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Improved Extraction Methods to Isolate High Molecular Weight DNA From Magnaporthaceae and Other Grass Root Fungi for Long-Read Whole Genome Sequencing
Full text linkThis manuscript details two modified protocols for the isolation of long-stranded or high molecular weight (HMW) DNA from Magnaporthaceae (Ascomycota) fungal mycelium intended for whole genome sequencing. The Cytiva Nucleon PhytoPure and the Macherey-Nagel NucleoBond HMW DNA kits were selected because the former requires lower amounts of starting material and the latter utilizes gentler methods to maximize DNA length, albeit at a higher requirement for input material. The Cytiva Nucleon PhytoPure kit successfully recovered HMW DNA for half of our fungal species by increasing the amount of RNase A treatment and adding in a proteinase K treatment. To reduce the impact of pigmentation development, which occurs toward later stages of culturing, extractions were run in quadruplicate to increase overall DNA concentration. We also adapted the Macherey-Nagel NucleoBond HMW DNA kit for high-quality HMW DNA by grinding the sample to a fine powder, overnight lysis, and splitting the sample before washing the precipitated DNA. For both kits, precipitated DNA was spooled out pre-washing, ensuring a higher percentage of high-integrity long strands. The Macherey-Nagel protocol offers advantages over the first through the utilization of gravity columns that provide gentler treatment, yielding >50% of high-purity DNA strands exceeding 40 kbp. The limitation of this method is the requirement for a large quantity of starting material (1 g). By triaging samples based on the rate of growth relative to the accumulation of secondary metabolites, our methodologies hold promise for yielding reliable and high-quality HMW DNA from a variety of fungal samples, improving sequencing outcome
Sulfur's Long Game: 145 Years of Soil Sulfur Speciation in the World's Oldest Agricultural Experiments
Full text linkSulfur (S) is an essential plant nutrient, but ongoing decreases in inorganic S inputs to soil continue to reduce S availability in agricultural soils globally. This study investigated long-term trends in soil S speciation after land use change and the application of different soil amendments using the world's longest-running agricultural experiments at the Rothamsted Research Centre, UK. Soil samples spanning 145 years were obtained from the Broadbalk Wheat Experiment (continuous cropping with different amendments) and two Wilderness sites, Broadbalk Wilderness and Geescroft Wilderness (cropping land left to rewild) and analysed using synchrotron-based x-ray absorption near-edge structure (XANES) spectroscopy. It was found that changes in S speciation were linked to changes in soil organic carbon (SOC). In the Broadbalk Winter Wheat experiment, farmyard manure applications increased the proportion of reduced C-bonded S by 40% over 145 years, while the S speciation in the inorganic fertiliser (NPKMgS) and Control treatments remained unchanged and was comprised of ~48% oxidised S. In the Wilderness sites (cropping ceased 143–146 years from present), SOC accumulation during rewilding generally increased the proportions of reduced organic S. However, soil acidification at the Geescroft site initially increased the average oxidation state of S (from +3.7 in 1883 to +4.4 in 1965) despite increasing SOC. Thus, whilst SOC is important in controlling S speciation, soil pH also has a central effect. These findings provide new insights into the long-term dynamics of S speciation under different agricultural practices and land-use changes and contribute to our understanding of S and its availability in cropping systems
An agent-based model of farmer decision making: Application to shared water resources in Arid and semi-arid regions
Full text linkThe study presents an agent-based modelling framework that integrates behavioural and biophysical models to investigate shared irrigation water management in an arid region. The behavioural model simulates farmers' decisions about their water irrigation sources (dam or groundwater) and whether to continue cultivating in the face of drought. This model was parameterised using survey data. The biophysical model component quantifies the impact of water availability and irrigation sources on soil salinity accumulation and its effects on crop productivity. Applied to the Al Haouz Basin, in Morocco, the integrated model reveals several key findings: (1) Increased groundwater access through water abstraction authorization can initially boost productivity but leads to widespread salinisation and farm abandonment, particularly under climate change scenarios. (2) Scenarios with reduced dam water availability demonstrate that mixed irrigation strategies mitigate short-term productivity losses but fail to prevent long-term soil salinity issues. (3) Land abandonment is significantly influenced by the level of water abstraction authorizations, with higher abstraction leading to more severe environmental degradation and social impacts. (4) Policy scenarios reveal that there is a theoretical optimal level of groundwater abstraction that maximises productivity while minimising land abandonment and salinity build-up. These results highlight the complex trade-offs between short-term gains and long-term sustainability, emphasising the need for holistic water governance policies that balance individual and collective interests
Trade-off between pollinator-wildflower diversity & grassland yields
Full text linkThis is a critical moment for land use policy globally, with many countries (e.g. the UK and the European Union) currently undertaking significant green reforms of their agricultural policies. Despite their importance for maintaining agricultural outputs and plant diversity, the effects of artificial soil enrichment on pollinators remain poorly understood. Our two-year study at the world’s longest-running ecological experiment, Park Grass, Rothamsted, examines the relationship between soil fertilisation, grassland yield and biodiversity. Our data show a large and significant negative effect of the major plant nutrients (NPK) on the abundance, species richness and functional diversity of both pollinators and flowering plants. The results also indicate a large and significant trade-off between productivity and biodiversity. Our findings are a salutary reminder of the challenge in reconciling conflicting aims in farmland management and strongly suggest that financial incentives are necessary to offset yield reductions to improve biodiversity outcomes in agricultural grasslands
Born of frustration: the emergence of Camelina sativa as a platform for lipid biotechnology
Full text linkThe emerging crop Camelina sativa (L.) Crantz (camelina) is a Brassicaceae oilseed with a rapidly growing reputation for the deployment of advanced lipid biotechnology and metabolic engineering. Camelina is recognised by agronomists for its traits including yield, oil/protein content, drought tolerance, limited input requirements, plasticity and resilience. Its utility as a platform for metabolic engineering was then quickly recognised, and biotechnologists have benefited from its short life cycle and facile genetic transformation, producing numerous transgenic interventions to modify seed lipid content and generate novel products. The desire to work with a plant that is both a model and crop has driven the expansion of research resources for camelina, including increased availability of genome and other “-omics” data sets. Collectively the expansion of these resources has established camelina as an ideal plant to study the regulation of lipid metabolism and genetic improvement. Furthermore, the unique characteristics of camelina enables the design-build-test-learn cycle to be transitioned from the controlled environment to the field. Complex metabolic engineering to synthesize and accumulate high levels of novel fatty acids and modified oils in seeds, can be deployed, tested and undergo rounds of iteration in agronomically relevant environments. Engineered camelina oils are now increasingly being developed and used to sustainably supply, improved nutrition, feed, biofuels and fossil fuel replacements for high-value chemical products. In this review, we provide a summary of seed fatty acid synthesis and oil assembly in camelina, highlighting how discovery research in camelina supports the advance of metabolic engineering towards the predictive manipulation of metabolism to produce desirable bio-based products. Further examples of innovation in camelina seed lipid engineering and crop improvement are then provided, describing how technologies (e.g., genetic modification (GM), gene editing (GE), RNAi, alongside GM and GE stacking) can be applied to produce new products and denude undesirable traits. Focusing on the production of long chain polyunsaturated omega-3 fatty acids in camelina, we describe how lipid biotechnology can transition from discovery to a commercial prototype. The prospects to produce structured triacylglycerol with fatty acids in specified stereospecific positions are also discussed, alongside the future outlook for the agronomic uptake of camelina lipid biotechnology
The North Wyke Farm Platform: Forage Quantity and Quality Data
Full text linkThe North Wyke Farm Platform (NWFP) was established in 2010 to study and improve grassland livestock production at the farm-scale. The NWFP uses a combination of environmental sensors, routine field and lab-based measurements, and detailed management records to monitor livestock and crop production, emissions to water, emissions to air, soil health, and biodiversity. The rich NWFP datasets help researchers to evaluate the effectiveness of different grassland (and arable) farming systems, which in turn, contributes to the development of sustainable, resilient and net zero land management strategies. This document serves as a user guide to the forage (pasture, silage) quantity and quality data and is associated with other dedicated user guides that detail the collection, and quality control processing of all the datasets produced on the NWFP
Colletotrichum species associated with durian (Durio zibethinus Murray) in Hainan, China
No full textDurian (Durio zibethinus Murray), the king of fruits, is an edible and economically important tropical fruit endemic to Southeast Asia. Durian is affected by Colletotrichum, which is one of the most important genera of plant pathogenic fungi, especially on tropical and subtropical crops. In this study, Colletotrichum species associated with durian in Hainan (China) which was a new region for durian cultivation were studied using phylogenetic and morphological analyses. The results of molecular identification based on internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), β-tubulin (TUB2) and the mating type locus MAT1-2 (ApMat) along with microscopic identification indicated that seven species from three species complexes were identified, including C. fructicola, C. siamense, C. queenslandicum and C. endophyticum from the gloeosporioides species complex; C.plurivorum and C. musicola from the orchidearum species complex, and C. gigasporum from the gigasporum species complex. Colletotrichum fructicola and C. siamense were the main Colletotrichum species associated with durian in Hainan. Pathogenicity tests showed that all seven species could infect durian leaves using a wound inoculation method but only C. fructicola, C. queenslandicum and C. endophyticum could infect using a non-wound inoculation method. The findings from this study enhance the knowledge of durian diseases and lay an essential foundation for devising effective disease management approaches. Further largescale surveys are necessary, including sampling different sites and plant tissues, pathogenicity testing on a wider range of host genotypes and fungicide-sensitivity evaluation among and within the different Colletotrichum species collected
A conserved fungal Knr4/Smi1 protein is crucial for maintaining cell wall stress tolerance and host plant pathogenesis
Full text linkFilamentous plant pathogenic fungi pose significant threats to global food security, particularly through diseases like Fusarium Head Blight (FHB) and Septoria Tritici Blotch (STB) which affects cereals. With mounting challenges in fungal control and increasing restrictions on fungicide use due to environmental concerns, there is an urgent need for innovative control strategies. Here, we present a comprehensive analysis of the stage-specific infection process of Fusarium graminearum in wheat spikes by generating a dual weighted gene co-expression network (WGCN). Notably, the network contained a mycotoxin-enriched fungal module that exhibited a significant correlation with a detoxification gene-enriched wheat module. This correlation in gene expression was validated through quantitative PCR.Byexamininga fungal module with genes highly expressed during early symptomless infection, we identified a gene encoding FgKnr4, a protein containing a Knr4/Smi1 disordered domain. Through comprehensive analysis, we confirmed the pivotal role of FgKnr4 in various biological processes, including morphogenesis, growth, cell wall stress tolerance, and pathogenicity. Further studies confirmed the observed phenotypes are partially due to the involvement of FgKnr4 in regulating the fungal cell wall integrity pathway by modulating the phosphorylation of the MAP-kinase MGV1. Orthologues of the FgKnr4 gene are widespread across the fungal kingdom but are absent in other Eukaryotes, suggesting the protein has potential as a promising intervention target. Encouragingly, the restricted growth and highly reduced virulence phenotypes observed for ΔFgknr4 were replicated upon deletion of the orthologous gene in the wheat fungal pathogen Zymoseptoria tritici. Overall, this study demonstrates the utility of an integrated network-level analytical approach to pinpoint genes of high interest to pathogenesis and disease control
Predicting wheat powdery mildew epidemics in China using meteorological data and machine learning approaches
No full textoai:repository.rothamsted.ac.uk:99518BACKGROUND: Prediction is vital for plant disease management. This study developed machine learning models that used meteorological data to predict wheat powdery mildew (WPM) occurrence severity degree and area in China. Six machine learning algorithms were trained and cross-validated to predict WPM severity degree with 411 pieces of meteorological data from 48 counties (1981–2021) across China. Areas of WPM occurrence were also derived from WPM severity degrees [which were predicted by the K-Nearest Neighbor (KNN) model] with spatial interpolation models.
RESULTS: The best-performing machine learning severity prediction models were based on meteorological data during the coldest month (January) of the wheat overwintering period, and also the wheat jointing stage–heading stage. In each case the times were subdivided into 5-day periods. In particular, the prediction model showed that the best performance was based on the support vector machine algorithm. Climate variable importance ranked via random forest identified eight key predictors. Using these, KNN achieved high performance, demonstrating its suitability for predicting WPM severity degree. Nationwide severity distributions were produced using inverse distance weighted (IDW) and ordinary kriging methods, based on severity degrees predicted by the KNN model from 1990 to 2019. Validation via chi-squared and error reference methods confirmed that the IDW_4.0 model outperformed the others.
CONCLUSIONS: Machine learning models effectively predict WPM severity degree and area of occurrence at a national scale
using meteorological data. The disease severity distribution of WPM displays disease severity spatial patterns visually and can improve management strategies for WPM across China
