Physical Contact between Torulaspora delbrueckii and Saccharomyces cerevisiae Alters Cell Growth and Molecular Interactions in Grape Must

The use of multi-starters in oenological conditions (Saccharomyces cerevisiae and non-Saccharomyces species) is becoming increasingly common. For the past ten years, the combination of Torulaspora delbrueckii and S. cerevisiae has been proposed to winemakers to improve the wine aromatic profile compared to pure inoculation with Saccharomyces cerevisiae. In this work, two commercial strains, T. delbrueckii Zymaflore® Alpha and S. cerevisiae Zymaflore® X5 (Laffort compagny, Floirac, France), were investigated in Sauvignon blanc must using a fermentor with a double compartment allowing for physical separation of the two yeast species. The physical separation of the two species resulted in significant differences in the growth, fermentation kinetics (maximum fermentation rate (+13%)), fermentation duration (-14%) and the production of 3SH (+35%) in comparison to mixed cultures with contact. Proteomic analysis confirmed cell–cell contact interactions, as strong differences were observed for both species between mixed cultures with and without physical contact. T. delbrueckii mortality in mixed cultures with physical contact may be explained by an oxidative stress. Indeed two proteins implicated in the oxidative stress response were found in significantly higher amounts: a cytosolic catalase T and a cytoplasmic thioredoxin isoenzyme. For S. cerevisiae, an increase in proteins involved in the respiratory chain and proton transport were found in higher amounts in pure cultures and mixed culture without physical contact. Our results confirmed that the two mixed inoculations increased certain minor esters (ethylpropanoate, ethyl dihydrocinnamate and ethyl isobutanoate) specifically produced by T. delbrueckii, 3.4-fold more compared to in the pure S. cerevisiae culture. In conclusion, these results provide new insights into the underlying mechanisms involved in cell–cell contact and confirm the benefits of using T. delbrueckii species under winemaking conditions

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

Nonlinear network-based quantitative trait prediction from biological data

International audienceAbstract Quantitatively predicting phenotypic variables using biomarkers is a challenging task for several reasons. First, the collected biological observations might be heterogeneous and correspond to different biological mechanisms. Second, the biomarkers used to predict the phenotype are potentially highly correlated since biological entities (genes, proteins, and metabolites) interact through unknown regulatory networks. In this paper, we present a novel approach designed to predict multivariate quantitative traits from biological data which address the 2 issues. The proposed model performs well on prediction but it is also fully parametric, with clusters of individuals and regulatory networks, which facilitates the downstream biological interpretation

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

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

Proteomic data from leaves of twenty-four sunflower genotypes under water deficit

This article describes a proteomic data set produced from sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower. They included both inbred lines and their hybrids. Water deficit was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. We present here the identification of 3062 proteins and the quantification of 1211 of them in the leaves of the 24 genotypes grown under two watering conditions. These data allow the study of both the effects of genetic variations and watering conditions. They constitute a valuable resource for the community to study adaptation of crops to drought and the molecular basis of heterosis

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

Abstract 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. Whereas we observed similar performances in terms of prediction, the feature ranking from various imputation-based methods was strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods

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

Full native timsTOF PASEF-enabled quantitative proteomics with the i2MassChroQ software package

International audienceIon mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting

Obituary: Dominique Job (1947-2022)

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