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Bakshi, R.

Publications and source records attributed to Bakshi, R..

7 recordsLinked to original sources

UV induces common cutaneous amyloid-like melanosomal protein aggregates

Misfolding of aggregation-prone proteins underpins diseases known as proteinopathies. One of these proteins, alpha-synuclein, is a component of aggregates in neurodegenerative conditions such as Parkinsons disease. The melanosomal protein PMEL, which forms physiologic amyloid scaffold structures on which melanin is organized in melanosomes, similarly ectopically accumulates in the dermis in many forms of cutaneous hyperpigmentation. Here, we demonstrate in a wide range of common clinical pigmentary disorders, as well as in primary melanocyte and mouse models examined by molecular, proteomic, and electron microscopic tools, that melanocytic alpha-synuclein is a prominent component of intracellular protein aggregates bound to similar proteins as in Parkinsons disease, as well as melanized extracellular protein deposits. Using the Real Time Quaking-Induced Conversion Assay (RT-QuIC), we demonstrate that UV induces misfolded melanosomal proteins to self-propagate, augmenting this pathology in prion-like fashion. CUT&RUN chromatin profiling and single-cell RNA-seq demonstrate that melanocytes utilize microphthalmia-associated transcription factor (MITF)-regulated autophagy to counteract protein aggregation, identifying aggregate removal as a core function of tanning. In contrast to extracellular aggregation, impaired intracellular aggregate removal contributes to melanocyte senescence, which conversely exacerbates chronic hypopigmentation and photoaging-related discoloration. These findings identify melanosomal proteinopathy as a common contributor to melanocyte dysfunction and suggest aggregate-focused management approaches.

molecular biology↗

ODC1 restricts meningeal B cell age-associated-like phenotype and function in multiple sclerosis: a human and experimental study

Meningeal inflammation, as a clinical feature of multiple sclerosis (MS), is associated with worse clinical disease outcomes. In both relapsing and secondary progressive MS and the experimental autoimmune encephalomyelitis (EAE) MS model, the meninges have been found to contain ectopic lymphoid follicles enriched with B cells. The metabolic requirement of meningeal B cell function in MS or EAE is not well elucidated. Using 7-Tesla MRI brain scans of MS patients and leptomeningeal enhancement (LME) as a marker, we found a correlation between meningeal inflammation and metabolites of the arginine/polyamine pathway, a finding recapitulated in the EAE model. Ornithine Decarboxylase (ODC1), the rate limiting enzyme for polyamine biosynthesis, as well as polyamine metabolism was diminished in the dura meningeal B cells from mice with MOG35-55 induced EAE mice as compared to naive controls. Pharmacological inhibition of ODC1 restricted meningeal T cells but promoted meningeal B cell proliferation. B cell-specific deletion of ODC1 resulted in expansion of B cells with age-associated B cell-like phenotype (CD11c+CD21/35-CD23-IgD-) and exacerbated disease in the MOG1-125 EAE model. Together, these findings demonstrate a divergent role of polyamines in regulating B and T cell responses in the meninges during autoimmunity. Significance StatementThis study identified the polyamine pathway to be associated with meningeal inflammation in multiple sclerosis, a clinical phenotype associated with worse disease outcomes and without targeted therapy. Using a mouse experimental model, we found that ODC1, the rate limiting enzyme of the polyamine biosynthesis pathway, was suppressed in meningeal B cells, restricted the development of age-associated B cells in the meninges and limited disease severity. This study elucidated a metabolic pathway regulating meningeal B cell function, informing its therapeutic applications in autoimmune diseases.

immunology↗

Developmental Toxicity and Lethality of Structurally Diverse PFAS in Zebrafish

Per-and polyfluoroalkyl substances (PFAS) are ubiquitous environmental contaminants that have been associated with adverse health effects in highly exposed populations. Manufacturers have taken steps to replace toxic long-chain perfluoroalkyl acids (PFAAs) with short-chain PFAAs and perfluoroether acids (PFEAs). There is little to no toxicity data for many of these chemicals. Most of the data that are available are taken from studies that do not account for the pH of highly concentrated PFAS solutions, resulting in highly acidic conditions that do not accurately reflect real-world exposures. The goal of this study was to evaluate the lethality and developmental toxicity of 17 structurally diverse PFAS in zebrafish in a pH-neutral environment. We then compared results to determine the impacts of chain length, head group, and ether linkages on toxicity. The potency of PFAS to induce mortality and developmental toxicity endpoints increased with chain length, and sulfonic acids were more potent than carboxylic acids. The inclusion of ether oxygens was associated with reduced potency relative to PFAAs with equal chain length and head group. Perfluorooctane sulfonic acid (PFOS) was the most potent compound, followed by perfluoroundecanoic acid (PFUnDA) and perfluorododecanoic acid (PFDoDA). Short-chain compounds perfluoro-2-methoxyacetic acid (PFMOAA) and perfluorobutanoic acid (PFBA) were the least potent. These data were used to construct a multiple linear regression model for PFAS potency. Failed swim bladder inflation was the most sensitive developmental toxicity endpoint for all assessed chemicals. Other common phenotypes included spinal curvature, edema, craniofacial malformations, and ocular malformations. Ocular malformations were more common in response to sulfonic acids. No other phenotypes exhibited significant structural specificity. Our study provides new toxicity data for a diverse set of PFAS under environmentally relevant conditions. Future studies should be expanded to include more branched structures and head groups not present in our testing set to allow for improved understanding of how other structural features impact toxicity.

pharmacology and toxicology↗

Deconfounded Dimension Reduction via Partial Embeddings

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. Our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

neuroscience↗

Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis

Background/PurposeMulticenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at 4 sites. Materials and MethodsImages were acquired on Siemens or Philips scanners at 3-tesla. Automated white matter lesion detection and whole brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 and T2 (FLAIR) lesions. Random effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites. ResultsRandom effect modeling demonstrated model assumption violations for most comparisons of interest. Non-parametric modeling indicated that site explained > 50% of the variation for most estimated volumes. This expanded to > 75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intra-scanner differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact. ConclusionDifferences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating inter-scanner differences show promise in reducing biases in multi-center studies of MS.

neuroscience↗

TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods, yet, manual delineation remains the gold standard approach. These approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes Sorensen-Dice Similarity Coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a general additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Womens Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data, we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding is mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicate no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

neuroscience↗

An automated statistical technique for counting distinct multiple sclerosis lesions can recover aspects of lesion history and provide relevant disease information

BackgroundLesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest associations with clinical outcomes. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (i.e. spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions.\n\nMethodsMRI is used to assess the probability of lesion at each location. The texture of this map is quantified using a novel technique, and clusters resembling the center of a lesion are counted.\n\nResultsValidity was demonstrated by comparing the proposed count to a gold-standard count in 60 subjects observed longitudinally. The counts were highly correlated (r = .97, p < .001) and not significantly different (t59 = -0.83, p > .40). Reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites, and variability of lesion count was equivalent to that of lesion load. Accounting for lesion load and age, lesion count was negatively associated (t58 = -2.73, p < .01) with the Expanded Disability Status Scale (EDSS). Average lesion size had a higher association with EDSS (r =.35, p < .01) than lesion load (r = .10, p > .40) or lesion count (r = -.12, p > .30) alone.\n\nConclusionThese findings demonstrate that it is possible to recover important aspects of the natural history of lesion formation without longitudinal data, and suggest that lesion size provides complementary information about disease.\n\nGrant SupportThe project described was supported in part by the NIH grants R01 NS085211, R21 NS093349, and R01 NS094456 from the National Institute of Neurological Disorders and Stroke (NINDS). The study was also supported by the Intramural Research Program of NINDS and the Race to Erase MS Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

neuroscience↗