Proteomic analysis of mouse astrocytes and their secretome by a combination of FASP and StageTip-based, high pH, reversed-phase fractionation

Integrated proteomic and genomic analysis to identify predictive biomarkers for valproate response in bipolar disorder: a 6-month follow-up study

BACKGROUND

Several genetic studies have been undertaken to elucidate the intricate interplay between genetics and drug responses in bipolar disorder (BD). However, there has been notably limited research on biomarkers specifically linked to valproate, with only a few studies investigating integrated proteomic and genomic factors in response to valproate treatment. Therefore, this study aimed to identify biological markers for the therapeutic response to valproate treatment in BD. Patients with BD in remission were assessed only at baseline, whereas those experiencing acute mood episodes were evaluated at three points (baseline, 8 ± 2 weeks, and 6 ± 1 months). The response to valproate treatment was measured using the Alda scale, with individuals scoring an Alda A score ≥ 5 categorized into the acute-valproate responder (acute-VPAR) group. We analyzed 158 peptides (92 proteins) from peripheral blood samples using multiple reaction monitoring mass spectrometry, and proteomic result-guided candidate gene association analyses, with 1,627 single nucleotide variants (SNVs), were performed using the Korean chip.

RESULTS

The markers of 37 peptides (27 protein) showed temporal upregulation, indicating possible association with response to valproate treatment. A total of 58 SNVs in 22 genes and 37 SNVs in 16 genes showed nominally significant associations with the Alda A continuous score and the acute-VPAR group, respectively. No SNVs reached the genome-wide significance threshold; however, three SNVs (rs115788299, rs11563197, and rs117669164) in the secreted phosphoprotein 2 gene reached a gene-based false discovery rate-corrected significance threshold with response to valproate treatment. Significant markers were associated with the pathophysiological processes of bipolar disorders, including the immune response, acute phase reaction, and coagulation cascade. These results suggest that valproate effectively suppresses mechanisms associated with disease progression.

CONCLUSIONS

The markers identified in this study could be valuable indicators of the underlying mechanisms associated with response to valproate treatment.

PCSK9 stimulates Syk, PKCδ, and NF-κB, leading to atherosclerosis progression independently of LDL receptor

Proprotein convertase subtilisin/kexin type-9 (PCSK9) binds to and degrades low-density lipoprotein (LDL) receptor, leading to increase of LDL cholesterol in blood. Its blockers have emerged as promising therapeutics for cardiovascular diseases. Here we show that PCSK9 itself directly induces inflammation and aggravates atherosclerosis independently of the LDL receptor. PCSK9 exacerbates atherosclerosis in LDL receptor knockout mice. Adenylyl cyclase-associated protein 1 (CAP1) is the main binding partner of PCSK9 and indispensable for the inflammatory action of PCSK9, including induction of cytokines, Toll like receptor 4, and scavenger receptors, enhancing the uptake of oxidized LDL. We find spleen tyrosine kinase (Syk) and protein kinase C delta (PKCδ) to be the key mediators of inflammation after PCSK9-CAP1 binding. In human peripheral blood mononuclear cells, serum PCSK9 levels are positively correlated with Syk, PKCδ, and p65 phosphorylation. The CAP1-fragment crystallizable region (CAP1-Fc) mitigates PCSK9-mediated inflammatory signal transduction more than the PCSK9 blocking antibody evolocumab does.

Neutrophil extracellular traps promote ΔNp63+ basal cell hyperplasia in chronic rhinosinusitis

BACKGROUND

Neutrophil extracellular traps (NETs) are observed in chronic rhinosinusitis (CRS), although their role remains unclear.

OBJECTIVES

This study aimed to investigate the influence of NETs on the CRS epithelium.

METHODS

Forty-five sinonasal biopsy specimens were immunofluorescence-stained to identify NETs and p63+ basal stem cells. Investigators treated human nasal epithelial cells with NETs and studied them with immunofluorescence staining, Western blotting, and quantitative real-time PCR. NET inhibitors were administered to a murine neutrophilic nasal polyp model.

RESULTS

NETs existed in tissues in patients with CRS with nasal polyps, especially in noneosinophilic nasal polyp tissues. p63+ basal cell expression had a positive correlation with the release of NETs. NETs induced the expansion of Ki-67+p63+ cells. We found that ΔNp63, an isoform of p63, was mainly expressed in the nasal epithelium and controlled by NETs. Treatment with deoxyribonuclease (DNase) I or Sivelestat (NET inhibitors) prevented the overexpression of ΔNp63+ epithelial stem cells and reduced polyp formation.

CONCLUSIONS

These results reveal that NETs are implicated in CRS pathogenesis via basal cell hyperplasia. This study suggests a novel possibility of treating CRS by targeting NETs.

Identification of VWA5A as a novel biomarker for inhibiting metastasis in breast cancer by machine-learning based protein prioritization

Distant metastasis is the leading cause of death in breast cancer (BC). The timing of distant metastasis differs according to subtypes of BCs and there is a need for identification of biomarkers for the prediction of early and late metastasis. To identify biomarker candidates whose abundance level can discriminate metastasis types, we performed a high-throughput proteomics assay using tissue samples from BCs with no metastasis, late metastasis, and early metastasis, processed data with machine learning-based feature selection, and found that low VWA5A could be responsible for shorter duration of metastasis-free interval. Low expression of VWA5A gene in METABRIC cohort was associated with poor survival in BCs, especially in hormone receptor (HR)-positive BCs. In-vitro experiments confirmed tumor suppressive effect of VWA5A on BCs in HR+ and triple-negative BC cell lines. We found that expression of VWA5A can be assessed by immunohistochemistry (IHC) on archival tissue samples. Decreasing nuclear expression of VWA5A was significantly associated with advanced T stage and lymphatic invasion in consecutive BCs of all subtypes. We discovered lower expression of VWA5A as the potential biomarker for metastasis-prone BCs, and our results support the clinical utility of VWA5A IHC, as an adjunctive tools for prognostication of BCs.

Proteomic profiling in the progression of psychosis: Analysis of clinical high-risk, first episode psychosis, and healthy controls

BACKGROUND AND HYPOTHESIS

Recent evidence has highlighted the benefits of early detection and treatment for better clinical outcomes in patients with psychosis. Biological markers of the disease have become a focal point of research. This study aimed to identify protein markers detectable in the early stages of psychosis and indicators of progression by comparing them with those of healthy controls (HC) and first episode psychosis (FEP).

STUDY DESIGN

The participants comprised 28 patients in the clinical high-risk (CHR) group, 49 patients with FEP, and 61 HCs aged 15-35 years. Blood samples were collected and analyzed using multiple reaction monitoring-mass spectrometry to measure the expression of 158 peptide targets. Data were adjusted for age, sex, and use of psychotropic drugs.

STUDY RESULTS

A total of 18 peptides (17 proteins) differed significantly among the groups. The protein PRDX2 was higher in the FEP group than in the CHR and HC groups and showed increased expression according to disease progression. The levels of six proteins were significantly higher in the FEP group than in the CHR group. Nine proteins differed significantly in the CHR group compared to the other groups. Sixteen proteins were significantly correlated with symptom severity. These proteins are primarily related to the coagulation cascade, inflammatory response, brain structure, and synaptic plasticity.

CONCLUSIONS

Our findings suggested that peripheral protein markers reflect disease progression in patients with psychosis. Further longitudinal research is needed to confirm these findings and to identify the specific roles of these markers in the pathogenesis of schizophrenia.

An Introduction to Analytical Challenges, Approaches, and Applications in Mass Spectrometry-Based Secretomics

The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS-based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry-based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science.

Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure

BACKGROUND

Proteomics and genomics studies have contributed to understanding the pathogenesis of chronic obstructive pulmonary disease (COPD), but previous studies have limitations. Here, using a machine learning (ML) algorithm, we attempted to identify pathways in cultured bronchial epithelial cells of COPD patients that were significantly affected when the cells were exposed to a cigarette smoke extract (CSE).

METHODS

Small airway epithelial cells were collected from patients with COPD and those without COPD who underwent bronchoscopy. After expansion through primary cell culture, the cells were treated with or without CSEs, and the proteomics of the cells were analyzed by mass spectrometry. ML-based feature selection was used to determine the most distinctive patterns in the proteomes of COPD and non-COPD cells after exposure to smoke extract. Publicly available single-cell RNA sequencing data from patients with COPD (GSE136831) were used to analyze and validate our findings.

RESULTS

Five patients with COPD and five without COPD were enrolled, and 7,953 proteins were detected. Ferroptosis was enriched in both COPD and non-COPD epithelial cells after their exposure to smoke extract. However, the ML-based analysis identified ferroptosis as the most dramatically different response between COPD and non-COPD epithelial cells, adjusted P value = 4.172 × 10^-6, showing that epithelial cells from COPD patients are particularly vulnerable to the effects of smoke. Single-cell RNA sequencing data showed that in cells from COPD patients, ferroptosis is enriched in basal, goblet, and club cells in COPD but not in other cell types.

CONCLUSION

Our ML-based feature selection from proteomic data reveals ferroptosis to be the most distinctive feature of cultured COPD epithelial cells compared to non-COPD epithelial cells upon exposure to smoke extract.

Proteomics of the astrocyte secretome reveals changes in their response to soluble oligomeric Aβ

Astrocytes associate with amyloid plaques in Alzheimer's disease (AD). Astrocytes react to changes in the brain environment, including increasing concentrations of amyloid-β (Aβ). However, the precise response of astrocytes to soluble small Aβ oligomers at concentrations similar to those present in the human brain has not been addressed. In this study, we exposed astrocytes to media from neurons that express the human amyloid precursor protein (APP) transgene with the double Swedish mutation (APPSwe), and which contains APP-derived fragments, including soluble human Aβ oligomers. We then used proteomics to investigate changes in the astrocyte secretome. Our data show dysregulated secretion of astrocytic proteins involved in the extracellular matrix and cytoskeletal organization and increase secretion of proteins involved in oxidative stress responses and those with chaperone activity. Several of these proteins have been identified in previous transcriptomic and proteomic studies using brain tissue from human AD and cerebrospinal fluid (CSF). Our work highlights the relevance of studying astrocyte secretion to understand the brain response to AD pathology and the potential use of these proteins as biomarkers for the disease.

Monitoring the interactions between N-degrons and N-recognins of the Arg/N-degron pathway

As defined by the N-degron pathway, single N-terminal (Nt) amino acids can function as N-degrons that induce the degradation of proteins and other biological materials. Central to this pathway is the selective recognition of N-degrons by cognate N-recognins that direct the substrates to either the ubiquitin (Ub)-proteasome system (UPS) or autophagy-lysosome pathway (ALP). Eukaryotic cells have developed diverse pathways to utilize all 20 amino acids in the genetic code as pro-N-degrons or N-degrons which can be generated through endoproteolytic cleavage or post-translational modifications. Amongst these, the arginine (Arg) N-degron plays a key role in both cis- and trans-degradation of a large spectrum of cellular materials by the proteasome or lysosome. In mammals, Arg/N-degrons can be generated through endoproteolytic cleavage or post-translational conjugation of the amino acid L-Arg by ATE1-encoded R-transferases (EC 2.3.2.8), which requires Arg-tRNAArg as a cofactor. Arg/N-degrons of short-lived substrates are recognized by a family of N-recognins characterized by the UBR box for polyubiquitination and proteasomal degradation. Under stresses, however, the same degrons can be recognized for autophagic degradation by the ZZ domain of the N-recognin p62/SQSTSM-1/Sequestosome-1 or KCMF1. Biochemical tools were developed to monitor the interaction of Arg/N-degrons with its cognate N-recognins. These assays were employed to identify new N-recognins and to characterize their biochemical properties and physiological functions. The principles of these assays may be applied for other types of N-degron pathways. Below, we describe the methods that analyze the interaction of Arg/N-degrons and their chemical mimics to N-recognins.

Discovery of Novel Digital Biomarkers for Type 2 Diabetic Nephropathy Classification via Integration of Urinary Proteomics and Pathology

Background

The heterogeneous phenotype of diabetic nephropathy (DN) from type 2 diabetes complicates appropriate treatment approaches and outcome prediction. Kidney histology helps diagnose DN and predict its outcomes, and an artificial intelligence (AI)-based approach will maximize clinical utility of histopathological evaluation. Herein, we addressed whether AI-based integration of urine proteomics and image features improves DN classification and its outcome prediction, altogether augmenting and advancing pathology practice.

Methods

We studied whole slide images (WSIs) of periodic acid-Schiff-stained kidney biopsies from 56 DN patients with associated urinary proteomics data. We identified urinary proteins differentially expressed in patients who developed end-stage kidney disease (ESKD) within two years of biopsy. Extending our previously published human-AI-loop pipeline, six renal sub-compartments were computationally segmented from each WSI. Hand-engineered image features for glomeruli and tubules, and urinary protein measurements, were used as inputs to deep-learning frameworks to predict ESKD outcome. Differential expression was correlated with digital image features using the Spearman rank sum coefficient.

Results

A total of 45 urinary proteins were differentially detected in progressors, which was most predictive of ESKD (AUC=0.95), while tubular and glomerular features were less predictive (AUC=0.71 and AUC=0.63, respectively). Accordingly, a correlation map between canonical cell-type proteins, such as epidermal growth factor and secreted phosphoprotein 1, and AI-based image features was obtained, which supports previous pathobiological results.

Conclusions

Computational method-based integration of urinary and image biomarkers may improve the pathophysiological understanding of DN progression as well as carry clinical implications in histopathological evaluation.

Monitoring regional astrocyte diversity by cell type-specific proteomic labeling in vivo

Astrocytes exhibit regional heterogeneity in morphology, function and molecular composition to support and modulate neuronal function and signaling in a region-specific manner. To characterize regional heterogeneity of astrocytic proteomes of different brain regions we established an inducible Aldh1l1-methionyl-tRNA-synthetase^L274G (MetRS^L274G ) mouse line that allows astrocyte-specific metabolic labeling of newly synthesized proteins by azidonorleucine (ANL) in vivo and subsequent isolation of tagged proteins by click chemistry. We analyzed astrocytic proteins from four different brain regions by mass spectrometry. The induced expression of MetRS^L274G is restricted to astrocytes and identified proteins show a high overlap with proteins compiled in "AstroProt," a newly established database for astrocytic proteins. Gene enrichment analysis reveals a high similarity among brain regions with subtle differences in enriched biological processes and in abundances of key astrocytic proteins for hippocampus, cortex and striatum. However, the cerebellar proteome stands out with proteins being highly associated with the calcium signaling pathway or with bipolar disorder. Subregional analysis of single astrocyte TAMRA intensities in hippocampal layers indicates distinct subregional heterogeneity of astrocytes and highlights the applicability of our toolbox to study differences of astrocytic proteomes in vivo.

Dual viscosity mixture vehicle for intratympanic steroid treatment modifies the ROS and inflammation related proteomes

Until recently, the most standard treatment for sensorineural or sudden hearing loss, which is caused by inner ear damage or deterioration, has been systemic oral steroid administration. In recent, intratympanic steroid injections such as dexamethasone have been used for the treatment of sudden hearing loss as well. It is injected into the tympanic cavity through its membrane and is expected to diffuse over the round window located between the tympanic cavity and the inner ear. However, in clinical situations, the delivery time of steroids to the inner ear is shorter than 24 h, which does not allow for a sufficient therapeutic effect. Therefore, we applied a previously invented dual viscosity mixture vehicle (DVV) for intratympanic dexamethasone to a guinea pig model, which could reduce the side effects of systemic steroid administration with sufficient dwelling time for the treatment of hearing loss, and we investigated the physiological changes with a global proteomic approach. In this study, we extracted perilymph in three different conditions from guinea pigs treated with dexamethasone-embedded DVV, dexamethasone mixed in saline, and control groups to compare proteomic changes using tandem mass spectrometry analysis. After liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analysis, we first identified 46 differentially expressed proteins (DEPs) that were statistically significant after one-way ANOVA multiple-sample test. We also performed pairwise comparisons among each group to identify DEPs closely related to the treatment response of dexamethasone-embedded DVV. Gene ontology enrichment analysis showed that these DEPs were mostly related to inflammation, immune, actin remodeling, and antioxidant-related processes. As a result, the proteome changes in the DVV-treated groups revealed that most upregulated proteins activate the cell proliferation process, and downregulated proteins inhibit apoptosis and inflammatory reactions. Moreover, the reactive oxygen process was also regulated by DEPs after DVV treatment.

Dual Oxidase 2 (DUOX2) as a Proteomic Biomarker for Predicting Treatment Response to Chemoradiation Therapy for Locally Advanced Rectal Cancer: Using High-Throughput Proteomic Analysis and Machine Learning Algorithm

High-throughput mass-spectrometry-based quantitative proteomic analysis was performed using formalin-fixed, paraffin-embedded (FFPE) biopsy samples obtained before treatment from 13 patients with locally advanced rectal cancer (LARC), who were treated with concurrent chemoradiation therapy (CCRT) followed by surgery. Patients were divided into complete responder (CR) and non-complete responder (nCR) groups. Immunohistochemical (IHC) staining of 79 independent FFPE tissue samples was performed to validate the predictive ability of proteomic biomarker candidates. A total of 3637 proteins were identified, and the expression of 498 proteins was confirmed at significantly different levels (differentially expressed proteins—DEPs) between two groups. In Gene Ontology enrichment analyses, DEPs enriched in biological processes in the CR group included proteins linked to cytoskeletal organization, immune response processes, and vesicle-associated protein transport processes, whereas DEPs in the nCR group were associated with biosynthesis, transcription, and translation processes. Dual oxidase 2 (DUOX2) was selected as the most predictive biomarker in machine learning algorithm analysis. Further IHC validation ultimately confirmed DUOX2 as a potential biomarker for predicting the response of nCR to CCRT. In conclusion, this study suggests that the treatment response to RT may be affected by the pre-treatment tumor microenvironment. DUOX2 is a potential biomarker for the early prediction of nCR after CCRT.

Proteomic profiling of postmortem prefrontal cortex tissue of suicide completers

Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography-tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.

Discovery of Proteins Responsible for Resistance to Three Chemotherapy Drugs in Breast Cancer Cells Using Proteomics and Bioinformatics Analysis

Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs in breast cancer are very limited. In this study, we employed proteomics and bioinformatics approaches on chemoresistant breast cancer cell lines to understand the underlying resistance mechanisms that resulted from doxorubicin (DR), paclitaxel (PR), and tamoxifen (TAR). In total, 10,385 proteins were identified and quantified from three TMT 6-plex and one TMT 10-plex experiments. Bioinformatics analysis showed that Notch signaling, immune response, and protein re-localization processes were uniquely associated with DR, PR, and TAR resistance, respectively. In addition, proteomic signatures related to drug resistance were identified as potential targets of many FDA-approved drugs. Furthermore, we identified potential prognostic proteins with significant effects on overall survival. Representatively, PLXNB2 expression was associated with a highly significant increase in risk, and downregulation of ACOX3 was correlated with a worse overall survival rate. Consequently, our study provides new insights into the proteomic aspects of the distinct mechanisms underlying chemoresistance in breast cancer.

Alterations in blood proteins in the prodromal stage of bipolar II disorders

Although early intervention may help prevent the progression of bipolar disorder, there are some controversies over early pharmacological intervention. In this study, we recruited 40 subjects in the prodromal stage of BD-II (BP), according to bipolar at-risk state criteria. We compared the expression of their plasma proteins with that of 48 BD-II and 75 healthy control (HC) to identify markers that could be detected in a high-risk state. The multiple reaction monitoring method was used to measure target peptide levels with high accuracy. A total of 26 significant peptides were identified through analysis of variance with multiple comparisons, of which 19 were differentially expressed in the BP group when compared to the BD-II and HC groups. Two proteins were overexpressed in the BP group; and were related to pro-inflammation and impaired neurotransmission. The other under-expressed peptides in the BP group were related to blood coagulation, immune reactions, lipid metabolism, and the synaptic plasticity. In this study, significant markers observed in the BP group have been reported in patients with psychiatric disorders. Overall, the results suggest that the pathophysiological changes included in BD-II had already occurred with BP, thus justifying early pharmacological treatment to prevent disease progression.

Computational Integration of Renal Histology and Urinary Proteomics using Neural Networks

Histological image data and molecular profiles provide context into renal condition. Often, a biopsy is drawn to diagnose or monitor a suspected kidney problem. However, molecular profiles can go beyond a pathologist's ability to see and diagnose. Using AI, we computationally incorporated urinary proteomic profiles with microstructural morphology from renal biopsy to investigate new and existing molecular links to image phenotypes. We studied whole slide images of periodic acid-Schiff stained renal biopsies from 56 DN patients matched with 2,038 proteins measured from each patient's urine. Using Seurat, we identified differentially expressed proteins in patients that developed end-stage renal disease within 2 years of biopsy. Glomeruli, globally sclerotic glomeruli, and tubules were segmented from WSI using our previously published HAIL pipeline. For each glomerulus, 315 handcrafted digital image features were measured, and for tubules, 207 features. We trained fully connected networks to predict urinary protein measurements that were differentially expressed between patients who did/ did not progress to ESRD within 2 years of biopsy. The input to this network was either glomerular or tubular histomorphological features in biopsy. Trained network weights were used as a proxy to rank which morphological features correlated most highly with specific urinary proteins. We identified significant image feature-protein pairs by ranking network weights by magnitude. We also looked at which features on average were most significant in predicting proteins. For both glomeruli and tubules, RGB color values and variance in PAS+ areas (specifically basement membrane for tubules) were, on average, more predictive of molecular profiles than other features. There is a strong connection between molecular profile and image phenotype, which can be elucidated through computational methods. These discovered links can provide insight to disease pathways, and discover new factors contributing to incidence and progression.

Development of a high-pH reversed-phase well plate for peptide fractionation and deep proteome analysis of cells and exosomes

The complexity of the proteome often limits the number of identified proteins in the nanoflow LC-MS (nanoLC-MS) analysis of samples. Therefore, peptide fractionation is essential for reducing the sample complexity and improving the proteome coverage. In this study, to achieve high-pH reversed-phase (RP)-well plate fractionation for high-throughput proteomics analysis, C18 particles were coated on a 96-well plate, and the sample-loading processes were optimized for high-pH fractionation. The sample capacity of the high-pH RP-well plate was estimated to be ~6 μg of protein. There were 1.85- and 1.71-fold increases in the number of protein groups and peptides identified, respectively, with high-pH RP-well plate fractionation, compared to those without fractionation. In addition, with alkaline C18 well plate fractionation, exosome markers could be detected using ~1 μg of a protein digest of exosomes by microflow LC-MS (microLC-MS). These results illustrate that high-pH RP-well plate fractionation has superior sensitivity and effectiveness in preparing trace amounts of proteins for deep proteome analysis.

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

Plasma proteomic data in bipolar II disorders and major depressive disorders

The proteomics data included in this article supplement the research article titled "Predictive protein markers for the severity of depression in mood disorders: A preliminary trans-diagnostic approach study (manuscript ID: JPSYCHIATRES-D-20-00437)." Plasma protein was analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). This data article included 370 plasma protein profiles expressed in patients with bipolar II disorder (BD-II) and major depressive disorder (MDD). The tables present the comparison of protein expressions between BD-II and MDD, and the relationship between the severity of the depressive symptoms and protein expression. In addition, details of results adjusting the use of each psychotropic medication (antipsychotics, mood stabilizers, and antidepressants) for 20 proteins that showed a significant relationship with the severity of the depressive symptom were presented in the table. Results of the bioinformatics analysis of proteins, which were significantly related to the severity of depressive symptom, are presented. The blood protein profiles and the results of the analyses presented in this data article provide detailed information on the proteins associated with mood disorders, and could be used as the basis for further mass spectrometry studies in psychiatric disorders.

Proteins related to ictogenesis and seizure clustering in chronic epilepsy

Seizure clustering is a common phenomenon in epilepsy. Protein expression profiles during a seizure cluster might reflect the pathomechanism underlying ictogenesis. We performed proteomic analyses to identify proteins with a specific temporal expression pattern in cluster phases and to demonstrate their potential pathomechanistic role. Pilocarpine epilepsy model mice with confirmed cluster pattern of spontaneous recurrent seizures by long-term video-electroencpehalography were sacrificed at the onset, peak, or end of a seizure cluster or in the seizure-free period. Proteomic analysis was performed in the hippocampus and the cortex. Differentially expressed proteins (DEPs) were identified and classified according to their temporal expression pattern. Among the five hippocampal (HC)-DEP classes, HC-class 1 (66 DEPs) represented disrupted cell homeostasis due to clustered seizures, HC-class 2 (63 DEPs) cluster-onset downregulated processes, HC-class 3 (42 DEPs) cluster-onset upregulated processes, and HC-class 4 (103 DEPs) consequences of clustered seizures. Especially, DEPs in HC-class 3 were hippocampus-specific and involved in axonogenesis, synaptic vesicle assembly, and neuronal projection, indicating their pathomechanistic roles in ictogenesis. Key proteins in HC-class 3 were highly interconnected and abundantly involved in those biological processes. This study described the seizure cluster-associated spatiotemporal regulation of protein expression. HC-class 3 provides insights regarding ictogenesis-related processes.

In-depth proteomic profiling captures subtype-specific features of craniopharyngiomas

Craniopharyngiomas are rare epithelial tumors derived from pituitary gland embryonic tissue. This epithelial tumor can be categorized as an adamantinomatous craniopharyngioma (ACP) or papillary craniopharyngioma (PCP) subtype with histopathological and genetic differences. Genomic and transcriptomic profiles of craniopharyngiomas have been investigated; however, the proteomic profile has yet to be elucidated and added to these profiles. Recent improvements in high-throughput quantitative proteomic approaches have introduced new opportunities for a better understanding of these diseases and the efficient discovery of biomarkers. We aimed to confirm subtype-associated proteomic changes between ACP and PCP specimens. We performed a system-level proteomic study using an integrated approach that combines mass spectrometry-based quantitative proteomic, statistical, and bioinformatics analyses. The bioinformatics analysis showed that differentially expressed proteins between ACP and PCP were significantly involved in mitochondrial organization, fatty acid metabolic processes, exocytosis, the inflammatory response, the cell cycle, RNA splicing, cell migration, and neuron development. Furthermore, using network analysis, we identified hub proteins that were positively correlated with ACP and PCP phenotypes. Our findings improve our understanding of the pathogenesis of craniopharyngiomas and provide novel insights that may ultimately translate to the development of craniopharyngioma subtype-specific therapeutics.

Next-generation Proteomics-Based Discovery, Verification, and Validation of Urine Biomarkers for Bladder Cancer Diagnosis

Purpose

We aimed to identify, verify, and validate a multiplex urinary biomarker-based prediction model for diagnosis and surveillance of urothelial carcinoma of bladder, using high-throughput proteomics methods.

Materials and Methods

Label-free quantification of data-dependent and data-independent acquisition of 12 and 24 individuals was performed in each of the discovery and verification phases using mass spectrometry, simultaneously using urinary exosome and proteins. Based on five scoring system based on proteomics data and statistical methods, we selected eight proteins. Enzyme-linked immunosorbent assay on urine from 120 patients with bladder mass lesions used for validation. Using multivariable logistic regression, we selected final candidate models for predicting bladder cancer.

Results

Comparing the discovery and verification cohorts, 38% (50/132 exosomal differentially expressed proteins [DEPs]) and 44% (109/248 urinary DEPs) are consistent at statistically significance, respectively. The 20 out of 50 exosome proteins and 27 out of 109 urinary proteins were upregulated in cancer patients. From eight selected proteins, we developed two diagnostic models for bladder cancer. The area under the receiver operating characteristic curve (AUROC) of two models were 0.845 and 0.842, which outperformed AUROC of urine cytology.

Conclusion

The results showed that the two diagnostic models developed here were more accurate than urine cytology. We successfully developed and validated a multiplex urinary protein-based prediction, which will have wide applications for the rapid diagnosis of urothelial carcinoma of the bladder. External validation for this biomarker panel in large population is required.

Predictive protein markers for depression severity in mood disorders: A preliminary trans-diagnostic approach study

Depression is a common symptom of many mental disorders, especially major depressive disorder (MDD) and bipolar disorder (BD). Previous studies have reported that these diseases share common pathophysiological pathways; therefore, this study elucidated whether the plasma levels of protein markers related to common depressive symptoms differed between patients with BD and those with MDD. Plasma samples of 71 patients with mood disorders and clinical manifestations were analyzed in this study. After depleting the abundant proteins, liquid chromatography-tandem mass spectrometry and label-free quantification were performed. Five proteins, viz., cholesteryl ester transfer protein (CETP), apolipoprotein D (APOD), mannan-binding lectin serine protease 2 (MASP2), Ig lambda chain V-II region BO (IGLV2-8) and Ig kappa chain V-III region NG9 (IGKV3-20) were negatively associated with the total scores of the Hamilton depression rating scale (HAM-D), after adjusting for the covariates. CETP and APOD also showed significant negative correlations with the anhedonia/retardation and guilt/agitation scores of the HAM-D. Four proteins, namely, Ig kappa chain V-II region TEW (IGKC; IGKV2D-28), Ig lambda variable 5-45 (IGLV5-45), complement factor H (CFH) and attractin (ATRN), showed significant associations with anhedonia/retardation after adjusting for covariates. Proteins that significantly correlated with the symptoms could predict the remission state of depression (area under the curve [AUC], 0.83) and anhedonia/retardation (AUC, 0.80). Bioinformatics analysis revealed that complement activation, immune response, and lipid metabolism were significantly enriched pathways. Although our study design was cross-sectional and no controls were included, protein markers identified in this preliminary study will be further investigated in our subsequent longitudinal study.

A large portion of the astrocyte proteome is dedicated to perivascular endfeet, including critical components of the electron transport chain

The perivascular astrocyte endfoot is a specialized and diffusion-limited subcellular compartment that fully ensheathes the cerebral vasculature. Despite their ubiquitous presence, a detailed understanding of endfoot physiology remains elusive, in part due to a limited understanding of the proteins that distinguish the endfoot from the greater astrocyte body. Here, we developed a technique to isolate astrocyte endfeet from brain tissue, which was used to study the endfoot proteome in comparison to the astrocyte somata. In our approach, brain microvessels, which retain their endfoot processes, were isolated from mouse brain and dissociated, whereupon endfeet were recovered using an antibody-based column astrocyte isolation kit. Our findings expand the known set of proteins enriched at the endfoot from 10 to 516, which comprised more than 1/5th of the entire detected astrocyte proteome. Numerous critical electron transport chain proteins were expressed only at the endfeet, while enzymes involved in glycogen storage were distributed to the somata, indicating subcellular metabolic compartmentalization. The endfoot proteome also included numerous proteins that, while known to have important contributions to blood-brain barrier function, were not previously known to localize to the endfoot. Our findings highlight the importance of the endfoot and suggest new routes of investigation into endfoot function.

Gliome database: a comprehensive web-based tool to access and analyze glia secretome data

Glial cells are phenotypically heterogeneous non-neuronal components of the central and peripheral nervous systems. These cells are endowed with diverse functions and molecular machineries to detect and regulate neuronal or their own activities by various secreted mediators, such as proteinaceous factors. In particular, glia-secreted proteins form a basis of a complex network of glia-neuron or glia-glia interactions in health and diseases. In recent years, the analysis and profiling of glial secretomes have raised new expectations for the diagnosis and treatment of neurological disorders due to the vital role of glia in numerous physiological or pathological processes of the nervous system. However, there is no online database of glia-secreted proteins available to facilitate glial research. Here, we developed a user-friendly 'Gliome' database (available at www.gliome.org), a web-based tool to access and analyze glia-secreted proteins. The database provides a vast collection of information on 3293 proteins that are released from glia of multiple species and have been reported to have differential functions under diverse experimental conditions. It contains a web-based interface with the following four key features regarding glia-secreted proteins: (i) fundamental information, such as signal peptide, SecretomeP value, functions and Gene Ontology category; (ii) differential expression patterns under distinct experimental conditions; (iii) disease association; and (iv) interacting proteins. In conclusion, the Gliome database is a comprehensive web-based tool to access and analyze glia-secretome data obtained from diverse experimental settings, whereby it may facilitate the integration of bioinformatics into glial research.

Antibiotic-Dependent Relationships Between the Nasal Microbiome and Secreted Proteome in Nasal Polyps

PURPOSE

Chronic rhinosinusitis (CRS) is a heterogeneous chronic inflammatory condition of the paranasal sinuses and nasal passages. Although antibiotics are used to reduce inflammation or to treat an episode of acute rhinosinusitis, their effects on the nasal environment and host response in CRS is unclear.

METHODS

We analyzed the effects of antibiotics on the nasal microbiome and secreted proteome in CRS using multi-omic analysis. Nasal secretions were collected from 29 controls, 30 CRS patients without nasal polyps (NP), and 40 CRS patients with NP. A total of 99 subjects were divided into 2 groups that included subjects who had taken antibiotics 3 months prior to sampling and those who had not. We performed 16S ribosomal DNA sequence analyses and Orbitrap mass spectrometry-based proteomic analyses. Spearman correlation was used to assess the correlations between the nasal microbiome and secreted proteome.

RESULTS

The associations between the nasal microbiome and secreted proteome were noted in subjects who had used antibiotics. Antibiotics could have stronger effects on their associations in patients with CRS with NP than in those without. It remains unknown whether these holistic changes caused by antibiotics are beneficial or harmful to CRS, however, the associations could be differentially affected by disease severity.

CONCLUSION

These findings provide new insight into the nasal environment and the host response in CRS.

Therapeutic Effects of hiPSC-Derived Glial and Neuronal Progenitor Cells-Conditioned Medium in Experimental Ischemic Stroke in Rats

Transplantation of various types of stem cells as a possible therapy for stroke has been tested for years, and the results are promising. Recent investigations have shown that the administration of the conditioned media obtained after stem cell cultivation can also be effective in the therapy of the central nervous system pathology (hypothesis of their paracrine action). The aim of this study was to evaluate the therapeutic effects of the conditioned medium of hiPSC-derived glial and neuronal progenitor cells in the rat middle cerebral artery occlusion model of the ischemic stroke. Secretory activity of the cultured neuronal and glial progenitor cells was evaluated by proteomic and immunosorbent-based approaches. Therapeutic effects were assessed by overall survival, neurologic deficit and infarct volume dynamics, as well as by the end-point values of the apoptosis- and inflammation-related gene expression levels, the extent of microglia/macrophage infiltration and the numbers of formed blood vessels in the affected area of the brain. As a result, 31% of the protein species discovered in glial progenitor cells-conditioned medium and 45% in neuronal progenitor cells-conditioned medium were cell type specific. The glial progenitor cell-conditioned media showed a higher content of neurotrophins (BDNF, GDNF, CNTF and NGF). We showed that intra-arterial administration of glial progenitor cells-conditioned medium promoted a faster decrease in neurological deficit compared to the control group, reduced microglia/macrophage infiltration, reduced expression of pro-apoptotic gene Bax and pro-inflammatory cytokine gene Tnf, increased expression of anti-inflammatory cytokine genes (Il4, Il10, Il13) and promoted the formation of blood vessels within the damaged area. None of these effects were exerted by the neuronal progenitor cell-conditioned media. The results indicate pronounced cytoprotective, anti-inflammatory and angiogenic properties of soluble factors secreted by glial progenitor cells.

In-depth proteome of perilymph in guinea pig model

The vast majority of sensorineural hearing loss is caused by impairment of the inner ear cells. Proteomic analysis of perilymph may therefore improve our understanding of inner ear diseases and hearing loss. However, the investigation of the human perilymph proteome was limited due to technical difficulties in perilymph sampling. The guinea pig (Cavia porcellus) is frequently used as an experimental model in preclinical hearing research. In this study, we analyzed samples of perilymph collected from 12 guinea pigs to overcome limited experimental information regarding its proteome. We identified a total of 1413 proteins, establishing a greatly expanded proteome of the previously inferred guinea pig perilymph. This provides a comprehensive proteomic resource for the research community, which will facilitate future molecular-phenotypic studies using the guinea pig as an experimental model of relevance to human inner ear biology.

Pancreatic Tissue Proteomics Unveils Key Proteins, Pathways, and Networks Associated with Type 1 Diabetes

PURPOSE

Type 1 diabetes (T1D) is characterized by autoimmune mediated self-destruction of the pancreatic islet beta cells and the resultant insulin deficiency. However, little is known about the underlying molecular pathogenesis at the pancreatic tissue level given the limited availability of clinical specimens.

EXPERIMENTAL DESIGN

Quantitative proteomic studies is performed on age-matched T1D and healthy cadaveric pancreatic tissues (n = 18 each) using TMT 10plex-based isobaric labeling and BoxCar-based label-free LC-MS/MS approaches. ELISA is used to validate the differentially expressed proteins (DEPs).

RESULTS

Overall, the two quantitative proteomics approaches identified 8824 proteins, of which 261 are DEPs. KEGG pathway and functional network analyses of the DEPs reveal dysregulations to pancreatic exocrine function, complement coagulation cascades, and extracellular matrix receptor interaction pathways in T1D. A selected list of the DEPs associated with pathways, subnetworks, and plasma proteome of T1D are validated using ELISA.

CONCLUSIONS AND CLINICAL RELEVANCE

Integrating labeling and label-free approaches improve the confidence in quantitative profiling of pancreatic tissue proteome, which furthers the understanding of the dysregulated pathways and functional subnetworks associated with T1D pathogenesis and may aid to develop diagnostic and therapeutic strategies for T1D.

Marker Identification of the Grade of Dysplasia of Intraductal Papillary Mucinous Neoplasm in Pancreatic Cyst Fluid by Quantitative Proteomic Profiling

The incidence of patients with pancreatic cystic lesions, particularly intraductal papillary mucinous neoplasm (IPMN), is increasing. Current guidelines, which primarily consider radiological features and laboratory data, have had limited success in predicting malignant IPMN. The lack of a definitive diagnostic method has led to low-risk IPMN patients undergoing unnecessary surgeries. To address this issue, we discovered IPMN marker candidates by analyzing pancreatic cystic fluid by mass spectrometry. A total of 30 cyst fluid samples, comprising IPMN dysplasia and other cystic lesions, were evaluated. Mucus was removed by brief sonication, and the resulting supernatant was subjected to filter-aided sample preparation and high-pH peptide fractionation. Subsequently, the samples were analyzed by LC-MS/MS. Using several bioinformatics tools, such as gene ontology and ingenuity pathway analysis, we detailed IPMNs at the molecular level. Among the 5834 proteins identified in our dataset, 364 proteins were differentially expressed between IPMN dysplasia. The 19 final candidates consistently increased or decreased with greater IPMN malignancy. CD55 was validated in an independent cohort by ELISA, Western blot, and IHC, and the results were consistent with the MS data. In summary, we have determined the characteristics of pancreatic cyst fluid proteins and discovered potential biomarkers for IPMN dysplasia.

Integrated Proteomics and Metabolomics Analysis Highlights Correlative Metabolite-Protein Networks in Soybean Seeds Subjected to Warm-Water Soaking

Soaking of soybean seeds is a prerequisite for the production of soy foods, and it has been shown that the extent of water absorbed during different imbibition conditions directly affects the quality of the subsequent soybean seed products by yet unknown mechanisms. In order to elucidate the molecular changes in soybean seeds during different soaking temperatures, we performed an integrated proteomics and metabolomics analysis of seeds soaked at 4, 25, and 55 °C. Proteomics analysis revealed that various enzymes related to carbohydrate and protein hydrolysis were activated in soybean seeds during water soaking at 55 °C. Interestingly, results obtained from this integrated proteomics and metabolomics study showed changes in various metabolites, including isoflavones, amino acids, and sugars, that were positively correlated with proteome changes occurring upon soaking at 55 °C. Furthermore, soaking of soybean seeds at 55 °C resulted in degradation of indigestible anti-nutrients such as raffinose oligosaccharides. Taken together, our results suggest that the seed soaking at a high temperature (55 °C) increases the nutritional value of soybean seeds by decreasing the contents of some of the common anti-nutrients.

Aggresomal sequestration and STUB1-mediated ubiquitylation during mammalian proteaphagy of inhibited proteasomes

The ubiquitin–proteasome system and autophagy are two major intracellular proteolytic pathways, and both remove misfolded and proteotoxic proteins from eukaryotic cells. This study describes the detailed regulatory pathway of proteasome degradation by autophagy for its own quality control. We discovered that a portion of inhibited proteasomes is actively sequestered into the aggresome, an insoluble fraction of the mammalian cell. The aggresome functions as a triage point for proteasome recovery and autophagic degradation. This mainly distinguishes proteasome quality control in mammals from that in other organisms. STUB1/CHIP E3 Ub ligase has a critical role in targeting inhibited proteasomes into the aggresome. These results provide strong insights into protein catabolism in various pathological conditions originating from impaired proteasomes.

The 26S proteasome, a self-compartmentalized protease complex, plays a crucial role in protein quality control. Multiple levels of regulatory systems modulate proteasomal activity for substrate hydrolysis. However, the destruction mechanism of mammalian proteasomes is poorly understood. We found that inhibited proteasomes are sequestered into the insoluble aggresome via HDAC6- and dynein-mediated transport. These proteasomes colocalized with the autophagic receptor SQSTM1 and cleared through selective macroautophagy, linking aggresomal segregation to autophagic degradation. This proteaphagic pathway was counterbalanced with the recovery of proteasomal activity and was critical for reducing cellular proteasomal stress. Changes in associated proteins and polyubiquitylation on inhibited 26S proteasomes participated in the targeting mechanism to the aggresome and autophagosome. The STUB1 E3 Ub ligase specifically ubiquitylated purified human proteasomes in vitro, mainly via Lys63-linked chains. Genetic and chemical inhibition of STUB1 activity significantly impaired proteasome processing and reduced resistance to proteasomal stress. These data demonstrate that aggresomal sequestration is the crucial upstream event for proteasome quality control and overall protein homeostasis in mammals.

In-Depth Investigation of Low-Abundance Proteins in Matured and Filling Stages Seeds of Glycine max Employing a Combination of Protamine Sulfate Precipitation and TMT-Based Quantitative Proteomic Analysis

Despite the significant technical advancements in mass spectrometry-based proteomics and bioinformatics resources, dynamic resolution of soybean seed proteome is still limited because of the high abundance of seed storage proteins (SSPs). These SSPs occupy a large proportion of the total seed protein and hinder the identification of low-abundance proteins. Here, we report a TMT-based quantitative proteome analysis of matured and filling stages seeds of high-protein (Saedanbaek) and low-protein (Daewon) soybean cultivars by application of a two-way pre-fractionation both at the levels of proteins (by PS) and peptides (by basic pH reverse phase chromatography). Interestingly, this approach led to the identification of more than 5900 proteins which is the highest number of proteins reported to date from soybean seeds. Comparative protein profiles of Saedanbaek and Daewon led to the identification of 2200 and 924 differential proteins in mature and filling stages seeds, respectively. Functional annotation of the differential proteins revealed enrichment of proteins related to major metabolism including amino acid, major carbohydrate, and lipid metabolism. In parallel, analysis of free amino acids and fatty acids in the filling stages showed higher contents of all the amino acids in the Saedanbaek while the fatty acids contents were found to be higher in the Daewon. Taken together, these results provide new insights into proteome changes during filling stages in soybean seeds. Moreover, results reported here also provide a framework for systemic and large-scale dissection of seed proteome for the seeds rich in SSPs by two-way pre-fractionation combined with TMT-based quantitative proteome analysis.

Identification of TUBB2A by quantitative proteomic analysis as a novel biomarker for the prediction of distant metastatic breast cancer

Background

Metastasis of breast cancer to distal organs is fatal. However, few studies have identified biomarkers that are associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers, such as HER2, ER, and PR, to differentiate between distant and nondistant metastatic breast cancers accurately has necessitated the development of novel biomarker candidates.

Methods

An integrated proteomics approach that combined filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data from FFPE distant metastatic breast cancer tissues. A bioinformatics analysis was performed with regard to gene ontology and signaling pathways using differentially expressed proteins (DEPs) to examine the molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and function of our protein targets.

Results

A total of 9441 and 8746 proteins were identified from the pooled and individual sample sets, respectively. Based on our criteria, TUBB2A was selected as a novel biomarker candidate. The metastatic activities of TUBB2A were subsequently validated. In our bioinformatics analysis using DEPs, we characterized the overall molecular features of distant metastasis and measured differences in the molecular functions of distant metastatic breast cancer between breast cancer subtypes.

Conclusions

Our report is the first study to examine the distant metastatic breast cancer proteome using FFPE tissues. The depth of our dataset allowed us to discover a novel biomarker candidate and a proteomic characteristics of distant metastatic breast cancer. Distinct molecular features of various breast cancer subtypes were also established. Our proteomic data constitute a valuable resource for research on distant metastatic breast cancer.

Moesin (MSN) as a Novel Proteome-Based Diagnostic Marker for Early Detection of Invasive Bladder Urothelial Carcinoma in Liquid-Based Cytology

Bladder urothelial carcinoma (BUC) is the most lethal malignancy of the urinary tract. Treatment for the disease highly depends on the invasiveness of cancer cells. Therefore, a predictive biomarker needs to be identified for invasive BUC. In this study, we employed proteomics methods on urine liquid-based cytology (LBC) samples and a BUC cell line library to determine a novel predictive biomarker for invasive BUC. Furthermore, an in vitro three-dimensional (3D) invasion study for biological significance and diagnostic validation through immunocytochemistry (ICC) were also performed. The proteomic analysis suggested moesin (MSN) as a potential biomarker to predict the invasiveness of BUC. The in vitro 3D invasion study showed that inhibition of MSN significantly decreased invasiveness in BUC cell lines. Further validation using ICC ultimately confirmed moesin (MSN) as a potential biomarker to predict the invasiveness of BUC (p = 0.023). In conclusion, we suggest moesin as a potential diagnostic marker for early detection of BUC with invasion in LBC and as a potential therapeutic target.

Comparison of serum protein profiles between major depressive disorder and bipolar disorder

BACKGROUND

Major depressive disorder and bipolar disorder are prevalent and debilitating psychiatric disorders that are difficult to distinguish, as their diagnosis is based on behavioural observations and subjective symptoms. Quantitative protein profile analysis might help to objectively distinguish between these disorders and increase our understanding of their pathophysiology. Thus, this study was conducted to compare the peripheral protein profiles between the two disorders.

METHODS

Serum samples were collected from 18 subjects with major depressive disorder and 15 subjects with bipolar disorder. After depleting abundant proteins, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and label-free quantification were performed. Data-dependent acquisition data were statistically analysed from the samples of 15 subjects with major depressive disorder and 10 subjects with bipolar disorder who were psychotropic drug-free. Two-sided t-tests were performed for pairwise comparisons of proteomes to detect differentially-expressed proteins (DEPs). Ingenuity Pathway Analysis of canonical pathways, disease and functions, and protein networks based on these DEPs was further conducted.

RESULTS

Fourteen DEPs were significant between subjects with major depressive disorder and those with bipolar disorder. Ras-related protein Rab-7a (t = 5.975, p = 4.3 × 10^- 6) and Rho-associated protein kinase 2 (t = 4.782, p = 8.0 × 10^- 5) were significantly overexpressed in subjects with major depressive disorder and Exportin-7 (t = -4.520, p = 1.5 × 10^- 4) was significantly overexpressed in subjects with bipolar disorder after considering multiple comparisons. Bioinformatics analysis showed that cellular functions and inflammation/immune pathways were significantly different.

CONCLUSIONS

Ras-related protein Rab-7a, Rho-associated protein kinase 2, and Exportin-7 were identified as potential peripheral protein candidates to distinguish major depressive disorder and bipolar disorder. Further large sample studies with longitudinal designs and validation processes are warranted.

Proteomic examination of the neuroglial secretome: lessons for the clinic

Introduction: Glial cells are closely associated with neurons located throughout the nervous system and regulate neuronal activity and function through various mechanisms including the secretion of proteins and other signaling molecules. Glia-secreted proteins play crucial roles in modulating neuronal function in physiological and pathological conditions. Aberrant activation of glial cells leading to neuroinflammation is a common phenomenon observed in various neurological disorders. Aberrantly activated glial cells secrete proteins in disease-specific manner and can be exploited as a repository for novel biomarker discovery.Areas covered: In this review, we describe the recent advances in proteomic techniques, highlighting the need for their application to the secretomic field. Studies regarding the secretome profile of glial cells published within the last 5 years are discussed in detail. The use of glia-based biomarkers in various neuroinflammatory and neurodegenerative diseases is also discussed.Expert opinion: Precise diagnosis and timely treatment of neurological disorders remains a challenge and glia-focused research to identify specific biomarkers appears to be a promising approach to combat these disorders. Recent technological advancement in proteomic research would open new frontiers for more rigorous analysis of glial secretome variations over time and the discovery/development of novel biomarkers for neurological disorders.

Proteomic identification of early urinary-biomarkers of acute kidney injury in preterm infants

The immature preterm kidney is likely to be vulnerable to acute kidney injury (AKI). However, the biomarkers currently used for AKI are not sensitive or specific and are also inadequate for the timely detection of AKI in preterm infants. The objectives of this study were to identify novel urinary biomarkers of AKI using proteomic techniques, and to verify and validate that the candidates can serve as early predictive biomarkers for AKI. In total, 1,810 proteins were identified in the discovery phase. Among those proteins, 174 were selected as the 1^st targeted proteins. A total of 168 proteins were quantified, and the levels of 6 were significantly increased in the AKI group in the verification phase. Using a clinical assay, the results were confirmed and validated using samples of the first urine after birth from the biorepository. Finally, enzyme-linked immunosorbent assays revealed that the levels of annexin A5, neutrophil gelatinase-associated lipocalin (NGAL), and protein S100-P were significantly higher in the samples of the first urine from patients with AKI than in those from patients without AKI. In conclusion, urinary annexin A5, NGAL and protein S100-P levels are promising biomarkers for early, accurate prediction of AKI in preterm infants.

Deep proteome profiling of the hippocampus in the 5XFAD mouse model reveals biological process alterations and a novel biomarker of Alzheimer's disease

Alzheimer’s disease (AD), which is the most common type of dementia, is characterized by the deposition of extracellular amyloid plaques. To understand the pathophysiology of the AD brain, the assessment of global proteomic dynamics is required. Since the hippocampus is a major region affected in the AD brain, we performed hippocampal analysis and identified proteins that are differentially expressed between wild-type and 5XFAD model mice via LC-MS methods. To reveal the relationship between proteomic changes and the progression of amyloid plaque deposition in the hippocampus, we analyzed the hippocampal proteome at two ages (5 and 10 months). We identified 9,313 total proteins and 1411 differentially expressed proteins (DEPs) in 5- and 10-month-old wild-type and 5XFAD mice. We designated a group of proteins showing the same pattern of changes as amyloid beta (Aβ) as the Aβ-responsive proteome. In addition, we examined potential biomarkers by investigating secretory proteins from the Aβ-responsive proteome. Consequently, we identified vitamin K-dependent protein S (PROS1) as a novel microglia-derived biomarker candidate in the hippocampus of 5XFAD mice. Moreover, we confirmed that the PROS1 level in the serum of 5XFAD mice increases as the disease progresses. An increase in PROS1 is also observed in the sera of AD patients and shows a close correlation with AD neuroimaging markers in humans. Therefore, our quantitative proteome data obtained from 5XFAD model mice successfully predicted AD-related biological alterations and suggested a novel protein biomarker for AD.

A protein newly implicated in Alzheimer’s disease could serve as a diagnostic biomarker or therapeutic target. A team led by Youngsoo Kim and Inhee Mook-Jung from Seoul National University, South Korea, analyzed all the proteins expressed in the hippocampus, the brain’s memory center, in mice with and without Alzheimer’s-like disease. They identified more than 1,400 proteins differentially expressed between the mouse model of Alzheimer’s and the normal mice. Among these were 36 secretory proteins that tended to increase their levels along with build-up of amyloid-beta, the protein found in clumps in the brains of people with Alzheimer’s. Many already had known links to Alzheimer’s, but the researchers also identified a novel protein called PROS1. Blood samples from Alzheimer’s patients also showed an increase in PROS1 levels, with a close correlation with amyloid-beta build-up in the brain.

Secretomics to Discover Regulators in Diseases

Secretory proteins play important roles in the cross-talk of individual functional units, including cells. Since secretory proteins are essential for signal transduction, they are closely related with disease development, including metabolic and neural diseases. In metabolic diseases, adipokines, myokines, and hepatokines are secreted from respective organs under specific environmental conditions, and play roles in glucose homeostasis, angiogenesis, and inflammation. In neural diseases, astrocytes and microglia cells secrete cytokines and chemokines that play roles in neurotoxic and neuroprotective responses. Mass spectrometry-based secretome profiling is a powerful strategy to identify and characterize secretory proteins. This strategy involves stepwise processes such as the collection of conditioned medium (CM) containing secretome proteins and concentration of the CM, peptide preparation, mass analysis, database search, and filtering of secretory proteins; each step requires certain conditions to obtain reliable results. Proteomic analysis of extracellular vesicles has become a new research focus for understanding the additional extracellular functions of intracellular proteins. Here, we provide a review of the insights obtained from secretome analyses with regard to disease mechanisms, and highlight the future prospects of this technology. Continued research in this field is expected to provide valuable information on cell-to-cell communication and uncover new pathological mechanisms.

ER complex proteins are required for rhodopsin biosynthesis and photoreceptor survival in Drosophila and mice

Defective rhodopsin homeostasis is one of the major causes of retinal degeneration, including the disease Retinitis pigmentosa. To identify cellular factors required for the biosynthesis of rhodopsin, we performed a genome-wide genetic screen in Drosophila for mutants with reduced levels of rhodopsin. We isolated loss-of-function alleles in endoplasmic reticulum membrane protein complex 3 (emc3), emc5, and emc6, each of which exhibited defective phototransduction and photoreceptor cell degeneration. EMC3, EMC5, and EMC6 were essential for rhodopsin synthesis independent of the ER associated degradation (ERAD) pathway, which eliminates misfolded proteins. We generated null mutations for all EMC subunits, and further demonstrated that different EMC subunits play roles in different cellular functions. Conditional knockout of the Emc3 gene in mice led to mislocalization of rhodopsin protein and death of cone and rod photoreceptor cells. These data indicate conserved roles for EMC subunits in maintaining rhodopsin homeostasis and photoreceptor function, and suggest that retinal degeneration may also be caused by defects in early biosynthesis of rhodopsin.

In-Depth, Proteomic Analysis of Nasal Secretions from Patients With Chronic Rhinosinusitis and Nasal Polyps

Purpose

Chronic rhinosinusitis (CRS) is a complex immunological condition, and novel experimental modalities are required to explore various clinical and pathophysiological endotypes; mere evaluation of nasal polyp (NP) status is inadequate. Therefore, we collected patient nasal secretions on filter paper and characterized the proteomes.

Methods

We performed liquid chromatography-mass spectrometry (MS)/MS in the data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes. Nasal secretions were collected from 10 controls, 10 CRS without NPs (CRSsNP) and 10 CRS with NPs (CRSwNP). We performed Orbitrap MS-based proteomic analysis in the DDA (5 controls, 5 CRSsNP and 5 CRSwNP) and the DIA (5 controls, 5 CRSsNP and 5 CRSwNP) modes, followed by a statistical analysis and a hierarchical clustering to identify differentially expressed proteins in the 3 groups.

Results

We identified 2,020 proteins in nasal secretions. Canonical pathway analysis and gene ontology (GO) evaluation revealed that interleukin (IL)-7, IL-9, IL-17A and IL-22 signaling and neutrophil-mediated immune responses like neutrophil degranulation and activation were significantly increased in CRSwNP compared to control. The GO terms related to the iron ion metabolism that may be associated with CRS and NP development.

Conclusions

Collection of nasal secretions on the filter paper is a practical and non-invasive method for in-depth study of nasal proteomics. Our proteomic signatures also support that Asian NPs could be characterized as non-eosinophilic inflammation features. Therefore, the proteomic profiling of nasal secretions from CRS patients may enhance our understanding of CRS endotypes.

Quantitative Proteomic Analysis Identifies AHNAK (Neuroblast Differentiation-associated Protein AHNAK) as a Novel Candidate Biomarker for Bladder Urothelial Carcinoma Diagnosis by Liquid-based Cytology

Cytological examination of urine is the most widely used noninvasive pathologic screen for bladder urothelial carcinoma (BLCA); however, inadequate diagnostic accuracy remains a major challenge. We performed mass spectrometry-based proteomic analysis of urine samples of ten patients with BLCA and ten paired patients with benign urothelial lesion (BUL) to identify ancillary proteomic markers for use in liquid-based cytology (LBC). A total of 4,839 proteins were identified and 112 proteins were confirmed as expressed at significantly different levels between the two groups. We also performed an independent proteomic profiling of tumor tissue samples where we identified 7,916 proteins of which 758 were differentially expressed. Cross-platform comparisons of these data with comparative mRNA expression profiles from The Cancer Genome Atlas identified four putative candidate proteins, AHNAK, EPPK1, MYH14 and OLFM4. To determine their immunocytochemical expression levels in LBC, we examined protein expression data from The Human Protein Atlas and in-house FFPE samples. We further investigated the expression of the four candidate proteins in urine cytology samples from two independent validation cohorts. These analyses revealed AHNAK as a unique intracellular protein differing in immunohistochemical expression and subcellular localization between tumor and non-tumor cells. In conclusion, this study identified a new biomarker, AHNAK, applicable to discrimination between BLCA and BUL by LBC. To our knowledge, the present study provides the first identification of a clinical biomarker for LBC based on in-depth proteomics.

Quantitative proteomic analysis of pancreatic cyst fluid proteins associated with malignancy in intraductal papillary mucinous neoplasms

Background

The application of advanced imaging technologies for identifying pancreatic cysts has become widespread. However, accurately differentiating between low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive intraductal papillary mucinous neoplasms (IPMNs) remains a diagnostic challenge with current biomarkers, necessitating the development of novel biomarkers that can distinguish IPMN malignancy.

Methods

Cyst fluid samples were collected from nine IPMN patients (3 LGD, 3 HGD, and 3 invasive IPMN) during their pancreatectomies. An integrated proteomics approach that combines filter-aided sample preparation, stage tip-based high-pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data of pancreatic cyst fluid and discover marker candidates for IPMN malignancy. Biological processes of differentially expressed proteins that are related to pancreatic cysts and aggressive malignancy were analyzed using bioinformatics tools such as gene ontology analysis and Ingenuity pathway analysis. In order to confirm the validity of the marker candidates, 19 cyst fluid samples were analyzed by western blot.

Results

A dataset of 2992 proteins was constructed from pancreatic cyst fluid samples. A subsequent analysis found 2963 identified proteins in individual samples, 2837 of which were quantifiable. Differentially expressed proteins between histological grades of IPMN were associated with pancreatic diseases and malignancy according to ingenuity pathway analysis. Eighteen biomarker candidates that were differentially expressed across IPMN histological grades were discovered—7 DEPs that were upregulated and 11 that were downregulated in more malignant grades. HOOK1 and PTPN6 were validated by western blot in an independent cohort, the results of which were consistent with our proteomic data.

Conclusions

This study demonstrates that novel biomarker candidates for IPMN malignancy can be discovered through proteomic analysis of pancreatic cyst fluid.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9193-1) contains supplementary material, which is available to authorized users.

Comparative proteomic analysis of human malignant ascitic fluids for the development of gastric cancer biomarkers

OBJECTIVES

Malignant ascites is a sign of peritoneal seeding, which is one of the most frequent forms of incurable distant metastasis. Because the development of malignant ascites is associated with an extremely poor prognosis, determining whether it resulted from peritoneal seeding has critical clinical implications in diagnosis, choice of treatment, and active surveillance. At present, the molecular characterizations of malignant ascites are especially limited in case of gastric cancer. We aimed to identify malignant ascites-specific proteins that may contribute to the development of alternative methods for diagnosis and therapeutic monitoring and also increase our understanding of the pathophysiology of peritoneal seeding.

DESIGN & METHODS

First, comprehensive proteomic strategies were employed to construct an in-depth proteome of ascitic fluids. Label-free quantitative proteomic analysis was subsequently performed to identify candidates that can differentiate between malignant ascitic fluilds of gastric cancer patients from benign ascitic fluids. Finally, two candidate proteins were verified by ELISA in 84 samples with gastric cancer or liver cirrhosis.

RESULTS

Comprehensive proteome profiling resulted in the identification of 5347 ascites proteins. Using label-free quantification, we identified 299 proteins that were differentially expressed in ascitic fluids between liver cirrhosis and stage IV gastric cancer patients. In addition, we identified 645 proteins that were significantly expressed in ascitic fluids between liver cirrhosis and gastric cancer patients with peritoneal seeding. Finally, Gastriscin and Periostin that can distinguish malignant ascites from benign ascites were verified by ELISA.

CONCLUSIONS

This study identified and verified protein markers that can distinguish malignant ascites with or without peritoneal seeding from benign ascites. Consequently, our results could be a significant resource for gastric cancer research and biomarker discovery in the diagnosis of malignant ascites.

Integrated Multi-Omic Analyses Support Distinguishing Secretory Carcinoma of the Breast from Basal-Like Triple-Negative Breast Cancer

PURPOSE

Secretory carcinoma (SC) of the breast is defined as an indolent tumor but is still categorized into a basal-like triple-negative breast cancer (BL-TNBC) subgroup that generally shows aggressive behavior according to the current classification. Despite the unique clinical behavior of SC, molecular characteristics that reflect biological behaviors of SC remain largely unknown.

EXPERIMENTAL DESIGN

A combinatorial approach of whole-exome sequencing and mass spectrometry-based in-depth quantitative proteomics to determine the entire molecular landscape of SC using three SC formalin-fixed paraffin-embedded (FFPE) tissues is employed.

RESULTS

Exome sequencing and proteomic analysis of SC identified 419 unique somatic mutations and 721 differentially expressed proteins as compared with triple-negative breast cancer (TNBC), respectively. Several pathways related to cancer metabolism were significantly upregulated in the SC group. Comparative analyses with multiple datasets revealed that SC shares genomic mutations and biological pathways more closely related to hormone receptor-positive breast cancer than BL-TNBC.

CONCLUSION AND CLINICAL RELEVANCE

These multi-omic analyses provide evidence that SC harbors substantially different molecular genomic and proteomic landscapes compared with BL-TNBC. These results provide an entire spectrum of in-depth molecular landscapes to support the hypothesis that SC is distinct from BL-TNBC.

SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes.

Cell adhesion and matricellular support by astrocytes of the tripartite synapse

Astrocytes contribute to the formation, function, and plasticity of synapses. Their processes enwrap the neuronal components of the tripartite synapse, and due to this close interaction they are perfectly positioned to modulate neuronal communication. The interaction between astrocytes and synapses is facilitated by cell adhesion molecules and matricellular proteins, which have been implicated in the formation and functioning of tripartite synapses. The importance of such neuron-astrocyte integration at the synapse is underscored by the emerging role of astrocyte dysfunction in synaptic pathologies such as autism and schizophrenia. Here we review astrocyte-expressed cell adhesion molecules and matricellular molecules that play a role in integration of neurons and astrocytes within the tripartite synapse.