Transcriptomic and cell wall proteomic datasets of rosettes and floral stems from five Arabidopsis thaliana ecotypes grown at optimal or sub-optimal temperature

Multi-omics Data Integration in the Context of Plant Abiotic Stress Signaling

In order to answer new biological questions, high-throughput data generated by new biotechnologies can be very meaningful but require specific and adapted statistical treatments. Thus, in the context of abiotic stress signaling studies, understanding the integration of cascading mechanisms from stress perception to biochemical and physiological adjustments necessarily entails efficient and valid analysis of multilevel and heterogeneous data. In this chapter, we propose examples to manage, analyze, and integrate multi-omics heterogeneous data. This workflow suggests and follows different general biological questions or issues answered with detailed code, data analysis, multiple visualizations, and always followed by brief interpretations. We illustrated this using the mixOmics package for the R software, as it specifically provides tools to address vertical and horizontal data integration issues. In order to illustrate this workflow, we used the usual omics datasets biologists can generate (phenomics, metabolomics, proteomics, and transcriptomics). These data were collected from two organs (leaf rosettes, floral stems) of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. They are available in the R package WallOmicsData. The workflow presented here is not limited to Arabidopsis thaliana and can be applied to any plant species. It can even be largely deployed to whatever the organisms of interest and the biological questions may be.

The PROSCOOP10 Gene Encodes Two Extracellular Hydroxylated Peptides and Impacts Flowering Time in Arabidopsis

The Arabidopsis PROSCOOP genes belong to a family predicted to encode secreted pro-peptides, which undergo maturation steps to produce peptides named SCOOP. Some of them are involved in defence signalling through their perception by a receptor complex including MIK2, BAK1 and BKK1. Here, we focused on the PROSCOOP10 gene, which is highly and constitutively expressed in aerial organs. The MS/MS analyses of leaf apoplastic fluids allowed the identification of two distinct peptides (named SCOOP10#1 and SCOOP10#2) covering two different regions of PROSCOOP10. They both possess the canonical S-X-S family motif and have hydroxylated prolines. This identification in apoplastic fluids confirms the biological reality of SCOOP peptides for the first time. NMR and molecular dynamics studies showed that the SCOOP10 peptides, although largely unstructured in solution, tend to assume a hairpin-like fold, exposing the two serine residues previously identified as essential for the peptide activity. Furthermore, PROSCOOP10 mutations led to an early-flowering phenotype and increased expression of the floral integrators SOC1 and LEAFY, consistent with the de-regulated transcription of PROSCOOP10 in several other mutants displaying early- or late-flowering phenotypes. These results suggest a role for PROSCOOP10 in flowering time, highlighting the functional diversity within the PROSCOOP family.

Plant Cell Wall Proteomes: The Core of Conserved Protein Families and the Case of Non-Canonical Proteins

Plant cell wall proteins (CWPs) play critical roles during plant development and in response to stresses. Proteomics has revealed their great diversity. With nearly 1000 identified CWPs, the Arabidopsis thaliana cell wall proteome is the best described to date and it covers the main plant organs and cell suspension cultures. Other monocot and dicot plants have been studied as well as bryophytes, such as Physcomitrella patens and Marchantia polymorpha. Although these proteomes were obtained using various flowcharts, they can be searched for the presence of members of a given protein family. Thereby, a core cell wall proteome which does not pretend to be exhaustive, yet could be defined. It comprises: (i) glycoside hydrolases and pectin methyl esterases, (ii) class III peroxidases, (iii) Asp, Ser and Cys proteases, (iv) non-specific lipid transfer proteins, (v) fasciclin arabinogalactan proteins, (vi) purple acid phosphatases and (vii) thaumatins. All the conserved CWP families could represent a set of house-keeping CWPs critical for either the maintenance of the basic cell wall functions, allowing immediate response to environmental stresses or both. Besides, the presence of non-canonical proteins devoid of a predicted signal peptide in cell wall proteomes is discussed in relation to the possible existence of alternative secretion pathways.

Arabidopsis PAP17 is a dual-localized purple acid phosphatase up-regulated during phosphate deprivation, senescence, and oxidative stress

A 35 kDa monomeric purple acid phosphatase (APase) was purified from cell wall extracts of Pi starved (-Pi) Arabidopsis thaliana suspension cells and identified as AtPAP17 (At3g17790) by mass spectrometry and N-terminal microsequencing. AtPAP17 was de novo synthesized and dual-localized to the secretome and/or intracellular fraction of -Pi or salt-stressed plants, or senescing leaves. Transiently expressed AtPAP17-green fluorescent protein localized to lytic vacuoles of the Arabidopsis suspension cells. No significant biochemical or phenotypical changes associated with AtPAP17 loss of function were observed in an atpap17 mutant during Pi deprivation, leaf senescence, or salinity stress. Nevertheless, AtPAP17 is hypothesized to contribute to Pi metabolism owing to its marked up-regulation during Pi starvation and leaf senescence, broad APase substrate selectivity and pH activity profile, and rapid repression and turnover following Pi resupply to -Pi plants. While AtPAP17 also catalyzed the peroxidation of luminol, which was optimal at pH 9.2, it exhibited a low Vmax and affinity for hydrogen peroxide relative to horseradish peroxidase. These results, coupled with absence of a phenotype in the salt-stressed or -Pi atpap17 mutant, do not support proposals that the peroxidase activity of AtPAP17 contributes to the detoxification of reactive oxygen species during stresses that trigger AtPAP17 up-regulation.

Comparison of mass spectrometry data and bioinformatics predictions to assess the bona fide localization of proteins identified in cell wall proteomics studies

Plant cell walls have complex architectures made of polysaccharides among which cellulose, hemicelluloses, pectins and cell wall proteins (CWPs). Some CWPs are anchored in the plasma membrane through a glycosylphosphatidylinositol (GPI)-anchor. The secretion pathway is the classical route to reach the extracellular space. Based on experimental data, a canonical signal peptide (SP) has been defined, and bioinformatics tools allowing the prediction of the sub-cellular localization of proteins have been designed. In the same way, the presence of GPI-anchor attachment sites can be predicted using bioinformatics programs. This article aims at comparing the bioinformatics predictions of the sub-cellular localization of proteins assumed to be CWPs to mass spectrometry (MS) data. The sub-cellular localization of a few CWPs exhibiting particular features has been checked by cell biology approaches. Although the prediction of SP length is confirmed in most cases, it is less conclusive for GPI-anchors. Three main observations were done: (i) the variability observed at the N-terminus of a few mature CWPs could play a role in the regulation of their biological activity; (ii) one protein was shown to have a double sub-cellular localization in the cell wall and the chloroplasts; and (iii) peptides were found to be located at the C-terminus of several CWPs previously identified in GPI-anchored proteomes, thus raising the issue of their actual anchoring to the plasma membrane.

Plastidial and cytosolic thiol reductases participate in the control of stomatal functioning

Stomatal movements via the control of gas exchanges determine plant growth in relation to environmental stimuli through a complex signalling network involving reactive oxygen species that lead to post-translational modifications of Cys and Met residues, and alter protein activity and/or conformation. Thiol-reductases (TRs), which include thioredoxins, glutaredoxins (GRXs) and peroxiredoxins (PRXs), participate in signalling pathways through the control of Cys redox status in client proteins. Their involvement in stomatal functioning remains poorly characterized. By performing a mass spectrometry-based proteomic analysis, we show that numerous thiol reductases, like PRXs, are highly abundant in guard cells. When investigating various Arabidopsis mutants impaired in the expression of TR genes, no change in stomatal density and index was noticed. In optimal growth conditions, a line deficient in cytosolic NADPH-thioredoxin reductases displayed higher stomatal conductance and lower leaf temperature evaluated by thermal infrared imaging. In contrast, lines deficient in plastidial 2-CysPRXs or type-II GRXs exhibited compared to WT reduced conductance and warmer leaves in optimal conditions, and enhanced stomatal closure in epidermal peels treated with abscisic acid or hydrogen peroxide. Altogether, these data strongly support the contribution of thiol redox switches within the signalling network regulating guard cell movements and stomatal functioning.

Proteomics and plant biology: contributions to date and a look towards the next decade

INTRODUCTION

This review presents the view of the author, that is opinionable and even speculative, on the field of proteomics, its application to plant biology knowledge, and translation to biotechnology. Written in a more academic than scientific style, it is based on past original and review articles by the author´s group, and those published by leading scientists in the last two years.

AREAS COVERED

Starting with a general definition and references to historical milestones, it covers sections devoted to the different platforms employed, the plant biology discourse in the protein language, challenges and future prospects, ending with the author opinion.

EXPERT OPINION

In 25 years, five proteomics platform generations have appeared. We are now moving from proteomics to Systems Biology. While feasible with model organisms, proteomics of orphan species remains challenging. Proteomics, even in its simplest approach, sheds light on plant biological processes, central dogma, and molecular bases of phenotypes of interest, and it can be translated to areas such as food traceability and allergen detection. Proteomics should be validated and optimized to each experimental system, objectives, and hypothesis. It has limitations, artifacts, and biases. We should not blindly accept proteomics data and just create a list of proteins, networks, and avoid speculative biological interpretations. From the hundred to thousand proteins identified and quantified, it is important to obtain a focus and validate some of them, otherwise it is merely. We are starting to have the protein pieces, so let, from now, build the proteomics and biological puzzle.

An integrative Study Showing the Adaptation to Sub-Optimal Growth Conditions of Natural Populations of Arabidopsis thaliana: A Focus on Cell Wall Changes

In the global warming context, plant adaptation occurs, but the underlying molecular mechanisms are poorly described. Studying natural variation of the model plant Arabidopsisthaliana adapted to various environments along an altitudinal gradient should contribute to the identification of new traits related to adaptation to contrasted growth conditions. The study was focused on the cell wall (CW) which plays major roles in the response to environmental changes. Rosettes and floral stems of four newly-described populations collected at different altitudinal levels in the Pyrenees Mountains were studied in laboratory conditions at two growth temperatures (22 vs. 15 °C) and compared to the well-described Col ecotype. Multi-omic analyses combining phenomics, metabolomics, CW proteomics, and transcriptomics were carried out to perform an integrative study to understand the mechanisms of plant adaptation to contrasted growth temperature. Different developmental responses of rosettes and floral stems were observed, especially at the CW level. In addition, specific population responses are shown in relation with their environment and their genetics. Candidate genes or proteins playing roles in the CW dynamics were identified and will deserve functional validation. Using a powerful framework of data integration has led to conclusions that could not have been reached using standard statistical approaches.

A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis

High-throughput data generated by new biotechnologies require specific and adapted statistical treatment in order to be efficiently used in biological studies. In this article, we propose a powerful framework to manage and analyse multi-omics heterogeneous data to carry out an integrative analysis. We have illustrated this using the mixOmics package for R software as it specifically addresses data integration issues. Our work also aims at applying the most recent functionalities of mixOmics to real datasets. Although multi-block integrative methodologies exist, we hope to encourage a more widespread use of such approaches in an operational framework by biologists. We have used natural populations of the model plant Arabidopsis thaliana in this work, but the framework proposed is not limited to this plant and can be deployed whatever the organisms of interest and the biological question may be. Four omics datasets (phenomics, metabolomics, cell wall proteomics and transcriptomics) were collected, analysed and integrated to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions. The methodologies presented here start from basic univariate statistics leading to multi-block integration analysis. We have also highlighted the fact that each method, either unsupervised or supervised, is associated with one biological issue. Using this powerful framework enabled us to arrive at novel conclusions on the biological system, which would not have been possible using standard statistical approaches.