Behavioral phenotyping enhanced--beyond (environmental) standardization

It is basic biology that the phenotype of an animal is the product of a complex and dynamic interplay between nature (genotype) and nurture (environment). It is far less clear, however, how this might translate into experimental design and the interpretation of animal experiments. Animal experiments are a compromise between modelling real world phenomena with maximal validity (complexity) and designing practicable research projects (abstraction). Textbooks on laboratory animal science generally favour abstraction over complexity. Depending on the area of research, however, abstraction can seriously compromise information gain, with respect to the real world phenomena an experiment is designed to model. Behavioral phenotyping of mouse mutants often deals with particularly complex manifestations of life, such as learning, memory or anxiety, that are strongly modulated by environmental factors. A growing body of evidence indicates that current approaches to behavioral phenotyping might often produce results that are idiosyncratic to the study in which they were obtained, because the interactive nature of genotype-environment relationships underlying behavioral phenotypes was not taken into account. This paper argues that systematic variation of genetic and environmental backgrounds, instead of excessive standardization, is needed to control the robustness of the results and to detect biologically relevant interactions between the mutation and the genetic and environmental background of the animals.     

Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (V_cmax25) and the maximum electron transport at a reference temperature (J_max25), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of V_cmax25 and J_max25; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, V_cmax25 and J_max25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for V_cmax25 and J_max25, based on the leaf reflectance. Results showed that these models yielded an R² of 0.57–0.65 and 0.48–0.58 for V_cmax25 and J_max25, respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity.     

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.     

浅议小麦赤霉病抗性类型与鉴定方法的对应性

由镰孢菌引起的赤霉病是小麦的重要病害,其抗性比较复杂。准确可靠的鉴定和评价方法是抗性改良成功的前提。评述了小麦赤霉病抗性类型及其不同赤霉病抗性鉴定和评价方法的优缺点,重点讨论了抗性类型与抗性鉴定方法的对应性。希望对赤霉病表型鉴定、不同抗性类型的理解和评价以及抗性改良有借鉴意义。     

Forensic DNA phenotyping in Europe: views “on the ground” from those who have a professional stake in the technology

Forensic DNA phenotyping (FDP) is an emerging technology that seeks to make probabilistic inferences regarding a person’s observable characteristics (“phenotype”) from DNA. The aim is to aid criminal investigations by helping to identify unknown suspected perpetrators, or to help with non-criminal missing persons cases. Here we provide results from the analysis of 36 interviews with those who have a professional stake in FDP, including forensic scientists, police officers, lawyers, government agencies and social scientists. Located in eight EU countries, these individuals were asked for their views on the benefits and problems associated with the prospective use of FDP. While all interviewees distinguished between those phenotypic tests perceived to either raise ethical, social or political concerns from those tests viewed as less ethically and socially problematic, there was wide variation regarding the criteria they used to make this distinction. We discuss the implications of this in terms of responsible technology development.     

Apolipoprotein E polymorphism in Northern Indian patients with coronary heart disease: Phenotype distribution and relation to serum lipids and lipoproteins

Apolipoprotein E (apo E), a genetic determinant of plasma lipid levels and coronary heart disease (CHD) needs to be investigated in Asian Indians since they have a propensity to develop dyslipidemia and accelerated atherosclerosis. We studied apo E phenotypes and plasma lipid levels in 52 Northern Indian male patients (aged 38–71 years) with angiographically proven CHD, and compared them to 50 healthy blood donors taken as the control group. High levels of Lp(a), (p < 0.05), and a definite trend towards lower levels of HDL-C (p < 0.05), was observed in the CHD patients as compared to the control subjects. The frequency of apo E allele 3 was 0.86 and 0.862, and 4 allele was 0.12 and 0.08 in the patients and controls, respectively. However, a lower frequency of the E2 allele was observed in the patient group (2 = 0.02) as compared to the controls (2 = 0.06) (p = ns). In individuals with apo E3/E3 phenotype, significantly lower HDL-C levels was observed in the CHD patients as compared to the control subjects (p < 0.05). A positive correlation was observed between apo E phenotypes and Lp(a) levels in the CHD subjects as compared to the controls (p < 0.05), the level being significantly high in CHD subjects with at least one E4 allele. To conclude, in this sample of Northern Indian subjects with CHD, there is a significant correlation between apo E3/E3 phenotype and low levels of HDL-C as compared to the control subjects. Further, apo E phenotype is positively correlated with high Lp(a) levels in the CHD subjects having at least one E4 allele. However, these relationships need to be explored in a larger sample of subjects.     

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.     

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。     

A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels

High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two-dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.