Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “proteome”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Astrocyte Reactivity by Alcohol Dependence in the Central Amygdala

Astrocytes play essential roles in the brain, but prolonged insult can result in the loss or diminishment of homeostatic functions and increased neuroinflammatory response. The central amygdala (CeA) is an important limbic region in reward and stress responsivity as well as in the development of alcohol dependence. Using a multi-omics approach with Aldh1l1-EGFP/Rpl10a mice and the chronic intermittent ethanol – two-bottle choice exposure model, we have characterized the translational response of CeA astrocytes, as well as the proteomic and phosphoproteomic changes in dependent, non-dependent, and naïve mice. We identified astrocyte-specific alterations in neuroimmune functions and antioxidant/oxidative stress pathways in dependent animals while non-dependent EtOH drinking altered cytoskeletal plasticity related pathways. Proteomic analysis showed down-regulation of astrocyte physiological functions in dependent animals while phosphoproteomic analysis identified cytoskeleton remodeling in both dependent and non-dependent animals suggesting the development of a reactive astrocyte phenotype. Astrocyte morphological reconstruction showed increased CeA astrocyte complexity in dependent and non-dependent groups compared to naïve mice. The astrocyte-specific increase in neuroimmune functions, down-regulation of astrocyte homeostatic functions, alteration in protein phosphorylation-mediated cytoskeleton remodeling, and increased astrocyte complexity demonstrate EtOH induced astrocyte activation in the CeA and suggest the presence of both adaptive and maladaptive reactive astrocytes. These findings highlight the roles CeA astrocytes play in the progression of alcohol intake to dependence and are the first step in the identification of novel astrocyte-specific therapeutic approaches for alcohol use disorder through the potentiation of adaptive changes and inhibition of maladaptive changes in astrocytes.

Hashimoto, Joel↗
DOE OSTI2026

Temporal multi-omic analysis uncovers sex-biased molecular programs underlying skeletal muscle adaptation to endurance training

Background. Exercise training is known to benefit health and reduce disease risk. While adaptations in skeletal muscles are fundamental to many of the health benefits of exercise training, the common and sex-specific molecular regulators that mediate these adaptations remain to be fully elucidated. Methods. To this end, we leveraged skeletal muscle multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), where 6 month-old male and female rats endurance trained for 1, 2, 4, or 8 weeks. Our objective was to identify shared and sex-specific multi-omic molecular responses to endurance training in skeletal muscle, and relate them to phenotypic adaptations. Results. We identified largely sexually-conserved transcriptomic and proteomic enrichments in the gastrocnemius, which correlated with skeletal muscle responses from a published exercise study in humans. We uncovered sex-consistent post-translational modifications, including decreased oxidation of MYH2 and deacetylation of the ß-oxidation enzyme HADHA. Pathway enrichment analyses revealed sex-specific remodeling across the acetylome, redox proteome, and phosphoproteome; females decreased mitochondrial protein oxidation and increased mitochondrial cristae proteins, indicative of enhanced redox buffering and mitochondrial efficiency. Despite observed decreases in the oxidation of key mitochondrial proteins, females displayed increases in the oxidation of proteins involved in glucose catabolism relative to males after 8 weeks of training, suggestive of sex-biased subcellular reactive oxygen species generation. Conclusions. This work shows a large portion of the adaptive response to endurance training in skeletal muscle is shared between females and males, while there are distinct and nuanced sex-specific adaptations that are evident, particularly at the level of post-translational regulation.

Many, Gina M.↗
DOE OSTI2023

Metatranscriptomics analysis reveals a novel transcriptional and translational landscape during Middle East respiratory syndrome coronavirus infection

Among all RNA viruses, coronavirus RNA transcription is the most complex and involves a process termed “discontinuous transcription” that results in the production of a set of 3'-nested, co-terminal genomic and subgenomic RNAs during infection. While the expression of the classic canonical set of subgenomic RNAs depends on the recognition of a 6- to 7-nt transcription regulatory core sequence (TRS), here, we use deep sequence and metagenomics analysis strategies and show that the coronavirus transcriptome is even more vast and more complex than previously appreciated and involves the production of leader-containing transcripts that have canonical and noncanonical leader-body junctions. Moreover, by ribosome protection and proteomics analyses, we show that both positive- and negative-sense transcripts are translationally active. The data support the hypothesis that the coronavirus proteome is much vaster than previously noted in the literature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Effect of disease progression on the podocyte cell cycle in Alport Syndrome

Progression of glomerulosclerosis is associated with loss of podocytes with subsequent glomerular tuft instability. It is thought that a diminished number of podocytes may be able to preserve tuft stability through cell hypertrophy associated with cell cycle reentry. At the same time, reentry into the cell cycle risks podocyte detachment if podocytes cross the G1/S checkpoint and undergo abortive cytokinesis. In order to study cell cycle dynamics during chronic kidney disease (CKD) development, we used a FUCCI model (fluorescence ubiquitination-based cell cycle indicator) of mice with X-linked Alport Syndrome. This model exhibits progressive CKD and expresses fluorescent reporters of cell cycle stage exclusively in podocytes. With the development of CKD, an increasing fraction of podocytes in vivo were found to be in G1 or later cell cycle stages. Podocytes in G1 and G2 were hypertrophic. Heterozygous female mice, with milder manifestations of CKD, showed G1 fraction numbers intermediate between wild-type and male Alport mice. Proteomic analysis of podocytes in different cell cycle phases showed differences in cytoskeleton reorganization and metabolic processes between G0 and G1 in disease. Additionally, in vitro experiments confirmed that damaged podocytes reentered the cell cycle comparable to podocytes in vivo. Importantly, we confirmed the upregulation of PDlim2, a highly expressed protein in podocytes in G1, in a patient with Alport Syndrome, confirming our proteomics data in the human setting. Thus, our data showed that in the Alport model of progressive CKD, podocyte cell cycle distribution is altered, suggesting that cell cycle manipulation approaches may have a role in the treatment of various progressive glomerular diseases characterized by podocytopenia.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Influence of the irradiated pulmonary microenvironment on macrophage and T cell dynamics

The lung is sensitive to radiation, which increases the risk of normal tissue toxicity following radiation therapy. Adverse outcomes include pneumonitis and pulmonary fibrosis, which are thought to result from dysregulated intercellular communication within the pulmonary microenvironment. Macrophage accumulation in the lung following radiation exposure is implicated in these pathogenic outcomes. Here we investigate the role of the microenvironment in macrophage and T cell dynamics. Materials and methods C57Bl/6 mice received 6Gyx5 x-ray irradiation to the right lung. Macrophage and T cell dynamics were investigated in ipsilateral right lungs, contralateral left lungs and compared to those from non-irradiated control subjects at 4-26wk post exposure. Lungs were evaluated by flow cytometry, histology and proteomics. Results In uni-lung irradiated subjects, focal regions of macrophage accumulation were noted in both lungs by 8wk, however fibrotic lesions were observed only in ipsilateral lungs by 26wk. Both infiltrating and alveolar macrophages populations transiently expanded in both lungs, however a transitional CD11b+ alveolar macrophage population persisted only in ipsilateral lungs and expressed lower CD206. Concurrently, arginase-1+ macrophages were noted within areas of macrophage accumulation in ipsilateral but not contralateral lungs at 8 and 26wk post exposure, while CD206+ macrophages were absent from these accumulations. While CD8+ T cells were present in both lungs of irradiated subjects, T regulatory cells were only increased in ipsilateral lungs. Unbiased proteomics analysis of immune cells revealed a substantial number of differentially expressed proteins in ipsilateral lungs when compared to either contralateral or non-irradiated lungs, as well as between contralateral lungs from irradiated subjects and non-irradiated controls. Conclusions Pulmonary macrophage and T cell dynamics are impacted by the microenvironmental conditions that develop in the lung following radiation exposure, both locally and systemically. Our findings demonstrate that while macrophages and T cells infiltrate and expand in both lungs, they diverge phenotypically depending on their environment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Characterization of the procoagulant phenotype of amniotic fluid across gestation in rhesus macaques and humans

Background: Amniotic fluid (AF) plays a key role in fetal development, yet the evolving composition of AF and its effects of hemostasis and thrombosis are poorly understood. Objectives: Here, we aim to determine how the evolving molecular composition of AF relates to its procoagulant properties. Methods: We analyzed the proteomes, lipidomes and procoagulant properties of AF obtained by amniocentesis from rhesus macaque and human pregnancies at gestational-age matched timepoints. Results When added to human plasma, both rhesus and human AF accelerated clotting time and fibrin generation. We identified proteomic modules associated with clotting time and enriched for coagulation-related pathways. Proteins known to be involved in hemostasis were highly correlated with each other and their intensity of expression varied across gestation in both rhesus and humans. Inhibition of contact pathway did not affect the procoagulant effect of AF. Blocking tissue factor pathway inhibitor reversed the ability of AF to block the generation of FXa. The prothrombinase activity of AF was inhibited by phospholipid inhibitors. The levels of phosphatidylserine in AF were inversely correlated with clotting time. AF promoted platelet activation and secretion in plasma. Conclusions: The addition of AF to plasma enhances coagulation in a manner dependent on phospholipids as well as the presence of proteases and other proteins that directly regulate coagulation. We describe a correlation between clotting time and expression of coagulation proteins and phosphatidylserine in both rhesus and human AF, supporting the use rhesus models for future studies of AF biology.

amniotic fluid↗
DOE OSTI2023

Revising the dynamic energy budget theory with a new reserve mobilization rule and three example applications to bacterial growth

Dynamic energy budget (DEB) theory has been applied to model a wide range of organisms, including microbes. In the standard DEB model, biomass is partitioned into reserve and structural compartments, where reserve biomass is mobilized in a pseudolinear manner (while the reserve biomass density, defined as the ratio between reserve and structural biomass, decays linearly) to drive maintenance and the growth of structural biomass (and extracellular enzyme production if it is considered). However, the linear dynamics of the reserve biomass density makes the standard DEB model incapable of explaining the slowdown of microbial growth at high reserve density that is caused by macromolecular crowding effect which reduces biochemical reaction rates (a typical situation occurs when microbes are experiencing severe moisture stress) and is inconsistent with the observation that intracellular enzymatic reactions generally follow non-linear kinetics. By partitioning biomass into reserve, kinetic, and structural compartments, we show here that the Equilibrium Chemistry Approximation (ECA) kinetics can be used to represent enzymatically catalyzed reserve biomass mobilization that can then drive the kinetic and structural biomass synthesis. This revised DEB model better represents the tradeoff in ribosome allocation for structural growth and internal enzyme production, is structurally compatible with metabolic models of cell individuals, and includes the standard DEB model and the popular compromise model as special cases for representing population growth. We then applied the revised DEB model to interpret components of bacterial respiration, their dependence on substrate availability, and emergent microbial carbon use efficiency dynamics for an exponentially growing population. We found that the revised DEB model enables a better understanding of bacterial substrates use (carbon in our examples) than that can be derived from a few other models in the literature. In particular, the revised DEB model explains why carbon use efficiency may first increase, then plateau, and finally decrease with growth rate (and substrate uptake rate), as a function of proteomics. Additionally, the revised DEB model explains why the kinetic biomass compartment needs to be divided to reasonably incorporate proteomic control of microbial growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep learning integrates histopathology and proteogenomics at a pan-cancer level

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Quality control of mislocalized and orphan proteins

A healthy and functional proteome is essential to cell physiology. However, this is constantly being challenged as most steps of protein metabolism are error-prone and changes in the physico-chemical environment can affect protein structure and function, thereby disrupting proteome homeostasis. Among a variety of potential mistakes, proteins can be targeted to incorrect compartments or subunits of protein complexes may fail to assemble properly with their partners, resulting in the formation of mislocalized and orphan proteins, respectively. Quality control systems are in place to handle these aberrant proteins, and to minimize their detrimental impact on cellular functions. Here, we discuss recent findings on quality control mechanisms handling mislocalized and orphan proteins. We highlight common principles involved in their recognition and summarize how accumulation of these aberrant molecules is associated with aging and disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Lipid accumulation in nitrogen and phosphorus-limited yeast is caused by less growth-related dilution

Oleaginous yeasts are used commercially to produce oleochemicals and hold potential also for biodiesel production. In response to nitrogen or phosphorous limitation, oleaginous yeasts accumulate lipids in the form of triacylglycerols. Previous work has investigated potential mechanisms by which nutrient limitation induces lipid biosynthesis without verifying whether lipid biosynthesis flux is actually enhanced. Here, in this work, we show, using 13C-glucose tracing, that in nitrogen or phosphorous limitation, lipid accumulation occurs without consistent increases in biosynthetic flux. Instead, the main driver of increased lipid pools is decreased growth-related dilution. This conclusion holds across two divergent oleaginous yeasts: Rhodotorula toruloides and Yarrowia lipolytica . Quantitative proteomics shows a substantial proteome reallocation in response to nitrogen and phosphorous limitation, with ribosomal proteins strongly downregulated, while lipid enzymes are preserved but not consistently upregulated in absolute quantity. Thus, nutrient limitation, rather than triggering greatly enhanced lipid synthesis, results in roughly sustained lipid enzyme levels and biosynthetic flux. Due to slower lipid dilution by cell division, this suffices to drive marked lipid accumulation.

09 BIOMASS FUELS↗
DOE OSTI2020

Development of Multiplexed Immuno-N-Terminomics to Reveal the Landscape of Proteolytic Processing in Early Embryogenesis of Drosophila melanogaster

Protein expression levels are regulated through both translation and degradation mechanisms. Levels of degradation intermediates, i.e., partially degraded proteins, cannot be distinguished from those of intact proteins by global proteomics analysis, which quantify total protein abundance levels. This study aimed to develop a tool for assessing the aspects of degradation regulation via proteolytic processing through a new multiplexed N-terminomics method involving selective isobaric labeling of protein N-termini and immunoaffinity capture of the labeled N-terminal peptides. Our method allows for not only identification or proteolytic cleavage sites, but also highly multiplexed quantification of proteolytic processing. In this paper, we profiled a number of potential cleavage sites by signal peptidase and provided experimental confirmation of predicted cleavage sites of signal peptide. Furthermore, the present method uniquely represents the landscape of proteomic proteolytic processing rate during early embryogenesis in Drosophila melanogaster, revealing the underlying mechanism of stringent decay regulation of zygotically expressed proteins during early stages of embryogenesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

SPE-CZE-MS Quantifies Zeptomole Amounts of Phosphorylated Peptides

Capillary zone electrophoresis (CZE) is gaining attention in the field of single-cell proteomics for its ultralow-flow and high-resolution separation abilities. Even more sample-limited yet rich in biological information are phosphoproteomics experiments, as the phosphoproteome composes only a fraction of the whole cellular proteome. Rapid analysis, high sensitivity, and maximization of sample utilization are paramount for single-cell analysis. Some challenges of coupling CZE analysis with mass spectrometry analysis (MS) of complex mixtures include 1. sensitivity due to volume loading limitations of CZE and 2. incompatibility of MS duty cycles with electropherographic time scales. Here, we address these two challenges as applied to singlecell- equivalent phosphoproteomics experiments by interfacing a microchip-based CZE device integrated with a solid-phaseextraction (SPE) bed with the Orbitrap Astral mass spectrometer. Using 225 phosphorylated peptide standards and phosphorylated peptide-enriched mouse brain tissue, we investigate microchip-based SPE-CZE functionality, quantitative performance, and complementarity to nano-LC-MS (nLC-MS) analysis. We highlight unique SPE-CZE separation mechanisms that can empower fitfor- purpose applications in single-cell-equivalent phosphoproteomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Computationally efficient Bayesian estimation of graphical networks for omics data

Graphical networks are useful, widely-used modeling approaches to represent complex biological processes with biological measurements generated by platforms such as mass spectrometry. Bayesian analyses of graphical networks for omics data have several advantages over their frequentist counterparts, such as the inclusion of prior knowledge in the estimation of models. However, Bayesian approaches to date have only been feasible for data with a couple hundred biomolecules due to prohibitive computational time, but omics data often contains tens of thousands of biomolecules. Here, we present and illustrate a more computationally efficient approach named BPlane (Bayesian PseudoLikelihood-based Algorithm for Network Estimation) to extend Bayesian modeling capabilities for larger-sized datasets, such as most untargeted proteomics data. Via simulation, we demonstrate that BPlane produces substantial computational savings over a current state-of-the-art Bayesian algorithm while maintaining competitive edge detection accuracy. On a SARS-CoV2 proteomics data with 7000 proteins, the competing algorithm takes three times as long to complete the first iteration as BPlane takes to converge after over 100 iterations.

EM algorithm↗
DOE OSTI2025

An Integral Activity-Based Protein Profiling Method for Higher Throughput Determination of Protein Target Sensitivity to Small Molecules

Activity-based protein profiling (ABPP) is a chemoproteomic technique that uses small molecule probes to label active enzymes selectively and covalently in complex proteomes. Competitive ABPP, which involves treatment of the active proteome with an analyte of interest, is especially powerful for profiling how small molecules impact specific protein activities. Advances in higher throughput workflows have made it possible to generate extensive competitive ABPP data across diverse biological samples, making this approach highly appealing for characterizing shared and unique proteins affected by perturbations such as drug or chemical exposures. To use the competitive ABPP approach effectively to understand potential adverse effects of chemicals of concern (CoC), a wide range of concentrations may be needed, particularly for chemicals that lack potency or toxicity data. In this work, we present an integral competitive ABPP method that enables target sensitivity determination for different organophosphate (OP) pesticides as model toxicants. Using previously developed OP-ABPs, we optimized conditions for tandem mass tag (TMT) multiplexing of ABPP samples and compared conventional competitive ABPP involving samples at discrete paraoxon concentrations to pooled samples across that same concentration range. We then expanded our approach to compare protein target sensitivities toward two additional OP pesticides, chlorpyrifos oxon and malaoxon. The results showed that differences in integral intensities for the pooled competition sample can be used to evaluate the relative sensitivity of specific proteins without increasing the overall number of samples. For 8 CoC concentrations of interest, this strategy reduced the number of TMT plexes and the corresponding number of LC–MS/MS analyses 3-fold. In conclusion, we envision the integral ABPP (IABPP) method will provide a means to screen diverse chemicals more rapidly to identify both high and low sensitivity protein targets.

activity-based probes↗
DOE OSTI2022

Metabolomics Analysis of Bacterial Pathogen Burkholderia thailandensis and Mammalian Host Cells in Co-culture

The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism’s physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this work, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen’s secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host–pathogen interactions underlying Burkholderia infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks

Identifying the molecular systems and proteins that modify the progression of Alzheimer’s disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

CryoET reveals organelle phenotypes in huntington disease patient iPSC-derived and mouse primary neurons

Huntington’s disease (HD) is caused by an expanded CAG repeat in the huntingtin gene, yielding a Huntingtin protein with an expanded polyglutamine tract. While experiments with patient-derived induced pluripotent stem cells (iPSCs) can help understand disease, defining pathological biomarkers remains challenging. Here, we used cryogenic electron tomography to visualize neurites in HD patient iPSC-derived neurons with varying CAG repeats, and primary cortical neurons from BACHD, deltaN17-BACHD, and wild-type mice. In HD models, we discovered sheet aggregates in double membrane-bound organelles, and mitochondria with distorted cristae and enlarged granules, likely mitochondrial RNA granules. We used artificial intelligence to quantify mitochondrial granules, and proteomics experiments reveal differential protein content in isolated HD mitochondria. Knockdown of Protein Inhibitor of Activated STAT1 ameliorated aberrant phenotypes in iPSC- and BACHD neurons. We show that integrated ultrastructural and proteomic approaches may uncover early HD phenotypes to accelerate diagnostics and the development of targeted therapeutics for HD.

59 BASIC BIOLOGICAL SCIENCES↗