Challenges in Peptide-Spectrum Matching: A Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits.

Workflows for large-scale (MS)-based shotgun proteomics can potentially lead to costly errors in the form of incorrect peptide-spectrum matches (PSMs). To improve the robustness of these workflows, we have investigated the use of the precursor mass discrepancy (PMD) to detect and filter potentially false PSMs that have, nonetheless, a high confidence score. We identified and addressed three cases of unexpected bias in PMD results: time of acquisition within a liquid chromatography-mass spectrometry (LC-MS) run, decoy PSMs, and length of the peptide. We created a postanalysis Bayesian confidence measure based on score and PMD, called PMD-false discovery rate (FDR). We tested PMD-FDR on four data sets across three types of MS-based proteomics projects: standard (single organism; reference database), proteogenomics (single organism; customized genomic-based database plus reference), and metaproteomics (microorganism community; customized conglomerate database). On a ground-truth data set and other representative data, PMD-FDR was able to detect 60-80% of likely incorrect PSMs (false-hits) while losing only 5% of correct PSMs (true-hits). PMD-FDR can also be used to evaluate data quality for results generated within different experimental PSM-generating workflows, assisting in method development. Going forward, PMD-FDR should provide detection of high scoring but likely false-hits, aiding applications that rely heavily on accurate PSMs, such as proteogenomics and metaproteomics.

Quantitative proteomics reveals factors regulating RNA biology as dynamic targets of stress-induced SUMOylation in Arabidopsis.

The stress-induced attachment of small ubiquitin-like modifier (SUMO) to a diverse collection of nuclear proteins regulating chromatin architecture, transcription, and RNA biology has been implicated in protecting plants and animals against numerous environmental challenges. In order to better understand stress-induced SUMOylation, we combined stringent purification of SUMO conjugates with isobaric tag for relative and absolute quantification mass spectrometry and an advanced method to adjust for sample-to-sample variation so as to study quantitatively the SUMOylation dynamics of intact Arabidopsis seedlings subjected to stress. Inspection of 172 SUMO substrates during and after heat shock (37 °C) revealed that stress mostly increases the abundance of existing conjugates, as opposed to modifying new targets. Some of the most robustly up-regulated targets participate in RNA processing and turnover and RNA-directed DNA modification, thus implicating SUMO as a regulator of the transcriptome during stress. Many of these targets were also strongly SUMOylated during ethanol and oxidative stress, suggesting that their modification is crucial for general stress tolerance. Collectively, our quantitative data emphasize the importance of SUMO to RNA-related processes protecting plants from adverse environments.

A dynamic model of proteome changes reveals new roles for transcript alteration in yeast.

The transcriptome and proteome change dynamically as cells respond to environmental stress; however, prior proteomic studies reported poor correlation between mRNA and protein, rendering their relationships unclear. To address this, we combined high mass accuracy mass spectrometry with isobaric tagging to quantify dynamic changes in ~2500 Saccharomyces cerevisiae proteins, in biological triplicate and with paired mRNA samples, as cells acclimated to high osmolarity. Surprisingly, while transcript induction correlated extremely well with protein increase, transcript reduction produced little to no change in the corresponding proteins. We constructed a mathematical model of dynamic protein changes and propose that the lack of protein reduction is explained by cell-division arrest, while transcript reduction supports redistribution of translational machinery. Furthermore, the transient 'burst' of mRNA induction after stress serves to accelerate change in the corresponding protein levels. We identified several classes of post-transcriptional regulation, but show that most of the variance in protein changes is explained by mRNA. Our results present a picture of the coordinated physiological responses at the levels of mRNA, protein, protein-synthetic capacity, and cellular growth.

Mass Distributions of Linear Chain Polymers.

Biochemistry has many examples of linear chain polymers, i.e., molecules formed from a sequence of units from a finite set of possibilities; examples include proteins, RNA, single-stranded DNA, and paired DNA. In the field of mass spectrometry, it is useful to consider the idea of weighted alphabets, with a word inheriting weight from its letters. We describe the distribution of the mass of these words in terms of a simple recurrence relation, the general solution to that relation, and a canonical form that explicitly describes both the exponential form of this distribution and its periodic features, thus explaining a wave pattern that has been observed in protein mass databases. Further, we show that a pure exponential term dominates the distribution and that there is exactly one such purely exponential term. Finally, we illustrate the use of this theorem by describing a formula for the integer mass distribution of peptides and we compare our theoretical results with mass distributions of human and yeast peptides.

Valence parity renders z(*)-type ions chemically distinct.

Here we report that the odd electron z (*) -type ions formed by the electron-based peptide dissociation methods (electron capture or transfer, ECD or ETD) have distinctive chemical compositions from other common product ion types. Specifically, b-, c-, and y-type ions have an odd number of atoms with an odd valence (e.g., N and H), while z (*)-type ions contain an even number of atoms with an odd valence. This tenet, referred to as the valence parity rule, mandates that no c-type ion shall have the same chemical composition, and by extension mass, as a z (*) -type ion. By experiment we demonstrate that nearly half of all observed c- and z (*) -type product ions resulting from 226 ETD product ion spectra can be assigned to a single, correct, chemical composition and ion type by simple inspection of the m/ z peaks. The assignments provide (1) a platform to directly determine amino acid composition, (2) an input for database search algorithms, or (3) a basis for de novo sequence analysis.

1,25-Dihydroxyvitamin D3 increases the methionine cycle, CD4+ T cell DNA methylation and Helios+Foxp3+ T regulatory cells to reverse autoimmune neurodegenerative disease.

We investigated how one calcitriol dose plus vitamin D3 reverses experimental autoimmune encephalomyelitis (EAE), a multiple sclerosis model. This protocol rapidly increased CD4+ T cell Ikzf2 transcripts, Helios protein, and CD4+Helios+FoxP3+ T regulatory cells. It also rapidly increased CD4+ T cell Bhmt1 transcripts, betaine:homocysteine methyltransferase-1 (BHMT1) enzyme activity, and global DNA methylation. BHMT1 transmethylates homocysteine to replenish methionine. Targeting the Vdr gene in T cells decreased Ikzf2 and Bhmt1 gene expression, reduced DNA methylation, and elevated systemic homocysteine in mice with EAE. We hypothesize that calcitriol drives a transition from encephalitogenic CD4+ T cell to Treg cell dominance by upregulating Ikzf2 and Bhmt1, recycling homocysteine to methionine, reducing homocysteine toxicity, maintaining DNA methylation, and stabilizing CD4+Helios+FoxP3+Tregulatory cells. Conserved vitamin D-responsive element (VDRE)-type sequences in the Bhmt1 and Ikzf2 promoters, the universal need for methionine in epigenetic regulation, and betaine's protective effects in MTHFR-deficiency suggest similar regulatory mechanisms exist in humans.

Vitamin D Actions on CD4(+) T Cells in Autoimmune Disease.

This review summarizes and integrates research on vitamin D and CD4(+) T-lymphocyte biology to develop new mechanistic insights into the molecular etiology of autoimmune disease. A deep understanding of molecular mechanisms relevant to gene-environment interactions is needed to deliver etiology-based autoimmune disease prevention and treatment strategies. Evidence linking sunlight, vitamin D, and the risk of multiple sclerosis and type 1 diabetes is summarized to develop the thesis that vitamin D is the environmental factor that most strongly influences autoimmune disease development. Evidence for CD4(+) T-cell involvement in autoimmune disease pathogenesis and for paracrine calcitriol signaling to CD4(+) T lymphocytes is summarized to support the thesis that calcitriol is sunlight's main protective signal transducer in autoimmune disease risk. Animal modeling and human mechanistic data are summarized to support the view that vitamin D probably influences thymic negative selection, effector Th1 and Th17 pathogenesis and responsiveness to extrinsic cell death signals, FoxP3(+)CD4(+) T-regulatory cell and CD4(+) T-regulatory cell type 1 (Tr1) cell functions, and a Th1-Tr1 switch. The proposed Th1-Tr1 switch appears to bridge two stable, self-reinforcing immune states, pro- and anti-inflammatory, each with a characteristic gene regulatory network. The bi-stable switch would enable T cells to integrate signals from pathogens, hormones, cell-cell interactions, and soluble mediators and respond in a biologically appropriate manner. Finally, unanswered questions and potentially informative future research directions are highlighted to speed delivery of etiology-based strategies to reduce autoimmune disease.

Periodic patterns in distributions of peptide masses.

We are investigating the distribution of the number of peptides for given masses, and especially the observation that peptide density reaches a local maximum approximately every 14Da. This wave pattern exists across species (e.g. human or yeast) and enzyme digestion techniques. To analyze this phenomenon we have developed a mathematical method for computing the mass distributions of peptides, and we present both theoretical and empirical evidence that this 14-Da periodicity does not arise from species selection of peptides but from the number- theoretic properties of the masses of amino acid residues. We also describe other, more subtle periodic patterns in the distribution of peptide masses. We also show that these periodic patterns are robust under a variety of conditions, including the addition of amino acid modifications and selection of mass accuracy scale. The method used here is also applicable to any family of sequential molecules, such as linear hydrocarbons, RNA, single- and double-stranded DNA.

A quantitative map of the liver mitochondrial phosphoproteome reveals posttranslational control of ketogenesis.

Mitochondria are dynamic organelles that play a central role in a diverse array of metabolic processes. Elucidating mitochondrial adaptations to changing metabolic demands and the pathogenic alterations that underlie metabolic disorders represent principal challenges in cell biology. Here, we performed multiplexed quantitative mass spectrometry-based proteomics to chart the remodeling of the mouse liver mitochondrial proteome and phosphoproteome during both acute and chronic physiological transformations in more than 50 mice. Our analyses reveal that reversible phosphorylation is widespread in mitochondria, and is a key mechanism for regulating ketogenesis during the onset of obesity and type 2 diabetes. Specifically, we have demonstrated that phosphorylation of a conserved serine on Hmgcs2 (S456) significantly enhances its catalytic activity in response to increased ketogenic demand. Collectively, our work describes the plasticity of this organelle at high resolution and provides a framework for investigating the roles of proteome restructuring and reversible phosphorylation in mitochondrial adaptation.

Characterization and diagnostic value of amino acid side chain neutral losses following electron-transfer dissociation.

Using a large set of high mass accuracy and resolution ETD tandem mass spectra, we characterized ETD-induced neutral losses. From these data we deduced the chemical formula for 20 of these losses. Many of them have been previously observed in electron-capture dissociation (ECD) spectra, such as losses of the side chains of arginine, aspartic acid, glutamic acid, glutamine, asparagine, leucine, histidine, and carbamidomethylated cysteine residues. With this information, we examined the diagnostic value of these amino acid-specific losses. Among 1285 peptide-spectrum matches, 92.5% have agreement between neutral loss-derived peptide amino acid composition and the peptide sequences. Moreover, we show that peptides can be uniquely identified by using only the accurate precursor mass and amino acid composition based on neutral losses; the median number of sequence candidates from an accurate mass query is reduced from 21 to 8 by adding side chain loss information. Besides increasing confidence in peptide identification, our findings suggest the potential use of these diagnostic losses in ETD spectra to improve false discovery rate estimation and to enhance the performance of scoring functions in database search algorithms.