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Biology subjects

Grasso, S.

Publications and source records attributed to Grasso, S..

2 recordsLinked to original sources

A cortical mechanism linking saliency detection and motor reactivity in rhesus monkeys

Sudden and surprising sensory events trigger neural processes that swiftly adjust behavior. To study the phylogenesis and the mechanism of this phenomenon, we trained two rhesus monkeys to keep a cursor inside a visual target by exerting force on an isometric joystick. We examined the effect of surprising auditory stimuli on exerted force, scalp electroencephalographic (EEG) activity, and local field potentials (LFP) recorded from the dorso-lateral prefrontal cortex. Auditory stimuli elicited (1) a biphasic modulation of isometric force: a transient decrease followed by a corrective tonic increase, and (2) EEG and LFP deflections dominated by two large negative-positive waves (N70 and P130). The EEG potential was maximal at the scalp vertex, in all respects similar to the human vertex potential. Electrocortical potentials and force were tightly coupled: the P130 amplitude predicted the magnitude of the corrective force increase, particularly in the EEG electrodes contralateral to the limb exerting force, and in the LFP recorded from deep rather than superficial cortical layers, suggesting a direct effect of the vertex potential on the motor output determining the behavior. These results disclose a phylogenetically-preserved cortico-motor mechanism supporting adaptive behavior in response to salient sensory events.

neuroscience↗

Signal peptide efficiency: from high-throughput data to prediction and explanation

The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides. To elucidate relevant features of the signal peptide sequence that influence secretion efficiency, an evaluation of ~12,000 different designed signal peptides was performed using a novel miniaturized high-throughput assay. The results were used to train a machine learning model, and a post-hoc explanation of the model is provided. By describing each signal peptide with a selection of 156 physicochemical features, it is now possible to both quantify feature importance and predict the protein secretion levels directed by each signal peptide. Our analyses allow the detection and explanation of the relevant signal peptide features influencing the efficiency of protein secretion, generating a versatile tool for the in silico evaluation of signal peptides.

synthetic biology↗