Novel therapies and biomarkers are needed for the treatment of acute ischemic stroke (AIS). This study aimed to provide comprehensive insights into the dynamic proteome changes and underlying molecular mechanisms post-ischemic stroke. TMT-coupled proteomic analysis was conducted on mouse brain cortex tissue from five time points up to 4 weeks poststroke in the distal hypoxic-middle cerebral artery occlusion (DH-MCAO) model. We found that nearly half of the detected proteome was altered following stroke, but only ∼8.6% of the changes were at relatively large scales. Clustering on the changed proteome defined four distinct expression patterns characterized by temporal and quantitative changes in innate and adaptive immune response pathways and cytoskeletal and neuronal remodeling. Further analysis on a subset of 309 "top hits", which temporally responded to stroke with relatively large and sustained changes, revealed that they were mostly secreted proteins, highly correlated to different cortical cytokines, and thereby potential pharmacodynamic biomarker candidates for inflammation-targeting therapies. Closer examination of the top enriched neurophysiologic pathways identified 57 proteins potentially associated with poststroke recovery. Altogether, our study generated a rich dataset with candidate proteins worthy of further validation as biomarkers and/or therapeutic targets for stroke. The proteomics data are available in the PRIDE Archive with identifier PXD025077.

The spindle assembly checkpoint (SAC) is critical for sensing defective microtubule-kinetochore attachments and tension across the kinetochore and functions to arrest cells in prometaphase to allow time to repair any errors before proceeding into anaphase. Dysregulation of the SAC leads to chromosome segregation errors that have been linked to human diseases like cancer. Although much has been learned about the composition of the SAC and the factors that regulate its activity, the proximity associations of core SAC components have not been explored in a systematic manner. Here, we have taken a BioID2-proximity-labeling proteomic approach to define the proximity protein environment for each of the five core SAC proteins BUB1, BUB3, BUBR1, MAD1L1, and MAD2L1 in mitotic-enriched populations of cells where the SAC is active. These five protein association maps were integrated to generate a SAC proximity protein network that contains multiple layers of information related to core SAC protein complexes, protein-protein interactions, and proximity associations. Our analysis validated many known SAC complexes and protein-protein interactions. Additionally, it uncovered new protein associations, including the ELYS-MAD1L1 interaction that we have validated, which lend insight into the functioning of core SAC proteins and highlight future areas of investigation to better understand the SAC.

The COVID-19 pandemic has become a worldwide health crisis. So far, most studies have focused on the epidemiology and pathogenesis of this infectious disease. Little attention has been given to the disease sequelae in patients recovering from COVID-19, and nothing is known about the mechanisms underlying these sequelae. Herein, we profiled the serum proteome of a cohort of COVID-19 patients in the disease onset and recovery stages. Based on the close integration of our proteomic analysis with clinical data, we propose that COVID-19 is associated with prolonged disorders in cholesterol metabolism and myocardium, even in the recovery stage. We identify potential biomarkers for these disorders. Moreover, severely affected patients presented more serious disturbances in these pathways. Our findings potentially support clinical decision-making to improve the prognosis and treatment of patients.

Parkinson's disease (PD) is the second most common neurodegenerative disorder that results in motor dysfunction and, eventually, cognitive impairment. α-Synuclein protein is known as a central protein to the pathophysiology of PD, but the underlying pathological mechanism still remains to be elucidated. In an effort to understand how α-synuclein underlies the pathology of PD, various PD mouse models with α-synuclein overexpression have been developed. However, systemic analysis of the brain proteome of those mouse models is lacking. In this study, we established two mouse models of PD by injecting α-synuclein preformed fibrils (PFF) or by inducing overexpression of human A53T α-synuclein to investigate common pathways in the two different types of the PD mouse models. For more accurate quantification of mouse brain proteome, the proteins were quantified using the method of stable isotope labeling with amino acids in mammals . We identified a total of 8355 proteins from the two mouse models; ∼6800 and ∼7200 proteins from α-synuclein PFF-injected mice and human A53T α-synuclein transgenic mice, respectively. Through pathway analysis of the differentially expressed proteins common to both PD mouse models, it was discovered that the complement and coagulation cascade pathways were enriched in the PD mice compared to control animals. Notably, a validation study demonstrated that complement component 3 (C3)-positive astrocytes were increased in the ventral midbrain of the intrastriatal α-synuclein PFF-injected mice and C3 secreted from astrocytes could induce the degeneration of dopaminergic neurons. This is the first study that highlights the significance of the complement and coagulation pathways in the pathogenesis of PD through proteome analyses with two sophisticated mouse models of PD.

As optimum temperature is essential for all living organisms, heat shock represents a challenging problem for their survival. Therefore, cellular response to heat shock is among the most extensively investigated stress response pathways; however, how the human proteome responds to heat shock has not been comprehensively investigated. In this study, we employed stable isotope labeling by amino acids in cell culture (SILAC), together with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, to fulfill an in-depth analysis of the alterations in the human proteome in M14 human melanoma cells in response to heat shock stress. We found that, after heat shock, 284 and 278 out of the 4319 quantified proteins were with substantially diminished and elevated expressions, respectively. We also examined the alterations in human kinome after heat shock by using our recently developed targeted proteomic method relying on parallel-reaction monitoring. Our results showed that the expression levels of 11 and 22 kinase proteins were increased and decreased, respectively, by at least 1.5-fold upon heat shock. By interrogating publicly available RNA-seq and m6A sequencing data, we observed that the elevated expression of more than 30 proteins, including CHEK1 and CCND3 kinases, could occur via an m6A-mediated mechanism. Furthermore, our results from single-base elongation and ligation-based quantitative polymerase chain reaction (qPCR) amplification (SELECT) and luciferase reporter assays revealed that heat shock gave rise to elevated m6A levels at A280 and A286 sites in the 5'-untranslated region of HSPH1 mRNA, thereby leading to increased translation of HSPH1 protein. Together, our discovery and targeted proteomic methods revealed the reprogramming of human proteome and kinome upon heat shock stress and provided insights into cellular responses toward heat shock stress.

Antibody sequence information is crucial to understanding the structural basis for antigen binding and enables the use of antibodies as therapeutics and research tools. Here, we demonstrate a method for direct de novo sequencing of monoclonal IgG from the purified antibody products. The method uses a panel of multiple complementary proteases to generate suitable peptides for de novo sequencing by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a bottom-up fashion. Furthermore, we apply a dual fragmentation scheme, using both stepped high-energy collision dissociation (stepped HCD) and electron-transfer high-energy collision dissociation (EThcD), on all peptide precursors. The method achieves full sequence coverage of the monoclonal antibody herceptin, with an accuracy of 99% in the variable regions. We applied the method to sequence the widely used anti-FLAG-M2 mouse monoclonal antibody, which we successfully validated by remodeling a high-resolution crystal structure of the Fab and demonstrating binding to a FLAG-tagged target protein in Western blot analysis. The method thus offers robust and reliable sequences of monoclonal antibodies.

MS1-based label-free quantification can compare precursor ion peaks across runs, allowing reproducible protein measurements. Among bioinformatic platforms enabling MS1-based quantification, MaxQuant (MQ) is one of the most used, while Proteome Discoverer (PD) has recently introduced the Minora tool. Here, we present a comparative evaluation of six MS1-based quantification methods available in MQ and PD. Intensity (MQ and PD) and area (PD only) of the precursor ion peaks were measured and then subjected or not to normalization. The six methods were applied to data sets simulating various differential proteomics scenarios and covering a wide range of protein abundance ratios and amounts. PD outperformed MQ in terms of quantification yield, dynamic range, and reproducibility, although neither platform reached a fully satisfactory quality of measurements at low-abundance ranges. PD methods including normalization were the most accurate in estimating the abundance ratio between groups and the most sensitive when comparing groups with a narrow abundance ratio; on the contrary, MQ methods generally reached slightly higher specificity, accuracy, and precision values. Moreover, we found that applying an optimized log ratio-based threshold can maximize specificity, accuracy, and precision. Taken together, these results can help researchers choose the most appropriate MS1-based protein quantification strategy for their studies.

Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep, a package for nonparametric description and differential analysis of genome-wide data, available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/blacksheepr.html) and Bioconda (https://bioconda.github.io/recipes/blksheep/README.html). BlackSheep is a complementary tool to other differential expression analysis methods, which is particularly useful when analyzing small subgroups in a larger cohort.

Toxic amyloid-beta (Aβ) peptides, produced by sequential proteolytic cleavage of the amyloid precursor protein (APP), play a key role in the initial stage of Alzheimer's disease (AD). Increasing evidence indicates that Aβ42 induces neuronal circuit hyperexcitability in the early stages of AD pathology. As a result, researchers have investigated treatments that modulate the excitatory/inhibitory imbalance as potential AD therapies. For example, levetiracetam, an atypical antiepileptic drug used to quell hyperexcitability, has garnered recent interest in the AD field, even though its exact mechanism(s) of action remains elusive. Here, we show that in APP knock-in mouse models of amyloid pathology, chronic levetiracetam administration decreases cortical Aβ42 levels and lowers the amyloid plaque burden. In addition, using multiplexed tandem mass tag-quantitative mass spectrometry-based proteomic analysis, we determined that chronic levetiracetam administration selectively normalizes levels of presynaptic endocytic proteins. Finally, we found that levetiracetam treatment selectively lowers beta carboxyl-terminal fragment levels, while the abundance of full-length APP remains unchanged. In summary, this work reports that chronic treatment with levetiracetam serves as a useful therapeutic in AD by normalizing levels of presynaptic endocytic proteins and altering APP cleavage preference, leading to a decrease in both Aβ42 levels and the amyloid plaque burden. These novel findings provide novel evidence for the previously documented therapeutic value of levetiracetam to mitigate AD pathology.

Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms, which do not account for the function of a protein in a particular phosphorylation state. Analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We propose a method that combines shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associated to potential functions using a random walk on the heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSitePlus and were able to associate differentially regulated sites on the same proteins to their previously described specific functions. We further tested the algorithm on three previously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles.

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Epithelial-mesenchymal transition (EMT) plays a critical role in airway injury, repair, and structural remodeling. IκB kinase (IKK)-NFκB signaling regulates late EMT-associated gene expression. However, IKK-mediated mesenchymal transition occurs earlier than NFκB/RelA subunit-dependent EMT gene expression, leading us to investigate the hypothesis that IKK plays an independent mechanism in transforming growth factor-β (TGFβ)-induced EMT. Time-resolved dissection of early proteome and phosphoproteome changes in response to TGFβ and a specific IKK inhibitor, BMS-345541, revealed that IKK regulates cascades of 23 signaling pathways essential in EMT, including TGFβ signaling, p38 mitogen associate protein kinase (MAPK), Toll receptor signaling, and integrin pathways. We identified early IKK-dependent phosphorylation of core regulatory proteins in essential EMT signaling cassettes, including ATF2, JUN, NFKB1/p105, and others. Interestingly, we found that IKKβ directly complexes with and phosphorylates the spliced X-box-binding protein 1 (XBP1s). XBP1s is an arm of the unfolded protein response (UPR) that activates the hexosamine biosynthetic pathway (HBP), a pathway that mediates protein N-glycosylation and survival from ER stress-induced apoptosis in EMT. We found that inhibition of IKK activity abolishes the phosphorylation of XBP1-T48, blocks XBP1s nuclear translocation, and inhibits the activation of HBP. Our study elucidates a previously unrecognized IKKβ-XBP1s-HBP crosstalk pathway that couples inflammation and glucose metabolic reprogramming in ETM. Because XBP1-HBP controls N-glycosylation of the extracellular matrix (ECM) in EMT, this novel IKKβ-XBP1-HBP pathway may contain therapeutic targets whose inhibition could prevent ECM remodeling in lung fibrosis or other airway remodeling diseases.

Erratum in J Proteome Res. 2022 Oct 7;21(10):2552.

Recently, we have found that two urinary glycoproteins, prostatic acid phosphatase (ACPP) and clusterin (CLU), combined with serum prostate-specific antigen (PSA) can serve as a three-signature panel for detecting aggressive prostate cancer (PCa) based on a quantitative glycoproteomic study. To facilitate the translation of candidates into clinically applicable tests, robust and accurate targeted parallel reaction monitoring (PRM) assays that can be widely adopted in multiple labs were developed in this study. The developed PRM assays for the urinary glycopeptides, FLN*ESYK from ACPP and EDALN*ETR from CLU, demonstrated good repeatability and a sufficient working range covering three to four orders of magnitude, and their performance in differentiating aggressive PCa was assessed by the quantitative analysis of urine specimens collected from 69 nonaggressive (Gleason score = 6) and 73 aggressive (Gleason ≥ 8) PCa patients. When ACPP combined with CLU, the discrimination power was improved from an area under a curve (AUC) of 0.66 to 0.78. By combining ACPP, CLU, and serum PSA to form a three-signature panel, the AUC was further improved to 0.83 (sensitivity: 84.9%, specificity: 66.7%). Since the serum PSA test alone had an AUC of 0.68, our results demonstrated that the new urinary glycopeptide PRM assays can serve as an adjunct to the serum PSA test to achieve better predictive power toward aggressive PCa. In summary, our developed PRM assays for urinary glycopeptides were successfully applied to clinical PCa urine samples with a promising performance in aggressive PCa detection.

SARS-CoV-2 infection has become a major public health burden and affects many organs including lungs, kidneys, the liver, and the brain. Although the virus is readily detected and diagnosed using nasopharyngeal swabs by reverse transcriptase polymerase chain reaction (RT-PCR), detection of its presence in body fluids is fraught with difficulties. A number of published studies have failed to detect viral RNA by RT-PCR methods in urine. Although microbial identification in clinical microbiology using mass spectrometry is undertaken after culture, here we undertook a mass spectrometry-based approach that employed an enrichment step to capture and detect SARS-CoV-2 nucleocapsid protein directly from urine of COVID-19 patients without any culture. We detected SARS-CoV-2 nucleocapsid protein-derived peptides from 13 out of 39 urine samples. Further, a subset of COVID-19 positive and COVID-19 negative urine samples validated by mass spectrometry were used for the quantitative proteomics analysis. Proteins with increased abundance in urine of SARS-CoV-2 positive individuals were enriched in the acute phase response, regulation of complement system, and immune response. Notably, a number of renal proteins such as podocin (NPHS2), an amino acid transporter (SLC36A2), and sodium/glucose cotransporter 5 (SLC5A10), which are intimately involved in normal kidney function, were decreased in the urine of COVID-19 patients. Overall, the detection of viral antigens in urine using mass spectrometry and alterations of the urinary proteome could provide insights into understanding the pathogenesis of COVID-19.