A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics using SCIEX TripleTOF5600 and 6600+, Orbitrap QE-HFX, Waters Synapt GS-Si and Synapt XS and Bruker timsTOF Pro.

To assess the ClpC2 interactome, a IP-MS analysis was performed using ectopically expressed tagged ClpC2 in a ClpC2 deficient cell line. To evaluate potential binding sites on ClpC2, CymA treatment was applied to compete with potential interactors for a shared ClpC2 binding site.

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Label Free Quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyse and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardisation in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range.

To assess the impact of the bactericidal compounds CymA and ecumicin on the mycobacterial proteome and to monitor proteome remodeling by the AAA+ ClpC1 protease machinery, we performed a LFQ proteomics analysis treating the cells at sub-bactericidal compound concentration. We identified new players of the mycobacterial ClpC1 stress response (ClpC2 and ClpC3), as novel bacterial antibiotic resistance mechanism.

Senolytic drugs are designed to selectively clear senescent cells (SnCs) that accumulate with injury or aging. In a mouse model of osteoarthritis (OA), senolysis yields a pro-regenerative response. However, recent research studies showed that the therapeutic benefit is reduced in aged mice. Increased oxidative stress (redox) mediates metabolic dysregulation and accumulation of posttranslational modifications (PTM) on proteins from the cartilage and is one of the major hallmarks of advanced age. This research investigated whether the senolytic treatment differentially affects oxidative load in the joints from young and aged animals. We employed label-free quantitative (LFQ) analysis of changes in the protein expression profiles and associated carbonylated PTMs in the extracted joint proteins. Our proteomic survey focused to a set of PTMs described as advanced glycation-end products (AGEs) or advanced lipooxidation-end products (ALEs), known to accumulate during aging and age-associated diseases. AGEs and ALEs are the result of non-enzymatic reactions of protein with reducing sugars, or with oxidized sugar or lipid degradation products, which may alter and sometimes crosslink the positively charged amino acids lysine and arginine on proteins. Our proteomic dataset supported detection of several AGEs and ALEs, including the adducts 4-ONE (4-oxononenal), 4-ONE+Delta:H(-2)O(-1) (dehydrated 4-oxononenal Michael adduct), carboxyethyl, carboxymethyl, 3-deoxyglucosone derived dihydroxyimidazoline, G-H1 (glyoxal-derived hydroimidazolone), HNE (4-hydroxynonenal), several carbonylated adducts including proline to pyrrolidinone or pyrrolidone Other detected modifications of note include mono-oxidation and di-oxidation products, including tryptophan to kynurenine. We also detected lysine-epsilon-gly-gly (GlyGly), which marks ubiquitylated sites, indicating potential substrates of proteasome-dependent degradation. The grand total PTM change was negative for treated aged OA mice but positive for treated young OA mice, regardless of whether summed across modifications or across proteins. In other words, senolytic treatment reduced oxidation associated PTMs more effectively in aged OA mice than in young OA mice. The LFQ proteomics analysis was complemented with new biophysical computational tools aimed to predict the stability of carbonylated proteins extracted from the joints. The biophysical model predicted divergent proteomic responses between young and aged animals. Altogether, our results showed that senolysis reduces overall oxidative stress in the aged arthritic joints in vivo.

protein-peptide: the peptide area exported from LFQ from PEAKs Studio for the 49 samples. This is used for protein quantification, and PTM analysis.Filter: The filters applied for LFQ analysis. protein-peptide: the peptide area exported from LFQ from PEAKs Studio for the 49 samples. This is used for protein quantification, and PTM analysis. Filter: The filters applied for LFQ analysis. (XLSX)

Comparison of number of differentially expressed proteins identified by LFQ and iTRAQ approaches.

The Planctomycetes have unique cell architectures with heavily invaginated membrane networks, as here confirmed by three-dimensional models reconstructed from FIB-SEM images of Tuwongella immobilis and Gemmata obscuriglobus. We studied the subcellular proteomes of T. immobilis, and for comparison Escherichia coli, by differential solubilisation followed by LC-MS/MS analysis. More than one thousand proteins were identified in each species. The first Tris-soluble fraction contained mostly cytoplasmic proteins, whereas membrane proteins dominated the second Triton X-100 cell extract. About 50 proteins were exclusive to the third SDS-soluble extract in E. coli, mostly outer membrane and cell surface proteins. A 5-fold higher number of proteins were identified in this fraction in T. immobilis, including predicted cell surface proteins with a prepilin cleavage motif or a Planctomycetes-specific signal peptide. Surprisingly, 50% of the predicted cytoplasmic proteins were exclusively associated with the SDS-soluble protein fraction in T. immobilis. Proteins involved in signal transduction pathway and transcriptional regulation were highly overrepresented in this set, as were also enzymes involved in DNA repair and processing of stable RNAs. Some of these proteins are unique to the Planctomycetes, while others have evolved by gene duplication and domain shuffling. In cases where the paralogs showed different fractionation patterns, it was the most divergent gene copy that was uniquely associated with the SDS-soluble fraction. These results are consistent with the hypothesis that gene duplication and domain shuffling underlie the evolution of new gene functions in the Planctomycetes. We further suggest that repair and recycling of “ageing” molecules play a more important role in the Planctomycetes than in other bacteria due to their large cell sizes, long generation times and life styles.

Burkholderia pseudomallei is the causative agent of melioidosis a disease endemic in South-East Asia and Northern Australia. The mortality rates in these areas are unacceptably high even with antibiotic treatment, attributed to intrinsic and acquired resistance of B. pseudomallei to antibiotics. With very few options for therapeutics there is an urgent requirement to identify anti-bacterial targets for the development of novel, effective treatments. In this study we examine the role and effect of ppiB on the proteome. Using LFQ analysis we show loss of ppiB has dramatic effect on the Burkholderia pseudomallei proteome.

Accurate quantification of individual proteoforms is a crucial step in identifying proteome-wide alterations in different biological conditions. Intact proteoforms have been analyzed predominantly by liquid chromatography–mass spectrometry (LC-MS)-based top-down proteomics (TDP) and quantified primarily by the label-free quantification (LFQ) method, as it requires no additional costly labeling. In TDP, due to frequent coelution and complex signal structures, overlapping signals deriving from multiple proteoforms complicate accurate quantification. Here, we introduce FLASHQuant for MS1-level LFQ analysis in TDP, which is capable of automatically resolving and quantifying coeluting proteoforms. In benchmark tests performed with both spike-in proteins and proteome-level mixture data sets, FLASHQuant was shown to perform highly accurate and reproducible quantification in short runtimes of just a few minutes per LC-MS run. In particular, it was demonstrated that resolving overlapping proteoforms boosts the quantification accuracy. FLASHQuant is publicly available as platform-independent open-source software at https://openms.org/flashquant/, accompanied by the simple alignment algorithm ConsensusFeatureGroupDetector for multiple LC-MS runs.

AtACINUS protein is involved in regulation of alternative transcription and splicing(AS). Identifying interaction partners and protein complex compositions for AtACINUS can produce valuable information on the mechanisms by which they regulate transcription and AS, as well as post-translational modifications on AtACINUS. A homozygous 35S::AtACINUS-GFP/acinus-2 plant was selected for similar protein expression level to the endogenous AtACINUS protein of wild-type plants using a native α-AtACINUS antibody. We isolated putative AtACINUS interaction partners from young Arabidopsis seedlings using a modified LaG16LaG2 nanobody. Plants expressing TAP-GFP under 35S promoter were used as controls.

The impact of NatC subunits NAA30, NAA35 and NAA38 on the human proteome (protein abundance). Analysis of HAP1 WT, NAA30 KO, NAA35 KO and NAA38 KO cells

The project contains raw and result files from a proteomic profiling of a male breast cancer (MBC) case. Label-free quantification-mass spectrometry (LFQ-MS) and bioinformatics analysis were employed to investigate the differentially expressed proteins (DEPs) among distinct tissue samples: the primary breast tumor, axillary metastatic lymph nodes and the adjacent non-tumor breast tissue. An additional proteomic comparative analysis was performed with a primary breast tumor of a female patient. A number of Ingenuity® Pathway Analysis (IPA) (QIAGEN Inc.) and functional annotation tools were used to further analyze the DEPs. Altogether, our findings revealed deregulated proteins into signaling pathways involved in the cancer development and provided a landscape of proteomic data for the MBC research.

Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded (FFPE) tissues of patients with closely related subtypes of diffuse large B-cell lymphoma (DLBCL). We combined a super-SILAC approach with label-free quantification (hybrid LFQ), to address situations where the protein is absent in the super-SILAC standard yet present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of DLBCL patients according to their cell-of-origin, using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb et al. MCP 2012 PMID 22442255). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistent with known trends between the subtypes. We employed machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8 and TBC1D4) classified the patients with very low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology.

Similarly to the calculation of the relative abundance at the protein level, the peptide ratio values were calculated based on the log-2 transformed average vales for cases (pT2+) and controls (pTa).Comparison of the quantification results at the peptide and protein level for identifications with conflicting expression trends between fractionated iTRAQ and LFQ.

Proteomics is a powerful approach to study the molecular mechanisms of cancer. In this study, we aim to characterize the proteomic profile of gastric cancer (GC) in patients with diabetes mellitus (DM) type 2. Forty GC tissue samples including 19 cases from diabetic patients and 21 cases from individuals without diabetes (control group) were selected for the proteomics analysis. Gastric tissues were processed following the single-pot, solid-phase-enhanced sample preparation approach—SP3 and enzymatic digestion with trypsin. The resulting peptides were analyzed by LC-MS Liquid Chromatography—Mass Spectrometry (LC-MS). The comparison of protein expression levels between GC samples from diabetic and non-diabetic patients was performed by label-free quantification (LFQ). A total of 6599 protein groups were identified in the 40 samples. Thirty-seven proteins were differentially expressed among the two groups, with 16 upregulated and 21 downregulated in the diabetic cohort. Statistical overresentation tests were considered for different annotation sets including the Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Disease functional databases. Upregulated proteins in the GC samples from diabetic patients were particularly enriched in respiratory electron transport and alcohol metabolic biological processes, while downregulated proteins were associated with epithelial cancers, intestinal diseases, and cell–cell junction cellular components. Taken together, these results support the data already obtained by previous studies that associate diabetes with metabolic disorders and diabetes-associated diseases, such as Alzheimer’s and Parkinson’s, and also provide valuable insights into seven GC-associated protein targets, claudin-3, polymeric immunoglobulin receptor protein, cadherin-17, villin-1, transglutaminase-2, desmoglein-2, and mucin-13, which warrant further investigation.

Supplementary material for paired comparative metabolomics and proteomics on Mycobacterium smegmatis mc²155 during hypoxia, as part of chapter 2 for the thesis "Biochemistry and physiology of mycobacterial adaptations to energy starvation".

Description below is identical to that provided in 'Summary.docx'.

Proteomics_analysis.xlsx

Includes raw and annotated data for comparative proteomics experiments for chapter 2.

The tab ‘Annotated comparisons’ contains fold change and p values for the comparisons for each protein from Mycobacterium smegmatis mc²155 derived from LFQ-Analyst. Functional annotations are derived from KEGG pathways and modules, which utilise the spreadsheets in ‘MSMEG gene annotation’ ‘Protein ids to KEGG pathway’ and ‘KEGG Pathway and Modules’ to link KEGG annotations to MSMEG_XXXX gene identifiers and MSMEG_XXXX to Uniprot ID. Output from LFQ-Analyst is provided in the ‘Full_dataset’, ‘Imputed_matrix’ and ‘Original_matrix’ tabs.

Data provided by the Monash Proteomics and Metabolomics Facility for upload into LFQ-analyst are provided as the ‘combined_protein.tsv’ and ‘LFQ-Analyst_experimental_design.txt’.

Metabolism_analysis.xlsx

Includes annotated data for comparative metabolomics experiments for chapter 2. Within the spreadsheet, TR refers to transition, ST refers to stationary phase and EXP refers to exponential phase. The tabs ‘TRvsEXP’, ‘STvsTR’ and ‘STvsEXP’ contain fold change and p values for each metabolite detected for each comparison. The remaining tabs categorise the metabolites based on KEGG database and IDEOM annotations. For broader categories (‘Lipid metabolism’,’ Carbohydrate metabolism’, ‘Cofactor metabolism’, ‘Nucleotide metabolism’, ‘Amino acid metabolism’ and ‘Peptides’ tabs), annotations were derived directly from filtering the ‘Map’ column of ‘Comparisons’ tab of the IDEOM worksheet (IDEOM_analysis.xlsb). Screenshots are pasted into each tab to show the filtering settings. The remaining tabs comprise narrower categories which were manually annotated with reference to KEGG pathways and maps, and also include rows corresponding to the proteomics data for these categories, so the proteomics and metabolomics data can be interpreted together. The ‘Proteomics’ tab contains the proteomics data referenced by these tabs, which is a copy of the ‘Annotated comparisons’ tab from the ‘Proteomics_analysis.xlsx’ file. A value of ‘N’ indicates the metabolite or protein (at least according to the name in the same row) was not found in these datasets.

The IDEOM worksheet (IDEOM_analysis.xlsb) was provided by the Monash Proteomics and Metabolomics Facility and was used for further analysis and for annotations. ‘Data_for_MA_no_normalization.csv’ was also provided by the Monash Proteomics and Metabolomics Facility for upload into Metaboanalyst (https://www.metaboanalyst.ca/).

Large-scale proteome analysis requires rapid and high-throughput analytical methods. We recently reported a new paradigm in proteome analysis where direct infusion and ion mobility are used instead of liquid chromatography (LC) to achieve rapid and high-throughput proteome analysis. Here, we introduce an improved direct infusion shotgun proteome analysis protocol including label-free quantification (DISPA-LFQ) using CsoDIAq software. With CsoDIAq analysis of DISPA data, we can now identify up to ∼2000 proteins from the HeLa and 293T proteomes, and with DISPA-LFQ, we can quantify ∼1000 proteins from no more than 1 μg of sample within minutes. The identified proteins are involved in numerous valuable pathways including central carbon metabolism, nucleic acid replication and transport, protein synthesis, and endocytosis. Together with a high-throughput sample preparation method in a 96-well plate, we further demonstrate the utility of this technology for performing high-throughput drug analysis in human 293T cells. The total time for data collection from a whole 96-well plate is approximately 8 h. We conclude that the DISPA-LFQ strategy presents a valuable tool for fast identification and quantification of proteins in complex mixtures, which will power a high-throughput proteomic era of drug screening, biomarker discovery, and clinical analysis.

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