Structural and functional analysis of the active cow rumen's microbial community provides a catalogue of genes and microbes participating in the deconstruction of cardoon biomass.

BACKGROUND: Ruminal microbial communities enriched on lignocellulosic biomass have shown considerable promise for the discovery of microorganisms and enzymes involved in digesting cell wall compounds, a key bottleneck in the development of second-generation biofuels and bioproducts, enabling a circular bioeconomy. Cardoon (Cynara cardunculus) is a promising inedible energy crop for current and future cellulosic biorefineries and the emerging bioenergy and bioproducts industries. The rumen microbiome can be considered an anaerobic "bioreactor", where the resident microbiota carry out the depolymerization and hydrolysis of plant cell wall polysaccharides (PCWPs) through the catalytic action of fibrolytic enzymes. In this context, the rumen microbiota represents a potential source of microbes and fibrolytic enzymes suitable for biofuel production from feedstocks. In this study, metatranscriptomic and 16S rRNA sequencing were used to profile the microbiome and to investigate the genetic features within the microbial community adherent to the fiber fractions of the rumen content and to the residue of cardoon biomass incubated in the rumen of cannulated cows. RESULTS: The metatranscriptome of the cardoon and rumen fibre-adherent microbial communities were dissected in their functional and taxonomic components. From a functional point of view, transcripts involved in the methanogenesis from CO2 and H2, and from methanol were over-represented in the cardoon-adherent microbial community and were affiliated with the Methanobrevibacter and Methanosphaera of the Euryarchaeota phylum. Transcripts encoding glycoside hydrolases (GHs), carbohydrate-binding modules (CBMs), carbohydrate esterases (CEs), polysaccharide lyases (PLs), and glycoside transferases (GTs) accounted for 1.5% (6,957) of the total RNA coding transcripts and were taxonomically affiliated to major rumen fibrolytic microbes, such as Oscillospiraceae, Fibrobacteraceae, Neocallimastigaceae, Prevotellaceae, Lachnospiraceae, and Treponemataceae. The comparison of the expression profile between cardoon and rumen fiber-adherent microbial communities highlighted that specific fibrolytic enzymes were potentially responsible for the breakdown of cardoon PCWPs, which was driven by specific taxa, mainly Ruminococcus, Treponema, and Neocallimastigaceae. CONCLUSIONS: Analysis of 16S rRNA and metatranscriptomic sequencing data revealed that the cow rumen microbiome harbors a repertoire of new enzymes capable of degrading PCWPs. Our results demonstrate the feasibility of using metatranscriptomics of enriched microbial RNA as a potential approach for accelerating the discovery of novel cellulolytic enzymes that could be harnessed for biotechnology. This research contributes a relevant perspective towards degrading cellulosic biomass and providing an economical route to the production of advanced biofuels and high-value bioproducts.

Dryland microbiomes reveal community adaptations to desertification and climate change.

Drylands account for 45% of the Earth's land area, supporting ~40% of the global population. These regions support some of the most extreme environments on Earth, characterized by extreme temperatures, low and variable rainfall, and low soil fertility. In these biomes, microorganisms provide vital ecosystem services and have evolved distinctive adaptation strategies to endure and flourish in the extreme. However, dryland microbiomes and the ecosystem services they provide are under threat due to intensifying desertification and climate change. In this review, we provide a synthesis of our current understanding of microbial life in drylands, emphasizing the remarkable diversity and adaptations of these communities. We then discuss anthropogenic threats, including the influence of climate change on dryland microbiomes and outline current knowledge gaps. Finally, we propose research priorities to address those gaps and safeguard the sustainability of these fragile biomes.

AI ethics on the road to responsible AI plant science and societal welfare.

The swiftness of artificial intelligence (AI) progress in plant science begets relevant ethical questions with significant scientific and societal implications. Embracing a principled approach to regulation, ethics review and monitoring, and human-centric interpretable informed AI (HIAI), we can begin to navigate our voyage towards ethical and socially responsible AI.

Extended reality gives digital agricultural biotechnology a new dimension.

Facing up to the global challenges of designing climate-resilient biotech crops involves a great deal of out-of-the-box thinking. Extended reality is coming of age in digital agricultural biotechnology. Here, we seek to stimulate technological innovation by empowering future innovators, researchers, academics, and startups to think and partner creatively.

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey.

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.

Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis.

High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.

Promoting Ethically Responsible Use of Agricultural Biotechnology.

Growing global demands for food, bioenergy, and specialty products, along with the threat posed by various environmental changes, present substantial challenges for agricultural production. Agricultural biotechnology offers a promising avenue for meeting these challenges; however, ethical and sociocultural concerns must first be addressed, to ensure widespread public trust and uptake. To be effective, we need to develop solutions that are ethically responsible, socially responsive, relevant to people of different cultural and social backgrounds, and conveyed to the public in a convincing and straightforward manner. Here, we highlight how ethical approaches, principled decision-making strategies, citizen-stakeholder participation, effective science communication, and bioethics education should be used to guide responsible use of agricultural biotechnology.

Creating Bioethics Distance Learning Through Virtual Reality.

Bioethics education is a central element in the biotechnology curriculum. Re-imagining distance learning, virtual reality (VR) is taking student involvement to the next level of interaction, offering a real classroom experience and a new way to gain ethical reasoning skills. Here, we explore a new paradigm for bioethics education that involves VR.

Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?

Human population growth and accelerated climate change necessitate agricultural improvements using designer crop ideotypes (idealized plants that can grow in niche environments). Diverse and highly skilled research groups must integrate efforts to bridge the gaps needed to achieve international goals toward sustainable agriculture. Given the scale of global agricultural needs and the breadth of multiple types of omics data needed to optimize these efforts, explainable artificial intelligence (AI with a decipherable decision making process that provides a meaningful explanation to humans) and exascale computing (computers that can perform 1018 floating-point operations per second, or exaflops) are crucial. Accurate phenotyping and daily-resolution climatype associations are equally important for refining ideotype production to specific environments at various levels of granularity. We review advances toward tackling technological hurdles to solve multiple United Nations Sustainable Development Goals and discuss a vision to overcome gaps between research and policy.

Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence.

Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.

Salicylic acid induces H2O2 production and endochitinase gene expression but not ethylene biosynthesis in Castanea sativa in vitro model system.

Salicylic acid (SA), ethylene (ET), and wounding are all known to influence plant defense response. Experiments attempting to determine SA's relation to ET biosynthesis and defense gene expression have shown conflicting results. To confront this, we developed an in vitro model system to investigate how SA affects ET biosynthesis, hydrogen peroxide (H(2)O(2)) production and endochitinase gene expression in the European chestnut. ET measurements of in vitro shoots indicated a critical time point for SA exogenous application, enabling us to study its effects independent of ET. In addition, ET measurements demonstrated that its own increased biosynthesis was a response to wounding but not to SA treatment. Application of the ET biosynthesis inhibitor, aminoethoxyvinylglycine (AVG), on wounded and SA-treated shoots blocked wounding-induced ET production. Interestingly, SA inhibited ET production, but to a lesser extent than AVG. Additionally, SA also induced the accumulation of endochitinase transcript level. Likewise, a sensitive tissue-print assay showed that SA further increased the level of H(2)O(2). Yet, SA-induced endochitinase gene expression and SA-enhanced H(2)O(2) production levels were independent of ET. The cumulative results indicate that SA acts as an inducer of endochitinase PR gene expression and of H(2)O(2) oxidative burst. This suggests that SA is a component of the signal transduction pathway leading to defense against pathogens in chestnut. Further, the model system developed for this experiment should facilitate the deciphering of defense signaling pathways and their cross-talk. Moreover, it should also benefit the study of trees of long generation time that are known to be recalcitrant to in vitro studies.

The Arabidopsis tetratricopeptide repeat-containing protein TTL1 is required for osmotic stress responses and abscisic acid sensitivity.

Mutations in the Arabidopsis (Arabidopsis thaliana) TETRATRICOPEPTIDE-REPEAT THIOREDOXIN-LIKE 1 (TTL1) cause reduced tolerance to NaCl and osmotic stress that is characterized by reduced root elongation, disorganization of the root meristem, and impaired osmotic responses during germination and seedling development. Expression analyses of genes involved in abscisic acid (ABA) biosynthesis and catabolism suggest that TTL1 is not involved in the regulation of ABA levels but is required for ABA-regulated responses. TTL1 regulates the transcript levels of several dehydration-responsive genes, such as the transcription factor DREB2A, and genes encoding dehydration response proteins, such as ERD1 (early response to dehydration 1), ERD3, and COR15a. The TTL1 gene encodes a novel plant protein with tetratricopeptide repeats and a region with homology to thioredoxin proteins. Based on homology searches, there are four TTL members in the Arabidopsis genome with similar intron-exon structure and conserved amino acid domains. Proteins containing tetratricopeptide repeat motifs act as scaffold-forming multiprotein complexes and are emerging as essential elements for plant hormonal responses (such as gibberellin responses and ethylene biosynthesis). In this report, we identify TTL1 as a positive regulator of ABA signaling during germination and seedling development under stress.

Ethylene signaling mediates a maize defense response to insect herbivory.

The signaling pathways that enable plants to mount defenses against insect herbivores are known to be complex. It was previously demonstrated that the insect-resistant maize (Zea mays L.) genotype Mp708 accumulates a unique defense cysteine proteinase, Mirl-CP, in response to caterpillar feeding. In this study, the role of ethylene in insect defense in Mp708 and an insect-susceptible line Tx601 was explored. Ethylene synthesis was blocked with either cobalt chloride or aminoethoxyvinylglycine. Alternatively, ethylene perception was inhibited with 1-methylcyclopropene. Blocking ethylene synthesis and perception resulted in Mp708 plants that were more susceptible to caterpillar feeding. In addition, fall armyworm (Spodoptera frugiperda) larvae that fed on inhibitor-treated Mp708 plants had signifycantly higher growth rates than those reared on untreated plants. In contrast, these responses were not significantly altered in Tx601. The ethylene synthesis and perception inhibitors also reduced the accumulation of Mirl-CP and its transcript mir1 in response to herbivory. These results indicate that ethylene is a component of the signal transduction pathway leading to defense against insect herbivory in the resistant genotype Mp708.