C. elegans pathogenic learning confers multigenerational pathogen avoidance, bioRxiv, 2018-11-22

AbstractThe ability to pass on learned information to progeny could present an evolutionary advantage for many generations. While apparently evolutionarily conserved1–12, transgenerational epigenetic inheritance (TEI) is not well understood at the molecular or behavioral levels. Here we describe our discovery that C. elegans can pass on a learned pathogenic avoidance behavior to their progeny for several generations through epigenetic mechanisms. Although worms are initially attracted to the gram-negative bacteria P. aeruginosa (PA14)13, they can learn to avoid this pathogen13. We found that prolonged PA14 exposure results in transmission of avoidance behavior to progeny that have themselves never been exposed to PA14, and this behavior persists through the fourth generation. This form of transgenerational inheritance of bacterial avoidance is specific to pathogenic P. aeruginosa, requires physical contact and infection, and is distinct from CREB-dependent long-term associative memory and larval imprinting. The TGF-β ligand daf-7, whose expression increases in the ASJ upon initial exposure to PA1414, is highly expressed in the ASI neurons of progeny of trained mothers until the fourth generation, correlating with transgenerational avoidance behavior. Mutants of histone modifiers and small RNA mediators display defects in naïve PA14 attraction and aversive learning. By contrast, the germline-expressed PRG-1Piwi homolog15 is specifically required for transgenerational inheritance of avoidance behavior. Our results demonstrate a novel and natural paradigm of TEI that may optimize progeny decisions and subsequent survival in the face of changing environmental conditions.

biorxiv genetics 100-200-users 2018

Hunger for Knowledge How the Irresistible Lure of Curiosity is Generated in the Brain, bioRxiv, 2018-11-22

Introductory ParagraphCuriosity is often portrayed as a desirable feature of human faculty. For example, a meta-analysis revealed that curiosity predicts academic performance above and beyond intelligence1, corroborating findings that curiosity supported long-term consolidation of learning2,3. However, curiosity may come at a cost of strong seductive power that sometimes puts people in a harmful situation. Here, with a set of three behavioural and two neuroimaging experiments including novel stimuli that strongly trigger curiosity (i.e. magic tricks), we examined the psychological and neural mechanisms underlying the irresistible lure of curiosity. We consistently demonstrated that across different samples people were indeed willing to gamble to expose themselves to physical risks (i.e. electric shocks) in order to satisfy their curiosity for trivial knowledge that carries no apparent instrumental values. Also, underlying this seductive power of curiosity is its incentive salience properties, which share common neural mechanisms with extrinsic incentives (i.e. hunger for foods). In particular, the two independent fMRI experiments using different kinds of curiosity-stimulating stimuli found replicable results that acceptance (compared to rejection) of curiosityincentive-driven gambles was accompanied with an enhanced activity in the striatum.

biorxiv neuroscience 100-200-users 2018

BEAST 2.5 An Advanced Software Platform for Bayesian Evolutionary Analysis, bioRxiv, 2018-11-20

AbstractElaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments.Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.Author summaryBayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

biorxiv evolutionary-biology 100-200-users 2018

 

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