The nitrate transporter-sensor MtNPF6.8 regulates the branched chain amino acid/pantothenate metabolic pathway in barrel medic (Medicago truncatula Gaertn.) root tip.

Nitrogen is the most limiting nutrient for plants, and it is preferentially absorbed in the form of nitrate by roots, which adapt to nitrate fluctuations by remodelling their architecture. Although core mechanisms of the response to nitrate availability are relatively well-known, signalling events controlling root growth and architecture have not all been identified, in particular in Legumes. However, the developmental effect of nitrate in Legumes is critical since external nitrate not only regulates root architecture but also N2-fixing nodule development. We have previously shown that in barrel medic (Medicago truncatula), the nitrate transporter MtNPF6.8 is required for nitrate sensitivity in root tip. However, uncertainty remains as to whether nitrogen metabolism itself is involved in the MtNPF6.8-mediated response. Here, we examine the metabolic effects of MtNPF6.8-dependent nitrate signalling using metabolomics and proteomics in WT and mtnpf6.8 root tips in presence or absence of nitrate. We found a reorchestration of metabolism due to the mutation, in favour of the branched chain amino acids/pantothenate metabolic pathway, and lipid catabolism via glyoxylate. That is, the mtnpf6.8 mutation was likely associated with a specific rerouting of acetyl-CoA production (glyoxylic cycle) and utilisation (pantothenate and branched chain amino acid synthesis). In agreement with our previous findings, class III peroxidases were confirmed as the main protein class responsive to nitrate, although in an MtNPF6.8-independent fashion. Our data rather suggest the involvement of other pathways within mtnpf6.8 root tips, such as Ca2+ signalling or cell wall methylation.

Fast alignment of mass spectra in large proteomics datasets, capturing dissimilarities arising from multiple complex modifications of peptides.

BACKGROUND: In proteomics, the interpretation of mass spectra representing peptides carrying multiple complex modifications remains challenging, as it is difficult to strike a balance between reasonable execution time, a limited number of false positives, and a huge search space allowing any number of modifications without a priori. The scientific community needs new developments in this area to aid in the discovery of novel post-translational modifications that may play important roles in disease. RESULTS: To make progress on this issue, we implemented SpecGlobX (SpecGlob eXTended to eXperimental spectra), a standalone Java application that quickly determines the best spectral alignments of a (possibly very large) list of Peptide-to-Spectrum Matches (PSMs) provided by any open modification search method, or generated by the user. As input, SpecGlobX reads a file containing spectra in MGF or mzML format and a semicolon-delimited spreadsheet describing the PSMs. SpecGlobX returns the best alignment for each PSM as output, splitting the mass difference between the spectrum and the peptide into one or more shifts while considering the possibility of non-aligned masses (a phenomenon resulting from many situations including neutral losses). SpecGlobX is fast, able to align one million PSMs in about 1.5 min on a standard desktop. Firstly, we remind the foundations of the algorithm and detail how we adapted SpecGlob (the method we previously developed following the same aim, but limited to the interpretation of perfect simulated spectra) to the interpretation of imperfect experimental spectra. Then, we highlight the interest of SpecGlobX as a complementary tool downstream to three open modification search methods on a large simulated spectra dataset. Finally, we ran SpecGlobX on a proteome-wide dataset downloaded from PRIDE to demonstrate that SpecGlobX functions just as well on simulated and experimental spectra. We then carefully analyzed a limited set of interpretations. CONCLUSIONS: SpecGlobX is helpful as a decision support tool, providing keys to interpret peptides carrying complex modifications still poorly considered by current open modification search software. Better alignment of PSMs enhances confidence in the identification of spectra provided by open modification search methods and should improve the interpretation rate of spectra.

Plasticity QTLs specifically contribute to the genotype × water availability interaction in maize.

KEY MESSAGE: Multi-trial genome wide association study of plasticity indices allow to detect QTLs specifically involved in the genotype x water availability interaction. Concerns regarding high maize yield losses due to increasing occurrences of drought events are growing, and breeders are still looking for molecular markers for drought tolerance. However, the genetic determinism of traits in response to drought is highly complex and identification of causal regions is a tremendous task. Here, we exploit the phenotypic data obtained from four trials carried out on a phenotyping platform, where a diversity panel of 254 maize hybrids was grown under well-watered and water deficit conditions, to investigate the genetic bases of the drought response in maize. To dissociate drought effect from other environmental factors, we performed multi-trial genome-wide association study on well-watered and water deficit phenotypic means, and on phenotypic plasticity indices computed from measurements made for six ecophysiological traits. We identify 102 QTLs and 40 plasticity QTLs. Most of them were new compared to those obtained from a previous study on the same dataset. Our results show that plasticity QTLs cover genetic regions not identified by QTLs. Furthermore, for all ecophysiological traits, except one, plasticity QTLs are specifically involved in the genotype by water availability interaction, for which they explain between 60 and 100% of the variance. Altogether, QTLs and plasticity QTLs captured more than 75% of the genotype by water availability interaction variance, and allowed to find new genetic regions. Overall, our results demonstrate the importance of considering phenotypic plasticity to decipher the genetic architecture of trait response to stress.

Sunflower Hybrids and Inbred Lines Adopt Different Physiological Strategies and Proteome Responses to Cope with Water Deficit.

Sunflower is a hybrid crop that is considered moderately drought-tolerant and adapted to new cropping systems required for the agro-ecological transition. Here, we studied the impact of hybridity status (hybrids vs. inbred lines) on the responses to drought at the molecular and eco-physiological level exploiting publicly available datasets. Eco-physiological traits and leaf proteomes were measured in eight inbred lines and their sixteen hybrids grown in the high-throughput phenotyping platform Phenotoul-Heliaphen. Hybrids and parental lines showed different growth strategies: hybrids grew faster in the absence of water constraint and arrested their growth more abruptly than inbred lines when subjected to water deficit. We identified 471 differentially accumulated proteins, of which 256 were regulated by drought. The amplitude of up- and downregulations was greater in hybrids than in inbred lines. Our results show that hybrids respond more strongly to water deficit at the molecular and eco-physiological levels. Because of presence/absence polymorphism, hybrids potentially contain more genes than their parental inbred lines. We propose that detrimental homozygous mutations and the lower number of genes in inbred lines lead to a constitutive defense mechanism that may explain the lower growth of inbred lines under well-watered conditions and their lower reactivity to water deficit.

Dynamics of Protein Phosphorylation during Arabidopsis Seed Germination.

Seed germination is critical for early plantlet development and is tightly controlled by environmental factors. Nevertheless, the signaling networks underlying germination control remain elusive. In this study, the remodeling of Arabidopsis seed phosphoproteome during imbibition was investigated using stable isotope dimethyl labeling and nanoLC-MS/MS analysis. Freshly harvested seeds were imbibed under dark or constant light to restrict or promote germination, respectively. For each light regime, phosphoproteins were extracted and identified from dry and imbibed (6 h, 16 h, and 24 h) seeds. A large repertoire of 10,244 phosphopeptides from 2546 phosphoproteins, including 110 protein kinases and key regulators of seed germination such as Delay Of Germination 1 (DOG1), was established. Most phosphoproteins were only identified in dry seeds. Early imbibition led to a similar massive downregulation in dormant and non-dormant seeds. After 24 h, 411 phosphoproteins were specifically identified in non-dormant seeds. Gene ontology analyses revealed their involvement in RNA and protein metabolism, transport, and signaling. In addition, 489 phosphopeptides were quantified, and 234 exhibited up or downregulation during imbibition. Interaction networks and motif analyses revealed their association with potential signaling modules involved in germination control. Our study provides evidence of a major role of phosphosignaling in the regulation of Arabidopsis seed germination.

A combined test for feature selection on sparse metaproteomics data-an alternative to missing value imputation.

One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely "at random" or "not at random". To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets, as well as simulated data with various scenarios regarding the missingness mechanisms and the nature of the difference of expression (differential intensity or differential presence). Whereas we observed similar performances in terms of prediction on the experimental data set, the feature ranking and selection from various imputation-based methods were strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods, and remains efficient in all simulated scenarios unlike imputation-based feature selection methods.

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance.

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the "gold standard" for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Benefits of Iterative Searches of Large Databases to Interpret Large Human Gut Metaproteomic Data Sets.

The gut microbiota are increasingly considered as a main partner of human health. Metaproteomics enables us to move from the functional potential revealed by metagenomics to the functions actually operating in the microbiome. However, metaproteome deciphering remains challenging. In particular, confident interpretation of a myriad of MS/MS spectra can only be pursued with smart database searches. Here, we compare the interpretation of MS/MS data sets from 48 individual human gut microbiomes using three interrogation strategies of the dedicated Integrated nonredundant Gene Catalog (IGC 9.9 million genes from 1267 individual fecal samples) together with the Homo sapiens database: the classical single-step interrogation strategy and two iterative strategies (in either two or three steps) aimed at preselecting a reduced-sized, more targeted search space for the final peptide spectrum matching. Both iterative searches outperformed the single-step classical search in terms of the number of peptides and protein clusters identified and the depth of taxonomic and functional knowledge, and this was the most convincing with the three-step approach. However, iterative searches do not help in reducing variability of repeated analyses, which is inherent to the traditional data-dependent acquisition mode, but this variability did not affect the hierarchical relationship between replicates and all other samples.

Maize metabolome and proteome responses to controlled cold stress partly mimic early-sowing effects in the field and differ from those of Arabidopsis.

In Northern Europe, sowing maize one-month earlier than current agricultural practices may lead to moderate chilling damage. However, studies of the metabolic responses to low, non-freezing, temperatures remain scarce. Here, genetically-diverse maize hybrids (Zea mays, dent inbred lines crossed with a flint inbred line) were cultivated in a growth chamber at optimal temperature and then three decreasing temperatures for 2 days each, as well as in the field. Leaf metabolomic and proteomic profiles were determined. In the growth chamber, 50% of metabolites and 18% of proteins changed between 20 and 16°C. These maize responses, partly differing from those of Arabidopsis to short-term chilling, were mapped on genome-wide metabolic maps. Several metabolites and proteins showed similar variation for all temperature decreases: seven MS-based metabolite signatures and two proteins involved in photosynthesis decreased continuously. Several increasing metabolites or proteins in the growth-chamber chilling conditions showed similar trends in the early-sowing field experiment, including trans-aconitate, three hydroxycinnamate derivatives, a benzoxazinoid, a sucrose synthase, lethal leaf-spot 1 protein, an allene oxide synthase, several glutathione transferases and peroxidases. Hybrid groups based on field biomass were used to search for the metabolite or protein responses differentiating them in growth-chamber conditions, which could be of interest for breeding.

A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize.

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms makes the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype-phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic, and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein coexpression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified 15 loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein coexpression network, and a reprogramming of the genetic control of the abundance of many proteins, including those involved in stress response. Colocalizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the coexpression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

Transcriptomic and proteomic data in developing tomato fruit.

Transcriptomic and proteomic analyses were performed on three replicates of tomato fruit pericarp samples collected at nine developmental stages, each replicate resulting from the pooling of at least 15 fruits. For transcriptome analysis, Illumina-sequenced libraries were mapped on the tomato genome with the aim to obtain absolute quantification of mRNA abundance. To achieve this, spikes were added at the beginning of the RNA extraction procedure. From 34,725 possible transcripts identified in the tomato, 22,877 were quantified in at least one of the nine developmental stages. For the proteome analysis, label-free liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) was used. Peptide ions, and subsequently the proteins from which they were derived, were quantified by integrating the signal intensities obtained from extracted ion currents (XIC) with the MassChroQ software. Absolute concentrations of individual proteins were estimated for 2375 proteins by using a mixed effects model from log10-transformed intensities and normalized to the total protein content. Transcriptomics data are available via GEO repository with accession number GSE128739. The raw MS output files and identification data were deposited on-line using the PROTICdb database (http://moulon.inra.fr/protic/tomato_fruit_development) and MS proteomics data have also been deposited to the ProteomeXchange with the dataset identifier PXD012877. The main added value of these quantitative datasets is their use in a mathematical model to estimate protein turnover in developing tomato fruit.

Identification of Hanks-Type Kinase PknB-Specific Targets in the Streptococcus thermophilus Phosphoproteome.

Protein phosphorylation especially on serine/threonine/tyrosine residues are frequent in many bacteria. This post-translational modification has been associated with pathogenicity and virulence in various species. However, only few data have been produced so far on generally recognized as safe bacteria used in food fermentations. A family of kinases known as Hanks-type kinases is suspected to be responsible for, at least, a part of these phosphorylations in eukaryotes as in bacteria. The objective of our work was to establish the first phosphoproteome of Streptococcus thermophilus, a lactic acid bacterium widely used in dairy fermentations in order to identified the proteins and pathways tagged by Ser/Thr/Tyr phosphorylations. In addition, we have evaluated the role in this process of the only Hanks-type kinase encoded in the S. thermophilus genome. We have constructed a mutant defective for the Hanks type kinase in S. thermophilus and established the proteomes and phosphoproteomes of the wild type and the mutant strains. To do that, we have enriched our samples in phosphopeptides with titane beads and used dimethyl tags to compare phosphopeptide abundances. Peptides and phosphopeptides were analyzed on a last generation LC-MS/MS system. We have identified and quantified 891 proteins representing half of the theoretical proteome. Among these proteins, 106 contained phosphorylated peptides. Various functional groups of proteins (amino acid, carbon and nucleotide metabolism, translation, cell cycle, stress response, …) were found phosphorylated. The phosphoproteome was only weakly reduced in the Hanks-type kinase mutant indicating that this enzyme is only one of the players in the phosphorylation process. The proteins that are modified by the Hanks-type kinase mainly belong to the divisome.

Modeling Protein Destiny in Developing Fruit.

Protein synthesis and degradation are essential processes that regulate cell status. Because labeling in bulky organs, such as fruits, is difficult, we developed a modeling approach to study protein turnover at the global scale in developing tomato (Solanum lycopersicum) fruit. Quantitative data were collected for transcripts and proteins during fruit development. Clustering analysis showed smaller changes in protein abundance compared to mRNA abundance. Furthermore, protein and transcript abundance were poorly correlated, and the coefficient of correlation decreased during fruit development and ripening, with transcript levels decreasing more than protein levels. A mathematical model with one ordinary differential equation was used to estimate translation (kt ) and degradation (kd ) rate constants for almost 2,400 detected transcript-protein pairs and was satisfactorily fitted for >1,000 pairs. The model predicted median values of ∼2 min for the translation of a protein, and a protein lifetime of ∼11 d. The constants were validated and inspected for biological relevance. Proteins involved in protein synthesis had higher kt and kd values, indicating that the protein machinery is particularly flexible. Our model also predicts that protein concentration is more strongly affected by the rate of translation than that of degradation.

Peptide filtering differently affects the performances of XIC-based quantification methods.

In bottom-up proteomics, data are acquired on peptides resulting from proteolysis. In XIC-based quantification, the quality of the estimation of protein abundance depends on how peptide data are filtered and on which quantification method is used to express peptide intensity as protein abundance. So far, these two questions have been addressed independently. Here, we studied to what extent the relative performances of the quantification methods depend on the filters applied to peptide intensity data. To this end, we performed a spike-in experiment using Universal Protein Standard to evaluate the performances of five quantification methods in five datasets obtained after application of four peptide filters. Estimated protein abundances were not equally affected by filters depending on the computation mode and the type of data for quantification. Furthermore, we found that filters could have contrasting effects depending on the quantification objective. Intensity modeling proved to be the most robust method, providing the best results in the absence of any filter. However, the different quantification methods can achieve similar performances when appropriate peptide filters are used. Altogether, our findings provide insights into how best to handle intensity data according to the quantification objective and the experimental design. SIGNIFICANCE: We believe that our results are of major importance because they address, as far as we know for the first time, the crossed-effects of peptide intensity data filtering and XIC-based quantification methods on protein quantification. While previous papers have dealt with peptide filtering independently of the quantification method, here we combined four peptide filters (based on peptide sharing between proteins, retention time variability, peptides occurrence and peptide intensity profiles) with five XIC-based quantification methods representing different modes of calculating protein abundances from peptide intensities. For these different combinations, we analyzed the quality of protein quantification in terms of precision, accuracy and linearity of response to increasing protein concentration using a spike-in experiment. We showed that not only filters effect on the estimation of protein abundances depend on the quantification methods but also that quantification methods can reach similar performances when appropriate peptide filters are used. Also, depending on the quantification objective, i.e. absolute or relative, filters can have contrasting effects and we demonstrated that protein quantification by the peptide intensity modeling was the most robust method.

Evaluation of Optimized Tube-Gel Methods of Sample Preparation for Large-Scale Plant Proteomics.

The so-called tube-gel method is a sample preparation protocol allowing for management of SDS for protein solubilization through in-gel protein trapping. Because of its simplicity, we assumed that once miniaturized, this method could become a standard for large scale experiments. We evaluated the performances of two variants of the miniaturized version of the tube-gel method based on different solubilization buffers (Tris-SDS or urea-SDS). To this end, we compared them to two other digestion methods: (i) liquid digestion after protein solubilization in the absence of SDS (liquid method) and (ii) filter-aided sample preparation (FASP). As large-scale experiments may require long term gel storage, we also examined to which extent gel aging affected the results of the proteomics analysis. We showed that both tube-gel and FASP methods extracted membrane proteins better than the liquid method, while the latter allowed the identification and quantification of a greater number of proteins. All methods were equivalent regarding quantitative stability. However, important differences were observed regarding post-translational modifications. In particular, methionine oxidation was higher with the tube-gel method than with the other methods. Based on these results, and considering time, simplicity, and cost aspects, we conclude that the miniaturized tube-gel method is suitable for sample preparation in the context of large-scale experiments.

Quantitative Proteomics Analysis Confirmed Oxidative Metabolism Predominates in Streptomyces coelicolor versus Glycolytic Metabolism in Streptomyces lividans.

Recent physiological studies indicated that S. lividans metabolism was mainly glycolytic, whereas S. coelicolor metabolism was mainly oxidative. To determine whether such metabolic characteristics were correlated with consistent proteomics features, a comparative label-free, shotgun proteomics analysis of these strains was carried out. Among 2024 proteins identified, 360 showed significant differences in abundance between the strains. This study revealed that S. coelicolor catabolized glucose less actively than S. lividans, whereas the amino acids present in the medium were catabolized less actively by S. lividans than by S. coelicolor. The abundance of glycolytic proteins in S. lividans was consistent with its high glycolytic activity, whereas the abundance of proteins involved in the catabolism of amino acids in S. coelicolor provided an explanatory basis for its predominantly oxidative metabolism. In this study, conducted under conditions of low O2 availability, proteins involved in resistance to oxidative stress and those belonging to a DosR-like dormancy regulon were abundant in S. coelicolor, whereas tellurium resistance proteins were abundant in S. lividans. This indicated that the strains reacted differently to O2 limitation. Proteins belonging to the CDA, RED, and ACT pathways, usually highly expressed in S. coelicolor, were not detected under these conditions, whereas proteins of siderophores, 5-hydroxyectoine, and terpenoid biosynthetic pathways were present.

X!TandemPipeline: A Tool to Manage Sequence Redundancy for Protein Inference and Phosphosite Identification.

X!TandemPipeline is a software designed to perform protein inference and to manage redundancy in the results of phosphosite identification by database search. It provides the minimal list of proteins or phosphosites that are present in a set of samples using grouping algorithms based on the principle of parsimony. Regarding proteins, a two-level classification is performed, where groups gather proteins sharing at least one peptide and subgroups gather proteins that are not distinguishable according to the identified peptides. Regarding phosphosites, an innovative approach based on the concept of phosphoisland is used to gather overlapping phosphopeptides. The graphical interface of X!TandemPipeline allows the users to launch X!tandem identification, to inspect spectra and to manually validate their assignment to peptides, to launch the grouping program, and to visualize elementary data as well as grouping and redundancy information. Identification results obtained from other search engines can also be processed. X!TandemPipeline results can be exported as ready-to-use tabulated files or as XML files that can be directly used by the PROTICdb database or by the MassChroQ quantification software. X!TandemPipeline runs fast, is easy to use, and can process hundreds of samples simultaneously. It is freely available under the GNU General Public License v3.0 at http://pappso.inra.fr/bioinfo/xtandempipeline/ .

Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics.

How to process and analyze MS data to quantify and statistically compare protein abundances in bottom-up proteomics has been an open debate for nearly fifteen years. Two main approaches are generally used: the first is based on spectral data generated during the process of identification (e.g. peptide counting, spectral counting), while the second makes use of extracted ion currents to quantify chromatographic peaks and infer protein abundances based on peptide quantification. These two approaches actually refer to multiple methods which have been developed during the last decade, but were submitted to deep evaluations only recently. In this paper, we compiled these different methods as exhaustively as possible. We also summarized the way they address the different problems raised by bottom-up protein quantification such as normalization, the presence of shared peptides, unequal peptide measurability and missing data. This article is part of a Special Issue entitled: Plant Proteomics--a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.

A Systems Approach to Elucidate Heterosis of Protein Abundances in Yeast.

Heterosis is a universal phenomenon that has major implications in evolution and is of tremendous agro-economic value. To study the molecular manifestations of heterosis and to find factors that maximize its strength, we implemented a large-scale proteomic experiment in yeast. We analyzed the inheritance of 1,396 proteins in 55 inter- and intraspecific hybrids obtained from Saccharomyces cerevisiae and S. uvarum that were grown in grape juice at two temperatures. We showed that the proportion of heterotic proteins was highly variable depending on the parental strain and on the temperature considered. For intraspecific hybrids, this proportion was higher at nonoptimal temperature. Unexpectedly, heterosis for protein abundance was strongly biased toward positive values in interspecific hybrids but not in intraspecific hybrids. Computer modeling showed that this observation could be accounted for by assuming concave relationships between protein abundances and their controlling factors, in line with the metabolic model of heterosis. These results point to nonlinear processes that could play a central role in heterosis.

Yeast proteome variations reveal different adaptive responses to grape must fermentation.

Saccharomyces cerevisiae and S. uvarum are two domesticated species of the Saccharomyces sensu stricto clade that diverged around 100 Ma after whole-genome duplication. Both have retained many duplicated genes associated with glucose fermentation and are characterized by the ability to achieve grape must fermentation. Nevertheless, these two species differ for many other traits, indicating that they underwent different evolutionary histories. To determine how the evolutionary histories of S. cerevisiae and S. uvarum are mirrored on the proteome, we analyzed the genetic variability of the proteomes of domesticated strains of these two species by quantitative mass spectrometry. Overall, 445 proteins were quantified. Massive variations of protein abundances were found, that clearly differentiated the two species. Abundance variations in specific metabolic pathways could be related to phenotypic traits known to discriminate the two species. In addition, proteins encoded by duplicated genes were shown to be differently recruited in each species. Comparing the strain differentiation based on the proteome variability to those based on the phenotypic and genetic variations further revealed that the strains of S. uvarum and some strains of S. cerevisiae displayed similar fermentative performances despite strong proteomic and genomic differences. Altogether, these results indicate that the ability of S. cerevisae and S. uvarum to complete grape must fermentation arose through different evolutionary roads, involving different metabolic pathways and duplicated genes.