Plant Abiotic Stress Responses: Comparison
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Please note this is a comparison between Version 2 by Catherine Yang and Version 1 by Luca Ambrosino.

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority.

The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes.

We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

  • bioinformatics
  • genomics
  • transcriptomics
  • proteomics
  • metabolomics
  • data integration
  • stress

1. Introduction

Plants display an amazing diversity and, owing to their sessile nature, they evolved a broad range of molecular mechanisms to respond to complex network of environmental signals, which activate multiple pathways, modulated by different responsive genes, in case conferring tolerance to the pressure determined by stressor factors [1][2][3].

Abiotic stresses, such as heat and cold, drought, salinity and flooding [4][5][6][7], however, dramatically affect plant growth and crop yield [2][8][9][10][11][12][13][14][15][16], and these are among the reasons why abiotic stress management is one of the most important challenges in agriculture. In current climate change scenarios, exposure to abiotic stresses is more frequent and the consequent effects are so relevant also considering the exponential increase of the world food supply due to the rapid population growth [2][9][12][13][17][18][19][20][21], and the widespread attention to promote a sustainable productivity. This is why extensive studies have been focused on understanding the molecular basis of abiotic stress response and the research for improved, productive plants, adapted for stress tolerance [10][13][20][22]. These activities were strongly favored by the evolving -omics technologies, which provide key strategies to promote molecular investigations on plant organization and functionality, also under stress conditions[23][24][25][26], and novel approaches for omics assisted crop improvement[27][28]. Since their initial introduction, they permitted unexpected views on different levels of cell functionality, ranging from genome to transcriptome, to proteome and metabolome, and more recently covering also investigation on chromatin organization by epigenome approaches [29][30][31][32].

These approaches, that cover different levels of biological functionalities, enable deeper investigations at each level, also offering the opportunity of integrated views [33][34][35], to study the complexity of the molecular response of plants and to abiotic stresses as well. Moreover, the technological evolution and cheaper methodologies offer faster and more accessible approaches favoring research considering an increasing number of crops[36][37]. 

2. Bioinformatics Resources for Plant Abiotic Stress Responses 

The so-called “Next Generation Sequencing” (NGS) technologies, as one of the major examples, largely favored deeper insights on plant genome organization [38][39][40][41][42][43] and on functional responses to variable environmental parameters, elucidating the first level of gene expression, i.e., the transcriptome analysis, by promoting the transition from expressed sequence tags (ESTs) and microarray based techniques [44][45], to more powerful approaches such as RNA-seq [46][47][48] and associated technologies [29][49][50].

Simultaneously, the development of proteomics procedures by 2D-Gels coupled to mass spectrometry (MS) [51] or, more recently, via high-throughput shotgun approaches [52], and robust LC–MS (liquid chromatography-mass spectrometry) [53] and GC–MS (gas chromatography-mass spectrometry) [54] metabolomics technologies, able to unravel fluctuations of non-volatile and volatile metabolites, are paving the way to a deep understanding of the effects of the biological processes under investigation [33].

In this context, the integration of results from different levels of molecular information favors holistic views to decipher key components that are playing roles in complex molecular processes involved in plant responses to unfavorable or changing environmental conditions [55][56][57].

Bioinformatics is necessary for data production in support of the different omics technologies, fundamental for data organization and for data mining. It favors the data sharing, the interpretation of the massive amount of information provided by high throughput technologies, permitting the filtering of valuable information for human driven interpretation, therefore assisting single level approach and multilevel data integration for comprehensive views on systems functionality [34][35][58].

Moreover, bioinformatics also provides overwhelming amount of accessible resources to the scientific community, driving pioneering research based either on the exploitation of -omics technologies [33][59][60][61], or of the manifold resources that may support specific subsequent analyses, such as those based on sequence comparisons, gene family investigations and molecular modeling [62][63][64].

Bioinformatics resources implementation and maintenance, and data sharing, are therefore among the main drivers of the success of this research field and of the evolution of the omics technologies, since the data exploitation revealed to be a very powerful approach to support the overall scientific community.

One of the key points to carefully sustain to this aim remains data accessibility and care. Data collection should be reliable and interoperable, suitable to be compared, touching the new challenges in the field, which mainly fall in the so called integrative bioinformatics [65]. However, data exploitation is today still relying on scientist consciousness about the opportunities and limits offered by the different data sources, about the sensitivity and specificity of the different technologies, and about the quality of the organized results. Additionally, inexpert users must be aware of the opportunities and rules in the field, to profitable handle, analyze and compare data from the available resources and to obtain novel insights into the organization and functionality of the biological systems. This requires education, collaborative efforts, transdisciplinarity.

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