Soil pH, developmental stages and geographical origin differently influence the root metabolomic diversity and root-related microbial diversity of Echium vulgare from native habitats.

Improved understanding of the complex interaction between plant metabolism, environmental conditions and the plant-associated microbiome requires an interdisciplinary approach: Our hypothesis in our multiomics study posited that several environmental and biotic factors have modulating effects on the microbiome and metabolome of the roots of wild Echium vulgare plants. Furthermore, we postulated reciprocal interactions between the root metabolome and microbiome. We investigated the metabolic content, the genetic variability, and the prokaryotic microbiome in the root systems of wild E. vulgare plants at rosette and flowering stages across six distinct locations. We incorporated the assessment of soil microbiomes and the measurement of selected soil chemical composition factors. Two distinct genetic clusters were determined based on microsatellite analysis without a consistent alignment with the geographical proximity between the locations. The microbial diversity of both the roots of E. vulgare and the surrounding bulk soil exhibited significant divergence across locations, varying soil pH characteristics, and within the identified plant genetic clusters. Notably, acidophilic bacteria were characteristic inhabitants of both soil and roots under acidic soil conditions, emphasizing the close interconnectedness between these compartments. The metabolome of E. vulgare significantly differed between root samples from different developmental stages, geographical locations, and soil pH levels. The developmental stage was the dominant driver of metabolome changes, with significantly higher concentrations of sugars, pyrrolizidine alkaloids, and some of their precursors in rosette stage plant roots. Our study featured the complex dynamics between soil pH, plant development, geographical locations, plant genetics, plant metabolome and microbiome, shedding light on existing knowledge gaps.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis.

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

Dynamics of alkannin/shikonin biosynthesis in response to jasmonate and salicylic acid in Lithospermum officinale.

Alkannin/shikonin and their derivatives are specialised metabolites of high pharmaceutical and ecological importance exclusively produced in the periderm of members of the plant family Boraginaceae. Previous studies have shown that their biosynthesis is induced in response to methyl jasmonate but not salicylic acid, two phytohormones that play important roles in plant defence. However, mechanistic understanding of induction and non-induction remains largely unknown. In the present study, we generated the first comprehensive transcriptomic dataset and metabolite profiles of Lithospermum officinale plants treated with methyl jasmonate and salicylic acid to shed light on the underlying mechanisms. Our results highlight the diverse biological processes activated by both phytohormones and reveal the important regulatory role of the mevalonate pathway in alkannin/shikonin biosynthesis in L. officinale. Furthermore, by modelling a coexpression network, we uncovered structural and novel regulatory candidate genes connected to alkannin/shikonin biosynthesis. Besides providing new mechanistic insights into alkannin/shikonin biosynthesis, the generated methyl jasmonate and salicylic acid elicited expression profiles together with the coexpression networks serve as important functional genomic resources for the scientific community aiming at deepening the understanding of alkannin/shikonin biosynthesis.

Unraveling metabolic patterns and molecular mechanisms underlying storability in sugar beet.

BACKGROUND: Sugar beet is an important crop for sugar production. Sugar beet roots are stored up to several weeks post-harvest waiting for processing in the sugar factories. During this time, sucrose loss and invert sugar accumulation decreases the final yield and processing quality. To improve storability, more information about post-harvest metabolism is required. We investigated primary and secondary metabolites of six sugar beet varieties during storage. Based on their variety-specific sucrose loss, three storage classes representing well, moderate, and bad storability were compared. Furthermore, metabolic data were visualized together with transcriptome data to identify potential mechanisms involved in the storage process. RESULTS: We found that sugar beet varieties that performed well during storage have higher pools of 15 free amino acids which were already observable at harvest. This storage class-specific feature is visible at harvest as well as after 13 weeks of storage. The profile of most of the detected organic acids and semi-polar metabolites changed during storage. Only pyroglutamic acid and two semi-polar metabolites, including ferulic acid, show higher levels in well storable varieties before and/or after 13 weeks of storage. The combinatorial OMICs approach revealed that well storable varieties had increased downregulation of genes involved in amino acid degradation before and after 13 weeks of storage. Furthermore, we found that most of the differentially genes involved in protein degradation were downregulated in well storable varieties at both timepoints, before and after 13 weeks of storage. CONCLUSIONS: Our results indicate that increased levels of 15 free amino acids, pyroglutamic acid and two semi-polar compounds, including ferulic acid, were associated with a better storability of sugar beet taproots. Predictive metabolic patterns were already apparent at harvest. With respect to elongated storage, we highlighted the role of free amino acids in the taproot. Using complementary transcriptomic data, we could identify potential underlying mechanisms of sugar beet storability. These include the downregulation of genes for amino acid degradation and metabolism as well as a suppressed proteolysis in the well storable varieties.

Transcriptional dynamics of Chitinophaga sp. strain R-73072-mediated alkannin/shikonin biosynthesis in Lithospermum officinale.

Plants are colonized by a wide range of bacteria, several of which are known to confer benefits to their hosts such as enhancing plant growth and the biosynthesis of secondary metabolites (SMs). Recently, it has been shown that Chitinophaga sp. strain R-73072 enhances the production of alkannin/shikonin, SMs of pharmaceutical and ecological importance. However, the mechanisms by which this bacterial strain increases these SMs in plants are not yet understood. To gain insight into these mechanisms, we analyzed the molecular responses of Lithospermum officinale, an alkannin/shikonin producing member of Boraginaceae, to inoculation with R-73072 in a gnotobiotic system using comparative transcriptomics and targeted metabolite profiling of root samples. We found that R-73072 modulated the expression of 1,328 genes, of which the majority appeared to be involved in plant defense and SMs biosynthesis including alkannin/shikonin derivatives. Importantly, bacterial inoculation induced the expression of genes that predominately participate in jasmonate and ethylene biosynthesis and signaling, suggesting an important role of these phytohormones in R-73072-mediated alkannin/shikonin biosynthesis. A detached leaf bioassay further showed that R-73072 confers systemic protection against Botrytis cinerea. Finally, R-73072-mediated coregulation of genes involved in plant defense and the enhanced production of alkannin/shikonin esters further suggest that these SMs could be important components of the plant defense machinery in alkannin/shikonin producing species.

Editorial: Functional Genomics in Plant Breeding 2.0.

Scientists agree that the increased human impact on the environment since the 19th century has positioned our planet in a period of rapid and intense change, particularly to our natural ecosystems [...].