Biology subjects

Nesvizhskii, A. I.

Publications and source records attributed to Nesvizhskii, A. I..

At least 19 recordsLinked to original sources

bioRxiv2026

Ficd loss rescues motor impairments and reverses oligodendrocyte maturation deficits in a mouse model of spinocerebellar ataxia type 3

Spinocerebellar ataxia type 3 (SCA3) is an inherited, fatal neurodegenerative disease caused by a pathological CAG repeat expansion in the ATXN3 gene, resulting in the selective degeneration of vulnerable neuronal populations. Recent work has identified impairments in oligodendrocyte maturation as a novel and robust feature of SCA3 pathogenesis. Oligodendrocytes synthesize myelin structural components through the endoplasmic reticulum (ER), rendering this organelle essential for white matter integrity. Despite this, the role of ER function in SCA3 remains unclear. In this study, we show that loss of FICD-mediated AMPylation, a post-translational modification regulating the ER-resident HSP70 chaperone, BiP, rescues motor impairments in a transgenic SCA3 mouse model. Ficd-/- SCA3 mice exhibit significantly reduced levels of nuclear ATXN3 in vulnerable brain regions, while Ficd+/+ littermates show an increased burden of AMPylated BiP in the spinal cord, identifying aberrant AMPylation as a novel contributor of SCA3 pathology. Using unbiased proteomics, we demonstrate that Ficd deletion mitigates the pathological decrease in myelin structural proteins and oligodendrocyte maturation factors, restoring levels of mature, myelinating oligodendrocytes. In parallel, we show that Ficd activates SREBP2-dependent cholesterol biosynthesis to support myelination. Taken as a whole, these findings posit ER homeostasis as a critical driver of oligodendrocyte pathology and identify FICD as a novel target for alleviating non-neuronal toxicity in SCA3.

molecular biology↗

bioRxiv2026

The inner nuclear membrane protein SUN1 regulates cullin-3 neddylation to maintain insulin signaling

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease and strongly linked to obesity and insulin resistance. We previously reported that the common nuclear envelope variant rs6461378 (g.842031C>T; SUN1 H118Y) associated with MASLD and related traits including insulin resistance. To gain insight into how wild-type (WT) and H118Y SUN1 might differentially impact insulin signaling, we performed affinity purification-mass spectrometry (AP-MS) in human liver-derived cells stably expressing WT or H118Y SUN1. Unbiased AP-MS revealed a novel SUN1-CUL3 interaction, with comparative analysis showing that WT SUN1 interacted robustly with CUL3, while CUL3 interaction was markedly diminished with H118Y SUN1. Cells in which SUN1 was silenced via siRNA, or in which H118Y SUN1 was ectopically expressed, showed increased CUL3 neddylation, which is required for cullin RING ligase (CRL)-mediated ubiquitination of insulin receptor substrate (IRS) proteins. Inhibition of neddylation restored IRS-1 levels and insulin signaling in H118Y SUN1-expressing cells. Together, our findings provide a potential mechanism of H118Y SUN1-driven insulin resistance and a viable therapeutic approach for its reversal.

physiology↗

bioRxiv2026

Expanding Glycopeptide Identification with Match-Between-Glycans in FragPipe

Glycosylation is one of the most important, but also most complex, post-translational modifications of proteins, playing a pivotal role in various pathological processes. Mass spectrometry-based large-scale glycoproteomics analysis offers a powerful approach to explore the fundamental roles of glycosylation in both physiological and pathological contexts. Traditionally, DDA glycopeptide assignment relies on information-dense MS2 spectra, containing sufficient fragmentation information to identify both the peptide and glycan moieties. Achieving this fragmentation can be difficult, especially for low-abundant glycopeptides and/or large, complex glycans. These glycopeptides are often not assigned using current data analysis software, yet they can be of biological relevance. Here, we introduce a method called match-between-glycans (MBG), which expands glycopeptide identification while maintaining the existing glycoproteome analysis workflow. MBG enables expanding the set of identified glycopeptides to include those without MS2 spectra, or with lower quality MS2 spectra, by looking for MS1 signals displaced from other identified glycopeptides by one or multiple monosaccharide unit(s). MBG can also identify glycans not included in the glycan database, such as those containing adducts or modifications, allowing these glycans to be recovered without a drastic expansion of the search space. Combined with target-decoy FDR control, we show this method is capable of accurately expanding glycopeptide identifications and providing a more complete quantitative profile of glycosylation at each glycosite. MBG is fully integrated into the glycoproteomics workflows in FragPipe, allowing seamless, one-click operation.

bioinformatics↗

bioRxiv2026

Single-Cell Proteomics of Human Peripheral Blood Mononuclear Cells Exceeding 600 Cells per Day

Single-cell proteomic (scProteomic) measurements of peripheral blood mononuclear cells (PBMCs) are of considerable value in human health, given their involvement in the maintenance of healthy and diseased states. However, the high heterogeneity and relatively small size of immune cell types demand maximal throughput and sensitivity in proteomic measurements that have yet to be fully realized. Here, we describe an approach that addresses sensitivity and throughput through the implementation of Real-Time spectral Library Searching (RTLS), TMTpro 32-plex labelling, an updated nested-nanodroplet processing in One pot for Trace Samples (N2), and a dual-column liquid chromatography system. By prioritizing tandem mass spectrometry (MS2) features with high similarity to library spectra, RTLS enables greater identification depth and feature reproducibility than a standard shotgun MS2 approach in low-input and single-cell samples. The platform permitted 660 single PBMCs to be measured per day, with an average of 750 protein identifications per cell and 1,648 proteins in total, achieving the necessary throughput and depth to characterize immune cell populations. Application of this scProteomic method and a new cell typing informatics approach to 2,130 PBMCs enabled the identification of both major and low-frequency cell types ([~]1-2%), as well as associated proteomic markers.

immunology↗

bioRxiv2026

Endothelial Cell Secretome Alterations Induced by Inflammatory Stress

Endothelial cells detect pathogens through pattern recognition receptors, such as Toll-like receptor 4 (TLR4), which triggers the synthesis and secretion of molecules that initiate the innate immune response. Proteins bearing signal peptides are secreted through the classical endoplasmic reticulum (ER)-Golgi-dependent route, whereas select signal-peptide-lacking cytoplasmic proteins are secreted via less well-characterized ER-Golgi-independent mechanisms, collectively termed unconventional cytoplasmic protein secretion (UCPS). To systematically characterize the secretome of human umbilical vein-derived endothelial cells (HUVECs) and delineate the contribution of UCPS, we performed deep quantitative proteomics on HUVEC cell lysates and conditioned medium before and after TLR4 stimulation with lipopolysaccharide (LPS). Of 5205 proteins detected in either fraction, 381 were enriched in the conditioned medium and therefore classified as secreted. Of these, 333 proteins (87.4%) were secreted via the conventional pathway, and 48 (12.6%) were secreted via UCPS, 43 of which were not previously associated with this process. Predicted functions of UCPS-secreted proteins include redox regulation, proteostasis, cytoskeletal remodeling, and innate immune signaling. We confirmed that -globin (HBA1), which functions as a redox sensor and regulator of nitric oxide in endothelial cells, is secreted constitutively by UCPS and at higher levels following inflammatory activation. Notably, UCPS cargo identity showed poor concordance with current computational predictors, underscoring the need for empirical datasets. Overall, our findings suggest that the HUVEC secretome includes both conventionally and unconventionally secreted proteins that regulate coagulation, angiogenesis, and immune function. Our findings establish a high-quality secretome dataset for HUVECs, providing a novel resource for future efforts to define the molecular determinants governing UCPS cargo selection and trafficking related to endothelial cell function.

cell biology↗

bioRxiv2025

A New Detailed Mass Offset Search in MSFragger for Improved Interpretation of Complex PTMs

Conventional database search methods for proteomics struggle when tasked to identify dozens or hundreds of modifications simultaneously. Open or error-tolerant searches can address this limitation, but at the cost of increased difficulty in downstream interpretation of the results and quantification. We and others have previously described "mass offset" or multi-notch searches that sit in between closed and open searches, allowing simultaneous search for hundreds of modifications with more straightforward downstream interpretation than open search. The original mass offset searches were closer to open search, lacking the ability to restrict modifications to specific amino acids. Here, we describe a new "detailed" mass offset (DMO) search implemented in the MSFragger search engine, which allows each mass offset to have its own site restrictions and fragmentation rules. The benefits of DMO search over existing mass offset searches are shown with three example searches of complex modification sets: nearly one hundred post-translational modifications, fast photochemical oxidation of proteins (FPOP)-derived modifications, and amino acid substitutions. The DMO search further improves the interpretability of results by reducing ambiguity in site localization, particularly when modifications have overlapping masses, and provides benefits that scale with the complexity of the search.

bioinformatics↗

bioRxiv2025

MHC1-TIP enables single-tube multimodal immunopeptidome profiling and uncovers intratumoral heterogeneity in antigen presentation

Profiling antigens presented on MHC class I molecules on the cell surface is essential to identify candidate antigens for targeted and personalized immunotherapies. However, mass spectrometry-based immunopeptidomics has traditionally been limited by high input requirements, extensive sample manipulation, and expensive reagents. To overcome these challenges, we developed MHC1-TIP: a scalable, single-tube and cost-effective workflow to enable robust MHC-I ligandome recovery from cell lines, patient-derived organoids, and sub-milligram amounts of clinical tissues. Moreover, MHC1-TIP also preserves compatibility with additional omics profiling technologies and we demonstrate its capacity for quantitative and multimodal profiling of the proteome and immunopeptidome from the same sample to enable integrated analyses of protein expression and antigen presentation. Application of MHC1-TIP to primary renal cell carcinoma fragments revealed extensive intratumoral heterogeneity in antigen presentation that was poorly correlated with source protein expression. MHC1-TIP represents a broadly applicable and sensitive approach for low-input, multimodal immunopeptidomics with clinical and translational relevance.

systems biology↗

bioRxiv2025

Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform

Isobaric mass tags, such as iTRAQ and TMT, are widely utilized for peptide and protein quantification in multiplex quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene, protein, peptide, and post-translational modification site levels. We demonstrate the versatility of TMT-Integrator using five publicly available TMT datasets: an E. coli dataset with 13 spike-in proteins, the clear cell renal cell carcinoma (ccRCC) whole proteome and phosphopeptide-enriched datasets from the Clinical Proteomic Tumor Analysis Consortium, and two human cell lysate datasets showcasing the latest advances with the Astral instrument and TMT 35-plex reagents. Integrated into the widely used FragPipe computational platform (https://fragpipe.nesvilab.org/), TMT-Integrator is a core component of TMT and iTRAQ data analysis workflows. We evaluate the FragPipe/TMT-Integrator analysis pipelines performance against MaxQuant and Proteome Discoverer with multiple benchmarks, facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe/TMT-Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, identifies more phosphorylated sites in the ccRCC phosphoproteome dataset, and delivers overall more robust quantification performance compared to other tools.

bioinformatics↗

bioRxiv2025

A modular, Galaxy-based immunopeptidogenomic (iPepGen) analysis pipeline for discovery, verification, and prioritization of candidate cancer neoantigen peptides

Characterizing tumor-specific neoantigen peptides, derived from genomic or transcriptomic aberrations and presented to the immune system, is critical for immuno-oncology studies. To this end, the modular iPepGen immunopeptidogenomics pipeline provides these functions: (1) Neoantigen prediction and protein database generation from genomic or transcriptomic sequencing data; (2) Peptide identification (3) Verification from immunopeptidomic mass spectral data; (4) Neoantigen classification and visualization; (5) Candidate prioritization for further study. Easy access via a publicly available, scalable cloud-based gateway coupled with online, interactive training materials streamlines the adoption by cancer researchers who require immunopeptidogenomic analysis tools but lack advanced computational expertise and resources.

bioinformatics↗

bioRxiv2025

A Novel Algorithm for the Harmonization of Pan-cancer Proteomics

Proteomic characterization of cancer tissues holds the potential to advance therapeutic options and reveal novel biomarkers by unlocking insights available only on the proteome level. However, proteomics data analysis is greatly challenged by systematic technical variability in experimental protocols, instrumentation and data processing, restricting comparisons between studies. With the continued and unprecedented growth of proteomics datasets, a comprehensive strategy for harmonizing these datasets is necessary to enable large-scale integrative analyses. Herein, we describe a novel framework for pan-cancer harmonization and imputation, which offers the scientific community an updated approach to this challenge. Rather than relying on a single batch-effect correction algorithm, our multi-step approach accurately addresses critical systematic differences with custom-tailored solutions, including standardized reanalysis of raw data and an autoencoder for pan-cancer integration. By introducing a suite of benchmarks, we bridge the critical gap in reliable harmonization evaluation. Using this framework, we created a harmonized pan-cancer dataset and demonstrated its superiority over existing solutions and previous pan-cancer harmonization efforts. We further demonstrated its utility in revealing prognostic markers, estimating indication-wide biomarker prevalence, and facilitating target discovery for cancer subtypes. We expect our work to provide powerful tools supporting proteomics research for precision cancer medicine.

bioinformatics↗

bioRxiv2025

PepCentric Enables Fast Repository-Scale Proteogenomics Searches

Identifying novel peptides arising from alternative splicing, mutations, or non-canonical translations is a crucial yet challenging aspect of proteogenomics. We introduce PepCentric, a scalable computational platform and a web-based portal utilizing advanced 2-D fragment indexing for rapid peptide-centric searches across extensive mass spectrometry datasets. With robust false discovery rate control and optimized search performance, PepCentric offers an efficient tool for validating novel peptides and exploring proteomic variations. In a matter of seconds, users can search their novel peptides or proteins against 2.3 billion spectra collected from 66700 mass spectrometry runs, making it practical to rapidly validate proteogenomic hypotheses.

bioinformatics↗

bioRxiv2025

TRAF6 integrates innate immune signals to regulate glucose homeostasis via Parkin-dependent and -independent mitophagy

Activation of innate immune signaling occurs during the progression of immunometabolic diseases, including type 2 diabetes (T2D), yet the impact of innate immune signaling on glucose homeostasis is controversial. Here, we report that the E3 ubiquitin ligase TRAF6 integrates innate immune signals following diet-induced obesity to promote glucose homeostasis through the induction of mitophagy. Whereas TRAF6 was dispensable for glucose homeostasis and pancreatic {beta}-cell function under basal conditions, TRAF6 was pivotal for insulin secretion, mitochondrial respiration, and increases in mitophagy following metabolic stress in both mouse and human islets. Indeed, TRAF6 was critical for the recruitment and function of machinery within both the ubiquitin-mediated (Parkin-dependent) and receptor-mediated (Parkin-independent) mitophagy pathways upon metabolic stress. Intriguingly, the effect of TRAF6 deficiency on glucose homeostasis and mitophagy was fully reversed by concomitant Parkin deficiency. Thus, our results implicate a role for TRAF6 in the cross-regulation of both ubiquitin-and receptor-mediated mitophagy through the restriction of Parkin. Together, we illustrate that {beta}-cells engage innate immune signaling to adaptively respond to a diabetogenic environment.

molecular biology↗

bioRxiv2024

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

bioinformatics↗

bioRxiv2024

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Brukers timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

bioinformatics↗

bioRxiv2024

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

bioinformatics↗

bioRxiv2024

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of [~]3500 proteins at a spatial resolution of 50 {micro}m and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

systems biology↗

bioRxiv2023

Efficient Analysis of Proteome-wide FPOP Data by FragPipe

Monitoring protein structure before and after perturbations can give insights into the role and function of proteins. Fast photochemical oxidation of proteins (FPOP) coupled with mass spectrometry (MS) allows monitoring of structural rearrangements by exposing proteins to OH radicals that oxidize solvent accessible residues, indicating protein regions undergoing movement. Some of the benefits of FPOP include high throughput and lack of scrambling due to label irreversibility. However, the challenges of processing FPOP data have thus far limited its proteome-scale uses. Here, we present a computational workflow for fast and sensitive analysis of FPOP datasets. Our workflow combines the speed of MSFragger search with a unique hybrid search method to restrict the large search space of FPOP modifications. Together, these features enable more than 10-fold faster FPOP searches that identify 50% more modified peptide spectra than previous methods. We hope this new workflow will increase the accessibility of FPOP to enable more protein structure and function relationships to be explored.

bioinformatics↗

bioRxiv2023

Autoinhibited kinesin-1 adopts a hierarchical folding pattern

Conventional kinesin-1 is the primary anterograde motor in cells for transporting cellular cargo. While there is a consensus that the C-terminal tail of kinesin-1 inhibits motility, the molecular architecture of a full-length autoinhibited kinesin-1 remains unknown. Here, we combine cross-linking mass spectrometry (XL-MS), electron microscopy (EM), and AlphaFold structure prediction to determine the architecture of the full-length autoinhibited kinesin-1 homodimer [kinesin-1 heavy chain (KHC)] and kinesin-1 heterotetramer [KHC bound to kinesin light chain 1 (KLC1)]. Our integrative analysis shows that kinesin-1 forms a compact, bent conformation through a break in coiled coil 3. Moreover, our XL-MS analysis demonstrates that kinesin light chains stabilize the folded inhibited state rather than inducing a new structural state. Using our structural model, we show that disruption of multiple interactions between the motor, stalk, and tail domains is required to activate the full-length kinesin-1. Our work offers a conceptual framework for understanding how cargo adaptors and microtubule-associated proteins relieve autoinhibition to promote activation.

biophysics↗