Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high‐throughput image analysis

Phenotyping large numbers of genotypes still represents the rate‐limiting step in many plant genetic experiments and in breeding. To address this issue, novel automated phenotyping technologies have been developed. We investigated for a core set of barley cultivars if high‐throughput image analysis can help to dissect vegetative biomass accumulation in response to two different watering regimes under semi‐controlled greenhouse conditions. We found that experiments, treatments, genotypes and genotype by environment interaction (G × E) can be characterized at any time point by certain digital traits. Biomass accumulation under control and stress conditions was highly heritable. Growth model‐derived maximum vegetative biomass (K_max), inflection point (I) and regrowth rate (k) were identified as promising candidate traits for genome‐wide association studies. Drought stress symptoms can be visualized, dissected and modelled. Especially the highly heritable regrowth rate, which had the biggest influence on biomass accumulation in stress treatment, seems promising for future studies to improve drought tolerance in different crop species. A proof of concept study revealed potential correlations between digital traits obtained from pot experiments under greenhouse conditions and agronomic traits from field experiments. Overall, non‐invasive, imaging‐based phenotyping platforms under greenhouse conditions offer excellent possibilities for trait discovery, trait development and industrial applications.     

Sequence and transfection of BoLA-DRB3 cDNAs

Bovine MHC (BoLA-) DRB3 alleles encoded by the DH8A, DH22A and DH24A class II haplotypes were cloned from cDNA and characterized by sequence analysis. Comparison with other full-length DRB3 sequences suggested that DRB3 alleles may have evolved through multiple lineages. All three BoLA-DRB3 alleles were shown to express on the surface of transfected cells, and the transfectants were used to define or confirm the class II specificity of a panel of monoclonal antibodies.     

Future impact of integrated high-throughput methylome analyses on human health and disease

A spate of high-powered genome-wide association studies (GWAS) have recently identified numerous single-nucleotide polymor- phisms (SNPs) robustly linked with complex disease. Despite interrogating the majority of common human variation, these SNPs only account for a small proportion of the phenotypic variance, which suggests genetic factors are acting in concert with non-genetic factors. Although environmental measures are logical covariants for genotype-phenotype investigations, another non-genetic intermediary exists: epigenetics. Epigenetics is the analysis of somatically-acquired and, in some cases, transgenerationally inherited epigenetic modifications that regulate gene expression, and offers to bridge the gap between genetics and environment to understand phenotype. The most widely studied epigenetic mark is DNA methylation. Aberrant methylation at gene promoters is strongly implicated in disease etiology, most notably cancer. This review will highlight the importance of DNA methylation as an epigenetic regulator, outline techniques to character- ize the DNA methylome and present the idea of reverse phenotyping, where multiple layers of analysis are integrated at the individual level to create personalized digital phenotypes and, at a phenotype level, to identify novel molecular signatures of disease.     

萝卜种质资源对芜菁花叶病毒的抗性鉴定与评价

病毒病是危害萝卜作物的主要病害之一,芜菁花叶病毒(TuMV)作为萝卜病毒病的主要毒源,给萝卜生产造成严重经济损失。由于缺乏精准鉴定,萝卜育种中缺乏稳定可靠的抗TuMV材料。本研究就国家蔬菜种质资源中期库提供的来自中国25个省份125个县市和其他3个国家的150份代表性萝卜种质资源对TuMV的抗性进行了苗期鉴定和ELISA检测,筛选出23份抗病材料,其中1份表现免疫。这些材料对萝卜抗病毒病基因定位和新品种培育具有重要意义。     

The Mouse Phenome Project

The laboratory mouse is the organism of choice for many studies in biology and medicine. Reliable phenotypic data are essential for the full utility of genotypic information emerging from efforts to sequence human and mouse genomes. The Mouse Phenome Project has been organized to help accomplish this task by establishing a collection of baseline phenotypic data on commonly used and genetically diverse inbred mouse strains and making this information publicly available through a web-accessible database. The Mouse Phenome Database (MPD) is being developed to manage these data and to provide researchers with tools for exploring both raw phenotypic data and comparative summary analyses. The MPD serves as a repository for detailed protocols and raw data. This resource enables investigators to identify appropriate strains for (1) physiological testing, (2) drug discovery, (3) toxicology studies, (4) mutagenesis, (5) modeling human diseases, (6) QTL analyses and identification of new genes and (7) unraveling the influence of environment on genotype.     

High-throughput MS-based protein phenotyping: application to haptoglobin

A high-throughput affinity capture and reduction approach was developed for phenotype and post-translational modification analysis of a complexed globular protein, haptoglobin (Hp), directly from human plasma. Hp was selectively retrieved utilizing anti-Hp antibodies immobilized onto affinity pipette tips, eluted onto a formatted mass spectrometer target for reduction of Hp alpha-chains (Hpalpha1 and Hpalpha2) and subjected to subsequent MALDI-MS analysis. The affinity capture and reduction approach was originally developed from a pre-extraction reduction methodology that was optimized to an affinity capture post-reduction technique for intact Hp alpha-chain variant analysis, phenotype classification and ensuing post-translational variant detection. Three common Hp phenotypes (1-1, 2-1 and 2-2) were assigned according to detection of Hpalpha1 and/or Hpalpha2 reduced intact chain(s) average mass(es). The affinity capture post-reduction approach was scaled for high-throughput Hp alpha-chain phenotype analysis from a normal plasma cohort. The entire sample cohort was successfully analyzed and phenotyped using the developed approach. Additionally, Hp post-translational variants were detected and assigned via accurate MS analyses. The results of this study suggest use of the methodology in future analyses of other similarly complexed proteins and in normal versus disease cohort population proteomics studies.     

Cognitive assessment of mice strains heterozygous for cell-adhesion genes reveals strain-specific alterations in timing

We used a fully automated system for the behavioural measurement of physiologically meaningful properties of basic mechanisms of cognition to test two strains of heterozygous mutant mice, Bfc (batface) and L1, and their wild-type littermate controls. Both of the target genes are involved in the establishment and maintenance of synapses. We find that the Bfc heterozygotes show reduced precision in their representation of interval duration, whereas the L1 heterozygotes show increased precision. These effects are functionally specific, because many other measures made on the same mice are unaffected, namely: the accuracy of matching temporal investment ratios to income ratios in a matching protocol, the rate of instrumental and classical conditioning, the latency to initiate a cued instrumental response, the trials on task and the impulsivity in a switch paradigm, the accuracy with which mice adjust timed switches to changes in the temporal constraints, the days to acquisition, and mean onset time and onset variability in the circadian anticipation of food availability.     

Image-based cell phenotyping with deep learning

A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells. Coupled with the rapid acceleration of deep learning–based computational tools, these advances have opened up new avenues for innovation across a wide variety of high-throughput cell biology applications. Here, we review applications wherein deep learning is powering the recognition, profiling, and prediction of visual phenotypes to answer important biological questions. As the complexity and scale of imaging assays increase, deep learning offers computational solutions to elucidate the details of previously unexplored cellular phenotypes.     

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.     

Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions

We have analysed daily patterns of leaf elongation rate (LER) in large data sets with 318 genotypes placed in naturally fluctuating temperature and evaporative demand, and examined the effect of targeted alleles on these patterns. The method consisted, firstly, in expressing elongation rate per unit thermal time, so it became temperature independent; secondly, in a joint analysis of diurnal fluctuations of elongation rate and of micrometeorological conditions in several experiments, and finally, in a comparison of daily patterns between groups of genotypes possessing targeted alleles. (1) Conditions for using thermal time at a time step of 15 min were first tested successfully in 318 recombinant inbred lines (RILs) of three mapping populations. (2) An analysis of 1989 time courses revealed a robust daily pattern of LER per unit thermal time (LERth) over several experiments. LERth was constant during the night and was reproducible (for a given RIL) over up to 10 consecutive nights in different experiments. It declined rapidly during the early morning, closely following the daily pattern of transpiration rate. (3) Groups of RILs carrying alleles conferring a high response to temperature had markedly higher night-time plateau of LER than those with low responses. Groups of RILs with high response to evaporative demand had rapid decreases in elongation rate at the transition between night and day, while this decrease was slower in groups of RILs with low response. These results open the way for using kinetics of responses to environmental stimuli as a phenotyping tool in genetic analyses.     

Governing anticipatory technology practices. Forensic DNA phenotyping and the forensic genetics community in Europe

Forensic geneticists have attempted to make the case for continued investment in forensic genetics research, despite its seemingly consolidated evidentiary role in criminal justice, by shifting the focus to technologies that can provide intelligence. Forensic DNA phenotyping (FDP) is one such emerging set of techniques, promising to infer external appearance and ancestry of an unknown person. On this example, I consider the repertoire of anticipatory practices deployed by scientists, expanding the concept to not only focus on promissory but also include epistemic and operational aspects of anticipatory work in science. I explore these practices further as part of anticipatory self-governance efforts, attending to the European forensic genetics community and its construction of FDP as a reliable and legitimate technology field for use in delivering public goods around security and justice. In this context, I consider three types of ordering devices that translate anticipatory practices into anticipatory self-governance.     

An improved method for butyrylcholinesterase phenotyping

An improved method for the identification of butyrylcholinesterase phenotypes is proposed. It is based on modifications of a method that uses -naphthyl acetate as substrate anddl-propranolol and Ro2-0683 as inhibitors. The proposed modifications make the method more rapid and increase the accuracy of the determinations of the phenotypes tested (BCHE U, BCHE UF, BCHE UA, BCHE AK, BCHE AF, and BCHE A). These modifications make the method even more adequate for population studies and clinical routine.We are grateful to the CNPq for research grants and scholarships.A preliminary report was presented at the Third International Meeting on Cholinesterases, France, May 12–16, 1990.