Multidimensional proteomics for cell biology

Human bronchial-pulmonary proteomics in coronavirus disease 2019 (COVID-19) pandemic: applications and implications

INTRODUCTION

The outbreak of the newly discovered human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has disrupted the normal life of almost every civilization worldwide. Studies have shown that the coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 can affect multiple human organs and physiological systems, but the respiratory system remains the primary location for viral infection.

AREAS COVERED

We summarize how omics technologies are used in SARS-CoV-2 research and specifically review the current knowledge of COVID-19 from the aspect of human bronchial-pulmonary proteomics. Also, knowledge gaps in COVID-19 that can be fulfilled by proteomics are discussed.

EXPERT OPINION

Overall, human bronchial-pulmonary proteomics plays an important role in revealing the dynamics, functions, tropism, and pathogenicity of SARS-CoV-2, which is crucial for COVID-19 biomarker and therapeutic target discoveries. To more fully understand the impact of COVID-19, research from various angles using multi-omics approaches should also be conducted on the lungs as well as other organs.

Advances in Multi-Omics Applications in HBV-Associated Hepatocellular Carcinoma

Hepatitis B virus (HBV) specifically infects liver cells, leading to progressive liver cirrhosis and significantly increasing the risk of hepatocellular carcinoma (HCC). The maturity of sequencing technology, improvement in bioinformatics data analysis and progress of omics technologies had improved research efficiency. The occurrence and progression of HCC are affected by multisystem and multilevel pathological changes. With the application of single-omics technologies, including genomics, transcriptomics, metabolomics and proteomics in tissue and body fluid samples, and even the novel development of multi-omics analysis on a single-cell platform, HBV-associated HCC changes can be better analyzed. The review summarizes the application of single omics and combined analysis of multi-omics data in HBV-associated HCC and proposes the importance of multi-omics analysis in the type of HCC, which provide the possibility for the precise diagnosis and therapy of HBV-associated HCC.

A modified density gradient proteomic-based method to analyze endolysosomal proteins in cardiac tissue

The importance of lysosomes in cardiac physiology and pathology is well established, and evidence for roles in calcium signaling is emerging. We describe a label-free proteomics method suitable for small cardiac tissue biopsies based on density-separated fractionation, which allows study of endolysosomal (EL) proteins. Density gradient fractions corresponding to tissue lysate; sarcoplasmic reticulum (SR), mitochondria (Mito) (1.3 g/mL); and EL with negligible contamination from SR or Mito (1.04 g/mL) were analyzed using Western blot, enzyme activity assay, and liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis (adapted discontinuous Percoll and sucrose differential density gradient). Kyoto Encyclopedia of Genes and Genomes, Reactome, Panther, and Gene Ontology pathway analysis showed good coverage of RAB proteins and lysosomal cathepsins (including cardiac-specific cathepsin D) in the purified EL fraction. Significant EL proteins recovered included catalytic activity proteins. We thus present a comprehensive protocol and data set of guinea pig atrial EL organelle proteomics using techniques also applicable for non-cardiac tissue.

A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs

Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed.

Proteomic profiling of the oncogenic septin 9 reveals isoform-specific interactions in breast cancer cells

Septins are a family of multimeric GTP-binding proteins, which are abnormally expressed in cancer. Septin 9 (SEPT9) is an essential and ubiquitously expressed septin with multiple isoforms, which have differential expression patterns and effects in breast cancer cells. It is unknown, however, if SEPT9 isoforms associate with different molecular networks and functions. Here, we performed a proteomic screen in MCF-7 breast cancer cells to identify the interactome of GFP-SEPT9 isoforms 1, 4 and 5, which vary significantly in their N-terminal extensions. While all three isoforms associated with SEPT2 and SEPT7, the truncated SEPT9_i4 and SEPT9_i5 interacted with septins of the SEPT6 group more promiscuously than SEPT9_i1, which bound predominately SEPT8. Spatial mapping and functional clustering of non-septin partners showed isoform-specific differences in interactions with proteins of distinct subcellular organelles (e.g., nuclei, centrosomes, cilia) and functions such as cell signalling and ubiquitination. The interactome of the full length SEPT9_i1 was more enriched in cytoskeletal regulators, while the truncated SEPT9_i4 and SEPT9_i5 exhibited preferential and isoform-specific interactions with nuclear, signalling, and ubiquitinating proteins. These data provide evidence for isoform-specific interactions, which arise from truncations in the N-terminal extensions of SEPT9, and point to novel roles in the pathogenesis of breast cancer.

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Identification of covalent modifications regulating immune signaling complex composition and phenotype

Cells signal through rearrangements of protein communities governed by covalent modifications and reversible interactions of distinct sets of proteins. A method that identifies those post‐transcriptional modifications regulating signaling complex composition and functional phenotypes in one experimental setup would facilitate an efficient identification of novel molecular signaling checkpoints. Here, we devised modifications, interactions and phenotypes by affinity purification mass spectrometry (MIP‐APMS), comprising the streamlined cloning and transduction of tagged proteins into functionalized reporter cells as well as affinity chromatography, followed by MS‐based quantification. We report the time‐resolved interplay of more than 50 previously undescribed modification and hundreds of protein–protein interactions of 19 immune protein complexes in monocytes. Validation of interdependencies between covalent, reversible, and functional protein complex regulations by knockout or site‐specific mutation revealed ISGylation and phosphorylation of TRAF2 as well as ARHGEF18 interaction in Toll‐like receptor 2 signaling. Moreover, we identify distinct mechanisms of action for small molecule inhibitors of p38 (MAPK14). Our method provides a fast and cost‐effective pipeline for the molecular interrogation of protein communities in diverse biological systems and primary cells.

Comparative Proteomics and Phosphoproteomics Analysis Reveal the Possible Breed Difference in Yorkshire and Duroc Boar Spermatozoa

Sperm cells are of unique elongated structure and function, the development of which is tightly regulated by the existing proteins and the posttranslational modifications (PTM) of these proteins. Based on the phylogenetic relationships of various swine breeds, Yorkshire boar is believed to be distinctly different from Duroc boar. The comprehensive differential proteomics and phosphoproteomics profilings were performed on spermatozoa from both Yorkshire and Duroc boars. By both peptide and PTM peptide quantification followed by statistical analyses, 167 differentially expressed proteins were identified from 1,745 proteins, and 283 differentially expressed phosphopeptides corresponding to 102 unique differentially phosphorylated proteins were measured from 1,140 identified phosphopeptides derived from 363 phosphorylated proteins. The representative results were validated by Western blots. Pathway enrichment analyses revealed that majority of differential expression proteins and differential phosphorylation proteins were primarily concerned with spermatogenesis, male gamete generation, sperm motility, energy metabolism, cilium morphogenesis, axonemal dynein complex assembly, sperm–egg recognition, and capacitation. Remarkably, axonemal dynein complex assembly related proteins, such as SMCP, SUN5, ODF1, AKAP3, and AKAP4 that play a key regulatory role in the sperm physiological functions, were significantly higher in Duroc spermatozoa than that of Yorkshire. Furthermore, phosphorylation of sperm-specific proteins, such as CABYR, ROPN1, CALM1, PRKAR2A, and PRKAR1A, participates in regulation of the boar sperm motility mainly through the cAMP/PKA signal pathway in different breeds, demonstrating that protein phosphorylation may be an important mechanism underlying the sperm diversity. Protein–protein interaction analysis revealed that the 14 overlapped proteins between differential expression proteins and differential phosphorylation proteins potentially played a key role in sperm development and motility of the flagellum, including the proteins ODF1, SMCP, AKAP4, FSIP2, and SUN5. Taken together, these physiologically and functionally differentially expressed proteins (DEPs) and differentially expressed phosphorylated proteins (DPPs) may constitute the proteomic backgrounds between the two different boar breeds. The validation will be performed to delineate the roles of these PTM proteins as modulators of Yorkshire and Duroc boar spermatozoa.

Exploring the diversity of plant proteome

The tremendous functional, spatial, and temporal diversity of the plant proteome is regulated by multiple factors that continuously modify protein abundance, modifications, interactions, localization, and activity to meet the dynamic needs of plants. Dissecting the proteome complexity and its underlying genetic variation is attracting increasing research attention. Mass spectrometry (MS)-based proteomics has become a powerful approach in the global study of protein functions and their relationships on a systems level. Here, we review recent breakthroughs and strategies adopted to unravel the diversity of the proteome, with a specific focus on the methods used to analyze posttranslational modifications (PTMs), protein localization, and the organization of proteins into functional modules. We also consider PTM crosstalk and multiple PTMs temporally regulating the life cycle of proteins. Finally, we discuss recent quantitative studies using MS to measure protein turnover rates and examine future directions in the study of the plant proteome.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

Spatiotemporally resolved subcellular phosphoproteomics

Proteome-wide profiling of protein phosphorylation has been widely used to reveal the underlying mechanism of diverse cellular signaling events. Yet, characterizing subcellular phosphoproteome with high spatial-temporal resolution has remained challenging. Herein, we developed a subcellular-specific uncaging-assisted biotinylation and mapping of phosphoproteome (SubMAPP) strategy to monitor the phosphorylation dynamics of subcellular proteome in living cells and animals. Our method capitalizes on the genetically encoded bioorthogonal decaging strategy, which enables the rapid activation of subcellular localized proximity labeling biotin ligase through either light illumination or small-molecule triggers. By further adopting an integrated orthogonal pull-down strategy with quantitative mass spectrometry, SubMAPP allowed for the investigation of subcellular phosphoproteome dynamics, revealing the altered phosphorylation patterns of endoplasmic reticulum (ER) luminal proteins under ER stress. Finally, we further expanded the scope of the SubMAPP strategy to primary neuron culture and living mice.

Towards Precision Dermatology: Emerging Role of Proteomic Analysis of the Skin

BACKGROUND

The skin is the largest organ in the human body and serves as a multilayered protective shield from the environment as well as a sensor and thermal regulator. However, despite its importance, many details about skin structure and function at the molecular level remain incompletely understood. Recent advances in liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics have enabled the quantification and characterization of the proteomes of a number of clinical samples, including normal and diseased skin.

SUMMARY

Here, we review the current state of the art in proteomic analysis of the skin. We provide a brief overview of the technique and skin sample collection methodologies as well as a number of recent examples to illustrate the utility of this strategy for advancing a broader understanding of the pathology of diseases as well as new therapeutic options.

KEY MESSAGES

Proteomic studies of healthy skin and skin diseases can identify potential molecular biomarkers for improved diagnosis and patient stratification as well as potential targets for drug development. Collectively, efforts such as the Human Skinatlas offer improved opportunities for enhancing clinical practice and patient outcomes.

Proteomic Analyses of Vitreous in Proliferative Diabetic Retinopathy: Prior Studies and Future Outlook

Vitreous fluid is becoming an increasingly popular medium for the study of retinal disease. Numerous studies have demonstrated that proteomic analysis of the vitreous from patients with proliferative diabetic retinopathy yields valuable molecular information regarding known and novel proteins and pathways involved in this disease. However, there is no standardized methodology for vitreous proteomic studies. Here, we share a suggested protocol for such studies and outline the various experimental and analytic methods that are currently available. We also review prior mass spectrometry-based proteomic studies of the vitreous from patients with proliferative diabetic retinopathy, discuss common pitfalls of these studies, and propose next steps for moving the field forward.

Current State of SLC and ABC Transporters in the Skin and Their Relation to Sweat Metabolites and Skin Diseases

With a relatively large surface area (2 m²) and 15% of total body mass, the skin forms the largest organ of the human body. The main functions of the skin include regulation of body temperature by insulation or sweating, regulation of the nervous system, regulation of water content, and protection against external injury. To perform these critical functions, the skin encodes genes for transporters responsible for the cellular trafficking of essential nutrients and metabolites to maintain cellular hemostasis. However, the knowledge on the expression, regulation, and function of these transporters is very limited and needs more work to elucidate how these transporters play a role both in disease progression and in healing. Furthermore, SLC and ABC transporters are understudied, and even less studied in skin. There are sparse reports on relation between transporters in skin and sweat metabolites. This mini review focuses on the current state of SLC and ABC transporters in the skin and their relation to sweat metabolites and skin diseases.

An integrated landscape of protein expression in human cancer

Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

Proteomic analysis of tyrosine phosphorylation induced by exogenous expression of oncogenic kinase fusions identified in lung adenocarcinoma

Kinase fusions are considered oncogenic drivers in numerous types of cancer. In lung adenocarcinoma 5-10% of patients harbor kinase fusions. The most frequently detected kinase fusion involves the Anaplastic Lymphoma Kinase (ALK) and Echinoderm Microtubule-associated protein-Like 4 (EML4). In addition, oncogenic kinase fusions involving the tyrosine kinases RET and ROS1 are found in smaller subsets of patients. In this study, we employed quantitative mass spectrometry-based phosphoproteomics to define the cellular tyrosine phosphorylation patterns induced by different oncogenic kinase fusions identified in patients with lung adenocarcinoma. We show that exogenous expression of the kinase fusions in HEK 293T cells leads to widespread tyrosine phosphorylation. Direct comparison of different kinase fusions demonstrates that the kinase part and not the fusion partner primarily defines the phosphorylation pattern. The tyrosine phosphorylation patterns differed between ALK, ROS1, and RET fusions, suggesting that oncogenic signaling induced by these kinases involves the modulation of different cellular processes.

The World of Stable Ribonucleoproteins and Its Mapping With Grad-Seq and Related Approaches

Macromolecular complexes of proteins and RNAs are essential building blocks of cells. These stable supramolecular particles can be viewed as minimal biochemical units whose structural organization, i.e., the way the RNA and the protein interact with each other, is directly linked to their biological function. Whether those are dynamic regulatory ribonucleoproteins (RNPs) or integrated molecular machines involved in gene expression, the comprehensive knowledge of these units is critical to our understanding of key molecular mechanisms and cell physiology phenomena. Such is the goal of diverse complexomic approaches and in particular of the recently developed gradient profiling by sequencing (Grad-seq). By separating cellular protein and RNA complexes on a density gradient and quantifying their distributions genome-wide by mass spectrometry and deep sequencing, Grad-seq charts global landscapes of native macromolecular assemblies. In this review, we propose a function-based ontology of stable RNPs and discuss how Grad-seq and related approaches transformed our perspective of bacterial and eukaryotic ribonucleoproteins by guiding the discovery of new RNA-binding proteins and unusual classes of noncoding RNAs. We highlight some methodological aspects and developments that permit to further boost the power of this technique and to look for exciting new biology in understudied and challenging biological models.

KIF5A-dependent axonal transport deficiency disrupts autophagic flux in trimethyltin chloride-induced neurotoxicity

Trimethyltin chloride (TMT) is widely used as a constituent of fungicides and plastic stabilizers in the industrial and agricultural fields, and is generally acknowledged to have potent neurotoxicity, especially in the hippocampus; however, the mechanism of induction of neurotoxicity by TMT remains elusive. Herein, we exposed Neuro-2a cells to different concentrations of TMT (2, 4, and 8 μM) for 24 h. Proteomic analysis, coupled with bioinformatics analysis, revealed the important role of macroautophagy/autophagy-lysosome machinery in TMT-induced neurotoxicity. Further analysis indicated significant impairment of autophagic flux by TMT via suppressed lysosomal function, such as by inhibiting lysosomal proteolysis and changing the lysosomal pH, thereby contributing to defects in autophagic clearance and subsequently leading to nerve cell death. Mechanistically, molecular interaction networks of Ingenuity Pathway Analysis identified a downregulated molecule, KIF5A (kinesin family member 5A), as a key target in TMT-impaired autophagic flux. TMT decreased KIF5A protein expression, disrupted the interaction between KIF5A and lysosome, and impaired lysosomal axonal transport. Moreover, Kif5a overexpression restored axonal transport, increased lysosomal dysfunction, and antagonized TMT-induced neurotoxicity in vitro. Importantly, in TMT-administered mice with seizure symptoms and histomorphological injury in the hippocampus, TMT inhibited KIF5A expression in the hippocampus. Gene transfer of Kif5a enhanced autophagic clearance in the hippocampus and alleviated TMT-induced neurotoxicity in vivo. Our results are the first to demonstrate KIF5A-dependent axonal transport deficiency to cause autophagic flux impairment via disturbance of lysosomal function in TMT-induced neurotoxicity; manipulation of KIF5A may be a therapeutic approach for antagonizing TMT-induced neurotoxicity.Abbreviations: 3-MA: 3-methyladenine; AAV: adeno-associated virus; ACTB: actin beta; AGC: automatic gain control; ATG: autophagy-related; ATP6V0D1: ATPase H+ transporting lysosomal V0 subunit D1; ATP6V1E1: ATPase H+ transporting lysosomal V1 subunit E1; CA: cornu ammonis; CQ: chloroquine; CTSB: cathepsin B; CTSD: cathepsin D; DCTN1: dynactin subunit 1; DG: dentate gyrus; DYNLL1: dynein light chain LC8-type 1; FBS: fetal bovine serum; GABARAP: GABA type A receptor-associated protein; GABARAPL1: GABA type A receptor associated protein like 1; GABARAPL2: GABA type A receptor associated protein like 2; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; IPA: Ingenuity Pathway Analysis; KEGG: Kyoto Encyclopedia of Genes and Genomes; KIF5A: kinesin family member 5A; LAMP: lysosomal-associated membrane protein; MAP1LC3B/LC3B: microtubule-associated protein 1 light chain 3 beta; NBR1: NBR1 autophagy cargo receptor; OPTN: optineurin; PBS: phosphate-buffered saline; PFA: paraformaldehyde; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; PRM: parallel reaction monitoring; siRNA: small interfering RNA; SQSTM1/p62: sequestosome 1; SYP: synaptophysin; TAX1BP1: Tax1 binding protein 1; TMT: trimethyltin chloride; TUB: tubulin.

T cells: a dedicated effector kinase pathways for every trait?

Signaling pathways play critical roles in regulating the activation of T cells. Recognition of foreign peptide presented by MHC to the T cell receptor (TCR) triggers a signaling cascade of proximal kinases and adapter molecules that lead to the activation of Effector kinase pathways. These effector kinase pathways play pivotal roles in T cell activation, differentiation, and proliferation. RNA sequencing-based methods have provided insights into the gene expression programs that support the above-mentioned cell biological responses. The proteome is often overlooked. A recent study by Damasio et al. [Biochem. J. (2021) 478, 79-98. doi:10.1042/BCJ20200661] focuses on characterizing the effect of extracellular signal-regulated kinase (ERK) on the remodeling of the proteome of activated CD8+ T cells using Mass spectrometric analysis. Surprisingly, the Effector kinase ERK pathway is responsible for only a select proportion of the proteome that restructures during T cell activation. The primary targets of ERK signaling are transcription factors, cytokines, and cytokine receptors. In this commentary, we discuss the recent findings by Damasio et al. [Biochem. J. (2021) 478, 79-98. doi:10.1042/BCJ20200661] in the context of different Effector kinase pathways in activated T cells.

Systems Wide Analysis of CCM Signaling Complex Alterations in CCM-Deficient Models Using Omics Approaches

Omics research has garnered popularity recently to integrate in-depth analysis of alterations at the molecular level to elucidate observable phenotypes resulting from knockdown/knockout models. Genomics, performed through RNA-seq, allows the user to evaluate alterations at the transcription level, oftentimes more sensitive than other types of analysis, especially when attempting to understand lack of observation of an expected phenotype. Proteomics facilitates an understanding of mechanisms being altered at the translational level allowing for an understanding of multiple layers of regulation occurring, elucidating discrepancies between what is seen at the RNA level compared to what is translated to a functional protein. Here we describe the methods currently being used to evaluate CCM-deficient strains in human brain microvascular endothelial cells (HBMVEC), zebrafish embryos as well as in vivo mouse model to evaluate impacts on various signaling cascades resulting from deficiencies in KRIT1 (CCM1), MGC4607 (CCM2), and PDCD10 (CCM3). The integration of data from genomics and proteomics analysis allows for the composition of interactomes, elucidating systems wide impacts resulting from disruption of the CCM signaling complex (CSC).

Proteomics reveals distinct mechanisms regulating the release of cytokines and alarmins during pyroptosis

A major pathway for proinflammatory protein release by macrophages is inflammasome-mediated pyroptotic cell death. As conventional secretion, unconventional secretion, and cell death are executed simultaneously, however, the cellular mechanisms regulating this complex paracrine program remain incompletely understood. Here, we devise a quantitative proteomics strategy to define the cellular exit route for each protein by pharmacological and genetic dissection of cellular checkpoints regulating protein release. We report the release of hundreds of proteins during pyroptosis, predominantly due to cell lysis. They comprise constitutively expressed and transcriptionally induced proteins derived from the cytoplasm and specific intracellular organelles. Many low-molecular-weight proteins including the cytokine interleukin-1β, alarmins, and lysosomal-cargo proteins exit cells in the absence of cell lysis. Cytokines and alarmins are released in an endoplasmic reticulum (ER)-Golgi-dependent manner as free proteins rather than by extracellular vesicles. Our work provides an experimental framework for the dissection of cellular exit pathways and a resource for pyroptotic protein release.

Identification of Celecoxib-Targeted Proteins Using Label-Free Thermal Proteome Profiling on Rat Hippocampus

Celecoxib, or Celebrex, a nonsteroidal anti-inflammatory drug, is one of the most common medicines for treating inflammatory diseases. Recently, it has been shown that celecoxib is associated with implications in complex diseases, such as Alzheimer disease and cancer as well as with cardiovascular risk assessment and toxicity, suggesting that celecoxib may affect multiple unknown targets. In this project, we detected targets of celecoxib within the nervous system using a label-free thermal proteome profiling method. First, proteins of the rat hippocampus were treated with multiple drug concentrations and temperatures. Next, we separated the soluble proteins from the denatured and sedimented total protein load by ultracentrifugation. Subsequently, the soluble proteins were analyzed by nano-liquid chromatography tandem mass spectrometry to determine the identity of the celecoxib-targeted proteins based on structural changes by thermal stability variation of targeted proteins toward higher solubility in the higher temperatures. In the analysis of the soluble protein extract at 67°C, 44 proteins were uniquely detected in drug-treated samples out of all 478 identified proteins at this temperature. Ras-associated binding protein 4a, 1 out of these 44 proteins, has previously been reported as one of the celecoxib off targets in the rat central nervous system. Furthermore, we provide more molecular details through biomedical enrichment analysis to explore the potential role of all detected proteins in the biologic systems. We show that the determined proteins play a role in the signaling pathways related to neurodegenerative disease-and cancer pathways. Finally, we fill out molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets. SIGNIFICANCE STATEMENT: This study determined 44 off-target proteins of celecoxib, a nonsteroidal anti-inflammatory and one of the most common medicines for treating inflammatory diseases. It shows that these proteins play a role in the signaling pathways related to neurodegenerative disease and cancer pathways. Finally, the study provides molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets.

Development of comprehensive two-dimensional low-flow liquid-chromatography setup coupled to high-resolution mass spectrometry for shotgun proteomics

Bottom-up proteomics provides often small amounts of highly complex samples that cannot be analysed by direct mass spectrometry (MS). To gain a better insight in the sample composition, liquid chromatography (LC) and (comprehensive) two-dimensional liquid chromatography (2D-LC or LC × LC) can be coupled to the MS. Low-flow separations are attractive for HRMS analysis, but they tend to be lengthy. In this work, a low-flow, online, actively modulated LC × LC system, based on hydrophilic-interaction liquid chromatography (HILIC) in the first dimension and reversed-phase liquid chromatography (RPLC) in the second dimension, was developed to separate complex mixtures of peptides. Miniaturization permitted the analysis of small sample amounts (1-5 μg) and direct coupling with micro-ESI MS (1 μL min^-1). All components were focused and automatically transferred from HILIC to RPLC using stationary-phase-assisted active modulation (C18 traps) to deal with solvent-incompatibility or dilution issues. Optimization of the setup was performed for the HILIC columns and the RPLC columns to provide a more efficient separation and higher identification rates than obtained using one-dimensional (1D) LC. A 60% increase in peak capacity was obtained with the 2D setup compared to a 1D-RPLC separation and a 17-34% increase in the number of proteins identified was achieved for the samples analysed (2D-yeast-8280 peptides and 2D-kidney tissue-8843 peptides), without increasing the analysis time (2 h).

Improved Proteomics-Based Drug Mechanism-of-Action Studies Using 16-Plex Isobaric Mass Tags

Multiplexed quantitative proteomics enabled complex workflows to study the mechanisms by which small molecule drugs interact with the proteome such as thermal proteome profiling (TPP) or multiplexed proteome dynamics profiling (mPDP). TPP measures changes in protein thermal stability in response to drug treatment and thus informs on direct targets and downstream regulation events, while the mPDP approach enables the discovery of regulated protein synthesis and degradation events caused by small molecules and other perturbations. The isobaric mass tags available for multiplexed proteomics have thus far limited the efficiency and sensitivity by which such experiments could be performed. Here we evaluate a recent generation of 16-plex isobaric mass tags and demonstrate the sensitive and time efficient identification of Staurosporine targets in HepG2 cell extracts by recording full thermal denaturation/aggregation profiles of vehicle and compound treated samples in a single mass spectrometry experiment. In 2D-TPP experiments, isothermal titration over seven concentrations per temperature enabled comprehensive selectivity profiling of Staurosporine with EC₅₀ values for kinase targets tightly matching to the kinobeads gold standard assay. Finally, we demonstrate time and condition-based multiplexing of dynamic SILAC labeling experiments to delineate proteome-wide effects of the molecular glue Indisulam on synthesis and degradation rates.

Proteomic insights into synaptic signaling in the brain: the past, present and future

Chemical synapses in the brain connect neurons to form neural circuits, providing the structural and functional bases for neural communication. Disrupted synaptic signaling is closely related to a variety of neurological and psychiatric disorders. In the past two decades, proteomics has blossomed as a versatile tool in biological and biomedical research, rendering a wealth of information toward decoding the molecular machinery of life. There is enormous interest in employing proteomic approaches for the study of synapses, and substantial progress has been made. Here, we review the findings of proteomic studies of chemical synapses in the brain, with special attention paid to the key players in synaptic signaling, i.e., the synaptic protein complexes and their post-translational modifications. Looking toward the future, we discuss the technological advances in proteomics such as data-independent acquisition mass spectrometry (DIA-MS), cross-linking in combination with mass spectrometry (CXMS), and proximity proteomics, along with their potential to untangle the mystery of how the brain functions at the molecular level. Last but not least, we introduce the newly developed synaptomic methods. These methods and their successful applications marked the beginnings of the synaptomics era.

Plasma Proteomes Can Be Reidentifiable and Potentially Contain Personally Sensitive and Incidental Findings

The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual-beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of-and argue for the desirability for-ethical and human-rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.

Virus systems biology: Proteomics profiling of dynamic protein networks during infection

The host cell proteome undergoes a variety of dynamic changes during viral infection, elicited by the virus itself or host cell defense mechanisms. Studying these changes on a global scale by integrating functional and physical interactions within protein networks during infection is an important tool to understand pathology. Indeed, proteomics studies dissecting protein signaling cascades and interaction networks upon infection showed how global information can significantly improve understanding of disease mechanisms of diverse viral infections. Here, we summarize and give examples of different experimental designs, proteomics approaches and bioinformatics analyses that allow profiling proteome changes and host-pathogen interactions to gain a molecular systems view of viral infection.

Bottom-Up Community Proteome Analysis of Saliva Samples and Tongue Swabs by Data-Dependent Acquisition Nano LC-MS/MS Mass Spectrometry

Analysis using mass spectrometry enables the characterization of metaproteomes in their native environments and overcomes the limitation of proteomics of pure cultures. Metaproteomics is a promising approach to link functions of currently actively expressed genes to the phylogenetic composition of the microbiome in their habitat. In this chapter, we describe the preparation of saliva samples and tongue swabs for nLC-MS/MS measurements and their bioinformatic analysis based on the Trans-Proteomic Pipeline and Prophane to study the oral microbiome .

Phosphoproteome and Proteome Sample Preparation from Mouse Tissues for Circadian Analysis

Recent advances in mass spectrometry (MS)-based quantitative proteomics now allow the identification and quantification of deep proteomes and post-translational modifications (PTMs) in relatively short times. Therefore, in the last few years, this technology has proven successful in the circadian field to characterize temporal oscillations of the proteome and more recently PTMs in cellular systems and in tissues. In this chapter, we describe a robust and simple protocol, based on the EasyPhos workflow, to enable preparation of large number of proteomes and phosphoproteomes from mouse tissues for MS-based quantitative analysis. We additionally discuss computational methods to analyze proteome and phosphoproteome time series to determine circadian oscillations.

DIA-based systems biology approach unveils E3 ubiquitin ligase-dependent responses to a metabolic shift

The yeast Saccharomyces cerevisiae is a powerful model system for systems-wide biology screens and large-scale proteomics methods. Nearly complete proteomics coverage has been achieved owing to advances in mass spectrometry. However, it remains challenging to scale this technology for rapid and high-throughput analysis of the yeast proteome to investigate biological pathways on a global scale. Here we describe a systems biology workflow employing plate-based sample preparation and rapid, single-run, data-independent mass spectrometry analysis (DIA). Our approach is straightforward, easy to implement, and enables quantitative profiling and comparisons of hundreds of nearly complete yeast proteomes in only a few days. We evaluate its capability by characterizing changes in the yeast proteome in response to environmental perturbations, identifying distinct responses to each of them and providing a comprehensive resource of these responses. Apart from rapidly recapitulating previously observed responses, we characterized carbon source-dependent regulation of the GID E3 ligase, an important regulator of cellular metabolism during the switch between gluconeogenic and glycolytic growth conditions. This unveiled regulatory targets of the GID ligase during a metabolic switch. Our comprehensive yeast system readout pinpointed effects of a single deletion or point mutation in the GID complex on the global proteome, allowing the identification and validation of targets of the GID E3 ligase. Moreover, this approach allowed the identification of targets from multiple cellular pathways that display distinct patterns of regulation. Although developed in yeast, rapid whole-proteome-based readouts can serve as comprehensive systems-level assays in all cellular systems.

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

Proximal Protein Interaction Landscape of RAS Paralogs

Simple Summary

RAS paralogs (HRAS, NRAS and KRAS) are of major interest in biology because they are involved in developmental disorders (e.g., Costello and Noonan syndromes) and in a broad variety of human neoplasia. Many research groups have devoted tremendous efforts to deepen our understanding of the RAS proteins functions and regulations, notably through identifying their functional protein partners. However, while most of these studies were focused on pathogenic RAS mutants, much less research has been dedicated to deciphering the normal activities of RAS paralogs. However, such characterization appears as a prerequisite to clearly identify pathogenic features. We delineated and compared the wild type RAS paralogs proximal interactomes. We detected more than 800 RAS high confident proximal interactors, either shared between paralogs or unique, and validated a subset of data through proximity ligation assays-based validation. Our results describe differential interactors enrichment between RAS paralogs and uncover novel ties between RAS signaling and cellular metabolism. We believe that our findings will support further studies aiming at better understanding how RAS paralogs could be differentially involved in discrete cellular processes and could serve as a basis to template oncogenic mechanism investigations.

Abstract

RAS proteins (KRAS, NRAS and HRAS) are frequently activated in different cancer types (e.g., non-small cell lung cancer, colorectal cancer, melanoma and bladder cancer). For many years, their activities were considered redundant due to their high degree of sequence homology (80% identity) and their shared upstream and downstream protein partners. However, the high conservation of the Hyper-Variable-Region across mammalian species, the preferential activation of different RAS proteins in specific tumor types and the specific post-translational modifications and plasma membrane-localization of each paralog suggest they could ensure discrete functions. To gain insights into RAS proteins specificities, we explored their proximal protein–protein interaction landscapes using the proximity-dependent biotin identification technology (BioID) in Flp-In T-REx 293 cell lines stably transfected and inducibly expressing wild type KRAS4B, NRAS or HRAS. We identified more than 800 high-confidence proximal interactors, allowing us to propose an unprecedented comparative analysis of wild type RAS paralogs protein networks. These data bring novel information on poorly characterized RAS functions, e.g., its putative involvement in metabolic pathways, and on shared as well as paralog-specific protein networks that could partially explain the complexity of RAS functions. These networks of protein interactions open numerous avenues to better understand RAS paralogs biological activities.

Proteomic approaches to investigate gammaherpesvirus biology and associated tumorigenesis

The DNA viruses, Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV), are members of the gammaherpesvirus subfamily, a group of viruses whose infection is associated with multiple malignancies, including cancer. The primary host for these viruses is humans and, like all herpesviruses, infection with these pathogens is lifelong. Due to the persistence of gammaherpesvirus infection and the potential for cancer formation in infected individuals, there is a driving need to understand not only the biology of these viruses and how they remain undetected in host cells but also the mechanism(s) by which tumorigenesis occurs. One of the methods that has provided much insight into these processes is proteomics. Proteomics is the study of all the proteins that are encoded by a genome and allows for (i) identification of existing and novel proteins derived from a given genome, (ii) interrogation of protein-protein interactions within a system, and (iii) discovery of druggable targets for the treatment of malignancies. In this chapter, we explore how proteomics has contributed to our current understanding of gammaherpesvirus biology and their oncogenic processes, as well as the clinical applications of proteomics for the detection and treatment of gammaherpesvirus-associated cancers.

Spatially and cell-type resolved quantitative proteomic atlas of healthy human skin

Human skin provides both physical integrity and immunological protection from the external environment using functionally distinct layers, cell types and extracellular matrix. Despite its central role in human health and disease, the constituent proteins of skin have not been systematically characterized. Here, we combine advanced tissue dissection methods, flow cytometry and state-of-the-art proteomics to describe a spatially-resolved quantitative proteomic atlas of human skin. We quantify 10,701 proteins as a function of their spatial location and cellular origin. The resulting protein atlas and our initial data analyses demonstrate the value of proteomics for understanding cell-type diversity within the skin. We describe the quantitative distribution of structural proteins, known and previously undescribed proteins specific to cellular subsets and those with specialized immunological functions such as cytokines and chemokines. We anticipate that this proteomic atlas of human skin will become an essential community resource for basic and translational research ( https://skin.science/ ).

Cell-Type-Specific Proteogenomic Signal Diffusion for Integrating Multi-Omics Data Predicts Novel Schizophrenia Risk Genes

Accumulation of diverse types of omics data on schizophrenia (SCZ) requires a systems approach to model the interplay between genome, transcriptome, and proteome. We introduce Markov affinity-based proteogenomic signal diffusion (MAPSD), a method to model intra-cellular protein trafficking paradigms and tissue-wise single-cell protein abundances. MAPSD integrates multi-omics data to amplify the signals at SCZ risk loci with small effect sizes, and reveal convergent disease-associated gene modules in the brain. We predicted a set of high-confidence SCZ risk loci followed by characterizing the subcellular localization of proteins encoded by candidate SCZ risk genes, and illustrated that most are enriched in neuronal cells in the cerebral cortex as well as Purkinje cells in the cerebellum. We demonstrated how the identified genes may be involved in neurodevelopment, how they may alter SCZ-related biological pathways, and how they facilitate drug repurposing. MAPSD is applicable in other polygenic diseases and can facilitate our understanding of disease mechanisms.

The Origins of Organellar Mapping by Protein Correlation Profiling

Cells have a rich inner structure that is commonly explored by microscopy. Classical biochemical methods that break apart the cells and fractionate them along a gradient have now gotten a new lease on life through modern methods of mass spectrometry-based proteomics. Their common principle is to comprehensively measure all the proteins in each of the fractions. The resulting quantitative profile then associates thousands of proteins to their cellular homes. Here, the author recounts how protein correlation profiling, the first such technique, was conceived and how it was applied to answer intricate cell biological questions.

New Insights into Inflammatory Bowel Diseases from Proteomic and Lipidomic Studies

Ulcerative colitis (UC) and Crohn's disease (CD) represent the two main forms of chronic inflammatory bowel diseases (IBD). The exact IBD etiology is not yet revealed but CD and UC are likely induced by an excessive immune response against normal constituents of the intestinal microbial flora. IBD diagnosis is based on clinical symptoms often combined with invasive and costly procedures. Thus, the need for more non-invasive markers is urgent. Several routine laboratory investigations have been explored as indicators of intestinal inflammation in IBD, including blood testing for C-reactive protein, erythrocyte sedimentation rate, and specific antibodies, in addition to stool testing for calprotectin and lactoferrin. However, none has been universally adopted, some have been well-characterized, and others hold great promise. In recent years, the technological developments within the field of mass spectrometry (MS) and bioinformatics have greatly enhanced the ability to retrieve, characterize, and analyze large amounts of data. High-throughput research allowed enhancing the understanding of the biology of IBD permitting a more accurate biomarker discovery than ever before. In this review, we summarize currently used IBD serological and stool biomarkers and how proteomics and lipidomics are contributing to the identification of IBD biomarkers.

Investigation of cancer drug resistance mechanisms by phosphoproteomics

Cancer cell mutations can be identified by genomic and transcriptomic techniques. However, they are not sufficient to understand the full complexity of cancer heterogeneity. Analyses of proteins expressed in cancers and their modification profiles show how these mutations could be translated at the functional level. Protein phosphorylation is a major post-translational modification critical for regulating several cellular functions. The covalent addition of phosphate groups to serine, threonine, and tyrosine is catalyzed by protein kinases. Over the past years, kinases were strongly associated with cancer, thus inhibition of protein kinases emanated as novel cancer treatment. However, cancers frequently develop drug resistance. Therefore, a better understanding of drug effects on tumors is urgently needed. In this perspective, phosphoproteomics arose as advanced tool to monitor cancer therapies and to discover novel drugs. This review highlights the role of phosphoproteomics in predicting sensitivity or resistance of cancers towards tyrosine kinase inhibitors and cytotoxic drugs. It also shows the importance of phosphoproteomics in identifying biomarkers that could be applied in clinical diagnostics to predict responses to drugs.

Biochemical analysis of oral fluids for disease detection

The field of diagnostics using invasive blood testing represents the majority of diagnostic tests used as part of routine health monitoring. The relatively recent introduction of salivary diagnostics has lead to a major paradigm shift in diagnostic analyses. Additionally, in this era of big data, oral fluid testing has shown promising outcomes in a number of fields, particularly the areas of genomics, microbiomics, proteomics, metabolomics, and transcriptomics. Despite the analytical challenges involved in the interpretation of large datasets generated from biochemical studies involving bodily fluids, including saliva, many studies have identified novel oral biomarkers for diagnosing oral and systemic diseases. In this regard, oral biofluids, including saliva, gingival crevicular fluid (GCF), peri-implant crevicular fluid (PICF), dentinal tubular fluid (DTF), are now attracting increasing attention due to their important attributes, such as noninvasive sampling, easy handling, low cost, and more accurate diagnosis of oral diseases. Recently, the utilization of salivary diagnostics to evaluate systemic diseases and monitor general health has increased in popularity among clinicians. Saliva contains a wide range of protein, DNA and RNA biomarkers, which assist in the diagnosis of multiple diseases and conditions, including cancer, cardiovascular diseases (CVD), auto-immune and degenerative diseases, respiratory infections, oral diseases, and microbial (viral, bacterial and fungal) diseases. Moreover, due to its noninvasive nature and ease-of-adoption by children, it is now being used in mass screening programs, oral health-related studies and clinical trials in support of the development of therapeutic agents. The recent advent of highly sensitive technologies, such as next-generation sequencing, mass spectrometry, highly sensitives ELISAs, and homogeneous immunoassays, suggests that even small quantities of salivary biomarkers are able to be assayed accurately, providing opportunities for the development of many future diagnostic applications (including emerging technologies, such as point-of-care and rapid molecular technologies). The present article explores the omics and biochemical compositions of various oral biofluids with important value in diagnostics and monitoring.

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β-cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein-protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in ‘inflammatory response’ and ‘lipid metabolic process’, and 37 downregulated candidate genes were involved in ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. These candidate genes were also involved in different signaling pathways associated with ‘PI3K/Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ and ‘MAPK signaling pathway’, which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)-target gene regulatory network and a transcription factor-target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa-miR-192-5p, hsa-miR-124-5p and hsa-miR-335-5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C-endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C-C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic-based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.

Plasma Fibronectin Levels Identified via Quantitative Proteomics Profiling Predicts Hepatitis B Surface Antigen Seroclearance in Chronic Hepatitis B

BACKGROUND

Seroclearance of hepatitis B surface antigen (HBsAg) is a potentially achievable target of chronic hepatitis B (CHB). Plasma proteins relevant to HBsAg seroclearance remain undetermined.

METHODS

We prospectively recruited treatment-naive CHB patients with spontaneous HBsAg seroclearance and matched HBsAg-positive controls. Plasma protein profiling was performed using isobaric tags for relative and absolute quantitation-based proteomics, with the expression of candidate proteins validated in a separate cohort. The predictive value of fibronectin was assessed at 3 years, 1 year (Year -1) before, and at the time (Year 0) of HBsAg seroclearance.

RESULTS

Four hundred eighty-seven plasma proteins were identified via proteomics, with 97 proteins showing altered expression. In the verification cohort (n = 90), median plasma fibronectin levels in patients with HBsAg seroclearance was higher than in controls (P = .009). In the longitudinal cohort (n = 164), patients with HBsAg seroclearance, compared with controls, had a higher median fibronectin levels at Year -1 (413.26 vs 227.95 µg/mL) and Year 0 (349.45 vs 208.72 µg/mL) (both P < .001). In patients with an annual HBsAg log reduction >0.5, Year -1 fibronectin level achieved an area under the receiving operator characteristic of 0.884 in predicting HBsAg seroclearance.

CONCLUSIONS

Using proteomics-based technology, plasma fibronectin may be associated with HBsAg seroclearance and a potential predictor of "functional cure".

Proximity proteomics identifies novel function of Rab14 in trafficking of Ebola virus matrix protein VP40

Ebola virus is a member of Filoviridae family of viruses that causes fetal hemorrhagic fever in human. Matrix protein VP40 of the Ebola virus is involved in multiple stages of viral maturation processes. In order to fully understand the interacting partners of VP40 in host cells, we applied proximity-dependent biotin-identification (BioID) approach to systematically screen for potential proteins at different time points of VP40 expression. By immunoprecipitation and subsequent proteomics analysis, we found over 100 candidate proteins with various cellular components and molecular functions. Among them, we identified Rab14 GTPase that appears to function at the late stage of VP40 expression. Imaging studies demonstrated that VP40 and Rab14 have substantial colocalization when expressed in HeLa cells. Overexpression of the dominant-negative Rab14(S25N) diminished the plasma membrane (PM) localization of VP40. In addition, we found that secreted VP40 protein can be endocytosed into Rab14 positive compartments. In summary, our study provides evidence that Rab14 is a novel regulator of the intracellular trafficking of Ebola virus matrix protein VP40 in HeLa cells.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.