Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results

Human proteome distribution atlas for tissue-specific plasma proteome dynamics

The plasma proteome is maintained by the influx and efflux of proteins from surrounding organs and cells. To quantify the extent to which different organs and cells impact the plasma proteome in healthy and diseased conditions, we developed a mass-spectrometry-based proteomics strategy to infer the tissue origin of proteins detected in human plasma. We first constructed an extensive human proteome atlas from 18 vascularized organs and the 8 most abundant cell types in blood. The atlas was interfaced with previous RNA and protein atlases to objectively define proteome-wide protein-organ associations to infer the origin and enable the reproducible quantification of organ-specific proteins in plasma. We demonstrate that the resource can determine disease-specific quantitative changes of organ-enriched protein panels in six separate patient cohorts, including sepsis, pancreatitis, and myocardial injury. The strategy can be extended to other diseases to advance our understanding of the processes contributing to plasma proteome dynamics.

Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomics technologies are rapidly evolving and of critical importance to the field of biomedical research. Here, we report a technical evaluation of six notable plasma proteomics technologies─unenriched (Neat), acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, and Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total, we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4500 proteins detected), while Olink detected ∼2600 proteins. Other MS-based methods ranged from ∼500-2200 proteins detected. In our analysis, Neat, Mag-Net, Seer, and Olink had good reproducibility, while PreOmics and Acid had higher variability (>20% median coefficient of variation). All MS methods showed good linearity with spiked-in C-reactive protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer produced the highest number of quantifiable proteins with a measurable LOD (4407) and LOQ (2696). Olink had the next highest number of quantifiable proteins, with 2002 having an LOD and 1883 having an LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a nonsmall cell lung cancer (NSCLC) cohort. All six methods detected differentially abundant proteins between the cancer and healthy samples but disagreed on which proteins were significant, highlighting the contrast between each method.

EZH2 inhibition mitigates HIV immune evasion, reduces reservoir formation, and promotes skewing of CD8+ T cells toward less-exhausted phenotypes

Persistent HIV reservoirs in CD4+ T cells pose a barrier to curing HIV infection. We identify overexpression of enhancer of zeste homolog 2 (EZH2) in HIV-infected CD4+ T cells that survive cytotoxic T lymphocyte (CTL) exposure, suggesting a mechanism of CTL resistance. Inhibition of EZH2 with the US Food and Drug Administration-approved drug tazemetostat increases surface expression of major histocompatibility complex (MHC) class I on CD4+ T cells, counterbalancing HIV Nef-mediated MHC class I downregulation. This improves CTL-mediated elimination of HIV-infected cells and suppresses viral replication in vitro. In a participant-derived xenograft mouse model, tazemetostat elevates MHC class I and the pro-apoptotic protein BIM in CD4+ T cells, facilitating CD8+ T cell-mediated reductions of HIV reservoir seeding. Additionally, tazemetostat promotes sustained skewing of CD8+ T cells toward less-differentiated and exhausted phenotypes. Our findings reveal EZH2 overexpression as a mechanism of CTL resistance and support the clinical evaluation of tazemetostat as a method of enhancing clearance of HIV reservoirs and improving CD8+ T cell function.

The proteomic and transcriptomic profiles of gastric mucosa from health volunteers

Few studies chose healthy samples from the normal as their study object in cancer research. Compared with the normal tissue adjacent to the tumor (NAT), the normal tissue reflects the real baseline molecular expression used to detect the changes in tumorigenesis accurately. In this study, 49 mucosa biopsies were collected from 7 histoanatomical regions of 7 young healthy volunteers to establish a multi-omics profiling of the normal stomach by SWATH-MS and RNA-seq. The z-score of the expression was used to build a reference for outlier detection, applied in the public omic datasets. Our research revealed that 7 histoanatomical regions (cardia, fundus, lesser curvature, greater curvature, angular incisure, antrum, pylorus) could be clustered into the proximal part involved in energy/biosynthetic metabolism and the distal part related to tissue construction/immunity. 2879 proteins and 10967 genes identified in this study, exhibited stable regional expression and were quantified across all cases with high Spearman correlation coefficients (protein_average = 0.90, RNA_average = 0.92) between each sample, indicating individual impacts are not noticeable. Based on the reference intervals built by the normal samples, we filtered protein outliers (tumor = 594, abnormal = 545, apparently normal = 450), and mRNA outliers (tumor = 4753, abnormal = 1645), and identified distinct consensus clusters associated with malignancy risks. Moreover, the outlier proteins in the apparently normal tissues show hints that exosomes play a role along with tumorigenesis. Our study highlights the important value of the multi-omic data strategy and the potential of the normal reference in cancer research, establishing a novel benchmark for gene expression that illuminates the distinctive traits of unhealthy specimens and contributes to the molecular characteristics of the stomach in clinical physiology.

Serum proteomics reveals survival-associated biomarkers in pancreatic cancer patients treated with chemoimmunotherapy

Immunotherapy has transformed the landscape of cancer treatment but remains largely ineffective for patients with pancreatic ductal adenocarcinoma (PDAC). Some patients, however, show improved outcomes when treated with a combination of immunotherapy and chemotherapy. Here, we conducted deep serum proteome analysis to investigate the protein profiles of PDAC patients and changes during this combinatorial treatment. Utilizing an advanced serum workflow, we quantified 1,011 proteins across 211 samples from 62 patients. Glycolytic enzymes were associated with survival in anti-PD-1-treated patients, with their abundances significantly correlating with expression levels in tumor biopsies. Notably, a set of protein biomarkers was found to be highly predictive of survival in anti-PD-1-treated patients (area under the curve [AUC] = 0.91). Overall, our data demonstrate the potential of deep serum proteomics for precision medicine, offering a powerful tool to guide patient selection for treatment through minimally invasive serum protein biomarker measurements.

Turnover atlas of proteome and phosphoproteome across mouse tissues and brain regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications such as phosphorylation are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites in eight mouse tissues and various brain regions using advanced proteomics and stable isotope labeling. We reveal tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discover a remarkable pattern of turnover changes for peroxisome proteins in specific tissues and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides fundamental insights into protein stability, tissue dynamic proteotypes, and functional protein phosphorylation and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

Crystal structure of Isthmin-1 and reassessment of its functional role in pre-adipocyte signaling

Isthmin-1 (ISM1) is a recently described adipokine with insulin-like properties that can control hyperglycemia and liver steatosis. Additionally, ISM1 is proposed to play critical roles in patterning, angiogenesis, vascular permeability, and apoptosis. A key feature of ISM1 is its AMOP (adhesion-associated domain in MUC4 (Mucin-4) and other proteins) domain which is essential for many of its functions. However, the molecular details of AMOP domains remain elusive as there are no descriptions of their structure. Here we determined the crystal structure of ISM1 including its thrombospondin type I repeat (TSR) and AMOP domain. Interestingly, ISM1's AMOP domain exhibits a distinct fold with similarities to bacterial streptavidin. When comparing our structure to predicted structures of other AMOP domains, we observed that while the core streptavidin-like barrel is conserved, the surface helices and loops vary greatly. Thus, the AMOP domain fold allows for structural plasticity that may underpin its diverse functions. Furthermore, and contrary to prior studies, we show that highly purified ISM1 does not stimulate AKT phosphorylation on 3T3-F442A pre-adipocytes. Rather, we find that co-purifying growth factors are responsible for this activity. Together, our data reveal the structure and clarify functional studies of this enigmatic protein.

Integrated liver-secreted and plasma proteomics identify a predictive model that stratifies MASH

Obesity is a major risk factor for metabolic-associated steatotic liver disease (MASLD), which can progress to metabolic-associated steatohepatitis (MASH). There are no validated non-invasive tests to stratify persons with obesity with a greater risk for MASH. Herein, we assess plasma and liver from 266 obese individuals spanning the MASLD spectrum. Ninety-six human livers were precision-cut, and mass spectrometry-based proteomics identifies 3,333 proteins in the liver-secretion medium, of which 107 are differentially secreted in MASH compared with no pathology. The plasma proteome is markedly remodeled in MASH but is not different between patients with steatosis and no pathology. The APASHA model, comprising plasma apolipoprotein F (APOF), proprotein convertase subtilisin/kexin type 9 (PCSK9), afamin (AFM), S100 calcium-binding protein A6 (S100A6), HbA1c, and zinc-alpha-2-glycoprotein (AZGP1), stratifies MASH (area under receiver operating characteristic [AUROC] = 0.88). Our investigations detail the evolution of liver-secreted and plasma proteins with MASLD progression, providing a rich resource defining human liver-secreted proteins and creating a predictive model to stratify patients with obesity at risk of MASH.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

A nonenzymatic dependency on inositol-requiring enzyme 1 controls cancer cell cycle progression and tumor growth

Endoplasmic-reticulum resident inositol-requiring enzyme 1α (IRE1) supports protein homeostasis via its cytoplasmic kinase-RNase module. Known cancer dependency on IRE1 entails its enzymatic activation of the transcription factor XBP1s and of regulated RNA decay. We discovered surprisingly that some cancer cell lines require IRE1 but not its enzymatic activity. IRE1 knockdown but not enzymatic IRE1 inhibition or XBP1 disruption attenuated cell cycle progression and tumor growth. IRE1 silencing led to activation of TP53 and CDKN1A/p21 in conjunction with increased DNA damage and chromosome instability, while decreasing heterochromatin as well as DNA and histone H3K9me3 methylation. Immunoelectron microscopy detected some endogenous IRE1 protein at the nuclear envelope. Thus, cancer cells co-opt IRE1 either enzymatically or nonenzymatically, which has significant implications for IRE1's biological role and therapeutic targeting.

Exosomes Released from Senescent Cells and Circulatory Exosomes Isolated from Human Plasma Reveal Aging-associated Proteomic and Lipid Signatures

Senescence emerged as significant mechanism of aging and age-related diseases, offering an attractive target for clinical interventions. Senescent cells release a senescence-associated secretory phenotype (SASP), including exosomes that may act as signal transducers between distal tissues, propagating secondary or bystander senescence and signaling throughout the body. However, the composition of exosome SASP remains underexplored, presenting an opportunity for novel unbiased discovery. We present a detailed proteomic and lipidomic analysis of exosome SASP using mass spectrometry from human plasma from young and older individuals and from tissue culture of senescent primary human lung fibroblasts. We identified ∼1,300 exosome proteins released by senescent cells induced by three different senescence inducers. In parallel, a human plasma cohort from young (20-26 years) and old (65-74 years) individuals revealed over 1,350 exosome proteins and 171 plasma exosome proteins were regulated when comparing old vs young individuals. Of the age-regulated plasma exosome proteins, we observed 52 exosome SASP factors that were also regulated in exosomes from the senescent fibroblasts, including serine protease inhibitors (SERPINs), Prothrombin, Coagulation factor V, Plasminogen, and Reelin. 247 lipids were identified in exosome samples. Following senescence induction, identified phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins increased significantly indicating cellular membrane changes. Interestingly, significantly changed proteins were related to extracellular matrix remodeling and inflammation, both potentially detrimental pathways that can damage surrounding tissues and even induce secondary senescence. Our findings reveal mechanistic insights and potential senescence biomarkers, enabling a better approach to surveilling the senescence burden in the aging population and offering therapeutic targets for interventions.

Targeting the NPY/NPY1R signaling axis in mutant p53-dependent pancreatic cancer impairs metastasis

Pancreatic cancer (PC) is a highly metastatic malignancy. More than 80% of patients with PC present with advanced-stage disease, preventing potentially curative surgery. The neuropeptide Y (NPY) system, best known for its role in controlling energy homeostasis, has also been shown to promote tumorigenesis in a range of cancer types, but its role in PC has yet to be explored. We show that expression of NPY and NPY1R are up-regulated in mouse PC models and human patients with PC. Moreover, using the genetically engineered, autochthonous KPR172HC mouse model of PC, we demonstrate that pancreas-specific and whole-body knockout of Npy1r significantly decreases metastasis to the liver. We identify that treatment with the NPY1R antagonist BIBO3304 significantly reduces KPR172HC migratory capacity on cell-derived matrices. Pharmacological NPY1R inhibition in an intrasplenic model of PC metastasis recapitulated the results of our genetic studies, with BIBO3304 significantly decreasing liver metastasis. Together, our results reveal that NPY/NPY1R signaling is a previously unidentified antimetastatic target in PC.

PRAME Epitopes are T-Cell Immunovulnerabilities in BRD4::NUTM1 Initiated NUT Carcinoma

NUT carcinoma ("NC") is a rare but highly lethal solid tumor without an effective standard of care. NC is caused by bromodomain-containing NUTM1 fusion genes, most commonly BRD4::NUTM1 . BRD4::NUTM1 recruits p300 to acetylate H3K27 forming "megadomains" with the overexpression of encapsulated oncogenes, most notably MYC . Akin to MYC , we hypothesized that transcriptional dysregulation caused by BRD4::NUTM1 would lead to the generation of cancer specific antigens that could be therapeutically actionable. Integrating genomics, immunopeptidomics, and computational biology approaches, we identified PRAME as the predominantly transcribed and HLA Class I-presented cancer/testis antigen in NC. Further, we show that a PRAME epitope-specific T-cell receptor ("TCR") x CD3 activator bispecific molecule modeled after brenetafusp has potent T-cell mediated activity against PRAME+ NC. Our results show that PRAME is often highly expressed in NC due to BRD4::NUTM1, and that BRD4::NUTM1 induced PRAME antigens are promising TCR targets for forthcoming clinical trials in NC.

Statement of Significance

NC is one of the most aggressive solid tumors to afflict humans and is refractory to chemotherapy, T-cell checkpoint blockade, and targeted therapies. We show PRAME epitopes are promising targets for TCR-based therapeutics like brenetafusp in NC, adding to growing momentum for addressing challenging fusion malignancies with TCR therapeutics.

A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://platform.msaid.io.

Enhanced sensitivity and scalability with a Chip-Tip workflow enables deep single-cell proteomics

Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow named Chip-Tip, identifying >5,000 proteins in individual HeLa cells. It also facilitated direct detection of post-translational modifications in single cells, making the need for specific post-translational modification-enrichment unnecessary. Our study demonstrates the feasibility of processing up to 120 label-free SCP samples per day. An optimized tissue dissociation buffer enabled effective single-cell disaggregation of drug-treated cancer cell spheroids, refining overall SCP analysis. Analyzing nondirected human-induced pluripotent stem cell differentiation, we consistently quantified stem cell markers OCT4 and SOX2 in human-induced pluripotent stem cells and lineage markers such as GATA4 (endoderm), HAND1 (mesoderm) and MAP2 (ectoderm) in different embryoid body cells. Our workflow sets a benchmark in SCP for sensitivity and throughput, with broad applications in basic biology and biomedicine for identification of cell type-specific markers and therapeutic targets.

Altered hepatic metabolism mediates sepsis preventive effects of reduced glucose supply in infected preterm newborns

Preterm infants are susceptible to neonatal sepsis, a syndrome of pro-inflammatory activity, organ damage, and altered metabolism following infection. Given the unique metabolic challenges and poor glucose regulatory capacity of preterm infants, their glucose intake during infection may have a high impact on the degree of metabolism dysregulation and organ damage. Using a preterm pig model of neonatal sepsis, we previously showed that a drastic restriction in glucose supply during infection protects against sepsis via suppression of glycolysis-induced inflammation, but results in severe hypoglycemia. Now we explored clinically relevant options for reducing glucose intake to decrease sepsis risk, without causing hypoglycemia and further explore the involvement of the liver in these protective effects. We found that a reduced glucose regime during infection increased survival via reduced pro-inflammatory response, while maintaining normoglycemia. Mechanistically, this intervention enhanced hepatic oxidative phosphorylation and possibly gluconeogenesis, and dampened both circulating and hepatic inflammation. However, switching from a high to a reduced glucose supply after the debut of clinical symptoms did not prevent sepsis, suggesting metabolic conditions at the start of infection are key in driving the outcome. Finally, an early therapy with purified human inter-alpha inhibitor protein, a liver-derived anti-inflammatory protein, partially reversed the effects of low parenteral glucose provision, likely by inhibiting neutrophil functions that mediate pathogen clearance. Our findings suggest a clinically relevant regime of reduced glucose supply for infected preterm infants could prevent or delay the development of sepsis in vulnerable neonates.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography-mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.

Positively charged specificity site in cyclin B1 is essential for mitotic fidelity

Phosphorylation of substrates by cyclin-dependent kinases (CDKs) is the driving force of cell cycle progression. Several CDK-activating cyclins are involved, yet how they contribute to substrate specificity is still poorly understood. Here, we discover that a positively charged pocket in cyclin B1, which is exclusively conserved within B-type cyclins and binds phosphorylated serine- or threonine-residues, is essential for correct execution of mitosis. HeLa cells expressing pocket mutant cyclin B1 are strongly delayed in anaphase onset due to multiple defects in mitotic spindle function and timely activation of the E3 ligase APC/C. Pocket integrity is essential for APC/C phosphorylation particularly at non-consensus CDK1 sites and full in vitro ubiquitylation activity. Our results support a model in which cyclin B1's pocket facilitates sequential substrate phosphorylations involving initial priming events that assist subsequent pocket-dependent phosphorylations even at non-consensus CDK1 motifs.

Substrate stiffness dictates unique doxorubicin-induced senescence-associated secretory phenotypes and transcriptomic signatures in human pulmonary fibroblasts

Cells are subjected to dynamic mechanical environments which impart forces and induce cellular responses. In age-related conditions like pulmonary fibrosis, there is both an increase in tissue stiffness and an accumulation of senescent cells. While senescent cells produce a senescence-associated secretory phenotype (SASP), the impact of physical stimuli on both cellular senescence and the SASP is not well understood. Here, we show that mechanical tension, modeled using cell culture substrate rigidity, influences senescent cell markers like SA-β-gal and secretory phenotypes. Comparing human primary pulmonary fibroblasts (IMR-90) cultured on physiological (2 kPa), fibrotic (50 kPa), and plastic (approximately 3 GPa) substrates, followed by senescence induction using doxorubicin, we identified unique high-stiffness-driven secretory protein profiles using mass spectrometry and transcriptomic signatures, both showing an enrichment in collagen proteins. Consistently, clusters of p21 + cells are seen in fibrotic regions of bleomycin induced pulmonary fibrosis in mice. Computational meta-analysis of single-cell RNA sequencing datasets from human interstitial lung disease confirmed these stiffness SASP genes are highly expressed in disease fibroblasts and strongly correlate with mechanotransduction and senescence-related pathways. Thus, mechanical forces shape cell senescence and their secretory phenotypes.

β-hydroxybutyrate is a metabolic regulator of proteostasis in the aged and Alzheimer disease brain

Loss of proteostasis is a hallmark of aging and Alzheimer disease (AD). We identify β-hydroxybutyrate (βHB), a ketone body, as a regulator of protein solubility. βHB primarily provides ATP substrate during periods of reduced glucose availability, and regulates other cellular processes through protein interactions. We demonstrate βHB-induced protein insolubility is not dependent on covalent protein modification, pH, or solute load, and is observable in mouse brain in vivo after delivery of a ketone ester. This mechanism is selective for pathological proteins such as amyloid-β, and exogenous βHB ameliorates pathology in nematode models of amyloid-β aggregation toxicity. We generate libraries of the βHB-induced protein insolublome using mass spectrometry proteomics, and identify common protein domains and upstream regulators. We show enrichment of neurodegeneration-related proteins among βHB targets and the clearance of these targets from mouse brain. These data indicate a metabolically regulated mechanism of proteostasis relevant to aging and AD.

Proteomic Profiling Towards a Better Understanding of Genetic Based Muscular Diseases: The Current Picture and a Look to the Future

Proteomics accelerates diagnosis and research of muscular diseases by enabling the robust analysis of proteins relevant for the manifestation of neuromuscular diseases in the following aspects: (i) evaluation of the effect of genetic variants on the corresponding protein, (ii) prediction of the underlying genetic defect based on the proteomic signature of muscle biopsies, (iii) analysis of pathophysiologies underlying different entities of muscular diseases, key for the definition of new intervention concepts, and (iv) patient stratification according to biochemical fingerprints as well as (v) monitoring the success of therapeutic interventions. This review presents-also through exemplary case studies-the various advantages of mass proteomics in the investigation of genetic muscle diseases, discusses technical limitations, and provides an outlook on possible future application concepts. Hence, proteomics is an excellent large-scale analytical tool for the diagnostic workup of (hereditary) muscle diseases and warrants systematic profiling of underlying pathophysiological processes. The steady development may allow to overcome existing limitations including a quenched dynamic range and quantification of different protein isoforms. Future directions may include targeted proteomics in diagnostic settings using not only muscle biopsies but also liquid biopsies to address the need for minimally invasive procedures.

A Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomic technologies are rapidly evolving and of critical importance to the field of biomedical research. Here we report a technical evaluation of six notable plasma proteomic technologies - unenriched (Neat), Acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4,500), while Olink detected ∼2,600 proteins. Other MS-based methods ranged from ∼500-2,200. Neat, Mag-Net, Seer, and Olink had strong reproducibility, while PreOmics and Acid showed higher variability. All MS methods showed good linearity with spiked-in C-Reactive Protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer had more than double the number of quantifiable proteins (4,800) for both LOD and LOQ than the next best method. Olink was comparable to Neat and Mag-Net for LOD, but worse for LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a non-small cell lung cancer (NSCLC) cohort.

A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set's suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named 'lfproQC' and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

GABAA receptor π forms channels that stimulate ERK through a G-protein-dependent pathway

The rare γ-aminobutyric acid type-A receptor (GABAAR) subunit π (GABRP) is highly expressed in certain cancers, where it stimulates growth through extracellular-regulated kinase (ERK) signaling by an uncharacterized pathway. To elucidate GABRP's signaling mechanism, we determined cryoelectron microscopy (cryo-EM) structures of GABRP embedded in native nanodiscs, both in the presence and absence of GABA. Structurally, GABRP homopentamers closely resemble heteropentameric GABAAR anion channels, transitioning from a closed "resting" state to an open "active" state upon GABA binding. However, functional assays reveal that GABRP responds more like a type-B metabotropic receptor. At physiological concentrations of GABA, chloride flux is not detected. Rather, GABRP activates a G-protein-coupled pathway leading to ERK signaling. Ionotropic activity is only triggered at supraphysiological GABA concentrations, effectively decoupling it from GABRP's signaling functions. These findings provide a structural and functional blueprint for GABRP, opening new avenues for targeted inhibition of GABA growth signals in GABRP-positive cancers.

Exogenous expression of ATP8, a mitochondrial encoded protein, from the nucleus in vivo

Replicative errors, inefficient repair, and proximity to sites of reactive oxygen species production make mitochondrial DNA (mtDNA) susceptible to damage with time. We explore in vivo allotopic expression (re-engineering mitochondrial genes and expressing them from the nucleus) as an approach to rescue defects arising from mtDNA mutations. We used a mouse strain C57BL/6J(mtFVB) with a natural polymorphism (m.7778 G>T) in the mitochondrial ATP8 gene that encodes a protein subunit of the ATP synthase. We generated a transgenic mouse with an epitope-tagged recoded mitochondrial-targeted ATP8 gene expressed from the ROSA26 locus in the nucleus and used the C57BL/6J(mtFVB) strain to verify successful incorporation. The allotopically expressed ATP8 protein in transgenic mice was constitutively expressed across all tested tissues, successfully transported into the mitochondria, and incorporated into ATP synthase. The ATP synthase with transgene had similar activity to non-transgenic control, suggesting successful integration and function. Exogenous ATP8 protein had no negative impact on measured mitochondrial function, metabolism, or behavior. Successful allotopic expression of a mitochondrially encoded protein in vivo in a mammal is a step toward utilizing allotopic expression as a gene therapy in humans to repair physiological consequences of mtDNA defects that may accumulate in congenital mitochondrial diseases or with age.

The Analysis of Plasma Proteomics for Luminal A Breast Cancer

BACKGROUND

Breast cancer is the prevailing malignancy among women, exhibiting a discernible escalation in incidence within our nation; hormone receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common subtype. In this study, we aimed to search for a non-invasive, specific, blood-based biomarker for the early detection of luminal A breast cancer through proteomic studies.

METHODS

To explore new potential plasma biomarkers, we applied data-independent acquisition (DIA), a technique combining liquid chromatography and tandem mass spectrometry, to quantify breast cancer-associated plasma protein abundance from a small number of plasma samples in 10 patients with luminal A breast cancer, 10 patients with benign breast tumors, and 10 healthy controls.

RESULTS

The proteomes of 30 participants in all cohorts were analyzed using the DIA method, and a total of 517 proteins and 3584 peptides were quantified. We found that there were significant differences in plasma protein expression profiles between breast cancer patients and non-breast cancer patients, and breast cancer was mainly related to lipid metabolism pathways. Finally, the optimal protein combinations for the diagnosis of breast cancer were PON3, IGLV3-10, and IGHV3-73 through multi-model analysis, which had a high prediction accuracy for breast cancer (AUC = 0.92), and the model could also distinguish breast cancer from HC (AUC = 0.92) and breast cancer from benign breast tumor (AUC = 0.91).

CONCLUSIONS

The study revealed proteomic signatures of patients with luminal A breast cancer, identified multiple differential proteins, and identified three plasma proteins as potential diagnostic biomarkers for breast cancer. It provides a reference for the screening of biomarkers for breast cancer.

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Substrate Stiffness Dictates Unique Doxorubicin-induced Senescence-associated Secretory Phenotypes and Transcriptomic Signatures in Human Pulmonary Fibroblasts

Cells are subjected to dynamic mechanical environments which impart forces and induce cellular responses. In age-related conditions like pulmonary fibrosis, there is both an increase in tissue stiffness and an accumulation of senescent cells. While senescent cells produce a senescence-associated secretory phenotype (SASP), the impact of physical stimuli on both cellular senescence and the SASP is not well understood. Here, we show that mechanical tension, modeled using cell culture substrate rigidity, influences senescent cell markers like SA-β-gal and secretory phenotypes. Comparing human primary pulmonary fibroblasts (IMR-90) cultured on physiological (2 kPa), fibrotic (50 kPa), and plastic (approximately 3 GPa) substrates, followed by senescence induction using doxorubicin, we identified unique high-stiffness-driven secretory protein profiles using mass spectrometry and transcriptomic signatures, both showing an enrichment in collagen proteins. Consistently, clusters of p21+ cells are seen in fibrotic regions of bleomycin induced pulmonary fibrosis in mice. Computational meta-analysis of single-cell RNA sequencing datasets from human interstitial lung disease confirmed these stiffness SASP genes are highly expressed in disease fibroblasts and strongly correlate with mechanotransduction and senescence-related pathways. Thus, mechanical forces shape cell senescence and their secretory phenotypes.

Proteomic and metabolomic insights into the mechanisms of calcium-mediated salt stress tolerance in hemp

Industrial hemp (Cannabis sativa L.) is a multifaced crop that has the potential to be exploited for many industrial applications, and making use of salt lands is considered to be a sustainable development strategy for the hemp industry. However, no elite salt-tolerant hemp varieties have been developed, and therefore supplementing appropriate exogenous substances to saline soil is one possible solution. Calcium-containing compounds are well-known for their salt tolerance enhancing effects, but the underlying molecular mechanisms remain largely unclear. Here, we first assessed the ameliorative effects of calcium amendments on salt-stressed hemp plants and then investigated these mechanisms on hemp using integrative analysis of proteomics and metabolomics. The stress phenotypes could be lessened by Ca2+ treatment. Certain concentrations of Ca2+ maintained relative electrical conductivity and the contents of malondialdehyde and chlorophyll. Ca2+ treatment also generally led to greater accumulations of soluble proteins, soluble carbohydrates and proline, and enhanced the activities of superoxide dismutase and peroxidase. Through functional classification, pathway enrichment, and network analysis, our data reveal that accumulation of dipeptides is a prominent metabolic signature upon exogenous Ca2+ treatment, and that changes in mitochondrial properties may play an important role in enhancing the salt tolerance. Our results outline the complex metabolic alternations involved in calcium-mediated salt stress resistance, and these data and analyses would be useful for future functional studies.

Master regulators governing protein abundance across ten human cancer types

Protein abundance correlates only moderately with mRNA levels, and are modulated post-transcriptionally by a network of regulators including ribosomes, RNA-binding proteins (RBPs), and the proteasome. Here, we identified Master Protein abundance Regulators (MaPRs) across ten cancer types by devising a new computational pipeline that jointly analyzed transcriptomes and proteomes from 1,305 tumor samples. We identified 232 to 1,394 MaPRs per cancer type, mediating up to 79% of post-transcriptional regulatory networks. MaPRs exhibit high network connectivity, strong genetic dependency in cancer cells, and significant enrichment for RBPs. Combining tumor up-regulation, druggability, and target network analyses identified cancer-specific vulnerabilities. MaPRs predict tumor proteomic subtypes more accurately than other proteins. Finally, significant portions of RBP MaPR-target relationships were validated by experimental evidence from eCLIP binding and knockdown assays. Our findings uncover central MaPRs that govern post-transcriptional networks, highlighting diverse processes underlying human proteome regulation and identifying key regulators in cancer biology.

Evaluation of Serum Proteome Sample Preparation Methods to Support Clinical Proteomics Applications

Serum contains several proteins that are associated with disease-related processes. Mass spectrometry (MS)-based proteomics approaches greatly facilitate serum protein biomarker development. However, the serum proteome complexity presents a technical challenge for the accurate, sensitive, and reproducible quantification of proteins by MS. Thus, efficient sample preparation methods are of critical importance for serum proteome analyses. In this study, we evaluated the technical performance of two serum proteome sample preparation methods using sera from patients with high-grade serous ovarian cancer and patients with benign nongynecological conditions with a goal of providing insight into their compatibility with clinical proteomics workflows. One method entailed the use of immobilized trypsin (SMART Digest Trypsin) with RapiGest SF, an acid-labile surfactant designed to enhance the in-solution enzymatic digestion of proteins. The other method incorporated a commercially available sample preparation kit, iST-BCT, which contains standardized reagents. Significantly higher protein sequence coverage, albeit with lower digestion efficiency, was obtained with the immobilized trypsin + RapiGest SF workflow, whereas the iST-BCT workflow was quicker and had marginally better reproducibility. Protein relative abundance analysis revealed that the serum proteomes clustered primarily by the sample processing workflow and secondarily by disease state. We conducted a time course study to determine whether differences in the relative abundance of diagnostic high-grade serous ovarian cancer serum protein biomarker candidates were biased according to the duration of enzymatic digestion. Our results highlight the importance of optimizing enzymatic digestion kinetics according to the peptide targets of interest while considering the sensitivity of the downstream analytical method utilized in clinical proteomics workflows designed to measure biomarkers.

Capillary zone electrophoresis-tandem mass spectrometry for in-depth proteomics analysis via data-independent acquisition

A capillary zone electrophoresis (CZE) system was coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode for in-depth proteomics analysis. The performance of this CZE-DIA-MS system was systemically evaluated and optimized under different operating conditions. The performance of the fully optimized CZE-DIA-MS system was subsequently compared to the one by using the same CZE-MS system operating in a data-dependent acquisition (DDA) mode. The experimental results show that the numbers of identified peptides and proteins acquired in the DIA mode are much higher than the ones acquired in the DDA mode, especially with the small sample loading amount. Specifically, the numbers of identified peptides and proteins acquired in the DIA mode are 1.8-fold and 2-fold higher than the ones acquired in the DDA mode by using 12.5 ng Hela digests. The proteins identified in the DIA mode also cover almost all the proteins identified in the DDA mode. In addition, a potential cancer biomarker protein, carbohydrate antigen 125, undetected in the DDA mode, can be easily identified in the DIA mode even with 12.5 ng Hela digests. The performance of the CZE-DIA-MS system for in-depth proteomics analysis with a limited sample amount has been fully demonstrated for the first time through this study.

Circulating Proteins Associated with Anti-IL6 Receptor Therapeutic Resistance in the Sera of Patients with Severe COVID-19

Circulating proteomes provide a snapshot of the physiological state of a human organism responding to pathogenic challenges and drug interventions. The outcomes of patients with COVID-19 and acute respiratory distress syndrome triggered by the SARS-CoV2 virus remain uncertain. Tocilizumab is an anti-interleukin-6 treatment that exerts encouraging clinical activity by controlling the cytokine storm and improving respiratory distress in patients with COVID-19. We investigate the biological determinants of therapeutic outcomes after tocilizumab treatment. Overall, 28 patients hospitalized due to severe COVID-19 who were treated with tocilizumab intravenously were included in this study. Sera were collected before and after tocilizumab, and the patient's outcome was evaluated until day 30 post-tocilizumab infusion for favorable therapeutic response to tocilizumab and mortality. Hyperreaction monitoring measurements by liquid chromatography-mass spectrometry-based proteomic analysis with data-independent acquisition quantified 510 proteins and 7019 peptides in the serum of patients. Alterations in the serum proteome reflect COVID-19 outcomes in patients treated with tocilizumab. Our results suggested that circulating proteins associated with the most significant prognostic impact belonged to the complement system, platelet degranulation, acute-phase proteins, and the Fc-epsilon receptor signaling pathway. Among these, upregulation of the complement system by activation of the classical pathway was associated with poor response to tocilizumab, and upregulation of Fc-epsilon receptor signaling was associated with lower mortality.

Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics

Biological sex is key information for archeological and forensic studies, which can be determined by proteomics. However, the lack of a standardized approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for the determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to a high-resolution parallel reaction monitoring (PRM) assay allowing for a throughput of 200 samples per day (SPD) with high quantitative performance enabling confident identification of both males and females. Additionally, we developed a streamlined data analysis pipeline and integrated it into a Shiny interface for ease of use. The method was first developed and optimized using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by 10-fold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens. Data are available via ProteomeXchange with the identifier PXD049326.

A Comprehensive and Robust Multiplex-DIA Workflow Profiles Protein Turnover Regulations Associated with Cisplatin Resistance

Measuring protein turnover is essential for understanding cellular biological processes and advancing drug discovery. The multiplex DIA mass spectrometry (DIA-MS) approach, combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC), has proven to be a reliable method for analyzing protein turnover and degradation kinetics. Previous multiplex DIA-MS workflows have employed various strategies, including leveraging the highest isotopic labeling channels of peptides to enhance the detection of isotopic MS signal pairs or clusters. In this study, we introduce an improved and robust workflow that integrates a novel machine learning strategy and channel-specific statistical filtering, enabling dynamic adaptation to systematic or temporal variations in channel ratios. This allows comprehensive profiling of protein turnover throughout the pSILAC experiment without relying solely on the highest channel signals. Additionally, we developed KdeggeR , a data processing and analysis package optimized for pSILAC-DIA experiments, which estimates and visualizes peptide and protein degradation rates and dynamic profiles. Our integrative workflow was benchmarked on both 2-channel and 3-channel standard DIA datasets and across two mass spectrometry platforms, demonstrating its broad applicability. Finally, applying this workflow to an aneuploid cancer cell model before and after cisplatin resistance development demonstrated a strong negative correlation between transcript regulation and protein degradation for major protein complex subunits. We also identified specific protein turnover signatures associated with cisplatin resistance.

A multi-subunit autophagic capture complex facilitates degradation of ER stalled MHC-I in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDA) evades immune detection partly via autophagic capture and lysosomal degradation of major histocompatibility complex class I (MHC-I). Why MHC-I is susceptible to capture via autophagy remains unclear. By synchronizing exit of proteins from the endoplasmic reticulum (ER), we show that PDAC cells display prolonged retention of MHC-I in the ER and fail to efficiently route it to the plasma membrane. A capture-complex composed of NBR1 and the ER-phagy receptor TEX264 facilitates targeting of MHC-I for autophagic degradation, and suppression of either receptor is sufficient to increase total levels and re-route MHC-I to the plasma membrane. Binding of MHC-I to the capture complex is linked to antigen presentation efficiency, as inhibiting antigen loading via knockdown of TAP1 or beta 2-Microglobulin led to increased binding between MHC-I and the TEX264-NBR1 capture complex. Conversely, expression of ER directed high affinity antigenic peptides led to increased MHC-I at the cell surface and reduced lysosomal degradation. A genome-wide CRISPRi screen identified NFXL1, as an ER-resident E3 ligase that binds to MHC-I and mediates its autophagic capture. High levels of NFXL1 are negatively correlated with MHC-I protein expression and predicts poor patient prognosis. These data highlight an ER resident capture complex tasked with sequestration and degradation of non-conformational MHC-I in PDAC cells, and targeting this complex has the potential to increase PDAC immunogenicity.

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models.

An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications like phosphorylation, are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites across eight mouse tissues and various brain regions, using advanced proteomics and stable isotope labeling. We revealed tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discovered that peroxisomes are regulated by protein turnover across tissues, and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides new fundamental insights into protein stability, tissue dynamic proteotypes, and the role of protein phosphorylation, and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

Feeding cessation and antibiotics improve clinical symptoms and alleviate gut and systemic inflammation in preterm pigs sensitive to necrotizing enterocolitis

Necrotizing enterocolitis (NEC) is a microbiota- and feeding-related gut inflammatory disease in preterm infants. The standard of care (SOC) treatment for suspected NEC is antibiotic treatment and reduced enteral feeding, but how SOC treatment mitigates NEC remains unclear. We explored whether SOC treatment alone or combined with an anti-inflammatory protein (inter-alpha inhibitor protein, IAIP) supplementation improves outcomes in a preterm piglet model of formula-induced NEC. Seventy-one cesarean-delivered preterm piglets were initially fed formula, developing NEC symptoms by day 3, and then randomized into CON (continued feeding) or SOC groups (feeding cessation and antibiotics), each with or without human IAIP (2×2 factorial design). By day 5, IAIP treatment did not significantly influence outcomes, whereas SOC treatment effectively reduced NEC lesions, diarrhea, and bloody stools. Notably, SOC treatment improved gut morphology and function, dampened gut inflammatory responses, altered the colonic microbiota composition, and modulated systemic immune responses. Plasma proteomic analysis revealed the effects of SOC treatment on organ development and systemic inflammatory responses. Collectively, these findings suggest that SOC treatment significantly prevents NEC progression in preterm piglets via effects on gut structure, function, and microbiota, as well as systemic immune and inflammatory responses. Timely feeding cessation and antibiotics are critical factors in preventing NEC progression in preterm infants, while the benefits of additional human IAIP treatment remain to be established.

Characterization of Amnion-Derived Membrane for Clinical Wound Applications

Human amniotic membrane (hAM), the innermost placental layer, has unique properties that allow for a multitude of clinical applications. It is a common misconception that birth-derived tissue products, such as dual-layered dehydrated amnion-amnion graft (dHAAM), are similar regardless of the manufacturing steps. A commercial dHAAM product, Axolotl Biologix DualGraft™, was assessed for biological and mechanical characteristics. Testing of dHAAM included antimicrobial, cellular biocompatibility, proteomics analysis, suture strength, and tensile, shear, and compressive modulus testing. Results demonstrated that the membrane can be a scaffold for fibroblast growth (cellular biocompatibility), containing an average total of 7678 unique proteins, 82,296 peptides, and 96,808 peptide ion variants that may be antimicrobial. Suture strength results showed an average pull force of 0.2 N per dHAAM sample (equating to a pull strength of 8.5 MPa). Tensile modulus data revealed variation, with wet samples showing 5× lower stiffness than dry samples. The compressive modulus and shear modulus displayed differences between donors (lots). This study emphasizes the need for standardized processing protocols to ensure consistency across dHAAM products and future research to explore comparative analysis with other amniotic membrane products. These findings provide baseline data supporting the potential of amniotic membranes in clinical applications.

DEWNA: dynamic entropy weight network analysis and its application to the DNA-binding proteome in A549 cells with cisplatin-induced damage

Cisplatin is one of the most commonly used chemotherapy drugs for treating solid tumors. As a genotoxic agent, cisplatin binds to DNA and forms platinum-DNA adducts that cause DNA damage and activate a series of signaling pathways mediated by various DNA-binding proteins (DBPs), ultimately leading to cell death. Therefore, DBPs play crucial roles in the cellular response to cisplatin and in determining cell fate. However, systematic studies of DBPs responding to cisplatin damage and their temporal dynamics are still lacking. To address this, we developed a novel and user-friendly stand-alone software, DEWNA, designed for dynamic entropy weight network analysis to reveal the dynamic changes of DBPs and their functions. DEWNA utilizes the entropy weight method, multiscale embedded gene co-expression network analysis and generalized reporter score-based analysis to process time-course proteome expression data, helping scientists identify protein hubs and pathway entropy profiles during disease progression. We applied DEWNA to a dataset of DBPs from A549 cells responding to cisplatin-induced damage across 8 time points, with data generated by data-independent acquisition mass spectrometry (DIA-MS). The results demonstrate that DEWNA can effectively identify protein hubs and associated pathways that are significantly altered in response to cisplatin-induced DNA damage, and offer a comprehensive view of how different pathways interact and respond dynamically over time to cisplatin treatment. Notably, we observed the dynamic activation of distinct DNA repair pathways and cell death mechanisms during the drug treatment time course, providing new insights into the molecular mechanisms underlying the cellular response to DNA damage.

Sirt2 Regulates Liver Metabolism in a Sex-Specific Manner

Sirtuin-2 (Sirt2), an NAD+-dependent lysine deacylase enzyme, has previously been implicated as a regulator of glucose metabolism, but the specific mechanisms remain poorly defined. Here, we observed that Sirt2-/- males, but not females, have decreased body fat, moderate hypoglycemia upon fasting, and perturbed glucose handling during exercise compared to wild type controls. Conversion of injected lactate, pyruvate, and glycerol boluses into glucose via gluconeogenesis was impaired, but only in males. Primary Sirt2-/- male hepatocytes exhibited reduced glycolysis and reduced mitochondrial respiration. RNAseq and proteomics were used to interrogate the mechanisms behind this liver phenotype. Loss of Sirt2 did not lead to transcriptional dysregulation, as very few genes were altered in the transcriptome. In keeping with this, there were also negligible changes to protein abundance. Site-specific quantification of the hepatic acetylome, however, showed that 13% of all detected acetylated peptides were significantly increased in Sirt2-/- male liver versus wild type, representing putative Sirt2 target sites. Strikingly, none of these putative target sites were hyperacetylated in Sirt2-/- female liver. The target sites in the male liver were distributed across mitochondria (44%), cytoplasm (32%), nucleus (8%), and other compartments (16%). Despite the high number of putative mitochondrial Sirt2 targets, Sirt2 antigen was not detected in purified wild type liver mitochondria, suggesting that Sirt2's regulation of mitochondrial function occurs from outside the organelle. We conclude that Sirt2 regulates hepatic protein acetylation and metabolism in a sex-specific manner.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

Comprehensive profiling of the human fecal proteome from IBD patients with DIA-MS enables evaluation of disease-relevant proteins

PURPOSE

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), is characterized by chronic gastrointestinal inflammation. A high unmet need exists for noninvasive biomarkers in IBD to monitor changes in disease activity and guide treatment decisions. Stool is an easily accessed, disease proximal matrix in IBD, however the composition of the IBD fecal proteome remains poorly characterized.

EXPERIMENTAL DESIGN

A data-independent acquisition LC-MS/MS approach was used to profile the human fecal proteome in two independent cohorts (Cohort 1: healthy n = 5, UC n = 5, CD n = 5, Cohort 2: healthy n = 20, UC n = 10, and CD n = 10) to identify noninvasive biomarkers reflective of disease activity.

RESULTS

688 human proteins were quantified, with 523 measured in both cohorts. In UC stool 96 proteins were differentially abundant and in CD stool 126 proteins were differentially abundant compared to healthy stool (absolute log2 fold change > 1, p-value < 0.05). Many of these fecal proteins are associated with infiltrating immune cells and ulceration/rectal bleeding, which are hallmarks of IBD pathobiology. Mapping the identified fecal proteins to a whole blood single-cell RNA sequencing data set revealed the involvement of various immune cell subsets to the IBD fecal proteome.

CONCLUSIONS AND CLINICAL RELEVANCE

Findings from this study not only confirmed the presence of established fecal biomarkers for IBD, such as calprotectin and lactoferrin, but also revealed new fecal proteins from multiple pathways known to be dysregulated in IBD. These novel proteins could serve as potential noninvasive biomarkers to monitor specific aspects of IBD disease activity which could expedite clinical development of novel therapeutic targets.

Role of AMPK in Atrial Metabolic Homeostasis and Substrate Preference

Atrial fibrillation is the most common clinical arrhythmia and may be due in part to metabolic stress. Atrial specific deletion of the master metabolic sensor, AMP-activated protein kinase (AMPK), induces atrial remodeling culminating in atrial fibrillation in mice, implicating AMPK signaling in the maintenance of atrial electrical and structural homeostasis. However, atrial substrate preference for mitochondrial oxidation and the role of AMPK in regulating atrial metabolism are unknown. Here, using LC-MS/MS methodology combined with infusions of [ 13 C 6 ]glucose and [ 13 C 4 ]β-hydroxybutyrate in conscious mice, we demonstrate that conditional deletion of atrial AMPK catalytic subunits shifts mitochondrial atrial metabolism away from fatty acid oxidation and towards pyruvate oxidation. LC-MS/MS-based quantification of acyl-CoAs demonstrated decreased atrial tissue content of long-chain fatty acyl-CoAs. Proteomic analysis revealed a broad downregulation of proteins responsible for fatty acid uptake (LPL, CD36, FABP3), acylation and oxidation. Atrial AMPK deletion reduced expression of atrial PGC1-α and downstream PGC1-α/PPARα/RXR regulated gene transcripts. In contrast, atrial [ 14 C]2-deoxyglucose uptake and GLUT1 expression increased with fasting in mice with AMPK deletion, while the expression of glycolytic enzymes exhibited heterogenous changes. Thus, these results highlight the crucial homeostatic role of AMPK in the atrium, with loss of atrial AMPK leading to downregulation of the PGC1-α/PPARα pathway and broad metabolic reprogramming with a loss of fatty acid oxidation, which may contribute to atrial remodeling and arrhythmia.

A Combined Proteomic and Transcriptomic Signature Is Predictive of Response to Anti-PD-1 Treatment: A Retrospective Study in Metastatic Melanoma Patients

Resistance biomarkers are needed to identify patients with advanced melanoma obtaining a response to ICI treatment and developing resistance later. We searched a combination of molecular signatures of response to ICIs in patients with metastatic melanoma. In a retrospective study on patients with metastatic melanoma treated with an anti-PD-1 agent carried out at Istituto Nazionale Tumori-IRCCS-Fondazione "G. Pascale", Naples, Italy. We integrated a whole proteome profiling of metastatic tissue with targeted transcriptomics. To assess the prognosis of patients according to groups of low and high risk, we used PFS and OS as outcomes. To identify the proteins and mRNAs gene signatures associated with the patient's response groups, the discriminant analysis for sparse data performed via partial least squares procedure was performed. Tissue samples from 22 patients were analyzed. A combined protein and gene signature associated with poorer response to ICI immunotherapy in terms of PFS and OS was identified. The PFS and OS Kaplan-Meier curves were significantly better for patients with high expression of the protein signature compared to patients with low expression of the protein signature and who were high-risk (Protein: HR = 0.023, 95% CI: 0.003-0.213; p < 0.0001. Gene: HR = 0.053, 95% CI: 0.011-0.260; p < 0.0001). The Kaplan-Meier curves showed that patients with low-risk gene signatures had better PFS (HR = 0 0.221, 95% CI: 0.071-0.68; p = 0.007) and OS (HR = 0.186, 95% CI: 0.05-0.695; p = 0.005). The proteomic and transcriptomic combined analysis was significantly associated with the outcomes of the anti-PD-1 treatment with a better predictive value compared to a single signature. All the patients with low expression of protein and gene signatures had progression within 6 months of treatment (median PFS = 3 months, 95% CI: 2-3), with a significant difference vs. the low-risk group (median PFS = not reached; p < 0.0001), and significantly poorer survival (OS = 9 months, 95% CI: 5-9) compared to patients with high expression of protein and gene signatures (median OS = not reached; p < 0.0001). We propose a combined proteomic and transcriptomic signature, including genes involved in pro-tumorigenic pathways, thereby identifying patients with reduced probability of response to immunotherapy with ICIs for metastatic melanoma.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer

Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method, we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology is benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We apply this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detect 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of phosphorylation events relevant to mitochondrial and brain biology.

diaPASEF Enables High-Throughput Proteomic Analysis of Host Cell Proteins for Biopharmaceutical Process Development

Monitoring and quantifying host cell proteins (HCPs) in biotherapeutic production processes is crucial to ensure product quality, stability, and safety. Liquid chromatography-mass spectrometry (LC-MS) analysis has emerged as an important tool for identifying and quantifying individual HCPs. However, LC-MS-based approaches face challenges due to the wide dynamic range between HCPs and the therapeutic protein as well as laborious sample preparation and long instrument time. To address these limitations, we evaluated the application of parallel accumulation-serial fragmentation combined with data-independent acquisition (diaPASEF) to HCP analysis for biopharmaceutical process development applications. We evaluated different library generation strategies and LC methods, demonstrating the suitability of these workflows for various HCP analysis needs, such as in-depth characterization and high-throughput analysis of process intermediates. Remarkably, the diaPASEF approach enabled the quantification of hundreds of HCPs that were undetectable by a standard data-dependent acquisition mode while considerably improving sample requirement, throughput, coverage, quantitative precision, and data completeness.