Biology subjects

Jean, F.

Publications and source records attributed to Jean, F..

3 recordsLinked to original sources

bioRxiv2020

The Axenfeld-Rieger syndrome gene FOXC1 contributes to left-right patterning

Normal body situs requires precise spatiotemporal expression of the Nodal-Lefty-Pitx2 cascade in the lateral plate mesoderm. The ultimate output of this patterning is establishment of the left-right axis, which provides vital cues for correct organ formation and function. Mutations, deletions and duplications in PITX2 and FOXC1 lead to the rare genetic disease Axenfeld-Rieger syndrome (ARS). While situs defects are not a recognised feature of ARS, partial penetrance of cardiac septal defects and valve incompetence is observed; both of these congenital heart defects (CHDs) also occur following disruption of left-right patterning. Here we investigated whether foxc1 genes have a critical role in specifying organ situs. We demonstrate that CRISPR/Cas9 generated mutants for the zebrafish paralogs foxc1a and foxc1b recapitulate ARS phenotypes including craniofacial dysmorphism, hydrocephalus and intracranial haemorrhage. Furthermore, foxc1a-/-; foxc1b-/- mutant animals display cardiac and gut situs defects. Modelling FOXC1 duplication by transient mRNA overexpression revealed that increased foxc1 dosage also results in organ situs defects. Analysis of known left-right patterning genes revealed a loss in expression of the NODAL antagonist lefty2 in the lateral plate mesoderm. Consistently, LEFTY2 mutations are known to cause human cardiac situs defects. Our data reveal a novel role for the forkhead-box transcription factor foxc1 in patterning of the left-right axis, and provide a plausible mechanism for the incidence of congenital heart defects in Axenfeld-Rieger syndrome patients. Author SummaryThis manuscript investigates the functional consequences of abrogating the activity of Foxc1 (Forkhead Box C1). We demonstrate that loss of zebrafish foxc1a and foxc1b results in phenotypes that resemble human patients with deletions in the FOXC1 locus. Notably, such phenotypes include alterations to the morphology of the heart. Investigations into the mechanisms underlying this phenotype led to the discovery that Foxc1 functions as a regulator of left-right patterning. Most components of left-right specification function normally in foxc1a/b mutants, but there is a pronounced loss of lefty2, a known inhibitor of Nodal signaling. This supports a model in which Foxc1 regulates situs of the heart via the regulation of Lefty2.

genetics↗

bioRxiv2020

SUPER RESOLUTION MICROSCOPY AND DEEP LEARNING IDENTIFY ZIKA VIRUS REORGANIZATION OF THE ENDOPLASMIC RETICULUM

The endoplasmic reticulum (ER) is a complex subcellular organelle composed of diverse structures such as tubules, sheets and tubular matrices. Flaviviruses such as Zika virus (ZIKV) induce reorganization of endoplasmic reticulum (ER) membranes to facilitate viral replication. Here, using 3D super resolution microscopy, ZIKV infection is shown to induce the formation of dense tubular matrices associated with viral replication in the central ER. Viral non-structural proteins NS4B and NS2B associate with replication complexes within the ZIKV-induced tubular matrix and exhibit distinct ER distributions outside this central ER region. Deep neural networks trained to identify ZIKV-infected versus mock-infected cells successfully identified ZIKV-induced central ER tubular matrices as a determinant of viral infection. Super resolution microscopy and deep learning are therefore able to identify and localize morphological features of the ER and may be of use to screen for inhibitors of infection by ER-reorganizing viruses.

cell biology↗

bioRxiv2019

Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction

Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail explain a common phenomenon known as muscle co-contraction. Co-contraction of agonist and antagonist muscles contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint stiffness) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to the noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Optimal feedback (closedloop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limbs mechanical impedance. The stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.\n\nAuthor summaryThis study presents a novel computational theory to explain the planning of force and impedance (e.g. stiffness) in the neural control of movement. It assumes that one main goal of motor planning is to elaborate feedforward motor commands that determine both the force and the impedance required for the task at hand. These feedforward motor commands (i.e. that are defined prior to movement execution) are designed to minimize effort and variance costs considering the uncertainty arising from sensorimotor noise. A major outcome of this mathematical framework is the explanation of a long-known phenomenon called muscle co-contraction (i.e. the concurrent contraction of opposing muscles). Muscle co-contraction has been shown to occur in many situations but previous modeling works struggled to account for it. Although effortful, co-contraction contributes to increase the robustness of motor behavior (e.g. small variance) upstream of sophisticated optimal feedback control processes that require state estimation from delayed sensory feedback to function. This work may have implications regarding our understanding of the neural control of movement in computational terms. It also provides a theoretical ground to explain how to optimally plan force and impedance within a general and versatile framework.

neuroscience↗