Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

The heterogeneity of idiopathic pulmonary fibrosis (IPF) limits its diagnosis and treatment. The association between the pathophysiological features and the serum protein signatures of IPF currently remains unclear. The present study analyzed the specific proteins and patterns associated with the clinical parameters of IPF based on a serum proteomic dataset by data-independent acquisition using MS. Differentiated proteins in sera distinguished patients with IPF into three subgroups in signal pathways and overall survival. Aging-associated signatures by weighted gene correlation network analysis coincidently provided clear and direct evidence that aging is a critical risk factor for IPF rather than a single biomarker. Expression of LDHA and CCT6A, which was associated with glucose metabolic reprogramming, was correlated with high serum lactic acid content in patients with IPF. Cross-model analysis and machine learning showed that a combinatorial biomarker accurately distinguished patients with IPF from healthy individuals with an area under the curve of 0.848 (95% CI = 0.684-0.941) and validated from another cohort and ELISA assay. This serum proteomic profile provides rigorous evidence that enables an understanding of the heterogeneity of IPF and protein alterations that could help in its diagnosis and treatment decisions.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery of proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990 and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Single-cell proteomics as an emerging field has exhibited potential in revealing cellular heterogeneity at the functional level. However, accurate interpretation of single-cell proteomics data suffers from challenges such as measurement noise, internal heterogeneity, and the limited sample size of label-free quantitative mass spectrometry. Herein, the author describes peptide-level differential expression analysis for single-cell proteomic (pepDESC), a method for detecting differentially expressed proteins using peptide-level information designed for label-free quantitative mass spectrometry-based single-cell proteomics. While, in this study, the author focuses on the heterogeneity among the limited number of samples, pepDESC is also applicable to regular-size proteomics data. By balancing proteome coverage and quantification accuracy using peptide quantification, pepDESC is demonstrated to be effective in real-world single-cell and spike-in benchmark datasets. By applying pepDESC to published single-mouse macrophage data, the author found a large fraction of differentially expressed proteins among three types of cells, which remarkably revealed distinct dynamics of different cellular functions responding to lipopolysaccharide stimulation.

Pancreatic cancer, in most cases being pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancers with a median survival time of less than 6 months. Therapeutic options are very limited for patients with PDAC, and surgery is still the most effective treatment, making improvements in early diagnosis critical. One typical characteristic of PDAC is the desmoplastic reaction of its stroma microenvironment, which actively interacts with cancer cells to orchestrate key components in tumorigenesis, metastasis, and chemoresistance. A global exploration of cancer-stroma crosstalk is essential to decipher PDAC biology and design intervention strategies. Over the past decade, the dramatic improvement in proteomics technologies has enabled the profiling of proteins, post-translational modifications (PTMs), and their protein complexes at unprecedented sensitivity and dimensionality. Here, starting with our current understanding of PDAC characteristics, including precursor lesions, progression models, tumor microenvironment, and therapeutic advancements, we describe how proteomics contributes to the functional and clinical exploration of PDAC, providing insights into PDAC carcinogenesis, progression, and chemoresistance. We summarize recent achievements enabled by proteomics to systematically investigate PTMs-mediated intracellular signaling in PDAC, cancer-stroma interactions, and potential therapeutic targets revealed by these functional studies. We also highlight proteomic profiling of clinical tissue and plasma samples to discover and verify useful biomarkers that can aid early detection and molecular classification of patients. In addition, we introduce spatial proteomic technology and its applications in PDAC for deconvolving tumor heterogeneity. Finally, we discuss future prospects of applying new proteomic technologies in comprehensively understanding PDAC heterogeneity and intercellular signaling networks. Importantly, we expect advances in clinical functional proteomics for exploring mechanisms of cancer biology directly by high-sensitivity functional proteomic approaches starting from clinical samples.

The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (www.uniprot.org) is the world's leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information and presents a summary of experimentally verified, or computationally predicted, functional information added by our expert biocuration team for each protein in the proteome. Researchers in the field of mass spectrometry-based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases.

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely posttranslationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry-based proteomic approaches to define the ECM composition, or "matrisome," of tissues. This first part of this review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Lastly, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. Mass spectrometry-based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics.

Lysine β-hydroxybutyrylation (Kbhb) is an evolutionarily conserved and widespread post-translational modification that is associated with active gene transcription and cellular proliferation. However, its role in phytopathogenic fungi remains unknown. Here, we characterized Kbhb in the rice false smut fungus Ustilaginoidea virens. We identified 2204 Kbhb sites in 852 proteins, which are involved in diverse biological processes. The mitogen-activated protein kinase UvSlt2 is a Kbhb protein, and a strain harboring a point mutation at K72, the Kbhb site of this protein, had decreased UvSlt2 activity and reduced fungal virulence. Molecular dynamic simulations revealed that K72bhb increases the hydrophobic solvent-accessible surface area of UvSlt2, thereby affecting its binding to its substrates. The mutation of K298bhb in the septin UvCdc10 resulted in reduced virulence and altered the subcellular localization of this protein. Moreover, we confirmed that the NAD+-dependent histone deacetylases UvSirt2 and UvSirt5 are the major enzymes that remove Kbhb in U. virens. Collectively, our findings identify regulatory elements of the Kbhb pathway and reveal important roles for Kbhb in regulating protein localization and enzymatic activity. These findings provide insight into the regulation of virulence in phytopathogenic fungi via post-translational modifications.

Acute myeloid leukemia (AML) is a highly heterogeneous cancer of the hematopoietic system with no cure for most patients. In addition to chemotherapy, treatment options for AML include recently approved therapies that target proteins with roles in AML pathobiology, such as FLT3, BLC2, and IDH1/2. However, due to disease complexity, these therapies produce very diverse responses, and survival rates are still low. Thus, despite considerable advances, there remains a need for therapies that target different aspects of leukemic biology and for associated biomarkers that define patient populations likely to respond to each available therapy. To meet this need, drugs that target different AML vulnerabilities are currently in advanced stages of clinical development. Here, we review proteomics and phosphoproteomics studies that aimed to provide insights into AML biology and clinical disease heterogeneity not attainable with genomic approaches. To place the discussion in context, we first provide an overview of genetic and clinical aspects of the disease, followed by a summary of proteins targeted by compounds that have been approved or are under clinical trials for AML treatment and, if available, the biomarkers that predict responses. We then discuss proteomics and phosphoproteomics studies that provided insights into AML pathogenesis, from which potential biomarkers and drug targets were identified, and studies that aimed to rationalize the use of synergistic drug combinations. When considered as a whole, the evidence summarized here suggests that proteomics and phosphoproteomics approaches can play a crucial role in the development and implementation of precision medicine for AML patients.

The N termini of proteins contain information about their biochemical properties and functions. These N termini can be processed by proteases and can undergo other co- or posttranslational modifications. We have developed LATE (LysN Amino Terminal Enrichment), a method that uses selective chemical derivatization of α-amines to isolate the N-terminal peptides, in order to improve N-terminome identification in conjunction with other enrichment strategies. We applied LATE alongside another N-terminomic method to study caspase-3-mediated proteolysis both in vitro and during apoptosis in cells. This has enabled us to identify many unreported caspase-3 cleavages, some of which cannot be identified by other methods. Moreover, we have found direct evidence that neo-N-termini generated by caspase-3 cleavage can be further modified by Nt-acetylation. Some of these neo-Nt-acetylation events occur in the early phase of the apoptotic process and may have a role in translation inhibition. This has provided a comprehensive overview of the caspase-3 degradome and has uncovered previously unrecognized cross talk between posttranslational Nt-acetylation and caspase proteolytic pathways.

The cell bodies of hypothalamic magnocellular neurones are densely packed in the hypothalamic supraoptic nucleus, whereas their axons project to the anatomically discrete posterior pituitary gland. We have taken advantage of this unique anatomical structure to establish proteome and phosphoproteome dynamics in neuronal cell bodies and axonal terminals in response to physiological stimulation. We have found that proteome and phosphoproteome responses to neuronal stimulation are very different between somatic and axonal neuronal compartments, indicating the need of each cell domain to differentially adapt. In particular, changes in the phosphoproteome in the cell body are involved in the reorganization of the cytoskeleton and in axonal terminals the regulation of synaptic and secretory processes. We have identified that prohormone precursors including vasopressin and oxytocin are phosphorylated in axonal terminals and are hyperphosphorylated following stimulation. By multiomic integration of transcriptome and proteomic data, we identify changes to proteins present in afferent inputs to this nucleus.

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity, and half-life. The increasingly mature toolbox with glycomic and glycoproteomic strategies is applied for the development of biopharmaceuticals and the discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end, it is noted that the identification of biomarkers has received more attention than the corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes, but developments to accurately quantify these have so far been limited. In this perspective, a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address the quantification of a multimarker panel are discussed.

Extracellular vesicles (EVs) are nanoscopic, heterogenous, lipid-rich particles that carry a multitude of cargo biomolecules including proteins, nucleic acids, and metabolites. Although historically EVs were regarded as cellular debris with no intrinsic value, growing understanding of EV biogenesis has led to the realization that EVs facilitate intercellular communication and are sources of liquid biomarkers. EVs can be isolated and analyzed from a wide variety of accessible biofluids for biomarker discovery and diagnostic applications. There is a diversity of EVs from different biological compartments (e.g., cells and tissues), and some of these EVs are present at extremely low concentrations. Consequently, a challenge in the field is to find appropriate markers that enable selective isolation of these rare EVs. Many conventional protein detection technologies have limited sensitivity to detect low abundance biomarkers in EVs, limiting their use in EV research. Advances in ultrasensitive detection technologies are needed to harness the potential of EVs for clinical application. This Perspective highlights current EV research focusing on ultrasensitive detection technologies, their limitations, and areas of potential growth in the future.

Membrane proteins play critical roles at the cell surface and their misfunction is a hallmark of many human diseases. A precise evaluation of the plasma membrane proteome is therefore essential for cell biology and for discovering novel biomarkers and therapeutic targets. However, the low abundance of this proteome relative to soluble proteins makes it difficult to characterize, even with the most advanced proteomics technologies. Here, we apply the peptidisc membrane mimetic to purify the cell membrane proteome. Using the HeLa cell line as a reference, we capture 500 different integral membrane proteins, with half annotated to the plasma membrane. Notably, the peptidisc library is enriched with several ABC, SLC, GPCR, CD, and cell adhesion molecules that generally exist at low to very low copy numbers in the cell. We extend the method to compare two pancreatic cell lines, Panc-1 and hPSC. Here we observe a striking difference in the relative abundance of the cell surface cancer markers L1CAM, ANPEP, ITGB4, and CD70. We also identify two novel SLC transporters, SLC30A1 and SLC12A7, that are highly present in the Panc-1 cell only. The peptidisc library thus emerges as an effective way to survey and compare the membrane proteome of mammalian cells. Furthermore, since the method stabilizes membrane proteins in a water-soluble state, members of the library, here SLC12A7, can be specifically isolated.

Lysosomes, the main degradative organelles of mammalian cells, play a key role in the regulation of metabolism. It is becoming more and more apparent that they are highly active, diverse, and involved in a large variety of processes. The essential role of lysosomes is exemplified by the detrimental consequences of their malfunction, which can result in lysosomal storage disorders, neurodegenerative diseases, and cancer. Using lysosome enrichment and mass spectrometry, we investigated the lysosomal proteomes of HEK293, HeLa, HuH-7, SH-SY5Y, MEF, and NIH3T3 cells. We provide evidence on a large scale for cell type-specific differences of lysosomes, showing that levels of distinct lysosomal proteins are highly variable within one cell type, while expression of others is highly conserved across several cell lines. Using differentially stable isotope-labeled cells and bimodal distribution analysis, we furthermore identify a high confidence population of lysosomal proteins for each cell line. Multi-cell line correlation of these data reveals potential novel lysosomal proteins, and we confirm lysosomal localization for six candidates. All data are available via ProteomeXchange with identifier PXD020600.

Reproductive traits often evolve rapidly between species. Understanding the causes and consequences of this rapid divergence requires characterization of female and male reproductive proteins and their effect on fertilization success. Species in the Drosophila virilis clade exhibit rampant interspecific reproductive incompatibilities, making them ideal for studies on diversification of reproductive proteins and their role in speciation. Importantly, the role of intraejaculate protein abundance and allocation in interspecific divergence is poorly understood. Here, we identify and quantify the transferred male ejaculate proteome using multiplexed isobaric labeling of the lower female reproductive tract before and immediately after mating using three species of the virilis group. We identified over 200 putative male ejaculate proteins, many of which show differential abundance between species, suggesting that males transfer a species-specific allocation of seminal fluid proteins during copulation. We also identified over 2000 female reproductive proteins, which contain female-specific serine-type endopeptidases that showed differential abundance between species and elevated rates of molecular evolution, similar to that of some male seminal fluid proteins. Our findings suggest that reproductive protein divergence can also manifest in terms of species-specific protein abundance patterns.

Non-obstructive azoospermia (NOA), the most severe form of male infertility, could be treated with intracytoplasmic sperm injection, providing spermatozoa were retrieved with the microdissection testicular sperm extraction (mTESE). We hypothesized that testis-specific and germ cell-specific proteins would facilitate flow cytometry-assisted identification of rare spermatozoa in semen cell pellets of NOA patients, thus enabling non-invasive diagnostics prior to mTESE. Data mining, targeted proteomics, and immunofluorescent microscopy identified and verified a panel of highly testis-specific proteins expressed at the continuum of germ cell differentiation. Late germ cell-specific proteins AKAP4_HUMAN and ASPX_HUMAN (ACRV1 gene) revealed exclusive localization in spermatozoa tails and acrosomes, respectively. A multiplex imaging flow cytometry assay facilitated fast and unambiguous identification of rare but morphologically intact AKAP4+/ASPX+/Hoechst+ spermatozoa within debris-laden semen pellets of NOA patients. While the previously suggested markers for spermatozoa retrieval suffered from low diagnostic specificity, the multistep gating strategy and visualization of AKAP4+/ASPX+/Hoechst+ cells with elongated tails and acrosome-capped nuclei facilitated fast and unambiguous identification of the mature intact spermatozoa. AKAP4+/ASPX+/Hoechst+ assay may emerge as a noninvasive test to predict retrieval of morphologically intact spermatozoa by mTESE, thus improving diagnostics and treatment of severe forms of male infertility.

Regular exercise has many favorable effects on human health, which may be mediated in part by the release of circulating bioactive factors during each bout of exercise. Limited data exist regarding the kinetic responses of plasma proteins during and after acute exercise. Proteomic profiling of 4163 proteins was performed using a large-scale, affinity-based platform in 75 middle-aged adults who were referred for treadmill exercise stress testing. Plasma proteins were quantified at baseline, peak exercise, and 1-h postexercise, and those with significant changes at both exercise timepoints were further examined for their associations with cardiometabolic traits and change with aerobic exercise training in the Health, Risk Factors, Exercise Training and Genetics Family Study, a 20-week exercise intervention study. A total of 765 proteins changed (false discovery rate < 0.05) at peak exercise compared to baseline, and 128 proteins changed (false discovery rate < 0.05) at 1-h postexercise. The 56 proteins that changed at both timepoints included midkine, brain-derived neurotrophic factor, metalloproteinase inhibitor 4, and coiled-coil domain-containing protein 126 and were enriched for secreted proteins. The majority had concordant direction of change at both timepoints. Across all proteins assayed, gene set enrichment analysis showed increased abundance of coagulation-related proteins at 1-h postexercise. Forty-five proteins were associated with at least one measure of adiposity, lipids, glucose homeostasis, or cardiorespiratory fitness in Health, Risk Factors, Exercise Training and Genetics Family Study, and 20 proteins changed with aerobic exercise training. We identified hundreds of novel proteins that change during acute exercise, most of which resolved by 1 h into recovery. Proteins with sustained changes during exercise and recovery may be of particular interest as circulating biomarkers and pathways for further investigation in cardiometabolic diseases. These data will contribute to a biochemical roadmap of acute exercise that will be publicly available for the entire scientific community.

Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms, and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this article we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium, which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization.

Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnostic approaches and new targeted drug treatments.

Different brain cell types play distinct roles in brain development and disease. Molecular characterization of cell-specific mechanisms using cell type-specific approaches at the protein (proteomic) level can provide biological and therapeutic insights. To overcome the barriers of conventional isolation-based methods for cell type-specific proteomics, in vivo proteomic labeling with proximity-dependent biotinylation of cytosolic proteins using biotin ligase TurboID, coupled with mass spectrometry (MS) of labeled proteins, emerged as a powerful strategy for cell type-specific proteomics in the native state of cells without the need for cellular isolation. To complement in vivo proximity labeling approaches, in vitro studies are needed to ensure that cellular proteomes using the TurboID approach are representative of the whole-cell proteome and capture cellular responses to stimuli without disruption of cellular processes. To address this, we generated murine neuroblastoma (N2A) and microglial (BV2) lines stably expressing cytosolic TurboID to biotinylate the cellular proteome for downstream purification and analysis using MS. TurboID-mediated biotinylation captured 59% of BV2 and 65% of N2A proteomes under homeostatic conditions. TurboID labeled endolysosome, translation, vesicle, and signaling proteins in BV2 microglia and synaptic, neuron projection, and microtubule proteins in N2A neurons. TurboID expression and biotinylation minimally impacted homeostatic cellular proteomes of BV2 and N2A cells and did not affect lipopolysaccharide-mediated cytokine production or resting cellular respiration in BV2 cells. MS analysis of the microglial biotin-labeled proteins captured the impact of lipopolysaccharide treatment (>500 differentially abundant proteins) including increased canonical proinflammatory proteins (Il1a, Irg1, and Oasl1) and decreased anti-inflammatory proteins (Arg1 and Mgl2).

Comprehensive and in-depth identification of the human leukocyte antigen class I (HLA-I) and class II (HLA-II) tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is a powerful technology for direct identification of HLA peptides from patient-derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare and clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high-throughput, sensitive, and single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight MS on the Bruker timsTOF single-cell proteomics system (SCP). We demonstrate greater than twofold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation, and reduces input requirements to as few as 1e6 A375 cells for >800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen and noncanonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor-derived samples, enabling sensitive, high-throughput, and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.

The ancestral SARS-CoV-2 strain that initiated the Covid-19 pandemic at the end of 2019 has rapidly mutated into multiple variants of concern with variable pathogenicity and increasing immune escape strategies. However, differences in host cellular antiviral responses upon infection with SARS-CoV-2 variants remain elusive. Leveraging whole-cell proteomics, we determined host signaling pathways that are differentially modulated upon infection with the clinical isolates of the ancestral SARS-CoV-2 B.1 and the variants of concern Delta and Omicron BA.1. Our findings illustrate alterations in the global host proteome landscape upon infection with SARS-CoV-2 variants and the resulting host immune responses. Additionally, viral proteome kinetics reveal declining levels of viral protein expression during Omicron BA.1 infection when compared to ancestral B.1 and Delta variants, consistent with its reduced replication rates. Moreover, molecular assays reveal deferral activation of specific host antiviral signaling upon Omicron BA.1 and BA.2 infections. Our study provides an overview of host proteome profile of multiple SARS-CoV-2 variants and brings forth a better understanding of the instigation of key immune signaling pathways causative for the differential pathogenicity of SARS-CoV-2 variants.

Profiling the nucleic acid-binding proteins (NABPs) during aging process is critical to elucidate its roles in biological systems as well as transcriptional and translational regulation. Here, we developed a comprehensive strategy to survey the NABPs of mouse immune organs by using single cell preparation and selective capture technology-based proteomics. Our approach provided a global view of tissue NABPs from different organs under normal physiological conditions with extraction specificity of 70 to 90%. Through quantitative proteomics analysis of mouse spleen and thymus at 1, 4, 12, 24, 48, and 72 weeks, we investigated the molecular features of aging-related NABPs. A total of 2674 proteins were quantified in all six stages, demonstrating distinct and time-specific expression pattern of NABPs. Thymus and spleen exhibited unique aging signatures, and differential proteins and pathways were enriched across the mouse lifespan. Three core modules and 16 hub proteins associated with aging were revealed through weighted gene correlation network analysis. Significant candidates were screened for immunoassay verification, and six hub proteins were confirmed. The integrated strategy pertains the capability to decipher the dynamic functions of NABPs in aging physiology and benefit further mechanism research.

The canonical view of PI3Kα signaling describes phosphatidylinositol-3,4,5-trisphosphate (PtdIns(3,4,5)P3) generation and activation of downstream effectors at the plasma membrane or at microtubule-bound endosomes. Here, we show that colorectal cancer (CRC) cell lines exhibit a diverse plasma membrane-nuclear distribution of PI3Kα, controlling corresponding levels of subcellular PtdIns(3,4,5)P3 pools. PI3Kα nuclear translocation was mediated by the importin β-dependent nuclear import pathway. By PtdIns(3,4,5)P3 affinity capture mass spectrometry done in the presence of SDS on CRC cell lines with PI3Kα nuclear localization, we identified 867 potential nuclear PtdIns(3,4,5)P3 effector proteins. Nuclear PtdIns(3,4,5)P3 interactome proteins were characterized by noncanonical PtdIns(3,4,5)P3-binding domains and showed overrepresentation for nuclear membrane, nucleolus, and nuclear speckles. The nuclear PtdIns(3,4,5)P3 interactome was enriched for proteins related to RNA metabolism, with splicing reporter assays and SC-35 foci staining suggesting a role of epidermal growth factor-stimulated nuclear PI3Kα signaling in modulating pre-mRNA splicing. In patient tumors, nuclear p110α staining was associated with lower T stage and mucinous histology. These results indicate that PI3Kα translocation mediates nuclear PtdIns(3,4,5)P3 effector signaling in human CRC, modulating signaling responses.

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification, and quantification, making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In in a previous review in 2017, we described technological and conceptual limitations that had held back success. We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification, and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. Shorter gradients, new scan modes, and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multiprotein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Ovarian cancer, a leading cause of cancer-related deaths among women, has been notoriously difficult to screen for and diagnose early, as early detection significantly improves survival. Researchers and clinicians seek routinely usable and noninvasive screening methods; however, available methods (i.e., biomarker screening) lack desirable sensitivity/specificity. The most fatal form, high-grade serous ovarian cancer, often originate in the fallopian tube; therefore, sampling from the vaginal environment provides more proximal sources for tumor detection. To address these shortcomings and leverage proximal sampling, we developed an untargeted mass spectrometry microprotein profiling method and identified cystatin A, which was validated in an animal model. To overcome the limits of detection inherent to mass spectrometry, we demonstrated that cystatin A is present at 100 pM concentrations using a label-free microtoroid resonator and translated our workflow to patient-derived clinical samples, highlighting the potential utility of early stage detection where biomarker levels would be low.

Proteomic studies in facioscapulohumeral muscular dystrophy (FSHD) could offer new insight into disease mechanisms underpinned by post-transcriptional processes. We used stable isotope (deuterium oxide; D2O) labeling and peptide mass spectrometry to investigate the abundance and turnover rates of proteins in cultured muscle cells from two individuals affected by FSHD and their unaffected siblings (UASb). We measured the abundance of 4420 proteins and the turnover rate of 2324 proteins in each (n = 4) myoblast sample. FSHD myoblasts exhibited a greater abundance but slower turnover rate of subunits of mitochondrial respiratory complexes and mitochondrial ribosomal proteins, which may indicate an accumulation of "older" less viable mitochondrial proteins in myoblasts from individuals affected by FSHD. Treatment with a 2'-O-methoxyethyl modified antisense oligonucleotide targeting exon 3 of the double homeobox 4 (DUX4) transcript tended to reverse mitochondrial protein dysregulation in FSHD myoblasts, indicating the effect on mitochondrial proteins may be a DUX4-dependent mechanism. Our results highlight the importance of post-transcriptional processes and protein turnover in FSHD pathology and provide a resource for the FSHD research community to explore this burgeoning aspect of FSHD.

PKC epsilon (PKCε) plays important roles in behavioral responses to alcohol and in anxiety-like behavior in rodents, making it a potential drug target for reducing alcohol consumption and anxiety. Identifying signals downstream of PKCε could reveal additional targets and strategies for interfering with PKCε signaling. We used a chemical genetic screen combined with mass spectrometry to identify direct substrates of PKCε in mouse brain and validated findings for 39 of them using peptide arrays and in vitro kinase assays. Prioritizing substrates with several public databases such as LINCS-L1000, STRING, GeneFriends, and GeneMAINA predicted interactions between these putative substrates and PKCε and identified substrates associated with alcohol-related behaviors, actions of benzodiazepines, and chronic stress. The 39 substrates could be broadly classified in three functional categories: cytoskeletal regulation, morphogenesis, and synaptic function. These results provide a list of brain PKCε substrates, many of which are novel, for future investigation to determine the role of PKCε signaling in alcohol responses, anxiety, responses to stress, and other related behaviors.

Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex [human leukocyte antigen (HLA) in humans]. These HLA-peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.

Rheumatoid arthritis (RA) is a typical autoimmune disease characterized by synovial inflammation, synovial tissue hyperplasia, and destruction of bone and cartilage. Protein glycosylation plays key roles in the pathogenesis of RA but in-depth glycoproteomics analysis of synovial tissues is still lacking. Here, by using a strategy to quantify intact N-glycopeptides, we identified 1260 intact N-glycopeptides from 481 N-glycosites on 334 glycoproteins in RA synovium. Bioinformatics analysis revealed that the hyper-glycosylated proteins in RA were closely linked to immune responses. By using DNASTAR software, we identified 20 N-glycopeptides whose prototype peptides were highly immunogenic. We next calculated the enrichment scores of nine types of immune cells using specific gene sets from public single-cell transcriptomics data of RA and revealed that the N-glycosylation levels at some sites, such as IGSF10_N2147, MOXD2P_N404, and PTCH2_N812, were significantly correlated with the enrichment scores of certain immune cell types. Furthermore, we showed that aberrant N-glycosylation in the RA synovium was related to increased expression of glycosylation enzymes. Collectively, this work presents, for the first time, the N-glycoproteome of RA synovium and describes immune-associated glycosylation, providing novel insights into RA pathogenesis.

Apolipoprotein (apo) E4 is the major genetic risk factor for Alzheimer's disease. While neurons generally produce a minority of the apoE in the central nervous system, neuronal expression of apoE increases dramatically in response to stress and is sufficient to drive pathology. Currently, the molecular mechanisms of how apoE4 expression may regulate pathology are not fully understood. Here, we expand upon our previous studies measuring the impact of apoE4 on protein abundance to include the analysis of protein phosphorylation and ubiquitylation signaling in isogenic Neuro-2a cells expressing apoE3 or apoE4. ApoE4 expression resulted in a dramatic increase in vasodilator-stimulated phosphoprotein (VASP) S235 phosphorylation in a protein kinase A (PKA)-dependent manner. This phosphorylation disrupted VASP interactions with numerous actin cytoskeletal and microtubular proteins. Reduction of VASP S235 phosphorylation via PKA inhibition resulted in a significant increase in filopodia formation and neurite outgrowth in apoE4-expressing cells, exceeding levels observed in apoE3-expressing cells. Our results highlight the pronounced and diverse impact of apoE4 on multiple modes of protein regulation and identify protein targets to restore apoE4-related cytoskeletal defects.

Current proteomic tools permit the high-throughput analysis of the blood proteome in large cohorts, including those enriched for chronic kidney disease (CKD) or its risk factors. To date, these studies have identified numerous proteins associated with cross-sectional measures of kidney function, as well as with the longitudinal risk of CKD progression. Representative signals that have emerged from the literature include an association between levels of testican-2 and favorable kidney prognosis and an association between levels of TNFRSF1A and TNFRSF1B and worse kidney prognosis. For these and other associations, however, understanding whether the proteins play a causal role in kidney disease pathogenesis remains a fundamental challenge, especially given the strong impact that kidney function can have on blood protein levels. Prior to investing in dedicated animal models or randomized trials, methods that leverage the availability of genotyping in epidemiologic cohorts-including Mendelian randomization, colocalization analyses, and proteome-wide association studies-can add evidence for causal inference in CKD proteomics research. In addition, integration of large-scale blood proteome analyses with urine and tissue proteomics, as well as improved assessment of posttranslational protein modifications (e.g., carbamylation), represent important future directions. Taken together, these approaches seek to translate progress in large-scale proteomic profiling into the promise of improved diagnostic tools and therapeutic target identification in kidney disease.

Bacteria are the most abundant and diverse organisms among the kingdoms of life. Due to this excessive variance, finding a unified, comprehensive, and safe workflow for quantitative bacterial proteomics is challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition, and data analysis strategies in bacterial proteomics. We investigated workflow performances on six representative species with highly different physiologic properties to mimic bacterial diversity. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in-solution digest. Peptides were separated on a 30-min linear microflow liquid chromatography gradient and analyzed in data-independent acquisition mode. Data analysis was performed with DIA-NN using a predicted spectral library. Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs, and biological safety. With this rapid workflow, over 40% of all encoded genes were detected per bacterial species. We demonstrated the general applicability of our workflow on a set of 23 taxonomically and physiologically diverse bacterial species. We could confidently identify over 45,000 proteins in the combined dataset, of which 30,000 have not been experimentally validated before. Our work thereby provides a valuable resource for the microbial scientific community. Finally, we grew Escherichia coli and Bacillus cereus in replicates under 12 different cultivation conditions to demonstrate the high-throughput suitability of the workflow. The proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom.

Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.

Nasopharyngeal carcinoma (NPC), a malignant tumor distinctly characterized by ethnic and geographic distribution, is highly prevalent in Southern China and Southeast Asia. However, the molecular mechanisms of NPC have not been fully revealed at the proteomic level. In this study, 30 primary NPC samples and 22 normal nasopharyngeal epithelial tissues were collected for proteomics analysis, and a relatively complete proteomics landscape of NPC was depicted for the first time. By combining differential expression analysis, differential co-expression analysis, and network analysis, potential biomarkers and therapeutic targets were identified. Some identified targets were verified by biological experiments. We found that 17-AAG, a specific inhibitor of the identified target heat shock protein 90 (HSP90), could be a potential therapeutic drug for NPC. Finally, consensus clustering identified two NPC subtypes with specific molecular features. The subtypes and the related molecules were verified by an independent data set and may have different progression-free survival. The results of this study provide a comprehensive understanding of the proteomics molecular signatures of NPC and provide new perspectives and inspiration for prognostic determination and treatment of NPC.

Mass spectrometry (MS) enables high-throughput identification and quantification of proteins in complex biological samples and can provide insights into the global function of biological systems. Label-free quantification is cost-effective and suitable for the analysis of human samples. Despite rapid developments in label-free data acquisition workflows, the number of proteins quantified across samples can be limited by technical and biological variability. This variation can result in missing values which can in turn challenge downstream data analysis tasks. General purpose or gene expression-specific imputation algorithms are widely used to improve data completeness. Here, we propose an imputation algorithm designated for label-free MS data that is aware of the type of missingness affecting data. On published datasets acquired by data-dependent and data-independent acquisition workflows with variable degrees of biological complexity, we demonstrate that the proposed missing value estimation procedure by barycenter computation competes closely with the state-of-the-art imputation algorithms in differential abundance tasks while outperforming them in the accuracy of variance estimates of the peptide abundance measurements, and better controls the false discovery rate in label-free MS experiments. The barycenter estimation procedure is implemented in the msImpute software package and is available from the Bioconductor repository.

Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications.

Neurologic manifestations are among the most frequently reported complications of COVID-19. However, given the paucity of tissue samples and the highly infectious nature of the etiologic agent of COVID-19, we have limited information to understand the neuropathogenesis of COVID-19. Therefore, to better understand the impact of COVID-19 on the brain, we used mass-spectrometry-based proteomics with a data-independent acquisition mode to investigate cerebrospinal fluid (CSF) proteins collected from two different nonhuman primates, Rhesus Macaque and African Green Monkeys, for the neurologic effects of the infection. These monkeys exhibited minimal to mild pulmonary pathology but moderate to severe central nervous system (CNS) pathology. Our results indicated that CSF proteome changes after infection resolution corresponded with bronchial virus abundance during early infection and revealed substantial differences between the infected nonhuman primates and their age-matched uninfected controls, suggesting these differences could reflect altered secretion of CNS factors in response to SARS-CoV-2-induced neuropathology. We also observed the infected animals exhibited highly scattered data distributions compared to their corresponding controls indicating the heterogeneity of the CSF proteome change and the host response to the viral infection. Dysregulated CSF proteins were preferentially enriched in functional pathways associated with progressive neurodegenerative disorders, hemostasis, and innate immune responses that could influence neuroinflammatory responses following COVID-19. Mapping these dysregulated proteins to the Human Brain Protein Atlas found that they tended to be enriched in brain regions that exhibit more frequent injury following COVID-19. It, therefore, appears reasonable to speculate that such CSF protein changes could serve as signatures for neurologic injury, identify important regulatory pathways in this process, and potentially reveal therapeutic targets to prevent or attenuate the development of neurologic injuries following COVID-19.

Spermatogenesis defects are important for male infertility; however, the etiology and pathogenesis are still unknown. Herein, we identified two loss-of-function mutations of STK33 in seven individuals with non-obstructive azoospermia. Further functional studies of these frameshift and nonsense mutations revealed that Stk33-/KI male mice were sterile, and Stk33-/KI sperm were abnormal with defects in the mitochondrial sheath, fibrous sheath, outer dense fiber, and axoneme. Stk33KI/KI male mice were subfertile and had oligoasthenozoospermia. Differential phosphoproteomic analysis and in vitro kinase assay identified novel phosphorylation substrates of STK33, fibrous sheath components A-kinase anchoring protein 3 and A-kinase anchoring protein 4, whose expression levels decreased in testis after deletion of Stk33. STK33 regulated the phosphorylation of A-kinase anchoring protein 3/4, affected the assembly of fibrous sheath in the sperm, and played an essential role in spermiogenesis and male infertility.

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

Spontaneous deamidation of asparaginyl residues in proteins, if not repaired or cleared, can set in motion a cascade that leads to deteriorated health. Previously, we have discovered that deamidated human serum albumin (HSA) is elevated in the blood of patients with Alzheimer's disease and other neurodegenerative diseases, while the level of endogenous antibodies against deamidated HSA is significantly diminished, creating an imbalance between the risk factor and the defense against it. Endogenous antibodies against deamidated proteins are still unexplored. In the current study, we employed the SpotLight proteomics approach to identify novel amino acid sequences in antibodies specific to deamidated HSA. The results provide new insights into the clearance mechanism of deamidated proteins, a possible avenue for prevention of neurodegeneration.

We have successfully developed a bioinertized nanoflow LC/MS/MS (nanoLC/MS/MS) system for the highly sensitive analysis of phosphopeptides by depleting metal ions from the mobile phase. We found that not only direct contact of phosphopeptides with metal components, but also indirect contact with nanoLC pumps through the mobile phase causes significant losses during the recovery of phosphopeptides. Moreover, electrospray ionization was adversely affected by the mobile phase containing multiple metal ions as well as by the sample solvents contaminated with metal ions used in immobilized metal ion affinity chromatography for phosphopeptide enrichment. To solve these problems, metal ions were depleted by inserting an online metal ion removal device containing metal-chelating membranes between the gradient mixer and the autosampler. As a result, the peak areas of the identified phosphopeptides increased an average of 9.9-fold overall and 77-fold for multiply phosphorylated peptides with the insertion of the online metal ion removal system. This strategy would be applicable to the highly sensitive analysis of other phosphorylated biomolecules by microscale-LC/MS/MS.

While altered protein glycosylation is regarded a trait of oral squamous cell carcinoma (OSCC), the heterogeneous and dynamic glycoproteome of tumor tissues from OSCC patients remain unmapped. To this end, we here employ an integrated multi-omics approach comprising unbiased and quantitative glycomics and glycoproteomics applied to a cohort of resected primary tumor tissues from OSCC patients with (n = 19) and without (n = 12) lymph node metastasis. While all tumor tissues displayed relatively uniform N-glycome profiles suggesting overall stable global N-glycosylation during disease progression, altered expression of six sialylated N-glycans was found to correlate with lymph node metastasis. Notably, glycoproteomics and advanced statistical analyses uncovered altered site-specific N-glycosylation revealing previously unknown associations with several clinicopathological features. Importantly, the glycomics and glycoproteomics data unveiled that comparatively high abundance of two core-fucosylated and sialylated N-glycans (Glycan 40a and Glycan 46a) and one N-glycopeptide from fibronectin were associated with low patient survival, while a relatively low abundance of N-glycopeptides from both afamin and CD59 were also associated with poor survival. This study provides insight into the complex OSCC tissue N-glycoproteome, thereby forming an important resource to further explore the underpinning disease mechanisms and uncover new prognostic glycomarkers for OSCC.

Biomarkers remain the highest value proposition in cancer medicine today-especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Histidine-rich glycoprotein (HRG) is a liver-produced protein circulating in human serum at high concentrations of around 125 μg/ml. HRG belongs to the family of type-3 cystatins and has been implicated in a plethora of biological processes, albeit that its precise function is still not well understood. Human HRG is a highly polymorphic protein, with at least five variants with minor allele frequencies of more than 10%, variable in populations from different parts of the world. Considering these five mutations we can theoretically expect 35 = 243 possible genetic HRG variants in the population. Here, we purified HRG from serum of 44 individual donors and investigated by proteomics the occurrence of different allotypes, each being either homozygote or heterozygote for each of the five mutation sites. We observed that some mutational combinations in HRG were highly favored, while others were apparently missing, although they ought to be present based on the independent assembly of these five mutation sites. To further explore this behavior, we extracted data from the 1000 genome project (n ∼ 2500 genomes) and assessed the frequency of different HRG mutants in this larger dataset, observing a prevailing agreement with our proteomics data. From all the proteogenomic data we conclude that the five different mutation sites in HRG are not occurring independently, but several mutations at different sites are fully mutually exclusive, whereas others are highly intwined. Specific mutations do also affect HRG glycosylation. As the levels of HRG have been suggested as a protein biomarker in a variety of biological processes (e.g., aging, COVID-19 severity, severity of bacterial infections), we here conclude that the highly polymorphic nature of the protein needs to be considered in such proteomics evaluations, as these mutations may affect HRG's abundance, structure, posttranslational modifications, and function.

Protein aggregation of amyloid-β peptides and tau are pathological hallmarks of Alzheimer's disease (AD), which are often resistant to detergent extraction and thus enriched in the insoluble proteome. However, additional proteins that coaccumulate in the detergent-insoluble AD brain proteome remain understudied. Here, we comprehensively characterized key proteins and pathways in the detergent-insoluble proteome from human AD brain samples using differential extraction, tandem mass tag (TMT) labeling, and two-dimensional LC-tandem mass spectrometry. To improve quantification accuracy of the TMT method, we developed a complement TMT-based strategy to correct for ratio compression. Through the meta-analysis of two independent detergent-insoluble AD proteome datasets (8914 and 8917 proteins), we identified 190 differentially expressed proteins in AD compared with control brains, highlighting the pathways of amyloid cascade, RNA splicing, endocytosis/exocytosis, protein degradation, and synaptic activity. To differentiate the truly detergent-insoluble proteins from copurified background during protein extraction, we analyzed the fold of enrichment for each protein by comparing the detergent-insoluble proteome with the whole proteome from the same AD samples. Among the 190 differentially expressed proteins, 84 (51%) proteins of the upregulated proteins (n = 165) were enriched in the insoluble proteome, whereas all downregulated proteins (n = 25) were not enriched, indicating that they were copurified components. The vast majority of these enriched 84 proteins harbor low-complexity regions in their sequences, including amyloid-β, Tau, TARDBP/TAR DNA-binding protein 43, SNRNP70/U1-70K, MDK, PTN, NTN1, NTN3, and SMOC1. Moreover, many of the enriched proteins in AD were validated in the detergent-insoluble proteome by five steps of differential extraction, proteomic analysis, or immunoblotting. Our study reveals a resource list of proteins and pathways that are exclusively present in the detergent-insoluble proteome, providing novel molecular insights to the formation of protein pathology in AD.

Basal cell carcinomas (BCCs) and cutaneous squamous cell carcinomas (SCCs) are the most frequent types of cancer, and both originate from the keratinocyte transformation, giving rise to the group of tumors called keratinocyte carcinomas (KCs). The invasive behavior is different in each group of KC and may be influenced by their tumor microenvironment. The principal aim of the study is to characterize the protein profile of the tumor interstitial fluid (TIF) of KC to evaluate changes in the microenvironment that could be associated with their different invasive and metastatic capabilities. We obtained TIF from 27 skin biopsies and conducted a label-free quantitative proteomic analysis comparing seven BCCs, 16 SCCs, and four normal skins. A total of 2945 proteins were identified, 511 of them quantified in more than half of the samples of each tumoral type. The proteomic analysis revealed differentially expressed TIF proteins that could explain the different metastatic behavior in both KCs. In detail, the SCC samples disclosed an enrichment of proteins related to cytoskeleton, such as Stratafin and Ladinin-1. Previous studies found their upregulation positively correlated with tumor progression. Furthermore, the TIF of SCC samples was enriched with the cytokines S100A8/S100A9. These cytokines influence the metastatic output in other tumors through the activation of NF-kB signaling. According to this, we observed a significant increase in nuclear NF-kB subunit p65 in SCCs but not in BCCs. In addition, the TIF of both tumors was enriched with proteins involved in the immune response, highlighting the relevance of this process in the composition of the tumor environment. Thus, the comparison of the TIF composition of both KCs provides the discovery of a new set of differential biomarkers. Among them, secreted cytokines such as S100A9 may help explain the higher aggressiveness of SCCs, while Cornulin is a specific biomarker for BCCs. Finally, the proteomic landscape of TIF provides key information on tumor growth and metastasis, which can contribute to the identification of clinically applicable biomarkers that may be used in the diagnosis of KC, as well as therapeutic targets.

Recent advances in mass spectrometry-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Although peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratio-based approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 min and 100,000 proteomes in less than 2 h, a 1000-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition and data-independent acquisition. In addition, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is an important part of an overall quantitative proteomic pipeline that also needs to include high sensitive statistical analysis leading to proteoform resolution. Available as an open-source Python package and a graphical user interface with a one-click installer, it can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

Obstructive nephropathy is one of the leading causes of kidney injury and renal fibrosis in pediatric patients. Although considerable advances have been made in understanding the pathophysiology of obstructive nephropathy, most of them were based on animal experiments and a comprehensive understanding of obstructive nephropathy in pediatric patients at the molecular level remains limited. Here, we performed a comparative proteomics analysis of obstructed kidneys from pediatric patients with ureteropelvic junction obstruction and healthy kidney tissues. Intriguingly, the proteomics revealed extensive metabolic reprogramming in kidneys from individuals with ureteropelvic junction obstruction. Moreover, we uncovered the dysregulation of NAD+ metabolism and NAD+-related metabolic pathways, including mitochondrial dysfunction, the Krebs cycle, and tryptophan metabolism, which led to decreased NAD+ levels in obstructed kidneys. Importantly, the major NADase CD38 was strongly induced in human and experimental obstructive nephropathy. Genetic deletion or pharmacological inhibition of CD38 as well as NAD+ supplementation significantly recovered NAD+ levels in obstructed kidneys and reduced obstruction-induced renal fibrosis, partially through the mechanisms of blunting the recruitment of immune cells and NF-κB signaling. Thus, our work not only provides an enriched resource for future investigations of obstructive nephropathy but also establishes CD38-mediated NAD+ decline as a potential therapeutic target for obstruction-induced renal fibrosis.

Ubiquitination has crucial roles in many cellular processes, and dysregulation of ubiquitin machinery enzymes can result in various forms of pathogenesis. Cells only have a limited set of ubiquitin-conjugating (E2) enzymes to support the ubiquitination of many cellular targets. As individual E2 enzymes have many different substrates and interactions between E2 enzymes and their substrates can be transient, it is challenging to define all in vivo substrates of an individual E2 and the cellular processes it affects. Particularly challenging in this respect is UBE2D3, an E2 enzyme with promiscuous activity in vitro but less defined roles in vivo. Here, we set out to identify in vivo targets of UBE2D3 by using stable isotope labeling by amino acids in cell culture-based and label-free quantitative ubiquitin diGly proteomics to study global proteome and ubiquitinome changes associated with UBE2D3 depletion. UBE2D3 depletion changed the global proteome, with the levels of proteins from metabolic pathways, in particular retinol metabolism, being the most affected. However, the impact of UBE2D3 depletion on the ubiquitinome was much more prominent. Interestingly, molecular pathways related to mRNA translation were the most affected. Indeed, we find that ubiquitination of the ribosomal proteins RPS10 and RPS20, critical for ribosome-associated protein quality control, is dependent on UBE2D3. We show by Targets of Ubiquitin Ligases Identified by Proteomics 2 methodology that RPS10 and RPS20 are direct targets of UBE2D3 and demonstrate that the catalytic activity of UBE2D3 is required to ubiquitinate RPS10 in vivo. In addition, our data suggest that UBE2D3 acts at multiple levels in autophagic protein quality control. Collectively, our findings show that depletion of an E2 enzyme in combination with quantitative diGly-based ubiquitinome profiling is a powerful tool to identify new in vivo E2 substrates, as we have done here for UBE2D3. Our work provides an important resource for further studies on the in vivo functions of UBE2D3.

Dampening functional levels of the mitochondrial deubiquitylating enzyme Ubiquitin-specific protease 30 (USP30) has been suggested as an effective therapeutic strategy against neurodegenerative disorders such as Parkinson's Disease. USP30 inhibition may counteract the deleterious effects of impaired turnover of damaged mitochondria, which is inherent to both familial and sporadic forms of the disease. Small-molecule inhibitors targeting USP30 are currently in development, but little is known about their precise nature of binding to the protein. We have integrated biochemical and structural approaches to gain novel mechanistic insights into USP30 inhibition by a small-molecule benzosulfonamide-containing compound, USP30inh. Activity-based protein profiling mass spectrometry confirmed target engagement, high selectivity, and potency of USP30inh for USP30 against 49 other deubiquitylating enzymes in a neuroblastoma cell line. In vitro characterization of USP30inh enzyme kinetics inferred slow and tight binding behavior, which is comparable with features of covalent modification of USP30. Finally, we blended hydrogen-deuterium exchange mass spectrometry and computational docking to elucidate the molecular architecture and geometry of USP30 complex formation with USP30inh, identifying structural rearrangements at the cleft of the USP30 thumb and palm subdomains. These studies suggest that USP30inh binds to this thumb-palm cleft, which guides the ubiquitin C terminus into the active site, thereby preventing ubiquitin binding and isopeptide bond cleavage, and confirming its importance in the inhibitory process. Our data will pave the way for the design and development of next-generation inhibitors targeting USP30 and associated deubiquitinylases.

p38α (encoded by MAPK14) is a protein kinase that regulates cellular responses to almost all types of environmental and intracellular stresses. Upon activation, p38α phosphorylates many substrates both in the cytoplasm and nucleus, allowing this pathway to regulate a wide variety of cellular processes. While the role of p38α in the stress response has been widely investigated, its implication in cell homeostasis is less understood. To investigate the signaling networks regulated by p38α in proliferating cancer cells, we performed quantitative proteomic and phosphoproteomic analyses in breast cancer cells in which this pathway had been either genetically targeted or chemically inhibited. Our study identified with high confidence 35 proteins and 82 phosphoproteins (114 phosphosites) that are modulated by p38α and highlighted the implication of various protein kinases, including MK2 and mTOR, in the p38α-regulated signaling networks. Moreover, functional analyses revealed an important contribution of p38α to the regulation of cell adhesion, DNA replication, and RNA metabolism. Indeed, we provide experimental evidence supporting that p38α facilitates cancer cell adhesion and showed that this p38α function is likely mediated by the modulation of the adaptor protein ArgBP2. Collectively, our results illustrate the complexity of the p38α-regulated signaling networks, provide valuable information on p38α-dependent phosphorylation events in cancer cells, and document a mechanism by which p38α can regulate cell adhesion.

Heat shock proteins are chaperones, and they are responsible for protein folding in cells. Heat shock protein 90 (HSP90) is one of the most important chaperones in human cells, and its inhibition is promising for cancer therapy. However, despite the development of multiple HSP90 inhibitors, none of them has been approved for disease treatment due to unexpected cellular toxicity and side effects. Hence, a more comprehensive investigation of cellular response to HSP90 inhibitors can aid in a better understanding of the molecular mechanisms of the cytotoxicity and side effects of these inhibitors. The thermal stability shifts of proteins, which represent protein structure and interaction alterations, can provide valuable information complementary to the results obtained from commonly used abundance-based proteomics analysis. Here, we systematically investigated cell response to different HSP90 inhibitors through global quantification of protein thermal stability changes using thermal proteome profiling, together with the measurement of protein abundance changes. Besides the targets and potential off-targets of the drugs, proteins with significant thermal stability changes under the HSP90 inhibition are found to be involved in cell stress responses and the translation process. Moreover, proteins with thermal stability shifts under the inhibition are upstream of those with altered expression. These findings indicate that the HSP90 inhibition perturbs cell transcription and translation processes. The current study provides a different perspective for achieving a better understanding of cellular response to chaperone inhibition.

The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller pixel/voxel number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of cell types involved while maintaining spatial information and the degree of the islet's sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be detected in the adjacent tissue. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.

The secreted metalloproteases ADAMTS9 and ADAMTS20 are implicated in extracellular matrix proteolysis and primary cilium biogenesis. Here, we show that clonal gene-edited RPE-1 cells in which ADAMTS9 was inactivated, and which constitutively lack ADAMTS20 expression, have morphologic characteristics distinct from parental RPE-1 cells. To investigate underlying proteolytic mechanisms, a quantitative terminomics method, terminal amine isotopic labeling of substrates was used to compare the parental and gene-edited RPE-1 cells and their medium to identify ADAMTS9 substrates. Among differentially abundant neo-amino (N) terminal peptides arising from secreted and transmembrane proteins, a peptide with lower abundance in the medium of gene-edited cells suggested cleavage at the Tyr314-Gly315 bond in the ectodomain of the transmembrane metalloprotease membrane type 1-matrix metalloproteinase (MT1-MMP), whose mRNA was also reduced in gene-edited cells. This cleavage, occurring in the MT1-MMP hinge, that is, between the catalytic and hemopexin domains, was orthogonally validated both by lack of an MT1-MMP catalytic domain fragment in the medium of gene-edited cells and restoration of its release from the cell surface by reexpression of ADAMTS9 and ADAMTS20 and was dependent on hinge O-glycosylation. A C-terminally semitryptic MT1-MMP peptide with greater abundance in WT RPE-1 medium identified a second ADAMTS9 cleavage site in the MT1-MMP hemopexin domain. Consistent with greater retention of MT1-MMP on the surface of gene-edited cells, pro-MMP2 activation, which requires cell surface MT1-MMP, was increased. MT1-MMP knockdown in gene-edited ADAMTS9/20-deficient cells restored focal adhesions but not ciliogenesis. The findings expand the web of interacting proteases at the cell surface, suggest a role for ADAMTS9 and ADAMTS20 in regulating cell surface activity of MT1-MMP, and indicate that MT1-MMP shedding does not underlie their observed requirement in ciliogenesis.

Osteoarthritis (OA) is the most prevalent rheumatic pathology. However, OA is not simply a process of wear and tear affecting articular cartilage but rather a disease of the entire joint. One of the most common locations of OA is the knee. Knee tissues have been studied using molecular strategies, generating a large amount of complex data. As one of the goals of the Rheumatic and Autoimmune Diseases initiative of the Human Proteome Project, we applied a text-mining strategy to publicly available literature to collect relevant information and generate a systematically organized overview of the proteins most closely related to the different knee components. To this end, the PubPular literature-mining software was employed to identify protein-topic relationships and extract the most frequently cited proteins associated with the different knee joint components and OA. The text-mining approach searched over eight million articles in PubMed up to November 2022. Proteins associated with the six most representative knee components (articular cartilage, subchondral bone, synovial membrane, synovial fluid, meniscus, and cruciate ligament) were retrieved and ranked by their relevance to the tissue and OA. Gene ontology analyses showed the biological functions of these proteins. This study provided a systematic and prioritized description of knee-component proteins most frequently cited as associated with OA. The study also explored the relationship of these proteins to OA and identified the processes most relevant to proper knee function and OA pathophysiology.

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

To date, studies of development have mainly focused on the embryonic stage and a short time thereafter. There has been little research on the whole life of an individual from childhood to aging and death. For the first time, we used noninvasive urinary proteome technology to track changes in several important developmental time points in a group of rats, covering 10 time points from childhood, adolescence, young adulthood, middle adulthood, and near-death in old age. Similar to previous studies on puberty, proteins were detected and they are involved in sexual or reproductive maturation, mature spermatozoa in seminiferous tubules (first seen), gonadal hormones, decline of estradiol, brain growth, and central nervous system myelination, and our differential protein enrichment pathways also included reproductive system development, tube development, response to hormone, response to estradiol, brain development, and neuron development. Similar to previous studies in young adults, proteins were detected and they are involved in musculoskeletal maturity, peak bone mass, development of the immune system, and growth and physical development, and our differential proteins enrichment pathways also included skeletal system development, bone regeneration, system development, immune system processes, myeloid leukocyte differentiation, and developmental growth. Studies on aging-related changes in neurons and neurogenesis have been reported, and we also found relevant pathways in aged rats, such as regulation of neuronal synaptic plasticity and positive regulation of long-term neuronal synaptic plasticity. However, at all time points throughout life, there were many biological pathways revealed by differential urinary protein enrichment involving multiple organs, tissues, systems, etc. that have not been mentioned in existing studies. This study shows comprehensive and detailed changes in rat lifetime development through the urinary proteome, helping to fill the gap in development research. Moreover, it provides a new approach to monitoring changes in human health and diseases of aging using the urinary proteome.

Energy homeostasis of mammals during cold exposure involves complicated neural regulation and is affected by gut microbiota. However, the regulatory mechanism remains unclear partially due to a lack of comprehensive knowledge of the signaling molecules involved. Herein, we performed region-resolvable quantitative profiling of the brain peptidome using cold-exposed mouse models and interrogated the interaction between gut microbes and brain peptides in response to cold. Region-specific alterations in the brain peptidome were observed during chronic cold exposure and were correlated with gut microbiome composition. Several proSAAS-derived peptides exhibited a positive correlation with Lactobacillus. The hypothalamus-pituitary axis exhibited a sensitive response to cold exposure. We obtained a candidate pool of bioactive peptides that potentially participate in the regulation of cold-induced energy homeostasis. Intervention with cold-adapted microbiota in mice decreased the abundance of hypothalamic neurokinin B and subsequently contributed to shifting the fuel source for energy consumption from lipids to glucose. Collectively, this study demonstrated that gut microbes modulate brain peptides contributing to energy metabolism, providing a data resource for understanding the regulatory mechanism of energy homeostasis upon cold exposure.

Posttranslational spliced peptides (PTSPs) are a unique class of peptides that have been found to be presented by HLA class-I molecules in cancer. Thus far, no consensus has been reached on the proportion of PTSPs in the immunopeptidome, with estimates ranging from 2% to as high as 45% and stirring significant debate. Furthermore, the role of the HLA class-II pathway in PTSP presentation has been studied only in diabetes. Here, we exploit our large-scale cancer peptidomics database and our newly devised pipeline for filtering spliced peptide predictions to identify recurring spliced peptides, both for HLA class-I and class-II complexes. Our results indicate that HLA class-I-spliced peptides account for a low percentage of the immunopeptidome (less than 3.1%) yet are larger in number relative to other types of identified aberrant peptides. Therefore, spliced peptides significantly contribute to the repertoire of presented peptides in cancer cells. In addition, we identified HLA class-II-bound spliced peptides, but to a lower extent (less than 0.5%). The identified spliced peptides include cancer- and immune-associated genes, such as the MITF oncogene, DAPK1 tumor suppressor, and HLA-E, which were validated using synthetic peptides. The potential immunogenicity of the DAPK1- and HLA-E-derived PTSPs was also confirmed. In addition, a reanalysis of our published mouse single-cell clone immunopeptidome dataset showed that most of the spliced peptides were found repeatedly in a large number of the single-cell clones. Establishing a novel search-scheme for the discovery and evaluation of recurring PTSPs among cancer patients may assist in identifying potential novel targets for immunotherapy.

Chondroitin sulfate proteoglycans (CSPGs) control key events in human health and disease and are composed of chondroitin sulfate (CS) polysaccharide(s) attached to different core proteins. Detailed information on the biological effects of site-specific CS structures is scarce as the polysaccharides are typically released from their core proteins prior to analysis. Here we present a novel glycoproteomic approach for site-specific sequencing of CS modifications from human urine. Software-assisted and manual analysis revealed that certain core proteins carried CS with abundant sulfate modifications, while others carried CS with lower levels of sulfation. Inspection of the amino acid sequences surrounding the attachment sites indicated that the acidity of the attachment site motifs increased the levels of CS sulfation, and statistical analysis confirmed this relationship. However, not only the acidity but also the sequence and characteristics of specific amino acids in the proximity of the serine glycosylation site correlated with the degree of sulfation. These results demonstrate attachment site-specific characteristics of CS polysaccharides of CSPGs in human urine and indicate that this novel method may assist in elucidating the biosynthesis and functional roles of CSPGs in cellular physiology.

Proteins containing a CAAX motif at the C-terminus undergo prenylation for localization and activity and include a series of key regulatory proteins, such as RAS superfamily members, heterotrimeric G proteins, nuclear lamina protein, and several protein kinases and phosphatases. However, studies of prenylated proteins in esophageal cancer are limited. Here, through research on large-scale proteomic data of esophageal cancer in our laboratory, we found that paralemmin-2 (PALM2), a potential prenylated protein, was upregulated and associated with poor prognosis in patients. Low-throughput verification showed that the expression of PALM2 in esophageal cancer tissues was higher than that in their paired normal esophageal epithelial tissues, and it was generally expressed in the membrane and cytoplasm of esophageal cancer cells. PALM2 interacted with the two subunits of farnesyl transferase (FTase), FNTA and FNTB. Either the addition of an FTase inhibitor or mutation in the CAAX motif of PALM2 (PALM2C408S) impaired its membranous localization and reduced the membrane location of PALM2, indicating PALM2 was prenylated by FTase. Overexpression of PALM2 enhanced the migration of esophageal squamous cell carcinoma cells, whereas PALM2C408S lost this ability. Mechanistically, PALM2 interacted with the N-terminal FERM domain of ezrin of the ezrin/radixin/moesin (ERM) family. Mutagenesis indicated that lysine residues K253/K254/K262/K263 in ezrin's FERM domain and C408 in PALM2's CAAX motif were important for PALM2/ezrin interaction and ezrin activation. Knockout of ezrin prevented enhanced cancer cell migration by PALM2 overexpression. PALM2, depending on its prenylation, increased both ezrin membrane localization and phosphorylation of ezrin at Y146. In summary, prenylated PALM2 enhances the migration of cancer cells by activating ezrin.

Hepatocellular carcinoma (HCC) is a prevalent cancer in China, with chronic hepatitis B (CHB) and liver cirrhosis (LC) being high-risk factors for developing HCC. Here, we determined the serum proteomes (762 proteins) of 125 healthy controls and Hepatitis B virus-infected CHB, LC, and HCC patients and constructed the first cancerous trajectory of liver diseases. The results not only reveal that the majority of altered biological processes were involved in the hallmarks of cancer (inflammation, metastasis, metabolism, vasculature, and coagulation) but also identify potential therapeutic targets in cancerous pathways (i.e., IL17 signaling pathway). Notably, the biomarker panels for detecting HCC in CHB and LC high-risk populations were further developed using machine learning in two cohorts comprised of 200 samples (discovery cohort = 125 and validation cohort = 75). The protein signatures significantly improved the area under the receiver operating characteristic curve of HCC (CHB discovery and validation cohort = 0.953 and 0.891, respectively; LC discovery and validation cohort = 0.966 and 0.818, respectively) compared to using the traditional biomarker, alpha-fetoprotein, alone. Finally, selected biomarkers were validated with parallel reaction monitoring mass spectrometry in an additional cohort (n = 120). Altogether, our results provide fundamental insights into the continuous changes of cancer biology processes in liver diseases and identify candidate protein targets for early detection and intervention.

Esophageal cancer is the seventh most common cancer in the world. Although traditional treatment methods such as radiotherapy and chemotherapy have good effects, their side effects and drug resistance remain problematic. The repositioning of drug function provides new ideas for the research and development of anticancer drugs. We previously showed that the Food and Drug Administration-approved drug sulconazole can effectively inhibit the growth of esophageal cancer cells, but its molecular mechanism is not clear. Here, our study demonstrated that sulconazole had a broad spectrum of anticancer effects. It can not only inhibit the proliferation but also inhibit the migration of esophageal cancer cells. Both transcriptomic sequencing and proteomic sequencing showed that sulconazole could promote various types of programmed cell death and inhibit glycolysis and its related pathways. Experimentally, we found that sulconazole induced apoptosis, pyroptosis, necroptosis, and ferroptosis. Mechanistically, sulconazole triggered mitochondrial oxidative stress and inhibited glycolysis. Finally, we showed that low-dose sulconazole can increase radiosensitivity of esophageal cancer cells. Taken together, these new findings provide strong laboratory evidence for the clinical application of sulconazole in esophageal cancer.

Current proteomic technologies focus on the quantification of protein levels, while little effort is dedicated to the development of system approaches to simultaneously monitor proteome variability and abundance. Protein variants may display different immunogenic epitopes detectable by monoclonal antibodies. Epitope variability results from alternative splicing, posttranslational modifications, processing, degradation, and complex formation and possesses dynamically changing availability of interacting surface structures that frequently serve as reachable epitopes and often carry different functions. Thus, it is highly likely that the presence of some of the accessible epitopes correlates with function under physiological and pathological conditions. To enable the exploration of the impact of protein variation on the immunogenic epitome first, here, we present a robust and analytically validated PEP technology for characterizing immunogenic epitopes of the plasma. To this end, we prepared mAb libraries directed against the normalized human plasma proteome as a complex natural immunogen. Antibody producing hybridomas were selected and cloned. Monoclonal antibodies react with single epitopes, thus profiling with the libraries is expected to profile many epitopes which we define by the mimotopes, as we present here. Screening blood plasma samples from control subjects (n = 558) and cancer patients (n = 598) for merely 69 native epitopes displayed by 20 abundant plasma proteins resulted in distinct cancer-specific epitope panels that showed high accuracy (AUC 0.826-0.966) and specificity for lung, breast, and colon cancer. Deeper profiling (≈290 epitopes of approximately 100 proteins) showed unexpected granularity of the epitope-level expression data and detected neutral and lung cancer-associated epitopes of individual proteins. Biomarker epitope panels selected from a pool of 21 epitopes of 12 proteins were validated in independent clinical cohorts. The results demonstrate the value of PEP as a rich and thus far unexplored source of protein biomarkers with diagnostic potential.

There is still much to uncover regarding the molecular details of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. As the most abundant protein, coronavirus nucleocapsid (N) protein encapsidates viral RNAs, serving as the structural component of ribonucleoprotein and virion, and participates in transcription, replication, and host regulations. Virus-host interaction might give clues to better understand how the virus affects or is affected by its host during infection and identify promising therapeutic candidates. Considering the critical roles of N, we here established a new cellular interactome of SARS-CoV-2 N by using a high-specific affinity purification (S-pulldown) assay coupled with quantitative mass spectrometry and immunoblotting validations, uncovering many N-interacting host proteins unreported previously. Bioinformatics analysis revealed that these host factors are mainly involved in translation regulations, viral transcription, RNA processes, stress responses, protein folding and modification, and inflammatory/immune signaling pathways, in line with the supposed actions of N in viral infection. Existing pharmacological cellular targets and the directing drugs were then mined, generating a drug-host protein network. Accordingly, we experimentally identified several small-molecule compounds as novel inhibitors against SARS-CoV-2 replication. Furthermore, a newly identified host factor, DDX1, was verified to interact and colocalize with N mainly by binding to the N-terminal domain of the viral protein. Importantly, loss/gain/reconstitution-of-function experiments showed that DDX1 acts as a potent anti-SARS-CoV-2 host factor, inhibiting the viral replication and protein expression. The N-targeting and anti-SARS-CoV-2 abilities of DDX1 are consistently independent of its ATPase/helicase activity. Further mechanism studies revealed that DDX1 impedes multiple activities of N, including the N-N interaction, N oligomerization, and N-viral RNA binding, thus likely inhibiting viral propagation. These data provide new clues to better depiction of the N-cell interactions and SARS-CoV-2 infection and may help inform the development of new therapeutic candidates.

The molecular mechanisms and pathways enabling certain individuals to remain cognitively normal despite high levels of Alzheimer's disease (AD) pathology remain incompletely understood. These cognitively normal people with AD pathology are described as preclinical or asymptomatic AD (AsymAD) and appear to exhibit cognitive resilience to the clinical manifestations of AD dementia. Here we present a comprehensive network-based approach from cases clinically and pathologically defined as asymptomatic AD to map resilience-associated pathways and extend mechanistic validation. Multiplex tandem mass tag MS (TMT-MS) proteomic data (n = 7787 proteins) was generated on brain tissue from Brodmann area 6 and Brodmann area 37 (n = 109 cases, n = 218 total samples) and evaluated by consensus weighted gene correlation network analysis. Notably, neuritin (NRN1), a neurotrophic factor previously linked to cognitive resilience, was identified as a hub protein in a module associated with synaptic biology. To validate the function of NRN1 with regard to the neurobiology of AD, we conducted microscopy and physiology experiments in a cellular model of AD. NRN1 provided dendritic spine resilience against amyloid-β (Aβ) and blocked Aβ-induced neuronal hyperexcitability in cultured neurons. To better understand the molecular mechanisms of resilience to Aβ provided by NRN1, we assessed how exogenous NRN1 alters the proteome by TMT-MS (n = 8238 proteins) of cultured neurons and integrated the results with the AD brain network. This revealed overlapping synapse-related biology that linked NRN1-induced changes in cultured neurons with human pathways associated with cognitive resilience. Collectively, this highlights the utility of integrating the proteome from the human brain and model systems to advance our understanding of resilience-promoting mechanisms and prioritize therapeutic targets that mediate resilience to AD.

The yeast Saccharomyces cerevisiae is a widely-used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimizing biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates, and standardized sample preparation procedures ensured reproducibility and accuracy. In addition, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimization of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared with the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step toward better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

Huntington's disease (HD) is a neurodegenerative disease caused by a CAG repeat expansion in the Huntingtin (HTT) gene. The resulting polyglutamine (polyQ) tract alters the function of the HTT protein. Although HTT is expressed in different tissues, the medium-spiny projection neurons (MSNs) in the striatum are particularly vulnerable in HD. Thus, we sought to define the proteome of human HD patient-derived MSNs. We differentiated HD72-induced pluripotent stem cells and isogenic controls into MSNs and carried out quantitative proteomic analysis. Using data-dependent acquisitions with FAIMS for label-free quantification on the Orbitrap Lumos mass spectrometer, we identified 6323 proteins with at least two unique peptides. Of these, 901 proteins were altered significantly more in the HD72-MSNs than in isogenic controls. Functional enrichment analysis of upregulated proteins demonstrated extracellular matrix and DNA signaling (DNA replication pathway, double-strand break repair, G1/S transition) with the highest significance. Conversely, processes associated with the downregulated proteins included neurogenesis-axogenesis, the brain-derived neurotrophic factor-signaling pathway, Ephrin-A:EphA pathway, regulation of synaptic plasticity, triglyceride homeostasis cholesterol, plasmid lipoprotein particle immune response, interferon-γ signaling, immune system major histocompatibility complex, lipid metabolism, and cellular response to stimulus. Moreover, proteins involved in the formation and maintenance of axons, dendrites, and synapses (e.g., septin protein members) were dysregulated in HD72-MSNs. Importantly, lipid metabolism pathways were altered, and using quantitative image analysis, we found that lipid droplets accumulated in the HD72-MSN, suggesting a deficit in the turnover of lipids possibly through lipophagy. Our proteomics analysis of HD72-MSNs identified relevant pathways that are altered in MSNs and confirm current and new therapeutic targets for HD.

High-density lipoprotein (HDL) levels are reduced in patients with coronavirus disease 2019 (COVID-19), and the extent of this reduction is associated with poor clinical outcomes. While lipoproteins are known to play a key role during the life cycle of the hepatitis C virus, their influence on coronavirus (CoV) infections is poorly understood. In this study, we utilize cross-linking mass spectrometry (XL-MS) to determine circulating protein interactors of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoprotein. XL-MS of plasma isolated from patients with COVID-19 uncovered HDL protein interaction networks, dominated by acute-phase serum amyloid proteins, whereby serum amyloid A2 was shown to bind to apolipoprotein (Apo) D. XL-MS on isolated HDL confirmed ApoD to interact with SARS-CoV-2 spike but not SARS-CoV-1 spike. Other direct interactions of SARS-CoV-2 spike upon HDL included ApoA1 and ApoC3. The interaction between ApoD and spike was further validated in cells using immunoprecipitation-MS, which uncovered a novel interaction between both ApoD and spike with membrane-associated progesterone receptor component 1. Mechanistically, XL-MS coupled with data-driven structural modeling determined that ApoD may interact within the receptor-binding domain of the spike. However, ApoD overexpression in multiple cell-based assays had no effect upon viral replication or infectivity. Thus, SARS-CoV-2 spike can bind to apolipoproteins on HDL, but these interactions do not appear to alter infectivity.

Liquid biopsy is a noninvasive technique that can provide valuable information for disease characterization by using biofluids as a source of biomarkers. Proteins found in biofluids can offer a wealth of information for understanding pathological processes. In this study, we used early-stage clear cell renal cell carcinoma (ccRCC) as a model to explore the proteomic relationships among tissue, plasma, and urine. We analyzed samples of tumor tissue, plasma, and urine from a cohort of 27 ccRCC patients with T1-2 stage and 27 matched healthy controls, using liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis. We integrated the differential proteins found in the three types of samples to explore ccRCC-associated molecular changes. Our results showed that both plasma and urine proteomes could reflect functional changes in tumor tissue. In plasma, cytoskeletal proteins and metabolic enzymes were differentially expressed, while in urine, adhesion molecules and defense proteins showed differential levels. The differential proteins found in plasma and urine both reflect the binding and catalytic activity of tumor tissue. Additionally, proteins only changed in biofluids could reflect body immune response changes, with plasma proteins involved in actin cytoskeleton and oxidative stress, and urine proteins involved in granulocyte adhesion and leukocyte extravasation signaling. Plasma and urine proteins could effectively distinguish RCC from control, with good performances (plasma/urine: 92.6%/92.6% specificity, 96.3%/92.6% sensitivity, and an area under the curve of 0.981/0.97). In conclusion, biofluids could not only reflect functional changes in tumor tissue but also reflect changes in the body's immune response. These findings will benefit the understanding of body biomarkers in tumors and the discovery of potential disease biomarkers.

Excitotoxicity, a neuronal death process in neurological disorders such as stroke, is initiated by the overstimulation of ionotropic glutamate receptors. Although dysregulation of proteolytic signaling networks is critical for excitotoxicity, the identity of affected proteins and mechanisms by which they induce neuronal cell death remain unclear. To address this, we used quantitative N-terminomics to identify proteins modified by proteolysis in neurons undergoing excitotoxic cell death. We found that most proteolytically processed proteins in excitotoxic neurons are likely substrates of calpains, including key synaptic regulatory proteins such as CRMP2, doublecortin-like kinase I, Src tyrosine kinase and calmodulin-dependent protein kinase IIβ (CaMKIIβ). Critically, calpain-catalyzed proteolytic processing of these proteins generates stable truncated fragments with altered activities that potentially contribute to neuronal death by perturbing synaptic organization and function. Blocking calpain-mediated proteolysis of one of these proteins, Src, protected against neuronal loss in a rat model of neurotoxicity. Extrapolation of our N-terminomic results led to the discovery that CaMKIIα, an isoform of CaMKIIβ, undergoes differential processing in mouse brains under physiological conditions and during ischemic stroke. In summary, by identifying the neuronal proteins undergoing proteolysis during excitotoxicity, our findings offer new insights into excitotoxic neuronal death mechanisms and reveal potential neuroprotective targets for neurological disorders.

Lysine methylation is a conserved and dynamic regulatory posttranslational modification performed by lysine methyltransferases (KMTs). KMTs catalyze the transfer of mono-, di-, or tri-methyl groups to substrate proteins and play a critical regulatory role in all domains of life. To date, only one KMT has been identified in cyanobacteria. Here, we tested all of the predicted KMTs in the cyanobacterium Synechocystis sp. PCC 6803 (Synechocystis), and we biochemically characterized sll1526 that we termed cKMT1 (cyanobacterial lysine methyltransferase 1) and determined that it can catalyze lysine methylation both in vivo and in vitro. Loss of cKMT1 alters photosynthetic electron transfer in Synechocystis. We analyzed cKMT1-regulated methylation sites in Synechocystis using a timsTOF Pro instrument. We identified 305 class I lysine methylation sites within 232 proteins, and of these, 80 methylation sites in 58 proteins were hypomethylated in ΔcKMT1 cells. We further demonstrated that cKMT1 could methylate ferredoxin-NADP(+) oxidoreductase (FNR) and its potential sites of action on FNR were identified. Amino acid residues H118 and Y219 were identified as key residues in the putative active site of cKMT1 as indicated by structure simulation, site-directed mutagenesis, and KMT activity measurement. Using mutations that mimic the unmethylated forms of FNR, we demonstrated that the inability to methylate K139 residues results in a decrease in the redox activity of FNR and affects energy transfer in Synechocystis. Together, our study identified a new KMT in Synechocystis and elucidated a methylation-mediated molecular mechanism catalyzed by cKMT1 for the regulation of energy transfer in cyanobacteria.

B4GALT1 encodes β-1,4-galactosyltransferase 1, an enzyme that plays a major role in glycan synthesis in the Golgi apparatus by catalyzing the addition of terminal galactose. Studies increasingly suggest that B4GALT1 may be involved in the regulation of lipid metabolism pathways. Recently, we discovered a single-site missense variant Asn352Ser (N352S) in the functional domain of B4GALT1 in an Amish population, which decreases the level of LDL-cholesterol (LDL-c) as well as the protein levels of ApoB, fibrinogen, and IgG in the blood. To systematically evaluate the effects of this missense variant on protein glycosylation, expression, and secretion, we developed a nano-LC-MS/MS-based platform combined with TMT-labeling for in-depth quantitative proteomic and glycoproteomic analyses in the plasma of individuals homozygous for the B4GALT1 missense variant N352S versus non-carriers (n = 5 per genotype). A total of 488 secreted proteins in the plasma were identified and quantified, 34 of which showed significant fold changes in protein levels between N352S homozygotes and non-carriers. We determined N-glycosylation profiles from 370 glycosylation sites in 151 glycoproteins and identified ten proteins most significantly associated with decreased galactosylation and sialyation in B4GALT1 N352S homozygotes. These results further support that B4GALT1 N352S alters the glycosylation profiles of a variety of critical target proteins, thus governing the functions of these proteins in multiple pathways, such as those involved in lipid metabolism, coagulation, and the immune response.

Successful placentation requires delicate communication between the endometrium and trophoblasts. The invasion and integration of trophoblasts into the endometrium during early pregnancy are crucial to placentation. Dysregulation of these functions is associated with various pregnancy complications, such as miscarriage and preeclampsia. The endometrial microenvironment has an important influence on trophoblast cell functions. The precise effect of the endometrial gland secretome on trophoblast functions remains uncertain. We hypothesized that the hormonal environment regulates the miRNA profile and secretome of the human endometrial gland, which subsequently modulates trophoblast functions during early pregnancy. Human endometrial tissues were obtained from endometrial biopsies with written consent. Endometrial organoids were established in matrix gel under defined culture conditions. They were treated with hormones mimicking the environment of the proliferative phase (Estrogen, E2), secretory phase (E2+Progesterone, P4), and early pregnancy (E2+P4+Human Chorionic Gonadotropin, hCG). miRNA-seq was performed on the treated organoids. Organoid secretions were also collected for mass spectrometric analysis. The viability and invasion/migration of the trophoblasts after treatment with the organoid secretome were determined by cytotoxicity assay and transwell assay, respectively. Endometrial organoids with the ability to respond to sex steroid hormones were successfully developed from human endometrial glands. By establishing the first secretome profiles and miRNA atlas of these endometrial organoids to the hormonal changes followed by trophoblast functional assays, we demonstrated that sex steroid hormones modulate aquaporin (AQP)1/9 and S100A9 secretions through miR-3194 activation in endometrial epithelial cells, which in turn enhanced trophoblast migration and invasion during early pregnancy. By using a human endometrial organoid model, we demonstrated for the first time that the hormonal regulation of the endometrial gland secretome is crucial to regulating the functions of human trophoblasts during early pregnancy. The study provides the basis for understanding the regulation of early placental development in humans.

Seminal plasma contains many morphologically heterogeneous extracellular vesicles (sEVs). These are sequentially released by cells of the testis, epididymis, and accessory sex glands and involved in male and female reproductive processes. This study aimed to define in depth sEV subsets isolated by ultrafiltration and size exclusion chromatography, decode their proteomic profiles using liquid chromatography-tandem mass spectrometry, and quantify identified proteins using sequential window acquisition of all theoretical mass spectra. The sEV subsets were defined as large (L-EVs) or small (S-EVs) by their protein concentration, morphology, size distribution, and EV-specific protein markers and purity. Liquid chromatography-tandem mass spectrometry identified a total of 1034 proteins, 737 of them quantified by SWATH in S-EVs, L-EVs, and non-EVs-enriched samples (18-20 size exclusion chromatography-eluted fractions). The differential expression analysis revealed 197 differentially abundant proteins between both EV subsets, S-EVs and L-EVs, and 37 and 199 between S-EVs and L-EVs versus non-EVs-enriched samples, respectively. The gene ontology enrichment analysis of differentially abundant proteins suggested, based on the type of protein detected, that S-EVs could be mainly released through an apocrine blebbing pathway and be involved in modulating the immune environment of the female reproductive tract as well as during sperm-oocyte interaction. In contrast, L-EVs could be released by fusion of multivesicular bodies with the plasma membrane becoming involved in sperm physiological processes, such as capacitation and avoidance of oxidative stress. In conclusion, this study provides a procedure capable of isolating subsets of EVs from pig seminal plasma with a high degree of purity and shows differences in the proteomic profile between EV subsets, indicating different sources and biological functions for the sEVs.

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nanoflow and microflow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized, and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH) data-independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness, and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive, and robust proteomic workflows using analytical flow and is amenable to large-scale studies.

Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ∼4500 predicted protein-coding regions on average, which can help discover several variants at the proteome level. Many of them are potentially associated with virulence; thus, identifying such proteins can be a helpful feature in developing panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using (i) a protein database for Mycobacterium tuberculosis, (ii) an M. avium genome six-frame-translated database, and (iii) a variant protein database of M. avium. A search of mass spectrometry data against M. avium protein database resulted in identifying 2954 proteins. Further, proteogenomic analyses aided in identifying 1301 novel peptide sequences and correcting translation start sites for 15 proteins. Ultimately, we created a spectral library of M. avium proteins, including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present an M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.

Ovarian cancer is a gynecological tumor with extremely high mortality and poor prognosis. Exosomes derived from tumor cells contain abundant proteins that may influence tumor metastasis. The purpose of our study was to explore the proteomic profile of serum exosomes from epithelial ovarian cancer (EOC) patients and to find potential diagnostic markers for EOC. We obtained purified exosomes from serum using ultracentrifugation. Migration assay was used to evaluate the effects of exosomes on the migration of EOC cells. Proteomic profile of serum exosomes was analyzed by liquid chromatogram-tandem mass spectrometry. The levels of low-density lipoprotein receptor-related protein 1 (LRP1) in serum and serum exosomes were determined by enzyme-linked immunosorbent assay. Western blot and Immunohistochemistry were used to determine the level of LRP1 in tissues. Moreover, we performed small-interfering RNA-mediated knockdown of LRP1 in EOC cells to obtain SI-LRP1-Exos and SI-NC-Exos. The detailed mechanisms by which exosomal LRP1 affected the migration of EOC cells in vitro and in vivo were also explored. We found that serum exosomes from EOC patients contributed to the migration of EOC cells. The level of serum exosomal LRP1 of EOC patients was significantly upregulated compared with that of healthy volunteers, which was consistent with the result of enzyme-linked immunosorbent assay. We found that exosomal LRP1 regulated the expression of MMP2 and MMP9 through ERK signaling pathway and affected the migration of EOC cells in vitro and in vivo. Therefore, we propose that exosomal LRP1 contributes to the migration of EOC and may act as an important diagnostic and prognostic biomarker of EOC.

The 2nd CASMS conference was held virtually through Gather. Town platform from October 17 to 21, 2022, with a total of 363 registrants including an outstanding and diverse group of scientists at the forefront of their research fields from both academia and industry worldwide, especially in the United States and China. The conference offered a 5-day agenda with an exciting scientific program consisting of two plenary lectures, 14 parallel symposia, and 4 special sessions in which a total of 97 invited speakers presented technological innovations and their applications in proteomics & biological mass spectrometry and metabo-lipidomics & pharmaceutical mass spectrometry. In addition, 18 invited speakers/panelists presented at 3 research-focused and 2 career development workshops. Moreover, 144 posters, 54 lightning talks, 5 sponsored workshops, and 14 exhibitions were presented, from which 20 posters and 8 lightning talks received presentation awards. Furthermore, the conference featured 1 MCP lectureship and 5 young investigator awardees for the first time to highlight outstanding mid-career and early-career rising stars in mass spectrometry from our society. The conference provided a unique scientific platform for young scientists (i.e., graduate students, postdocs and junior faculty/investigators) to present their research, meet with prominent scientists, and learn about career development and job opportunities (http://casms.org).

Carbon metabolism is central to photosynthetic organisms and involves the coordinated operation and regulation of numerous proteins. In cyanobacteria, proteins involved in carbon metabolism are regulated by multiple regulators including the RNA polymerase sigma factor SigE, the histidine kinases Hik8, Hik31 and its plasmid-borne paralog Slr6041, and the response regulator Rre37. To understand the specificity and the cross-talk of such regulations, we simultaneously and quantitatively compared the proteomes of the gene knockout mutants for the regulators. A number of proteins showing differential expression in one or more mutants were identified, including four proteins that are unanimously upregulated or downregulated in all five mutants. These represent the important nodes of the intricate and elegant regulatory network for carbon metabolism. Moreover, serine phosphorylation of PII, a key signaling protein sensing and regulating in vivo carbon/nitrogen (C/N) homeostasis through reversible phosphorylation, is massively increased with a concomitant significant decrease in glycogen content only in the hik8-knockout mutant, which also displays impaired dark viability. An unphosphorylatable PII S49A substitution restored the glycogen content and rescued the dark viability of the mutant. Together, our study not only establishes the quantitative relationship between the targets and the corresponding regulators and elucidated their specificity and cross-talk but also unveils that Hik8 regulates glycogen accumulation through negative regulation of PII phosphorylation, providing the first line of evidence that links the two-component system with PII-mediated signal transduction and implicates them in the regulation of carbon metabolism.

Plant vacuoles serve as the primary intracellular compartments for inorganic phosphate (Pi) storage. Passage of Pi across vacuolar membranes plays a critical role in buffering the cytoplasmic Pi level against fluctuations of external Pi and metabolic activities. To gain new insights into the proteins and processes, vacuolar Pi level regulated by vacuolar phosphate transporter 1 (VPT1) in Arabidopsis, we carried out tandem mass tag labeling proteome and phosphoproteome profiling of Arabidopsis WT and vpt1 loss-of-function mutant plants. The vpt1 mutant had a marked reduced vacuolar Pi level and a slight increased cytosol Pi level. The mutant was stunted as reflected in the reduction of the fresh weight compared with WT plants and bolting earlier under normal growth conditions in soil. Over 5566 proteins and 7965 phosphopeptides were quantified. About 146 and 83 proteins were significantly changed at protein abundance or site-specific phosphorylation levels, but only six proteins were shared between them. Functional enrichment analysis revealed that the changes of Pi states in vpt1 are associated with photosynthesis, translation, RNA splicing, and defense response, consistent with similar studies in Arabidopsis. Except for PAP26, EIN2, and KIN10, which were reported to be associated with phosphate starvation signal, we also found that many differential proteins involved in abscisic acid signaling, such as CARK1, SnRK1, and AREB3, were significantly changed in vpt1. Our study illuminates several new aspects of the phosphate response and identifies important targets for further investigation and potential crop improvement.

GSK3α and GSK3β are two GSK3 isoforms with 84% overall identity and 98% identity in their catalytic domains. GSK3β plays important roles in the pathogenesis of cancer, while GSK3α has long been considered a functionally redundant protein of GSK3β. Few studies have specifically investigated the functions of GSK3α. In this study, unexpectedly, we found that the expression of GSK3α, but not GSK3β, was significantly correlated with the overall survival of colon cancer patients in 4 independent cohorts. To decipher the roles of GSK3α in colon cancer, we profiled the phosphorylation substrates of GSK3α and uncovered 156 phosphosites from 130 proteins specifically regulated by GSK3α. A number of these GSK3α-mediated phosphosites have never been reported before or have been incorrectly identified as substrates of GSK3β. Among them, the levels of HSF1S303p, CANXS583p, MCM2S41p, POGZS425p, SRRM2T983p, and PRPF4BS431p were significantly correlated with the overall survival of colon cancer patients. Further pull-down assays identified 23 proteins, such as THRAP3, BCLAF1, and STAU1, showing strong binding affinity to GSK3α. The interaction between THRAP3 and GSK3α was verified by biochemical experiments. Notably, among the 18 phosphosites of THRAP3, phosphorylation at S248, S253, and S682 is specifically mediated by GSK3α. Mutation of S248 to D (S248D), which mimics the effect of phosphorylation, obviously increased cancer cell migration and the binding affinity to proteins related to DNA damage repair. Collectively, this work not only discloses the specific function of GSK3α as a kinase but also suggests GSK3α as a promising therapeutic target for colon cancer.

Adenomatous polyposis coli (APC) is an important tumor suppressor and is mostly linked to the regulation of the Wnt/β-catenin signaling pathway. APC mutation has been identified as an early event in more than 80% of sporadic colorectal cancers (CRCs). Moreover, prognostic differences are observed in CRC patients with APC mutations. Although previous genomics studies have investigated the roles of concomitant gene mutations in determining the phenotypic heterogeneity of APC-mutant tumors, valuable prognostic determinants for APC-mutant CRC patients are still lacking. Based on the proteome and phosphoproteome data, we classified APC-mutant colon cancer patients and revealed genomic, proteomic, and phosphoproteomic heterogeneity in APC-mutant tumors. More importantly, we identified RAI14 as a key prognostic determinant for APC-mutant but not APC-wildtype colon cancer patients. The heterogeneity and the significance of prognostic biomarkers in APC-mutant tumors were further validated in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) colon cancer cohort. In addition, we found that colon cancer patients with high expression of RAI14 were less responsive to chemotherapy. Knockdown of RAI14 in cell lines led to reduced cell migration and changes in epithelial-mesenchymal transition (EMT)-related markers. Mechanistically, knockdown of RAI14 remodeled the phosphoproteome associated with cell adhesion, which might affect EMT marker expression and promote F-actin degradation. Collectively, this work describes the phenotypic heterogeneity of APC-mutant tumors and identifies RAI14 as an important prognostic determinant for APC-mutant colon cancer patients. The prognostic utility of RAI14 in APC-mutant colon cancer will provide early warning and increase the chance of successful treatment.