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Many intra- and extracellular signals induce structural changes in proteins. Schopper et al. , describe a limited proteolysis–based mass spectrometry (LiP-MS) approach to characterizing these changes at a proteome-wide scale. Protein structural changes induced by external perturbations or internal cues can profoundly influence protein activity and thus modulate cellular physiology. A number of biophysical approaches are available to probe protein structural changes, but these are not applicable to a whole proteome in a biological extract. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a recently developed proteomics approach that enables the identification of protein structural changes directly in their complex biological context on a proteome-wide scale. After perturbations of interest, proteome extracts are subjected to a double-protease digestion step with a nonspecific protease applied under native conditions, followed by complete digestion with the sequence-specific protease trypsin under denaturing conditions. This sequential treatment generates structure-specific peptides amenable to bottom-up MS analysis. Next, a proteomics workflow involving shotgun or targeted MS and label-free quantification is applied to measure structure-dependent proteolytic patterns directly in the proteome extract. Possible applications of LiP-MS include discovery of perturbation-induced protein structural alterations, identification of drug targets, detection of disease-associated protein structural states, and analysis of protein aggregates directly in biological samples. The approach also enables identification of the specific protein regions involved in the structural transition or affected by the binding event. Sample preparation takes approximately 2 d, followed by one to several days of MS and data analysis time, depending on the number of samples analyzed. Scientists with basic biochemistry training can implement the sample preparation steps. MS measurement and data analysis require a background in proteomics.

Coupling limited proteolysis and a proteomics workflow enables measurement of both subtle and wholesale protein conformational changes in a eukaryotic proteome. Changes in protein conformation can affect protein function, but methods to probe these structural changes on a global scale in cells have been lacking. To enable large-scale analyses of protein conformational changes directly in their biological matrices, we present a method that couples limited proteolysis with a targeted proteomics workflow. Using our method, we assessed the structural features of more than 1,000 yeast proteins simultaneously and detected altered conformations for ∼300 proteins upon a change of nutrients. We find that some branches of carbon metabolism are transcriptionally regulated whereas others are regulated by enzyme conformational changes. We detect structural changes in aggregation-prone proteins and show the functional relevance of one of these proteins to the metabolic switch. This approach enables probing of both subtle and pronounced structural changes of proteins on a large scale.

Enhancing the removal of aggregate-prone toxic proteins is a rational therapeutic strategy for a number of neurodegenerative diseases, especially Huntington’s disease and various spinocerebellar ataxias. Ideally, such approaches should preferentially clear the mutant/misfolded species, while having minimal impact on the stability of wild-type/normally-folded proteins. Furthermore, activation of both ubiquitin-proteasome and autophagy-lysosome routes may be advantageous, as this would allow effective clearance of both monomeric and oligomeric species, the latter which are inaccessible to the proteasome. Here we find that compounds that activate the D1 ATPase activity of VCP/p97 fulfill these requirements. Such effects are seen with small molecule VCP activators like SMER28, which activate autophagosome biogenesis by enhancing interactions of PI3K complex components to increase PI(3)P production, and also accelerate VCP-dependent proteasomal clearance of such substrates. Thus, this mode of VCP activation may be a very attractive target for many neurodegenerative diseases. Several neurodegenerative diseases are characterized by the aggregation of cytoplasmic proteins. Here, the authors demonstrate that the small molecule SMER28 activates VCP, which enhances both autophagic and proteasomal clearance of aggregate-prone proteins.

The thioredoxin (TXN) system is an NADPH + H + /FAD redox-triggered effector that sustains homeostasis, bioenergetics, detoxifying drug networks, and cell survival in oxidative stress-related diseases. Elovanoid (ELV)-N34 is an endogenously formed lipid mediator in neural cells from omega-3 fatty acid precursors that modulate neuroinflammation and senescence gene programming when reduction-oxidation (redox) homeostasis is disrupted, enhancing cell survival. Limited proteolysis (LiP) screening of human retinal pigment epithelial (RPE) cells identified TXNRD1 isoforms 2, 3, or 5, the reductase of the TXN system, as an intracellular target of ELV-N34. TXNRD1 silencing confirmed that the ELV-N34 target was isoform 2 or 3. This lipid mediator induces TXNRD1 structure changes that modify the FAD interface domain, leading to its activity modulation. The addition of ELV-N34 decreased membrane and cytosolic TXNRD1 activity, suggesting localizations for the targeted reductase. These results show for the first time that the lipid mediator ELV-N34 directly modulates TXNRD1 activity, underling its protection in several pathologies when uncompensated oxidative stress (UOS) evolves.

Background Normal cognitive aging is accompanied by molecular changes in the brain and periphery, but the specific proteomic and peptide-level alterations remain poorly defined. This study aimed to characterize age-related protein and peptide modifications by analyzing matched cerebrospinal fluid (CSF) and plasma samples from cognitively normal individuals. Methods Mass spectrometry was used to profile the proteome and peptide-level data of CSF and plasma samples from young ( n  = 52; mean age 29 ± 5.9 years; 4% male) and older ( n  = 40; mean age 69 ± 6.3 years; 48% male) adults. Differential abundance analysis, gene set enrichment, and weighted correlation network analyses were performed to identify age-associated pathways. Protein cleavage, alternative splicing, and phosphorylation events were examined to capture post-translational modifications linked to aging. Results In CSF, aging is associated with significant upregulation of extracellular matrix (ECM) components, coagulation, and inflammatory pathways. In plasma, the insulin-like growth factor-1 (IGF-1) signaling pathway is notably downregulated. Peptide-level analysis reveals novel alternative cleavage and phosphorylation patterns in key proteins involved in lipid metabolism, ECM structure, axonogenesis, and synaptic activity, including APP, APOE, COL4A2, NRXN1, and NRCAM. These findings highlight distinct and compartment-specific molecular changes that occur with aging. Conclusions This study provides a comprehensive proteomic and peptide-level landscape of normal cognitive aging, identifying both protein-level shifts and novel age-associated cleavage and phosphorylation events. Plain language summary As people age, their bodies and brains undergo gradual molecular changes, even in the absence of disease. In this study, we analyzed blood and cerebrospinal fluid (the fluid surrounding the brain and spinal cord) from healthy younger and older adults to understand how the proteins in these fluids change with normal aging. To do this, we used advanced mass spectrometry (MS), a powerful analytical method that measures the mass of molecules with extremely high precision. Our study found age-related changes in proteins involved in brain structure, inflammation, and metabolism. Notably, we also discovered subtle modifications in key proteins that may influence how cells communicate and maintain brain health over time. These findings provide new insights into the biology of healthy aging and may help researchers distinguish normal aging from the early stages of neurodegenerative diseases in the future. Kamalian et al. presents a deep proteomic and peptide-level analysis of matched cerebrospinal fluid and plasma from cognitively normal adults (From 18- to 83-year-olds). This study reveals distinct molecular signatures of normal aging and uncovers age-associated cleavage and phosphorylation events in key proteins such as APP, APOE, and NRXN1.

BackgroundColorectal cancer (CRC) is currently the second most common cause of cancer related mortality in the world. One of the hallmarks of CRC is its complex mutational landscape that contributes to the derivation of neoantigens which aid the immunosurveillance of the cancer. Immunopeptides play an essential role in adaptive immunity by activating and ensuring T-cell specificity. Mass spectrometry (MS) is currently the only technology that can measure and identify the immunopeptide profiles of biological samples at a large scale, however, MS-based studies are frequently limited by sample input and poor scalability. Here, we introduce a semi-automated workflow requiring low sample input to robustly identify immunopeptides and apply it to a cohort of fresh frozen, high quality CRC samples for immunopeptide profiling and neoantigen identification.MethodsWe initially optimized a workflow that allowed the native lysis and sequential immunoprecipitation of class-I and class-II immunopeptides while ensuring scalability and reproducibility. The immunopeptides were thereafter subjected to our TrueDiscovery FAIMS-DIA LC-MS/MS platform. We utilized data from WGS on both tumor and adjacent normal tissue for the calling of high-confidence somatic variations and the definition of the resulting neoantigens.ResultsWe characterized 131,578 unique immunopeptides from 15 mg of fresh-frozen tumor tissue across 20 patients with CRC lesions that mapped to 12,488 genes. On average we identified 9,767 class-I and 16,445 class-II immunopeptides from all patients indicating significant inter-patient heterogeneity. Overall, we found the identification numbers of class-II immunopeptides to be more variable than that of class-I which might be linked to immune infiltration levels.From our pool of identified immunopeptides, we were able to detect 16 class-I and 29 class-II neoantigens, covering 87% of the microsatellite instability-high (MSI-H) samples in the cohort. Coverage of both classes was essential to increase neoantigen discovery as 53% MSI-H samples had neoantigens mapping to a single class. Interestingly we also detected a significant correlation between the mutational burden and the number of detected neoantigens in our patient cohort suggesting that the genetic set up of the cancer might influence the repertoire of presented neoantigens thereby controlling immunosurveillance.ConclusionsIn summary, we have established a scalable and efficient pipeline for cell line and tissue immunopeptidomics for both class-I and II immunopeptides. Our pipeline generates high-quality identifications and can be deployed to help shed light on (neo)antigen heterogeneity through large-scale profiling of patients as exemplified in the case of MSI-H CRC.

BackgroundExtracellular vesicles (EV) play an important role in melanoma progression but their potential as clinical biomarkers has yet to be realized. EVs can be found in most liquid biopsies (e.g., blood, urine, CSF) and exosomes are the most prominent subcategory of EVs. Exosomes are 50–200 nm lipid-bilayer enclosed particles secreted by all body cells, including tumor cells, and serve as mediators of metastasis formation and typically contain several classes of bioactive molecules such as RNA, proteins, lipids, and metabolites. Blood and its liquid components plasma/serum are the most frequently used matrix for biomarker discovery due to the ease of collection. However, most proteomic platforms for plasma/serum profiling are unable to profile EV proteins due to the high dynamic range of protein concentrations in EV preparations. This is due to 1) EV isolation methods that vary in their potential to separate EVs from free proteins, and 2) the presence of a natural corona of high-abundant blood proteins attached to the EV surface.MethodsTo tackle this challenge, we developed SEC-DIA-MS an integrated workflow combining size-exclusion chromatography, EV concentration, and optimized mass spectrometry to enable deep profiling of the proteome content of the enriched vesicles.ResultsFrom 200 µl of plasma or serum from a test melanoma patient cohort (6 patients and 3 matched controls), we quantified 2,242 exosome-associated proteins, achieving a 2.5-fold increase in depth compared to previous melanoma studies. To gain a better understanding of the exosome enrichment efficiency, we extensively characterized the plasma/serum proteome by analyzing native, depleted, and EV-enriched blood from the same donors. We successfully validated well-known exosome markers such as CD9, CD63, CD81, PDCD6IP, and TSG101, and found that EV samples are significantly enriched in intact membrane proteins and those related to T cell biology, further underlining the uniqueness of the EV proteome composition. We further assessed the differences between plasma and serum EVs and suggest the use of plasma samples for future studies due to better separation of healthy and melanoma samples.Furthermore, we deployed this workflow to identify predictive biomarkers of response in a clinical NSCLC cohort subjected to immune-checkpoint inhibitor treatment in combination with chemotherapy.ConclusionsTaken together, we demonstrated the workflow for biomarker discovery in plasma and serum. The ease of automating and scaling up such an approach enables a broader application to other indications and biological matrices.

Protein aggregation is mostly viewed as deleterious and irreversible causing several pathologies. However, reversible protein aggregation has recently emerged as a novel concept for cellular regulation. Here, we characterize stress-induced, reversible aggregation of yeast pyruvate kinase, Cdc19. Aggregation of Cdc19 is regulated by oligomerization and binding to allosteric regulators. We identify a region of low compositional complexity (LCR) within Cdc19 as necessary and sufficient for reversible aggregation. During exponential growth, shielding the LCR within tetrameric Cdc19 or phosphorylation of the LCR prevents unscheduled aggregation, while its dephosphorylation is necessary for reversible aggregation during stress. Cdc19 aggregation triggers its localization to stress granules and modulates their formation and dissolution. Reversible aggregation protects Cdc19 from stress-induced degradation, thereby allowing cell cycle restart after stress. Several other enzymes necessary for G1 progression also contain LCRs and aggregate reversibly during stress, implying that reversible aggregation represents a conserved mechanism regulating cell growth and survival. Saad et al.  identify stress-induced reversible protein aggregation as a protective mechanism to ensure cell cycle resumption and cell survival after stress in yeast.

Background Aging is the greatest risk factor for Alzheimer's disease (AD), yet the biological pathways that distinguish healthy aging from pathological aging, which leads to neurodegeneration, remain poorly understood. There is a critical need for novel biomarkers that can detect the earliest changes in the disease process, particularly well before the onset of cognitive symptoms and preferably even before amyloid conversion that are currently diagnosed by blood/CSF Aβ42/Aβ40 ratio. Plasma represents an ideal biological matrix for novel biomarker discovery due to its ease of collection compared to CSF, its non‐invasive nature, and its suitability for clinical biomarker discovery. To address this challenge, we developed a novel MS‐based proteomics workflow to enable unbiased biomarker discovery. Method A sub‐cohort of participants was selected from the BIOCARD cohort. Fifty‐five participants were classified as amyloid converters based on the following criteria: (1) at least three measurements of the CSF Aβ42/Aβ40 ratio spanning over 10 years and (2) two or more early time points with a ratio >0.068 followed by later measurements <0.068. Fifty‐five non‐converters were selected as age‐, sex‐, and interval‐matched controls, with all CSF Aβ42/Aβ40 ratios remaining >0.068. A total of 578 plasma samples from 110 participants were analyzed using Biognosys’ P2 workflow. Samples underwent P2 particle‐based pre‐treatment, enzymatic digestion to peptides, and subsequent quantitative mass spectrometry analysis. Additional data, including cognitive assessments, clinical biomarker panels, and MRI scans, were collected at each plasma/CSF sampling time point. Result Mass spectrometry‐based proteomics provided valuable insights into protein‐ and peptide‐level changes associated with cognitive decline, illuminating the biological pathways involved in the transition from healthy aging to mild cognitive impairment (MCI). Key pathways identified include lipid metabolism, extracellular matrix remodeling, axonogenesis, and synaptic activity. Integrating proteomics data with available clinical biomarker and cognitive data enhances the ability to pinpoint specific molecular changes associated with cognitive aging. This approach enables the identification of plasma‐based signatures that precede amyloid conversion and cognitive decline, highlighting markers detectable at early time points before participants clinically convert. Conclusion This integrated approach bridges the gap between molecular changes and clinical phenotypes, highlighting plasma‐derived biomarkers as a less invasive alternative to CSF collection.

Aging is the greatest risk factor for Alzheimer's disease (AD), yet the biological pathways that distinguish healthy aging from pathological aging, which leads to neurodegeneration, remain poorly understood. There is a critical need for novel biomarkers that can detect the earliest changes in the disease process, particularly well before the onset of cognitive symptoms and preferably even before amyloid conversion that are currently diagnosed by blood/CSF Aβ42/Aβ40 ratio. Plasma represents an ideal biological matrix for novel biomarker discovery due to its ease of collection compared to CSF, its non-invasive nature, and its suitability for clinical biomarker discovery. To address this challenge, we developed a novel MS-based proteomics workflow to enable unbiased biomarker discovery. A sub-cohort of participants was selected from the BIOCARD cohort. Fifty-five participants were classified as amyloid converters based on the following criteria: (1) at least three measurements of the CSF Aβ42/Aβ40 ratio spanning over 10 years and (2) two or more early time points with a ratio >0.068 followed by later measurements <0.068. Fifty-five non-converters were selected as age-, sex-, and interval-matched controls, with all CSF Aβ42/Aβ40 ratios remaining >0.068. A total of 578 plasma samples from 110 participants were analyzed using Biognosys' P2 workflow. Samples underwent P2 particle-based pre-treatment, enzymatic digestion to peptides, and subsequent quantitative mass spectrometry analysis. Additional data, including cognitive assessments, clinical biomarker panels, and MRI scans, were collected at each plasma/CSF sampling time point. Mass spectrometry-based proteomics provided valuable insights into protein- and peptide-level changes associated with cognitive decline, illuminating the biological pathways involved in the transition from healthy aging to mild cognitive impairment (MCI). Key pathways identified include lipid metabolism, extracellular matrix remodeling, axonogenesis, and synaptic activity. Integrating proteomics data with available clinical biomarker and cognitive data enhances the ability to pinpoint specific molecular changes associated with cognitive aging. This approach enables the identification of plasma-based signatures that precede amyloid conversion and cognitive decline, highlighting markers detectable at early time points before participants clinically convert. This integrated approach bridges the gap between molecular changes and clinical phenotypes, highlighting plasma-derived biomarkers as a less invasive alternative to CSF collection.