Biology subjects

Corbin, C.

Publications and source records attributed to Corbin, C..

4 recordsLinked to original sources

bioRxiv2023

A deep-learning-based screening platform in aging-relevant human motor neurons to identify therapeutic compounds for amyotrophic lateral sclerosis

Age is the primary risk factor for most neurodegenerative diseases including amyo-trophic lateral sclerosis (ALS). Despite the clear importance of age on the development and progression of ALS, age is rarely considered a factor in cellular or animal models of ALS. The recent advent of direct reprogramming methodologies, whereby somatic cells such as fibroblasts can be converted into neurons while retaining the age signature of the host, has facilitated the study of age-relevant human cells in vitro for the first time. Despite the promise of this technology, age is a complex, multidimensional, and dy-namic phenotype that makes the interpretation of the interplay between age and dis-ease a great challenge. To circumvent these challenges, we developed a screening platform for directly reprogrammed neurons in an age-relevant human cellular model to better model and identify disease-modifying targets and pathways for ALS. This plat-form uses deep learning image analysis to screen compounds for efficacy against ALS-associated cellular phenotypes and efficiently classifies ALS-like phenotypic signatures as well as predicts the age of the original fibroblast donor. Notably, our work identified NCB-0846, a TNIK/MAP4K7 inhibitor, as a novel compound with the ability to revert ALS-like phenotypes. Moreover, we show that chronic dosing of NCB-0846 in the SOD1G93A mouse model of ALS significantly reduced serum neurofilament-L levels. Our findings establish the power of a platform that combines direct reprogramming of human cells and deep learning as a powerful tool for combatting aging and age-related diseases.

neuroscience↗

bioRxiv2020

Thermal sensitivity of the Spiroplasma-Drosophila hydei protective symbiosis: The best of climes, the worst of climes.

The outcome of natural enemy attack in insects has commonly been found to be influenced by the presence of protective symbionts in the host. The degree to which protection functions in natural populations, however, will depend on the robustness of the phenotype to variation in the abiotic environment. We studied the impact of a key environmental parameter - temperature - on the efficacy of the protective effect of the symbiont Spiroplasma on its host Drosophila hydei, against attack by the parasitoid wasp Leptopilina heterotoma. In addition, we investigated the thermal sensitivity of the symbionts vertical transmission, which may be a key determinant of the ability of the symbiont to persist. We found that vertical transmission was more robust than previously considered, with Spiroplasma being maintained at 25 {degrees}C, 18 {degrees}C and with 18/15 {degrees}C diurnal cycles, with rates of segregational loss only increasing at 15 {degrees}C. Protection against wasp attack was ablated before symbiont transmission was lost, with the symbiont failing to rescue the fly host at 18 {degrees}C. We conclude that the presence of a protective symbiosis in natural populations cannot be simply inferred from presence of a symbiont whose protective capacity has been tested under narrow controlled conditions. More broadly, we argue that the thermal environment is likely to represent an important determinant of the evolutionary ecology of defensive symbioses in natural environments, potentially driving seasonal, latitudinal and altitudinal variation in symbiont frequency, and modulating the strength of selection for symbiotic protective systems compared to defensive systems encoded in the nuclear genomes.

ecology↗

bioRxiv2017

White matter differences in Parkinson’s disease mapped using tractometry

Neurodegenerative disorders are characterized by a progressive loss of brain function. Improved precision in mapping the altered brain pathways can provide a deep understanding of the trajectory of decline. We propose a tractometry workflow for conducting group statistical analyses of point-wise microstructural measures along white matter fasciculi to identify patterns of abnormalities associated with disease. We combined state-of-the-art tools including fiber registration, tract simplification and fiber matching for accurate point-wise statistical analyses across populations. We test the utility of this method by identifying group differences between Parkinsons disease (PD) patients and healthy controls. We find statistically significant group differences in diffusion MRI derived measures along the anterior thalamic radiations (ATR), corticospinal tract (CST) and regions of the corpus callosum (CC). These pathways are essential for motor control systems within cortico-cortical and cortico-subcortical brain networks. Moreover, the reported pathological changes were not widespread but rather localized along several tracts. Point-wise tract analyses may therefore offer an advantage in anatomical specificity over traditional methods that assess mean microstructural measures across large regions of interest.

neuroscience↗

bioRxiv2017

FiberNet 2.0: An Automatic Neural Network Based Tool for Clustering White Matter Fibers in the Brain

The brains white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimers disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brains neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.

neuroscience↗