Mapping parental DMRs predictive of local and distal methylome remodeling in epigenetic F1 hybrids

F1 hybrids derived from a cross between two inbred parental lines often display widespread changes in DNA methylation and gene expression patterns relative to their parents. An emerging challenge is to understand how parental epigenomic differences contribute to these events. Here, we generated a large mapping panel of F1 epigenetic hybrids, whose parents are isogenic but variable in their DNA methylation patterns. Using a combination of multi-omic profiling and epigenetic mapping strategies we show that differentially methylated regions in parental pericentromeres act as major reorganizers of hybrid methylomes and transcriptomes, even in the absence of genetic variation. These parental differentially methylated regions are associated with hybrid methylation remodeling events at thousands of target regions throughout the genome, both locally (in cis) and distally (in trans). Many of these distally-induced methylation changes lead to nonadditive expression of nearby genes and associate with phenotypic heterosis. Our study highlights the pleiotropic potential of parental pericentromeres in the functional remodeling of hybrid genomes and phenotypes.

High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models

Detection of spikes is the first important step toward image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leaves that exhibit similar colors to spike regions. Consequently, accurate detection of grain spikes renders, in general, a non-trivial task even for advanced, state-of-the-art deep neural networks (DNNs). To improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module. The performance of our extended FRCNN-A vs. conventional FRCNN was compared on images of different European wheat cultivars, including "difficult" bushy phenotypes from 2 different phenotyping facilities and optical setups. Our experimental results show that introduced architectural adaptations in FRCNN-A helped to improve spike detection accuracy in inner regions. The mean average precision (mAP) of FRCNN and FRCNN-A on inner spikes is 76.0% and 81.0%, respectively, while on the state-of-the-art detection DNNs, Swin Transformer mAP is 83.0%. As a lightweight network, FRCNN-A is faster than FRCNN and Swin Transformer on both baseline and augmented training datasets. On the FastGAN augmented dataset, FRCNN achieved a mAP of 84.24%, FRCNN-A attained a mAP of 85.0%, and the Swin Transformer achieved a mAP of 89.45%. The increase in mAP of DNNs on the augmented datasets is proportional to the amount of the IPK original and augmented images. Overall, this study indicates a superior performance of attention mechanisms-based deep learning models in detecting small and subtle features of grain spikes.

Strong prevalence of light regime-specific QTL in Arabidopsis detected using automated high-throughput phenotyping in fluctuating or constant light

Plants have evolved and adapted under dynamic environmental conditions, particularly to fluctuating light, but plant research has often focused on constant growth conditions. To quantitatively asses the adaptation to fluctuating light, a panel of 384 natural Arabidopsis thaliana accessions was analyzed in two parallel independent experiments under fluctuating and constant light conditions in an automated high-throughput phenotyping system upgraded with supplemental LEDs. While the integrated daily photosynthetically active radiation was the same under both light regimes, plants in fluctuating light conditions accumulated significantly less biomass and had lower leaf area during their measured vegetative growth than plants in constant light. A total of 282 image-derived architectural and/or color-related traits at six common time points, and 77 photosynthesis-related traits from one common time point were used to assess their associations with genome-wide natural variation for both light regimes. Out of the 3000 significant marker-trait associations (MTAs) detected, only 183 (6.1%) were common for fluctuating and constant light conditions. The prevalence of light regime-specific QTL indicates a complex adaptation. Genes in linkage disequilibrium with fluctuating light-specific MTAs with an adjusted repeatability value >0.5 were filtered for gene ontology terms containing "photo" or "light", yielding 15 selected candidates. The candidate genes are involved in photoprotection, PSII maintenance and repair, maintenance of linear electron flow, photorespiration, phytochrome signaling, and cell wall expansion, providing a promising starting point for further investigations into the response of Arabidopsis thaliana to fluctuating light conditions.

Integrated multi-omics analyses and genome-wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola

Genome-wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High-throughput omics technologies, such as genomics, transcriptomics, proteomics and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalise on a diverse canola population with 477 spring-type lines which was previously analysed by high-throughput phenotyping of growth-related traits and by RNA sequencing and metabolite profiling for multi-omics-based hybrid performance prediction. We deepened the phenotypic data analysis, now providing 123 time-resolved image-based traits, to gain insight into the complex relations during early vegetative growth and reanalysed the transcriptome data based on the latest Darmor-bzh v10 genome assembly. Genome-wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritised candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1) and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production.

Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation

In plant science, the suboptimal match of growing conditions hampers the transfer of knowledge from controlled environments in glasshouses or climate chambers to field environments. Here we present the PhenoSphere, a plant cultivation infrastructure designed to simulate field-like environments in a reproducible manner. To benchmark the PhenoSphere, the effects on plant growth of weather conditions of a single maize growing season and of an averaged season over three years are compared to those of a standard glasshouse and of four years of field trials. The single season simulation proves superior to the glasshouse and the averaged season in the PhenoSphere: The simulated weather regime of the single season triggers plant growth and development progression very similar to that observed in the field. Hence, the PhenoSphere enables detailed analyses of performance-related trait expression and causal biological mechanisms in plant populations exposed to weather conditions of current and anticipated future climate scenarios.

Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis

Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.

Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought

In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.

The microbial growth potential of antiscalants used in seawater desalination

20 years since the first report on the biofouling potential of chemicals used for scale control, still, antiscalants with high bacterial growth potential are used in practice. Evaluating the bacterial growth potential of commercially available antiscalants is therefore essential for a rational selection of these chemicals. Previous antiscalant growth potential tests were conducted in drinking water or seawater inoculated with model bacterial species which do not represent natural bacterial communities. To reflect better on the conditions of desalination systems, we investigated the bacterial growth potential of eight different antiscalants in natural seawater and an autochthonous bacterial population as inoculum. The antiscalants differed strongly in their bacterial growth potential varying from ≤ 1 to 6 μg easily biodegradable C equivalents/mg antiscalant. The six phosphonate-based antiscalants investigated showed a broad range of growth potential, which depended on their chemical composition, whilst the biopolymer and the synthetic carboxylated polymers-based antiscalants showed limited or no significant bacterial growth. Moreover, nuclear magnetic resonance (NMR) scans enabled antiscalant fingerprinting, identifying components and contaminants, providing a rapid and sensitive characterization, and opening opportunities for rational selection of antiscalants for biofouling control.

Novel exotic alleles of EARLY FLOWERING 3 determine plant development in barley

EARLY FLOWERING 3 (ELF3) is an important regulator of various physiological and developmental processes and hence may serve to improve plant adaptation which will be substantial for future plant breeding. To expand the limited knowledge on barley ELF3 in determining agronomic traits, we conducted field studies with heterogeneous inbred families (HIFs) derived from selected lines of the wild barley nested association mapping population HEB-25. During two growing seasons, phenotypes of nearly isogenic HIF sister lines, segregating for exotic and cultivated alleles at the ELF3 locus, were compared for ten developmental and yield-related traits. We determine novel exotic ELF3 alleles and show that HIF lines, carrying the exotic ELF3 allele, accelerated plant development compared to the cultivated ELF3 allele, depending on the genetic background. Remarkably, the most extreme effects on phenology could be attributed to one exotic ELF3 allele differing from the cultivated Barke ELF3 allele in only one SNP. This SNP causes an amino acid substitution (W669G), which predictively has an impact on the protein structure of ELF3, thereby possibly affecting phase separation behaviour and nano-compartment formation of ELF3 and, potentially, also affecting its local cellular interactions causing significant trait differences between HIF sister lines.

Seawater Reverse Osmosis Performance Decline Caused by Short-Term Elevated Feed Water Temperature

The shortage of fresh water resources has made the desalination of seawater a widely adopted technology. Seawater reverse osmosis (SWRO) is the most commonly used method for desalination. The SWRO process is energy-intensive, and most of the energy in SWRO is spent on pressurizing the seawater to overcome the osmotic barrier for producing fresh water. The pressure needed depends on the salinity of the seawater, its temperature, and the membrane surface properties. Membrane compaction occurs in SWRO due to hydraulic pressure application for long-term operations and operating temperature fluctuations due to seasonal seawater changes. This study investigates the effects of short-term feed water temperature increase on the SWRO process in a full-scale pilot with pretreatment and a SWRO installation consisting of a pressure vessel which contains seven industrial-scale 8" diameter spiral wound membrane elements. A SWRO feed water temperature of 40 °C, even for a short period of 7 days, caused a permanent performance decline illustrated by a strong specific energy consumption increase of 7.5%. This study highlights the need for membrane manufacturer data that account for the water temperature effect on membrane performance over a broad temperature range. There is a need to develop new membranes that are more tolerant to temperature fluctuations.

Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)

BACKGROUND

Automated analysis of large image data is highly demanded in high-throughput plant phenotyping. Due to large variability in optical plant appearance and experimental setups, advanced machine and deep learning techniques are required for automated detection and segmentation of plant structures in complex optical scenes.

METHODS

Here, we present a GUI-based software tool (DeepShoot) for efficient, fully automated segmentation and quantitative analysis of greenhouse-grown shoots which is based on pre-trained U-net deep learning models of arabidopsis, maize, and wheat plant appearance in different rotational side- and top-views.

RESULTS

Our experimental results show that the developed algorithmic framework performs automated segmentation of side- and top-view images of different shoots acquired at different developmental stages using different phenotyping facilities with an average accuracy of more than 90% and outperforms shallow as well as conventional and encoder backbone networks in cross-validation tests with respect to both precision and performance time.

CONCLUSION

The DeepShoot tool presented in this study provides an efficient solution for automated segmentation and phenotypic characterization of greenhouse-grown plant shoots suitable also for end-users without advanced IT skills. Primarily trained on images of three selected plants, this tool can be applied to images of other plant species exhibiting similar optical properties.

Initial Description of the Genome of Aeluropus littoralis, a Halophile Grass

The use of wild plant species or their halophytic relatives has been considered in plant breeding programs to improve salt and drought tolerance in crop plants. Aeluropus littoralis serves as halophyte model for identification and isolation of novel stress adaptation genes. A. littoralis, a perennial monocot grass, grows in damp or arid areas, often salt-impregnated places and wasteland in cultivated areas, can survive periodically high water salinity, and tolerate high salt concentrations in the soil up to 1,100 mM sodium chloride. Therefore, it serves as valuable genetic resource to understand molecular mechanisms of stress-responses in monocots. The knowledge can potentially be used for improving tolerance to abiotic stresses in economically important crops. Several morphological, anatomical, ecological, and physiological traits of A. littoralis have been investigated so far. After watering with salt water the grass is able to excrete salt via its salt glands. Meanwhile, a number of ESTs (expressed sequence tag), genes and promoters induced by the salt and drought stresses were isolated, sequenced and annotated at a molecular level. Transfer of stress related genes to other species resulted in enhanced stress resistance. Here we describe the genome sequence and structure of A. littoralis analyzed by whole genome sequencing and histological analysis. The chromosome number was determined to be 20 (2n = 2x = 20). The genome size was calculated to be 354 Mb. This genomic information provided here, will support the functional investigation and application of novel genes improving salt stress resistance in crop plants. The utility of the sequence information is exemplified by the analysis of the DREB-transcription factor family.

Immunogenicity of standard and extended dosing intervals of BNT162b2 mRNA vaccine

Extension of the interval between vaccine doses for the BNT162b2 mRNA vaccine was introduced in the United Kingdom to accelerate population coverage with a single dose. At this time, trial data were lacking, and we addressed this in a study of United Kingdom healthcare workers. The first vaccine dose induced protection from infection from the circulating alpha (B.1.1.7) variant over several weeks. In a substudy of 589 individuals, we show that this single dose induces severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralizing antibody (NAb) responses and a sustained B and T cell response to the spike protein. NAb levels were higher after the extended dosing interval (6-14 weeks) compared with the conventional 3- to 4-week regimen, accompanied by enrichment of CD4+ T cells expressing interleukin-2 (IL-2). Prior SARS-CoV-2 infection amplified and accelerated the response. These data on dynamic cellular and humoral responses indicate that extension of the dosing interval is an effective immunogenic protocol.

Symptomatic relief from at-home use of activated Bifidobacterium infantis EVC001 probiotic in infants: results from a consumer survey on the effects on diaper rash, colic symptoms, and sleep

The gut microbiome during infancy is directly involved in the digestion of human milk, development of the immune system, and long-term health outcomes. Gut dysbiosis in early life has been linked to multiple short-term ailments, from diaper dermatitis and poor stooling habits, to poor sleep and fussiness, with mixed results in the scientific literature on the efficacy of probiotics for symptom resolution. Despite the growing interest in probiotics for consumer use, observed symptomatic relief is rarely documented. This study aims to evaluate observed symptomatic relief from at-home use of activated Bifidobacterium infantis EVC001 in infants. Consumer feedback was collected over a 2-year period via a 30-day post-purchase online survey of B. infantis EVC001 (Evivo^®) customers. Outcome measures included observed changes in diaper rash, symptoms of colic, and sleep behaviours in infants fed B. infantis EVC001. A total of 1,621 respondents completed the survey. Before purchasing B. infantis EVC001, the majority of respondents visited the product website, researched infant probiotics online, or consulted with their doctor or other healthcare professional. Of the participants whose infants had ever experienced diaper rash, 72% (n=448) reported improvements, and 57% of those reported complete resolution of this problem. Of those who responded to questions about gassiness/fussiness, naptime sleep, and night-time sleep behaviours, 63% (n=984), 33% (n=520), and 52% (n=806) reported resolution or improvements, respectively. Although clinical data regarding probiotic use are often inconclusive for symptom resolution, home use of B. infantis EVC001 in infants improved diaper rash, gassiness/fussiness, and sleep quality within the first week of use in a significant number of respondents who engaged in a voluntary post-purchase survey. These outcomes may be a result of the unique genetic capacity of B. infantis EVC001 to colonise the infant gut highlighting the importance of strain selection in evaluating the effects of probiotic products.

Clinical Autopsy of a Reverse Osmosis Membrane Module

The desalination of seawater using reverse osmosis membranes is an attractive solution to global freshwater scarcity. However, membrane performance is reduced by (bio)fouling. Membrane autopsies are essential for identifying the type of fouling material, and applying corrective measures to minimize membrane fouling. Information from full-scale membrane autopsies guiding improved plant operations is scant in the formal literature. In this case-study, a reverse osmosis membrane from a full-scale seawater desalination plant with a feed channel pressure drop increase of about 218% over the pressure vessel was autopsied. The simultaneous determination of microbial cells, ATP, and total organic carbon (TOC) abundances per membrane area allowed estimating the contributions of biofouling and organic fouling. The abundance of microbial cells determined by flow cytometry (up to 7 × 108 cells/cm2), and ATP (up to 21,000 pg/cm2) as well as TOC (up to 98 μg/cm2) were homogeneously distributed on the membrane. Inorganic fouling was also measured, and followed a similar coverage distribution to that of biofouling. Iron (∼150 μg/cm2, estimated by ICP-MS) was the main inorganic foulant. ATR-FTIR spectra supported that membrane fouling was both organic/biological and inorganic. High-resolution SEM-EDS imaging of cross-sectioned membranes allowed assessing the thickness of the fouling layer (up to 20 μm) and its elemental composition. Imaging results further supported the results of homogeneous fouling coverage. Moreover, imaging revealed both zones with and without compression of the polysulfone membrane layer, suggesting that the stress due to operating pressure was heterogeneous. The procedure for this membrane autopsy provided a reasonable overview of the diverse contributors of fouling and might be a starting point to building a consensus autopsy protocol. Next, it would be valuable to build a RO membrane autopsy database, which can be used as a guidance and diagnostic tool to improve the management and operation of RO desalination plants.

Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image-Based Phenotyping Under Controlled Conditions

Changes in climate are likely to have a negative impact on water availability and soil fertility in many maize-growing agricultural areas. The development of high-throughput phenotyping platforms provides a new prospect for dissecting the dynamic complex plant traits such as abiotic stress tolerance into simple components. The growth phenotypes of 20 maize (Zea mays L.) inbred lines were monitored in a non-invasive way under control, nitrogen, and water limitation as well as under combined nitrogen and water stress using an automated phenotyping system in greenhouse conditions. Thirteen biomass-related and morphophysiological traits were extracted from RGB images acquired at 33 time points covering developmental stages from leaf count 5 at the first imaging date to leaf count 10-13 at the final harvest. For these traits, genetic differences were identified and dynamic developmental trends during different maize growth stages were analyzed. The difference between control and water stress was detectable 3-10 days after the beginning of stress depending on the genotype, while the effect of limited nitrogen supply only induced subtle phenotypic effects. Phenotypic traits showed different response dynamics as well as multiple and changing interaction patterns with stress progression. The estimated biovolume, leaf area index, and color ratios were found to be stress-responsive at different stages of drought stress progression and thereby represent valuable reference indicators in the selection of drought-adaptive genotypes. Furthermore, genotypes could be grouped according to two typical growth dynamic patterns in water stress treatments by c-means clustering analysis. Inbred lines with high drought adaptability across time and development were identified and could serve as a basis for designing novel genotypes with desired, stage-specific growth phenotypes under water stress through pyramiding. Drought recovery potential may play an equal role as drought tolerance in plant drought adaptation.

P011 Sharing is caring: a regional service development project exploring secondary immunosuppression in children

Background/Aims 

The range of approved immunosuppressive and immunomodulatory (IM) agents has grown considerably with an increasing list of indications across paediatric specialties. At present, there is limited evidence supporting best practice for prescribing and monitoring of IM agents in children and young people (CYP). We present a staged service development project exploring cross-specialty prescribing and monitoring of IM agents at a tertiary children’s hospital (Great North Children’s Hospital, GNCH) and data sharing with local hospitals across northeast England.

Methods 

In Phase 1, we searched pharmacy databases and surveyed specialty teams in GNCH to identify clinicians regularly prescribing IM agents to CYP over a twelve-month period. Phase 2 was a cross-specialty retrospective case-notes review of prescribing, monitoring and infection surveillance in a representative sample of CYP on IM agents. Phase 3 explored information sharing with six other hospitals in the region and acute presentations to these sites involving CYP on IM agents.

Results 

Phase 1 identified 9 paediatric and 2 adult specialties prescribing IM agents to 416 CYP. 32 discrete IM therapies were prescribed with significant between-specialty overlap in drugs prescribed but a wide range of prescribing and monitoring practices. Phase 2 assessed 77 CYP on IM agents in detail - 57% were prescribed >1 IM agent, 100% had FBC measured at least once (range once only to weekly), 18% developed lymphopenia at least once and 40% were prescribed prophylactic antibiotics. Previous varicella exposure had been assessed in 70%. Phase 3 data are summarised in Table 1. P011 Table 1:Information sharing and acute presentations to regional hospitals local to immunosuppressed patientsTotal number of patients141Mean age in years (range)11 (2 - 17)NDiagnosisJIA without uveitis108JIA with uveitis9Uveitis alone8Systemic JIA4Period fever4Behçet’s disease2Juvenile dermatomyositis2Scleroderma1Juvenile systemic lupus erythematosus1Mixed connective tissue disease1Granulomatosis with polyangiitis1Immunosuppressive or immunomodulatory agent usedAdalimumab65Methotrexate42Tocilizumab22Mycophenolate mofetil10Etanercept10Infliximab5Sulfasalazine5Prednisolone4Abatacept4Leflunomide4Canakinumab2Colchicine2Anakinra2Rituximab2Cyclophosphamide1Number of immunosuppressive or immunomodulatory agents per patient3 agents52 agents441 agent92Number of acute presentations by diagnosis or presenting complaint (n = 19)Fever4Chickenpox4Viral upper respiratory tract infection2Joint pain2Abdominal pain2Rash2Eye infection1Tonsilitis1Wheeze1Yes (%)No (%)Named local consultant (n = 129)3763Correct diagnosis recorded locally (n = 130)8020Correct immunosuppressive or immunomodulatory agent recorded locally (n = 130)5050Open access for febrile illness (n = 116)4159Reviewed in past 2 years for acute illness (n = 109)1783Note: presented numbers for immunosuppressive or immunomodulatory agents are not mutually exclusive. JIA, juvenile idiopathic arthritis

Conclusion 

IM agents are central to modern paediatric clinical care across a wide range of diseases. This staged project identified significant variation in IM prescribing and monitoring practice between specialties at GNCH. Communication between specialty and local teams is inadequate. Particular areas of concern include limited diagnostic, blood monitoring and medication information sharing and limited local information governing management of intercurrent illness and vaccination. Although different disease processes can necessitate different advice and prescribing practices, sharing examples of good practice will minimise unnecessary variation. We propose the development of a regional immunosuppression working group to improve quality and safety across our region.

Disclosure 

J.L. Bennett: None. C. Tsilifis: None. A. Flinn: None. T. Altmann: None. N. Jansen: None. H. Tumelty: None. K. Aitken: None. S. Bhopal: None. E. Harrison: None. S. Ravenscroft: None. E. Sen: None. E. Williams: None. T. Flood: None. S. Sampath: None. A. Battersby: None. F. McErlane: None.

Multi-omics-based prediction of hybrid performance in canola

Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F₁ hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

Temporal dynamics of QTL effects on vegetative growth in Arabidopsis thaliana

We assessed early vegetative growth in a population of 382 accessions of Arabidopsis thaliana using automated non-invasive high-throughput phenotyping. All accessions were imaged daily from 7 d to 18 d after sowing in three independent experiments and genotyped using the Affymetrix 250k SNP array. Projected leaf area (PLA) was derived from image analysis and used to calculate relative growth rates (RGRs). In addition, initial seed size was determined. The generated datasets were used jointly for a genome-wide association study that identified 238 marker-trait associations (MTAs) individually explaining up to 8% of the total phenotypic variation. Co-localization of MTAs occurred at 33 genomic positions. At 21 of these positions, sequential co-localization of MTAs for 2-9 consecutive days was observed. The detected MTAs for PLA and RGR could be grouped according to their temporal expression patterns, emphasizing that temporal variation of MTA action can be observed even during the vegetative growth phase, a period of continuous formation and enlargement of seemingly similar rosette leaves. This indicates that causal genes may be differentially expressed in successive periods. Analyses of the temporal dynamics of biological processes are needed to gain important insight into the molecular mechanisms of growth-controlling processes in plants.

A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping

BACKGROUND

Automated segmentation of large amount of image data is one of the major bottlenecks in high-throughput plant phenotyping. Dynamic optical appearance of developing plants, inhomogeneous scene illumination, shadows and reflections in plant and background regions complicate automated segmentation of unimodal plant images. To overcome the problem of ambiguous color information in unimodal data, images of different modalities can be combined to a virtual multispectral cube. However, due to motion artefacts caused by the relocation of plants between photochambers the alignment of multimodal images is often compromised by blurring artifacts.

RESULTS

Here, we present an approach to automated segmentation of greenhouse plant images which is based on co-registration of fluorescence (FLU) and of visible light (VIS) camera images followed by subsequent separation of plant and marginal background regions using different species- and camera view-tailored classification models. Our experimental results including a direct comparison with manually segmented ground truth data show that images of different plant types acquired at different developmental stages from different camera views can be automatically segmented with the average accuracy of 93 % ( S D = 5 % ) using our two-step registration-classification approach.

CONCLUSION

Automated segmentation of arbitrary greenhouse images exhibiting highly variable optical plant and background appearance represents a challenging task to data classification techniques that rely on detection of invariances. To overcome the limitation of unimodal image analysis, a two-step registration-classification approach to combined analysis of fluorescent and visible light images was developed. Our experimental results show that this algorithmic approach enables accurate segmentation of different FLU/VIS plant images suitable for application in fully automated high-throughput manner.

Image Phenotyping of Spring Barley (Hordeum vulgare L.) RIL Population Under Drought: Selection of Traits and Biological Interpretation

Image-based phenotyping is a non-invasive method that permits the dynamic evaluation of plant features during growth, which is especially important for understanding plant adaptation and temporal dynamics of responses to environmental cues such as water deficit or drought. The aim of the present study was to use high-throughput imaging in order to assess the variation and dynamics of growth and development during drought in a spring barley population and to investigate associations between traits measured in time and yield-related traits measured after harvesting. Plant material covered recombinant inbred line population derived from a cross between European and Syrian cultivars. After placing the plants on the platform (28th day after sowing), drought stress was applied for 2 weeks. Top and side cameras were used to capture images daily that covered the visible range of the light spectrum, fluorescence signals, and the near infrared spectrum. The image processing provided 376 traits that were subjected to analysis. After 32 days of image phenotyping, the plants were cultivated in the greenhouse under optimal watering conditions until ripening, when several architecture and yield-related traits were measured. The applied data analysis approach, based on the clustering of image-derived traits into groups according to time profiles of statistical and genetic parameters, permitted to select traits representative for inference from the experiment. In particular, drought effects for 27 traits related to convex hull geometry, texture, proportion of brown pixels and chlorophyll intensity were found to be highly correlated with drought effects for spike traits and thousand grain weight.

Age-dependent loss of seed viability is associated with increased lipid oxidation and hydrolysis

The accumulation of reactive oxygen species has been associated with a loss of seed viability. Therefore, we have investigated the germination ability of a range of seed stocks, including two wheat collections and one barley collection that had been dry-aged for 5-40 years. Metabolite profiling analysis revealed that the accumulation of glycerol was negatively correlated with the ability to germinate in all seed sets. Furthermore, lipid degradation products such as glycerol phosphates and galactose were accumulated in some seed sets. A quantitative analysis of nonoxidized and oxidized lipids was performed in the wheat seed set that showed the greatest variation in germination. This analysis revealed that the levels of fully acylated and nonoxidized storage lipids like triacylglycerols and structural lipids like phospho- and galactolipids were decreasing. Moreover, the abundance of oxidized variants and hydrolysed products such as mono-/diacylglycerols, lysophospholipids, and fatty acids accumulated as viability decreased. The proportional formation of oxidized and nonoxidized fatty acids provides evidence for an enzymatic hydrolysis of specifically oxidized lipids in dry seeds. The results link reactive oxygen species with lipid oxidation, structural damage, and death in long-term aged seeds.

Biomarkers for grain yield stability in rice under drought stress

Crop yield stability requires an attenuation of the reduction of yield losses caused by environmental stresses such as drought. Using a combination of metabolomics and high-throughput colorimetric assays, we analysed central metabolism and oxidative stress status in the flag leaf of 292 indica rice (Oryza sativa) accessions. Plants were grown in the field and were, at the reproductive stage, exposed to either well-watered or drought conditions to identify the metabolic processes associated with drought-induced grain yield loss. Photorespiration, protein degradation, and nitrogen recycling were the main processes involved in the drought-induced leaf metabolic reprogramming. Molecular markers of drought tolerance and sensitivity in terms of grain yield were identified using a multivariate model based on the values of the metabolites and enzyme activities across the population. The model highlights the central role of the ascorbate-glutathione cycle, particularly dehydroascorbate reductase, in minimizing drought-induced grain yield loss. In contrast, malondialdehyde was an accurate biomarker for grain yield loss, suggesting that drought-induced lipid peroxidation is the major constraint under these conditions. These findings highlight new breeding targets for improved rice grain yield stability under drought.

Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola

A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping. Time-resolved data for early growth-related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome-wide SNP array data provided the framework for genome-wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker-trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time-resolved analyses to effectively unravel the complex and stage-specific contributions of genes affecting growth processes that operate at different developmental phases.

Semi-automated Root Image Analysis (saRIA)

Quantitative characterization of root system architecture and its development is important for the assessment of a complete plant phenotype. To enable high-throughput phenotyping of plant roots efficient solutions for automated image analysis are required. Since plants naturally grow in an opaque soil environment, automated analysis of optically heterogeneous and noisy soil-root images represents a challenging task. Here, we present a user-friendly GUI-based tool for semi-automated analysis of soil-root images which allows to perform an efficient image segmentation using a combination of adaptive thresholding and morphological filtering and to derive various quantitative descriptors of the root system architecture including total length, local width, projection area, volume, spatial distribution and orientation. The results of our semi-automated root image segmentation are in good conformity with the reference ground-truth data (mean dice coefficient = 0.82) compared to IJ_Rhizo and GiAroots. Root biomass values calculated with our tool within a few seconds show a high correlation (Pearson coefficient = 0.8) with the results obtained using conventional, pure manual segmentation approaches. Equipped with a number of adjustable parameters and optional correction tools our software is capable of significantly accelerating quantitative analysis and phenotyping of soil-, agar- and washed root images.

Combining next-generation sequencing and progeny testing for rapid identification of induced recessive and dominant mutations in maize M2 individuals

Molecular identification of mutant alleles responsible for certain phenotypic alterations is a central goal of genetic analyses. In this study we describe a rapid procedure suitable for the identification of induced recessive and dominant mutations applied to two Zea mays mutants expressing a dwarf and a pale green phenotype, respectively, which were obtained through pollen ethyl methanesulfonate (EMS) mutagenesis. First, without prior backcrossing, induced mutations (single nucleotide polymorphisms, SNPs) segregating in a (M₂ ) family derived from a heterozygous (M₁ ) parent were identified using whole-genome shotgun (WGS) sequencing of a small number of (M₂ ) individuals with mutant and wild-type phenotypes. Second, the state of zygosity of the mutation causing the phenotype was determined for each sequenced individual by phenotypic segregation analysis of the self-pollinated (M₃ ) offspring. Finally, we filtered for segregating EMS-induced SNPs whose state of zygosity matched the determined state of zygosity of the mutant locus in each sequenced (M₂ ) individuals. Through this procedure, combining sequencing of individuals and Mendelian inheritance, three and four SNPs in linkage passed our zygosity filter for the homozygous dwarf and heterozygous pale green mutation, respectively. The dwarf mutation was found to be allelic to the an1 locus and caused by an insertion in the largest exon of the AN1 gene. The pale green mutation affected the nuclear W2 gene and was caused by a non-synonymous amino acid exchange in encoded chloroplast DNA polymerase with a predicted deleterious effect. This coincided with lower cpDNA levels in pale green plants.

Genetic diversity for nitrogen use efficiency in Arabidopsis thaliana

The plasticity of plant growth response to differing nitrate availability renders the identification of biomarkers difficult, but allows access to genetic factors as tools to modulate root systems to a wide range of soil conditions. Nitrogen availability is a major determinant of crop yield. While the application of fertiliser substantially increases the yield on poor soils, it also causes nitrate pollution of water resources and high costs for farmers. Increasing nitrogen use efficiency in crop plants is a necessary step to implement low-input agricultural systems. We exploited the genetic diversity present in the worldwide Arabidopsis thaliana population to study adaptive growth patterns and changes in gene expression associated with chronic low nitrate stress, to identify biomarkers associated with good plant performance under low nitrate availability. Arabidopsis accessions were grown on agar plates with limited and sufficient supply of nitrate to measure root system architecture as well as shoot and root fresh weight. Differential gene expression was determined using Affymetrix ATH1 arrays. We show that the response to differing nitrate availability is highly variable in Arabidopsis accessions. Analyses of vegetative shoot growth and root system architecture identified accession-specific reaction modes to cope with limited nitrate availability. Transcription and epigenetic factors were identified as important players in the adaption to limited nitrogen in a global gene expression analysis. Five nitrate-responsive genes emerged as possible biomarkers for NUE in Arabidopsis. The plasticity of plant growth in response to differing nitrate availability in the substrate renders the identification of morphological and molecular features as biomarkers difficult, but at the same time allows access to a multitude of genetic factors which can be used as tools to modulate and adjust root systems to a wide range of soil conditions.

Image-Derived Traits Related to Mid-Season Growth Performance of Maize Under Nitrogen and Water Stress

Phenotypic measurements under controlled cultivation conditions are essential to gain a mechanistic understanding of plant responses to environmental impacts and thus for knowledge-based improvement of their performance under natural field conditions. Twenty maize inbred lines (ILs) were phenotyped in response to two levels of water and nitrogen supply (control and stress) and combined nitrogen and water deficit. Over a course of 5 weeks (from about 4-leaf stage to the beginning of the reproductive stage), maize phenology and growth were monitored by using a high-throughput phenotyping platform for daily acquisition of images in different spectral ranges. The focus of the present study is on the measurements taken at the time of maximum water stress (for traits that reflect plant physiological properties) and at the end of the experiment (for traits that reflect plant architectural and biomass-related traits). Twenty-five phenotypic traits extracted from the digital image data that support biological interpretation of plant growth were selected for their predictive value for mid-season shoot biomass accumulation. Measured fresh and dry weights after harvest were used to calculate various indices (water-use efficiency, physiological nitrogen-use efficiency, specific plant weight) and to establish correlations with image-derived phenotypic features. Also, score indices based on dry weight were used to identify contrasting ILs in terms of productivity and tolerance to stress, and their means for image-derived and manually measured traits were compared. Color-related traits appear to be indicative of plant performance and photosystem II operating efficiency might be an importance physiological parameter of biomass accumulation, particularly under severe stress conditions. Also, genotypes showing greater leaf area may be better adapted to abiotic stress conditions.

Natural variation of BSK3 tunes brassinosteroid signaling to regulate root foraging under low nitrogen

Developmental plasticity of root system architecture is crucial for plant performance in nutrient-poor soils. Roots of plants grown under mild nitrogen (N) deficiency show a foraging response characterized by increased root length but mechanisms underlying this developmental plasticity are still elusive. By employing natural variation in Arabidopsis accessions, we show that the brassinosteroid (BR) signaling kinase BSK3 modulates root elongation under mild N deficiency. In particular, a proline to leucine substitution in the predicted kinase domain of BSK3 enhances BR sensitivity and signaling to increase the extent of root elongation. We further show that low N specifically upregulates transcript levels of the BR co-receptor BAK1 to activate BR signaling and stimulate root elongation. Altogether, our results uncover a role of BR signaling in root elongation under low N. The BSK3 alleles identified here provide targets for improving root growth of crops growing under limited N conditions.

Plant roots elongate under mild nitrogen deficiency as part of a foraging response that facilitates nutrient uptake. Here the authors show that natural variation in this response among Arabidopsis accessions depends on the brassinosteroid (BR) signaling kinase BSK3, which can enhance BR sensitivity and root growth.

Comparison and extension of three methods for automated registration of multimodal plant images

With the introduction of high-throughput multisensory imaging platforms, the automatization of multimodal image analysis has become the focus of quantitative plant research. Due to a number of natural and technical reasons (e.g., inhomogeneous scene illumination, shadows, and reflections), unsupervised identification of relevant plant structures (i.e., image segmentation) represents a nontrivial task that often requires extensive human-machine interaction. Registration of multimodal plant images enables the automatized segmentation of 'difficult' image modalities such as visible light or near-infrared images using the segmentation results of image modalities that exhibit higher contrast between plant and background regions (such as fluorescent images). Furthermore, registration of different image modalities is essential for assessment of a consistent multiparametric plant phenotype, where, for example, chlorophyll and water content as well as disease- and/or stress-related pigmentation can simultaneously be studied at a local scale. To automatically register thousands of images, efficient algorithmic solutions for the unsupervised alignment of two structurally similar but, in general, nonidentical images are required. For establishment of image correspondences, different algorithmic approaches based on different image features have been proposed. The particularity of plant image analysis consists, however, of a large variability of shapes and colors of different plants measured at different developmental stages from different views. While adult plant shoots typically have a unique structure, young shoots may have a nonspecific shape that can often be hardly distinguished from the background structures. Consequently, it is not clear a priori what image features and registration techniques are suitable for the alignment of various multimodal plant images. Furthermore, dynamically measured plants may exhibit nonuniform movements that require application of nonrigid registration techniques. Here, we investigate three common techniques for registration of visible light and fluorescence images that rely on finding correspondences between (i) feature-points, (ii) frequency domain features, and (iii) image intensity information. The performance of registration methods is validated in terms of robustness and accuracy measured by a direct comparison with manually segmented images of different plants. Our experimental results show that all three techniques are sensitive to structural image distortions and require additional preprocessing steps including structural enhancement and characteristic scale selection. To overcome the limitations of conventional approaches, we develop an iterative algorithmic scheme, which allows it to perform both rigid and slightly nonrigid registration of high-throughput plant images in a fully automated manner.

Automated Alignment of Multi-Modal Plant Images Using Integrative Phase Correlation Approach

Modern facilities for high-throughput phenotyping provide plant scientists with a large amount of multi-modal image data. Combination of different image modalities is advantageous for image segmentation, quantitative trait derivation, and assessment of a more accurate and extended plant phenotype. However, visible light (VIS), fluorescence (FLU), and near-infrared (NIR) images taken with different cameras from different view points in different spatial resolutions exhibit not only relative geometrical transformations but also considerable structural differences that hamper a straightforward alignment and combined analysis of multi-modal image data. Conventional techniques of image registration are predominantly tailored to detection of relative geometrical transformations between two otherwise identical images, and become less accurate when applied to partially similar optical scenes. Here, we focus on a relatively new technical problem of FLU/VIS plant image registration. We present a framework for automated alignment of FLU/VIS plant images which is based on extension of the phase correlation (PC) approach - a frequency domain technique for image alignment, which relies on detection of a phase shift between two Fourier-space transforms. Primarily tailored to detection of affine image transformations between two structurally identical images, PC is known to be sensitive to structural image distortions. We investigate effects of image preprocessing and scaling on accuracy of image registration and suggest an integrative algorithmic scheme which allows to overcome shortcomings of conventional single-step PC by application to non-identical multi-modal images. Our experimental tests with FLU/VIS images of different plant species taken on different phenotyping facilities at different developmental stages, including difficult cases such as small plant shoots of non-specific shape and non-uniformly moving leaves, demonstrate improved performance of our extended PC approach within the scope of high-throughput plant phenotyping.

Identification of Rapeseed (Brassica napus) Cultivars With a High Tolerance to Boron-Deficient Conditions

Boron (B) is an essential micronutrient for seed plants. Information on B-efficiency mechanisms and B-efficient crop and model plant genotypes is very scarce. Studies evaluating the basis and consequences of B-deficiency and B-efficiency are limited by the facts that B occurs as a trace contaminant essentially everywhere, its bioavailability is difficult to control and soil-based B-deficiency growth systems allowing a high-throughput screening of plant populations have hitherto been lacking. The crop plant Brassica napus shows a very high sensitivity toward B-deficient conditions. To reduce B-deficiency-caused yield losses in a sustainable manner, the identification of B-efficient B. napus genotypes is indispensable. We developed a soil substrate-based cultivation system which is suitable to study plant growth in automated high-throughput phenotyping facilities under defined and repeatable soil B conditions. In a comprehensive screening, using this system with soil B concentrations below 0.1 mg B (kg soil)-1, we identified three highly B-deficiency tolerant B. napus cultivars (CR2267, CR2280, and CR2285) among a genetically diverse collection comprising 590 accessions from all over the world. The B-efficiency classification of cultivars was based on a detailed assessment of various physical and high-throughput imaging-based shoot and root growth parameters in soil substrate or in in vitro conditions, respectively. We identified cultivar-specific patterns of B-deficiency-responsive growth dynamics. Elemental analysis revealed striking differences only in B contents between contrasting genotypes when grown under B-deficient but not under standard conditions. Results indicate that B-deficiency tolerant cultivars can grow with a very limited amount of B which is clearly below previously described critical B-tissue concentration values. These results suggest a higher B utilization efficiency of CR2267, CR2280, and CR2285 which would represent a unique trait among so far identified B-efficient B. napus cultivars which are characterized by a higher B-uptake capacity. Testing various other nutrient deficiency treatments, we demonstrated that the tolerance is specific for B-deficient conditions and is not conferred by a general growth vigor at the seedling stage. The identified B-deficiency tolerant cultivars will serve as genetic and physiological "tools" to further understand the mechanisms regulating the B nutritional status in rapeseed and to develop B-efficient elite genotypes.

Phenotyping roots in darkness: disturbance-free root imaging with near infrared illumination

Root systems architecture (RSA) and size properties are essential determinants of plant performance and need to be assessed in high-throughput plant phenotyping platforms. Thus, we tested a concept that involves near-infrared (NIR) imaging of roots growing along surfaces of transparent culture vessels using special long pass filters to block their exposure to visible light. Two setups were used to monitor growth of Arabidopsis, rapeseed, barley and maize roots upon exposure to white light, filter-transmitted radiation or darkness: root growth direction was analysed (1) through short-term cultivation on agar plates, and (2) using soil-filled transparent pots to monitor long-term responses. White light-triggered phototropic responses were detected for Arabidopsis in setup 1, and for rapeseed, barley and maize roots in setups 1 and 2, whereas light effects could be avoided by use of the NIR filter thus confirming its suitability to mimic darkness. NIR image-derived 'root volume' values correlated well with root dry weight. The root system fractions visible at the different pot sides and in different zones revealed species- and genotype-dependent variation of spatial root distribution and other RSA traits. Following this validated concept, root imaging setups may be integrated into shoot phenotyping facilities in order to enable root system analysis in the context of whole-plant performance investigations.

Exploring traditional aus-type rice for metabolites conferring drought tolerance

BACKGROUND

Traditional varieties and landraces belonging to the aus-type group of rice (Oryza sativa L.) are known to be highly tolerant to environmental stresses, such as drought and heat, and are therefore recognized as a valuable genetic resource for crop improvement. Using two aus-type (Dular, N22) and two drought intolerant irrigated varieties (IR64, IR74) an untargeted metabolomics analysis was conducted to identify drought-responsive metabolites associated with tolerance.

RESULTS

The superior drought tolerance of Dular and N22 compared with the irrigated varieties was confirmed by phenotyping plants grown to maturity after imposing severe drought stress in a dry-down treatment. Dular and N22 did not show a significant reduction in grain yield compared to well-watered control plants, whereas the intolerant varieties showed a significant reduction in both, total spikelet number and grain yield. The metabolomics analysis was conducted with shoot and root samples of plants at the tillering stage at the end of the dry-down treatment. The data revealed an overall higher accumulation of N-rich metabolites (amino acids and nucleotide-related metabolites allantoin and uridine) in shoots of the tolerant varieties. In roots, the aus-type varieties were characterised by a higher reduction of metabolites representative of glycolysis and the TCA cycle, such as malate, glyceric acid and glyceric acid-3-phosphate. On the other hand, the oligosaccharide raffinose showed a higher fold increase in both, shoots and roots of the sensitive genotypes. The data further showed that, for certain drought-responsive metabolites, differences between the contrasting rice varieties were already evident under well-watered control conditions.

CONCLUSIONS

The drought tolerance-related metabolites identified in the aus-type varieties provide a valuable set of protective compounds and an entry point for assessing genetic diversity in the underlying pathways for developing drought tolerant rice and other crops.

Structure Annotation and Quantification of Wheat Seed Oxidized Lipids by High-Resolution LC-MS/MS

Lipid oxidation is a process ubiquitous in life, but the direct and comprehensive analysis of oxidized lipids has been limited by available analytical methods. We applied high-resolution liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS/MS) to quantify oxidized lipids (glycerides, fatty acids, phospholipids, lysophospholipids, and galactolipids) and implemented a platform-independent high-throughput-amenable analysis pipeline for the high-confidence annotation and acyl composition analysis of oxidized lipids. Lipid contents of 90 different naturally aged wheat (Triticum aestivum) seed stocks were quantified in an untargeted high-resolution LC-MS experiment, resulting in 18,556 quantitative mass-to-charge ratio features. In a posthoc liquid chromatography-tandem mass spectrometry experiment, high-resolution MS/MS spectra (5 mD accuracy) were recorded for 8,957 out of 12,080 putatively monoisotopic features of the LC-MS data set. A total of 353 nonoxidized and 559 oxidized lipids with up to four additional oxygen atoms were annotated based on the accurate mass recordings (1.5 ppm tolerance) of the LC-MS data set and filtering procedures. MS/MS spectra available for 828 of these annotations were analyzed by translating experimentally known fragmentation rules of lipids into the fragmentation of oxidized lipids. This led to the identification of 259 nonoxidized and 365 oxidized lipids by both accurate mass and MS/MS spectra and to the determination of acyl compositions for 221 nonoxidized and 295 oxidized lipids. Analysis of 15-year aged wheat seeds revealed increased lipid oxidation and hydrolysis in seeds stored in ambient versus cold conditions.

Establishment of integrated protocols for automated high throughput kinetic chlorophyll fluorescence analyses

BACKGROUND

Automated plant phenotyping has been established as a powerful new tool in studying plant growth, development and response to various types of biotic or abiotic stressors. Respective facilities mainly apply non-invasive imaging based methods, which enable the continuous quantification of the dynamics of plant growth and physiology during developmental progression. However, especially for plants of larger size, integrative, automated and high throughput measurements of complex physiological parameters such as photosystem II efficiency determined through kinetic chlorophyll fluorescence analysis remain a challenge.

RESULTS

We present the technical installations and the establishment of experimental procedures that allow the integrated high throughput imaging of all commonly determined PSII parameters for small and large plants using kinetic chlorophyll fluorescence imaging systems (FluorCam, PSI) integrated into automated phenotyping facilities (Scanalyzer, LemnaTec). Besides determination of the maximum PSII efficiency, we focused on implementation of high throughput amenable protocols recording PSII operating efficiency (ΦPSII). Using the presented setup, this parameter is shown to be reproducibly measured in differently sized plants despite the corresponding variation in distance between plants and light source that caused small differences in incident light intensity. Values of ΦPSII obtained with the automated chlorophyll fluorescence imaging setup correlated very well with conventionally determined data using a spot-measuring chlorophyll fluorometer. The established high throughput operating protocols enable the screening of up to 1080 small and 184 large plants per hour, respectively. The application of the implemented high throughput protocols is demonstrated in screening experiments performed with large Arabidopsis and maize populations assessing natural variation in PSII efficiency.

CONCLUSIONS

The incorporation of imaging systems suitable for kinetic chlorophyll fluorescence analysis leads to a substantial extension of the feature spectrum to be assessed in the presented high throughput automated plant phenotyping platforms, thus enabling the simultaneous assessment of plant architectural and biomass-related traits and their relations to physiological features such as PSII operating efficiency. The implemented high throughput protocols are applicable to a broad spectrum of model and crop plants of different sizes (up to 1.80 m height) and architectures. The deeper understanding of the relation of plant architecture, biomass formation and photosynthetic efficiency has a great potential with respect to crop and yield improvement strategies.

QTL analysis of the developmental response to L-glutamate in Arabidopsis roots and its genotype-by-environment interactions

Primary root growth in Arabidopsis and a number of other species has previously been shown to be remarkably sensitive to the presence of external glutamate, with glutamate signalling eliciting major changes in root architecture. Using two recombinant inbred lines from reciprocal crosses between Arabidopsis accessions C24 and Col-0, we have identified one large-effect quantitative trait locus (QTL), GluS1, and two minor QTLs, GluS2 and GluS3, which together accounted for 41% of the phenotypic variance in glutamate sensitivity. The presence of the GluS1 locus on chromosome 3 was confirmed using a set of C24/Col-0 isogenic lines. GluS1 was mapped to an interval between genes At3g44830-At3g46880. When QTL mapping was repeated under a range of environmental conditions, including temperature, shading and nitrate supply, a strong genotype-by-environment interaction in the controls for the glutamate response was identified. Major differences in the loci controlling this trait were found under different environmental conditions. Here we present evidence for the existence of loci on chromosomes 1 and 5 epistatically controlling the response of the GluS1 locus to variations in ambient temperature, between 20°C and 26°C. In addition, a locus on the long arm of chromosome 1 was found to play a major role in controlling the ability of external nitrate signals to antagonize the glutamate effect. We conclude that there are multiple loci controlling natural variation in glutamate sensitivity in Arabidopsis roots and that epistatic interactions play an important role in modulating glutamate sensitivity in response to changes in environmental conditions.

Genetic dissection of metabolite variation in Arabidopsis seeds: evidence for mQTL hotspots and a master regulatory locus of seed metabolism

To gain insight into genetic factors controlling seed metabolic composition and its relationship to major seed properties, an Arabidopsis recombinant inbred line (RIL) population, derived from accessions Col-0 and C24, was studied using an MS-based metabolic profiling approach. Relative intensities of 311 polar primary metabolites were used to identify associated genomic loci and to elucidate their interactions by quantitative trait locus (QTL) mapping. A total of 786 metabolic QTLs (mQTLs) were unequally distributed across the genome, forming several hotspots. For the branched-chain amino acid leucine, mQTLs and candidate genes were elucidated in detail. Correlation studies displayed links between metabolite levels, seed protein content, and seed weight. Principal component analysis revealed a clustering of samples, with PC1 mapping to a region on the short arm of chromosome IV. The overlap of this region with mQTL hotspots indicates the presence of a potential master regulatory locus of seed metabolism. As a result of database queries, a series of candidate regulatory genes, including bZIP10, were identified within this region. Depending on the search conditions, metabolic pathway-derived candidate genes for 40-61% of tested mQTLs could be determined, providing an extensive basis for further identification and characterization of hitherto unknown genes causal for natural variation of Arabidopsis seed metabolism.

Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping

Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase-specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non-invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome-wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker-trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non-parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage-specific growth affecting genes to elucidate important processes operating at different developmental phases.

A naturally occurring promoter polymorphism of the Arabidopsis FUM2 gene causes expression variation, and is associated with metabolic and growth traits

Fumarate and malate are known intermediates of the TCA cycle, a mitochondrial metabolic pathway generating NADH for respiration. Arabidopsis thaliana and other Brassicaceae contain an additional cytosolic fumarase (FUM2) that functions in carbon assimilation and nitrogen use. Here, we report the identification of a hitherto unknown FUM2 promoter insertion/deletion (InDel) polymorphism found between the Col-0 and C24 accessions, which also divides a large number of Arabidopsis accessions carrying either the Col-0 or the C24 allele. The polymorphism consists of two stretches of 2.1 and 3.8 kb, which are both absent from the promotor region of Col-0 FUM2. By analysing mutants as well as mapping and natural populations with contrasting FUM2 alleles, the promotor insertion was linked to reduced FUM2 mRNA expression, reduced fumarase activity and reduced fumarate/malate ratio in leaves. In a large population of 174 natural accessions, the polymorphism was also found to be associated with the fumarate/malate ratio, malate and fumarate levels, and with dry weight at 15 days after sowing (DAS). The association with biomass production was confirmed in an even larger (251) accession population for dry weight at 22 DAS. The dominant Col-0 allele that results in increased fumarate/malate ratios and enhanced biomass production is predominantly found in central/eastern European accessions, whereas the C24 type allele is prevalent on the Iberian Peninsula, west of the Rhine and in the British Isles. Our findings support the role of FUM2 in diurnal carbon storage, and point to a growth advantage of accessions carrying the FUM2 Col-0 allele.

Measures for interoperability of phenotypic data: minimum information requirements and formatting

BACKGROUND

Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse.

RESULTS

In this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called "Minimum Information About a Plant Phenotyping Experiment", which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented.

CONCLUSIONS

Acceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.

DNA ligase IV syndrome; a review

DNA ligase IV deficiency is a rare primary immunodeficiency, LIG4 syndrome, often associated with other systemic features. DNA ligase IV is part of the non-homologous end joining mechanism, required to repair DNA double stranded breaks. Ubiquitously expressed, it is required to prevent mutagenesis and apoptosis, which can result from DNA double strand breakage caused by intracellular events such as DNA replication and meiosis or extracellular events including damage by reactive oxygen species and ionising radiation.

Within developing lymphocytes, DNA ligase IV is required to repair programmed DNA double stranded breaks induced during lymphocyte receptor development.

Patients with hypomorphic mutations in LIG4 present with a range of phenotypes, from normal to severe combined immunodeficiency. All, however, manifest sensitivity to ionising radiation. Commonly associated features include primordial growth failure with severe microcephaly and a spectrum of learning difficulties, marrow hypoplasia and a predisposition to lymphoid malignancy. Diagnostic investigations include immunophenotyping, and testing for radiosensitivity. Some patients present with microcephaly as a predominant feature, but seemingly normal immunity. Treatment is mainly supportive, although haematopoietic stem cell transplantation has been used in a few cases.

Association mapping for cold tolerance in two large maize inbred panels

BACKGROUND

Breeding for cold tolerance in maize promises to allow increasing growth area and production in temperate zones. The objective of this research was to conduct genome-wide association analyses (GWAS) in temperate maize inbred lines and to find strategies for pyramiding genes for cold tolerance. Two panels of 306 dent and 292 European flint maize inbred lines were evaluated per se and in testcrosses under cold and control conditions in a growth chamber. We recorded indirect measures for cold tolerance as the traits number of days from sowing to emergence, relative leaf chlorophyll content or quantum efficiency of photosystem II. Association mapping for identifying genes associated to cold tolerance in both panels was based on genotyping with 49,585 genome-wide single nucleotide polymorphism (SNP) markers.

RESULTS

We found 275 significant associations, most of them in the inbreds evaluated per se, in the flint panel, and under control conditions. A few candidate genes coincided between the current research and previous reports. A total of 47 flint inbreds harbored the favorable alleles for six significant quantitative trait loci (QTL) detected for inbreds per se evaluated under cold conditions, four of them had also the favorable alleles for the main QTL detected from the testcrosses. Only four dent inbreds (EZ47, F924, NK807 and PHJ40) harbored the favorable alleles for three main QTL detected from the evaluation of the dent inbreds per se under cold conditions. There were more QTL in the flint panel and most of the QTL were associated with days to emergence and ΦPSII.

CONCLUSIONS

These results open new possibilities to genetically improve cold tolerance either with genome-wide selection or with marker assisted selection.

Phenomic prediction of maize hybrids

Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging.