 0.001), better motor responses (p < 0.001), transitory consciousness improvement (p = 0.008), and a tendency toward lower NSE (p = 0.067). One patient recovering EEG reactivity survived with good functional outcome at 3 months. CONCLUSIONS: Recovery of EEG reactivity from TH to NT seems to distinguish two patients' subgroups regarding early neurological assessment and transitory consciousness improvement, corroborating the role of EEG in providing information about cerebral functions. Understanding these dynamic changes encourages maintenance of intensive support in selected patients even after a non-reactive EEG background in TH, as a small subgroup may indeed recover with good functional outcome.

A model for the evolution of reinforcement learning in fluctuating games

Many species are able to learn to associate behaviours with rewards as this gives fitness advantages in changing environments. Social interactions between population members may, however, require more cognitive abilities than simple trial-and-error learning, in particular the capacity to make accurate hypotheses about the material payoff consequences of alternative action combinations. It is unclear in this context whether natural selection necessarily favours individuals to use information about payoffs associated with nontried actions (hypothetical payoffs), as opposed to simple reinforcement of realized payoff. Here, we develop an evolutionary model in which individuals are genetically determined to use either trial-and-error learning or learning based on hypothetical reinforcements, and ask what is the evolutionarily stable learning rule under pairwise symmetric two-action stochastic repeated games played over the individual's lifetime. We analyse through stochastic approximation theory and simulations the learning dynamics on the behavioural timescale, and derive conditions where trial-and-error learning outcompetes hypothetical reinforcement learning on the evolutionary timescale. This occurs in particular under repeated cooperative interactions with the same partner. By contrast, we find that hypothetical reinforcement learners tend to be favoured under random interactions, but stable polymorphisms can also obtain where trial-and-error learners are maintained at a low frequency. We conclude that specific game structures can select for trial-and-error learning even in the absence of costs of cognition, which illustrates that cost-free increased cognition can be counterselected under social interactions.

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.

Visualizing Business Model Evolution with the Business Model Canvas: Concept and Tool

The Business Model Canvas (BMC) assists in the design of companies' business models. As strategies evolve so too does the business model. Unfortunately, each BMC is a standalone representation. Thus, there is a need to be able to describe transformation from one version of a business model to the next as well as to visualize these operations. To address this issue, and to contribute to computer-assisted business model design, we propose a set of design principles for business model evolution. We also demonstrate a tool that can assist in the creation and navigation of business model versions in a visual and user-friendly way