Comparison of Efficiency and Specificity of CRISPR-Associated (Cas) Nucleases in Plants An Expanded Toolkit for Precision Genome Engineering, bioRxiv, 2018-09-21

Molecular tools adapted from bacterial CRISPR (Clustered Regulatory Interspaced Short Palindromic Repeats) systems for adaptive immunity have become widely used for plant genome engineering, both to investigate gene functions and to engineer desirable traits. A number of different Cas (CRISPR-associated) nucleases are now used but, as most studies performed to date have engineered different targets using a variety of plant species and molecular tools, it has been difficult to draw conclusions about the comparative performance of different nucleases. Due to the time and effort required to regenerate engineered plants, efficiency is critical. In addition, there have been several reports of mutations at sequences with less than perfect identity to the target. While in some plant species it is possible to remove these so-called ‘off-targets’ by backcrossing to a parental line, the specificity of genome engineering tools is important when targeting specific members of closely-related gene families, especially when recent paralogues are co-located in the genome and unlikely to segregate. Specificity is also important for species that take years to reach sexual maturity or that are clonally propagated. Here, we directly compare the efficiency and specificity of Cas nucleases from different bacterial species together with engineered variants of Cas9. We find that the nucleotide content correlates with efficiency and that Cas9 from Staphylococcus aureus is comparatively most efficient at inducing mutations. We also demonstrate that ‘high-fidelity’ variants of Cas9 can reduce off-target mutations in plants. We present these molecular tools as standardised DNA parts to facilitate their re-use.

biorxiv plant-biology 0-100-users 2018

Proximity sensors reveal social information transfer in maternity colonies of Common noctule bats, bioRxiv, 2018-09-20

Summary<jatslist list-type=order><jatslist-item>Bats are a highly gregarious taxon suggesting that social information should be readily available for making decision. Social information transfer in maternity colonies might be a particularly efficient mechanism for naïve pups to acquire information on resources from informed adults. However, such behaviour is difficult to study in the wild, in particular in elusive and small-bodied animals such as bats.<jatslist-item><jatslist-item>The goal of this study was to investigate the role of social information in acquiring access to two types of resources, which are crucial in the life of a juvenile bat suitable roosting sites and fruitful feeding grounds. We hypothesized that fledging offspring will make use of social information by following informed members of the social groups to unknown roosts or foraging sites.<jatslist-item><jatslist-item>In the present study we applied for the first time the newly developed miniaturized proximity sensor system ‘BATS’, a fully automated system for documenting associations among individual bats both while roosting and while on the wing. We quantified associations among juveniles and other group member while switching roosts and during foraging.<jatslist-item><jatslist-item>We found clear evidence for information transfer while switching roosts, mainly among juveniles and their genetically identified mothers. Anecdotal observations suggest intentional guidance behaviour by mothers, indicated by repeated commuting flights among the pup and the target roost. Infrequent, short meetings with colony members other than the mother indicate local enhancement at foraging sites, but no intentional information transfer.<jatslist-item><jatslist-item>Our study illustrates how advances in technology enable researchers to solve long-standing puzzles. Miniaturized proximity sensors facilitate the automated collection of continuous data sets and represent an ideal tool to gain novel insights into the sociobiology of elusive and small-bodied species.<jatslist-item>

biorxiv animal-behavior-and-cognition 0-100-users 2018

Probing variability in a cognitive map using manifold inference from neural dynamics, bioRxiv, 2018-09-17

Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task,1-3 and are thought to form a cognitive map of task-relevant variables.1,4,5 However, their activity varies over repeated behavioral conditions,6 such as different runs through the same position or repeated trials. Although widely observed across the brain,7-10 such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information.6,11-13 Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold—a space of possible network states. The manifold’s structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low-dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take different trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory,1,5,14 planning,12,15,16 and reinforcement learning.17,18 In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.

biorxiv neuroscience 0-100-users 2018

 

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