ProteomeXchange provides globally coordinated proteomics data submission and dissemination

Blocking muscle wasting via deletion of the muscle-specific E3 ligase MuRF1 impedes pancreatic tumor growth

Cancer-induced muscle wasting reduces quality of life, complicates or precludes cancer treatments, and predicts early mortality. Herein, we investigate the requirement of the muscle-specific E3 ubiquitin ligase, MuRF1, for muscle wasting induced by pancreatic cancer. Murine pancreatic cancer (KPC) cells, or saline, were injected into the pancreas of WT and MuRF1^-/- mice, and tissues analyzed throughout tumor progression. KPC tumors induces progressive wasting of skeletal muscle and systemic metabolic reprogramming in WT mice, but not MuRF1^-/- mice. KPC tumors from MuRF1^-/- mice also grow slower, and show an accumulation of metabolites normally depleted by rapidly growing tumors. Mechanistically, MuRF1 is necessary for the KPC-induced increases in cytoskeletal and muscle contractile protein ubiquitination, and the depression of proteins that support protein synthesis. Together, these data demonstrate that MuRF1 is required for KPC-induced skeletal muscle wasting, whose deletion reprograms the systemic and tumor metabolome and delays tumor growth.

Enzymatic Phosphorylation of Oxidized Tyrosine Residues

Post-translational modifications (PTMs) alter the function and fate of proteins and cells in almost every conceivable way. Protein modifications can occur as a result of specific regulating actions of enzymes, such as tyrosine kinases phosphorylating tyrosine residues or by nonenzymatic reactions, such as oxidation related to oxidative stress and diseases. While many studies have addressed the multisite, dynamic, and network-like properties of PTMs, only little is known of the interplay of the same site modifications. In this work, we studied the enzymatic phosphorylation of oxidized tyrosine (l-DOPA) residues using synthetic insulin receptor peptides, in which tyrosine residues were replaced with l-DOPA. The phosphorylated peptides were identified by liquid chromatography-high-resolution mass spectrometry and the site of phosphorylation by tandem mass spectrometry. The results clearly show that the oxidized tyrosine residues are phosphorylated, displaying a specific immonium ion peak in the MS² spectra. Furthermore, we detected this modification in our reanalysis (MassIVE ID: MSV000090106) of published bottom-up phosphoproteomics data. The modification, where both oxidation and phosphorylation take place at the same amino acid, has not yet been published in PTM databases. Our data indicate that there can be multiple PTMs that do not exclude each other at the same modification site.

New Insights into the Impact of Human Papillomavirus on Oral Cancer in Young Patients: Proteomic Approach Reveals a Novel Role for S100A8

Human papillomavirus (HPV) infection has recently been linked to a subset of cancers affecting the oral cavity. However, the molecular mechanisms underlying HPV-driven oral squamous cell carcinoma (OSCC) onset and progression are poorly understood.

METHODS

We performed MS-based proteomics profiling based on HPV status in OSCC in young patients, following biological characterization and cell assays to explore the proteome functional landscape.

RESULTS

Thirty-nine proteins are differentially abundant between HPV (+) and HPV (-) OSCC. Among them, COPS3, DYHC1, and S100A8 are unfavorable for tumor recurrence and survival, in contrast to A2M and Serpine1, low levels of which show an association with better DFS. Remarkably, S100A8 is considered an independent prognostic factor for lower survival rates, and at high levels, it alters tumor-associated immune profiling, showing a lower proportion of M1 macrophages and dendritic cells. HPV (+) OSCC also displayed the pathogen-associated patterns receptor that, when activated, triggered the S100A8 and NFκB inflammatory responses.

CONCLUSION

HPV (+) OSCC has a peculiar microenvironment pattern distinctive from HPV (-), involving the expression of pathogen-associated pattern receptors, S100A8 overexpression, and NFκB activation and responses, which has important consequences in prognosis and may guide therapeutic decisions.

Structures of Ric-8B in complex with Gα protein folding clients reveal isoform specificity mechanisms

Mammalian Ric-8 proteins act as chaperones to regulate the cellular abundance of heterotrimeric G protein α subunits. The Ric-8A isoform chaperones Gαi/o, Gα12/13, and Gαq/11 subunits, while Ric-8B acts on Gαs/olf subunits. Here, we determined cryoelectron microscopy (cryo-EM) structures of Ric-8B in complex with Gαs and Gαolf, revealing isoform differences in the relative positioning and contacts between the C-terminal α5 helix of Gα within the concave pocket formed by Ric-8 α-helical repeat elements. Despite the overall architectural similarity with our earlier structures of Ric-8A complexed to Gαq and Gαi1, Ric-8B distinctly accommodates an extended loop found only in Gαs/olf proteins. The structures, along with results from Ric-8 protein thermal stability assays and cell-based Gαolf folding assays, support a requirement for the Gα C-terminal region for binding specificity, and highlight that multiple structural elements impart specificity for Ric-8/G protein binding.

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.

Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning

Proteomics instrumentation and the corresponding bioinformatics tools have evolved at a rapid pace in the last 20 years, whereas the exploitation of deep learning techniques in proteomics is on the horizon. The ability to revisit proteomics raw data, in particular, could be a valuable resource for machine learning applications seeking new insight into protein expression and functions of previously acquired data from different instruments under various lab conditions. We map publicly available proteomics repositories (such as ProteomeXchange) and relevant publications to extract MS/MS data to form one large database that contains the patient history and mass spectrometric data acquired for the patient sample. The extracted mapped dataset should enable the research to overcome the issues attached to the dispersions of proteomics data on the internet, which makes it difficult to apply emerging new bioinformatics tools and deep learning algorithms. The workflow proposed in this study enables a linked large dataset of heart-related proteomics data, which could be easily and efficiently applied to machine learning and deep learning algorithms for futuristic predictions of heart diseases and modeling. Data scraping and crawling offer a powerful tool to harvest and prepare the training and test datasets; however, the authors advocate caution because of ethical and legal issues, as well as the need to ensure the quality and accuracy of the data that are being collected.

Mass spectrometry-based retina proteomics

A subfield of neuroproteomics, retina proteomics has experienced a transformative growth since its inception due to methodological advances in enabling chemical, biochemical, and molecular biology techniques. This review focuses on mass spectrometry's contributions to facilitate mammalian and avian retina proteomics to catalog and quantify retinal protein expressions, determine their posttranslational modifications, as well as its applications to study the proteome of the retina in the context of biology, health and diseases, and therapy developments.

Persulfidation protects from oxidative stress under nonphotorespiratory conditions in Arabidopsis

Hydrogen sulfide is a signaling molecule in plants that regulates essential biological processes through protein persulfidation. However, little is known about sulfide-mediated regulation in relation to photorespiration. Here, we performed label-free quantitative proteomic analysis and observed a high impact on protein persulfidation levels when plants grown under nonphotorespiratory conditions were transferred to air, with 98.7% of the identified proteins being more persulfidated under suppressed photorespiration. Interestingly, a higher level of reactive oxygen species (ROS) was detected under nonphotorespiratory conditions. Analysis of the effect of sulfide on aspects associated with non- or photorespiratory growth conditions has demonstrated that it protects plants grown under suppressed photorespiration. Thus, sulfide amends the imbalance of carbon/nitrogen and restores ATP levels to concentrations like those of air-grown plants; balances the high level of ROS in plants under nonphotorespiratory conditions to reach a cellular redox state similar to that in air-grown plants; and regulates stomatal closure, to decrease the high guard cell ROS levels and induce stomatal aperture. In this way, sulfide signals the CO₂ -dependent stomata movement, in the opposite direction of the established abscisic acid-dependent movement. Our findings suggest that the high persulfidation level under suppressed photorespiration reveals an essential role of sulfide signaling under these conditions.

Identification of key biomarkers associated with immune cells infiltration for myocardial injury in dermatomyositis by integrated bioinformatics analysis

BACKGROUND

Dermatomyositis (DM) is an acquired autoimmune disease that can cause damage to various organs, including the heart muscle. However, the mechanisms underlying myocardial injury in DM are not yet fully understood.

METHODS

In this study, we utilized publicly available datasets from the Gene Expression Omnibus (GEO) database to identify hub-genes that are enriched in the immune system process in DM and myocarditis. Weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) analysis, protein-protein interaction (PPI), and gene ontology (GO) analysis were employed to identify these hub-genes. We then used the CIBERSORT method to analyze immune cell infiltration in skeletal muscle specimens of DM and myocardium specimens of myocarditis respectively. Correlation analysis was performed to investigate the relationship between key genes and infiltrating immune cells. Finally, we predicted regulatory miRNAs of hub-genes through miRNet and validated their expression in online datasets and clinical samples.

RESULTS

Using integrated bioinformatics analysis, we identified 10 and 5 hub-genes that were enriched in the immune system process in the database of DM and myocarditis respectively. The subsequent intersections between hub-genes were IFIT3, OAS3, ISG15, and RSAD2. We found M2 macrophages increased in DM and myocarditis compared to the healthy control, associating with the expression of IFIT3, OAS3, ISG15, and RSAD2 in DM and myocarditis positively. Gene function enrichment analysis (GSEA) showed that IFIT3, OAS3, ISG15, and RSAD2 were mainly enriched in type I interferon (IFN) signaling pathway, cellular response to type I interferon, and response to type I interferon. Finally, we verified that the expression of miR-146a-5p was significantly higher in the DM with myocardial injury than those without myocardial injury (p = 0.0009).

CONCLUSION

Our findings suggest that IFIT3, OAS3, ISG15, and RSAD2 may play crucial roles in the underlying mechanism of myocardial injury in DM. Serum miR-146a-5p could be a potential biomarker for myocardial injury in DM.

Variant biomarker discovery using mass spectrometry-based proteogenomics

Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.

Alveolar-Capillary Barrier Protection In Vitro: Lung Cell Type-Specific Effects and Molecular Mechanisms Induced by 1α, 25-Dihydroxyvitamin D3

Low serum levels of 1α, 25-dihydroxyvitamin D3 (VD3) are associated with a higher mortality in trauma patients with sepsis or ARDS. However, the molecular mechanisms behind this observation are not yet understood. VD₃ is known to stimulate lung maturity, alveolar type II cell differentiation, or pulmonary surfactant synthesis and guides epithelial defense during infection. In this study, we investigated the impact of VD₃ on the alveolar-capillary barrier in a co-culture model of alveolar epithelial cells and microvascular endothelial cells respectively in the individual cell types. After stimulation with bacterial LPS (lipopolysaccharide), gene expression of inflammatory cytokines, surfactant proteins, transport proteins, antimicrobial peptide, and doublecortin-like kinase 1 (DCLK1) were analyzed by real-time PCR, while corresponding proteins were evaluated by ELISA, immune-fluorescence, or Western blot. The effect of VD3 on the intracellular protein composition in H441 cells was analyzed by quantitative liquid chromatography-mass spectrometry-based proteomics. VD₃ effectively protected the alveolar-capillary barrier against LPS treatment, as indicated by TEER measurement and morphological assessment. VD₃ did not inhibit the IL-6 secretion by H441 and OEC but restricted the diffusion of IL-6 to the epithelial compartment. Further, VD₃ could significantly suppress the surfactant protein A expression induced in the co-culture system by LPS treatment. VD₃ induced high levels of the antimicrobial peptide LL-37, which counteracted effects by LPS and strengthened the barrier. Quantitative proteomics identified VD3-dependent protein abundance changes ranging from constitutional extracellular matrix components and surfactant-associated proteins to immune-regulatory molecules. DCLK1, as a newly described target molecule for VD₃, was prominently stimulated by VD₃ (10 nM) and seems to influence the alveolar-epithelial cell barrier and regeneration.

Normalization of organ-on-a-Chip samples for mass spectrometry based proteomics and metabolomics via Dansylation-based assay

Mass spectrometry based 'omics pairs well with organ-on-a-chip-based investigations, which often have limited cellular material for sampling. However, a common issue with these chip-based platforms is well-to-well or chip-to-chip variability in the proteome and metabolome due to factors such as plate edge effects, cellular asynchronization, effluent flow, and limited cell count. This causes high variability in the quantitative multi-omics analysis of samples, potentially masking true biological changes within the system. Solutions to this have been approached via data processing tools and post-acquisition normalization strategies such as constant median, constant sum, and overall signal normalization. Unfortunately, these methods do not adequately correct for the large variations, resulting in a need for increased biological replicates. The methods in this work utilize a dansylation based assay with a subset of labeled metabolites that allow for pre-acquisition normalization to better correlate the biological perturbations that truly occur in chip-based platforms. BCA protein assays were performed in tandem with a proteomics pipeline to achieve pre-acquisition normalization. The CN Bio PhysioMimix was seeded with primary hepatocytes and challenged with VX after six days of culture, and the metabolome and proteome were analyzed using the described normalization methods. A decreased coefficient of variation percentage is achieved, significant changes are observed through the proteome and metabolome, and better classification of biological replicates acquired because of these strategies.

Proteome-wide lysine acetylation profiling to investigate the involvement of histone deacetylase HDA5 in the salt stress response of Arabidopsis leaves

Post-translational modifications (PTMs) of proteins play important roles in the acclimation of plants to environmental stress. Lysine acetylation is a dynamic and reversible PTM, which can be removed by histone deacetylases. Here we investigated the role of lysine acetylation in the response of Arabidopsis leaves to 1 week of salt stress. A quantitative mass spectrometry analysis revealed an increase in lysine acetylation of several proteins from cytosol and plastids, which was accompanied by altered histone deacetylase activities in the salt-treated leaves. While activities of HDA14 and HDA15 were decreased upon salt stress, HDA5 showed a mild and HDA19 a strong increase in activity. Since HDA5 is a cytosolic-nuclear enzyme from the class II histone deacetylase family with yet unknown protein substrates, we performed a lysine acetylome analysis on hda5 mutants and characterized its substrate proteins. Next to histone H2B, the salt stress-responsive transcription factor GT2L and the dehydration-related protein ERD7 were identified as HDA5 substrates. In addition, in protein-protein interaction studies, HDA18 was discovered, among other interacting proteins, to work in a complex together with HDA5. Altogether, this study revealed the substrate proteins of HDA5 and identified new lysine acetylation sites which are hyperacetylated upon salt stress. The identification of specific histone deacetylase substrate proteins, apart from histones, will be important to unravel the acclimation response of Arabidopsis to salt stress and their role in plant physiology.

Chemical pulldown combined with mass spectrometry to identify the molecular targets of antimalarials in cell-free lysates

Here, we provide a protocol using chemical pulldown combined with mass spectrometry (LC-MS/MS) to identify drug targets in Plasmodium falciparum. This approach works upon the principle that a resin-bound inhibitor selectively binds its molecular target(s) in cell-free lysates. We describe the preparation of drug beads and P. falciparum lysate, followed by chemical pulldown, sample fractionation, and LC-MS/MS analysis. We then detail how to identify specifically bound proteins by comparing protein enrichment in DMSO-treated relative to drug-treated lysates via quantitative proteomics. For complete details on the use and execution of this protocol, please refer to Milne et al. (2022).1.

Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames

All species continuously evolve short open reading frames (sORFs) that can be templated for protein synthesis and may provide raw materials for evolutionary adaptation. We analyzed the evolutionary origins of 7,264 recently cataloged human sORFs and found that most were evolutionarily young and had emerged de novo. We additionally identified 221 previously missed sORFs potentially translated into peptides of up to 15 amino acids-all of which are smaller than the smallest human microprotein annotated to date. To investigate the bioactivity of sORF-encoded small peptides and young microproteins, we subjected 266 candidates to a mass-spectrometry-based interactome screen with motif resolution. Based on these interactomes and additional cellular assays, we can associate several candidates with mRNA splicing, translational regulation, and endocytosis. Our work provides insights into the evolutionary origins and interaction potential of young and small proteins, thereby helping to elucidate this underexplored territory of the human proteome.

FungiProteomeDB: a database for the molecular weight and isoelectric points of the fungal proteomes

Proteins' molecular weight (MW) and isoelectric point (pI) are crucial for their subcellular localization and subsequent function. These are also useful in 2D gel electrophoresis, liquid chromatography-mass spectrometry and X-ray protein crystallography. Moreover, visualizations like a virtual 2D proteome map of pI vs. MW are worthwhile to discuss the proteome diversity among different species. Although the genome sequence data of the fungi kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the MW and pI of the fungi proteins and reported them in, FungiProteomeDB, an online database (DB) https://vision4research.com/fungidb/. We analyzed the proteome of 685 fungal species that contain 7 127 141 protein sequences. The DB provides an easy-to-use and efficient interface for various search options, summary statistics and virtual 2D proteome map visualizations. The MW and pI of a protein can be obtained by searching the name of a protein, a keyword or a list of accession numbers. It also allows querying protein sequences. The DB will be helpful in hypothesis formulation and in various biotechnological applications. Database URL https://vision4research.com/fungidb/.

Mapping the signaling network of BIN2 kinase using TurboID-mediated biotin labeling and phosphoproteomics

Elucidating enzyme-substrate relationships in posttranslational modification (PTM) networks is crucial for understanding signal transduction pathways but is technically difficult because enzyme-substrate interactions tend to be transient. Here, we demonstrate that TurboID-based proximity labeling (TbPL) effectively and specifically captures the substrates of kinases and phosphatases. TbPL-mass spectrometry (TbPL-MS) identified over 400 proximal proteins of Arabidopsis thaliana BRASSINOSTEROID-INSENSITIVE2 (BIN2), a member of the GLYCOGEN SYNTHASE KINASE 3 (GSK3) family that integrates signaling pathways controlling diverse developmental and acclimation processes. A large portion of the BIN2-proximal proteins showed BIN2-dependent phosphorylation in vivo or in vitro, suggesting that these are BIN2 substrates. Protein-protein interaction network analysis showed that the BIN2-proximal proteins include interactors of BIN2 substrates, revealing a high level of interactions among the BIN2-proximal proteins. Our proteomic analysis establishes the BIN2 signaling network and uncovers BIN2 functions in regulating key cellular processes such as transcription, RNA processing, translation initiation, vesicle trafficking, and cytoskeleton organization. We further discovered significant overlap between the GSK3 phosphorylome and the O-GlcNAcylome, suggesting an evolutionarily ancient relationship between GSK3 and the nutrient-sensing O-glycosylation pathway. Our work presents a powerful method for mapping PTM networks, a large dataset of GSK3 kinase substrates, and important insights into the signaling network that controls key cellular functions underlying plant growth and acclimation.

A large-scale proteogenomic atlas of pear

Pear is an important fruit tree that is widely distributed around the world. The first pear genome map was reported from our laboratory approximately 10 years ago. To further study global protein expression patterns in pear, we generated pear proteome data based on 24 major tissues. The tissue-resolved profiles provided evidence of the expression of 17 953 proteins. We identified 4294 new coding events and improved the pear genome annotation via the proteogenomic strategy based on 18 090 peptide spectra with peptide spectrum matches >1. Among the eight randomly selected new short coding open reading frames that were expressed in the style, four promoted and one inhibited the growth of pear pollen tubes. Based on gene coexpression module analysis, we explored the key genes associated with important agronomic traits, such as stone cell formation in fruits. The network regulating the synthesis of lignin, a major component of stone cells, was reconstructed, and receptor-like kinases were implicated as core factors in this regulatory network. Moreover, we constructed the online database PearEXP (http://www.peardb.org.cn) to enable access to the pear proteogenomic resources. This study provides a paradigm for in-depth proteogenomic studies of woody plants.

Integrated View of Baseline Protein Expression in Human Tissues

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

Proteomic analysis identifies dysregulated proteins and associated molecular pathways in a cohort of gallbladder cancer patients of African ancestry

BACKGROUND

Gallbladder cancer (GBC) is a lethal cancer with a poor prognosis. The lack of specific and sensitive biomarkers results in delayed diagnosis with most patients presenting at late stages of the disease. Furthermore, there is little known about the molecular mechanisms associated with GBC, especially in patients of African ancestry. This study aimed to determine dysregulated proteins in South African GBC patients to identify potential mechanisms of the disease progression and plausible biomarkers.

METHODS

Tissues (27 GBC, 13 Gallstone disease, and 5 normal tissues) and blood plasma (54 GBC and 73 Benign biliary pathology) were obtained from consenting patients. Protein extraction was performed on all tissues and liquid chromatography-mass spectrometry was used for proteomic profiling. A project-specific spectral library was built using the Pulsar search algorithm. Principal component and Spearman's rank correlation analyses were performed using PAST (V4.07b). Pathway and Network analyses were conducted using REACTOME (v3.7) and stringAPP (v1.7.0), respectively.

RESULTS

In the tissue sample group, there were 62 and 194 dysregulated proteins in GBC compared to normal and gallstone groups, respectively. In the plasma group, there were 33 altered proteins in GBC compared to the benign biliary pathology group. We found 9 proteins (APOA1, APOA2, RET4, TTR, HEMO, HBB, HBA, PIGR, and APOE) to be commonly dysregulated in both tissue and plasma. Furthermore, a subset analysis demonstrated that 2 proteins, S100A8 and S100A9, were downregulated in GBC patients with GD history compared to those without. Pathway analysis showed that the dysregulated proteins in GBC patients were enriched in pathways involved in smooth muscle contraction, metabolism, ECM organization, and integrin cell surface interactions.

CONCLUSION

The identified dysregulated proteins help in understanding GBC molecular mechanisms in our patient group. Furthermore, the alteration of specific proteins in both tissue and plasma samples suggests their potential utility as biomarkers of GBC in this sample cohort.

The Cyanobacterial "Nutraceutical" Phycocyanobilin Inhibits Cysteine Protease Legumain

The blue biliprotein phycocyanin, produced by photo-autotrophic cyanobacteria including spirulina (Arthrospira) and marketed as a natural food supplement or "nutraceutical," is reported to have anti-inflammatory, antioxidant, immunomodulatory, and anticancer activity. These diverse biological activities have been specifically attributed to the phycocyanin chromophore, phycocyanobilin (PCB). However, the mechanism of action of PCB and the molecular targets responsible for the beneficial properties of PCB are not well understood. We have developed a procedure to rapidly cleave the PCB pigment from phycocyanin by ethanolysis and then characterized it as an electrophilic natural product that interacts covalently with thiol nucleophiles but lacks any appreciable cytotoxicity or antibacterial activity against common pathogens and gut microbes. We then designed alkyne-bearing PCB probes for use in chemical proteomics target deconvolution studies. Target identification and validation revealed the cysteine protease legumain (also known as asparaginyl endopeptidase, AEP) to be a target of PCB. Inhibition of this target may account for PCB's diverse reported biological activities.

Serum proteomics profiling identifies a preliminary signature for the diagnosis of early-stage lung cancer

PURPOSE

Lung cancer is the most common cause of death from cancer worldwide, largely due to late diagnosis. Thus, there is an urgent need to develop new approaches to improve the detection of early-stage lung cancer, which would greatly improve patient survival.

EXPERIMENTAL DESIGN

The quantitative protein expression profiles of microvesicles isolated from the sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on a peptide library matching approach.

RESULTS

We identified 33 differentially expressed proteins that allow discriminating the two groups. We also built a machine learning model based on serum protein expression profiles that can correctly classify the majority of lung cancer cases and that highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors.

CONCLUSIONS AND CLINICAL RELEVANCE

Our study identified a preliminary, non-invasive protein signature able to discriminate with high specificity and selectivity early-stage lung cancer patients from high-risk healthy subjects. These results provide the basis for future validation studies for the development of a non-invasive diagnostic tool for lung cancer.

Achieving a Deeper Understanding of Drug Metabolism and Responses Using Single-Cell Technologies

Recent advancements in single-cell technologies have enabled detection of RNA, proteins, metabolites, and xenobiotics in individual cells, and the application of these technologies has the potential to transform pharmacological research. Single-cell data has already resulted in the development of human and model species cell atlases, identifying different cell types within a tissue, further facilitating the characterization of tumor heterogeneity, and providing insight into treatment resistance. Research discussed in this review demonstrates that distinct cell populations express drug metabolizing enzymes to different extents, indicating there may be variability in drug metabolism not only between organs, but within tissue types. Additionally, we put forth the concept that single-cell analyses can be used to expose underlying variability in cellular response to drugs, providing a unique examination of drug efficacy, toxicity, and metabolism. We will outline several of these techniques: single-cell RNA-sequencing and mass cytometry to characterize and distinguish different cell types, single-cell proteomics to quantify drug metabolizing enzymes and characterize cellular responses to drug, capillary electrophoresis-ultrasensitive laser-induced fluorescence detection and single-probe single-cell mass spectrometry for detection of drugs, and others. Emerging single-cell technologies such as these can comprehensively characterize heterogeneity in both cell-type-specific drug metabolism and response to treatment, enhancing progress toward personalized and precision medicine. SIGNIFICANCE STATEMENT: Recent technological advances have enabled the analysis of gene expression and protein levels in single cells. These types of analyses are important to investigating mechanisms that cannot be elucidated on a bulk level, primarily due to the variability of cell populations within biological systems. Here, we summarize cell-type-specific drug metabolism and how pharmacologists can utilize single-cell approaches to obtain a comprehensive understanding of drug metabolism and cellular heterogeneity in response to drugs.

Extracellular matrix drives tumor organoids toward desmoplastic matrix deposition and mesenchymal transition

Patient-derived tumor organoids have been established as promising tools for in vitro modelling of multiple tumors, including cholangiocarcinoma (CCA). However, organoids are commonly cultured in basement membrane extract (BME) which does not recapitulate the intricacies of the extracellular matrix (ECM). We combined CCA organoids (CCAOs) with native tumor and liver scaffolds, obtained by decellularization, to effectuate a model to study the interaction between epithelial tumor cells and their surrounding ECM. Decellularization resulted in removal of cells while preserving ECM structure and retaining important characteristics of the tissue origin, including stiffness and presence of desmoplasia. The transcriptome of CCAOs in a tumor scaffold much more resembled that of patient-paired CCA tissue in vivo compared to CCAOs cultured in BME or liver scaffolds. This was accompanied by an increase in chemoresistance to clinically-relevant chemotherapeutics. CCAOs in decellularized scaffolds revealed environment-dependent proliferation dynamics, driven by the occurrence of epithelial-mesenchymal transition. Furthermore, CCAOs initiated an environment-specific desmoplastic reaction by increasing production of multiple collagen types. In conclusion, convergence of organoid-based models with native ECM scaffolds will lead to better understanding of the in vivo tumor environment. STATEMENT OF SIGNIFICANCE: The extracellular matrix (ECM) influences various facets of tumor behavior. Understanding the exact role of the ECM in controlling tumor cell fate is pertinent to understand tumor progression and develop novel therapeutics. This is particularly the case for cholangiocarcinoma (CCA), whereby the ECM displays a distinct tumor environment, characterized by desmoplasia. However, current models to study the interaction between epithelial tumor cells and the environment are lacking. We have developed a fully patient-derived model encompassing CCA organoids (CCAOs) and human decellularized tumor and tumor-free liver ECM. The tumor ECM induced recapitulation of various aspects of CCA, including migration dynamics, transcriptome and proteome profiles, and chemoresistance. Lastly, we uncover that epithelial tumor cells contribute to matrix deposition, and that this phenomenon is dependent on the level of desmoplasia already present.

A Database of Accurate Electrophoretic Migration Patterns for Human Proteins

Native molecular weight (MW) is one of the defining features of proteins. Denaturing gel electrophoresis (SDS-PAGE) is a very popular technique for separating proteins and determining their MW. Coupled with antibody-based detection, SDS-PAGE is widely applied for protein identification and quantitation. Yet, electrophoresis is poorly reproducible and the MWs obtained are often inaccurate. This hampers antibody validation and negatively impacts the reliability of western blot data, resulting worldwide in a considerable waste of reagents and labour. We argue that, to alleviate these problems there is a need to establish a database of reference MWs measured by SDS-PAGE. Using mass spectrometry as an orthogonal detection method, we acquired electrophoretic migration patterns for approximately 10'000 human proteins in five commonly used cell lines. We applied a robust internal calibration of migration to determine accurate and reproducible molecular weights. This in turn allows merging replicates to increase accuracy, but also enables comparing different cell lines. Mining of the data obtained highlights structural factors that affect migration of distinct classes of proteins. When combined with peptide coverage, the data produced recapitulates known post-translational modifications and differential splicing and can be used to formulate hypotheses on new or poorly known processing events. The full information is freely accessible as a web resource through a user friendly graphical interface (https://pumba.dcsr.unil.ch/). We anticipate that this database will be useful to investigators worldwide for troubleshooting western blot experiments, but could also contribute to the characterization of human proteoforms.

CP204L Is a Multifunctional Protein of African Swine Fever Virus That Interacts with the VPS39 Subunit of the Homotypic Fusion and Vacuole Protein Sorting Complex and Promotes Lysosome Clustering

Virus replication depends on a complex interplay between viral and host proteins. In the case of African swine fever virus (ASFV), a large DNA virus, only a few virus-host protein-protein interactions have been identified to date. In this study, we demonstrate that the ASFV protein CP204L interacts with the cellular homotypic fusion and protein sorting (HOPS) protein VPS39, blocking its association with the lysosomal HOPS complex, which modulates endolysosomal trafficking and promotes lysosome clustering. Instead, CP204L and VPS39 are targeted to virus factories and localized at the periphery of the virus DNA replication sites. Furthermore, we show that loss of VPS39 reduces the levels of virus proteins synthesized in the early phase of infection and delays ASFV replication but does not completely inhibit it. Collectively, these results identify a novel virus-host protein interaction that modulates host membrane rearrangement during infection and provide evidence that CP204L is a multifunctional protein engaged in distinct steps of the ASFV life cycle. IMPORTANCE African swine fever virus (ASFV) was first identified over a hundred years ago. Since then, much effort has been made to understand the pathogenesis of ASFV. However, the specific roles of many individual ASFV proteins during the infection remain enigmatic. This study provides evidence that CP204L, one of the most abundant ASFV proteins, modulates endosomal trafficking during virus infection. Through protein-protein interaction, CP204L prevents the recruitment of VPS39 to the endosomal and lysosomal membranes, resulting in their accumulation. Consequently, CP204L and VPS39 become sequestered in the ASFV replication and assembly site, known as the virus factory. These results uncover a novel function of viral protein CP204L and extend our understanding of complex interaction between virus and host.

Harnessing transcriptionally driven chromosomal instability adaptation to target therapy-refractory lethal prostate cancer

Metastatic prostate cancer (PCa) inevitably acquires resistance to standard therapy preceding lethality. Here, we unveil a chromosomal instability (CIN) tolerance mechanism as a therapeutic vulnerability of therapy-refractory lethal PCa. Through genomic and transcriptomic analysis of patient datasets, we find that castration and chemotherapy-resistant tumors display the highest CIN and mitotic kinase levels. Functional genomics screening coupled with quantitative phosphoproteomics identify MASTL kinase as a survival vulnerability specific of chemotherapy-resistant PCa cells. Mechanistically, MASTL upregulation is driven by transcriptional rewiring mechanisms involving the non-canonical transcription factors androgen receptor splice variant 7 and E2F7 in a circuitry that restrains deleterious CIN and prevents cell death selectively in metastatic therapy-resistant PCa cells. Notably, MASTL pharmacological inhibition re-sensitizes tumors to standard therapy and improves survival of pre-clinical models. These results uncover a targetable mechanism promoting high CIN adaptation and survival of lethal PCa.

Profiling the Heme-Binding Proteomes of Bacteria Using Chemical Proteomics

Heme is a cofactor with myriad roles and essential to almost all living organisms. Beyond classical gas transport and catalytic functions, heme is increasingly appreciated as a tightly controlled signalling molecule regulating protein expression. However, heme acquisition, biosynthesis and regulation is poorly understood beyond a few model organisms, and the heme-binding proteome has not been fully characterised in bacteria. Yet as heme homeostasis is critical for bacterial survival, heme-binding proteins are promising drug targets. Herein we report a chemical proteomics method for global profiling of heme-binding proteins in live cells for the first time. Employing a panel of heme-based clickable and photoaffinity probes enabled the profiling of 32-54 % of the known heme-binding proteomes in Gram-positive and Gram-negative bacteria. This simple-to-implement profiling strategy could be interchangeably applied to different cell types and systems and fuel future research into heme biology.

Comparative membrane proteomics reveals diverse cell regulators concentrated at the nuclear envelope

The nuclear envelope (NE) is a subdomain of the ER with prominent roles in nuclear organization, largely mediated by its distinctive protein composition. We developed methods to reveal novel, low abundance transmembrane (TM) proteins concentrated at the NE relative to the peripheral ER. Using label-free proteomics that compared isolated NEs to cytoplasmic membranes, we first identified proteins with apparent NE enrichment. In subsequent authentication, ectopically expressed candidates were analyzed by immunofluorescence microscopy to quantify their targeting to the NE in cultured cells. Ten proteins from a validation set were found to associate preferentially with the NE, including oxidoreductases, enzymes for lipid biosynthesis and regulators of cell growth and survival. We determined that one of the validated candidates, the palmitoyltransferase Zdhhc6, modifies the NE oxidoreductase Tmx4 and thereby modulates its NE levels. This provides a functional rationale for the NE concentration of Zdhhc6. Overall, our methodology has revealed a group of previously unrecognized proteins concentrated at the NE and additional candidates. Future analysis of these can potentially unveil new mechanistic pathways associated with the NE.

Blocking muscle wasting via deletion of the muscle-specific E3 ubiquitin ligase MuRF1 impedes pancreatic tumor growth

Cancer-induced muscle wasting reduces quality of life, complicates or precludes cancer treatments, and predicts early mortality. Herein, we investigated the requirement of the muscle-specific E3 ubiquitin ligase, MuRF1, for muscle wasting induced by pancreatic cancer. Murine pancreatic cancer (KPC) cells, or saline, were injected into the pancreas of WT and MuRF1-/- mice, and tissues analyzed throughout tumor progression. KPC tumors induced progressive wasting of skeletal muscle and systemic metabolic reprogramming in WT mice, but not MuRF1-/- mice. KPC tumors from MuRF1-/- mice also grew slower, and showed an accumulation of metabolites normally depleted by rapidly growing tumors. Mechanistically, MuRF1 was necessary for the KPC-induced increases in cytoskeletal and muscle contractile protein ubiquitination, and the depression of proteins that support protein synthesis. Together, these data demonstrate that MuRF1 is required for KPC-induced skeletal muscle wasting, whose deletion reprograms the systemic and tumor metabolome and delays tumor growth.

Ultra-sensitive isotope probing to quantify activity and substrate assimilation in microbiomes

BACKGROUND

Stable isotope probing (SIP) approaches are a critical tool in microbiome research to determine associations between species and substrates, as well as the activity of species. The application of these approaches ranges from studying microbial communities important for global biogeochemical cycling to host-microbiota interactions in the intestinal tract. Current SIP approaches, such as DNA-SIP or nanoSIMS allow to analyze incorporation of stable isotopes with high coverage of taxa in a community and at the single cell level, respectively, however they are limited in terms of sensitivity, resolution or throughput.

RESULTS

Here, we present an ultra-sensitive, high-throughput protein-based stable isotope probing approach (Protein-SIP), which cuts cost for labeled substrates by 50-99% as compared to other SIP and Protein-SIP approaches and thus enables isotope labeling experiments on much larger scales and with higher replication. The approach allows for the determination of isotope incorporation into microbiome members with species level resolution using standard metaproteomics liquid chromatography-tandem mass spectrometry (LC-MS/MS) measurements. At the core of the approach are new algorithms to analyze the data, which have been implemented in an open-source software ( https://sourceforge.net/projects/calis-p/ ). We demonstrate sensitivity, precision and accuracy using bacterial cultures and mock communities with different labeling schemes. Furthermore, we benchmark our approach against two existing Protein-SIP approaches and show that in the low labeling range used our approach is the most sensitive and accurate. Finally, we measure translational activity using 18O heavy water labeling in a 63-species community derived from human fecal samples grown on media simulating two different diets. Activity could be quantified on average for 27 species per sample, with 9 species showing significantly higher activity on a high protein diet, as compared to a high fiber diet. Surprisingly, among the species with increased activity on high protein were several Bacteroides species known as fiber consumers. Apparently, protein supply is a critical consideration when assessing growth of intestinal microbes on fiber, including fiber-based prebiotics.

CONCLUSIONS

We demonstrate that our Protein-SIP approach allows for the ultra-sensitive (0.01 to 10% label) detection of stable isotopes of elements found in proteins, using standard metaproteomics data.

A subset of antibodies targeting citrullinated proteins confers protection from rheumatoid arthritis

Although elevated levels of anti-citrullinated protein antibodies (ACPAs) are a hallmark of rheumatoid arthritis (RA), the in vivo functions of these antibodies remain unclear. Here, we have expressed monoclonal ACPAs derived from patients with RA, and analyzed their functions in mice, as well as their specificities. None of the ACPAs showed arthritogenicity nor induced pain-associated behavior in mice. However, one of the antibodies, clone E4, protected mice from antibody-induced arthritis. E4 showed a binding pattern restricted to skin, macrophages and dendritic cells in lymphoid tissue, and cartilage derived from mouse and human arthritic joints. Proteomic analysis confirmed that E4 strongly binds to macrophages and certain RA synovial fluid proteins such as α-enolase. The protective effect of E4 was epitope-specific and dependent on the interaction between E4-citrullinated α-enolase immune complexes with FCGR2B on macrophages, resulting in increased IL-10 secretion and reduced osteoclastogenesis. These findings suggest that a subset of ACPAs have therapeutic potential in RA.

Senescent cells suppress macrophage-mediated corpse removal via upregulation of the CD47-QPCT/L axis

Progressive accrual of senescent cells in aging and chronic diseases is associated with detrimental effects in tissue homeostasis. We found that senescent fibroblasts and epithelia were not only refractory to macrophage-mediated engulfment and removal, but they also paralyzed the ability of macrophages to remove bystander apoptotic corpses. Senescent cell-mediated efferocytosis suppression (SCES) was independent of the senescence-associated secretory phenotype (SASP) but instead required direct contact between macrophages and senescent cells. SCES involved augmented senescent cell expression of CD47 coinciding with increased CD47-modifying enzymes QPCT/L. SCES was reversible by interfering with the SIRPα-CD47-SHP-1 axis or QPCT/L activity. While CD47 expression increased in human and mouse senescent cells in vitro and in vivo, another ITIM-containing protein, CD24, contributed to SCES specifically in human epithelial senescent cells where it compensated for genetic deficiency in CD47. Thus, CD47 and CD24 link the pathogenic effects of senescent cells to homeostatic macrophage functions, such as efferocytosis, which we hypothesize must occur efficiently to maintain tissue homeostasis.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.

Implementing the MSFragger Search Engine as a Node in Proteome Discoverer

Here, we describe the implementation of the fast proteomics search engine MSFragger as a processing node in the widely used Proteome Discoverer (PD) software platform. PeptideProphet (via the Philosopher tool kit) is also implemented as an additional PD node to allow validation of MSFragger open (mass-tolerant) search results. These two nodes, along with the existing Percolator validation module, allow users to employ different search strategies and conveniently inspect search results through PD. Our results have demonstrated the improved numbers of PSMs, peptides, and proteins identified by MSFragger coupled with Percolator and significantly faster search speed compared to the conventional SEQUEST/Percolator PD workflows. The MSFragger-PD node is available at https://github.com/nesvilab/PD-Nodes/releases/.

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data

Determining the correct localization of post-translational modifications (PTMs) on peptides aids in interpreting their effect on protein function. While most algorithms for this task are available as standalone applications or incorporated into software suites, improving their versatility through access from popular scripting languages facilitates experimentation and incorporation into novel workflows. Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. Access to internal objects for scoring and working with modified peptides adds to the toolbox for working with PTMs in Python. pyAscore is available as an open source package for Python 3.6+ on all major operating systems and can be found at pyascore.readthedocs.io.

Optimized extraction and large-scale proteomics of pig jejunum brush border membranes for use in in vitro digestion models

Despite the physiological importance of the hydrolases from the intestinal brush border membrane (BBM), a step simulating the intestinal digestion has not been included yet in the harmonized protocols of in vitro digestion, due to commercial unavailability of these enzymes and lack of consensus for the conditions of use. The proper utilize of BBM requires a detailed investigation of their enzymatic composition. BBM vesicles were purified from specimens of pig jejunum optimizing previously described methods and assayed for aminopeptidase N and dipeptidyl peptidase IV activity. Large-scale proteomics was carried out with a bottom-up shotgun approach, also performing a rough quantification with the iBAQ (intensity Based Absolute Quantification). Overall, 1428 proteins were identified and functionally classified by gene ontology enrichment analysis. The predominant enzyme fraction (220 gene products) was represented by hydrolases, including peptidases, glycosidases, and lipases. Aminopeptidase N and sucrase-isomaltase represented 52.9 % and 50.2 % of the peptidase and glycosidase abundance, respectively. In addition to expected transporters and cytoskeletal actin-binding proteins, purified BBM vesicles also contains a complex array of protease inhibitors, here described for the first time, that may modulate the activity of hydrolases. Considering the similarity with the human counterpart, intestinal porcine BBM are suited for simulating the human small intestinal digestion.

A truncated and catalytically inactive isoform of KDM5B histone demethylase accumulates in breast cancer cells and regulates H3K4 tri-methylation and gene expression

KDM5B histone demethylase is overexpressed in many cancers and plays an ambivalent role in oncogenesis, depending on the specific context. This ambivalence could be explained by the expression of KDM5B protein isoforms with diverse functional roles, which could be present at different levels in various cancer cell lines. We show here that one of these isoforms, namely KDM5B-NTT, accumulates in breast cancer cell lines due to remarkable protein stability relative to the canonical PLU-1 isoform, which shows a much faster turnover. This isoform is the truncated and catalytically inactive product of an mRNA with a transcription start site downstream of the PLU-1 isoform, and the consequent usage of an alternative ATG for translation initiation. It also differs from the PLU-1 transcript in the inclusion of an additional exon (exon-6), previously attributed to other putative isoforms. Overexpression of this isoform in MCF7 cells leads to an increase in bulk H3K4 methylation and induces derepression of a gene cluster, including the tumor suppressor Cav1 and several genes involved in the interferon-alpha and -gamma response. We discuss the relevance of this finding considering the hypothesis that KDM5B may possess regulatory roles independent of its catalytic activity.

EstG is a novel esterase required for cell envelope integrity in Caulobacter

Proper regulation of the bacterial cell envelope is critical for cell survival. Identification and characterization of enzymes that maintain cell envelope homeostasis is crucial, as they can be targets for effective antibiotics. In this study, we have identified a novel enzyme, called EstG, whose activity protects cells from a variety of lethal assaults in the ⍺-proteobacterium Caulobacter crescentus. Despite homology to transpeptidase family cell wall enzymes and an ability to protect against cell-wall-targeting antibiotics, EstG does not demonstrate biochemical activity toward cell wall substrates. Instead, EstG is genetically connected to the periplasmic enzymes OpgH and BglX, responsible for synthesis and hydrolysis of osmoregulated periplasmic glucans (OPGs), respectively. The crystal structure of EstG revealed similarities to esterases and transesterases, and we demonstrated esterase activity of EstG in vitro. Using biochemical fractionation, we identified a cyclic hexamer of glucose as a likely substrate of EstG. This molecule is the first OPG described in Caulobacter and establishes a novel class of OPGs, the regulation and modification of which are important for stress survival and adaptation to fluctuating environments. Our data indicate that EstG, BglX, and OpgH comprise a previously unknown OPG pathway in Caulobacter. Ultimately, we propose that EstG is a novel enzyme that instead of acting on the cell wall, acts on cyclic OPGs to provide resistance to a variety of cellular stresses.

Revealing the small proteome of Haloferax volcanii by combining ribosome profiling and small-protein optimized mass spectrometry

In contrast to extensively studied prokaryotic 'small' transcriptomes (encompassing all small noncoding RNAs), small proteomes (here defined as including proteins ≤70 aa) are only now entering the limelight. The absence of a complete small protein catalogue in most prokaryotes precludes our understanding of how these molecules affect physiology. So far, archaeal genomes have not yet been analyzed broadly with a dedicated focus on small proteins. Here, we present a combinatorial approach, integrating experimental data from small protein-optimized mass spectrometry (MS) and ribosome profiling (Ribo-seq), to generate a high confidence inventory of small proteins in the model archaeon Haloferax volcanii. We demonstrate by MS and Ribo-seq that 67% of the 317 annotated small open reading frames (sORFs) are translated under standard growth conditions. Furthermore, annotation-independent analysis of Ribo-seq data showed ribosomal engagement for 47 novel sORFs in intergenic regions. A total of seven of these were also detected by proteomics, in addition to an eighth novel small protein solely identified by MS. We also provide independent experimental evidence in vivo for the translation of 12 sORFs (annotated and novel) using epitope tagging and western blotting, underlining the validity of our identification scheme. Several novel sORFs are conserved in Haloferax species and might have important functions. Based on our findings, we conclude that the small proteome of H. volcanii is larger than previously appreciated, and that combining MS with Ribo-seq is a powerful approach for the discovery of novel small protein coding genes in archaea.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Whole lung proteome of an acute epithelial injury mouse model in comparison to spatially resolved proteomes

Epithelial injury is one of the major drivers of acute pulmonary diseases. Recurring injury followed by aberrant repair is considered as the primary cause of chronic lung diseases, such as idiopathic pulmonary fibrosis (IPF). Preclinical in vivo models allow studying early disease-driving mechanisms like the recently established adeno-associated virus-diphtheria toxin receptor (AAV-DTR) mouse model of acute epithelial lung injury, which utilises AAV mediated expression of the human DTR. We performed quantitative proteomics of homogenised lung samples from this model and compared the results to spatially resolved proteomics data of epithelial cell regions from the same animals. In whole lung tissue proteins involved in cGAS-STING and interferon pathways, proliferation, DNA replication and the composition of the provisional extracellular matrix were upregulated upon injury. Besides epithelial cell markers SP-A, SP-C and Scgb1a1, proteins involved in cilium assembly, lipid metabolism and redox pathways were among downregulated proteins. Comparison of the bulk to spatially resolved proteomics data revealed a large overlap of protein changes and striking differences. Together our study underpins the broad usability of bulk proteomics and pinpoints to the benefit of sophisticated proteomic analyses of specific tissue regions or single cell types.

Multi-omics analysis of magnetically levitated plasma biomolecules

We recently discovered that superparamagnetic iron oxide nanoparticles (SPIONs) can levitate plasma biomolecules in the magnetic levitation (MagLev) system and cause formation of ellipsoidal biomolecular bands. To better understand the composition of the levitated biomolecules in various bands, we comprehensively characterized them by multi-omics analyses. To probe whether the biomolecular composition of the levitated ellipsoidal bands correlates with the health of plasma donors, we used plasma from individuals who had various types of multiple sclerosis (MS), as a model disease with significant clinical importance. Our findings reveal that, while the composition of proteins does not show much variability, there are significant differences in the lipidome and metabolome profiles of each magnetically levitated ellipsoidal band. By comparing the lipidome and metabolome compositions of various plasma samples, we found that the levitated biomolecular ellipsoidal bands do contain information on the health status of the plasma donors. More specifically, we demonstrate that there are particular lipids and metabolites in various layers of each specific plasma pattern that significantly contribute to the discrimination of different MS subtypes, i.e., relapsing-remitting MS (RRMS), secondary-progressive MS (SPMS), and primary-progressive MS (PPMS). These findings will pave the way for utilization of MagLev of biomolecules in biomarker discovery for identification of diseases and discrimination of their subtypes.

Pitfalls and Solutions in Mass Spectrometry-Based Identification of Protein Glycation

Emerging evidence suggests that advanced glycation end-products (AGEs) such as Nε-(carboxymethyl)lysine (CML) and Nε-(carboxymethyl)lysine (CEL) may play important roles in certain human diseases. Reliable analytical methods are needed for their characterizations and measurements. Pitfalls have been reported for applications of LC-MS/MS to identify various types of post-translational modifications, but not yet for the case of AGEs. Here, we showed that in the absence of manual inspection, cysteine alkylation with 2-iodoacetamide (IAA) can result in false-positive/ambiguous identifications of CML >20%. They were attributed to offsite alkylation together with incorrect monoisotopic peak assignment (pitfall 1) or together with deamidation (pitfall 2). For pitfall 1, false-positive identifications can be alleviated using a peptide mass error tolerance ≤5 ppm during the database search. Pitfall 2 results in ambiguous modification assignments, which may be overcome by using other alkylation reagents. According to calculations of theoretical mass shifts, the use of other common alkylation reagents (iodoacetic acid, 2-chloroacetamide, and acrylamide) should face similar pitfalls. The use of acrylamide can result in false-positive identifications of CEL instead of CML. Subsequently, we showed that compared to IAA, the use of N-isopropylacrylamide (NIPAM) as an alkylation reagent achieved similar levels of proteome coverage, while reducing the offsite alkylation reactions at lysine by more than five times. Furthermore, false-positive/ambiguous identifications of CML due to the two types of pitfalls were absent when using NIPAM. NIPAM alkylation results in a unique mass shift that allows reliable identifications of CML and most likely other AGEs, such as CEL.

Retinal Proteome Analysis Reveals a Region-Specific Change in the Rabbit Myopia Model

Uncovering region-specific changes in the myopic retina can provide clues to the pathogenesis of myopia progression. After imposing form deprivation myopia in the right eye of 6-week-old rabbits, we investigated the proteome profile of each retinal region (central, mid-periphery, and far-periphery retina), using accurate high-resolution mass spectrometry. Protein expression was analyzed using gene ontology and network analysis compared with that of the control, the left eyes. Among 2065 proteins detected from whole retinal samples, 249 differentially expressed proteins (DEPs) were identified: 164 DEPs in the far-periphery, 39 in the mid-periphery, and 83 in the central retina. In network analysis, the far-periphery retina showed the most significant connectivity between DEPs. The regulation of coagulation was the most significant biological process in upregulated DEPs in the far-periphery retina. Proteasome was the most significant Kyoto Encyclopedia of Genes and Genomes pathway in downregulated DEPs in the central retina. Antithrombin-III, fibrinogen gamma chain, and fibrinogen beta chain were identified as hub proteins for myopia progression, which were upregulated in the far-periphery retina. Proteomic analysis in this study suggested that oxidative stress can be the primary pathogenesis of myopia progression and that the far-periphery retina plays a role as the key responder.

Exhaustive Cross-Linking Search with Protein Feedback

Improving the sensitivity of protein-protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/β mass detection scheme and establishes protein-peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links.