Polyphosphate kinase regulates LPS structure and polymyxin resistance during starvation in E. coli.

Polyphosphates (polyP) are chains of inorganic phosphates that can reach over 1,000 residues in length. In Escherichia coli, polyP is produced by the polyP kinase (PPK) and is thought to play a protective role during the response to cellular stress. However, the molecular pathways impacted by PPK activity and polyP accumulation remain poorly characterized. In this work, we used label-free mass spectrometry to study the response of bacteria that cannot produce polyP (Δppk) during starvation to identify novel pathways regulated by PPK. In response to starvation, we found 92 proteins significantly differentially expressed between wild-type and Δppk mutant cells. Wild-type cells were enriched for proteins related to amino acid biosynthesis and transport, while Δppk mutants were enriched for proteins related to translation and ribosome biogenesis, suggesting that without PPK, cells remain inappropriately primed for growth even in the absence of the required building blocks. From our data set, we were particularly interested in Arn and EptA proteins, which were down-regulated in Δppk mutants compared to wild-type controls, because they play a role in lipid A modifications linked to polymyxin resistance. Using western blotting, we confirm differential expression of these and related proteins in K-12 strains and a uropathogenic isolate, and provide evidence that this mis-regulation in Δppk cells stems from a failure to induce the BasRS two-component system during starvation. We also show that Δppk mutants unable to up-regulate Arn and EptA expression lack the respective L-Ara4N and pEtN modifications on lipid A. In line with this observation, loss of ppk restores polymyxin sensitivity in resistant strains carrying a constitutively active basR allele. Overall, we show a new role for PPK in lipid A modification during starvation and provide a rationale for targeting PPK to sensitize bacteria towards polymyxin treatment. We further anticipate that our proteomics work will provide an important resource for researchers interested in the diverse pathways impacted by PPK.

METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance.

MOTIVATION: Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. RESULTS: METAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the user's choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN) or Random Over-Sampling Examples (ROSE). To present the effect of over-sampling on the data META-BOA further displays both principal component analysis and t-distributed stochastic neighbor embedding visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their further analyses. AVAILABILITY AND IMPLEMENTATION: META-BOA is available at https://complimet.ca/meta-boa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteomics characterization of mitochondrial-derived vesicles under oxidative stress.

Mitochondria share attributes of vesicular transport with their bacterial ancestors given their ability to form mitochondrial-derived vesicles (MDVs). MDVs are involved in mitochondrial quality control and their formation is enhanced with stress and may, therefore, play a potential role in mitochondrial-cellular communication. However, MDV proteomic cargo has remained mostly undefined. In this study, we strategically used an in vitro MDV budding/reconstitution assay on cardiac mitochondria, followed by graded oxidative stress, to identify and characterize the MDV proteome. Our results confirmed previously identified cardiac MDV markers, while also revealing a complete map of the MDV proteome, paving the way to a better understanding of the role of MDVs. The oxidative stress vulnerability of proteins directed the cargo loading of MDVs, which was enhanced by antimycin A (Ant-A). Among OXPHOS complexes, complexes III and V were found to be Ant-A-sensitive. Proteins from metabolic pathways such as the TCA cycle and fatty acid metabolism, along with Fe-S cluster, antioxidant response proteins, and autophagy were also found to be Ant-A sensitive. Intriguingly, proteins containing hyper-reactive cysteine residues, metabolic redox switches, including professional redox enzymes and those that mediate iron metabolism, were found to be components of MDV cargo with Ant-A sensitivity. Last, we revealed a possible contribution of MDVs to the formation of extracellular vesicles, which may indicate mitochondrial stress. In conclusion, our study provides an MDV proteomics signature that delineates MDV cargo selectivity and hints at the potential for MDVs and their novel protein cargo to serve as vital biomarkers during mitochondrial stress and related pathologies.

PIGNON: a protein-protein interaction-guided functional enrichment analysis for quantitative proteomics.

BACKGROUND: Quantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches. RESULTS: Herein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein-protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein-protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON's results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes. CONCLUSION: PIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

Proximity Interactome Map of the Vac14-Fig4 Complex Using BioID.

Conversion between phosphatidylinositol-3-phosphate and phosphatidylinositol-3,5-bisphosphate on endosomal membranes is critical for the maturation of early endosomes to late endosomes/lysosomes and is regulated by the PIKfyve-Vac14-Fig4 complex. Despite the importance of this complex for endosomal homeostasis and vesicular trafficking, there is little known about how its activity is regulated or how it interacts with other cellular proteins. Here, we screened for the cellular interactome of Vac14 and Fig4 using proximity-dependent biotin labeling (BioID). After independently screening the interactomes of Vac14 and Fig4, we identified 89 high-confidence protein hits shared by both proteins. Network analysis of these hits revealed pathways with known involvement of the PIKfyve-Vac14-Fig4 complex, including vesicular organization and PI3K/Akt signaling, as well as novel pathways including cell cycle and mitochondrial regulation. We also identified subunits of coatomer complex I (COPI), a Golgi-associated complex with an emerging role in endosomal dynamics. Using proximity ligation assays, we validated the interaction between Vac14 and COPI subunit COPB1 and between Vac14 and Arf1, a GTPase required for COPI assembly. In summary, this study used BioID to comprehensively map the Vac14-Fig4 interactome, revealing potential roles for these proteins in diverse cellular processes and pathways, including preliminary evidence of an interaction between Vac14 and COPI. Data are available via ProteomeXchange with the identifier PXD027917.

pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies.

MOTIVATION: Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment. RESULTS: To combat this challenge, we developed pepFunk, a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and a gene set variation analysis-inspired pathway enrichment adapted for peptide-level data. Analysis using our peptide-centric workflow is fast and highly correlated to a protein-centric analysis, and can identify more enriched KEGG pathways than analysis using protein-level data. Our workflow is open source and available as a web application or source code to be run locally. AVAILABILITY AND IMPLEMENTATION: pepFunk is available online as a web application at https://shiny.imetalab.ca/pepFunk/ with open-source code available from https://github.com/northomics/pepFunk. CONTACT: dfigeys@uottawa.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

∆F508 CFTR interactome remodelling promotes rescue of cystic fibrosis.

Deletion of phenylalanine 508 of the cystic fibrosis transmembrane conductance regulator (∆F508 CFTR) is the major cause of cystic fibrosis, one of the most common inherited childhood diseases. The mutated CFTR anion channel is not fully glycosylated and shows minimal activity in bronchial epithelial cells of patients with cystic fibrosis. Low temperature or inhibition of histone deacetylases can partly rescue ∆F508 CFTR cellular processing defects and function. A favourable change of ∆F508 CFTR protein-protein interactions was proposed as a mechanism of rescue; however, CFTR interactome dynamics during temperature shift and inhibition of histone deacetylases are unknown. Here we report the first comprehensive analysis of the CFTR and ∆F508 CFTR interactome and its dynamics during temperature shift and inhibition of histone deacetylases. By using a novel deep proteomic analysis method, we identify 638 individual high-confidence CFTR interactors and discover a ∆F508 deletion-specific interactome, which is extensively remodelled upon rescue. Detailed analysis of the interactome remodelling identifies key novel interactors, whose loss promote ∆F508 CFTR channel function in primary cystic fibrosis epithelia or which are critical for CFTR biogenesis. Our results demonstrate that global remodelling of ∆F508 CFTR interactions is crucial for rescue, and provide comprehensive insight into the molecular disease mechanisms of cystic fibrosis caused by deletion of F508.

Computational Identification of Human Biological Processes and Protein Sequence Motifs Putatively Targeted by SARS-CoV-2 Proteins Using Protein-Protein Interaction Networks.

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK 293 cells published by Gordon et al. (Nature2020, 583, 459-468) to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on PPI networks generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in PPI networks. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in PPI networks. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

Studying the Temporal Dynamics of the Gut Microbiota Using Metabolic Stable Isotope Labeling and Metaproteomics.

The gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here, we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with 15N hydrolysate from Ralstonia eutropha, we identified 12 326 nonredundant unlabeled peptides, of which 8256 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse microbiome. MetaProfiler and the bioinformatic pipeline are available at https://github.com/northomics/MetaProfiler.git.

Proteomics INTegrator (PINT): An Online Tool To Store, Query, and Visualize Large Proteomics Experiment Results.

The characterization of complex biological systems based on high-throughput protein quantification through mass spectrometry commonly involves differential expression analysis between replicate samples originating from different experimental conditions. Here we present Proteomics INTegrator (PINT), a new user-friendly Web-based platform-independent system to store, visualize, and query proteomics experiment results. PINT provides an extremely flexible query interface that allows advanced Boolean algebra-based data filtering of many different proteomics features such as confidence values, abundance levels or ratios, data set overlaps, sample characteristics, as well as UniProtKB annotations, which are transparently incorporated into the system. In addition, PINT allows developers to incorporate data visualization and analysis tools, such as PSEA-Quant and Reactome pathway analysis, for data set enrichment analysis. PINT serves as a centralized hub for large-scale proteomics data and as a platform for data analysis, facilitating the interpretation of proteomics results and expediting biologically relevant conclusions.

Comparison of CRISPR Genomic Tagging for Affinity Purification and Endogenous Immunoprecipitation Coupled with Quantitative Mass Spectrometry To Identify the Dynamic AMPKα2 Interactome.

Recent advances in genome editing technologies have enabled the insertion of epitope tags at endogenous loci with relative efficiency. We describe an approach for investigation of protein interaction dynamics of the AMP-activated kinase complex AMPK using a catalytic subunit AMPKα2 (PRKAA2 gene) as the bait, based on CRISPR/Cas9-mediated genome editing coupled to stable isotope labeling in cell culture, multidimensional protein identification technology, and computational and statistical analyses. Furthermore, we directly compare this genetic epitope tagging approach to endogenous immunoprecipitations of the same gene under homologous conditions to assess differences in observed interactors. Additionally, we directly compared each enrichment strategy in the genetically modified cell-line with two separate endogenous antibodies. For each approach, we analyzed the interaction profiles of this protein complex under basal and activated states, and after implementing the same analytical, computational, and statistical analyses, we found that high-confidence protein interactors vary greatly with each method and between commercially available endogenous antibodies.

Quantitative Analysis of the Proteome Response to the Histone Deacetylase Inhibitor (HDACi) Vorinostat in Niemann-Pick Type C1 disease.

Niemann-Pick type C (NPC) disease is an inherited, progressive neurodegenerative disorder principally caused by mutations in the NPC1 gene. NPC disease is characterized by the accumulation of unesterified cholesterol in the late endosomes (LE) and lysosomes (Ly) (LE/Ly). Vorinostat, a histone deacetylase inhibitor (HDACi), restores cholesterol homeostasis in fibroblasts derived from NPC patients; however, the exact mechanism by which Vorinostat restores cholesterol level is not known yet. In this study, we performed comparative proteomic profiling of the response of NPC1I1061T fibroblasts to Vorinostat. After stringent statistical criteria to filter identified proteins, we observed 202 proteins that are differentially expressed in Vorinostat-treated fibroblasts. These proteins are members of diverse cellular pathways including the endomembrane dependent protein folding-stability-degradation-trafficking axis, energy metabolism, and lipid metabolism. Our study shows that treatment of NPC1I1061T fibroblasts with Vorinostat not only enhances pathways promoting the folding, stabilization and trafficking of NPC1 (I1061T) mutant to the LE/Ly, but alters the expression of lysosomal proteins, specifically the lysosomal acid lipase (LIPA) involved in the LIPA->NPC2->NPC1 based flow of cholesterol from the LE/Ly lumen to the LE/Ly membrane. We posit that the Vorinostat may modulate numerous pathways that operate in an integrated fashion through epigenetic and post-translational modifications reflecting acetylation/deacetylation balance to help manage the defective NPC1 fold, the function of the LE/Ly system and/or additional cholesterol metabolism/distribution pathways, that could globally contribute to improved mitigation of NPC1 disease in the clinic based on as yet uncharacterized principles of cellular metabolism dictating cholesterol homeostasis.

Functional 5' UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks.

Biological networks are rich representations of the relationships between entities such as genes or proteins and have become increasingly complete thanks to various high-throughput network mapping experimental approaches. Here, we propose a method to use such networks to guide the search for functional sequence motifs. Specifically, we introduce Local Enrichment of Sequence Motifs in biological Networks (LESMoN), an enumerative motif discovery algorithm that identifies 5' untranslated region (UTR) sequence motifs whose associated proteins form unexpectedly dense clusters in a given biological network. When applied to the human protein-protein interaction network from BioGRID, LESMoN identifies several highly significant 5' UTR sequence motifs, including both previously known motifs and uncharacterized ones. The vast majority of these motifs are evolutionary conserved and the genes containing them are significantly enriched for various gene ontology terms suggesting new associations between 5' UTR motifs and a number of biological processes. We validate in vivo the role in protein expression regulation of three motifs identified by LESMoN.

Quantitative Proteomics of Human Fibroblasts with I1061T Mutation in Niemann-Pick C1 (NPC1) Protein Provides Insights into the Disease Pathogenesis.

Niemann-Pick type C (NPC) disease is a fatal neurodegenerative disorder characterized by the accumulation of unesterified cholesterol in the late endosomal/lysosomal compartments. Mutations in the NPC1 protein are implicated in 95% of patients with NPC disease. The most prevalent mutation is the missense mutation I1061T that occurs in ∼ 15-20% of the disease alleles. In our study, an isobaric labeling-based quantitative analysis of proteome of NPC1(I1061T) primary fibroblasts when compared with wild-type cells identified 281 differentially expressed proteins based on stringent data analysis criteria. Gene ontology enrichment analysis revealed that these proteins play important roles in diverse cellular processes such as protein maturation, energy metabolism, metabolism of reactive oxygen species, antioxidant activity, steroid metabolism, lipid localization, and apoptosis. The relative expression level of a subset of differentially expressed proteins (TOR4A, DHCR24, CLGN, SOD2, CHORDC1, HSPB7, and GAA) was independently and successfully substantiated by Western blotting. We observed that treating NPC1(I1061T) cells with four classes of seven different compounds that are potential NPC drugs increased the expression level of SOD2 and DHCR24. We have also shown an abnormal accumulation of glycogen in NPC1(I1061T) fibroblasts possibly triggered by defective processing of lysosomal alpha-glucosidase. Our study provides a starting point for future more focused investigations to better understand the mechanisms by which the reported dysregulated proteins triggers the pathological cascade in NPC, and furthermore, their effect upon therapeutic interventions.

Physiological and Molecular Alterations Promoted by Schizotetranychus oryzae Mite Infestation in Rice Leaves.

Infestation of phytophagous mite Schizotetranychus oryzae in rice causes critical yield losses. To better understand this interaction, we employed Multidimensional Protein Identification Technology (MudPIT) approach to identify differentially expressed proteins. We detected 18 and 872 unique proteins in control and infested leaves, respectively, along with 32 proteins more abundant in control leaves. S. oryzae infestation caused decreased abundance of proteins related to photosynthesis (mostly photosystem II-related), carbon assimilation and energy production, chloroplast detoxification, defense, and fatty acid and gibberellin synthesis. On the contrary, infestation caused increased abundance of proteins involved in protein modification and degradation, gene expression at the translation level, protein partitioning to different organelles, lipid metabolism, actin cytoskeleton remodeling, and synthesis of jasmonate, amino acid, and molecular chaperones. Our results also suggest that S. oryzae infestation promotes cell-wall remodeling and interferes with ethylene biosynthesis in rice leaves. Proteomic data were positively correlated with enzymatic assays and RT-qPCR analysis. Our findings describe the protein expression patterns of infested rice leaves and suggest that the acceptor side of PSII is probably the major damaged target in the photosynthetic apparatus. These data will be useful in future biotechnological approaches aiming to induce phytophagous mite resistance in rice.

Curation of the Mammalian Palmitoylome Indicates a Pivotal Role for Palmitoylation in Diseases and Disorders of the Nervous System and Cancers.

Palmitoylation involves the reversible posttranslational addition of palmitate to cysteines and promotes membrane binding and subcellular localization. Recent advancements in the detection and identification of palmitoylated proteins have led to multiple palmitoylation proteomics studies but these datasets are contained within large supplemental tables, making downstream analysis and data mining time-consuming and difficult. Consequently, we curated the data from 15 palmitoylation proteomics studies into one compendium containing 1,838 genes encoding palmitoylated proteins; representing approximately 10% of the genome. Enrichment analysis revealed highly significant enrichments for Gene Ontology biological processes, pathway maps, and process networks related to the nervous system. Strikingly, 41% of synaptic genes encode a palmitoylated protein in the compendium. The top disease associations included cancers and diseases and disorders of the nervous system, with Schizophrenia, HD, and pancreatic ductal carcinoma among the top five, suggesting that aberrant palmitoylation may play a pivotal role in the balance of cell death and survival. This compendium provides a much-needed resource for cell biologists and the palmitoylation field, providing new perspectives for cancer and neurodegeneration.

A newly uncovered group of distantly related lysine methyltransferases preferentially interact with molecular chaperones to regulate their activity.

Methylation is a post-translational modification that can affect numerous features of proteins, notably cellular localization, turnover, activity, and molecular interactions. Recent genome-wide analyses have considerably extended the list of human genes encoding putative methyltransferases. Studies on protein methyltransferases have revealed that the regulatory function of methylation is not limited to epigenetics, with many non-histone substrates now being discovered. We present here our findings on a novel family of distantly related putative methyltransferases. Affinity purification coupled to mass spectrometry shows a marked preference for these proteins to associate with various chaperones. Based on the spectral data, we were able to identify methylation sites in substrates, notably trimethylation of K135 of KIN/Kin17, K561 of HSPA8/Hsc70 as well as corresponding lysine residues in other Hsp70 isoforms, and K315 of VCP/p97. All modification sites were subsequently confirmed in vitro. In the case of VCP, methylation by METTL21D was stimulated by the addition of the UBX cofactor ASPSCR1, which we show directly interacts with the methyltransferase. This stimulatory effect was lost when we used VCP mutants (R155H, R159G, and R191Q) known to cause Inclusion Body Myopathy with Paget's disease of bone and Fronto-temporal Dementia (IBMPFD) and/or familial Amyotrophic Lateral Sclerosis (ALS). Lysine 315 falls in proximity to the Walker B motif of VCP's first ATPase/D1 domain. Our results indicate that methylation of this site negatively impacts its ATPase activity. Overall, this report uncovers a new role for protein methylation as a regulatory pathway for molecular chaperones and defines a novel regulatory mechanism for the chaperone VCP, whose deregulation is causative of degenerative neuromuscular diseases.

Nuclear import of RNA polymerase II is coupled with nucleocytoplasmic shuttling of the RNA polymerase II-associated protein 2.

The RNA polymerase II (RNAP II)-associated protein (RPAP) 2 has been discovered through its association with various subunits of RNAP II in affinity purification coupled with mass spectrometry experiments. Here, we show that RPAP2 is a mainly cytoplasmic protein that shuttles between the cytoplasm and the nucleus. RPAP2 shuttling is tightly coupled with nuclear import of RNAP II, as RPAP2 silencing provokes abnormal accumulation of RNAP II in the cytoplasmic space. Most notably, RPAP4/GPN1 silencing provokes the retention of RPAP2 in the nucleus. Our results support a model in which RPAP2 enters the nucleus in association with RNAP II and returns to the cytoplasm in association with the GTPase GPN1/RPAP4. Although binding of RNAP II to RPAP2 is mediated by an N-terminal domain (amino acids 1-170) that contains a nuclear retention domain, and binding of RPAP4/GPN1 to RPAP2 occurs through a C-terminal domain (amino acids 156-612) that has a dominant cytoplasmic localization domain. In conjunction with previously published data, our results have important implications, as they indicate that RPAP2 controls gene expression by two distinct mechanisms, one that targets RNAP II activity during transcription and the other that controls availability of RNAP II in the nucleus.

PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data.

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

Sheathless capillary electrophoresis-tandem mass spectrometry for top-down characterization of Pyrococcus furiosus proteins on a proteome scale.

Intact protein analysis via top-down mass spectrometry (MS) provides the unique capability of fully characterizing protein isoforms and combinatorial post-translational modifications (PTMs) compared to the bottom-up MS approach. Front-end protein separation poses a challenge for analyzing complex mixtures of intact proteins on a proteomic scale. Here we applied capillary electrophoresis (CE) through a sheathless capillary electrophoresis-electrospray ionization (CESI) interface coupled to an Orbitrap Elite mass spectrometer to profile the proteome from Pyrococcus furiosus. CESI-top-down MS analysis of Pyrococcus furiosus cell lysate identified 134 proteins and 291 proteoforms with a total sample consumption of 270 ng in 120 min of total analysis time. Truncations and various PTMs were detected, including acetylation, disulfide bonds, oxidation, glycosylation, and hypusine. This is the largest scale analysis of intact proteins by CE-top-down MS to date.