Within- and cross-species predictions of plant specialized metabolism genes using transfer learning

Integrating machine learning and genome editing for crop improvement

Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

Arabidopsis as a model for translational research

Arabidopsis thaliana is currently the most-studied plant species on earth, with an unprecedented number of genetic, genomic, and molecular resources having been generated in this plant model. In the era of translating foundational discoveries to crops and beyond, we aimed to highlight the utility and challenges of using Arabidopsis as a reference for applied plant biology research, agricultural innovation, biotechnology, and medicine. We hope that this review will inspire the next generation of plant biologists to continue leveraging Arabidopsis as a robust and convenient experimental system to address fundamental and applied questions in biology. We aim to encourage lab and field scientists alike to take advantage of the vast Arabidopsis datasets, annotations, germplasm, constructs, methods, molecular and computational tools in our pursuit to advance understanding of plant biology and help feed the world's growing population. We envision that the power of Arabidopsis-inspired biotechnologies and foundational discoveries will continue to fuel the development of resilient, high-yielding, nutritious plants for the betterment of plant and animal health and greater environmental sustainability.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology

Advances in high-throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role in plant systems biology because of its excellent performance and wide application in the analysis of big data. However, to achieve ideal performance, supervised ML algorithms require large numbers of labeled samples as training data. In some cases, it is impossible or prohibitively expensive to obtain enough labeled training data; here, the paradigms of unsupervised learning (UL) and semi-supervised learning (SSL) play an indispensable role. In this review, we first introduce the basic concepts of ML techniques, as well as some representative UL and SSL algorithms, including clustering, dimensionality reduction, self-supervised learning (self-SL), positive-unlabeled (PU) learning and transfer learning. We then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research. Finally, we discuss the limitations and highlight the significance and challenges of UL and SSL strategies in plant systems biology.

Fruity, sticky, stinky, spicy, bitter, addictive, and deadly: evolutionary signatures of metabolic complexity in the Solanaceae

Covering: 2000-2022Plants collectively synthesize a huge repertoire of metabolites. General metabolites, also referred to as primary metabolites, are conserved across the plant kingdom and are required for processes essential to growth and development. These include amino acids, sugars, lipids, and organic acids. In contrast, specialized metabolites, historically termed secondary metabolites, are structurally diverse, exhibit lineage-specific distribution and provide selective advantage to host species to facilitate reproduction and environmental adaptation. Due to their potent bioactivities, plant specialized metabolites attract considerable attention for use as flavorings, fragrances, pharmaceuticals, and bio-pesticides. The Solanaceae (Nightshade family) consists of approximately 2700 species and includes crops of significant economic, cultural, and scientific importance: these include potato, tomato, pepper, eggplant, tobacco, and petunia. The Solanaceae has emerged as a model family for studying the biochemical evolution of plant specialized metabolism and multiple examples exist of lineage-specific metabolites that influence the senses and physiology of commensal and harmful organisms, including humans. These include, alcohols, phenylpropanoids, and carotenoids that contribute to fruit aroma and color in tomato (fruity), glandular trichome-derived terpenoids and acylsugars that contribute to plant defense (stinky & sticky, respectively), capsaicinoids in chilli-peppers that influence seed dispersal (spicy), and steroidal glycoalkaloids (bitter) from Solanum, nicotine (addictive) from tobacco, as well as tropane alkaloids (deadly) from Deadly Nightshade that deter herbivory. Advances in genomics and metabolomics, coupled with the adoption of comparative phylogenetic approaches, resulted in deeper knowledge of the biosynthesis and evolution of these metabolites. This review highlights recent progress in this area and outlines opportunities for - and challenges of-developing a more comprehensive understanding of Solanaceae metabolism.

Using interdisciplinary, phylogeny-guided approaches to understand the evolution of plant metabolism

To cope with relentless environmental pressures, plants produce an arsenal of structurally diverse chemicals, often called specialized metabolites. These lineage-specific compounds are derived from the simple building blocks made by ubiquitous core metabolic pathways. Although the structures of many specialized metabolites are known, the underlying metabolic pathways and the evolutionary events that have shaped the plant chemical diversity landscape are only beginning to be understood. However, with the advent of multi-omics data sets and the relative ease of studying pathways in previously intractable non-model species, plant specialized metabolic pathways are now being systematically identified. These large datasets also provide a foundation for comparative, phylogeny-guided studies of plant metabolism. Comparisons of metabolic traits and features like chemical abundances, enzyme activities, or gene sequences from phylogenetically diverse plants provide insights into how metabolic pathways evolved. This review highlights the power of studying evolution through the lens of comparative biochemistry, particularly how placing metabolism into a phylogenetic context can help a researcher identify the metabolic innovations enabling the evolution of structurally diverse plant metabolites.

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

Machine learning in plant science and plant breeding

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity

Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity.