PyQuant: A Versatile Framework for Analysis of Quantitative Mass Spectrometry Data

A defect in mitochondrial fatty acid synthesis impairs iron metabolism and causes elevated ceramide levels

In most eukaryotic cells, fatty acid synthesis (FAS) occurs in the cytoplasm and in mitochondria. However, the relative contribution of mitochondrial FAS (mtFAS) to the cellular lipidome is not well defined. Here we show that loss of function of Drosophila mitochondrial enoyl coenzyme A reductase (Mecr), which is the enzyme required for the last step of mtFAS, causes lethality, while neuronal loss of Mecr leads to progressive neurodegeneration. We observe a defect in Fe-S cluster biogenesis and increased iron levels in flies lacking mecr, leading to elevated ceramide levels. Reducing the levels of either iron or ceramide suppresses the neurodegenerative phenotypes, indicating an interplay between ceramide and iron metabolism. Mutations in human MECR cause pediatric-onset neurodegeneration, and we show that human-derived fibroblasts display similar elevated ceramide levels and impaired iron homeostasis. In summary, this study identifies a role of mecr/MECR in ceramide and iron metabolism, providing a mechanistic link between mtFAS and neurodegeneration.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Maternal serum lipidomics identifies lysophosphatidic acid as a predictor of small for gestational age neonates

To discover lipidomic alterations during pregnancy in mothers who subsequently delivered small for gestational age (SGA) neonates and identify predictive lipid markers that can help recognize and manage these mothers, we carried out untargeted lipidomics on maternal serum samples collected between 24-28 weeks of gestation. We used a nested case-control study design and serum from mothers who delivered SGA and appropriate for gestational age babies. We applied untargeted lipidomics using mass spectrometry to characterize lipids and discover changes associated with SGA births during pregnancy. Multivariate pattern recognition software Collaborative Laboratory Integrated Reports (CLIR) was used for the post-analytical recognition of range differences in lipid ratios that could differentiate between SGA and control mothers and their integration for complete separation between the two groups. Here, we report changes in lipids from serum collected during pregnancy in mothers who delivered SGA neonates. In contrast to normal pregnancies where lysophosphatidic acid increased over the course of the pregnancy owing to increased activity of lysophospholipase D, we observed a decrease (32%; P = 0.05) of 20:4-lysophosphatidic acid in SGA mothers, which could potentially compromise fetal growth and development. Integration of lipid ratios in an interpretive tool (CLIR) could completely separate SGA mothers from controls demonstrating the power of untargeted lipidomic analyses for identifying novel predictive biomarkers. Additional studies are required for further assessment of the lipid biomarkers identified in this report.

Proximity-Dependent Biotinylation to Elucidate the Interactome of TNK2 Nonreceptor Tyrosine Kinase

Nonreceptor tyrosine kinases (NRTKs) represent an important class of signaling molecules driving diverse cellular pathways. Aberrant expression and hyperphosphorylation of TNK2, an NRTK, have been implicated in multiple cancers. However, the exact proteins and cellular events that mediate phenotypic changes downstream of TNK2 are unclear. Biological systems that employ proximity-dependent biotinylation methods, such as BioID, are being increasingly used to map protein-protein interactions, as they provide increased sensitivity in discovering interaction partners. In this study, we employed stable isotope labeling with amino acids in cell culture and BioID coupled to the biotinylation site identification technology (BioSITe) method that we recently developed to quantitatively explore the interactome of TNK2. By performing a controlled comparative analysis between full-length TNK2 and its truncated counterpart, we were able to not only identify site-level biotinylation of previously well-established TNK2 binders and substrates including NCK1, NCK2, CTTN, and STAT3, but also discover several novel TNK2 interacting partners. We also performed co-immunoprecipitation and immunofluorescence analysis to validate the interaction between TNK2 and CLINT1, a novel TNK2 interacting protein. Overall, this work reveals the power of the BioSITe method coupled to BioID and highlights several molecules that warrant further exploration to assess their functional significance in TNK2-mediated signaling.

Expanding the clinical and metabolic phenotype of DPM2 deficient congenital disorders of glycosylation

Pathogenic alterations in the DPM2 gene have been previously described in patients with hypotonia, progressive muscle weakness, absent psychomotor development, intractable seizures, and early death. We identified biallelic DPM2 variants in a 23-year-old male with truncal hypotonia, hypertonicity, congenital heart defects, intellectual disability, and generalized muscle wasting. His clinical presentation was much less severe than that of the three previously described patients. This is the second report on this ultra-rare disorder. Here we review the characteristics of previously reported individuals with a defect in the DPM complex while expanding the clinical phenotype of DPM2-Congenital Disorders of Glycosylation. In addition, we offer further insights into the pathomechanism of DPM2-CDG disorder by introducing glycomics and lipidomics analysis.

Mutation-Specific and Common Phosphotyrosine Signatures of KRAS G12D and G13D Alleles

KRAS is one of the most frequently mutated genes across all cancer subtypes. Two of the most frequent oncogenic KRAS mutations observed in patients result in glycine to aspartic acid substitution at either codon 12 (G12D) or 13 (G13D). Although the biochemical differences between these two predominant mutations are not fully understood, distinct clinical features of the resulting tumors suggest involvement of disparate signaling mechanisms. When we compared the global phosphotyrosine proteomic profiles of isogenic colorectal cancer cell lines bearing either G12D or G13D KRAS mutation, we observed both shared as well as unique signaling events induced by the two KRAS mutations. Remarkably, while the G12D mutation led to an increase in membrane proximal and adherens junction signaling, the G13D mutation led to activation of signaling molecules such as nonreceptor tyrosine kinases, MAPK kinases, and regulators of metabolic processes. The importance of one of the cell surface molecules, MPZL1, which was found to be hyperphosphorylated in G12D cells, was confirmed by cellular assays as its knockdown led to a decrease in proliferation of G12D but not G13D expressing cells. Overall, our study reveals important signaling differences across two common KRAS mutations and highlights the utility of our approach to systematically dissect subtle differences between related oncogenic mutants and potentially lead to individualized treatments.

Diet posttranslationally modifies the mouse gut microbial proteome to modulate renal function

Associations between chronic kidney disease (CKD) and the gut microbiota have been postulated, yet questions remain about the underlying mechanisms. In humans, dietary protein increases gut bacterial production of hydrogen sulfide (H2S), indole, and indoxyl sulfate. The latter are uremic toxins, and H2S has diverse physiological functions, some of which are mediated by posttranslational modification. In a mouse model of CKD, we found that a high sulfur amino acid-containing diet resulted in posttranslationally modified microbial tryptophanase activity. This reduced uremic toxin-producing activity and ameliorated progression to CKD in the mice. Thus, diet can tune microbiota function to support healthy host physiology through posttranslational modification without altering microbial community composition.

Impact of Increased FUT8 Expression on the Extracellular Vesicle Proteome in Prostate Cancer Cells

Extracellular vesicles (EVs) are involved in intercellular communication, transporting proteins and nucleic acids to proximal and distal regions. There is evidence of glycosylation influencing protein routing into EVs; however, the impact of aberrant cellular glycotransferase expression on EV protein profiles has yet to be evaluated. In this study, we paired extracellular vesicle characterization and quantitative proteomics to determine the systemic impact of altered α(1,6)fucosyltranferase (FUT8) expression on prostate cancer-derived EVs. Our results showed that increased cellular expression of FUT8 could reduce the number of vesicles secreted by prostate cancer cells as well as increase the abundance of proteins associated with cell motility and prostate cancer metastasis. In addition, overexpression of FUT8 resulted in altered glycans on select EV-derived glycoproteins. This study presents the first evidence of altered cellular glycosylation impacting EV protein profiles and provides further rationale for exploring the functional role of glycosylation in EV biogenesis and biology.

A novel mass spectrometry method for the absolute quantification of several cytochrome P450 and uridine 5'-diphospho-glucuronosyltransferase enzymes in the human liver

Cytochrome P450 (CYP450) and 5'-diphosphate glucuronosyltransferases (UGT) are the two major families of drug-metabolizing enzymes in the human liver microsome (HLM). As a result of their frequent abundance fluctuation among populations, the accurate quantification of these enzymes in different individuals is important for designing patient-specific dosage regimens in the framework of precision medicine. The preparation and quantification of internal standards is an essential step for the quantitative analysis of enzymes. However, the commonly employed stable isotope labeling-based strategy (QconCAT) suffers from requiring very expensive isotopic reagents, tedious experimental procedures, and long labeling times. Furthermore, arginine-to-proline conversion during metabolic isotopic labeling compromises the quantification accuracy. Therefore, we present a new strategy that replaces stable isotope-labeled amino acids with lanthanide labeling for the preparation and quantification of QconCAT internal standard peptides, which leads to a threefold reduction in the reagent costs and a fivefold reduction in the time consumed. The absolute amount of trypsin-digested QconCAT peptides can be obtained by lanthanide labeling and inductively coupled plasma-optical emission spectrometry (ICP-OES) analysis with a high quantification accuracy (%RE < 20%). By taking advantage of the highly selective and facile ICP-OES procedure and multiplexed large-scale absolute target protein quantification using biological mass spectrometry, this strategy was successfully used for the absolute quantification of drug-metabolizing enzymes. We obtained good linearity (correlation coefficient > 0.95) over concentrations spanning 2.5 orders of magnitude with improved sensitivity (limit of quantification = 2 fmol) in nine HLM samples, indicating the potential of this method for large-scale absolute target protein quantification in clinical samples. Graphical abstract.

PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis

Summary

As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces.

Availability and implementation

PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/.

Supplementary information

Supplementary data are available at Bioinformatics online

High-Resolution Enabled 5-plex Mass Defect-Based N, N-Dimethyl Leucine Tags for Quantitative Proteomics

A mass defect-based labeling strategy provides high accuracy as an MS¹-centric quantification method, avoiding the ratio compression that affects isobaric label-based reporter ion quantification. We have developed cost-effective 5-plex mass defect N, N-dimethyl leucine (mdDiLeu) tags for quantification of various biological samples with increased multiplexing at a given resolving power afforded by the addition of mass difference isotopologues. The combination of mass difference and mass defect produces two labeled peak clusters separated by 5 Da in MS¹ spectra that are detected as five isotopic peaks at high resolution with mass differences of 15, 17, and 18 mDa per tag. Synthesis of each of the 5-plex mdDiLeu tags is accomplished by a single straightforward reaction step, making it accessible to any lab. To demonstrate 5-plex mdDiLeu for quantitative proteomics, we perform proof-of-principle experiments of mdDiLeu-labeled Saccharomyces cerevisiae lysate digest on an Orbitrap Fusion Lumos mass spectrometer.

Systematic analysis of protein turnover in primary cells

A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000-6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types.

Establishment of Dimethyl Labeling-based Quantitative Acetylproteomics in Arabidopsis

Protein acetylation, one of many types of post-translational modifications (PTMs), is involved in a variety of biological and cellular processes. In the present study, we applied both CsCl density gradient (CDG) centrifugation-based protein fractionation and a dimethyl-labeling-based 4C quantitative PTM proteomics workflow in the study of dynamic acetylproteomic changes in Arabidopsis. This workflow integrates the dimethyl chemical labeling with chromatography-based acetylpeptide separation and enrichment followed by mass spectrometry (MS) analysis, the extracted ion chromatogram (XIC) quantitation-based computational analysis of mass spectrometry data to measure dynamic changes of acetylpeptide level using an in-house software program, named Stable isotope-based Quantitation-Dimethyl labeling (SQUA-D), and finally the confirmation of ethylene hormone-regulated acetylation using immunoblot analysis. Eventually, using this proteomic approach, 7456 unambiguous acetylation sites were found from 2638 different acetylproteins, and 5250 acetylation sites, including 5233 sites on lysine side chain and 17 sites on protein N termini, were identified repetitively. Out of these repetitively discovered acetylation sites, 4228 sites on lysine side chain (i.e. 80.5%) are novel. These acetylproteins are exemplified by the histone superfamily, ribosomal and heat shock proteins, and proteins related to stress/stimulus responses and energy metabolism. The novel acetylproteins enriched by the CDG centrifugation fractionation contain many cellular trafficking proteins, membrane-bound receptors, and receptor-like kinases, which are mostly involved in brassinosteroid, light, gravity, and development signaling. In addition, we identified 12 highly conserved acetylation site motifs within histones, P-glycoproteins, actin depolymerizing factors, ATPases, transcription factors, and receptor-like kinases. Using SQUA-D software, we have quantified 33 ethylene hormone-enhanced and 31 hormone-suppressed acetylpeptide groups or called unique PTM peptide arrays (UPAs) that share the identical unique PTM site pattern (UPSP). This CDG centrifugation protein fractionation in combination with dimethyl labeling-based quantitative PTM proteomics, and SQUA-D may be applied in the quantitation of any PTM proteins in any model eukaryotes and agricultural crops as well as tissue samples of animals and human beings.

Mass Defect-Based Dimethyl Pyrimidinyl Ornithine (DiPyrO) Tags for Multiplex Quantitative Proteomics

We have developed a novel amine-reactive mass defect-based chemical tag, dimethyl pyrimidinyl ornithine (DiPyrO), that is compact in size, is suitable for various biological samples, and enables highly multiplexed quantification of peptides at the MS¹ level without increasing mass spectral complexity. The DiPyrO tag structure incorporates heavy isotopes in a variety of configurations to impart as much as 45.3 mDa or as little as 5.8 mDa per tag between labeled peptides. Notably, peptides containing lysine are labeled with two tags, doubling the imparted mass defect to up to 90.6 mDa for the duplex tags and effectively reducing the resolving power requirement compared to previously reported mass defect-based quantification approaches. This permits current and previous generation LTQ-Orbitrap platforms to perform confident quantitative analyses of two DiPyrO-labeled samples at 100K resolving power, whereas 3-plex and 6-plex quantifications are possible at 240K and 480K resolving powers, respectively. In this work, we discuss the design and synthesis of the DiPyrO tag, characterize its effect on labeled proteome analysis by nanoLC-MS², and demonstrate proof-of-principle applications of the duplex and triplex tags for quantitative proteomics using high-resolution MS acquisition on the Orbitrap Elite and Orbitrap Fusion Lumos.

pyQms enables universal and accurate quantification of mass spectrometry data

Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present pyQms, an open-source software for accurate quantification of all types of molecules measurable by MS. pyQms uses isotope pattern matching that offers an accurate quality assessment of all quantifications and the ability to directly incorporate mass spectrometer accuracy. pyQms is, due to its universal design, applicable to every research field, labeling strategy, and acquisition technique. This opens ultimate flexibility for researchers to design experiments employing innovative and hitherto unexplored labeling strategies. Importantly, pyQms performs very well to accurately quantify partially labeled proteomes in large scale and high throughput, the most challenging task for a quantification algorithm.