Biology subjects

Puppo, F.

Publications and source records attributed to Puppo, F..

3 recordsLinked to original sources

bioRxiv2020

Computing temporal sequences associated with dynamic patterns on the C. elegans connectome

Understanding how the structural connectivity of a network constrains the dynamics it is able to support is a very active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way, independent of the biophysical or molecular details of the cells themselves. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome. We show that contralateral motor neuron activations in ventral (VB) and dorsal (DB) classes of motor neurons emerged from the simulations, which are qualitatively similar to rhythmic motor neuron firing pattern associated with locomotion of the worm. One interpretation of these results is that there is an inherent - and we propose - purposeful structural wiring to the C. elegans connectome that has evolved to serve specific behavioral functions. To study network signaling pathways responsible for the dynamics we developed an analytic framework that constructs Temporal Sequences (TSeq), time-ordered walks of signals on graphs. We found that only 5% of TSeq are preserved between the isolated feeding network relative to its embedded counterpart. The remaining 95% of signaling pathways computed in the isolated network are not present in the embedded network. This suggests a cautionary note for computational studies of isolated neurobiological circuits and networks.

neuroscience↗

bioRxiv2020

Super-selective reconstruction of causal and direct connectivity with application to in-vitro iPSC neuronal networks

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in-vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective - direct and causal - connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then, reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect and apparent links. Our method can be generally applied to the functional characterization of any in-vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in-vitro iPSC-derived neuronal cultures.

neuroscience↗

bioRxiv2020

Splicing impact of deep exonic missense variants in CAPN3 explored systematically by minigene functional assay

Improving the accuracy of variant interpretation during diagnostic sequencing is a major goal for genomic medicine. In order to explore an often overlooked splicing effect of missense variants, we developed the functional assay ("minigene") for the majority of exons of CAPN3, the gene responsible for Limb Girdle Muscular Dystrophy (LGMD). By systematically screening 21 missense variants distributed along the gene, we found that eight clinically relevant missense variants located at a certain distance from the exon/intron borders (deep exonic missense variants) disrupted normal splicing of CAPN3 exons. Several recent machine learning based computational tools failed to predict splicing impact for the majority of these deep exonic missense variants, highlighting the importance of including variants of this type in the training sets during the future algorithm development. Overall, 24 variants in CAPN3 gene were explored, leading to the change in the ACMG classification of seven of them when results of the "minigene" functional assay were taken into account. Our findings reveal previously unknown splicing impact of several clinically important variants in CAPN3 and draw attention to the existence of deep exonic variants with a disruptive effect on gene splicing that could be overlooked by the current approaches in clinical genetics.

genetics↗