Design, expression and characterization of mutants of fasciculin optimized for interaction with its target, acetylcholinesterase

Predicting mutations that enhance protein–protein affinity remains a challenging task, especially for high-affinity complexes. To test our capability to improve the affinity of such complexes, we studied interaction of acetylcholinesterase with the snake toxin, fasciculin. Using the program ORBIT, we redesigned fasciculin's sequence to enhance its interactions with Torpedo californica acetylcholinesterase. Mutations were predicted in 5 out of 13 interfacial residues on fasciculin, preserving most of the polar inter-molecular contacts seen in the wild-type toxin/enzyme complex. To experimentally characterize fasciculin mutants, we developed an efficient strategy to over-express the toxin in Escherichia coli, followed by refolding to the native conformation. Despite our predictions, a designed quintuple fasciculin mutant displayed reduced affinity for the enzyme. However, removal of a single mutation in the designed sequence produced a quadruple mutant with improved affinity. Moreover, one designed mutation produced 7-fold enhancement in affinity for acetylcholinesterase. This led us to reassess our criteria for enhancing affinity of the toxin for the enzyme. We observed that the change in the predicted inter-molecular energy, rather than in the total energy, correlates well with the change in the experimental free energy of binding, and hence may serve as a criterion for enhancement of affinity in protein–protein complexes

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

Selective block of Ca2+-dependent K+ current in crayfish neuromuscular system and chromaffin cells by sea anemone Bunodosoma cangicum venom

The effects of the nematocyst venom of the sea anemone Bunodosoma cangicum on depolarization-activated currents were studied in opener crayfish muscle fibers and in cultured bovine chromaffin cells. The venom selectively and reversibly blocked the Ca2+-dependent K+ current (I(K(Ca))) present in crayfish muscle in a dose-dependent manner without affecting voltage-gated Ca2+ or K+ currents. Furthermore, the venom also reduced I(K(Ca)) in chromaffin cells, without modifying voltage-gated Na+, Ca2+, or K+ currents. Synaptic transmission in crayfish muscle was also affected by the venom. Repetitive excitatory and inhibitory postsynaptic currents (each associated with a presynaptic action potential) were evoked by each nerve stimulus, suggesting that presynaptic I(K(Ca)) may control the electrical activity of excitatory and inhibitory presynaptic fibers. We conclude that B. cangicum venom includes a toxin that selectively and reversibly blocks Ca2+-dependent K+ currents in crayfish muscle and in bovine chromaffin cells, and modifies excitatory and inhibitory synaptic transmission, probably abolishing a similar conductance at the presynaptic fibers.Peer Reviewe

Slow pressure waves in the cranial enclosure

International audienc

Modifications of spontaneous cerebral blood flow oscillations during cardiopulmonary bypass

International audienc