Specificity for latent C termini links the E3 ubiquitin ligase CHIP to caspases.

Protein-protein interactions between E3 ubiquitin ligases and protein termini help shape the proteome. These interactions are sensitive to proteolysis, which alters the ensemble of cellular N and C termini. Here we describe a mechanism wherein caspase activity reveals latent C termini that are then recognized by the E3 ubiquitin ligase CHIP. Using expanded knowledge of CHIP's binding specificity, we predicted hundreds of putative interactions arising from caspase activity. Subsequent validation experiments confirmed that CHIP binds the latent C termini at tauD421 and caspase-6D179. CHIP binding to tauD421, but not tauFL, promoted its ubiquitination, while binding to caspase-6D179 mediated ubiquitin-independent inhibition. Given that caspase activity generates tauD421 in Alzheimer's disease (AD), these results suggested a concise model for CHIP regulation of tau homeostasis. Indeed, we find that loss of CHIP expression in AD coincides with the accumulation of tauD421 and caspase-6D179. These results illustrate an unanticipated link between caspases and protein homeostasis.

A Comprehensive Resource for Induced Pluripotent Stem Cells from Patients with Primary Tauopathies.

Primary tauopathies are characterized neuropathologically by inclusions containing abnormal forms of the microtubule-associated protein tau (MAPT) and clinically by diverse neuropsychiatric, cognitive, and motor impairments. Autosomal dominant mutations in the MAPT gene cause heterogeneous forms of frontotemporal lobar degeneration with tauopathy (FTLD-Tau). Common and rare variants in the MAPT gene increase the risk for sporadic FTLD-Tau, including progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We generated a collection of fibroblasts from 140 MAPT mutation/risk variant carriers, PSP, CBD, and cognitively normal controls; 31 induced pluripotent stem cell (iPSC) lines from MAPT mutation carriers, non-carrier family members, and autopsy-confirmed PSP patients; 33 genome engineered iPSCs that were corrected or mutagenized; and forebrain neural progenitor cells (NPCs). Here, we present a resource of fibroblasts, iPSCs, and NPCs with comprehensive clinical histories that can be accessed by the scientific community for disease modeling and development of novel therapeutics for tauopathies.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. NEW METHOD: Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. RESULTS: Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. COMPARISON WITH EXISTING METHODS: We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. CONCLUSION: The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.