Christina Ludwig

Large-Scale Quantitative Assessment of Different In-Solution Protein Digestion Protocols Reveals Superior Cleavage Efficiency of Tandem Lys-C/Trypsin Proteolysis over Trypsin Digestion

The complete and specific proteolytic cleavage of protein samples into peptides is crucial for the success of every shotgun LC-MS/MS experiment. In particular, popular peptide-based label-free and targeted mass spectrometry approaches rely on efficient generation of fully cleaved peptides to ensure accurate and sensitive protein quantification. In contrast to previous studies, we globally and quantitatively assessed the efficiency of different digestion strategies using a yeast cell lysate, label-free quantification, and statistical analysis. Digestion conditions include double tryptic, surfactant-assisted, and tandem-combinatorial Lys-C/trypsin digestion. In comparison to tryptic digests, Lys-C/trypsin digests were found most efficient to yield fully cleaved peptides while reducing the abundance of miscleaved peptides. Subsequent sequence context analysis revealed improved digestion performances of Lys-C/trypsin for miscleaved sequence stretches flanked by charged basic and particulary acidic residues. Furthermore, targeted MS analysis demonstrated a more comprehensive protein cleavage only after Lys-C/trypsin digestion, resulting in a more accurrate absolute protein quantification and extending the number of peptides suitable for SRM assay development. Therefore, we conclude that a serial Lys-C/trypsin digestion is highly attractive for most applications in quantitative MS-based proteomics building on in-solution digestion schemes.

The Mtb Proteome Library: A Resource of Assays to Quantify the Complete Proteome of Mycobacterium tuberculosis

Research advancing our understanding of Mycobacterium tuberculosis (Mtb) biology and complex host-Mtb interactions requires consistent and precise quantitative measurements of Mtb proteins. We describe the generation and validation of a compendium of assays to quantify 97% of the 4,012 annotated Mtb proteins by the targeted mass spectrometric method selected reaction monitoring (SRM). Furthermore, we estimate the absolute abundance for 55% of all Mtb proteins, revealing a dynamic range within the Mtb proteome of over four orders of magnitude, and identify previously unannotated proteins. As an example of the assay library utility, we monitored the entire Mtb dormancy survival regulon (DosR), which is linked to anaerobic survival and Mtb persistence, and show its dynamic protein-level regulation during hypoxia. In conclusion, we present a publicly available research resource that supports the sensitive, precise, and reproducible quantification of virtually any Mtb protein by a robust and widely accessible mass spectrometric method.

Regulation of yeast central metabolism by enzyme phosphorylation

As a frequent post‐translational modification, protein phosphorylation regulates many cellular processes. Although several hundred phosphorylation sites have been mapped to metabolic enzymes in Saccharomyces cerevisiae, functionality was demonstrated for few of them. Here, we describe a novel approach to identify in vivo functionality of enzyme phosphorylation by combining flux analysis with proteomics and phosphoproteomics. Focusing on the network of 204 enzymes that constitute the yeast central carbon and amino‐acid metabolism, we combined protein and phosphoprotein levels to identify 35 enzymes that change their degree of phosphorylation during growth under five conditions. Correlations between previously determined intracellular fluxes and phosphoprotein abundances provided first functional evidence for five novel phosphoregulated enzymes in this network, adding to nine known phosphoenzymes. For the pyruvate dehydrogenase complex E1 α subunit Pda1 and the newly identified phosphoregulated glycerol‐3‐phosphate dehydrogenase Gpd1 and phosphofructose‐1‐kinase complex β subunit Pfk2, we then validated functionality of specific phosphosites through absolute peptide quantification by targeted mass spectrometry, metabolomics and physiological flux analysis in mutants with genetically removed phosphosites. These results demonstrate the role of phosphorylation in controlling the metabolic flux realised by these three enzymes.

Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the “best flyer” hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R2 of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

Absolute Proteome Composition and Dynamics during Dormancy and Resuscitation of Mycobacterium tuberculosis

Mycobacterium tuberculosis remains a health concern due to its ability to enter a non-replicative dormant state linked to drug resistance. Understanding transitions into and out of dormancy will inform therapeutic strategies. We implemented a universally applicable, label-free approach to estimate absolute cellular protein concentrations on a proteome-wide scale based on SWATH mass spectrometry. We applied this approach to examine proteomic reorganization of M. tuberculosis during exponential growth, hypoxia-induced dormancy, and resuscitation. The resulting data set covering >2,000 proteins reveals how protein biomass is distributed among cellular functions during these states. The stress-induced DosR regulon contributes 20% to cellular protein content during dormancy, whereas ribosomal proteins remain largely unchanged at 5%-7%. Absolute protein concentrations furthermore allow protein alterations to be translated into changes in maximal enzymatic reaction velocities, enhancing understanding of metabolic adaptations. Thus, global absolute protein measurements provide a quantitative description of microbial states, which can support the development of therapeutic interventions.

Interaction Studies and Alanine Scanning Analysis of a Semi-synthetic Split Intein Reveal Thiazoline Ring Formation from an Intermediate of the Protein Splicing Reaction

We recently reported an artificially split intein based on the Ssp DnaB mini-intein that consists of a synthetic N-terminal intein fragment (IntN) and a recombinant C-terminal part (IntC), which are 11 and 143 amino acids in length, respectively. This intein holds great promise for the preparation of semi-synthetic proteins by protein trans-splicing. In this work we synthesized a set of IntN peptide variants to investigate their structure-function relationship with regard to fragment association and promotion of protein trans-splicing. A further truncation of the IntN sequence below 11 amino acids resulted in loss of activity, whereas C-terminal extensions were tolerated. Alanine scanning analysis identified three essential hydrophobic residues, whereas substitutions at other positions were tolerated. We developed assays to monitor association of IntN with an IntC mutant blocked in protein splicing by native PAGE and fluorescence anisotropy. The kinetic parameters of intein complex formation were Kd = 1.1 μm, kon = 16.8 m–1 s–1, and koff = 1.8 × 10–5 s–1 for the native IntN11 sequence. Intriguingly, a G(–1)A substitution, previously known to significantly impair protein splicing, was revealed to result in thiazoline ring formation involving the catalytic Cys-1, likely by aberrant dehydration of a oxythiazolidine intermediate. This finding provides experimental evidence for the postulated intermediate during the initial N/S acyl shift and underlines the delicate spatial and temporal alignment required in the intein active site to prevent side reactions of the protein-splicing pathway.

Dynamic phosphoproteomics reveals TORC1-dependent regulation of yeast nucleotide and amino acid biosynthesis

The effect of phosphorylation on metabolic enzyme activity could be inferred by correlating phosphoproteomics and metabolomics data. How phosphorylation regulates metabolism The phosphorylation events triggered directly by or downstream of the protein complex TORC1 enable yeast to adjust their metabolism to respond to changes in nutrient availability or nutritional quality. Oliveira et al. subjected yeast to changes in nutrient quality or to treatment with rapamycin, an inhibitor of TORC1. By temporally correlating changes in metabolite concentrations with phosphorylation events, they identified metabolic enzymes downstream of TORC1 and inferred the effect of phosphorylation on the activity of these enzymes, which included enzymes involved in nucleotide and amino acid metabolism and in carbohydrate storage. Phosphoproteomics studies have unraveled the extent of protein phosphorylation as a key cellular regulation mechanism, but assigning functionality to specific phosphorylation events remains a major challenge. TORC1 (target of rapamycin complex 1) is a kinase-containing protein complex that transduces changes in nutrient availability into phosphorylation signaling events that alter cell growth and proliferation. To resolve the temporal sequence of phosphorylation responses to nutritionally and chemically induced changes in TORC1 signaling and to identify previously unknown kinase-substrate relationships in Saccharomyces cerevisiae, we performed quantitative mass spectrometry–based phosphoproteomic analyses after shifts in nitrogen sources and rapamycin treatment. From early phosphorylation events that were consistent over at least two experimental perturbations, we identified 51 candidate and 10 known proximal targets of TORC1 that were direct substrates of TORC1 or of one of its kinase or phosphatase substrates. By correlating these phosphoproteomics data with dynamic metabolomics data, we inferred the functional role of phosphorylation on the metabolic activity of 12 enzymes, including three candidate TORC1-proximal targets: Amd1, which is involved in nucleotide metabolism; Hom3, which is involved in amino acid metabolism; and Tsl1, which mediates carbohydrate storage. Finally, we identified the TORC1 substrates Sch9 and Atg1 as candidate kinases that phosphorylate Amd1 and Hom3, respectively.

Automatic generation of predictive dynamic models reveals nuclear phosphorylation as the key Msn2 control mechanism.

Topological filtering identifies biological networks compatible with known data and enables quantitative analysis of regulatory mechanisms. Reducing the Options Quantitative analysis of signaling systems is challenging because limited quantitative data are available and the data can be represented by many network models. Sunnåker et al. developed a computational approach called topological filtering to systematically and automatically integrate modeling and data acquisition to infer the set of mechanistically plausible models, thus vastly reducing the number of potential models. The approach iteratively eliminates reactions from the model to identify only those topological networks that fit the data. Application of their method to an extracellular signal–regulated kinase (ERK) pathway that could be represented by 512 possible network topologies reduced the possibilities to 16 and showed that a set of feedback reactions were necessary to quantitatively represent the results. Topological filtering applied to the regulation of the localization of Msn2, a yeast transcription factor controlled by phosphorylation by PKA (protein kinase A) in response to changes in glucose abundance, identified a single model that fit the data. Comparison of model predictions with experimental data showed that the nuclear phosphorylation rate was key to controlling Msn2 nuclear abundance in response to cAMP (cyclic adenosine monophosphate), a signal produced as cells recover from glucose starvation. Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate among systems biology models are still lacking. We describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model and automatically generates a set of simpler models compatible with observational data. As a proof of principle, we analyzed the dynamic control of the transcription factor Msn2 in Saccharomyces cerevisiae, specifically the short-term mechanisms mediating the cells’ recovery after release from starvation stress. Our method determined that 12 of 192 possible models were compatible with available Msn2 localization data. Iterations between model predictions and rationally designed phosphoproteomics and imaging experiments identified a single-circuit topology with a relative probability of 99% among the 192 models. Model analysis revealed that the coupling of dynamic phenomena in Msn2 phosphorylation and transport could lead to efficient stress response signaling by establishing a rate-of-change sensor. Similar principles could apply to mammalian stress response pathways. Systematic construction of dynamic models may yield detailed insight into nonobvious molecular mechanisms.

aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data

Motivation: The determination of absolute quantities of proteins in biological samples is necessary for multiple types of scientific inquiry. While relative quantification has been commonly used in proteomics, few proteomic datasets measuring absolute protein quantities have been reported to date. Various technologies have been applied using different types of input data, e.g. ion intensities or spectral counts, as well as different absolute normalization strategies. To date, a user-friendly and transparent software supporting large-scale absolute protein quantification has been lacking. Results: We present a bioinformatics tool, termed aLFQ, which supports the commonly used absolute label-free protein abundance estimation methods (TopN, iBAQ, APEX, NSAF and SCAMPI) for LC-MS/MS proteomics data, together with validation algorithms enabling automated data analysis and error estimation. Availability and implementation: aLFQ is written in R and freely available under the GPLv3 from CRAN (http://www.cran.r-project.org). Instructions and example data are provided in the R-package. The raw data can be obtained from the PeptideAtlas raw data repository (PASS00321). Contact: lars.malmstroem@imsb.biol.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Attenuation of pattern recognition receptor signaling is mediated by a MAP kinase kinase kinase

Pattern recognition receptors (PRRs) play a key role in plant and animal innate immunity. PRR binding of their cognate ligand triggers a signaling network and activates an immune response. Activation of PRR signaling must be controlled prior to ligand binding to prevent spurious signaling and immune activation. Flagellin perception in Arabidopsis through FLAGELLIN‐SENSITIVE 2 (FLS2) induces the activation of mitogen‐activated protein kinases (MAPKs) and immunity. However, the precise molecular mechanism that connects activated FLS2 to downstream MAPK cascades remains unknown. Here, we report the identification of a differentially phosphorylated MAP kinase kinase kinase that also interacts with FLS2. Using targeted proteomics and functional analysis, we show that MKKK7 negatively regulates flagellin‐triggered signaling and basal immunity and this requires phosphorylation of MKKK7 on specific serine residues. MKKK7 attenuates MPK6 activity and defense gene expression. Moreover, MKKK7 suppresses the reactive oxygen species burst downstream of FLS2, suggesting that MKKK7‐mediated attenuation of FLS2 signaling occurs through direct modulation of the FLS2 complex.