Mitogen-Activated Protein Kinase 4-Regulated Metabolic Networks

Mitogen-activated protein kinase 4 (MPK4) was first identified as a negative regulator of systemic acquired resistance. It is also an important kinase involved in many other biological processes in plants, including cytokinesis, reproduction, and photosynthesis. Arabidopsis thaliana mpk4 mutant is dwarf and sterile. Previous omics studies including genomics, transcriptomics, and proteomics have revealed new functions of MPK4 in different biological processes. However, due to challenges in metabolomics, no study has touched upon the metabolomic profiles of the mpk4 mutant. What metabolites and metabolic pathways are potentially regulated by MPK4 are not known. Metabolites are crucial components of plants, and they play important roles in plant growth and development, signaling, and defense. Here we used targeted and untargeted metabolomics to profile metabolites in the wild type and the mpk4 mutant. We found that in addition to the jasmonic acid and salicylic acid pathways, MPK4 is involved in polyamine synthesis and photosynthesis. In addition, we also conducted label-free proteomics of the two genotypes. The integration of metabolomics and proteomics data allows for an insight into the metabolomic networks that are potentially regulated by MPK4.

Resource allocation to cell envelopes and the scaling of bacterial growth rate

Although various empirical studies have reported a positive correlation between the specific growth rate and cell size across bacteria, it is currently unclear what causes this relationship. We conjecture that such scaling occurs because smaller cells have a larger surface-to-volume ratio and thus have to allocate a greater fraction of the total resources to the production of the cell envelope, leaving fewer resources for other biosynthetic processes. To test this theory, we developed a coarse-grained model of bacterial physiology composed of the proteome that converts nutrients into biomass, with the cell envelope acting as a resource sink. Assuming resources are partitioned to maximize the growth rate, the model yields expected scalings. Namely, the growth rate and ribosomal mass fraction scale negatively, while the mass fraction of envelope-producing enzymes scales positively with surface-to-volume. These relationships are compatible with growth measurements and quantitative proteomics data reported in the literature.

Resolving Missing Protein Problems Using Functional Class Scoring

Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in “data holes”. These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples. This hinders biomarker and drug discovery from proteomics data. Network-based approaches are powerful: The Functional Class Scoring (FCS) method using protein complexes was able to easily recover missed proteins with weak or partial support. However, there are limitations: The verification approach (in determining missing protein recovery) is potentially biased as the test data was based on relatively outdated Data-Dependent Acquisition (DDA) proteomics and FCS does not provide a scoring scheme for individual protein components (in significant complexes). To address these issues: First, we devised a more rigorous evaluation of FCS based on same-sample technical replicates. And second, we evaluate using data from more recent Data-Independent Acquisition (DIA) technologies (viz. SWATH).Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved. This confirms FCS as a viable method that selects biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p-values. Although cross-replicate verification rates are not spectacular, the predicted missing proteins as a whole, have higher peptide support than non-predicted proteins. FCS also has the capacity to predict missing proteins that are often lost due to weak specific peptide support. As a yet unresolved limitation, we find that FCS cannot assign meaningful probabilities to individual protein components (no relationship between actual probability of verification and FCS-assigned probability) as it only provides a p-value at the level of complexes.

Development of the “Applied Proteomics” Concept for Biotechnology Applications in Microalgae: Example of the Proteome Data in Nannochloropsis gaditana

Most of the marine ecosystems on our planet are still unknown. Among these ecosystems, microalgae act as a baseline due to their role as primary producers. The estimated millions of species of these microorganisms represent an almost infinite source of potentially active biocomponents offering unlimited biotechnology applications. This review considers current research in microalgae using the “omics” approach, which today is probably the most important biotechnology tool. These techniques enable us to obtain a large volume of data from a single experiment. The specific focus of this review is proteomics as a technique capable of generating a large volume of interesting information in a single proteomics assay, and particularly the concept of applied proteomics. As an example, this concept has been applied to the study of Nannochloropsis gaditana, in which proteomics data generated are transformed into information of high commercial value by identifying proteins with direct applications in the biomedical and agri-food fields, such as the protein designated UCA01 which presents antitumor activity, obtained from N. gaditana.

Sequence, Structure, and Function of DNA-Binding Protein in Deinococcus Wulumuqiensis R12

Deinococcus wulumuqiensis R12, which was isolated from arid irradiated soil in Xinjiang province of China, belongs to a genus Deinococcus that is well-known for its extreme resistance to ionizing radiation and oxidative stress. The DNA-binding protein Dps has been studied for its great contribution to oxidative resistance. To explore the role of Dps in D. wulumuqiensis R12, the Dps sequence and homologous structure were analyzed. In addition, the dps gene was knocked out and proteomics was used to verify the functions of Dps in D. wulumuqiensis R12. Docking data and DNA binding experiments in vitro showed that the R12 Dps has a better DNA binding ability with the N-terminal than the R1 Dps1. When the dps gene was deleted in D. wulumuqiensis R12, its resistance to H2O2 and UV rays was greatly reduced, and the cell envelope was destroyed by H2O2 treatment. Additionally, the qRT-PCR and proteomics data suggested that when the dps gene was deleted, the catalase gene was significantly down-regulated in cells. And the proteomics data indicated the metabolism, transport and oxidation-reduction processes in D. wulumuqiensis R12 were down-regulated after the deletion of dps gene. Dps protein might play an important role in Deinococcus wulumuqiensis R12.

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

The increasingly large amount of public proteomics data enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contains 23 individual datasets, which have a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. We compared protein expression between the different organs, including the distribution of proteins across them. We also performed gene ontology and pathway enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples. Finally, it should be noted that protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.

Six Biomarkers Expressed Stably in Urinary Exosomes During All Life Stages - As the Reference Markers in Urinary Quantification

BackgroundUrinary extracellular exosomes (uEVs) have been identified as a novel, stable and no-invasive source of biomarkers. However, the potential clinical value of uEVs is limited by the lack of standard quantitative proteomics data. It is necessary to uncover ubiquitous and stable proteins of uEVs as the reference markers in urinary quantification.Samples and methodsThe samples from 210 healthy individuals (3~90 years old), were divided into seven different stages of life. The uEVs samples were identified by LC-MS/MS and data-independent acquisition (DIA) methods. Eight stably expressed uEVs proteins were obtained by bioinformatics analysis. Moreover, 42 samples were used to validate by Western blot, ELISA, and immunofluorescence.ResultsA total of 3,002 proteins and 1,393 co-expression uEVs proteins were identified by LC-MS/MS. The bioinformatics analysis showed 1,393 co-expression proteins mostly enriched in endocytosis. Eight proteins were stably expressed throughout the seven age stages (p<0.05). Furthermore, RAB8A, RAB8B, Semaphorin-5A, Plexin-B2, JAMA, and STUB1 were validated by Western blot. Above all, RAB8A and RAB8B are the most stably expressed proteins in different age stages.ConclusionRAB8A, RAB8B, Semaphorin-5A, Plexin-B2, JAMA, and STUB1 were expressed stably proteins throughout the age stages. These six proteins might be the standard reference markers in the analysis of urine exosomal proteomics. RAB8A and RAB8B have been validated are the putative reference markers.

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

KRSA: An R package and R Shiny web application for an end-to-end upstream kinase analysis of kinome array data

Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.