A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

An Integrated Transcriptome and Proteome Analysis Reveals New Insights into Russeting of Bagging and Non-Bagging "Golden Delicious" Apple

Apple skin russeting naturally occurs in many varieties, particularly in “Golden Delicious” and its pedigree, and is regarded as a non-invasive physiological disorder partly caused by excessive deposition of lignin. However, the understanding of its molecular mechanism is still limited. In this study, we used iTRAQ (isobaric tags for relative and absolute quantitation) and RNA-seq to detect the changes in the expression levels of genes and proteins in three developmental stages of russeting formation, in russeted (non-bagging) and non-russeted (bagging) skin of “Golden Delicious” apple. 2856 differentially expressed genes and 942 differentially expressed proteins in the comparison groups were detected at the transcript level and protein level, respectively. A correlation analysis of the transcriptomics and proteomics data revealed that four genes (MD03G1059200, MD08G1009200, MD17G1092400, and MD17G1225100) involved in lignin biosynthesis are significant changed during apple russeting formation. Additionally, 92 transcription factors, including 4 LIM transcription factors, may be involved in apple russeting formation. Among them, one LIM transcription factor (MD15G1068200) was capable of binding to the PAL-box like (CCACTTGAGTAC) element, which indicated it was potentially involved in lignin biosynthesis. This study will provide further views on the molecular mechanisms controlling apple russeting formation.

Proteomic Response to Rising Temperature in the Marine Cyanobacterium Synechococcus Grown in Different Nitrogen Sources

Synechococcus is one of the most important contributors to global primary productivity, and ocean warming is predicted to increase abundance and distribution of Synechococcus in the ocean. Here, we investigated molecular response of an oceanic Synechococcus strain WH8102 grown in two nitrogen sources (nitrate and urea) under present (25°C) and predicted future (28°C) temperature conditions using an isobaric tag (IBT)-based quantitative proteomic approach. Rising temperature decreased growth rate, contents of chlorophyll a, protein and sugar in the nitrate-grown cells, but only decreased protein content and significantly increased zeaxanthin content of the urea-grown cells. Expressions of CsoS2 protein involved in carboxysome formation and ribosomal subunits in both nitrate- and urea-grown cells were significantly decreased in rising temperature, whereas carbohydrate selective porin and sucrose-phosphate synthase (SPS) were remarkably up-regulated, and carbohydrate degradation associated proteins, i.e., glycogen phosphorylase kinase, fructokinase and glucose-6-phosphate dehydrogenase, were down-regulated in the urea-grown cells. Rising temperature also increased expressions of three redox-sensitive enzymes (peroxiredoxin, thioredoxin, and CP12) in both nitrate- and urea-grown cells. Our results indicated that rising temperature did not enhance cell growth of Synechococcus; on the contrary, it impaired cell functions, and this might influence cell abundance and distribution of Synechococcus in a future ocean.

Decreased metabolism and increased tolerance to extreme environments in Staphylococcus warneri during long-term spaceflight

Many studies have shown that the space environment can affect bacteria by causing a range of mutations. However, to date, few studies have explored the effects of long‐term spaceflight (>1 month) on bacteria. In this study, a Staphylococcus warneri strain that was isolated from the Shenzhou‐10 spacecraft and had experienced a spaceflight (15 days) was carried into space again. After a 64‐day flight, combined phenotypic, genomic, transcriptomic, and proteomic analyses were performed to compare the influence of the two spaceflights on this bacterium. Compared with short‐term spaceflight, long‐term spaceflight increased the biofilm formation ability of S. warneri and the cell wall resistance to external environmental stress but reduced the sensitivity to chemical stimulation. Further analysis showed that these changes might be associated with the significantly upregulated gene expression of the phosphotransferase system, which regulates the metabolism of sugars, including glucose, mannose, fructose, and cellobiose. The mutation of S. warneri caused by the 15‐day spaceflight was limited at the phenotype and gene level after cultivation on the ground. After 79 days of spaceflight, significant changes in S. warneri were observed. The phosphotransferase system of S. warneri was upregulated by long‐term space stimulation, which resulted in a series of changes in the cell wall, biofilm, and chemical sensitivity, thus enhancing the resistance and adaptability of the bacterium to the external environment.

ProteomicsDB

ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC–MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein–protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.

Quantitative Proteome Reveals Variation in the Condition Factor of Sea Urchin Strongylocentrotus nudus during the Fishing Season Using an iTRAQ-based Approach

To investigate the variation in the condition factor of the sea urchin Strongylocentrotus nudus (S. nudus), gonads were collected in May (MAY), June (JUN), and July (JUL), at the beginning (AUG-b) and end of August (AUG-e). Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) detection of the gonads revealed an obvious enhancement of the band at about 37 kDa from July, which was identified as transforming growth factor-beta-induced protein ig-h3 (TGFBI) by nanoLC-ESI-MS/MS. Gonadal proteins were identified by isobaric tagging for relative and absolute quantitation (iTRAQ), and regulation of the identified proteins in pairs of the collected groups was observed. A total of 174 differentially expressed proteins (DEPs) were identified. Seven of the DEPs showed significant correlations with both the gonad index (GI) and protein content. These correlations included 6-phosphogluconate dehydrogenase, decarboxylating isoform X2 (6PGD), CAD protein, myoferlin isoform X8, ribosomal protein L36 (RL36), isocitrate dehydrogenase [NADP], mitochondrial isoform X2 (IDH), multifunctional protein ADE2 isoform X3, sperm-activating peptides (SAPs) and aldehyde dehydrogenase, and mitochondrial (ALDH). However, TGFBI had no correlation with gonad index (GI) or protein content. 6PGD, IDH, multifunctional protein ADE2 isoform X3, and ALDH were shown to interact with each other and might play key roles in changing the condition factor of S. nudus gonads.

pValid: Validation Beyond the Target-Decoy Approach for Peptide Identification in Shotgun Proteomics

As the de facto validation method in mass spectrometry-based proteomics, the target-decoy approach determines a threshold to estimate the false discovery rate and then filters those identifications beyond the threshold. However, the incorrect identifications within the threshold are still unknown and further validation methods are needed. In this study, we characterized a framework of validation and investigated a number of common and novel validation methods. We first defined the accuracy of a validation method by its false-positive rate (FPR) and false-negative rate (FNR) and, further, proved that a validation method with lower FPR and FNR led to identifications with higher sensitivity and precision. Then we proposed a validation method named pValid that incorporated an open database search and a theoretical spectrum prediction strategy via a machine-learning technology. pValid was compared with four common validation methods as well as a synthetic peptide validation method. Tests on three benchmark data sets indicated that pValid had an FPR of 0.03% and an FNR of 1.79% on average, both superior to the other four common validation methods. Tests on a synthetic peptide data set also indicated that the FPR and FNR of pValid were better than those of the synthetic peptide validation method. Tests on a large-scale human proteome data set indicated that pValid successfully flagged the highest number of incorrect identifications among all five methods. Further considering its cost-effectiveness, pValid has the potential to be a feasible validation tool for peptide identification.

Synaptic markers of cognitive decline in neurodegenerative diseases: a proteomic approach

See Attems and Jellinger (doi:10.1093/brain/awx360) for a scientific commentary on this article.Cognitive changes occurring throughout the pathogenesis of neurodegenerative diseases are directly linked to synaptic loss. We used in-depth proteomics to compare 32 post-mortem human brains in the prefrontal cortex of prospectively followed patients with Alzheimer's disease, Parkinson's disease with dementia, dementia with Lewy bodies and older adults without dementia. In total, we identified 10 325 proteins, 851 of which were synaptic proteins. Levels of 25 synaptic proteins were significantly altered in the various dementia groups. Significant loss of SNAP47, GAP43, SYBU (syntabulin), LRFN2, SV2C, SYT2 (synaptotagmin 2), GRIA3 and GRIA4 were further validated on a larger cohort comprised of 92 brain samples using ELISA or western blot. Cognitive impairment before death and rate of cognitive decline significantly correlated with loss of SNAP47, SYBU, LRFN2, SV2C and GRIA3 proteins. Besides differentiating Parkinson's disease dementia, dementia with Lewy bodies, and Alzheimer's disease from controls with high sensitivity and specificity, synaptic proteins also reliably discriminated Parkinson's disease dementia from Alzheimer's disease patients. Our results suggest that these particular synaptic proteins have an important predictive and discriminative molecular fingerprint in neurodegenerative diseases and could be a potential target for early disease intervention.

The Synthetic Phenotype of ΔbamB ΔbamE Double Mutants Results from a Lethal Jamming of the Bam Complex by the Lipoprotein RcsF

The selective permeability of the Gram-negative outer membrane (OM) is maintained by integral β-barrel outer membrane proteins (OMPs). The heteropentomeric β-barrel assembly machine (Bam) folds and inserts OMPs into the OM. Coordination of the essential proteins BamA and BamD is critical for OMP assembly and therefore the viability of the cell. The role of the nonessential lipoproteins BamBCE has yet to be characterized; however, genetic evidence suggests that they have nonoverlapping roles in OMP assembly. In this work, we quantify changes of the proteome in the conditional lethal ΔbamB ΔbamE double mutant. We show that cells lacking BamB and BamE have a global OMP defect that is a result of a lethal obstruction of an assembly-competent Bam complex by the lipoprotein RcsF. RcsF is a stress-sensing lipoprotein that is threaded through the lumen of abundant β-barrel OMPs by the Bam complex to expose the amino terminus on the cell surface. We demonstrate that simply removing this lipoprotein corrects the severe OMP assembly defect of the double mutant nearly as efficiently as a previously isolated suppressor mutation in bamA We propose that BamB and BamE play crucial, nonoverlapping roles to coordinate the activities of BamA and BamD during OMP biogenesis.IMPORTANCE Protein assembly into lipid bilayers is an essential process that ensures the viability of diverse organisms. In Gram-negative bacteria, the heteropentomeric β-barrel assembly machine (Bam) folds and inserts proteins into the outer membrane. Due to its essentiality, outer membrane protein (OMP) assembly by the Bam complex is an attractive target for antibiotic development. Here, we show that the conditional lethal phenotype of a mutant lacking two of the three nonessential lipoproteins, BamB and BamE, is caused by lethal jamming of the stripped-down Bam complex by a normally surface-exposed lipoprotein, RcsF. The heterotrimeric Bam complex (BamA, BamD, BamC) is nearly as efficient as the wild-type complex in OMP assembly if RcsF is removed. Our study highlights the importance of BamB and BamE in regulating the interaction between BamA and BamD and expands our understanding of the role of the Bam complex in outer membrane biogenesis.

Centriolar satellites are required for efficient ciliogenesis and ciliary content regulation

Centriolar satellites are ubiquitous in vertebrate cells. They have recently emerged as key regulators of centrosome/cilium biogenesis, and their mutations are linked to ciliopathies. However, their precise functions and mechanisms of action remain poorly understood. Here, we generated a kidney epithelial cell line (IMCD3) lacking satellites by CRISPR/Cas9-mediated PCM1 deletion and investigated the cellular and molecular consequences of satellite loss. Cells lacking satellites still formed full-length cilia but at significantly lower numbers, with changes in the centrosomal and cellular levels of key ciliogenesis factors. Using these cells, we identified new ciliary functions of satellites such as regulation of ciliary content, Hedgehog signaling, and epithelial cell organization in three-dimensional cultures. However, other functions of satellites, namely proliferation, cell cycle progression, and centriole duplication, were unaffected in these cells. Quantitative transcriptomic and proteomic profiling revealed that loss of satellites affects transcription scarcely, but significantly alters the proteome. Importantly, the centrosome proteome mostly remains unaltered in the cells lacking satellites. Together, our findings identify centriolar satellites as regulators of efficient cilium assembly and function and provide insight into disease mechanisms of ciliopathies.

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta

Here, we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis, we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy.

Blood cells travel through the blood vessels in a soupy mixture of proteins called plasma. Most of these proteins are plasma-specific, yet small amounts of proteins can leak into the plasma from other body parts and may provide hints about what is going on elsewhere in the body. This could allow doctors to use plasma samples to assess health or detect disease. But so far developing methods to detect these leaked proteins has proved difficult.

Plasma passing through the placenta can transfer proteins between a pregnant woman and her baby. Learning more about these protein exchanges may help scientists understand how the mother and baby adapt to each other and what triggers child birth. But, so far, they have been hard to study. Using DNA to help trace the origins of proteins found in mother or baby could make it easier.

Now, Pernemalm et al. have used DNA sequencing in combination with protein analysis to identify proteins passed between two pregnant mothers and their babies. Comparing the genetic sequences of each mother and child made it possible to trace the origin of the proteins. For example, if a mother had a version of the protein that matched genes the child inherited from its father, they knew it passed from the baby to the mother. This approach found 24 proteins in plasma from two pregnant mothers that had likely passed through the placenta during pregnancy. Pernemalm et al. also analyzed the plasma of 30 healthy individuals and confirmed that it contained several proteins that had likely leaked from other organs, including the lungs and pancreas.

Monitoring protein transfer between pregnant mother and baby may help scientists identify what triggers normal or premature deliveries. One advantage of the technique developed Pernemalm et al. is that it can analyze plasma proteins from large numbers of people, which could enable larger studies. More refinement of the technique may also allow scientists to identify leaked proteins in the plasma that provide an early warning of cancer or other diseases.

Breast cancer quantitative proteome and proteogenomic landscape

In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.

Gene expression profiles can classify breast cancer into five clinically relevant subtypes. Here, the authors perform an in-depth quantitative profiling of the proteome of 45 breast tumors, and show they can recapitulate the transcriptome-based classifications and identify many potentially antigenic tumour-specific peptides.

Proteogenomics and Hi-C reveal transcriptional dysregulation in high hyperdiploid childhood acute lymphoblastic leukemia

Hyperdiploidy, i.e. gain of whole chromosomes, is one of the most common genetic features of childhood acute lymphoblastic leukemia (ALL), but its pathogenetic impact is poorly understood. Here, we report a proteogenomic analysis on matched datasets from genomic profiling, RNA-sequencing, and mass spectrometry-based analysis of >8,000 genes and proteins as well as Hi-C of primary patient samples from hyperdiploid and ETV6/RUNX1-positive pediatric ALL. We show that CTCF and cohesin, which are master regulators of chromatin architecture, display low expression in hyperdiploid ALL. In line with this, a general genome-wide dysregulation of gene expression in relation to topologically associating domain (TAD) borders were seen in the hyperdiploid group. Furthermore, Hi-C of a limited number of hyperdiploid childhood ALL cases revealed that 2/4 cases displayed a clear loss of TAD boundary strength and 3/4 showed reduced insulation at TAD borders, with putative leukemogenic effects.

Accurate Estimation of Context-Dependent False Discovery Rates in Top-Down Proteomics

Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.

Proteome Analysis Revealed Changes in Protein Expression Patterns Caused by Mutations in Ehrlichia chaffeensis

The tick-borne rickettsial pathogen, Ehrlichia chaffeensis, causes monocytic ehrlichiosis in people and other vertebrate hosts. Mutational analysis in E. chaffeensis genome aids in better understanding of its infection and persistence in host cells and in the development of attenuated vaccines. Our recent RNA deep sequencing study revealed that three genomic mutations caused global changes in the gene expression patterns, which in turn affect the ability of pathogen's survival in a host and the host's ability to induce protection against the pathogen. In this follow-up study, we document the impact of mutations on the pathogen's global protein expression and the influence of protein abundance on a mutant's attenuation and protection of vertebrate host against infection. iTRAQ labeling and mass spectrometry analysis of E. chaffeensis wildtype and mutants identified 564 proteins covering about 63% of the genome. Mutation in ECH_0379 gene encoding for an antiporter protein, causing attenuated growth in vertebrate hosts, led to overexpression of p28 outer membrane proteins, molecular chaperons, and metabolic enzymes, while a mutation downstream to the ECH_0490 gene that caused minimal impact on the pathogen's in vivo growth resulted in major changes in the expression of outer membrane proteins, transcriptional regulators and T4SS proteins. ECH_0660 gene mutation, causing the pathogen's rapid clearance and offering protection against wild type infection challenge in a vertebrate host, had a minimal impact on proteome similar to our prior observations from transcriptome analysis. While the global proteome data revealed fewer translated proteins compared to the transcripts identified from RNA deep sequencing analysis, there is a great deal of correlation noted between the global proteome and transcriptome analysis. Further, global proteome analysis, including the assessment of 2D resolved total and immunoproteomes revealed greater variations in the highly immunogenic p28-Omp proteins.

Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics

Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differential proteins use intermediate filters to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered data sets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical data set we discovered 35 proteins at 5% FDR, whereas the original study discovered 1 and MaxQuant/Perseus 4 proteins at this threshold. Compellingly, these 35 proteins showed enrichment for functional annotation terms, whereas the top ranked proteins reported by MaxQuant/Perseus showed no enrichment. The model executes in minutes and is freely available at https://pypi.org/project/triqler/.

iTRAQ-based comparative proteomic analysis of differentially expressed proteins in Rhodococcus sp. BAP-1 induced by fluoranthene

To reveal the molecular mechanism at the level of regulation of proteins in Rhodococcus sp. BAP-1 induced by fluoranthene comparative proteomic analysis was performed on proteins extracted from fluoranthene-exposed cells on 1 d, 3 d, 6 d and 8 d compared with control cells using isobaric tags for relative and absolute quantization (iTRAQ) labeling and LC-MS/MS analysis to access differentially expressed proteins. As a result, we detected a total of 897 significantly differentially expressed proteins, including 30 shared proteins in four comparison clusters. We were able to short-list 190, 329, 101 and 90 proteins that were over-represented, and 394, 234, 65 and 49 under-represented proteins, in 1d/control, 3d/control, 6d/control and 8d/control comparisons, respectively. Functional analysis relied on Clusters of Orthologous Groups (COG), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that fluoranthene significantly altered the expression of proteins involved in metabolic and biosynthesis processes. Furthermore, BAP-1 up-regulates aldehyde dehydrogenase, cytochrome c oxidase, and oligopeptide transport ATP-binding protein, while down-regulates several other proteins in order to adapt to fluoranthene exposure. These findings provide important clues to reveal fluoranthene degradation mechanism in BAP-1 and promote its bioremediation applications.

Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae

The microbiome has a strong impact on human health and disease and is, therefore, increasingly studied in a clinical context. Metaproteomics is also attracting considerable attention, and such data can be efficiently generated today owing to improvements in mass spectrometry-based proteomics. As we will discuss in this study, there are still major challenges notably in data analysis that need to be overcome. Here, we analyzed 212 fecal samples from 56 hospitalized acute leukemia patients with multidrug-resistant Enterobactericeae (MRE) gut colonization using metagenomics and metaproteomics. This is one of the largest clinical metaproteomic studies to date, and the first metaproteomic study addressing the gut microbiome in MRE colonized acute leukemia patients. Based on this substantial data set, we discuss major current limitations in clinical metaproteomic data analysis to provide guidance to researchers in the field. Notably, the results show that public metagenome databases are incomplete and that sample-specific metagenomes improve results. Furthermore, biological variation is tremendous which challenges clinical study designs and argues that longitudinal measurements of individual patients are a valuable future addition to the analysis of patient cohorts.

Integrated analysis of transcriptomic and proteomic data from tree peony (P. ostii) seeds reveals key developmental stages and candidate genes related to oil biosynthesis and fatty acid metabolism

Tree peony (Paeonia section Moutan DC.) seeds are an excellent source of beneficial natural compounds that promote health, and they contain high levels of alpha-linolenic acid (ALA). In recent years, tree peony has been emerging as an oil crop. Therefore, combined analysis of the transcriptome and proteome of tree peony (P. ostii) seeds at 25, 32, 39, 53, 67, 81, 88, 95, and 109 days after pollination (DAP) was conducted to better understand the transcriptional and translational regulation of seed development and oil biosynthesis. A total of 38,482 unigenes and 2841 proteins were identified. A total of 26,912 differentially expressed genes (DEGs) and 592 differentially expressed proteins (DEPs) were clustered into three groups corresponding to the rapid growth, seed inclusion enrichment and conversion, and late dehydration and mature stages of seed development. Fifteen lipid metabolism pathways were identified at both the transcriptome and proteome levels. Pathway enrichment analysis revealed that a period of rapid fatty acid biosynthesis occurred at 53-88 DAP. Furthermore, 211 genes and 35 proteins associated with the fatty acid metabolism pathway, 63 genes and 11 proteins associated with the biosynthesis of unsaturated fatty acids (UFAs), and 115 genes and 24 proteins associated with ALA metabolism were identified. Phylogenetic analysis revealed that 16 putative fatty acid desaturase (FAD)-encoding genes clustered into four FAD groups, eight of which exhibited the highest expression at 53 DAP, suggesting that they play an important role in ALA accumulation. RT-qPCR analysis indicated that the temporal expression patterns of oil biosynthesis genes were largely similar to the RNA-seq results. The expression patterns of fatty acid metabolism- and seed development-related proteins determined by MRM were also highly consistent with the results obtained in the proteomic analysis. Correlation analysis indicated significant differences in the number and abundance of DEGs and DEPs but a high level of consistency in expression patterns and metabolic pathways. The results of the present study represent the first combined transcriptomic and proteomic analysis of tree peony seeds and provide insight into tree peony seed development and oil accumulation.

Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy

Optimization of chromatography and data analysis resulted in more than 10 000 proteins in a single shot at a validated FDR of 1% (two-species test) and revealed deep insights into the testis cancer physiology.

Lactobacillus gasseri LA39 Activates the Oxidative Phosphorylation Pathway in Porcine Intestinal Epithelial Cells

Intestinal microbial interactions with the host epithelium have important roles in host health. Our previous data have suggested that Lactobacillus gasseri LA39 is the predominant intestinal Lactobacillus in weaned piglets. However, the regulatory role of L. gasseri LA39 in the intestinal epithelial protein expression in piglets remains unclear. In the present study, we conducted comparative proteomics approach to investigate the intestinal epithelial protein profile alteration caused by L. gasseri LA39 in piglets. The expressions of 15 proteins significantly increased, whereas the expressions of 13 proteins significantly decreased in the IPEC-J2 cells upon L. gasseri LA39 treatment. Bioinformatics analyses, including COG function annotation, GO annotation, and KEGG pathway analysis for the differentially expressed proteins revealed that the oxidative phosphorylation (OXPHOS) pathway in IPEC-J2 cells was significantly activated by L. gasseri LA39 treatment. Further data indicated that two differentially expressed proteins UQCRC2 and TCIRG1, associated with the OXPHOS pathway, and cellular ATP levels in IPEC-J2 cells were significantly up-regulated by L. gasseri LA39 treatment. Importantly, the in vivo data indicated that oral gavage of L. gasseri LA39 significantly increased the expression of UQCRC2 and TCIRG1 and the cellular ATP levels in the intestinal epithelial cells of weaned piglets. Our results, both in vitro and in vivo, reveal that L. gasseri LA39 activates the OXPHOS pathway and increases the energy production in porcine intestinal epithelial cells. These findings suggest that L. gasseri LA39 may be a potential probiotics candidate for intestinal energy production promotion and confers health-promoting functions in mammals.

Accumulation of endogenous peptides triggers a pathogen stress response in Arabidopsis thaliana

The stepwise degradation of peptides to amino acids in plant mitochondria and chloroplasts is catalyzed by a network of oligopeptidases (presequence protease PreP, organellar oligopeptidase OOP) and aminopeptidases. In the present report, we show that the lack of oligopeptidase activity in Arabidopsis thaliana results in the accumulation of endogenous free peptides, mostly of chloroplastic origin (targeting peptides and degradation products). Using mRNA sequencing and deep coverage proteomics, allowing for the identification of 17 000 transcripts and 11 000 proteins, respectively, we uncover a peptide-stress response occurring in plants lacking PreP and OOP oligopeptidase activity. The peptide-stress response results in the activation of the classical plant defense pathways in the absence of pathogenic challenge. The constitutive activation of the pathogen-defense pathways imposes a strong growth penalty and a reduction of the plants reproductive fitness. Our results indicate that the absence of organellar oligopeptidases PreP1/2 and OOP results in the accumulation of peptides that are perceived as pathogenic effectors and activate the signaling pathways of plant-defense response.

ProteinExplorer: A Repository-Scale Resource for Exploration of Protein Detection in Public Mass Spectrometry Data Sets

High-throughput tandem mass spectrometry has enabled the detection and identification of over 75% of all proteins predicted to result in translated gene products in the human genome. In fact, the galloping rate of data acquisition and sharing of mass spectrometry data has led to the current availability of many tens of terabytes of public data in thousands of human data sets. The systematic reanalysis of these public data sets has been used to build a community-scale spectral library of 2.1 million precursors for over 1 million unique sequences from over 19,000 proteins (including spectra of synthetic peptides). However, it has remained challenging to find and inspect spectra of peptides covering functional protein regions or matching novel proteins. ProteinExplorer addresses these challenges with an intuitive interface mapping tens of millions of identifications to functional sites on nearly all human proteins while maintaining provenance for every identification back to the original data set and data file. Additionally, ProteinExplorer facilitates the selection and inspection of HPP-compliant peptides whose spectra can be matched to spectra of synthetic peptides and already includes HPP-compliant evidence for 107 missing (PE2, PE3, and PE4) and 23 dubious (PE5) proteins. Finally, ProteinExplorer allows users to rate spectra and to contribute to a community library of peptides entitled PrEdict (Protein Existance dictionary) mapping to novel proteins but whose preliminary identities have not yet been fully established with community-scale false discovery rates and synthetic peptide spectra. ProteinExplorer can be now be accessed at https://massive.ucsd.edu/ProteoSAFe/protein_explorer_splash.jsp .

Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine

We present a sequence-tag-based search engine, Open-pFind, to identify peptides in an ultra-large search space that includes coeluting peptides, unexpected modifications and digestions. Our method detects peptides with higher precision and speed than seven other search engines. Open-pFind identified 70-85% of the tandem mass spectra in four large-scale datasets and 14,064 proteins, each supported by at least two protein-unique peptides, in a human proteome dataset.

Discovery of novel plasma biomarkers for future incident venous thromboembolism by untargeted synchronous precursor selection mass spectrometry proteomics

Essentials Discovery of predictive biomarkers of venous thromboembolism (VTE) may aid risk stratification. A case-control study where plasma was sampled before the occurrence of VTE was established. We generated untargeted plasma proteomic profiles of 200 individuals by use of mass spectrometry. Assessment of the biomarker potential of 501 proteins yielded 46 biomarker candidates.

ABSTRACT

Background Prophylactic anticoagulant treatment may substantially reduce the incidence of venous thromboembolism (VTE) but entails considerable risk of severe bleeding. Identification of individuals at high risk of VTE through the use of predictive biomarkers is desirable in order to achieve a favorable benefit-to-harm ratio. Objective We aimed to identify predictive protein biomarker candidates of VTE. Methods We performed a case-control study of 200 individuals that participated in the Tromsø Study, a population-based cohort, where blood samples were collected before the VTE events occurred. Untargeted tandem mass tag-synchronous precursor selection-mass spectrometry (TMT-SPS-MS3)-based proteomic profiling was used to study the plasma proteomes of each individual. Results Of the 501 proteins detected in a sufficient number of samples to allow multivariate analysis, 46 proteins were associated with VTE case-control status with P-values below the 0.05 significance threshold. The strongest predictive biomarker candidates, assessed by statistical significance, were transthyretin, vitamin K-dependent protein Z and protein/nucleic acid deglycase DJ-1. Conclusions Our untargeted approach of plasma proteome profiling revealed novel predictive biomarker candidates of VTE and confirmed previously reported candidates, thereby providing conceptual support for the validity of the study. A larger nested case-control study will be conducted to validate our findings.

Assembling the Community-Scale Discoverable Human Proteome

The increasing throughput and sharing of proteomics mass spectrometry data have now yielded over one-third of a million public mass spectrometry runs. However, these discoveries are not continuously aggregated in an open and error-controlled manner, which limits their utility. To facilitate the reusability of these data, we built the MassIVE Knowledge Base (MassIVE-KB), a community-wide, continuously updating knowledge base that aggregates proteomics mass spectrometry discoveries into an open reusable format with full provenance information for community scrutiny. Reusing >31 TB of public human data stored in a mass spectrometry interactive virtual environment (MassIVE), the MassIVE-KB contains >2.1 million precursors from 19,610 proteins (48% larger than before; 97% of the total) and doubles proteome coverage to 6 million amino acids (54% of the proteome) with strict library-scale false discovery controls, thereby providing evidence for 430 proteins for which sufficient protein-level evidence was previously missing. Furthermore, MassIVE-KB can inform experimental design, helps identify and quantify new data, and provides tools for community construction of specialized spectral libraries.

Wang et al. introduce MassIVE-KB, a program designed to distill the entire community’s mass spectrometry data into reusable spectral library resources. As a result, the statistically-significant discovery of a peptide or protein in a single researcher’s data will thus be made available to the whole community to support its identification (in shotgun experiments) or quantitative detection (in targeted experiments) in all future analyses.

Metabolic reprogramming of acute lymphoblastic leukemia cells in response to glucocorticoid treatment

Glucocorticoids (GCs) are metabolic hormones with immunosuppressive effects that have proven effective drugs against childhood acute lymphoblastic leukemia (ALL). Yet, the role of metabolic reprogramming in GC-induced ALL cell death is poorly understood. GCs efficiently block glucose uptake and metabolism in ALL cells, but this does not fully explain the observed induction of autophagy and cell death. Here, we have performed parallel time-course proteomics, metabolomics, and isotope-tracing studies to examine in detail the metabolic effects of GCs on ALL cells. We observed metabolic events associated with growth arrest, autophagy, and catabolism prior to onset of apoptosis: nucleotide de novo synthesis was reduced, while certain nucleobases accumulated; polyamine synthesis was inhibited; and phosphatidylcholine synthesis was induced. GCs suppressed not only glycolysis but also entry of both glucose and glutamine into the TCA cycle. In contrast, expression of glutamine-ammonia ligase (GLUL) and cellular glutamine content was robustly increased by GC treatment, suggesting induction of glutamine synthesis, similar to nutrient-starved muscle. Modulating medium glutamine and dimethyl-α-ketoglutarate (dm-αkg) to favor glutamine synthesis reduced autophagosome content of ALL cells, and dm-αkg also rescued cell viability. These data suggest that glutamine synthesis affects autophagy and possibly onset of cell death in response to GCs, which should be further explored to understand mechanism of action and possible sources of resistance.

Complementary iTRAQ-based proteomic and RNA sequencing-based transcriptomic analyses reveal a complex network regulating pomegranate (Punica granatum L.) fruit peel colour

Peel colour is an important factor affecting the marketability of pomegranate fruits. Therefore, elucidating the genetic mechanism of fruit peel colour development may be useful for breeding pomegranate cultivars with enhanced fruit peel colours. In this study, we combined an iTRAQ-based proteome-level analysis with an RNA sequencing-based transcriptome-level analysis to detect the proteins and genes related to fruit peel colour development in pomegranate. We analysed the ‘Tunisia’ (red fruit) and ‘White’ (white fruit) pomegranate cultivars at two stages of fruit development. A total of 27 differentially abundant proteins (increased abundance) and 54 differentially expressed genes (16 up-regulated and 38 down-regulated) were identified from our proteomics and transcriptomics data. The identified proteins and genes contribute to pomegranate fruit peel colour by participating in the biosynthesis of anthocyanins, stilbenoids, diarylheptanoids, gingerols, flavonoids, and phenylpropanoids. Several candidate proteins and genes corresponded to enzymes related to general reactions (PAL, 4CL, DFR, LDOX/ANS, CHS, and F3′5′H) and glycosylation (GT1 and UGAT) of compounds and pigments related to the colour of pomegranate fruit peel. Complementary proteome- and transcriptome-level analyses revealed a complex molecular network controlling fruit peel colour. The candidate genes identified in this study may be useful for the marker-based breeding of new pomegranate cultivars.

Quantitative Proteomics Analysis of Membrane Proteins in Enterococcus faecalis With Low-Level Linezolid-Resistance

Despite increasing reports of low-level linezolid-resistant enterococci worldwide, the mechanism of this resistance remains poorly understood. Previous transcriptome studies of low-level linezolid-resistant Enterococcus faecalis isolates have demonstrated a number of significantly up-regulated genes potentially involved in mediation of drug resistance. However, whether the transcriptome faithfully reflects the proteome remains unknown. In this study, we performed quantitative proteomics analysis of membrane proteins in an E. faecalis isolate (P10748) with low-level linezolid-resistance in comparison with two linezolid-susceptible strains 3138 and ATCC 29212, all of which have been previously investigated by whole transcriptome analysis. A total of 8,197 peptides associated with 1,170 proteins were identified in all three isolates with false discovery rate (FDR) at 1% and P < 0.05. There were 14 significantly up-regulated and 6 significantly down-regulated proteins in strain P10748 compared to strains 3138 and ATCC 29212, which were in general positively correlated with transcription levels revealed in previous transcriptome studies. Our analysis suggests that the low-level linezolid-resistance in E. faecalis is conferred primarily by the ATP-binding cassette protein OptrA through ribosomal protection and, possibly, also by the enterococcal surface protein (Esp) and other proteins through biofilm formation. The genetic transfer of optrA is potentially regulated by the surface exclusion protein Sea1, conjugal transfer protein TraB, replication protein RepA and XRE family transcription regulator protein. This report represents the first investigation of the mechanisms of linezolid-resistance in E. faecalis by a quantitative proteomics approach.

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.

Extracellular nanovesicles released from the commensal yeast Malassezia sympodialis are enriched in allergens and interact with cells in human skin

Malassezia sympodialis is a dominant commensal fungi in the human skin mycobiome but is also associated with common skin disorders including atopic eczema (AE). M. sympodialis releases extracellular vesicles, designated MalaEx, which are carriers of small RNAs and allergens, and they can induce inflammatory cytokine responses. Here we explored how MalaEx are involved in host-microbe interactions by comparing protein content of MalaEx with that of the parental yeast cells, and by investigating interactions of MalaEx with cells in the skin. Cryo-electron tomography revealed a heterogeneous population of MalaEx. iTRAQ based quantitative proteomics identified in total 2439 proteins in all replicates of which 110 were enriched in MalaEx compared to the yeast cells. Among the MalaEx enriched proteins were two of the M. sympodialis allergens, Mala s 1 and s 7. Functional experiments indicated an active binding and internalization of MalaEx into human keratinocytes and monocytes, and MalaEx were found in close proximity of the nuclei using super-resolution fluorescence 3D-SIM imaging. Our results provides new insights into host-microbe interactions, supporting that MalaEx may have a role in the sensitization and maintenance of inflammation in AE by containing enriched amounts of allergens and with their ability to interact with skin cells.

Distinct sperm nucleus behaviors between genotypic and temperature-dependent sex determination males are associated with replication and expression-related pathways in a gynogenetic fish

BACKGROUND

Coexistence and transition of diverse sex determination strategies have been revealed in some ectothermic species, but the variation between males caused by different sex determination strategies and the underlying mechanism remain unclear. Here, we used the gynogenetic gibel carp (Carassius gibelio) with both genotypic sex determination (GSD) and temperature-dependent sex determination (TSD) strategies to illustrate this issue.

RESULTS

We found out that males of GSD and TSD in gibel carp had similar morphology, testicular histology, sperm structure and sperm vitality. However, when maternal individuals were mated with males of GSD, sperm nucleus swelling and fusing with the female pronucleus were observed in the fertilized eggs. On the contrary, when maternal individuals were mated with males of TSD, sperm nucleus remained in the condensed status throughout the whole process. Subsequently, semen proteomics analysis unveiled that DNA replication and gene expression-related pathways were inhibited in the sperm from males of TSD compared to males of GSD, and most differentially expressed proteins associated with DNA replication, transcription and translation were down-regulated. Moreover, via BrdU incorporation and immunofluorescence detection, male nucleus replication was revealed to be present in the fertilized eggs by the sperm from males of GSD, but absent in the fertilized eggs by the sperm from males of TSD.

CONCLUSIONS

These findings indicate that DNA replication and gene expression-related pathways are associated with the distinct sperm nucleus development behaviors in fertilized eggs in response to the sperm from males of GSD and TSD. And this study is the first attempt to screen the differences between males determined via GSD and TSD in gynogenetic species, which might give a hint for understanding evolutionary adaption of diverse sex determination mechanisms in unisexual vertebrates.

Accurate, Sensitive, and Precise Multiplexed Proteomics Using the Complement Reporter Ion Cluster

Quantitative analysis of proteomes across multiple time points, organelles, and perturbations is essential for understanding both fundamental biology and disease states. The development of isobaric tags (e.g., TMT) has enabled the simultaneous measurement of peptide abundances across several different conditions. These multiplexed approaches are promising in principle because of advantages in throughput and measurement quality. However, in practice, existing multiplexing approaches suffer from key limitations. In its simple implementation (TMT-MS2), measurements are distorted by chemical noise leading to poor measurement accuracy. The current state-of-the-art (TMT-MS3) addresses this but requires specialized quadrupole-iontrap-Orbitrap instrumentation. The complement reporter ion approach (TMTc) produces high accuracy measurements and is compatible with many more instruments, like quadrupole-Orbitraps. However, the required deconvolution of the TMTc cluster leads to poor measurement precision. Here, we introduce TMTc+, which adds the modeling of the MS2-isolation step into the deconvolution algorithm. The resulting measurements are comparable in precision to TMT-MS3/MS2. The improved duty cycle and lower filtering requirements make TMTc+ more sensitive than TMT-MS3 and comparable with TMT-MS2. At the same time, unlike TMT-MS2, TMTc+ is exquisitely able to distinguish signal from chemical noise even outperforming TMT-MS3. Lastly, we compare TMTc+ to quantitative label-free proteomics of total HeLa lysate and find that TMTc+ quantifies 7.8k versus 3.9k proteins in a 5-plex sample. At the same time, the median coefficient of variation improves from 13% to 4%. Thus, TMTc+ advances quantitative proteomics by enabling accurate, sensitive, and precise multiplexed experiments on more commonly used instruments.

A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms

A natural way to benchmark the performance of an analytical experimental setup is to use samples of known composition and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, one of the inherent problems of interpreting data is that the measured analytes are peptides and not the actual proteins themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. Hence, for a realistic benchmark of protein inference procedures, there is a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the application of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5–8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale.

Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow

Proteogenomics enable the discovery of novel peptides (from unannotated genomic protein-coding loci) and single amino acid variant peptides (derived from single-nucleotide polymorphisms and mutations). Increasing the reliability of these identifications is crucial to ensure their usefulness for genome annotation and potential application as neoantigens in cancer immunotherapy. We here present integrated proteogenomics analysis workflow (IPAW), which combines peptide discovery, curation, and validation. IPAW includes the SpectrumAI tool for automated inspection of MS/MS spectra, eliminating false identifications of single-residue substitution peptides. We employ IPAW to analyze two proteomics data sets acquired from A431 cells and five normal human tissues using extended (pH range, 3–10) high-resolution isoelectric focusing (HiRIEF) pre-fractionation and TMT-based peptide quantitation. The IPAW results provide evidence for the translation of pseudogenes, lncRNAs, short ORFs, alternative ORFs, N-terminal extensions, and intronic sequences. Moreover, our quantitative analysis indicates that protein production from certain pseudogenes and lncRNAs is tissue specific.

Proteogenomics enables the discovery of protein coding regions and disease-relevant mutations but their verification remains challenging. Here, the authors combine peptide discovery, curation and validation in an integrated proteogenomics workflow, robustly identifying unknown coding regions and mutations.

iTRAQ-Based Proteomic Analysis Reveals Potential Regulation Networks of IBA-Induced Adventitious Root Formation in Apple

Adventitious root (AR) formation, which is controlled by endogenous and environmental factors, is indispensable for vegetative asexual propagation. However, comprehensive proteomic data on AR formation are still lacking. The aim of this work was to study indole-3-butyric acid (IBA)-induced AR formation in the dwarf apple rootstock 'T337'. In this study, the effect of IBA on AR formation was analysed. Subsequent to treatment with IBA, both the rooting rate and root length of 'T337' increased significantly. An assessment of hormone levels in basal stem cuttings suggested that auxin, abscisic acid, and brassinolide were higher in basal stem cuttings that received the exogenous IBA application; while zeatin riboside, gibberellins, and jasmonic acid were lower than non-treated basal stem cuttings. To explore the underlying molecular mechanism, an isobaric tags for relative and absolute quantification (iTRAQ)-based proteomic technique was employed to identify the expression profiles of proteins at a key period of adventitious root induction (three days after IBA treatment). In total, 3355 differentially expressed proteins (DEPs) were identified. Many DEPs were closely related to carbohydrate metabolism and energy production, protein homeostasis, reactive oxygen and nitric oxide signaling, and cell wall remodeling biological processes; as well as the phytohormone signaling, which was the most critical process in response to IBA treatment. Further, RT-qPCR analysis was used to evaluate the expression level of nine genes that are involved in phytohormone signaling and their transcriptional levels were mostly in accordance with the protein patterns. Finally, a putative work model was proposed. Our study establishes a foundation for further research and sheds light on IBA-mediated AR formation in apple as well as other fruit rootstock cuttings.

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.

Proteomic evaluation of human umbilical cord tissue exposed to polybrominated diphenyl ethers in an e-waste recycling area

Parental exposure to polybrominated diphenyl ethers (PBDEs) is associated with adverse birth outcomes. This study aims to examine differentially-expressed protein profiles in umbilical cord tissue, derived from mothers exposed to PBDEs, and investigate candidate biomarkers to reveal the underlying molecular mechanisms. Umbilical cord samples were obtained from women residing in an electronic waste (e-waste) recycling area (Guiyu) and reference area (Haojiang) in China. The concentration of PBDEs in umbilical cord tissue was determined by gas chromatography and mass spectrometry (GC/MS). Isobaric tagging for relative and absolute quantification (iTRAQ)-based proteomic technology was conducted to analyze differentially-expressed protein profiles. The total PBDE concentration was approximately five-fold higher in umbilical cords from Guiyu than from Haojiang (median 71.92ng/g vs. 15.52ng/g lipid, P<0.01). Neonatal head circumference, body-mass index (BMI) and Apgar1 score were lower in Guiyu and negatively correlated with PBDE concentration (P<0.01). Proteomic analysis showed 697 proteins were differentially expressed in the e-waste-exposed group compared with the reference group. The differentially-expressed proteins were principally involved in antioxidant defense, apoptosis, cell structure and metabolism. Among them, catalase and glutathione S-transferase omega-1, were down-regulated, and cytochrome c was found to be up-regulated, changes which were further verified by enzyme-linked immunosorbent assays. These results suggest that an antioxidant imbalance and cell apoptosis in the umbilical cord following PBDE exposure is associated with neonatal birth outcomes.

Quantitative Proteomics for Xenopus Embryos II, Data Analysis

The oocytes, embryos, and cell-free lysates of the frog Xenopus laevis have emerged as powerful models for quantitative proteomic experiments. In the accompanying paper (Chapter 13) we describe how to prepare samples and acquire multiplexed proteomics spectra from those. As an illustrative example we use a 10-stage developmental time series from the egg to stage 35 (just before hatching). Here, we outline how to convert the ~700,000 acquired mass spectra from this time series into protein expression dynamics for ~9000 proteins. We first outline a preliminary quality-control analysis to discover any errors that occurred during sample preparation. We discuss how peptide and protein identification error rates are controlled, and how peptide and protein species are quantified. Our analysis relies on the freely available MaxQuant proteomics pipeline. Finally, we demonstrate how to start interpreting this large dataset by clustering and gene-set enrichment analysis.

Advances in the Chromosome-Centric Human Proteome Project: looking to the future

INTRODUCTION

The mission of the Chromosome-Centric Human Proteome Project (C-HPP), is to map and annotate the entire predicted human protein set (~20,000 proteins) encoded by each chromosome. The initial steps of the project are focused on 'missing proteins (MPs)', which lacked documented evidence for existence at protein level. In addition to remaining 2,579 MPs, we also target those annotated proteins having unknown functions, uPE1 proteins, alternative splice isoforms and post-translational modifications. We also consider how to investigate various protein functions involved in cis-regulatory phenomena, amplicons lncRNAs and smORFs. Areas covered: We will cover the scope, historic background, progress, challenges and future prospects of C-HPP. This review also addresses the question of how we can best improve the methodological approaches, select the optimal biological samples, and recommend stringent protocols for the identification and characterization of MPs. A new strategy for functional analysis of some of those annotated proteins having unknown function will also be discussed. Expert commentary: If the project moves well by reshaping the original goals, the current working modules and team work in the proposed extended planning period, it is anticipated that a progressively more detailed draft of an accurate chromosome-based proteome map will become available with functional information.

Plasma proteomics reveals coagulation, inflammation, and metabolic shifts in H-type hypertension patients with and without acute ischemic stroke

Systematic profiling of a larger portion of circulating plasma proteome provide opportunities for unbiased discovery of novel markers to improve diagnostic, therapeutic, or predictive accuracy. This study aimed to identify differentially expressed proteins (DEPs) in plasma that could provide overall insight into the molecular changes of both H- type hypertension (HH) and HH-related acute ischemic stroke (AIS). This study used an iTRAQ-based LC-MS/MS proteomics approach to screen for plasma DEPs in HH patients with and without AIS, and controls. After excluding highly abundant plasma proteins, more than 600 proteins, and their relative levels, were identified. Of these, 26 DEPs, each showing > 1.2-fold change, were identified in HH and HH-related AIS patients compared with controls. Bioinformatics analysis revealed that these DEPs were enriched in 21 functional gene ontology items; “blood coagulation” was the most predominant pathway showing enrichment. Of these, eight DEPs were located in the hub position of networks involved with protein-protein interactions. AT-3, CRP, ApoB, and AHSG were further validated in each group by enzyme-linked immune sorbent assays. Comparing HH-related AIS with HH, the areas under the curve for AT-3, CRP, ApoB, and AHSG were 0.698, 0.892, 0.626, and 0.847, respectively. This proteomic profiling study provided enhanced pathophysiological understanding of the regulatory processes involved in coagulation, inflammation, and metabolism, and identified a panel of novel biomarkers for detecting HH-related AIS during its pre-stroke stage.

Digging More Missing Proteins Using an Enrichment Approach with ProteoMiner

Human Proteome Project (HPP) aims at mapping entire human proteins with a systematic effort upon all the emerging techniques, which would enhance understanding of human biology and lay a foundation for development of medical applications. Until now, 2563 missing proteins (MPs, PE2-4) are still undetected even using the most sensitive approach of protein detection. Herein, we propose that enrichment of low-abundance proteins benefits MPs finding. ProteoMiner is an equalizing technique by reducing high-abundance proteins and enriching low-abundance proteins in biological liquids. With triton X-100/TBS buffer extraction, ProteoMiner enrichment, and peptide fractionation, 20 MPs (at least two non-nested unique peptides with more than eight a.a. length) with 60 unique peptides were identified from four human tissues including eight membrane/secreted proteins and five nucleus proteins. Then 15 of them were confirmed with two non-nested unique peptides (≥9 a.a.) identified by matching well with their chemically synthetic peptides in PRM assay. Hence, these results demonstrated ProteoMiner as a powerful means in discovery of MPs.