Discovery of novel indolinone-based,potent, selective and brain penetrant inhibitors of LRRK2

Mutations in leucine-rich repeat kinase-2 (LRRK2) are the most common genetic cause of Parkinson’s disease (PD). The most frequent kinase-enhancing mutation is the G2019S residing in the kinase activation domain. This opens up a promising therapeutic avenue for drug discovery targeting the kinase activity of LRRK2 in PD. Several LRRK2 inhibitors have been reported to date. Here, we report a selective, brain penetrant LRRK2 inhibitor and demonstrate by a competition pulldown assay in vivo target engagement in mice.     

The number of markers and samples needed for detecting bottlenecks under realistic scenarios,with and without recovery: a simulation‐based study

Detecting bottlenecks is a common task in molecular ecology. While several bottleneck detection methods exist, evaluations of their power have focused only on severe bottlenecks (e.g. to Ne ~10). As a component of a recent review, Peery et al. ( 2012 ) analysed the power of two approaches, the M‐ratio and heterozygote excess tests, to detect moderate bottlenecks (e.g. to Ne ~100), which is realistic for many conservation situations. In this Comment, we address three important points relevant to but not considered in Peery et al. Under moderate bottleneck scenarios, we test the (i) relative advantage of sampling more markers vs. more individuals, (ii) potential power to detect the bottleneck when utilizing dozens of microsatellites (a realistic possibility for contemporary studies) and (iii) reduction in power when postbottleneck recovery has occurred. For the realistic situations examined, we show that (i) doubling the number of loci shows equal or better power than tripling the number of individuals, (ii) increasing the number of markers (up to 100) results in continued additive gains in power, and (iii) recovery after a moderate amount of time or gradual change in size reduces power, by up to one‐half. Our results provide a practical supplement to Peery et al. and encourage the continued use of bottleneck detection methods in the genomic age, but also emphasize that the power under different sampling schemes should be estimated, using simulation modelling, as a routine component of molecular ecology studies.     

Origins and Evolution of the Etruscans’ mtDNA

The Etruscan culture is documented in Etruria, Central Italy, from the 8^th to the 1^st century BC. For more than 2,000 years there has been disagreement on the Etruscans’ biological origins, whether local or in Anatolia. Genetic affinities with both Tuscan and Anatolian populations have been reported, but so far all attempts have failed to fit the Etruscans’ and modern populations in the same genealogy. We extracted and typed the hypervariable region of mitochondrial DNA of 14 individuals buried in two Etruscan necropoleis, analyzing them along with other Etruscan and Medieval samples, and 4,910 contemporary individuals from the Mediterranean basin. Comparing ancient (30 Etruscans, 27 Medieval individuals) and modern DNA sequences (370 Tuscans), with the results of millions of computer simulations, we show that the Etruscans can be considered ancestral, with a high degree of confidence, to the current inhabitants of Casentino and Volterra, but not to the general contemporary population of the former Etruscan homeland. By further considering two Anatolian samples (35 and 123 individuals) we could estimate that the genetic links between Tuscany and Anatolia date back to at least 5,000 years ago, strongly suggesting that the Etruscan culture developed locally, and not as an immediate consequence of immigration from the Eastern Mediterranean shores.     

The Computational Development of Reinforcement Learning during Adolescence

Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment) and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed). Computational strategies changed during development: whereas adolescents’ behaviour was better explained by a basic reinforcement learning algorithm, adults’ behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback) and a value contextualisation module (enabling symmetrical reward and punishment learning). Unlike adults, adolescent performance did not benefit from counterfactual (complete) feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence.