PAK2 is necessary for myelination in the peripheral nervous system.

Myelination enables electrical impulses to propagate on axons at the highest speed, encoding essential life functions. The Rho family GTPases, RAC1 and CDC42, have been shown to critically regulate Schwann cell myelination. P21-activated kinase 2 (PAK2) is an effector of RAC1/CDC42, but its specific role in myelination remains undetermined. We produced a Schwann cell-specific knockout mouse of Pak2 (scPak2-/-) to evaluate PAK2's role in myelination. Deletion of Pak2 specifically in mouse Schwann cells resulted in severe hypomyelination, slowed nerve conduction velocity, and behavior dysfunctions in the scPak2-/- peripheral nerve. Many Schwann cells in scPak2-/-sciatic nerves were arrested at the stage of axonal sorting. These abnormalities were rescued by reintroducing Pak2, but not the kinase-dead mutation of Pak2, via lentivirus delivery to scPak2-/- Schwann cells in vivo. Moreover, ablation of Pak2 in Schwann cells blocked the promyelinating effect driven by neuregulin-1, prion protein, and inactivated RAC1/CDC42. Conversely, the ablation of Pak2 in neurons exhibited no phenotype. Such PAK2 activity can also be either enhanced or inhibited by different myelin lipids. We have identified a novel promyelinating factor, PAK2, that acts as a critical convergence point for multiple promyelinating signaling pathways. The promyelination by PAK2 is Schwann cell-autonomous. Myelin lipids, identified as inhibitors or activators of PAK2, may be utilized to develop therapies for repairing abnormal myelin in peripheral neuropathies.

Pmp22 super-enhancer deletion causes tomacula formation and conduction block in peripheral nerves.

Copy number variation of the peripheral nerve myelin gene Peripheral Myelin Protein 22 (PMP22) causes multiple forms of inherited peripheral neuropathy. The duplication of a 1.4 Mb segment surrounding this gene in chromosome 17p12 (c17p12) causes the most common form of Charcot-Marie-Tooth disease type 1A, whereas the reciprocal deletion of this gene causes a separate neuropathy termed hereditary neuropathy with liability to pressure palsies (HNPP). PMP22 is robustly induced in Schwann cells in early postnatal development, and several transcription factors and their cognate regulatory elements have been implicated in coordinating the gene's proper expression. We previously found that a distal super-enhancer domain was important for Pmp22 expression in vitro, with particular impact on a Schwann cell-specific alternative promoter. Here, we investigate the consequences of deleting this super-enhancer in vivo. We find that loss of the super-enhancer in mice reduces Pmp22 expression throughout development and into adulthood, with greater impact on the Schwann cell-specific promoter. Additionally, these mice display tomacula formed by excessive myelin folding, a pathological hallmark of HNPP, as have been previously observed in heterozygous Pmp22 mice as well as sural biopsies from patients with HNPP. Our findings demonstrate a mechanism by which smaller copy number variations, not including the Pmp22 gene, are sufficient to reduce gene expression and phenocopy a peripheral neuropathy caused by the HNPP-associated deletion encompassing PMP22.

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction.

BACKGROUND: Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropathies. In this study, we are testing whether deep learning-based model can automate quantification of the FF in individual muscles. While individual muscle is smaller with irregular shape, manually segmented muscle MRI images have been accumulated in this lab; and make the deep learning feasible. PURPOSE: To automate segmentation on muscle MRI images through deep learning for quantifying individual muscle FF in patients with peripheral neuropathies. STUDY TYPE: Retrospective. SUBJECTS: 24 patients and 19 healthy controls. FIELD STRENGTH/SEQUENCES: 3T; Interleaved 3D GRE. ASSESSMENT: A 3D U-Net model was implemented in segmenting muscle MRI images. This was enabled by leveraging a large set of manually segmented muscle MRI images. B1+ and B1- maps were used to correct image inhomogeneity. Accuracy of the automation was evaluated using Pixel Accuracy (PA), Dice Coefficient (DC) in binary masks; and Bland-Altman and Pearson correlation by comparing FF values between manual and automated methods. STATISTICAL TESTS: PA and DC were reported with their median value and standard deviation. Two methods were compared using the ± 95% confidence intervals (CI) of Bland-Altman analysis and the Pearson's coefficient (r2 ). RESULTS: DC values were from 0.83 ± 0.17 to 0.98 ± 0.02 in thigh and from 0.63 ± 0.18 to 0.96 ± 0.02 in calf muscles. For FF values, the overall ± 95% CI and r2 were [0.49, -0.56] and 0.989 in thigh and [0.84, -0.71] and 0.971 in the calf. DATA CONCLUSION: Automated results well agreed with the manual results in quantifying FF for individual muscles. This method mitigates the formidable time consumption and intense labor in manual segmentations; and enables the use of individual muscle FF as outcome measures in upcoming longitudinal studies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

Myelin abnormality in Charcot-Marie-Tooth type 4J recapitulates features of acquired demyelination.

OBJECTIVE: Charcot-Marie-Tooth type 4J (CMT4J) is a rare autosomal recessive neuropathy caused by mutations in FIG4 that result in loss of FIG4 protein. This study investigates the natural history and mechanisms of segmental demyelination in CMT4J. METHODS: Over the past 9 years, we have enrolled and studied a cohort of 12 CMT4J patients, including 6 novel FIG4 mutations. We evaluated these patients and related mouse models using morphological, electrophysiological, and biochemical approaches. RESULTS: We found sensory motor demyelinating polyneuropathy consistently in all patients. This underlying myelin pathology was associated with nonuniform slowing of conduction velocities, conduction block, and temporal dispersion on nerve conduction studies, which resemble those features in acquired demyelinating peripheral nerve diseases. Segmental demyelination was also confirmed in mice without Fig4 (Fig4-/- ). The demyelination was associated with an increase of Schwann cell dedifferentiation and macrophages in spinal roots where nerve-blood barriers are weak. Schwann cell dedifferentiation was induced by the increasing intracellular Ca2+ . Suppression of Ca2+ level by a chelator reduced dedifferentiation and demyelination of Schwann cells in vitro and in vivo. Interestingly, cell-specific knockout of Fig4 in mouse Schwann cells or neurons failed to cause segmental demyelination. INTERPRETATION: Myelin change in CMT4J recapitulates the features of acquired demyelinating neuropathies. This pathology is not Schwann cell autonomous. Instead, it relates to systemic processes involving interactions of multiple cell types and abnormally elevated intracellular Ca2+ . Injection of a Ca2+ chelator into Fig4-/- mice improved segmental demyelination, thereby providing a therapeutic strategy against demyelination. Ann Neurol 2018;83:756-770.

Morphometric analysis of peripheral myelinated nerve fibers through deep learning.

Irrespective of initial causes of neurological diseases, these disorders usually exhibit two key pathological changes-axonal loss or demyelination or a mixture of the two. Therefore, vigorous quantification of myelin and axons is essential in studying these diseases. However, the process of quantification has been labor intensive and time-consuming because of the requisite manual segmentation of myelin and axons from microscopic nerve images. As a part of AI development, deep learning has been utilized to automate certain tasks, such as image analysis. This study describes the development of a convolutional neural network (CNN)-based approach to segment images of mouse nerve cross sections. We adapted the U-Net architecture and used manually-produced segmentation data accumulated over many years in our lab for training. These images ranged from normal nerves to those afflicted by severe myelin and axon pathologies; thus, maximizing the trained model's ability to recognize atypical myelin structures. Morphometric data produced by applying the trained model to additional images were then compared to manually obtained morphometrics. The former effectively shortened the time consumption in the morphometric analysis with excellent accuracy in axonal density and g-ratio. However, we were not able to completely eliminate manual refinement of the segmentation product. We also observed small variations in axon diameter and myelin thickness within 9.5%. Nevertheless, we learned alternative ways to improve accuracy through the study. Overall, greatly increased efficiency in the CNN-based approach out-weighs minor limitations that will be addressed in future studies, thus justifying our confidence in its prospects. Note: All the relevant code is freely available at https://neurology.med.wayne.edu/drli-datashairing.

C698R mutation in Lrsam1 gene impairs nerve regeneration in a CMT2P mouse model.

Missense mutation C694R in the RING domain of the LRSAM1 gene results in a dominantly inherited polyneuropathy, Charcot-Marie-Tooth disease type 2P (CMT2P). We have generated and characterized a Lrsam1C698R knock-in mouse model produced through CRISPR/Cas9 technology. Both heterozygous (Lrsam1+/C698R) and homozygous (Lrsam1C698/C698R) knock-in mice exhibited normal motor functions on behavioral tests as well as normal on nerve conduction studies. Axonal density and myelin thickness were not significantly different between mutants and wild-type mice by sciatic nerve morphometric analysis up to 17 months of age. In line with these normal findings, protein-protein interactions between mutant LRSAM1 and RNA-binding proteins (such as FUS and G3BP1) were still present in mouse cells, which differs from the disrupted interactions between these proteins in human CMT2P cells. However, after crush nerve injury, Lrsam1+/C698R mice had a mild, but statistically significant, reduced compound nerve action potential and conduction velocity during recovery. Therefore, C698R mutation results in a mild impaired nerve regeneration in mice. We speculate that repetitive nerve injuries may, at least partially, contribute to the slowly progressive axonal loss in CMT2P.