Biology subjects

Kawai, H.

Publications and source records attributed to Kawai, H..

3 recordsLinked to original sources

bioRxiv2021

MeDUsA: A novel system for automated axon quantification to evaluate neuroaxonal degeneration

BackgroundDrosophila is an excellent model organism for studying human neurodegenerative diseases (NDs), and the rough eye phenotype (REP) assay is a convenient experimental system for analysing the toxicity of ectopically expressed human disease genes. However, the association between REP and axonal degeneration, an early sign of ND, remains unclear. To address this question, we developed a method to evaluate axonal degeneration by quantifying the number of retinal R7 axons in Drosophila; however, it requires expertise and is time-consuming. Therefore, there is a need for an easy-to-use software that can automatically quantify the axonal degeneration. ResultWe created MeDUsA (a method for the quantification of degeneration using fly axons), which is a standalone executable computer program based on Python that combines a pre-trained deep-learning masking tool with an axon terminal counting tool. This software automatically quantifies the number of axons from a confocal z-stack image series. Using this software, we have demonstrated for the first time directly that axons degenerate when the causative factors of NDs (Syn, Tau, TDP-43, HTT) were expressed in the Drosophila eye. Furthermore, we compared axonal toxicity of the representative causative genes of NDs and their pathological alleles with REP and found no significant correlation between them. ConclusionsMeDUsA rapidly and accurately quantifies axons in Drosophila eye. By simplifying and automating time-consuming manual efforts requiring significant expertise, it enables large-scale, complex research efforts on axonal degeneration, such as screening to identify genes or drugs that mediate axonal toxicity caused by ND disease proteins.

pathology↗

bioRxiv2021

A quantitative model of sporadic axonal degeneration in the Drosophila visual system

In human neurodegenerative diseases, neurons undergo axonal degeneration months to years before they die. Here, we developed a system modelling early degenerative events in Drosophila adult photoreceptor cells. Thanks to the stereotypy of their axonal projections, this system delivers quantitative data on sporadic and progressive axonal degeneration of photoreceptor cells. Using this method, we show that exposure of adult flies to a constant light stimulation for several days overcomes the intrinsic resilience of R7 photoreceptors and leads to progressive axonal degeneration. This was not associated with apoptosis. We furthermore provide evidence that loss of synaptic integrity between R7 and a postsynaptic partner preceded axonal degeneration, thus recapitulating features of human neurodegenerative diseases. Finally, our experiments uncovered that neurotransmission to postsynaptic partners of R7 and their response are required to initiate degeneration, suggesting that postsynaptic cells signal back to the photoreceptor to maintain axonal structure. This model can be used to dissect cellular circuit mechanisms involved in the early events of axonal degeneration, allowing for a better understanding of how neurons cope with stress and lose their resilience capacities.

neuroscience↗

bioRxiv2021

An interactive deep learning-based approach reveals mitochondrial cristae topologies

Outer and inner mitochondrial membranes are highly specialized structures with distinct functional properties. Reconstructing complex 3D ultrastructural features of mitochondrial membranes at the nanoscale requires analysis of large volumes of serial scanning electron tomography data. While deep-learning-based methods improved in sophistication recently, time-consuming human intervention processes remain major roadblocks for efficient and accurate analysis of organelle ultrastructure. In order to overcome this limitation, we developed a deep-learning image analysis platform called Python-based Human-In-the-LOop Workflows (PHILOW). Our implementation of an iterative segmentation algorithm and Three-Axis-Prediction method not only improved segmentation speed, but also provided unprecedented ultrastructural detail of whole mitochondria and cristae. Using PHILOW, we found that 42% of cristae surface exhibits tubular structures that are not recognizable in light microscopy and 2D electron microscopy. Furthermore, we unraveled a fundamental new regulatory function for the dynamin-related GTPase Optic Atrophy 1 (OPA1) in controlling the balance between lamellar versus tubular cristae subdomains.

cell biology↗