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.

Modus operandi: Chromatin recognition by α-helical histone readers.

Histone reader domains provide a mechanism for sensing states of coordinated nuclear processes marked by histone proteins' post-translational modifications (PTMs). Among a growing number of discovered histone readers, the 14-3-3s, ankyrin repeat domains (ARDs), tetratricopeptide repeats (TPRs), bromodomains (BRDs), and HEAT domains are a group of domains using various mechanisms to recognize unmodified or modified histones, yet they all are composed of an α-helical fold. In this review, we compare how these readers fold to create protein domains that are very diverse in their tertiary structures, giving rise to intriguing peptide binding mechanisms resulting in vastly different footprints of their targets.

Proteomics analysis reveals a role for E. coli polyphosphate kinase in membrane structure and polymyxin resistance during starvation.

Polyphosphates (polyP) are chains of inorganic phosphates that can reach over 1000 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 (∆ppk) during starvation to identify novel pathways regulated by PPK. In response to starvation, we found 92 proteins significantly differentially expressed between wild-type and ∆ppk 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 required building blocks. From our dataset, we were particularly interested in Arn and EptA proteins, which were downregulated in ∆ppk 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, and provide evidence that this mis-regulation in ∆ppk cells stems from a failure to induce the BasS/BasR two-component system during starvation. We also show that ∆ppk mutants unable to upregulate 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.

MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening.

Metaproteomics is used to explore the functional dynamics of microbial communities. However, acquiring metaproteomic data by tandem mass spectrometry (MS/MS) is time-consuming and resource-intensive, and there is a demand for computational methods that can be used to reduce these resource requirements. We present MetaProClust-MS1, a computational framework for microbiome feature screening developed to prioritize samples for follow-up MS/MS. In this proof-of-concept study, we tested and compared MetaProClust-MS1 results on gut microbiome data, from fecal samples, acquired using short 15-min MS1-only chromatographic gradients and MS1 spectra from longer 60-min gradients to MS/MS-acquired data. We found that MetaProClust-MS1 identified robust gut microbiome responses caused by xenobiotics with significantly correlated cluster topologies of comparable data sets. We also used MetaProClust-MS1 to reanalyze data from both a clinical MS/MS diagnostic study of pediatric patients with inflammatory bowel disease and an experiment evaluating the therapeutic effects of a small molecule on the brain tissue of Alzheimer's disease mouse models. MetaProClust-MS1 clusters could distinguish between inflammatory bowel disease diagnoses (ulcerative colitis and Crohn's disease) using samples from mucosal luminal interface samples and identified hippocampal proteome shifts of Alzheimer's disease mouse models after small-molecule treatment. Therefore, we demonstrate that MetaProClust-MS1 can screen both microbiomes and single-species proteomes using only MS1 profiles, and our results suggest that this approach may be generalizable to any proteomics experiment. MetaProClust-MS1 may be especially useful for large-scale metaproteomic screening for the prioritization of samples for further metaproteomic characterization, using MS/MS, for instance, in addition to being a promising novel approach for clinical diagnostic screening. IMPORTANCE Growing evidence suggests that human gut microbiome composition and function are highly associated with health and disease. As such, high-throughput metaproteomic studies are becoming more common in gut microbiome research. However, using a conventional long liquid chromatography (LC)-MS/MS gradient metaproteomics approach as an initial screen in large-scale microbiome experiments can be slow and expensive. To combat this challenge, we introduce MetaProClust-MS1, a computational framework for microbiome screening using MS1-only profiles. In this proof-of-concept study, we show that MetaProClust-MS1 identifies clusters of gut microbiome treatments using MS1-only profiles similar to those identified using MS/MS. Our approach allows researchers to prioritize samples and treatments of interest for further metaproteomic analyses and may be generally applicable to any proteomic analysis. In particular, this approach may be especially useful for large-scale metaproteomic screening or in clinical settings where rapid diagnostic evidence is required.

Ddp1 Cooperates with Ppx1 to Counter a Stress Response Initiated by Nonvacuolar Polyphosphate.

In diverse cells from bacterial to mammalian species, inorganic phosphate is stored in long chains called polyphosphate (polyP). These nearly universal polymers, ranging from three to thousands of phosphate moieties in length, are associated with molecular functions, including energy homeostasis, protein folding, and cell signaling. In many cell types, polyphosphate is concentrated in subcellular compartments or organelles. In the budding yeast Saccharomyces cerevisiae, polyP synthesis by the membrane-bound vacuolar transporter chaperone (VTC) complex is coupled to its translocation into the lumen of the vacuole, a lysosome-like organelle, where it is stored at high concentrations. In contrast, the ectopic expression of the bacterial polyphosphate kinase (PPK) results in the toxic accumulation of polyP outside the vacuole. In this study, we used label-free mass spectrometry to investigate the mechanisms underlying this toxicity. We find that PPK expression results in the activation of a stress response mediated in part by the Hog1 and Yak1 kinases and the Msn2/Msn4 transcription factors as well as by changes in protein kinase A (PKA) activity. This response is countered by the combined action of the Ddp1 and Ppx1 polyphosphatases that function together to counter polyP accumulation and downstream toxicity. In contrast, the ectopic expression of previously proposed mammalian polyphosphatases did not impact PPK-mediated toxicity in this model, suggesting either that these enzymes do not function directly as polyphosphatases in vivo or that they require cofactors unique to higher eukaryotes. Our work provides insight into why polyP accumulation outside lysosome-like organelles is toxic. Furthermore, it serves as a resource for exploring how polyP may impact conserved biological processes at a molecular level. IMPORTANCE Cells from bacteria to humans have a molecule called polyphosphate (polyP) that functions in diverse processes. In many microbes, polyP is sequestered in granules or lysosome-related organelles such as vacuoles. In this study, we use an ectopic expression system to force budding yeast to accumulate polyP outside the vacuole. We use proteomics to demonstrate that this nonvacuolar polyP initiates a stress response mediated by a signaling cascade involving the Yak1 and Hog1 kinases and the Msn2 and Msn4 transcription factors. This response is countered by a pair of polyphosphatases with different enzymatic activities that function in concert to degrade polyP. Our results provide new insights into why polyP is confined to specific cell locations in many microbial cells.

Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies.

Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.

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.

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.

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.

Identification of novel lipid droplet factors that regulate lipophagy and cholesterol efflux in macrophage foam cells.

Macrophage autophagy is a highly anti-atherogenic process that promotes the catabolism of cytosolic lipid droplets (LDs) to maintain cellular lipid homeostasis. Selective autophagy relies on tags such as ubiquitin and a set of selectivity factors including selective autophagy receptors (SARs) to label specific cargo for degradation. Originally described in yeast cells, "lipophagy" refers to the degradation of LDs by autophagy. Yet, how LDs are targeted for autophagy is poorly defined. Here, we employed mass spectrometry to identify lipophagy factors within the macrophage foam cell LD proteome. In addition to structural proteins (e.g., PLIN2), metabolic enzymes (e.g., ACSL) and neutral lipases (e.g., PNPLA2), we found the association of proteins related to the ubiquitination machinery (e.g., AUP1) and autophagy (e.g., HMGB, YWHA/14-3-3 proteins). The functional role of candidate lipophagy factors (a total of 91) was tested using a custom siRNA array combined with high-content cholesterol efflux assays. We observed that knocking down several of these genes, including Hmgb1, Hmgb2, Hspa5, and Scarb2, significantly reduced cholesterol efflux, and SARs SQSTM1/p62, NBR1 and OPTN localized to LDs, suggesting a role for these in lipophagy. Using yeast lipophagy assays, we established a genetic requirement for several candidate lipophagy factors in lipophagy, including HSPA5, UBE2G2 and AUP1. Our study is the first to systematically identify several LD-associated proteins of the lipophagy machinery, a finding with important biological and therapeutic implications. Targeting these to selectively enhance lipophagy to promote cholesterol efflux in foam cells may represent a novel strategy to treat atherosclerosis.Abbreviations: ADGRL3: adhesion G protein-coupled receptor L3; agLDL: aggregated low density lipoprotein; AMPK: AMP-activated protein kinase; APOA1: apolipoprotein A1; ATG: autophagy related; AUP1: AUP1 lipid droplet regulating VLDL assembly factor; BMDM: bone-marrow derived macrophages; BNIP3L: BCL2/adenovirus E1B interacting protein 3-like; BSA: bovine serum albumin; CALCOCO2: calcium binding and coiled-coil domain 2; CIRBP: cold inducible RNA binding protein; COLGALT1: collagen beta(1-O)galactosyltransferase 1; CORO1A: coronin 1A; DMA: deletion mutant array; Faa4: long chain fatty acyl-CoA synthetase; FBS: fetal bovine serum; FUS: fused in sarcoma; HMGB1: high mobility group box 1; HMGB2: high mobility group box 2: HSP90AA1: heat shock protein 90: alpha (cytosolic): class A member 1; HSPA5: heat shock protein family A (Hsp70) member 5; HSPA8: heat shock protein 8; HSPB1: heat shock protein 1; HSPH1: heat shock 105kDa/110kDa protein 1; LDAH: lipid droplet associated hydrolase; LIPA: lysosomal acid lipase A; LIR: LC3-interacting region; MACROH2A1: macroH2A.1 histone; MAP1LC3: microtubule-associated protein 1 light chain 3; MCOLN1: mucolipin 1; NBR1: NBR1, autophagy cargo receptor; NPC2: NPC intracellular cholesterol transporter 2; OPTN: optineurin; P/S: penicillin-streptomycin; PLIN2: perilipin 2; PLIN3: perilipin 3; PNPLA2: patatin like phospholipase domain containing 2; RAB: RAB, member RAS oncogene family; RBBP7, retinoblastoma binding protein 7, chromatin remodeling factor; SAR: selective autophagy receptor; SCARB2: scavenger receptor class B, member 2; SGA: synthetic genetic array; SQSTM1: sequestosome 1; TAX1BP1: Tax1 (human T cell leukemia virus type I) binding protein 1; TFEB: transcription factor EB; TOLLIP: toll interacting protein; UBE2G2: ubiquitin conjugating enzyme E2 G2; UVRAG: UV radiation resistance associated gene; VDAC2: voltage dependent anion channel 2; VIM: vimentin.

A functional ecological network based on metaproteomics responses of individual gut microbiomes to resistant starches.

Resistant starches (RS) are dietary compounds processed by the gut microbiota into metabolites, such as butyrate, that are beneficial to the host. The production of butyrate by the microbiome appears to be affected by the plant source and type of RS as well as the individual's microbiota. In this study, we used in vitro culture and metaproteomic methods to explore individual microbiome's functional responses to RS2 (enzymatically-resistant starch), RS3 (retrograded starch) and RS4 (chemically-modified starch). Results showed that RS2 and RS3 significantly altered the protein expressions in the individual gut microbiomes, while RS4 did not result in significant protein changes. Significantly elevated protein groups were enriched in carbohydrate metabolism and transport functions of families Eubacteriaceae, Lachnospiraceae and Ruminococcaceae. In addition, Bifidobacteriaceae was significantly increased in response to RS3. We also observed taxon-specific enrichments of starch metabolism and pentose phosphate pathways corresponding to this family. Functions related to starch utilization, ABC transporters and pyruvate metabolism pathways were consistently increased in the individual microbiomes in response to RS2 and RS3. Given that these taxon-specific responses depended on the type of carbohydrate sources, we constructed a functional ecological network to gain a system-level insight of functional organization. Our results suggest that while some microbes tend to be functionally independent, there are subsets of microbes that are functionally co-regulated by environmental changes, potentially by alterations of trophic interactions.

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.

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.

A Broad Response to Intracellular Long-Chain Polyphosphate in Human Cells.

Polyphosphates (polyPs) are long chains of inorganic phosphates linked by phosphoanhydride bonds. They are found in all kingdoms of life, playing roles in cell growth, infection, and blood coagulation. Unlike in bacteria and lower eukaryotes, the mammalian enzymes responsible for polyP metabolism are largely unexplored. We use RNA sequencing (RNA-seq) and mass spectrometry to define a broad impact of polyP produced inside of mammalian cells via ectopic expression of the E. coli polyP synthetase PPK. We find that multiple cellular compartments can support accumulation of polyP to high levels. Overproduction of polyP is associated with reprogramming of both the transcriptome and proteome, including activation of the ERK1/2-EGR1 signaling axis. Finally, fractionation analysis shows that polyP accumulation results in relocalization of nuclear/cytoskeleton proteins, including targets of non-enzymatic lysine polyphosphorylation. Our work demonstrates that internally produced polyP can activate diverse signaling pathways in human cells.

Quantitative analysis of global protein stability rates in tissues.

Protein degradation is an essential mechanism for maintaining proteostasis in response to internal and external perturbations. Disruption of this process is implicated in many human diseases. We present a new technique, QUAD (Quantification of Azidohomoalanine Degradation), to analyze the global degradation rates in tissues using a non-canonical amino acid and mass spectrometry. QUAD analysis reveals that protein stability varied within tissues, but discernible trends in the data suggest that cellular environment is a major factor dictating stability. Within a tissue, different organelles and protein functions were enriched with different stability patterns. QUAD analysis demonstrated that protein stability is enhanced with age in the brain but not in the liver. Overall, QUAD allows the first global quantitation of protein stability rates in tissues, which will allow new insights and hypotheses in basic and translational research.

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS' efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

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.

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.

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.