Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

Abstract

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

Keywords: Artificial intelligence; Bioinformatic tools; Climate change; Defense responses; Environmental pollution; Metal toxicity; Omics approaches.

Fig. 1

Overview of omics-assisted crop improvement. Integrating three major omics tools, transcriptomics, proteomics, and metabolomics (sometimes combined with high-throughput phenotyping and artificial intelligence), can help toward trait improvement, stress tolerance (single or multiple), development of high-yielding varieties, food security, and development of future crops. Abbreviations: capillary electrophoresis mass spectrometry (CE-MS), chromatin immunoprecipitation-sequencing (Chip-seq), direct-infusion mass spectrometry (DI-MS), Fourier transform ion cyclotron resonance (FT-IR), gas chromatography-mass spectrometry (GC–MS), global run-on sequencing (GRO-seq), high‐throughput chromosome conformation capture-sequencing (HiC-seq), high-resolution mass spectrometry (HRMS), high-performance thin layer chromatography (HPTLC), isobaric tag for relative absolute quantitation/tandem mass tags (iTRAQ/TMT), isotope-coded affinity-tag-based protein profiling (ICAT), liquid chromatography-mass spectrometry (LC–MS), mass spectrometry (MS), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), nuclear magnetic resonance (NMR) spectroscopy, RNA-sequencing (RNA-seq), ribosome profiling-sequencing (Ribo-seq), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), ultra-high-performance liquid chromatography (UHPLC)

Fig. 2

Schematic overview of the mechanisms underlying the restriction, uptake, and detoxification of metal(loid)s in plants and their responses. The uptake of metal(loid)s occurs via root cells, with excessive amounts stimulating root exudation containing various molecules such as OA, PCs, AA, En, SM, and MR. In the rhizosphere, these molecules form complexes with metal(loid)s that restrict their entry into root cells or transform them into less toxic materials. However, metal(loid)s absorbed into root cells can be translocated to the xylem and thus transported to aerial tissues. Abbreviations: arsenic (As), boron (B), chromium (Cr), cadmium (Cd), copper (Cu), enzymes (En), iron (Fe), lead (Pb), manganese (Mn), mycorrhizas (MR), nickel (Ni), organic acids (OA), phytochelatins (PCs), root exudate (RE), secondary metabolites (SM), zinc (Zn)

Fig. 3

Integrated omics approaches to develop metal(loid)-tolerant crop plants. (A) Metal(loid)-toxicity-mediated omics studies comprise four major steps: (1) sample collection against metal(loid) toxicity, (2) design and execution of single or multi-omics tools in one or multiple experiments, (3) integration and analysis of multi-omics datasets, and (4) results interpretation to reveal several key players and mechanisms for developing metal(loid)-tolerant crop plants with improved growth and productivity. (B) Inegrating omics data with artificial intelligence to design elite/superior cultivars. Recent innovations in computational algorithm and big data technology have deeply stimulated the growth of artificial intelligence. Using artificial intelligence models, integration of different omics approaches accelerates interpreting how plant phenotypes are accurately predicted and sequentially assists fast-forward breeding for elite/superior cultivars for the future. Abbreviations: Deep learning (DL), genomic selection (GS), machine learning (ML)

Fig. 4

Overview of five major challenges in integrating omics datasets. Modified from Misra et al. (2019)