Annual Review of Plant Biology - Volume 71, 2020

Plant diseases reduce crop yields and threaten global food security, making the selection of disease-resistant cultivars a major goal of crop breeding. Broad-spectrum resistance (BSR) is a desirable trait because it confers resistance against more than one pathogen species or against the majority of races or strains of the same pathogen. Many BSR genes have been cloned in plants and have been found to encode pattern recognition receptors, nucleotide-binding and leucine-rich repeat receptors, and defense-signaling and pathogenesis-related proteins. In addition, the BSR genes that underlie quantitative trait loci, loss of susceptibility and nonhost resistance have been characterized. Here, we comprehensively review the advances made in the identification and characterization of BSR genes in various species and examine their application in crop breeding. We also discuss the challenges and their solutions for the use of BSR genes in the breeding of disease-resistant crops.

The ancient DNA revolution of the past 35 years has driven an explosion in the breadth, nuance, and diversity of questions that are approachable using ancient biomolecules, and plant research has been a constant, indispensable facet of these developments. Using archaeological, paleontological, and herbarium plant tissues, researchers have probed plant domestication and dispersal, plant evolution and ecology, paleoenvironmental composition and dynamics, and other topics across related disciplines. Here, we review the development of the ancient DNA discipline and the role of plant research in its progress and refinement. We summarize our understanding of long-term plant DNA preservation and the characteristics of degraded DNA. In addition, we discuss challenges in ancient DNA recovery and analysis and the laboratory and bioinformatic strategies used to mitigate them. Finally, we review recent applications of ancient plant genomic research.

For millennia, humans have used plants for food, raw materials, and medicines, but only within the past two centuries have we begun to connect particular plant metabolites with specific properties and utilities. Since the utility of classical molecular genetics beyond model species is limited, the vast specialized metabolic systems present in the Earth's flora remain largely unstudied. With an explosion in genomics resources and a rapidly expanding toolbox over the past decade, exploration of plant specialized metabolism in nonmodel species is becoming more feasible than ever before. We review the state-of-the-art tools that have enabled this rapid progress. We present recent examples of de novo biosynthetic pathway discovery that employ various innovative approaches. We also draw attention to the higher-order organization of plant specialized metabolism at subcellular, cellular, tissue, interorgan, and interspecies levels, which will have important implications for the future design of comprehensive metabolic engineering strategies.

Genetic engineering is a molecular biology technique that enables a gene or genes to be inserted into a plant's genome. The first genetically engineered plants were grown commercially in 1996, and the most common genetically engineered traits are herbicide and insect resistance. Questions and concerns have been raised about the effects of these traits on the environment and human health, many of which are addressed in a pair of 2008 and 2009 Annual Review of Plant Biology articles. As new science is published and new techniques like genome editing emerge, reanalysis of some of these issues, and a look at emerging issues, is warranted. Herein, an analysis of relevant scientific literature is used to present a scientific perspective on selected topics related to genetic engineering and genome editing.

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

Cannabis sativa L. is an important yet controversial plant with a long history of recreational, medicinal, industrial, and agricultural use, and together with its sister genus Humulus, it represents a group of plants with a myriad of academic, agricultural, pharmaceutical, industrial, and social interests. We have performed a meta-analysis of pooled published genomics data, andwe present a comprehensive literature review on the evolutionary history of Cannabis and Humulus, including medicinal and industrial applications. We demonstrate that current Cannabis genome assemblies are incomplete, with ∼10% missing, 10–25% unmapped, and 45S and 5S ribosomal DNA clusters as well as centromeres/satellite sequences not represented. These assemblies are also ordered at a low resolution, and their consensus quality clouds the accurate annotation of complete, partial, and pseudogenized gene copies. Considering the importance of genomics in the development of any crop, this analysis underlines the need for a coordinated effort to quantify the genetic and biochemical diversity of this species.

The 1,000 Plants (1KP) initiative was the first large-scale effort to collect next-generation sequencing (NGS) data across a phylogenetically representative sampling of species for a major clade of life, in this case theViridiplantae, or green plants. As an international multidisciplinary consortium, we focused on plant evolution and its practical implications. Among the major outcomes were the inference of a reference species tree for green plants by phylotranscriptomic analysis of low-copy genes, a survey of paleopolyploidy (whole-genome duplications) across the Viridiplantae, the inferred evolutionary histories for many gene families and biological processes, the discovery of novel light-sensitive proteins for optogenetic studies in mammalian neuroscience, and elucidation of the genetic network for a complex trait (C4 photosynthesis). Altogether, 1KP demonstrated how value can be extracted from a phylodiverse sequencing data set, providing a template for future projects that aim to generate even more data, including complete de novo genomes, across the tree of life.

Synthetic signaling is a branch of synthetic biology that aims to understand native genetic regulatory mechanisms and to use these insights to engineer interventions and devices that achieve specified design parameters. Applying synthetic signaling approaches to plants offers the promise of mitigating the worst effects of climate change and providing a means to engineer crops for entirely novel environments, such as those in space travel. The ability to engineer new traits using synthetic signaling methods will require standardized libraries of biological parts and methods to assemble them; the decoupling of complex processes into simpler subsystems; and mathematical models that can accelerate the design-build-test-learn cycle. The field of plant synthetic signaling is relatively new, but it is poised for rapid advancement. Translation from the laboratory to the field is likely to be slowed, however, by the lack of constructive dialogue between researchers and other stakeholders.

The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.