ProteomeXchange provides globally coordinated proteomics data submission and dissemination

Evaluating Proteomics Imputation Methods with Improved Criteria

Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

Treatment of human cardiac fibroblasts with the protein arginine deiminase inhibitor BB-Cl-amidine activates the Nrf2/HO-1 signaling pathway

BACKGROUND

Cardiac fibrosis contributes to end-stage extracellular matrix remodeling and heart failure (HF). Cardiac fibroblasts (CFs) differentiate into myofibroblasts (myoFbs) to preserve the structural integrity of the heart; however, the molecular mechanisms regulating CF transdifferentiation remain poorly understood. Protein arginine deiminase (PAD), which converts arginine to citrulline, has been shown to play a role in myocardial infarction, fibrosis, and HF. This study aimed to investigate the role of PAD in CF differentiation to myoFbs and identify the citrullinated proteins that were associated with phenotypic changes in CFs.

RESULTS

Gene expression analysis showed that PAD1 and PAD2 isoforms, but not PAD4 isoforms, were abundant in both CFs and myoFbs, and PAD1 was significantly upregulated in myoFbs. The pan-PAD inhibitor BB-Cl-amidine (BB-Cl) downregulated the mRNA expression of PAD1 and PAD2 as well as the protein expression of the fibrosis marker COL1A1 in CFs and myoFbs. Interestingly, a proteomic approach pointed to the activation of the Nrf2/HO-1 signaling pathway upon BB-Cl treatment in CFs and myoFbs. BB-Cl administration resulted in the upregulation of HO-1 at both the gene and protein levels in CFs and myoFbs. Importantly, the protein citrullination landscape of CFs consisting of 86 novel citrullination sites associated with focal adhesion (FN1(R1054)), inflammation (TAGLN(R12)) and DNA replication (EEF2(R767)) pathways was identified.

CONCLUSIONS

In summary, we revealed that BB-Cl treatment resulted in increased HO-1 expression via the Nrf2 pathway, which could prevent excessive tissue damage, thereby leading to substantial clinical benefits for the treatment of cardiac fibrosis.

Phosphorylation of CFP10 modulates Mycobacterium tuberculosis virulence

IMPORTANCE

Secreted virulence factors play a critical role in bacterial pathogenesis. Virulence effectors not only help bacteria to overcome the host immune system but also aid in establishing infection. Mtb, which causes tuberculosis in humans, encodes various virulence effectors. Triggers that modulate the secretion of virulence effectors in Mtb are yet to be fully understood. To gain mechanistic insight into the secretion of virulence effectors, we performed high-throughput proteomic studies. With the help of system-level protein-protein interaction network analysis and empirical validations, we unravelled a link between phosphorylation and secretion. Taking the example of the well-known virulence factor of CFP10, we show that the dynamics of CFP10 phosphorylation strongly influenced bacterial virulence and survival ex vivo and in vivo. This study presents the role of phosphorylation in modulating the secretion of virulence factors.

New insights in the targets of action of dimethyl fumarate in endothelial cells: effects on energetic metabolism and serine synthesis in vitro and in vivo

Dimethyl fumarate is an ester from the Krebs cycle intermediate fumarate. This drug is approved and currently used for the treatment of psoriasis and multiple sclerosis, and its anti-angiogenic activity was reported some years ago. Due to the current clinical relevance of this compound and the recently manifested importance of endothelial cell metabolism on the angiogenic switch, we wanted to elucidate whether dimethyl fumarate has an effect on energetic metabolism of endothelial cells. Different experimental approximations were performed in endothelial cells, including proteomics, isotope tracing and metabolomics experimental approaches, in this work we studied the possible role of dimethyl fumarate in endothelial cell energetic metabolism. We demonstrate for the first time that dimethyl fumarate promotes glycolysis and diminishes cell respiration in endothelial cells, which could be a consequence of a down-regulation of serine and glycine synthesis through inhibition of PHGDH activity in these cells. Dimethyl fumarate alters the energetic metabolism of endothelial cells in vitro and in vivo through an unknown mechanism, which could be the cause or the consequence of its pharmacological activity. This new discovery on the targets of this compound could open a new field of study regarding the mechanism of action of dimethyl fumarate.

A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).

Recent advancement in targeted therapy and role of emerging technologies to treat cancer

Cancer is a complex disease that causes abnormal cell growth and spread. DNA mutations, chemical or environmental exposure, viral infections, chronic inflammation, hormone abnormalities, etc., are underlying factors that can cause cancer. Drug resistance and toxicity complicate cancer treatment. Additionally, the variability of cancer makes it difficult to establish universal treatment guidelines. Next-generation sequencing has made genetic testing inexpensive. This uncovers genetic mutations that can be treated with specialty drugs. AI (artificial intelligence), machine learning, biopsy, next-generation sequencing, and digital pathology provide personalized cancer treatment. This allows for patient-specific biological targets and cancer treatment. Monoclonal antibodies, CAR-T, and cancer vaccines are promising cancer treatments. Recent trial data incorporating these therapies have shown superiority in clinical outcomes and drug tolerability over conventional chemotherapies. Combinations of these therapies with new technology can change cancer treatment and help many. This review discusses the development and challenges of targeted therapies like monoclonal antibodies (mAbs), bispecific antibodies (BsAbs), bispecific T cell engagers (BiTEs), dual variable domain (DVD) antibodies, CAR-T therapy, cancer vaccines, oncolytic viruses, lipid nanoparticle-based mRNA cancer vaccines, and their clinical outcomes in various cancers. We will also study how artificial intelligence and machine learning help find new cancer treatment targets.

PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts

The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Fallopian tube lesions as potential precursors of early ovarian cancer: a comprehensive proteomic analysis

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of ovarian cancer. While the origin of ovarian tumors is still debated, it has been suggested that HGSC originates from cells in the fallopian tube epithelium (FTE), specifically the epithelial cells in the region of the tubal-peritoneal junction. Three main lesions, p53 signatures, STILs, and STICs, have been defined based on the immunohistochemistry (IHC) pattern of p53 and Ki67 markers and the architectural alterations of the cells, using the Sectioning and Extensively Examining the Fimbriated End Protocol. In this study, we performed an in-depth proteomic analysis of these pre-neoplastic epithelial lesions guided by mass spectrometry imaging and IHC. We evaluated specific markers related to each preneoplastic lesion. The study identified specific lesion markers, such as CAVIN1, Emilin2, and FBLN5. We also used SpiderMass technology to perform a lipidomic analysis and identified the specific presence of specific lipids signature including dietary Fatty acids precursors in lesions. Our study provides new insights into the molecular mechanisms underlying the progression of ovarian cancer and confirms the fimbria origin of HGSC.

4R-Tau seeding activity unravels molecular subtypes in patients with Progressive Supranuclear Palsy

Progressive Supranuclear palsy (PSP) is a 4-repeat (4-R) tauopathy. We hypothesized that the molecular diversity of tau could explain the heterogeneity seen in PSP disease progression. To test this hypothesis, we performed an extensive biochemical characterisation of the high molecular weight tau species (HMW-Tau) in 20 different brain regions of 25 PSP patients. We found a correlation between the HMW-Tau species and tau seeding capacity in the primary motor cortex, where we confirmed that an elevated 4R-Tau seeding activity correlates with a shorter disease duration. To identify factors that contribute to these differences, we performed proteomic and spatial transcriptomic analysis that revealed key mechanistic pathways, in particular those involving the immune system, that defined patients demonstrating high and low tau seeding capacity. These observations suggest that differences in the tau seeding activity may contribute to the considerable heterogeneity seen in disease progression of patients suffering from PSP.

A phospho-dawn of protein modification anticipates light onset in the picoeukaryote Ostreococcus tauri

Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. Here, we compare transcriptome data under light-dark cycles with partial proteome and phosphoproteome data, assayed using shotgun MS, from the alga Ostreococcus tauri, the smallest free-living eukaryote. A total of 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Mathematical modelling showed that light-stimulated protein synthesis can account for the observed clustering of protein peaks in the daytime. Prompted by night-peaking and apparently dark-stable proteins, we also tested cultures under prolonged darkness, where the proteome changed less than under the diel cycle. Among the dark-stable proteins were prasinophyte-specific sequences that were also reported to accumulate when O. tauri formed lipid droplets. In the phosphoproteome, 39% of rhythmic phospho-sites reached peak levels just before dawn. This anticipatory phosphorylation suggests that a clock-regulated phospho-dawn prepares green cells for daytime functions. Acid-directed and proline-directed protein phosphorylation sites were regulated in antiphase, implicating the clock-related casein kinases 1 and 2 in phase-specific regulation, alternating with the CMGC protein kinase family. Understanding the dynamic phosphoprotein network should be facilitated by the minimal kinome and proteome of O. tauri. The data are available from ProteomeXchange, with identifiers PXD001734, PXD001735, and PXD002909.

Massive Solubility Changes in Neuronal Proteins upon Simulated Traumatic Brain Injury Reveal the Role of Shockwaves in Irreversible Damage

We investigated the immediate molecular consequences of traumatic brain injuries (TBIs) using a novel proteomics approach. We simulated TBIs using an innovative laboratory apparatus that employed a 5.1 kg dummy head that held neuronal cells and generated a ≤4000 g-force acceleration upon impact. A Proteome Integral Solubility Alteration (PISA) assay was then employed to monitor protein solubility changes in a system-wide manner. Dynamic impacts led to both a reduction in neuron viability and massive solubility changes in the proteome. The affected proteins mapped not only to the expected pathways, such as those of cell adhesion, collagen, and laminin structures, as well as the response to stress, but also to other dense protein networks, such as immune response, complement, and coagulation cascades. The cellular effects were found to be mainly due to the shockwave rather than the g-force acceleration. Soft materials could reduce the impact's severity only until they were fully compressed. This study shows a way of developing a proteome-based meter for measuring irreversible shockwave-induced cell damage and provides a resource for identifying protein biomarkers of TBIs and potential drug targets for the development of products aimed at primary prevention and intervention.

Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification

Mass spectrometry coupled to liquid chromatography is one of the most powerful technologies for proteome quantification in biomedical samples. In peptide-centric workflows, protein mixtures are enzymatically digested to peptides prior their analysis. However, proteome-wide quantification studies rarely identify all potential peptides for any given protein, and targeted proteomics experiments focus on a set of peptides for the proteins of interest. Consequently, proteomics relies on the use of a limited subset of all possible peptides as proxies for protein quantitation. In this work, we evaluated the stability of the human proteotypic peptides during 21 days and trained a deep learning model to predict peptide stability directly from tryptic sequences, which together constitute a resource of broad interest to prioritize and select peptides in proteome quantification experiments.

Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy

BACKGROUND

Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value.

METHODS

Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP.

RESULTS

A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors.

CONCLUSIONS

Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.

iSMOD: an integrative browser for image-based single-cell multi-omics data

Genomic and transcriptomic image data, represented by DNA and RNA fluorescence in situ hybridization (FISH), respectively, together with proteomic data, particularly that related to nuclear proteins, can help elucidate gene regulation in relation to the spatial positions of chromatins, messenger RNAs, and key proteins. However, methods for image-based multi-omics data collection and analysis are lacking. To this end, we aimed to develop the first integrative browser called iSMOD (image-based Single-cell Multi-omics Database) to collect and browse comprehensive FISH and nucleus proteomics data based on the title, abstract, and related experimental figures, which integrates multi-omics studies focusing on the key players in the cell nucleus from 20 000+ (still growing) published papers. We have also provided several exemplar demonstrations to show iSMOD's wide applications-profiling multi-omics research to reveal the molecular target for diseases; exploring the working mechanism behind biological phenomena using multi-omics interactions, and integrating the 3D multi-omics data in a virtual cell nucleus. iSMOD is a cornerstone for delineating a global view of relevant research to enable the integration of scattered data and thus provides new insights regarding the missing components of molecular pathway mechanisms and facilitates improved and efficient scientific research.

Proteomic and functional characterisation of extracellular vesicles from collagen VI deficient human fibroblasts reveals a role in cell motility

Extracellular vesicles (EVs) are key mediators of cell-to-cell communication. Their content reflects the state of diseased cells representing a window into disease progression. Collagen-VI Related Muscular Dystrophy (COL6-RD) is a multi-systemic disease involving different cell types. The role of EVs in this disease has not been explored. We compared by quantitative proteomics the protein cargo of EVs released from fibroblasts from patients with COL6-RD and controls. Isolated EVs contained a significant proportion of the most frequently reported proteins in EVs according to Exocarta and Vesiclepedia. We identified 67 differentially abundant proteins associated with vesicle transport and exocytosis, actin remodelling and the cytoskeleton, hemostasis and oxidative stress. Treatment of control fibroblasts with EVs from either patient or healthy fibroblasts altered significantly the motility of cells on a cell migration assay highlighting the functional relevance of EVs. In parallel, we analysed the secretome from the same cells and found a distinctly different set of 48 differentially abundant proteins related to extracellular matrix organisation and remodelling, growth factor response, RNA metabolism and the proteasome. The EVs and secretome sets of proteins only shared two identifiers indicating that the sorting of proteins towards EVs or the secretory pathway is tightly regulated for different functions.

Salivary Chemical Barrier Proteins in Oral Squamous Cell Carcinoma-Alterations in the Defense Mechanism of the Oral Cavity

Oral squamous cell carcinoma (OSCC) is one of the most frequent types of head and neck cancer. Despite the genetic and environmental risk factors, OSCC is also associated with microbial infections and/or dysbiosis. The secreted saliva serves as the chemical barrier of the oral cavity and, since OSCC can alter the protein composition of saliva, our aim was to analyze the effect of OSCC on the salivary chemical barrier proteins. Publicly available datasets regarding the analysis of salivary proteins from patients with OSCC and controls were collected and examined in order to identify differentially expressed chemical barrier proteins. Network analysis and gene ontology (GO) classification of the differentially expressed chemical barrier proteins were performed as well. One hundred and twenty-seven proteins showing different expression pattern between the OSCC and control groups were found. Protein-protein interaction networks of up- and down-regulated proteins were constructed and analyzed. The main hub proteins (IL-6, IL-1B, IL-8, TNF, APOA1, APOA2, APOB, APOC3, APOE, and HP) were identified and the enriched GO terms were examined. Our study highlighted the importance of the chemical barrier of saliva in the development of OSCC.

Ionmob: a Python package for prediction of peptide collisional cross-section values

MOTIVATION

Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing.

RESULTS

We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task.

AVAILABILITY AND IMPLEMENTATION

The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.

Physiology of trans-translation deficiency in Bacillus subtilis - a comparative proteomics study

trans-Translation is the most effective ribosome rescue system known in bacteria. While it is essential in some bacteria, Bacillus subtilis possesses two additional alternative ribosome rescue mechanisms that require the proteins BrfA or RqcH. To investigate the physiology of trans-translation deficiency in the model organism B. subtilis, we compared the proteomes of B. subtilis 168 and a ΔssrA mutant in the mid-log phase using gel-free label-free quantitative proteomics. In chemically defined medium, the growth rate of the ssrA deletion mutant was 20% lower than that of B. subtilis 168. An 35 S-methionine incorporation assay demonstrated that protein synthesis rates were also lower in the ΔssrA strain. Alternative rescue factors were not detected. Among the 34 proteins overrepresented in the mutant strain were eight chemotaxis proteins. Indeed, both on LB agar and minimal medium the ΔssrA strain showed an altered motility and chemotaxis phenotype. Despite the lower growth rate, in the mutant proteome ribosomal proteins were more abundant while proteins related to amino acid biosynthesis were less abundant than in the parental strain. This overrepresentation of ribosomal proteins coupled with a lower protein synthesis rate and down-regulation of precursor supply reflects the slow ribosome recycling in the trans-translation-deficient mutant.

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, which are largely mediated by its distinctive protein composition. We developed methods to reveal low-abundance transmembrane (TM) proteins concentrated at the NE relative to the peripheral ER. Using label-free proteomics that compared isolated NEs with 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.

The SUMO-NIP45 pathway processes toxic DNA catenanes to prevent mitotic failure

SUMOylation regulates numerous cellular processes, but what represents the essential functions of this protein modification remains unclear. To address this, we performed genome-scale CRISPR-Cas9-based screens, revealing that the BLM-TOP3A-RMI1-RMI2 (BTRR)-PICH pathway, which resolves ultrafine anaphase DNA bridges (UFBs) arising from catenated DNA structures, and the poorly characterized protein NIP45/NFATC2IP become indispensable for cell proliferation when SUMOylation is inhibited. We demonstrate that NIP45 and SUMOylation orchestrate an interphase pathway for converting DNA catenanes into double-strand breaks (DSBs) that activate the G2 DNA-damage checkpoint, thereby preventing cytokinesis failure and binucleation when BTRR-PICH-dependent UFB resolution is defective. NIP45 mediates this new TOP2-independent DNA catenane resolution process via its SUMO-like domains, promoting SUMOylation of specific factors including the SLX4 multi-nuclease complex, which contributes to catenane conversion into DSBs. Our findings establish that SUMOylation exerts its essential role in cell proliferation by enabling resolution of toxic DNA catenanes via nonepistatic NIP45- and BTRR-PICH-dependent pathways to prevent mitotic failure.

Mouse skin peptidomic analysis of the hemorrhage induced by a snake venom metalloprotease

Hemorrhage induced by snake venom metalloproteases (SVMPs) results from proteolysis, capillary disruption, and blood extravasation. HF3, a potent SVMP of Bothrops jararaca, induces hemorrhage at pmol doses in the mouse skin. To gain insight into the hemorrhagic process, the main goal of this study was to analyze changes in the skin peptidome generated by injection of HF3, using approaches of mass spectrometry-based untargeted peptidomics. The results revealed that the sets of peptides found in the control and HF3-treated skin samples were distinct and derived from the cleavage of different proteins. Peptide bond cleavage site identification in the HF3-treated skin showed compatibility with trypsin-like serine proteases and cathepsins, suggesting the activation of host proteinases. Acetylated peptides, which originated from the cleavage at positions in the N-terminal region of proteins in both samples, were identified for the first time in the mouse skin peptidome. The number of peptides acetylated at the residue after the first Met residue, mostly Ser and Ala, was higher than that of peptides acetylated at the initial Met. Proteins cleaved in the hemorrhagic skin participate in cholesterol metabolism, PPAR signaling, and in the complement and coagulation cascades, indicating the impairment of these biological processes. The peptidomic analysis also indicated the emergence of peptides with potential biological activities, including pheromone, cell penetrating, quorum sensing, defense, and cell-cell communication in the mouse skin. Interestingly, peptides generated in the hemorrhagic skin promoted the inhibition of collagen-induced platelet aggregation and could act synergistically in the local tissue damage induced by HF3.

Inherited chitinases enable sustained growth and rapid dispersal of bacteria from chitin particles

Many biogeochemical functions involve bacteria utilizing solid substrates. However, little is known about the coordination of bacterial growth with the kinetics of attachment to and detachment from such substrates. In this quantitative study of Vibrio sp. 1A01 growing on chitin particles, we reveal the heterogeneous nature of the exponentially growing culture comprising two co-existing subpopulations: a minority replicating on chitin particles and a non-replicating majority which was planktonic. This partition resulted from a high rate of cell detachment from particles. Despite high detachment, sustained exponential growth of cells on particles was enabled by the enrichment of extracellular chitinases excreted and left behind by detached cells. The 'inheritance' of these chitinases sustains the colonizing subpopulation despite its reduced density. This simple mechanism helps to circumvent a trade-off between growth and dispersal, allowing particle-associated marine heterotrophs to explore new habitats without compromising their fitness on the habitat they have already colonized.

iTRAQ-based proteomic analysis provides novel insight into the postnatal testicular development of Hu sheep

Testicular development is an intricate and coordinated process in which thousands of proteins are involved in the regulation of somatic cells development and spermatogenesis. However, knowledge about the proteomic changes during postnatal testicular development in Hu sheep is still elusive. The study was conducted to characterize the protein profiles at four key stages during postnatal testicular development, including infant (0-month-old, M0), puberty (3-month-old, M3), sexual maturity (6-month-old, M6) and body maturity (12-month-old, M12), and between the large- and small-testis groups at 6 months in Hu sheep. Consequently, 5252 proteins were identified using isobaric tags for relative and absolute quantification (iTRAQ) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods, and 465, 1261, 231 and 1080 differentially abundant proteins (DAPs) were found between M0_vs_M3, M3_vs_M6L, M6L_vs_M12, and M6L_vs_M6S, respectively. GO and KEGG analysis revealed that the majority of DAPs were involved in cellular process, metabolic process and immune system-related pathways. Furthermore, a protein-protein interaction network was constructed using 86 fertility-related DAPs, and five proteins with the highest degree were represented as hub proteins, including CTNNB1, ADAM2, ACR, HSPA2 and GRB2. This study provided new insights into the regulation mechanisms of postnatal testicular development and identified several potential biomarkers for selecting the high-fertility rams. SIGNIFICANCE OF THE STUDY: Testicular development is an intricate developmental process in which thousands of proteins are involved in regulating the somatic cells development and spermatogenesis. However, knowledge about the proteome changes during postnatal testicular development in Hu sheep is still elusive. This study provides comprehensive insights into the dynamic changes in the sheep testis proteome during postnatal testicular development. Additionally, testis size is positively correlated with semen quality and ejaculation volume, also for the merits of easy measurement, high heritability and selection efficiency, is an important indicator to select candidate rams with high fertility. The functional analyses of the acquired candidate proteins may help us gain a better understanding of the molecular regulatory mechanisms of testicular development.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

Proteomics as a Metrological Tool to Evaluate Genome Annotation Accuracy Following De Novo Genome Assembly: A Case Study Using the Atlantic Bottlenose Dolphin (Tursiops truncatus)

The last decade has witnessed dramatic improvements in whole-genome sequencing capabilities coupled to drastically decreased costs, leading to an inundation of high-quality de novo genomes. For this reason, the continued development of genome quality metrics is imperative. Using the 2016 Atlantic bottlenose dolphin NCBI RefSeq annotation and mass spectrometry-based proteomic analysis of six tissues, we confirmed 10,402 proteins from 4711 protein groups, constituting nearly one-third of the possible predicted proteins. Since the identification of larger proteins with more identified peptides implies reduced database fragmentation and improved gene annotation accuracy, we propose the metric NP10, which attempts to capture this quality improvement. The NP10 metric is calculated by first stratifying proteomic results by identifying the top decile (or 10th 10-quantile) of identified proteins based on the number of peptides per protein and then returns the median molecular weight of the resulting proteins. When using the 2016 versus 2012 Tursiops truncatus genome annotation to search this proteomic data set, there was a 21% improvement in NP10. This metric was further demonstrated by using a publicly available proteomic data set to compare human genome annotations from 2004, 2013 and 2016, which showed a 33% improvement in NP10. These results demonstrate that proteomics may be a useful metrological tool to benchmark genome accuracy, though there is a need for reference proteomic datasets across species to facilitate the evaluation of new de novo and existing genome.

Heimdall, an alternative protein issued from a ncRNA related to kappa light chain variable region of immunoglobulins from astrocytes: a new player in neural proteome

The dogma "One gene, one protein" is clearly obsolete since cells use alternative splicing and generate multiple transcripts which are translated into protein isoforms, but also use alternative translation initiation sites (TISs) and termination sites on a given transcript. Alternative open reading frames for individual transcripts give proteins originate from the 5'- and 3'-UTR mRNA regions, frameshifts of mRNA ORFs or from non-coding RNAs. Longtime considered as non-coding, recent in-silico translation prediction methods enriched the protein databases allowing the identification of new target structures that have not been identified previously. To gain insight into the role of these newly identified alternative proteins in the regulation of cellular functions, it is crucial to assess their dynamic modulation within a framework of altered physiological modifications such as experimental spinal cord injury (SCI). Here, we carried out a longitudinal proteomic study on rat SCI from 12 h to 10 days. Based on the alternative protein predictions, it was possible to identify a plethora of newly predicted protein hits. Among these proteins, some presented a special interest due to high homology with variable chain regions of immunoglobulins. We focus our interest on the one related to Kappa variable light chains which is similarly highly produced by B cells in the Bence jones disease, but here expressed in astrocytes. This protein, name Heimdall is an Intrinsically disordered protein which is secreted under inflammatory conditions. Immunoprecipitation experiments showed that the Heimdall interactome contained proteins related to astrocyte fate keepers such as "NOTCH1, EPHA3, IPO13" as well as membrane receptor protein including "CHRNA9; TGFBR, EPHB6, and TRAM". However, when Heimdall protein was neutralized utilizing a specific antibody or its gene knocked out by CRISPR-Cas9, sprouting elongations were observed in the corresponding astrocytes. Interestingly, depolarization assays and intracellular calcium measurements in Heimdall KO, established a depolarization effect on astrocyte membranes KO cells were more likely that the one found in neuroprogenitors. Proteomic analyses performed under injury conditions or under lipopolysaccharides (LPS) stimulation, revealed the expression of neuronal factors, stem cell proteins, proliferation, and neurogenesis of astrocyte convertor factors such as EPHA4, NOTCH2, SLIT3, SEMA3F, suggesting a role of Heimdall could regulate astrocytic fate. Taken together, Heimdall could be a novel member of the gatekeeping astrocyte-to-neuroprogenitor conversion factors.

Proteomic analysis reveals mechanisms underlying increased efficacy of bleomycin by photochemical internalization in bladder cancer cells

Photochemical internalization (PCI) is a promising new technology for site-specific drug delivery, developed from photodynamic therapy (PDT). In PCI, light-induced activation of a photosensitizer trapped inside endosomes together with e.g. chemotherapeutics, nucleic acids or immunotoxins, allows cytosolic delivery and enhanced local therapeutic effect. Here we have evaluated the photosensitizer meso-tetraphenyl chlorine disulphonate (TPCS2a/fimaporfin) in a proteome analysis of AY-27 rat bladder cancer cells in combination with the chemotherapeutic drug bleomycin (BML). We find that BLMPCI attenuates oxidative stress responses induced by BLM alone, while concomitantly increasing transcriptional repression and DNA damage responses. BLMPCI also mediates downregulation of bleomycin hydrolase (Blmh), which is responsible for cellular degradation of BLM, as well as several factors known to be involved in fibrotic responses. PCI-mediated delivery might thus allow reduced dosage of BLM and alleviate unwanted side effects from treatment, including pulmonary fibrosis.

Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts

Efficient conversion of pentose sugars remains a significant barrier to the replacement of petroleum-derived chemicals with plant biomass-derived bioproducts. While the oleaginous yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) has a relatively robust native metabolism of pentose sugars compared to other wild yeasts, faster assimilation of those sugars will be required for industrial utilization of pentoses. To increase the rate of pentose assimilation in R. toruloides, we leveraged previously reported high-throughput fitness data to identify potential regulators of pentose catabolism. Two genes were selected for further investigation, a putative transcription factor (RTO4_12978, Pnt1) and a homolog of a glucose transceptor involved in carbon catabolite repression (RTO4_11990). Overexpression of Pnt1 increased the specific growth rate approximately twofold early in cultures on xylose and increased the maximum specific growth by 18% while decreasing accumulation of arabitol and xylitol in fast-growing cultures. Improved growth dynamics on xylose translated to a 120% increase in the overall rate of xylose conversion to fatty alcohols in batch culture. Proteomic analysis confirmed that Pnt1 is a major regulator of pentose catabolism in R. toruloides. Deletion of RTO4_11990 increased the growth rate on xylose, but did not relieve carbon catabolite repression in the presence of glucose. Carbon catabolite repression signaling networks remain poorly characterized in R. toruloides and likely comprise a different set of proteins than those mainly characterized in ascomycete fungi.

A Chemical Proteomic Strategy Reveals Inhibitors of Lipoate Salvage in Bacteria and Parasites

The development of novel anti-infectives requires unprecedented strategies targeting pathways which are solely present in pathogens but absent in humans. Following this principle, we developed inhibitors of lipoic acid (LA) salvage, a crucial pathway for the survival of LA auxotrophic bacteria and parasites but non-essential in human cells. An LA-based probe was selectively transferred onto substrate proteins via lipoate protein ligase (LPL) in intact cells, and their binding sites were determined by mass spectrometry. Probe labeling served as a proxy of LPL activity, enabling in situ screenings for cell-permeable LPL inhibitors. Profiling a focused compound library revealed two substrate analogs (LAMe and C3) as inhibitors, which were further validated by binding studies and co-crystallography. Importantly, LAMe exhibited low toxicity in human cells and achieved killing of Plasmodium falciparum in erythrocytes with an EC50 value of 15 μM, making it the most effective LPL inhibitor reported to date.

Mining proteomics data to extract post-translational modifications associated with gastric cancer

Gastric cancers are highly heterogeneous, deep-seated tumours associated with late diagnosis and poor prognosis. Post-translational modifications (PTMs) of proteins are known to be well-associated with oncogenesis and metastasis in most cancers. Several enzymes which drive PTMs have also been used as theranostics in cancers of the breast, ovary, prostate and bladder. However, there is limited data on PTMs in gastric cancers. Considering that experimental protocols for simultaneous analysis of multiple PTMs are being explored, a data-driven approach involving reanalysis of mass spectrometry-derived data is useful in cataloguing altered PTMs. We subjected publicly available mass spectrometry data on gastric cancer to an iterative searching strategy for fetching PTMs including phosphorylation, acetylation, citrullination, methylation and crotonylation. These PTMs were catalogued and further analyzed for their functional enrichment through motif analysis. This value-added approach delivered identification of 21,710 unique modification sites on 16,364 modified peptides. Interestingly, we observed 278 peptides corresponding to 184 proteins to be differentially abundant. Using bioinformatics approaches, we observed that majority of these altered PTMs/proteins belonged to cytoskeletal and extracellular matrix proteins, which are known to be perturbed in gastric cancer. The dataset derived by this mutiPTM investigation can provide leads to further investigate the potential role of altered PTMs in gastric cancer management.

Functional mapping of N-terminal residues in the yeast proteome uncovers novel determinants for mitochondrial protein import

N-terminal ends of polypeptides are critical for the selective co-translational recruitment of N-terminal modification enzymes. However, it is unknown whether specific N-terminal signatures differentially regulate protein fate according to their cellular functions. In this work, we developed an in-silico approach to detect functional preferences in cellular N-terminomes, and identified in S. cerevisiae more than 200 Gene Ontology terms with specific N-terminal signatures. In particular, we discovered that Mitochondrial Targeting Sequences (MTS) show a strong and specific over-representation at position 2 of hydrophobic residues known to define potential substrates of the N-terminal acetyltransferase NatC. We validated mitochondrial precursors as co-translational targets of NatC by selective purification of translating ribosomes, and found that their N-terminal signature is conserved in Saccharomycotina yeasts. Finally, systematic mutagenesis of the position 2 in a prototypal yeast mitochondrial protein confirmed its critical role in mitochondrial protein import. Our work highlights the hydrophobicity of MTS N-terminal residues and their targeting by NatC as important features for the definition of the mitochondrial proteome, providing a molecular explanation for mitochondrial defects observed in yeast or human NatC-depleted cells. Functional mapping of N-terminal residues thus has the potential to support the discovery of novel mechanisms of protein regulation or targeting.

Cross-Linking Mass Spectrometry Uncovers Interactions Between High-Density Lipoproteins and the SARS-CoV-2 Spike Glycoprotein

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

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

A Proteomic Study of the Bioactivity of Annona muricata Leaf Extracts in HT-1080 Fibrosarcoma Cells

Graviola (Annona muricata) is a tropical plant with many traditional ethnobotanic uses and pharmacologic applications. A metabolomic study of both aqueous and DMSO extracts from Annona muricata leaves recently allowed us to identify dozens of bioactive compounds. In the present study, we use a proteomic approach to detect altered patterns in proteins on both conditioned media and extracts of HT-1080 fibrosarcoma cells under treatment conditions, revealing new potential bioactivities of Annona muricata extracts. Our results reveal the complete sets of deregulated proteins after treatment with aqueous and DMSO extracts from Annona muricata leaves. Functional enrichment analysis of proteomic data suggests deregulation of cell cycle and iron metabolism, which are experimentally validated in vitro. Additional experimental data reveal that DMSO extracts protect HT-1080 fibrosarcoma cells and HMEC-1 endothelial cells from ferroptosis. Data from our proteomic study are available via ProteomeXchange with identifier PXD042354.

Comparison of Four Purification Methods on Serum Extracellular Vesicle Recovery, Size Distribution, and Proteomics

In recent decades, the role played by extracellular vesicles in physiological and pathological processes has attracted attention. Extracellular vesicles are released by different types of cells and carry molecules that could become biomarkers for the diagnosis of diseases. Extracellular vesicles are also moldable tools for the controlled release of bioactive substances in clinical and therapeutic applications. However, one of the significant challenges when studying these exciting and versatile vesicles is the purification process, which presents significant difficulties in terms of lack of purity, yield, and reproducibility, reflected in unreliable data. Therefore, our objective in the present study was to compare the proteomic profile of serum-derived EVs purified using ExoQuick™ (Systems Biosciences), Total Isolation Kit (Life Technologies), Ultracentrifugation, and Ultrafiltration. Each technique utilized for purification has shown different concentrations and populations of purified particles. The results showed marked differences in distribution, size, and protein content, demonstrating the need to develop reproducible and reliable protocols to isolate extracellular vesicles for their clinical application.

Strategies for mapping protein hydrolysate profiles and pharmacokinetics based on non-targeted proteomics combining skyline-aided quantitative techniques

Numerous works have been focused on the bioactivities of protein hydrolysates (PHs) and their application in food or drug formulations, but their composition and pharmacokinetics have never been addressed due to their complex constitutes, short half-life, extremely low concentrations and lack of authentic standards. The present study aims to develop systematic analytical strategy and technical platform with optimized sample preparation, separation and detection protocols for PHs. Lineal peptides (LPs), extraction of the spleen of healthy pigs or calves, were used as cases. First, solvents with polarity gradients were used to globally extract peptides of LP from biological matrix. Non-targeted proteomics based on a high-resolution MS system was used to establish a reliable qualitative analysis workflow for PHs. Based on the developed approach, 247 unique peptides were identified using NanoLC-Orbitrap-MS/MS, and then further verified on the MicroLC-Q-TOF/MS system. In the quantitative analysis workflow, Skyline software was used to predict and optimize the LC-MS/MS detection parameters of LPs followed by investigating the linearity and precision of the developed analytical assay. Note worthily, we innovatively prepared calibration curves by sequential dilution of LP solution to overcome the bottleneck of lacking authentic standards and complex PH composition. All the peptides exhibited good linearity and precision in biological matrix. The established qualitative and quantitative assays were successfully applied to study the distribution characteristics of LPs in mice, and would be conductive to systematically map the profile and pharmacokinetics of peptides in various PHs in vivo and in vitro.

The salivary proteome in relation to oral mucositis in autologous hematopoietic stem cell transplantation recipients: a labelled and label-free proteomics approach

BACKGROUND

Oral mucositis is a frequently seen complication in the first weeks after hematopoietic stem cell transplantation recipients which can severely affects patients quality of life. In this study, a labelled and label-free proteomics approach were used to identify differences between the salivary proteomes of autologous hematopoietic stem cell transplantation (ASCT) recipients developing ulcerative oral mucositis (ULC-OM; WHO score ≥ 2) or not (NON-OM).

METHODS

In the TMT-labelled analysis we pooled saliva samples from 5 ULC-OM patients at each of 5 timepoints: baseline, 1, 2, 3 weeks and 3 months after ASCT and compared these with pooled samples from 5 NON-OM patients. For the label-free analysis we analyzed saliva samples from 9 ULC-OM and 10 NON-OM patients at 6 different timepoints (including 12 months after ASCT) with Data-Independent Acquisition (DIA). As spectral library, all samples were grouped (ULC-OM vs NON-OM) and analyzed with Data Dependent Analysis (DDA). PCA plots and a volcano plot were generated in RStudio and differently regulated proteins were analyzed using GO analysis with g:Profiler.

RESULTS

A different clustering of ULC-OM pools was found at baseline, weeks 2 and 3 after ASCT with TMT-labelled analysis. Using label-free analysis, week 1-3 samples clustered distinctly from the other timepoints. Unique and up-regulated proteins in the NON-OM group (DDA analysis) were involved in immune system-related processes, while those proteins in the ULC-OM group were intracellular proteins indicating cell lysis.

CONCLUSIONS

The salivary proteome in ASCT recipients has a tissue protective or tissue-damage signature, that corresponded with the absence or presence of ulcerative oral mucositis, respectively.

TRIAL REGISTRATION

The study is registered in the national trial register (NTR5760; automatically added to the International Clinical Trial Registry Platform).

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

Hypoxia induced responses are reflected in the stromal proteome of breast cancer

Cancers are often associated with hypoxia and metabolic reprogramming, resulting in enhanced tumor progression. Here, we aim to study breast cancer hypoxia responses, focusing on secreted proteins from low-grade (luminal-like) and high-grade (basal-like) cell lines before and after hypoxia. We examine the overlap between proteomics data from secretome analysis and laser microdissected human breast cancer stroma, and we identify a 33-protein stromal-based hypoxia profile (33P) capturing differences between luminal-like and basal-like tumors. The 33P signature is associated with metabolic differences and other adaptations following hypoxia. We observe that mRNA values for 33P predict patient survival independently of molecular subtypes and basic prognostic factors, also among low-grade luminal-like tumors. We find a significant prognostic interaction between 33P and radiation therapy.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Structural insights into regulation of the PEAK3 pseudokinase scaffold by 14-3-3

PEAK pseudokinases are molecular scaffolds which dimerize to regulate cell migration, morphology, and proliferation, as well as cancer progression. The mechanistic role dimerization plays in PEAK scaffolding remains unclear, as there are no structures of PEAKs in complex with their interactors. Here, we report the cryo-EM structure of dimeric PEAK3 in complex with an endogenous 14-3-3 heterodimer. Our structure reveals an asymmetric binding mode between PEAK3 and 14-3-3 stabilized by one pseudokinase domain and the SHED domain of the PEAK3 dimer. The binding interface contains a canonical phosphosite-dependent primary interaction and a unique secondary interaction not observed in previous structures of 14-3-3/client complexes. Additionally, we show that PKD regulates PEAK3/14-3-3 binding, which when prevented leads to PEAK3 nuclear enrichment and distinct protein-protein interactions. Altogether, our data demonstrate that PEAK3 dimerization forms an unusual secondary interface for 14-3-3 binding, facilitating 14-3-3 regulation of PEAK3 localization and interactome diversity.

A cytosolic surveillance mechanism activates the mitochondrial UPR

The mitochondrial unfolded protein response (UPR^mt) is essential to safeguard mitochondria from proteotoxic damage by activating a dedicated transcriptional response in the nucleus to restore proteostasis^1,2. Yet, it remains unclear how the information on mitochondria misfolding stress (MMS) is signalled to the nucleus as part of the human UPR^mt (refs. ^3,4). Here, we show that UPR^mt signalling is driven by the release of two individual signals in the cytosol-mitochondrial reactive oxygen species (mtROS) and accumulation of mitochondrial protein precursors in the cytosol (c-mtProt). Combining proteomics and genetic approaches, we identified that MMS causes the release of mtROS into the cytosol. In parallel, MMS leads to mitochondrial protein import defects causing c-mtProt accumulation. Both signals integrate to activate the UPR^mt; released mtROS oxidize the cytosolic HSP40 protein DNAJA1, which leads to enhanced recruitment of cytosolic HSP70 to c-mtProt. Consequently, HSP70 releases HSF1, which translocates to the nucleus and activates transcription of UPR^mt genes. Together, we identify a highly controlled cytosolic surveillance mechanism that integrates independent mitochondrial stress signals to initiate the UPR^mt. These observations reveal a link between mitochondrial and cytosolic proteostasis and provide molecular insight into UPR^mt signalling in human cells.

Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER

Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has widespread clinical use for detection of inborn errors of metabolism, therapeutic drug monitoring, and numerous other applications. This technique detects proteolytic peptides as surrogates for protein biomarker expression, mutation, and post-translational modification in individual clinical assays and in cancer research with highly multiplexed quantitation across biological pathways. LC-MRM for protein biomarkers must be translated from multiplexed research-grade panels to clinical use. LC-MRM panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. An application to visualize and communicate targeted proteomics data will empower translational researchers to move protein biomarker panels from discovery to clinical use. Therefore, we have developed a web-based tool for targeted proteomics that provides pathway-level evaluations of key biological drivers (e.g., EGFR signaling), signature scores (representing phenotypes) (e.g., EMT), and the ability to quantify specific drug targets across a sample cohort. This tool represents a framework for integrating summary information, decision algorithms, and risk scores to support Physician-Interpretable Phenotypic Evaluation in R (PIPER) that can be reused or repurposed by other labs to communicate and interpret their own biomarker panels.

Detection and quantification of the histone code in the fungal genus Aspergillus

In eukaryotes, the combination of different histone post-translational modifications (PTMs) - the histone code - impacts the chromatin organization as compact and transcriptionally silent heterochromatin or accessible and transcriptionally active euchromatin. Although specific histone PTMs have been studied in fungi, an overview of histone PTMs and their relative abundance is still lacking. Here, we used mass spectrometry to detect and quantify histone PTMs in three fungal species belonging to three distinct taxonomic sections of the genus Aspergillus (Aspergillus niger, Aspergillus nidulans (two strains), and Aspergillus fumigatus). We overall detected 23 different histone PTMs, including a majority of lysine methylations and acetylations, and 23 co-occurrence patterns of multiple histone PTMs. Among those, we report for the first time the detection of H3K79me1, H3K79me2, and H4K31ac in Aspergilli. Although all three species harbour the same PTMs, we found significant differences in the relative abundance of H3K9me1/2/3, H3K14ac, H3K36me1 and H3K79me1, as well as the co-occurrence of acetylation on both K18 and K23 of histone H3 in a strain-specific manner. Our results provide novel insights about the underexplored complexity of the histone code in filamentous fungi, and its functional implications on genome architecture and gene regulation.

An ERK1/2-driven RNA-binding switch in nucleolin drives ribosome biogenesis and pancreatic tumorigenesis downstream of RAS oncogene

Oncogenic RAS signaling reprograms gene expression through both transcriptional and post-transcriptional mechanisms. While transcriptional regulation downstream of RAS is relatively well characterized, how RAS post-transcriptionally modulates gene expression to promote malignancy remains largely unclear. Using quantitative RNA interactome capture analysis, we here reveal that oncogenic RAS signaling reshapes the RNA-bound proteomic landscape of pancreatic cancer cells, with a network of nuclear proteins centered around nucleolin displaying enhanced RNA-binding activity. We show that nucleolin is phosphorylated downstream of RAS, which increases its binding to pre-ribosomal RNA (rRNA), boosts rRNA production, and promotes ribosome biogenesis. This nucleolin-dependent enhancement of ribosome biogenesis is crucial for RAS-induced pancreatic cancer cell proliferation and can be targeted therapeutically to inhibit tumor growth. Our results reveal that oncogenic RAS signaling drives ribosome biogenesis by regulating the RNA-binding activity of nucleolin and highlight a crucial role for this mechanism in RAS-mediated tumorigenesis.

On the identification of potential novel therapeutic targets for spinocerebellar ataxia type 1 (SCA1) neurodegenerative disease using EvoPPI3

EvoPPI (http://evoppi.i3s.up.pt), a meta-database for protein-protein interactions (PPI), has been upgraded (EvoPPI3) to accept new types of data, namely, PPI from patients, cell lines, and animal models, as well as data from gene modifier experiments, for nine neurodegenerative polyglutamine (polyQ) diseases caused by an abnormal expansion of the polyQ tract. The integration of the different types of data allows users to easily compare them, as here shown for Ataxin-1, the polyQ protein involved in spinocerebellar ataxia type 1 (SCA1) disease. Using all available datasets and the data here obtained for Drosophila melanogaster wt and exp Ataxin-1 mutants (also available at EvoPPI3), we show that, in humans, the Ataxin-1 network is much larger than previously thought (380 interactors), with at least 909 interactors. The functional profiling of the newly identified interactors is similar to the ones already reported in the main PPI databases. 16 out of 909 interactors are putative novel SCA1 therapeutic targets, and all but one are already being studied in the context of this disease. The 16 proteins are mainly involved in binding and catalytic activity (mainly kinase activity), functional features already thought to be important in the SCA1 disease.

Comprehensive glycoprofiling of oral tumours associates N-glycosylation with lymph node metastasis and patient survival

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

Biofilm-forming strains of P. aeruginosa and S. aureus isolated from cystic fibrosis patients differently affect inflammatory phenotype of macrophages

OBJECTIVE

Lung cystic fibrosis (CF) is characterized by chronic infections and hyperinflammatory response of neutrophils and macrophages. P. aeruginosa (PA) and S. aureus (MSSA, MRSA) are major pathogens of advanced CF. The main goal of this study was to compare the inflammatory phenotype of murine C57BL/6 macrophages exposed to PA57 with that exposed to MSSA60, both strains isolated from the same patient with severe CF. In the present study, we used C57BL/6 mice sensitive to lung infection with P. aeruginosa.

METHODS

We measured the release of cytokines and the expression of phenotypic markers of murine neutrophils and macrophages exposed to bacterial cells and biofilm components (i.e., EPS) of the selected bacteria. In addition, a quantitative proteomic approach was used for the characterization of proteome-wide changes in macrophages.

RESULTS

Neutrophils stimulated with PA57 and MSSA60 strains produced hyperinflammatory pattern of cytokines. The pro-inflammatory impact of PA57 was significantly higher than that of MSSA60 (IL-6/IL-10 ratio: PA57 = 9.3 vs. MSSA60 = 1.7). Macrophages produced significantly lower amount of cytokines, but showed classical pattern of M1 markers (iNOS-High; arginase-1 and mannose receptor MRC1-Low). Importantly, as evidenced by proteomic analysis, PA57 and PA57-EPS were stronger inducers of M1 macrophage polarization than the MSSA60 counterparts.

CONCLUSIONS

Our study demonstrated that strong biofilm P. aeruginosa strains, CF isolates, are dominant inducers of M1 macrophages, termed biofilm-associated macrophages (BAMs). We suggest that repolarization of detrimental BAMs might be a new therapeutic strategy to ameliorate the airway damage in CF.

Pancreatic ductal adenocarcinoma ubiquitination profiling reveals specific prognostic and theranostic markers

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has been widely studied at multiomics level. However, little is known about its specific ubiquitination, a major post-translational modification (PTM). As PTMs regulate the final function of any gene, we decided to establish the ubiquitination profiles of 60 PDAC.

METHODS

We used specific proteomic tools to establish the ubiquitin dependent proteome (ubiquitinome) of frozen PDXs (Patients' derived xenographs). Then, we performed bioinformatics analysis to identify the possible associations of these ubiquitination profiles with tumour phenotype, patient survival and resistance to chemotherapies. Finally, we used proximity ligation assays (PLA) to detect and quantify the ubiquitination level of one identified marker.

FINDINGS

We identified 38 ubiquitination site profiles correlating with the transcriptomic phenotype of tumours and four had notable prognostic capabilities. Seventeen ubiquitination profiles displayed potential theranostic marker for gemcitabine, seven for 5-FU, six for oxaliplatin and thirteen for irinotecan. Using PLA, we confirmed the use of one ubiquitination profile as a drug-response marker, directly on paraffin embedded tissues, supporting the possible application of these biomarkers in the clinical setting.

INTERPRETATION

These findings bring new and important insights on the relationship between ubiquitination levels of proteins and different molecular and clinical features of PDAC patients. Markers identified in this study could have a potential application in clinical settings to help to predict response to chemotherapies thereby allowing the personalization of treatments.

FUNDING

Fondation ARC (PJA 20181208270 and PGA 12021010002840_3562); INCa; Canceropôle PACA; DGOS; Amidex Foundation; Fondation de France; and INSERM.