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.

An AI-based Decision Support System for Predicting Mental Health Disorders.

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

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.

UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat.

Wheat is one of the world's most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike's temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike's temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.

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.

Identification of Resistance Genes and Response to Arsenic in Rhodococcus aetherivorans BCP1.

Arsenic (As) ranks among the priority metal(loid)s that are of public health concern. In the environment, arsenic is present in different forms, organic or inorganic, featured by various toxicity levels. Bacteria have developed different strategies to deal with this toxicity involving different resistance genetic determinants. Bacterial strains of Rhodococcus genus, and more in general Actinobacteria phylum, have the ability to cope with high concentrations of toxic metalloids, although little is known on the molecular and genetic bases of these metabolic features. Here we show that Rhodococcus aetherivorans BCP1, an extremophilic actinobacterial strain able to tolerate high concentrations of organic solvents and toxic metalloids, can grow in the presence of high concentrations of As(V) (up to 240 mM) under aerobic growth conditions using glucose as sole carbon and energy source. Notably, BCP1 cells improved their growth performance as well as their capacity of reducing As(V) into As(III) when the concentration of As(V) is within 30-100 mM As(V). Genomic analysis of BCP1 compared to other actinobacterial strains revealed the presence of three gene clusters responsible for organic and inorganic arsenic resistance. In particular, two adjacent and divergently oriented ars gene clusters include three arsenate reductase genes (arsC1/2/3) involved in resistance mechanisms against As(V). A sequence similarity network (SSN) and phylogenetic analysis of these arsenate reductase genes indicated that two of them (ArsC2/3) are functionally related to thioredoxin (Trx)/thioredoxin reductase (TrxR)-dependent class and one of them (ArsC1) to the mycothiol (MSH)/mycoredoxin (Mrx)-dependent class. A targeted transcriptomic analysis performed by RT-qPCR indicated that the arsenate reductase genes as well as other genes included in the ars gene cluster (possible regulator gene, arsR, and arsenite extrusion genes, arsA, acr3, and arsD) are transcriptionally induced when BCP1 cells were exposed to As(V) supplied at two different sub-lethal concentrations. This work provides for the first time insights into the arsenic resistance mechanisms of a Rhodococcus strain, revealing some of the unique metabolic requirements for the environmental persistence of this bacterial genus and its possible use in bioremediation procedures of toxic metal contaminated sites.

Breeding progress and preparedness for mass-scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar.

Genetic improvement through breeding is one of the key approaches to increasing biomass supply. This paper documents the breeding progress to date for four perennial biomass crops (PBCs) that have high output-input energy ratios: namely Panicum virgatum (switchgrass), species of the genera Miscanthus (miscanthus), Salix (willow) and Populus (poplar). For each crop, we report on the size of germplasm collections, the efforts to date to phenotype and genotype, the diversity available for breeding and on the scale of breeding work as indicated by number of attempted crosses. We also report on the development of faster and more precise breeding using molecular breeding techniques. Poplar is the model tree for genetic studies and is furthest ahead in terms of biological knowledge and genetic resources. Linkage maps, transgenesis and genome editing methods are now being used in commercially focused poplar breeding. These are in development in switchgrass, miscanthus and willow generating large genetic and phenotypic data sets requiring concomitant efforts in informatics to create summaries that can be accessed and used by practical breeders. Cultivars of switchgrass and miscanthus can be seed-based synthetic populations, semihybrids or clones. Willow and poplar cultivars are commercially deployed as clones. At local and regional level, the most advanced cultivars in each crop are at technology readiness levels which could be scaled to planting rates of thousands of hectares per year in about 5 years with existing commercial developers. Investment in further development of better cultivars is subject to current market failure and the long breeding cycles. We conclude that sustained public investment in breeding plays a key role in delivering future mass-scale deployment of PBCs.

Dynamic changes in ABA content in water-stressed Populus nigra: effects on carbon fixation and soluble carbohydrates.

BACKGROUND AND AIMS: Hydraulic and chemical signals operate in tandem to regulate systemic plant responses to drought. Transport of abscisic acid (ABA) through the xylem and phloem from the root to shoot has been suggested to serve as the main signal of water deficit. There is evidence that ABA and its ABA-glycosyl-ester (ABA-GE) are also formed in leaves and stems through the chloroplastic 2-C-methylerythritol-5-phosphate (MEP) pathway. This study aimed to evaluate how hormonal and hydraulic signals contribute to optimize stomatal (gs), mesophyll (gm) and leaf hydraulic (Kleaf) conductance under well-watered and water-stressed conditions in Populus nigra (black poplar) plants. In addition, we assessed possible relationships between ABA and soluble carbohydrates within the leaf and stem. METHODS: Plants were subjected to three water treatments: well-watered (WW), moderate stress (WS1) and severe stress (WS2). This experimental set-up enabled a time-course analysis of the response to water deficit at the physiological [leaf gas exchange, plant water relations, (Kleaf)], biochemical (ABA and its metabolite/catabolite quantification in xylem sap, leaves, wood, bark and roots) and molecular (gene expression of ABA biosynthesis) levels. KEY RESULTS: Our results showed strong coordination between gs, gm and Kleaf under water stress, which reduced transpiration and increased intrinsic water use efficiency (WUEint). Analysis of gene expression of 9-cis-epoxycarotenoid dioxygenase (NCED) and ABA content in different tissues showed a general up-regulation of the biosynthesis of this hormone and its finely-tuned catabolism in response to water stress. Significant linear relationships were found between soluble carbohydrates and ABA contents in both leaves and stems, suggesting a putative function for this hormone in carbohydrate mobilization under severe water stress. CONCLUSIONS: This study demonstrates the tight regulation of the photosynthetic machinery by levels of ABA in different plants organs on a daily basis in both well-watered and water stress conditions to optimize WUEint and coordinate whole plant acclimation responses to drought.

Corrigendum: An In vitro Study of Bio-Control and Plant Growth Promotion Potential of Salicaceae Endophytes.

[This corrects the article DOI: 10.3389/fmicb.2017.00386.].

Engineering Drought Resistance in Forest Trees.

Climatic stresses limit plant growth and productivity. In the past decade, tree improvement programs were mainly focused on yield but it is obvious that enhanced stress resistance is also required. In this review we highlight important drought avoidance and tolerance mechanisms in forest trees. Genomes of economically important trees species with divergent resistance mechanisms can now be exploited to uncover the mechanistic basis of long-term drought adaptation at the whole plant level. Molecular tree physiology indicates that osmotic adjustment, antioxidative defense and increased water use efficiency are important targets for enhanced drought tolerance at the cellular and tissue level. Recent biotechnological approaches focused on overexpression of genes involved in stress sensing and signaling, such as the abscisic acid core pathway, and down-stream transcription factors. By this strategy, a suite of defense systems was recruited, generally enhancing drought and salt stress tolerance under laboratory conditions. However, field studies are still scarce. Under field conditions trees are exposed to combinations of stresses that vary in duration and magnitude. Variable stresses may overrule the positive effect achieved by engineering an individual defense pathway. To assess the usability of distinct modifications, large-scale experimental field studies in different environments are necessary. To optimize the balance between growth and defense, the use of stress-inducible promoters may be useful. Future improvement programs for drought resistance will benefit from a better understanding of the intricate networks that ameliorate molecular and ecological traits of forest trees.

UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought.

Poplars are fast-growing, high-yielding forest tree species, whose cultivation as second-generation biofuel crops is of increasing interest and can efficiently meet emission reduction goals. Yet, breeding elite poplar trees for drought resistance remains a major challenge. Worldwide breeding programs are largely focused on intra/interspecific hybridization, whereby Populus nigra L. is a fundamental parental pool. While high-throughput genotyping has resulted in unprecedented capabilities to rapidly decode complex genetic architecture of plant stress resistance, linking genomics to phenomics is hindered by technically challenging phenotyping. Relying on unmanned aerial vehicle (UAV)-based remote sensing and imaging techniques, high-throughput field phenotyping (HTFP) aims at enabling highly precise and efficient, non-destructive screening of genotype performance in large populations. To efficiently support forest-tree breeding programs, ground-truthing observations should be complemented with standardized HTFP. In this study, we develop a high-resolution (leaf level) HTFP approach to investigate the response to drought of a full-sib F2 partially inbred population (termed here 'POP6'), whose F1 was obtained from an intraspecific P. nigra controlled cross between genotypes with highly divergent phenotypes. We assessed the effects of two water treatments (well-watered and moderate drought) on a population of 4603 trees (503 genotypes) hosted in two adjacent experimental plots (1.67 ha) by conducting low-elevation (25 m) flights with an aerial drone and capturing 7836 thermal infrared (TIR) images. TIR images were undistorted, georeferenced, and orthorectified to obtain radiometric mosaics. Canopy temperature (Tc) was extracted using two independent semi-automated segmentation techniques, eCognition- and Matlab-based, to avoid the mixed-pixel problem. Overall, results showed that the UAV platform-based thermal imaging enables to effectively assess genotype variability under drought stress conditions. Tc derived from aerial thermal imagery presented a good correlation with ground-truth stomatal conductance (gs) in both segmentation techniques. Interestingly, the HTFP approach was instrumental to detect drought-tolerant response in 25% of the population. This study shows the potential of UAV-based thermal imaging for field phenomics of poplar and other tree species. This is anticipated to have tremendous implications for accelerating forest tree genetic improvement against abiotic stress.

De novo assembly, functional annotation, and analysis of the giant reed (Arundo donax L.) leaf transcriptome provide tools for the development of a biofuel feedstock.

BACKGROUND: Arundo donax has attracted renewed interest as a potential candidate energy crop for use in biomass-to-liquid fuel conversion processes and biorefineries. This is due to its high productivity, adaptability to marginal land conditions, and suitability for biofuel and biomaterial production. Despite its importance, the genomic resources currently available for supporting the improvement of this species are still limited. RESULTS: We used RNA sequencing (RNA-Seq) to de novo assemble and characterize the A. donax leaf transcriptome. The sequencing generated 1249 million clean reads that were assembled using single-k-mer and multi-k-mer approaches into 62,596 unique sequences (unitranscripts) with an N50 of 1134 bp. TransDecoder and Trinotate software suites were used to obtain putative coding sequences and annotate them by mapping to UniProtKB/Swiss-Prot and UniRef90 databases, searching for known transcripts, proteins, protein domains, and signal peptides. Furthermore, the unitranscripts were annotated by mapping them to the NCBI non-redundant, GO and KEGG pathway databases using Blast2GO. The transcriptome was also characterized by BLAST searches to investigate homologous transcripts of key genes involved in important metabolic pathways, such as lignin, cellulose, purine, and thiamine biosynthesis and carbon fixation. Moreover, a set of homologous transcripts of key genes involved in stomatal development and of genes coding for stress-associated proteins (SAPs) were identified. Additionally, 8364 simple sequence repeat (SSR) markers were identified and surveyed. SSRs appeared more abundant in non-coding regions (63.18%) than in coding regions (36.82%). This SSR dataset represents the first marker catalogue of A. donax. 53 SSRs (PolySSRs) were then predicted to be polymorphic between ecotype-specific assemblies, suggesting genetic variability in the studied ecotypes. CONCLUSIONS: This study provides the first publicly available leaf transcriptome for the A. donax bioenergy crop. The functional annotation and characterization of the transcriptome will be highly useful for providing insight into the molecular mechanisms underlying its extreme adaptability. The identification of homologous transcripts involved in key metabolic pathways offers a platform for directing future efforts in genetic improvement of this species. Finally, the identified SSRs will facilitate the harnessing of untapped genetic diversity. This transcriptome should be of value to ongoing functional genomics and genetic studies in this crop of paramount economic importance.