Premise:This study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao. Methods:Despite recent stagnation in accuracy improvements in computer vision for image classification, our research demonstrates significant advancements in performance through semi-supervised learning, specialised loss functions, and the inclusion of a non-cocoa class. Results:Semi-supervised learning reduced overfitting and enhanced generalisability, particularly for subtle symptoms. The non-cocoa class exposed models to a broad range of relevant features, significantly improving model robustness and performance in difficult cases. Grad-CAM for qualitative assessment provided valuable insights into model behaviour, highlighting cases of overfitting missed by summary statistics. We also describe dynamic focal loss, a novel loss function that uses an empirical measure of difficulty to weight each image. Our results suggest that while PhytNet shows promise in terms of computational efficiency and superior handling of difficult images, ResNet18 with semi-supervised learning and dynamic focal loss emerged as the strongest contender for real-world deployment. Discussion:This research underscores the potential of semi-supervised learning and advanced loss functions in enhancing the applicability of deep learning models in agricultural disease management. It also presents a new high-quality benchmark dataset of 7220 images of diseased and healthy cocoa trees, offering a much greater and more realistic challenge than the Plan Village dataset.

AgriFoodPy is an open-source Python package for processing, simulation, and modeling of agrifood datasets and systems.By employing xarray (Hoyer & Hamman, 2017) as the primary data structure, AgriFoodPy provides methods to manipulate tabular data by extending xarray functionality via accessor classes.It acts as an accessibility and interoperability layer between data sources and external packages, and also bundles with a library of models for use without any additional requirements.

Premise:Automated disease, weed, and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet, and ConvNeXt often underperform on smaller, specialised datasets typical of such projects. Methods:We address this gap with informed data collection and the development of a new convolutional neural network architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. Data collection was informed by spectroscopy data, which provided useful insights into the spectral characteristics of cocoa trees. Cocoa was chosen as a focal species due to the diverse pathology of its diseases, which pose significant challenges for detection. Results:ResNet18 showed some signs of overfitting, while EfficientNet variants showed distinct signs of overfitting. By contrast, PhytNet displayed excellent attention to relevant features, almost no overfitting, and an exceptionally low computation cost of 1.19 GFLOPS. Conclusions:We show that PhytNet is a promising candidate for rapid disease or plant classification and for precise localisation of disease symptoms for autonomous systems. We also show that the most informative light spectra for detecting cocoa disease are outside the visible spectrum and that efforts to detect disease in cocoa should be focused on local symptoms, rather than the systemic effects of disease.

Our food system is giving rise to a growing social, health and environmental crisis. Much of the food consumed in the United Kingdom is cheap, nutrient-poor and highly processed, leading to under-consumption of essential foods such as grains, beans, vegetables and fruit. This has contributed to a rise in diet-related diseases, with approximately 22% of primary school leavers being overweight or obese. Food production is unsustainable with agriculture responsible for 10% of the UK's greenhouse gas emissions and intensive farming practices have led to a significant loss of soil carbon and a decline in biodiversity. COVID-19 increased inequalities in our food system. Therefore, there is an urgent need for interventions to counteract these adverse social, health and environmental impacts. Education can play a crucial role as an intervention to address challenges in the food system. We tested an innovative school initiative using portable aquaponic pods and aligned to the national curriculum, to engage pupils in food production and foster learning about sustainability, climate change and healthy eating. The evaluation, based on teacher surveys, aquapod chart data, student blogs and postcards and feedback from the development team, revealed positive impacts on students' environmental awareness, as well as sustainability and practical food production knowledge. However, the programme encountered logistical challenges and we therefore highlight future improvements to produce a curriculum programme that can be delivered at scale to enhance food education and empower pupils to drive the agenda on tackling food sustainability and climate change.

I feel very privileged to take over as Editor in Chief of The Plant Journal.I am enthusiastic to take on the role, building on the fantastic work of Lee Sweetlove, to continue growing the reputation of TPJ as a platform for excellent plant science and providing a high-quality, professional, and respectful publishing experience for authors, reviewers, and editors.TPJ has an engaged and pro-active Editorial Board and I look forward to working with them to ensure TPJ continues to make a significant contribution to communication of plant science across multiple species and fields and developing the Board to reflect the diversity of plant scientific research.As you know, a journal doesn't happen without the effort of many in our scientific community-the editors who work hard to provide efficient and fair assessment of submissions, the many reviewers who spend time reading and evaluating manuscripts and authors who trust us with their work.It is important, given this collective effort, to highlight that TPJ is part-owned by the Society of Experimental Biology (SEB) and contributes back to the scientific community through the SEB supporting scientific meetings and early career researchers.TPJ also has its own initiatives including TPJ Fellows, annual paper prizes and Features articles sharing insights on the

SAG21/LEA5 is an unusual late embryogenesis abundant protein in Arabidopsis thaliana, that is primarily mitochondrially located and may be important in regulating translation in both chloroplasts and mitochondria. SAG21 expression is regulated by a plethora of abiotic and biotic stresses and plant growth regulators indicating a complex regulatory network. To identify key transcription factors regulating SAG21 expression, yeast-1-hybrid screens were used to identify transcription factors that bind the 1685 bp upstream of the SAG21 translational start site. Thirty-three transcription factors from nine different families bound to the SAG21 promoter, including members of the ERF, WRKY and NAC families. Key binding sites for both NAC and WRKY transcription factors were tested through site directed mutagenesis indicating the presence of cryptic binding sites for both these transcription factor families. Co-expression in protoplasts confirmed the activation of SAG21 by WRKY63/ABO3, and SAG21 upregulation elicited by oligogalacturonide elicitors was partially dependent on WRKY63, indicating its role in SAG21 pathogen responses. SAG21 upregulation by ethylene was abolished in the erf1 mutant, while wound-induced SAG21 expression was abolished in anac71 mutants, indicating SAG21 expression can be regulated by several distinct transcription factors depending on the stress condition.

Accurate quantification of gene and transcript-specific expression, with the underlying knowledge of precise transcript isoforms, is crucial to understanding many biological processes. Analysis of RNA sequencing data has benefited from the development of alignment-free algorithms which enhance the precision and speed of expression analysis. However, such algorithms require a reference transcriptome. Here we generate a reference transcript dataset (LsRTDv1) for lettuce (cv. Saladin), combining long- and short-read sequencing with publicly available transcriptome annotations, and filtering to keep only transcripts with high-confidence splice junctions and transcriptional start and end sites. LsRTDv1 identifies novel genes (mostly long non-coding RNAs) and increases the number of transcript isoforms per gene in the lettuce genome from 1.4 to 2.7. We show that LsRTDv1 significantly increases the mapping rate of RNA-seq data from a lettuce time-series experiment (mock- and Botrytis cinerea-inoculated) and enables detection of genes that are differentially alternatively spliced in response to infection as well as transcript-specific expression changes. LsRTDv1 is a valuable resource for investigation of transcriptional and alternative splicing regulation in lettuce.

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Yield is impacted by the environmental conditions that plants are exposed to. Controlled environmental agriculture provides growers with an opportunity to fine-tune environmental conditions for optimising yield and crop quality. However, space and time constraints will limit the number of experimental conditions that can be tested, which will in turn limit the resolution to which environmental conditions can be optimised. Here we present an innovative experimental approach that utilises the existing heterogeneity in light quantity and quality across a vertical farm to evaluate hundreds of environmental conditions concurrently. It proposes a three-phase workflow for identifying critical light variables, which can guide targeted improvements in yield and energy use. Using an observational study design, we identify features in light quality that are most predictive of biomass in different microgreens crops (kale, radish and sunflower) that may inform future iterations of lighting technology development for vertical farms. The findings suggest that light quality, rather than just light intensity, plays a crucial role in uniform crop yields and that light sensitivities are variety-specific, highlighting the importance of tailored light recipes for different crops. ### Competing Interest Statement A.L. Tozer Limited supplied seed and Vertically Urban Limited provided the lighting.

Societies must transform their dynamics to support the flourishing of life. There is increasing interest in regeneration and regenerative practice as a solution, but also limited cohered understanding of what constitutes regenerative systems at social-ecological scales. In this perspective we present a conceptual, cross-disciplinary, and action-oriented regenerative systems framework, the Regenerative Lens, informed by a wide literature review. The framework emphasizes that regenerative systems maintain positive reinforcing cycles of wellbeing within and beyond themselves, especially between humans and wider nature, such that “life begets life.” We identify five key qualities needed in systems to encourage such dynamics: an ecological worldview embodied in human action; mutualism; high diversity; agency for humans and non-humans to act regeneratively; and continuous reflexivity. We apply the Lens to an envisioned future food system to illustrate its utility as a reflexive tool and for stretching ambition. We hope that the conceptual clarity provided here will aid the necessary acceleration of learning and action toward regenerative systems.

We report the results of a structured expert elicitation to identify the most likely types of potential food system disruption scenarios for the UK, focusing on routes to civil unrest. We take a backcasting approach by defining as an end-point a societal event in which 1 in 2000 people have been injured in the UK, which 40% of experts rated as “Possible (20–50%)”, “More likely than not (50–80%)” or “Very likely (>80%)” over the coming decade. Over a timeframe of 50 years, this increased to 80% of experts. The experts considered two food system scenarios and ranked their plausibility of contributing to the given societal scenario. For a timescale of 10 years, the majority identified a food distribution problem as the most likely. Over a timescale of 50 years, the experts were more evenly split between the two scenarios, but over half thought the most likely route to civil unrest would be a lack of total food in the UK. However, the experts stressed that the various causes of food system disruption are interconnected and can create cascading risks, highlighting the importance of a systems approach. We encourage food system stakeholders to use these results in their risk planning and recommend future work to support prevention, preparedness, response and recovery planning.

AbstractLettuce is susceptible to a wide range of plant pathogens including the fungal pathogensBotrytis cinereaandSclerotinia sclerotiorum, causal agents of grey mould and lettuce drop, respectively. Chemical control is routinely used but there is an urgent need to develop varieties with enhanced resistance given the economic and environmental costs of preventative pesticide sprays, the prevalence of fungicide-resistant isolates of both pathogens in the field, and the increasing withdrawal of approved fungicides through legislation. Resistance againstBotrytis cinereaandSclerotinia sclerotiorumis quantitative, governed by multiple small-medium impact loci, with plant responses involving large-scale transcriptional reprogramming. The elucidation of the gene regulatory networks (GRNs) mediating these responses will not only identify key transcriptional regulators but also interactions between regulators and show how the defence response is fine-tuned to a particular pathogen. We generated high-resolution (14 time points) time series expression data from lettuce leaves following mock-inoculation or inoculation withB. cinerea, capturing the dynamics of the transcriptional response to infection. Integrating this data with a time series dataset fromS. sclerotioruminfection of lettuce identified a core set of 4362 genes similarly differentially expressed in response to both pathogens. Using the expression data for these core genes (with additional single time point data from 21 different lettuce accessions) we inferred a GRN underlying the lettuce defence response to these pathogens. Using the GRN, we have predicted and validated key regulators of lettuce immunity, identifying both positive (LsBOS1) and negative (LsNAC53) regulators of defence againstB. cinerea, as well as downstream target genes. These data provide a high level of detail on defence-induced transcriptional change in a crop species and a GRN with the ability to predict transcription factors mediating disease resistance both in lettuce and other species.

Automated disease, weed and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet and ConvNeXt often underperform on smaller, specialised datasets typical of such projects. We address this gap with informed data collection and the development of a new CNN architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. Data collection was informed by analysis of spectroscopy data, which provided useful insights into the spectral characteristics of cocoa trees. Such information could inform future data collection and model development. Cocoa was chosen as a focal species due to the diverse pathology of its diseases, which pose significant challenges for detection. ResNet18 showed some signs of overfitting, while EfficientNet variants showed distinct signs of overfitting. By contrast, PhytNet displayed excellent attention to relevant features, no overfitting, and an exceptionally low computation cost (1.19 GFLOPS). As such PhytNet is a promising candidate for rapid disease or plant classification, or precise localisation of disease symptoms for autonomous systems.

Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in-depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.

Plants are resistant to most microbial species due to nonhost resistance (NHR), providing broad-spectrum and durable immunity. However, the molecular components contributing to NHR are poorly characterised. We address the question of whether failure of pathogen effectors to manipulate nonhost plants plays a critical role in NHR. RxLR (Arg-any amino acid-Leu-Arg) effectors from two oomycete pathogens, Phytophthora infestans and Hyaloperonospora arabidopsidis, enhanced pathogen infection when expressed in host plants (Nicotiana benthamiana and Arabidopsis, respectively) but the same effectors performed poorly in distantly related nonhost pathosystems. Putative target proteins in the host plant potato were identified for 64 P. infestans RxLR effectors using yeast 2-hybrid (Y2H) screens. Candidate orthologues of these target proteins in the distantly related non-host plant Arabidopsis were identified and screened using matrix Y2H for interaction with RxLR effectors from both P. infestans and H. arabidopsidis. Few P. infestans effector-target protein interactions were conserved from potato to candidate Arabidopsis target orthologues (cAtOrths). However, there was an enrichment of H. arabidopsidis RxLR effectors interacting with cAtOrths. We expressed the cAtOrth AtPUB33, which unlike its potato orthologue did not interact with P. infestans effector PiSFI3, in potato and Nicotiana benthamiana. Expression of AtPUB33 significantly reduced P. infestans colonization in both host plants. Our results provide evidence that failure of pathogen effectors to interact with and/or correctly manipulate target proteins in distantly related non-host plants contributes to NHR. Moreover, exploiting this breakdown in effector-nonhost target interaction, transferring effector target orthologues from non-host to host plants is a strategy to reduce disease.

Global emissions of CO2 and other greenhouse gases have increased steadily since the pre-industrial era (Pedersen et al., 2021) leading to global temperature increases of >1 °C with most of this increase occurring within the last 50 years (IPCC, 2021). The observed increase in global warming compared to historical averages (1850–1900) is unprecedented and has not been observed in the past 125 000 years. Greater heating will be accompanied by increases in extreme precipitation, tropical storms, and flooding events across all seasons, but more likely to occur in the autumn and winter months in wetter regions of the globe (Hirabayashi et al., 2021; IPCC, 2021; Tabari, 2021). Warming will also lead to increases in global sea level rise of up to 0.55 m or more by 2100 due to the melting of mountain and polar glaciers (IPCC, 2021; Vousdoukas et al., 2018). Coastal flooding events will be compounded by extreme meteorological tides, storm surge, and precipitation, which are predicted to increase by more than 25% by 2100 (Bevacqua et al., 2020). Flooding, which results in either submergence or waterlogging, is responsible for more crop production losses than any other abiotic stress besides drought (Kaur et al., 2020). In addition to the threats posed by extreme precipitation events and sea level rise, global climate changes will also lead to frequent and extreme heatwaves and droughts, which will drive changes in soil moisture, particularly in many regions across North, Central, and South America, the Mediterranean basin, northern and southern Africa, Australia, and western and southern Asia (IPCC, 2021; Naumann et al., 2018). About 50% of the Earth’s land surface is characterized as arid, semi-arid, or dry subhumid (Zika and Erb, 2009). For these drying areas, the duration and magnitude of drought is predicted to double for 30% of the global land mass resulting in a five-fold increase in water demand deficits during the 21st century (Naumann et al., 2018). In addition to the global expansion of dry lands, soil drying and the resulting leaf:air vapor pressure deficits will reduce terrestrial net primary production, reduce terrestrial carbon sinks, and reshape the geographical redistribution of plants (Zhao and Running, 2009). Rare, 1-in-100-year droughts, will become more common, occurring every 2–5 years for many parts of Africa, Australia, southern Europe, southern and central United States, central America, the Caribbean, northwest China, and regions of South America. Increased soil drying brought about by global climate change will threaten global food security and curtail biomass feedstock production for biofuels. Multi-ensemble modeling of the global impact of climate change for 1981–2010, compared with pre-industrial climactic conditions, demonstrates that global mean yields of wheat (Triticum aestivum), maize (Zea mays), and soybean (Glycine max) have declined by 1.8, 4.1, and 4.5%, respectively, even when CO2 fertilization effects and modernized agronomic practices are taken into account (Iizumi et al., 2018). Indeed, the physiological responses of plants to elevated CO2 in the 21st century are not likely to offset the negative consequences of surface drying (Dai et al., 2018). A meta-analysis of multiple published simulations of crop yields predicts aggregate production losses for wheat, rice (Oryza sativa), and maize in both temperate and tropical regions by 2 °C warming with even greater crop losses in the second half of the 21st century (Challinor et al., 2014). Yield loss risks under drought conditions arising from global warming are predicted by ensemble modeling to increase by 9–19% for major crops including wheat, maize, rice, and soybean by the end of the 21st century (Leng and Hall, 2019). In addition to rain-fed crop production, which depends upon ambient precipitation patterns, irrigated crops depend upon the availability of groundwater resources. Irrigated agriculture uses 20% of total cultivated land area, but contributes to 40% of global crop production, and accounts for approximately 70% of global water withdrawals (FAO, 2017). However, overreliance on ground water resources has led to alarming rates of aquifer depletion (Famiglietti and Rodell, 2013; Voss et al., 2013). Anthropogenic climate change has led to reduced and earlier annual snow pack melt (Bormann et al., 2018), which drives even greater ground water depletion (Cuthber et al., 2019). In addition to these biophysical impacts of global climate change, increased losses in agricultural production are anticipated due to the changing geographical range of pathogens (Bebber, 2015) and the impacts of, for example, increased temperature on plant–pathogen interaction (Desaint et al., 2021). Furthermore, sea level rise will submerge large coastal regions currently used for crop production, further limiting agricultural production potential (Wang et al., 2018). As the global climate crisis worsens, critical gains in agricultural production on the order of 60% will be necessary to meet the global food demands of a growing human population estimated to increase to more than 9 billion by 2050 (United Nations, 2019). Agriculture and land-use changes from agricultural expansion contribute up to 25% of greenhouse gas emissions, which poses additional challenges to maintaining a balance between terrestrial carbon sequestration strategies and attempts to meet current and future food demands (Crippa et al., 2021; Searchinger et al., 2018). Thus, a clear and present need exists to develop novel strategies to produce more climate-resilient crops. This special issue encompasses a range of topics that outline potentially useful strategies to fortify the climate resilience of crops to ensure a reliable supply of food, feed, fiber, and biofuels for humankind in the not-so-distant future. Drought is by far one of the most prevalent abiotic stress conditions to limit crop yield worldwide. In the coming years, drought periods are projected to increase in intensity and duration, due to climate change, inflicting a higher than ever yield penalty on agricultural production. In their review article, Berrío et al. (2022) discuss several strategies to enhance drought resilience in crops focusing on ‘growth-centered’ and ‘drought resilience without growth penalty’ strategies. They highlight several different molecular players that were successfully used to engineer drought tolerance in plants, as well as discuss the role of hormones such as abscisic acid, brassinosteroids, cytokinins, ethylene, and strigolactones. In addition, they discuss future perspectives for the development of new strategies to improve drought tolerance under field conditions. In their encompassing review, Kuromori et al. (2022) address plant responses to drought at the single-cell and whole-plant levels. They discuss recent advancements in our understanding of how drought is sensed in leaves and roots, and how different parts of the plant use long-distance signaling to coordinate responses to drought at the whole-plant level. Focusing on abscisic acid and peptide signaling they also highlight selected mechanisms for plant adaptation and resistance to drought and underscore the importance of phenotyping for measuring plant adaptation to drought conditions for the purpose of breeding. Environmental stress conditions, such as drought, heat, or cold stresses, disrupt cellular homeostasis and result in the accumulation of reactive oxygen species (ROS). Unopposed, ROS can cause oxidative stress that will damage many processes in plants and reduce yield. In their article, Kerchev and Van Breusegem (2022) review the decade-long research effort to improve oxidative stress resilience in crops by boosting their antioxidant machinery. They highlight the pros and cons of different strategies and propose new avenues for future research and development in this important subject. The review by Rivero et al. (2022) addresses the critical challenge that environmental stress conditions do not occur in isolation and that predictions of plant responses to multiple stresses is often not possible from our current understanding of responses to a single stress. Addressing combined abiotic stress as well as interactions between abiotic and biotic stress conditions, the authors outline our knowledge of the different physiological outcomes of stress combinations, as well as underlying molecular mechanisms – identifying integrators of combined stress responses as well as highlighting the complexity of this challenge. They end with recommendations of key technologies required – involving genetics, synthetic biology, and engineering – and a call to arms for collaboration across stresses and disciplines. Also focusing on drought and the different molecular mechanisms involved in drought tolerance and drought recovery, Tang and Bassham (2022) address the important process of autophagy and its role in inducing drought tolerance. Autophagy is a subcellular degradation and recycling process that functions during plant development and responses to different stresses. As discussed in their article, autophagy can selectively degrade important proteins such as aquaporins to adjust water permeability, remove damaged proteins to reduce toxicity, and degrade components of hormone signaling pathways to modulate stress responses. In addition, during recovery, autophagy helps reset the cell status. The authors conclude that manipulating autophagy is a promising approach to enhance drought resilience in crops. The review by Zsögön et al. (2022) discusses increasing climate resilience in the food system by diversification of the crops – in terms of both crop species and varieties of a crop – used in global agriculture. The authors outline the impact of climate change on agriculture, including combinations of stress discussed in more depth elsewhere in this special issue, and highlight key aspects of the crop domestication process and crop adaptations to specific environments beyond their center of origin. They ask what we can learn from domestication to apply in the design of new crops for future climatic conditions, be that through classical or new breeding technologies, and capturing of genetic diversity that may have been lost through domestication and artificial selection? In addition to diversifying our crop portfolio and altering the structure of crop canopies, altering root architecture has long been viewed as a critically important means of improving the climate resilience of crops. Lynch (2022) reviews the role of different root anatomical phenotypes as a means to improve water and nitrogen capture while also sequestering CO2 in the soil. The metabolic costs, constraints, and benefits of root growth angles, number of axial roots, rooting depth, and lateral branching are explored. The strengths and weaknesses of various root ideotypes are evaluated in the context of low- and high-input soil agroecosystems and various biotic and abiotic stressors, with the goal of defining the optimal fitness of root phenotypes using in-silico modeling tools across a range of scales and environments. In addition to climate-driven stresses brought about by greenhouse gas emissions, anthropogenic air pollutants such as ozone (O3) can result in major oxidative stress, reduced rates of photosynthesis, accelerated senescence, and decreased crop yields. In their review of the negative impacts that rising O3 levels have on crop performance, Montes et al. summarize the results gained from years of studies conducted at Free Air Concentration Enrichment (O3-FACE) facilities (Montes et al., 2022). The authors detail the major observations gathered from O3-FACE facilities throughout the globe in the Northern Hemisphere and the different responses of C3 photosynthesis (e.g., chickpea [Cicer arietinum], soybean, rice, snapbean [Phaseolus vulgaris], wheat, and various woody crops) compared with C4 photosynthesis (e.g., maize, sorghum [Sorghum bicolor], sugarcane [Saccharum officinarum], and switchgrass [Panicum virgatum]) crops. Several O3-FACE studies have also revealed large genetic variations in the sensitivity to O3 exposure, which provide opportunities to potentially reduce crop damage and reductions in crop yield brought about by O3 in the future. Furthermore, the authors discuss the interactions of O3 pollution with other climate change stressors including drought and heat and the potentially ameliorating effects of elevated atmospheric [CO2]. Climate change is altering the diversity and spatial distribution of many different pathogens, as well as the disease outcome from pathogen infection of crops, impacting agricultural production. As a wider range of pathosystems is investigated, and the identification of molecular mechanisms underlying multigenic quantitative resistance increases, Delplace et al. (2022) argue for the plant defense response to be seen as a complex network of interactions with a spectrum of disease outcomes. They demonstrate the networked nature of pathogen detection at the cell surface and intracellularly and the key role dynamic protein complexes play in both detection and signal transduction. The review highlights network properties with relevance to biological function in host immunity, as well as the need to examine immunity within fluctuating environmental conditions and the value of large-scale data (with increasing cellular and temporal resolution) and network integration to enable prediction of immunity under a changing climate. The reviews contained in this special issue not only provide new insights into the major challenges presented by the global climate crisis, but also illuminate our mechanistic understanding of the ways in which plants respond and adapt to a wide range of environmental stresses including anthropogenic, abiotic and biotic stressors, and combinatorial stresses to plants, soils, and associated microbiomes (Zandalinas et al., 2021). Such novel information will provide researchers with the knowledge they will need to formulate creative strategies for the development and testing of the climate-resilient crops essential for our immediate future to sustain and enhance global food security and plant biodiversity. The authors have no conflict of interest to declare.

Maf1 is a transcription factor that is conserved in sequence and structure between yeasts, animals and plants. Its principal molecular function is also well conserved, being to bind and repress RNA polymerase (pol) III, thereby inhibiting synthesis of tRNAs and other noncoding RNAs. Restrictions on tRNA production and hence protein synthesis can provide a mechanism to preserve resources under conditions that are suboptimal for growth. Accordingly, Maf1 is found in some organisms to influence growth and/or stress survival. Because of their sessile nature, plants are especially vulnerable to environmental changes and molecular adaptations that enhance growth under benign circumstances can increase sensitivity to external challenges. We tested if Maf1 depletion in the model plant Arabidopsis affects growth, pathogen resistance and tolerance of drought or soil salinity, a common physiological challenge that imposes both osmotic and ionic stress. We find that disruption of the Maf1 gene or RNAi-mediated depletion of its transcript is well-tolerated and confers a modest growth advantage without compromising resistance to common biotic and abiotic challenges.

AbstractLactuca sativaL. (lettuce) is an important leafy vegetable crop grown and consumed globally. Chemicals are routinely used to control major pathogens, including the causal agents of grey mould (Botrytis cinerea) and lettuce drop (Sclerotinia sclerotiorum). With increasing prevalence of pathogen resistance to fungicides and environmental concerns, there is an urgent need to identify sources of genetic resistance toB. cinereaandS. sclerotiorumin lettuce. We demonstrated genetic variation for quantitative resistance toB. cinereaandS. sclerotiorumin a set of 97 diverse lettuce and wild relative accessions, and between the parents of lettuce mapping populations. Transcriptome profiling across multiple lettuce accessions enabled us to identify genes with expression correlated with resistance, predicting the importance of post-transcriptional gene regulation in the lettuce defence response. We identified five genetic loci influencing quantitative resistance in a F10 mapping population derived from aLactuca serriola(wild relative) x lettuce cross, which each explained 5–10% of the variation. Differential gene expression analysis between the parent lines, and integration of data on correlation of gene expression and resistance in the diversity set, highlighted potential causal genes underlying the quantitative trait loci.Key MessageWe demonstrate genetic variation for quantitative resistance against important fungal pathogens in lettuce and its wild relatives, map loci conferring resistance and predict key molecular mechanisms using transcriptome profiling.

Background Accurate and comprehensive annotation of transcript sequences is essential for transcript quantification and differential gene and transcript expression analysis. Single-molecule long-read sequencing technologies provide improved integrity of transcript structures including alternative splicing, and transcription start and polyadenylation sites. However, accuracy is significantly affected by sequencing errors, mRNA degradation, or incomplete cDNA synthesis. Results We present a new and comprehensive Arabidopsis thaliana Reference Transcript Dataset 3 (AtRTD3). AtRTD3 contains over 169,000 transcripts-twice that of the best current Arabidopsis transcriptome and including over 1500 novel genes. Seventy-eight percent of transcripts are from Iso-seq with accurately defined splice junctions and transcription start and end sites. We develop novel methods to determine splice junctions and transcription start and end sites accurately. Mismatch profiles around splice junctions provide a powerful feature to distinguish correct splice junctions and remove false splice junctions. Stratified approaches identify high-confidence transcription start and end sites and remove fragmentary transcripts due to degradation. AtRTD3 is a major improvement over existing transcriptomes as demonstrated by analysis of an Arabidopsis cold response RNA-seq time-series. AtRTD3 provides higher resolution of transcript expression profiling and identifies cold-induced differential transcription start and polyadenylation site usage. Conclusions AtRTD3 is the most comprehensive Arabidopsis transcriptome currently. It improves the precision of differential gene and transcript expression, differential alternative splicing, and transcription start/end site usage analysis from RNA-seq data. The novel methods for identifying accurate splice junctions and transcription start/end sites are widely applicable and will improve single-molecule sequencing analysis from any species.