Critical assessment of proteome-wide label-free absolute abundance estimation strategies

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

Tumor tissue hnRNP M and HSP 90α as potential predictors of disease-specific mortality in patients with early-stage cutaneous head and neck melanoma: A proteomics-based study

Background: Breslow tumor thickness and mitotic rate are standardly used for risk stratification of patients with malignant melanoma. However, their prognostic value is relatively limited and a need for improved prognostication has been advocated. We aimed to screen the tumor tissue proteome in a search for potentially useful prognostic factors in early-stage cutaneous head and neck melanoma.

Methodology and Findings: Proteomic profiles of archival formalin-fixed tissue samples of 31 patients (age 23–90 years) with early-stage head and neck cutaneous malignant melanoma (American Joint Committee on Cancer, AJCC, stage I/II) were determined and expression intensities were compared to those of melanocytic nevi, yielding ratios used in data analysis. Medical charts were retrospectively reviewed to determine time elapsed since diagnosis to disease-specific death or censoring. In a multivariate recursive partitioning analysis (as per AJCC guidelines), higher expression levels of heterogeneous nuclear ribonucleoprotein M (hnRNP M) [n = 18, HR = 1.94 vs. the entire cohort; HR = 5.95 (95%CI 2.43–14.5) for “high” vs. “low” (n = 13)] and of heat shock protein 90 alpha (HSP 90α) [n = 17, HR = 2.09 vs. the entire cohort; HR = 4.59 (95%CI 1.87–11.2) for “high” vs. “low” (n = 14)] were each independently strongly associated with higher mortality (accounting for clinical and standard pathohistological features). Higher Breslow thickness and mitotic rate were associated with higher mortality only when proteomic data were disregarded.

Conclusions and Significance: Data suggest that tumor tissue expression of hnRNP M and/or of HSP 90α deserve further investigation and clinical validation as potential novel risk stratification aids in patients with stage I-II cutaneous head and neck malignant melanoma.

Proteomic Profiling of Purified Rabies Virus Particles

While host proteins incorporated into virions during viral budding from infected cell are known to play essential roles in multiple process of the life cycle of progeny virus, these characteristics have been largely neglected in studies on rabies virus (RABV). Here, we purified the RABV virions with good purity and integrity, and analyzed their proteome by nano LC–MS/MS, followed by the confirmation with immunoblot and immuno-electronic microscopy. In addition to the 5 viral proteins, 49 cellular proteins were reproducibly identified to be incorporated into matured RABV virions. Function annotation suggested that 24 of them were likely involved in virus replication. Furthermore, cryo-EM was employed to observe the purified RABV virions, generating high-resolution pictures of the bullet-shaped virion structure of RABV. This study has provided new insights into the host proteins composition in RABV virion and shed the light for further investigation on molecular mechanisms of RABV infection, as well as the discovery of new anti-RABV therapeutics.

Quantification of Dynamic Protein Interactions and Phosphorylation in LPS Signaling Pathway by SWATH-MS

Lipopolysaccharide (LPS)-induced macrophage activation is a prototype of innate immune response. Although key effector proteins in LPS signaling pathway have been revealed, the map of dynamic protein interactions and phosphorylation as well as the stoichiometry of protein complexes are lacking. Here we present a dynamic map of protein interactions and phosphorylation in MyD88, TRAF6 and NEMO complexes obtained by SWATH-MS. The comprehensive MS measurement leads to quantification of up to about 3,000 proteins across about 21-40 IP samples. We detected and quantified almost all known interactors of MyD88, TRAF6 and NEMO. By analyzing these quantitative data, we uncovered differential recruitment of IRAK family proteins to LPS-induced signaling complexes and determined the stoichiometry of the Myddosome complex. In addition, quantitative phosphoproteomics analysis identified a number of unreported high-confidence phosphosites on the key proteins in LPS signaling pathway. Collectively, data of dynamic protein interactions and phosphorylation presented by this study could be a resource for further study of the LPS signaling pathway.

Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

Background

Burkholderia pseudomallei is a human pathogen causing severe infections in tropical and subtropical regions and is classified as a bio-threat agent. B. thailandensis strain E264 has been proposed as less pathogenic surrogate for understanding the interactions of B. pseudomallei with host cells.

Results

We show that, unlike B. thailandensis strain E264, the pattern of growth of B. thailandensis strain E555 in macrophages is similar to that of B. pseudomallei. We have genome sequenced B. thailandensis strain E555 and using the annotated sequence identified genes and proteins up-regulated during infection. Changes in gene expression identified more of the known B. pseudomallei virulence factors than changes in protein levels and used together we identified 16% of the currently known B. pseudomallei virulence factors. These findings demonstrate the utility of B. thailandensis strain E555 to study virulence of B. pseudomallei.

Conclusions

A weakness of studies using B. thailandensis as a surrogate for B. pseudomallei is that the strains used replicate at a slower rate in infected cells. We show that the pattern of growth of B. thailandensis strain E555 in macrophages closely mirrors that of B. pseudomallei. Using this infection model we have shown that virulence factors of B. pseudomallei can be identified as genes or proteins whose expression is elevated on the infection of macrophages. This finding confirms the utility of B. thailandensis strain E555 as a surrogate for B. pseudomallei and this strain should be used for future studies on virulence mechanisms.

Electronic supplementary material

The online version of this article (10.1186/s12866-019-1469-8) contains supplementary material, which is available to authorized users.

Simple Tip-Based Sample Processing Method for Urinary Proteomic Analysis

Mass spectrometry-based urinary proteomics is one of the most attractive strategies to discover proteins for diagnosis, prognosis, monitoring, or prediction of therapeutic responses of urological diseases involving the kidney, prostate, and bladder; however, interfering compounds found in urine necessitate sample preparation strategies that are currently not suitable for urinary proteomics in the clinical setting. Herein, we describe the C4-tip method, comprising a simple, automated strategy utilizing a reverse-phase resin tip-based format and "on-tip" digestion to examine the urine proteome. We first determined the optimal conditions for protein isolation and protease digestion on the C4-tip using the standard protein bovine fetuin. Next, we applied the C4-tip method to urinary proteomics, identifying a total of 813 protein groups using LC-MS/MS, with identified proteins from the C4-tip method displaying a similar distribution of gene ontology (GO) cellular component assignments compared to identified proteins from an ultrafiltration preparation method. Finally, we assessed the reproducibility of the C4-tip method, revealing a high Spearman correlation R-value for shared proteins identified across all tips. Together, we have shown the C4-tip method to be a simple, robust method for high-throughput analysis of the urinary proteome by mass spectrometry in the clinical setting.

Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause

Transcription, translation, and turnover of transcripts and proteins are essential for cellular function. The contribution of those factors to protein levels is under debate, as transcript levels and cognate protein levels do not necessarily correlate due to regulation of translation and protein turnover. Here we propose neuronal polarity as a third factor that is particularly evident in the CNS, leading to considerable distances between somata and axon terminals. Consequently, transcript levels may negatively correlate with cognate protein levels in CNS regions, i.e., transcript and protein levels behave reciprocally. To test this hypothesis, we performed an integrative inter-omics study and analyzed three interconnected rat auditory brainstem regions (cochlear nuclear complex, CN; superior olivary complex, SOC; inferior colliculus, IC) and the rest of the brain as a reference. We obtained transcript and protein sets in these regions of interest (ROIs) by DNA microarrays and label-free mass spectrometry, and performed principal component and correlation analyses. We found 508 transcript|protein pairs and detected poor to moderate transcript|protein correlation in all ROIs, as evidenced by coefficients of determination from 0.34 to 0.54. We identified 57-80 negatively correlating gene products in the ROIs and intensively analyzed four of them for which the correlation was poorest. Three cognate proteins (Slc6a11, Syngr1, Tppp) were synaptic and hence candidates for a negative correlation because of protein transport into axon terminals. Thus, we systematically analyzed the negatively correlating gene products. Gene ontology analyses revealed overrepresented transport/synapse-related proteins, supporting our hypothesis. We present 30 synapse/transport-related proteins with poor transcript|protein correlation. In conclusion, our analyses support that protein transport in polar cells is a third factor that influences the protein level and, thereby, the transcript|protein correlation. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* and *Open Data* because it provided all relevant information to reproduce the study in the manuscript and because it made the data publicly available. The data can be accessed at https://osf.io/ha28n/. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.

Discovery and Quantification of Nonhuman Proteins in Human Milk

The question whether and which nonhuman peptides or proteins are present in human milk was raised many decades ago. However, due to cross-reactivity or nonspecific antibody recognition, the accuracy of detection by immunochemical methods has been a concern. Additionally, the relative low-abundance of nonhuman peptides/proteins in the complex milk sample makes them a challenging target to detect. Here, by deep proteome profiling, we detected several nonhuman peptides, which could be grouped as nonhuman proteins. We next estimated their concentration in human milk by combining data-dependent shotgun proteomics and parallel reaction monitoring. First, we fractionated human milk at the protein level and were able to detect 1577 human proteins. Additionally, we identified 109 nonhuman peptides, of which 71 were grouped into 36 nonhuman proteins. In the next step, we targeted 37 nonhuman peptides and nine of them could be repeatedly quantified in human milk samples. Peptides/proteins originating from bovine milk products were the dominant nonhuman proteins observed, notably bovine caseins (α-S1-, α-S2-, β-, κ-caseins) and β-lactoglobulin. The method we present here can be expanded to investigate more about nonhuman peptides and proteins in human milk and give a better understanding of how human milk plays a role in allergy prevention.

Identification of O-Glcnacylated Proteins in Trypanosoma cruzi

Originally an anthropozoonosis in the Americas, Chagas disease has spread from its previous borders through migration. It is caused by the protozoan Trypanosoma cruzi. Differences in disease severity have been attributed to a natural pleomorphism in T. cruzi. Several post-translational modifications (PTMs) have been studied in T. cruzi, but to date no work has focused on O-GlcNAcylation, a highly conserved monosaccharide-PTM of serine and threonine residues mainly found in nucleus, cytoplasm, and mitochondrion proteins. O-GlcNAcylation is thought to regulate protein function analogously to protein phosphorylation; indeed, crosstalk between both PTMs allows the cell to regulate its functions in response to nutrient levels and stress. Herein, we demonstrate O-GlcNAcylation in T. cruzi epimastigotes by three methods: by using specific antibodies against the modification in lysates and whole parasites, by click chemistry labeling, and by proteomics. In total, 1,271 putative O-GlcNAcylated proteins and six modification sequences were identified by mass spectrometry (data available via ProteomeXchange, ID PXD010285). Most of these proteins have structural and metabolic functions that are essential for parasite survival and evolution. Furthermore, O-GlcNAcylation pattern variations were observed by antibody detection under glucose deprivation and heat stress conditions, supporting their possible role in the adaptive response. Given the numerous biological processes in which O-GlcNAcylated proteins participate, its identification in T. cruzi proteins opens a new research field in the biology of Trypanosomatids, improve our understanding of infection processes and may allow us to identify new therapeutic targets.

Mass Spectrometry-Based Biomarkers in Drug Development

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

Absolute Quantification of Grapevine Red Blotch Virus in Grapevine Leaf and Petiole Tissues by Proteomics

Grapevine red blotch is a recently identified viral disease that was first recognized in the Napa Valley of California. Infected plants showed foliar symptoms similar to leafroll, another grapevine viral disease, on vines testing negative for known grapevine leafroll-associated virus. Later, the Grapevine red blotch virus (GRBV) was independently discovered in the US states of California and New York and was demonstrated to be the causal agent of red blotch disease. Due to its wide occurrence in the United States, vector transmission, and impacts on grape industry, this virus has the potential to cause serious economic losses. Despite numerous attempts, it has yet not been possible to isolate or visualize viral particles from GRBV-infected plants, thereby hampering the development of a serological assay that would facilitate GRBV detection in grapevine. In this work, mass spectrometry approaches were applied in order to quantify GRBV in infected plants and identify potential biomarkers for viral infection. We present for the first time the physical detection on the protein level of the two GRBV genes V1 (coat protein) and V2 in grapevine tissue lysates. The GRBV coat protein load in petioles was determined to be in the range of 100-900 million copies per milligram wet weight by using three heavy isotope labeled reference peptides as internal standards. In leaves on the other hand, the V1 copy number per unit wet tissue weight appeared to be about six times lower than in petioles, and about 300 times lower in terms of protein concentration in the extractable protein mass, albeit these estimations could only be made with one reference peptide detectable in leaf extracts. Moreover, we found in leaf and petiole extracts of GRBV-infected plants a consistent upregulation of several enzymes involved in flavonoid biosynthesis by label-free shotgun proteomics, indicating the activation of a defense mechanism against GRBV, a plant response already described for Grapevine leafroll-associated virus infection on the transcriptome level. Finally and importantly, we identified some other microorganisms belonging to the grapevine leaf microbiota, two bacterial species (Novosphingobium sp. Rr 2-17 and Methylobacterium) and one virus, Grapevine rupestris stem pitting-associated virus.

Sucrose-Induced Proteomic Response and Carbohydrate Utilization of Lactobacillus sakei TMW 1.411 During Dextran Formation

Lactobacillus (L.) sakei belongs to the dominating lactic acid bacteria in indigenous meat fermentations, while diverse strains of this species have also been isolated from plant fermentations. We could recently show, that L. sakei TMW 1.411 produces a high molecular weight dextran from sucrose, indicating its potential use as a dextran forming starter culture. However, the general physiological response of L. sakei to sucrose as carbohydrate source has not been investigated yet, especially upon simultaneous dextran formation. To address this lack of knowledge, we sequenced the genome of L. sakei TMW 1.411 and performed a label-free, quantitative proteomics approach to investigate the sucrose-induced changes in the proteomic profile of this strain in comparison to its proteomic response to glucose. In total, 21 proteins were found to be differentially expressed at the applied significance criteria (FDR ≤ 0.01). Among these, 14 were associated with the carbohydrate metabolism including several enzymes, which enable sucrose and fructose uptake, as well as, their subsequent intracellular metabolization, respectively. The plasmid-encoded, extracellular dextransucrase of L. sakei TMW 1.411 was expressed at high levels irrespective of the present carbohydrate and was predominantly responsible for sucrose consumption in growth experiments using sucrose as sole carbohydrate source, while the released fructose from the dextransucrase reaction was more preferably taken up and intracellularly metabolized than sucrose. Genomic comparisons revealed, that operons coding for uptake and intracellular metabolism of sucrose and fructose are chromosomally conserved among L. sakei, while plasmid-located dextransucrase genes are present only in few strains. In accordance with these findings, all 59 different L. sakei strains of our strain collection were able to grow on sucrose as sole carbohydrate source, while eight of them exhibited a mucous phenotype on agar plates indicating dextran formation from sucrose. Our study therefore highlights the intrinsic adaption of L. sakei to plant environments, where sucrose is abundant, and provides fundamental knowledge regarding the use of L. sakei as starter culture for sucrose-based food fermentation processes with in-situ dextran formation.

Nonsense-mediated mRNA decay involves two distinct Upf1-bound complexes

Nonsense-mediated mRNA decay (NMD) is a translation-dependent RNA degradation pathway involved in many cellular pathways and crucial for telomere maintenance and embryo development. Core NMD factors Upf1, Upf2 and Upf3 are conserved from yeast to mammals, but a universal NMD model is lacking. We used affinity purification coupled with mass spectrometry and an improved data analysis protocol to characterize the composition and dynamics of yeast NMD complexes in yeast (112 experiments). Unexpectedly, we identified two distinct complexes associated with Upf1: Upf1-23 (Upf1, Upf2, Upf3) and Upf1-decapping Upf1-decapping contained the mRNA decapping enzyme, together with Nmd4 and Ebs1, two proteins that globally affected NMD and were critical for RNA degradation mediated by the Upf1 C-terminal helicase region. The fact that Nmd4 association with RNA was partially dependent on Upf1-23 components and the similarity between Nmd4/Ebs1 and mammalian Smg5-7 proteins suggest that NMD operates through conserved, successive Upf1-23 and Upf1-decapping complexes. This model can be extended to accommodate steps that are missing in yeast, to serve for further mechanistic studies of NMD in eukaryotes.

mRNA-specific translation regulation by a ribosome-associated ncRNA in Haloferax volcanii

Regulation of gene expression at the translational level allows rapid adaptation of cellular proteomes to quickly changing environmental conditions and is thus central for prokaryotic organisms. Small non-coding RNAs (sRNAs) have been reported to effectively orchestrate translation control in bacteria and archaea mainly by targeting mRNAs by partial base complementarity. Here we report an unprecedented mechanism how sRNAs are capable of modulating protein biosynthesis in the halophilic archaeon Haloferax volcanii. By analyzing the ribosome-associated ncRNAs (rancRNAs) under different stress conditions we identified an intergenic sRNA, termed rancRNA_s194, that is primarily expressed during exponential growth under all tested conditions. By interaction with the ribosome rancRNA_s194 inhibits peptide bond formation and protein synthesis in vitro but appears to target a specific mRNA in vivo. The respective knock-out strain shows a reduced lag phase in media containing xylose as sole carbon source and outcompetes the wildtype cells under these conditions. Mass spectrometry, polysome profiling and mRNA binding competition experiments suggest that rancRNA_s194 prevents the cstA mRNA from being efficiently translated by H. volcanii ribosomes. These findings enlarge the regulatory repertoire of archaeal sRNAs in modulating post-transcriptional gene expression.

gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data

In quantitative mass spectrometry, the method by which peptides are grouped into proteins can have dramatic effects on downstream analyses. Here we describe gpGrouper, an inference and quantitation algorithm that offers an alternative method for assignment of protein groups by gene locus and improves pseudo-absolute iBAQ quantitation by weighted distribution of shared peptide areas. We experimentally show that distributing shared peptide quantities based on unique peptide peak ratios improves quantitation accuracy compared with conventional winner-take-all scenarios. Furthermore, gpGrouper seamlessly handles two-species samples such as patient-derived xenografts (PDXs) without ignoring the host species or species-shared peptides. This is a critical capability for proper evaluation of proteomics data from PDX samples, where stromal infiltration varies across individual tumors. Finally, gpGrouper calculates peptide peak area (MS1) based expression estimates from multiplexed isobaric data, producing iBAQ results that are directly comparable across label-free, isotopic, and isobaric proteomics approaches.

Probing the colorectal cancer proteome for biomarkers: Current status and perspectives

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.

Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics

Mass spectrometry-based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label-based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling-based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

Zika virus crosses an in vitro human blood brain barrier model

Zika virus (ZIKV) is a flavivirus that is highly neurotropic causing congenital abnormalities and neurological damage to the central nervous systems (CNS). In this study, we used a human induced pluripotent stem cell (iPSC)-derived blood brain barrier (BBB) model to demonstrate that ZIKV can infect brain endothelial cells (i-BECs) without compromising the BBB barrier integrity or permeability. Although no disruption to the BBB was observed post-infection, ZIKV particles were released on the abluminal side of the BBB model and infected underlying iPSC-derived neural progenitor cells (i-NPs). AXL, a putative ZIKV cellular entry receptor, was also highly expressed in ZIKV-susceptible i-BEC and i-NPs. This iPSC-derived BBB model can help elucidate the mechanism by which ZIKV can infect BECs, cross the BBB and gain access to the CNS.

Streptococcus pyogenes Infection and the Human Proteome with a Special Focus on the Immunoglobulin G-cleaving Enzyme IdeS

Infectious diseases are characterized by a complex interplay between host and pathogen, but how these interactions impact the host proteome is unclear. Here we applied a combined mass spectrometry-based proteomics strategy to investigate how the human proteome is transiently modified by the pathogen Streptococcus pyogenes, with a particular focus on bacterial cleavage of IgG in vivo. In invasive diseases, S. pyogenes evokes a massive host response in blood, whereas superficial diseases are characterized by a local leakage of several blood plasma proteins at the site of infection including IgG. S. pyogenes produces IdeS, a protease cleaving IgG in the lower hinge region and we find highly effective IdeS-cleavage of IgG in samples from local IgG poor microenvironments. The results show that IdeS contributes to the adaptation of S. pyogenes to its normal ecological niches. Additionally, the work identifies novel clinical opportunities for in vivo pathogen detection.

Merged Map of the Yeast Proteome

A comprehensive reference map of protein abundances in budding yeast is generated by combining the 21 largest quantitative proteome datasets currently available for this model organism.

Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Protein activity is the ultimate arbiter of function in most cellular pathways, and protein concentration is fundamentally connected to protein action. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukaryote, existing datasets are difficult to compare, and there is no consensus abundance value for each protein. We evaluated 21 quantitative analyses of the S. cerevisiae proteome, normalizing and converting all measurements of protein abundance into the intuitive measurement of absolute molecules per cell. We estimate the cellular abundance of 92% of the proteins in the yeast proteome and assess the variation in each abundance measurement. Using our protein abundance dataset, we find that a global response to diverse environmental stresses is not detected at the level of protein abundance, we find that protein tags have only a modest effect on protein abundance, and we identify proteins that are differentially regulated at the mRNA abundance, mRNA translation, and protein abundance levels.

Quantitating translational control: mRNA abundance-dependent and independent contributions and the mRNA sequences that specify them

Translation rate per mRNA molecule correlates positively with mRNA abundance. As a result, protein levels do not scale linearly with mRNA levels, but instead scale with the abundance of mRNA raised to the power of an ‘amplification exponent’. Here we show that to quantitate translational control, the translation rate must be decomposed into two components. One, TR_mD, depends on the mRNA level and defines the amplification exponent. The other, TR_mIND, is independent of mRNA amount and impacts the correlation coefficient between protein and mRNA levels. We show that in Saccharomyces cerevisiae TR_mD represents ∼20% of the variance in translation and directs an amplification exponent of 1.20 with a 95% confidence interval [1.14, 1.26]. TR_mIND constitutes the remaining ∼80% of the variance in translation and explains ∼5% of the variance in protein expression. We also find that TR_mD and TR_mIND are preferentially determined by different mRNA sequence features: TR_mIND by the length of the open reading frame and TR_mD both by a ∼60 nucleotide element that spans the initiating AUG and by codon and amino acid frequency. Our work provides more appropriate estimates of translational control and implies that TR_mIND is under different evolutionary selective pressures than TR_mD.

Investigating the Effect of Growth Phase on the Surface-Layer Associated Proteome of Lactobacillus acidophilus Using Quantitative Proteomics

Bacterial surface-layers (S-layers) are semi-porous crystalline arrays that self-assemble to form the outermost layer of some cell envelopes. S-layers have been shown to act as scaffolding structures for the display of auxiliary proteins externally. These S-layer associated proteins have recently gained attention in probiotics due to their direct physical contact with the intestinal mucosa and potential role in cell proliferation, adhesion, and immunomodulation. A number of studies have attempted to catalog the S-layer associated proteome of Lactobacillus acidophilus NCFM under a single condition. However, due to the versatility of the cell surface, we chose to employ a multiplexing-based approach with the intention of accurately contrasting multiple conditions. In this study, a previously described lithium chloride isolation protocol was used to release proteins bound to the L. acidophilus S-layer during logarithmic and early stationary growth phases. Protein quantification values were obtained via TMT (tandem mass tag) labeling combined with a triple-stage mass spectrometry (MS3) method. Results showed significant growth stage-dependent alterations to the surface-associated proteome while simultaneously highlighting the sensitivity and reproducibility of the technology. Thus, this study establishes a framework for quantifying condition-dependent changes to cell surface proteins that can easily be applied to other S-layer forming bacteria.

Absolute Quantification of Human Milk Caseins and the Whey/Casein Ratio during the First Year of Lactation

Whey proteins and caseins in breast milk provide bioactivities and also have different amino acid composition. Accurate determination of these two major protein classes provides a better understanding of human milk composition and function, and further aids in developing improved infant formulas based on bovine whey proteins and caseins. In this study, we implemented a LC-MS/MS quantitative analysis based on iBAQ label-free quantitation, to estimate absolute concentrations of α-casein, β-casein, and κ-casein in human milk samples (n = 88) collected between day 1 and day 360 postpartum. Total protein concentration ranged from 2.03 to 17.52 with a mean of 9.37 ± 3.65 g/L. Casein subunits ranged from 0.04 to 1.68 g/L (α-), 0.04 to 4.42 g/L (β-), and 0.10 to 1.72 g/L (α-), with β-casein having the highest average concentration among the three subunits. Calculated whey/casein ratio ranged from 45:55 to 97:3. Linear regression analyses show significant decreases in total protein, β-casein, κ-casein, total casein, and a significant increase of whey/casein ratio during the course of lactation. Our study presents a novel and accurate quantitative analysis of human milk casein content, demonstrating a lower casein content than earlier believed, which has implications for improved infants formulas.

DeepPep: Deep proteome inference from peptide profiles

Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/.

The accurate identification of proteins in a proteomics sample, called the protein inference problem, is a fundamental challenge in biomedical sciences. Current approaches are based on applications of traditional neural networks, linear optimization and Bayesian techniques. We here present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a standard proteomics mixture, given all protein sequences and a peptide profile. Comparison to leading methods shows that DeepPep has most robust performance with various instruments and datasets. Our results provide evidence that using sequence-level location information of a peptide in the context of proteome sequence can result in more accurate and robust protein inference. We conclude that Deep Learning on protein sequence leads to superior platforms for protein inference that can be further refined with additional features and extended for far reaching applications.

Hemolymph proteins of Anopheles gambiae larvae infected by Escherichia coli

Anopheles gambiae is a major vector of human malaria and its immune system in part determines the fate of ingested parasites. Proteins, hemocytes and fat body in hemolymph are critical components of this system, mediating both humoral and cellular defenses. Here we assessed differences in the hemolymph proteomes of water- and E. coli-pricked mosquito larvae by a gel-LC-MS approach. Among the 1756 proteins identified, 603 contained a signal peptide but accounted for two-third of the total protein amount on the quantitative basis. The sequence homology search indicated that 233 of the 1756 may be related to defense. In general, we did not detect substantial differences between the control and induced plasma samples in terms of protein numbers or levels. Protein distributions in the gel slices suggested post-translational modifications (e.g. proteolysis) and formation of serpin-protease complexes and high Mr immune complexes. Based on the twenty-five most abundant proteins, we further suggest that major functions of the larval hemolymph are storage, transport, and immunity. In summary, this study provided first data on constitution, levels, and possible functions of hemolymph proteins in the mosquito larvae, reflecting complex changes occurring in the fight against E. coli infection.

Quantitative proteome analysis of plasma microparticles for the characterization of HCV-induced hepatic cirrhosis and hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor and a leading cause of cancer-related deaths worldwide. Cirrhosis induced by hepatitis-C virus (HCV) infection is the most critical risk factor for HCC. However, the mechanism of HCV-induced carcinogenesis is not fully understood. Plasma microparticles (PMP) contribute to numerous physiological and pathological processes and contain proteins whose composition correlates to the respective pathophysiological conditions.

EXPERIMENTAL DESIGN

We analyzed PMP from 22 HCV-induced cirrhosis patients, 16 HCV-positive HCC patients with underlying cirrhosis and 18 healthy controls. PMP were isolated using ultracentrifugation and analyzed via label-free LC-MS/MS.

RESULTS

We identified 840 protein groups and quantified 507 proteins. 159 proteins were found differentially abundant between the three experimental groups. PMP in both disease entities displayed remarkable differences in the proteome composition compared to healthy controls. Conversely, the proteome difference between both diseases was minimal. GO analysis revealed that PMP isolated from both diseases were significantly enriched in proteins involved in complement activation, while endopeptidase activity was downregulated exclusively in HCC patients.

CONCLUSION

This study reports for the first time a quantitative proteome analysis for PMP from patients with HCV-induced cirrhosis and HCC. Data are available via ProteomeXchange with identifier PXD005777.

In-depth PtdIns(3,4,5)P3 signalosome analysis identifies DAPP1 as a negative regulator of GPVI-driven platelet function

The class I phosphoinositide 3-kinase (PI3K) isoforms play important roles in platelet priming, activation, and stable thrombus formation. Class I PI3Ks predominantly regulate cell function through their catalytic product, the signaling phospholipid phosphatidylinositol 3,4,5-trisphosphate [PtdIns(3,4,5)P₃], which coordinates the localization and/or activity of a diverse range of binding proteins. Notably, the complete repertoire of these class I PI3K effectors in platelets remains unknown, limiting mechanistic understanding of class I PI3K-mediated control of platelet function. We measured robust agonist-driven PtdIns (3,4,5)P₃ generation in human platelets by lipidomic mass spectrometry (MS), and then used affinity-capture coupled to high-resolution proteomic MS to identify the targets of PtdIns (3,4,5)P₃ in these cells. We reveal for the first time a diverse platelet PtdIns(3,4,5)P₃ interactome, including kinases, signaling adaptors, and regulators of small GTPases, many of which are previously uncharacterized in this cell type. Of these, we show dual adaptor for phosphotyrosine and 3-phosphoinositides (DAPP1) to be regulated by Src-family kinases and PI3K, while platelets from DAPP1-deficient mice display enhanced thrombus formation on collagen in vitro. This was associated with enhanced platelet α/δ granule secretion and α_IIbβ₃ integrin activation downstream of the collagen receptor glycoprotein VI. Thus, we present the first comprehensive analysis of the PtdIns(3,4,5)P₃ signalosome of human platelets and identify DAPP1 as a novel negative regulator of platelet function. This work provides important new insights into how class I PI3Ks shape platelet function.

The predictive nature of transcript expression levels on protein expression in adult human brain

BACKGROUND

Next generation sequencing methods are the gold standard for evaluating expression of the transcriptome. When determining the biological implications of such studies, the assumption is often made that transcript expression levels correspond to protein levels in a meaningful way. However, the strength of the overall correlation between transcript and protein expression is inconsistent, particularly in brain samples.

RESULTS

Following high-throughput transcriptomic (RNA-Seq) and proteomic (liquid chromatography coupled with tandem mass spectrometry) analyses of adult human brain samples, we compared the correlation in the expression of transcripts and proteins that support various biological processes, molecular functions, and that are located in different areas of the cell. Although most categories of transcripts have extremely weak predictive value for the expression of their associated proteins (R² values of < 10%), transcripts coding for protein kinases and membrane-associated proteins, including those that are part of receptors or ion transporters, are among those that are most predictive of downstream protein expression levels.

CONCLUSIONS

The predictive value of transcript expression for corresponding proteins is variable in human brain samples, reflecting the complex regulation of protein expression. However, we found that transcriptomic analyses are appropriate for assessing the expression levels of certain classes of proteins, including those that modify proteins, such as kinases and phosphatases, regulate metabolic and synaptic activity, or are associated with a cellular membrane. These findings can be used to guide the interpretation of gene expression results from primate brain samples.

Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob

Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob).

SIGNIFICANCE

The concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.

Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.

Proteomic Analysis of Lipid Droplets from Arabidopsis Aging Leaves Brings New Insight into Their Biogenesis and Functions

Lipid droplets (LDs) are cell compartments specialized for oil storage. Although their role and biogenesis are relatively well documented in seeds, little is known about their composition, structure and function in senescing leaves where they also accumulate. Here, we used a label free quantitative mass spectrometry approach to define the LD proteome of aging Arabidopsis leaves. We found that its composition is highly different from that of seed/cotyledon and identified 28 proteins including 9 enzymes of the secondary metabolism pathways involved in plant defense response. With the exception of the TRIGALACTOSYLDIACYLGLYCEROL2 protein, we did not identify enzymes implicated in lipid metabolism, suggesting that growth of leaf LDs does not occur by local lipid synthesis but rather through contact sites with the endoplasmic reticulum (ER) or other membranes. The two most abundant proteins of the leaf LDs are the CALEOSIN3 and the SMALL RUBBER PARTICLE1 (AtSRP1); both proteins have structural functions and participate in plant response to stress. CALEOSIN3 and AtSRP1 are part of larger protein families, yet no other members were enriched in the LD proteome suggesting a specific role of both proteins in aging leaves. We thus examined the function of AtSRP1 at this developmental stage and found that AtSRP1 modulates the expression of CALEOSIN3 in aging leaves. Furthermore, AtSRP1 overexpression induces the accumulation of triacylglycerol with an unusual composition compared to wild-type. We demonstrate that, although AtSRP1 expression is naturally increased in wild type senescing leaves, its overexpression in senescent transgenic lines induces an over-accumulation of LDs organized in clusters at restricted sites of the ER. Conversely, atsrp1 knock-down mutants displayed fewer but larger LDs. Together our results reveal that the abundancy of AtSRP1 regulates the neo-formation of LDs during senescence. Using electron tomography, we further provide evidence that LDs in leaves share tenuous physical continuity as well as numerous contact sites with the ER membrane. Thus, our data suggest that leaf LDs are functionally distinct from seed LDs and that their biogenesis is strictly controlled by AtSRP1 at restricted sites of the ER.

Label-Free and Standard-Free Absolute Quantitative Proteomics Using the "Total Protein" and "Proteomic Ruler" Approaches

Understanding biological systems and their variation upon stimuli requires knowledge on their composition, primarily including information on organization and dynamics of proteomes. The total protein approach (TPA) is a label- and standard-free method for absolute protein quantitation of proteins using large-scale proteomic data. The method relies on the assumption that the total MS signal from all identified proteins in the dataset reflects-in a biochemical sense-the total protein and the MS signal from a single protein corresponds its abundance in the studied sample. The method offers an easy way to quantify thousands of protein per sample. A related method, the "Proteomic Ruler," enables conversion of the protein abundance data calculated by TPA to compute numbers of protein copies per cell. TPA and the Proteomic Ruler are powerful tools for studying dynamics of cell architecture.

Characterization of a dynamic metabolon producing the defense compound dhurrin in sorghum

Metabolic highways may be orchestrated by the assembly of sequential enzymes into protein complexes, or metabolons, to facilitate efficient channeling of intermediates and to prevent undesired metabolic cross-talk while maintaining metabolic flexibility. Here we report the isolation of the dynamic metabolon that catalyzes the formation of the cyanogenic glucoside dhurrin, a defense compound produced in sorghum plants. The metabolon was reconstituted in liposomes, which demonstrated the importance of membrane surface charge and the presence of the glucosyltransferase for metabolic channeling. We used in planta fluorescence lifetime imaging microscopy and fluorescence correlation spectroscopy to study functional and structural characteristics of the metabolon. Understanding the regulation of biosynthetic metabolons offers opportunities to optimize synthetic biology approaches for efficient production of high-value products in heterologous hosts.

Gene-specific correlation of RNA and protein levels in human cells and tissues

An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on parallel reaction monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.

Robust Label-free, Quantitative Profiling of Circulating Plasma Microparticle (MP) Associated Proteins

Cells of the vascular system release spherical vesicles, called microparticles, in the size range of 0.1-1 μm induced by a variety of stress factors resulting in variable concentrations between health and disease. Furthermore, microparticles have intercellular communication/signaling properties and interfere with inflammation and coagulation pathways. Today's most used analytical technology for microparticle characterization, flow cytometry, is lacking sensitivity and specificity, which might have led to the publication of contradicting results in the past.We propose the use of nano-liquid chromatography two-stage mass spectrometry as a nonbiased tool for quantitative MP proteome analysis.For this, we developed an improved microparticle isolation protocol and quantified the microparticle protein composition of twelve healthy volunteers with a label-free, data-dependent and independent proteomics approach on a quadrupole orbitrap instrument.Using aliquots of 250 μl platelet-free plasma from one individual donor, we achieved excellent reproducibility with an interassay coefficient of variation of 2.7 ± 1.7% (mean ± 1 standard deviation) on individual peptide intensities across 27 acquisitions performed over a period of 3.5 months. We show that the microparticle proteome between twelve healthy volunteers were remarkably similar, and that it is clearly distinguishable from whole cell and platelet lysates. We propose the use of the proteome profile shown in this work as a quality criterion for microparticle purity in proteomics studies. Furthermore, one freeze thaw cycle damaged the microparticle integrity, articulated by a loss of cytoplasm proteins, encompassing a specific set of proteins involved in regulating dynamic structures of the cytoskeleton, and thrombin activation leading to MP clotting. On the other hand, plasma membrane protein composition was unaffected. Finally, we show that multiplexed data-independent acquisition can be used for relative quantification of target proteins using Skyline software. Mass spectrometry data are available via ProteomeXchange (identifier PXD003935) and panoramaweb.org (https://panoramaweb.org/labkey/N1OHMk.url).

Quantitative analysis of human centrosome architecture by targeted proteomics and fluorescence imaging

Centrioles are essential for the formation of centrosomes and cilia. While numerical and/or structural centrosomes aberrations are implicated in cancer, mutations in centriolar and centrosomal proteins are genetically linked to ciliopathies, microcephaly, and dwarfism. The evolutionarily conserved mechanisms underlying centrosome biogenesis are centered on a set of key proteins, including Plk4, Sas-6, and STIL, whose exact levels are critical to ensure accurate reproduction of centrioles during cell cycle progression. However, neither the intracellular levels of centrosomal proteins nor their stoichiometry within centrosomes is presently known. Here, we have used two complementary approaches, targeted proteomics and EGFP-tagging of centrosomal proteins at endogenous loci, to measure protein abundance in cultured human cells and purified centrosomes. Our results provide a first assessment of the absolute and relative amounts of major components of the human centrosome. Specifically, they predict that human centriolar cartwheels comprise up to 16 stacked hubs and 1 molecule of STIL for every dimer of Sas-6. This type of quantitative information will help guide future studies of the molecular basis of centrosome assembly and function.