Faculty Bibliography

Temporal lobe epilepsy represents a major cause of drug-resistant epilepsy. Cognitive impairment is a frequent comorbidity, but the mechanisms are not fully elucidated. We hypothesized that the cognitive impairment in drug-resistant temporal lobe epilepsy could be due to perturbations of amyloid and tau signalling pathways related to activation of stress kinases, similar to those observed in Alzheimer's disease. We examined these pathways, as well as amyloid-β and tau pathologies in the hippocampus and temporal lobe cortex of drug-resistant temporal lobe epilepsy patients who underwent temporal lobe resection (n = 19), in comparison with age- and region-matched samples from neurologically normal autopsy cases (n = 22). Post-mortem temporal cortex samples from Alzheimer's disease patients (n = 9) were used as positive controls to validate many of the neurodegeneration-related antibodies. Western blot and immunohistochemical analysis of tissue from temporal lobe epilepsy cases revealed increased phosphorylation of full-length amyloid precursor protein and its associated neurotoxic cleavage product amyloid-β*56. Pathological phosphorylation of two distinct tau species was also increased in both regions, but increases in amyloid-β1-42 peptide, the main component of amyloid plaques, were restricted to the hippocampus. Furthermore, several major stress kinases involved in the development of Alzheimer's disease pathology were significantly activated in temporal lobe epilepsy brain samples, including the c-Jun N-terminal kinase and the protein kinase R-like endoplasmic reticulum kinase. In temporal lobe epilepsy cases, hippocampal levels of phosphorylated amyloid precursor protein, its pro-amyloidogenic processing enzyme beta-site amyloid precursor protein cleaving enzyme 1, and both total and hyperphosphorylated tau expression, correlated with impaired preoperative executive function. Our study suggests that neurodegenerative and stress-related processes common to those observed in Alzheimer's disease may contribute to cognitive impairment in drug-resistant temporal lobe epilepsy. In particular, we identified several stress pathways that may represent potential novel therapeutic targets.

Pathological avoidance behavior in anxiety and anxiety-related disorders has a large role in the persistence and severity of disease. Individuals are cued to avoid potential aversive events by learned danger and safety signals in the environment. Individuals with anxiety demonstrate a bias to utilize danger signals more than safety signals, in contrast to those without these disorders. Therefore, the present study investigated if danger and safety signals differentially influenced persistent avoidance in an animal model of anxiety-vulnerability, the Wistar Kyoto (WKY) rat, relative to the outbred Sprague Dawley (SD) rat. Persistent avoidance was assessed using extinction protocols. When danger or safety signals were present during extinction, WKY rats were slower to extinguish the avoidance response compared to SD rats. In contrast, when danger and safety signals were both present during extinction, WKY and SD rats extinguished at a similar rate. Differences in contextual and configural learning were explored as potential causes of the strain differences in the use of safety and danger signals in avoidance extinction. Strains did not differ in avoidance extinction when context was manipulated. However, WKY rats were impaired in configural learning using a negative patterning task. The results indicate that danger and safety signals may impair avoidance extinction in anxiety-vulnerable individuals due to impaired configural learning. These findings have important implications for understanding the etiology of anxiety disorders and may improve their diagnosis and treatment.

PURPOSE/OBJECTIVE:T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive disease, affecting children and adults. Chemotherapy treatments show high response rates but have debilitating effects and carry risk of relapse. Previous work implicated NOTCH1 and other oncogenes. However, direct inhibition of these pathways affects healthy tissues and cancer alike. Our goal in this work has been to identify enzymes active in T-ALL whose activity could be targeted for therapeutic purposes. EXPERIMENTAL DESIGN/METHODS:To identify and characterize new NOTCH1 druggable partners in T-ALL, we coupled studies of the NOTCH1 interactome to expression analysis and a series of functional analyses in cell lines, patient samples and xenograft models. RESULTS:We demonstrate that ubiquitin-specific protease 7 (USP7) interacts with NOTCH1 and controls leukemia growth by stabilizing the levels of NOTCH1 and JMJD3 histone demethylase. USP7 is highly expressed in T-ALL and is transcriptionally regulated by NOTCH1. In turn, USP7 controls NOTCH1 levels through deubiquitination. USP7 binds oncogenic targets and controls gene expression through stabilization of NOTCH1 and JMJD3 and ultimately H3K27me3 changes. We also show that USP7 and NOTCH1 bind T-ALL superenhancers, and inhibition of USP7 leads to a decrease of the transcriptional levels of NOTCH1 targets and significantly blocks T-ALL cell growth in vitro and in vivo. CONCLUSIONS:These results provide a new model for USP7 deubiquitinase activity through recruitment to oncogenic chromatin loci and regulation of both oncogenic transcription factors and chromatin marks to promote leukemia. Our studies also show that targeting USP7 inhibition could be a therapeutic strategy in aggressive leukemia.

OBJECTIVE:To determine whether CSF biomarkers can be used as a predictor of freezing of gait (FOG) in Parkinson disease (PD) and to investigate the predictive value of clinical, dopamine transporter (DAT) imaging, and CSF parameters both separately and in combination. METHODS:to total tau at baseline. Demographic and clinical data and DAT imaging results were also investigated. Cox proportional-hazards regression analyses were performed to identify the factors predictive of FOG. From these results, we constructed a predictive model for the development of FOG. RESULTS:achieved a better discriminative ability (area under the curve 0.755, 95% CI 0.700-0.810) than any factor alone. CONCLUSION/CONCLUSIONS:to be a predictor of FOG in patients with early PD. Furthermore, the development of FOG within 4 years after diagnosis of PD can be predicted with acceptable accuracy with our risk model.

OBJECTIVE:With this prospective, observational study, we aimed to determine whether noninvasive language tasks, developed specifically for children, could reliably identify the hemisphere of seizure onset in pediatric epilepsy. METHODS:analyses and odds ratios were used to examine classification of left vs right hemisphere epilepsy for individual patients based on test performance. RESULTS:= 0.004). The odds of left hemisphere epilepsy were 4.2 times higher (95% confidence interval 1.4-11.7) than the odds of right hemisphere epilepsy with poor auditory naming performance. In the subset of patients with temporal lobe epilepsy (TLE), the odds of left TLE were 11.3 times higher (95% confidence interval 2.00-63.17) than the odds of right TLE with poor auditory naming performance. CONCLUSION/CONCLUSIONS:Contrary to previous findings, naming performance can lateralize hemisphere of seizure onset in children with epilepsy, thereby assisting in the preoperative workup for pediatric epilepsy surgery.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T₁-, T₂-, and T₂^∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.