Reaction phenotyping of vinblastine metabolism in dogs

Vinblastine is a vinca alkaloid used either as a single agent or in combination therapy for the treatment of canine mast cell tumours and lymphomas. The objective of this study was to determine which isoform of cytochrome P450 enzyme is responsible for the majority of vinblastine metabolism in dogs. A panel of eight recombinant canine cytochrome P450 enzymes (CYP1A1, CYP1A2, CYP3A12, CYP3A26, CYP2B11, CYP2C41, CYP2C21 and CYP2D15) were incubated in vitro with vinblastine. Findings were confirmed by the use of canine polyclonal antibodies of cytochrome P450 enzymes (CYP1A1, CYP3A12, CYP2B11 and CYP2C21) that were pre‐incubated with individual and pooled hepatic microsomes that were purified from canine liver. Substrate depletion was observed in the presence of recombinant CYP3A12, whereas depletion did not substantially occur when microsomes were pre‐incubated with polyclonal antibodies against CYP3A12. These findings confirmed that CYP3A12 is the major cytochrome P450 isoform responsible for the metabolism of vinblastine in dogs.     

Genetic dissection of root architectural traits by QTL and genome‐wide association mapping in rapeseed (Brassica napus)

Variation in root architecture is essential for the adaptation of plants to target environments. A non‐destructive gel‐based mini‐rhizotron system was used for root architecture trait phenotyping. This system has facilitated the visualization of root architectural traits in large genotype collection of rapeseed including 94 double haploid (DH) lines from “Express617” x “V8” and 439 inbred lines (ASSYST diversity set). A high‐density Express617‐V8 linkage map was used for quantitative trait loci (QTLs) identification in DH population based on standard composite interval mapping. 6K SNPs were analysed for association mapping of root traits in ASSYST diversity set. A large variation, broad segregation and medium–low heritability of root architectural traits, primary root length and growth rate, lateral root number, lateral root length and lateral root density, were observed. In the double haploid population, 11 QTL regions, and in the diversity set, 38 significant marker–trait associations were detected. Significant marker–trait associations proved that these are quantitatively inherited traits controlled by multiple genes which revealed to proceed for genetic improvement and selection of rapeseed lines with improved root system.     

水稻类病斑早衰突变体lmps1的表型鉴定与基因定位

经甲基磺酸乙酯(EMS)诱变优良籼型水稻恢复系缙恢10号,获得一个稳定遗传的水稻类病斑早衰突变体lmps1(lesion mimic and premature senescence 1)。该突变体苗期表型正常,分蘖早期出现褐色类病斑,且斑点数目随植株生长而增多,孕穗期叶片开始萎黄衰老。与野生型相比,突变体lmps1的每穗总粒数下降8%(P0.05),株高、穗长、有效穗数、每穗实粒数、结实率以及千粒重分别下降14.3%、24.3%、27.2%、50%、45.7%与14.5%,差异均达极显著水平(P0.01)。遮光处理表明,突变体lmps1的类病斑性状受光照诱导。孕穗期叶片光合色素含量下降且光合效率降低, H2O2含量增加,抗氧化酶SOD和CAT的活性显著降低。透射电镜观察结果显示,突变体lmps1叶肉细胞中叶绿体数目减少,叶绿体的类囊体片层结构损伤降解。qRT-PCR结果显示,突变体lmps1中防卫反应相关基因除POX22.3表达量降低外,POC1、PAL、PBZ1、PR1、NPR1、PR5表达量均极显著高于野生型。遗传分析表明突变体lmps1的类病斑早衰性状受1对隐性核基因控制,利用西农1A与突变体lmps1杂交所得F2群体中的突变株,将目标基因定位于第7染色体长臂端粒附近约167.3 kb的物理区段内。     

Precision phenotyping of contrasting potato(Solanum tuberosum L.) varieties in a novel aeroponics system for improving nitrogen use efficiency: In search of key traits and genes

With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment and achieve sufficient tuber yields with less N fertilizer supply. Recently, we have developed an aeroponics system to study NUE in potato using genomics, physiology, and breeding approaches. This study aims on precision phenotyping of plants of two distinct potato varieties(Kufri Gaurav, N efficient; Kufri Jyoti, N inefficient) in the novel aeroponics system. Plants were grown in aeroponics under controlled conditions with low N(0.75 mmol L~(-1) NO_3~-) and high N(7.5 mmol L~(–1) NO_3~-) levels. Plant biomass, root traits, total chlorophyll content, and plant N were increased with increasing N supply, whereas higher NUE parameters namely NUE, agronomic NUE(Ag NUE), N uptake efficiency(NUp E), harvest index(HI), and N harvest index(NHI) were observed at low N. An NUE efficient cv. Kufri Gaurav showed higher tuber dry weight, fresh tuber yield, tuber number per plant, early start of tuber harvesting, root traits, stolon traits, NUE parameters, and higher amino acid(aspartic acid and asparagine) content at low N supply. Higher expression of nitrate reductase(NR), nitrite reductase(NIR), and asparagine synthetase(AS) genes was observed in the leaf tissues of Kufri Gaurav at high N. Thus, aeroponics-based precision phenotyping enables identification of NUE efficient genotypes based on key traits and genes involved in improving NUE in potato. Further, this study suggests that the potential of aeroponics can be utilized to investigate N biology in potato under different N regimes.     

Metabolic phenotyping of mouse mutants in the German Mouse Clinic

The German Mouse Clinic was established as a phenotyping center to provide the scientific community with systematic standardized phenotyping of mouse models from various genetic backgrounds. We found metabolic phenotypes in nine out of 20 mutant lines screened in a primary screen. Based on these findings, the mutants were analyzed in secondary and tertiary screens. Mice of a sample mutant line, isolated from the ENU‐screen at the National Research Center for Environment and Health in Munich, were found to have lower body weight, consume less food, but have higher ratios of metabolized energy per unit body weight compared with their wild‐type littermates. Basal metabolic rate and heat production were simultaneously increased by 16–18%, whereas body fat content was reduced by 11–16%. The combination of various parameters of energy consumption, expenditure and energy storage illustrate the metabolic demands of the sample mutant mouse line and demonstrate the utility of the powerful phenotyping tool used at the German Mouse Clinic.     

On the use of spectral reflectance indices to assess agro‐morphological traits of wheat plants grown under simulated saline field conditions

Successful breeding of plants for salinity stress tolerance requires realistic growing conditions and fast, non‐destructive evaluation techniques for phenotypic traits associated with salinity tolerance. In this study, we used subsurface water retention technique (SWRT) as a growing condition and spectral measurements as an evaluation method to assess different agro‐morphological traits of salt‐tolerant (Sakha 93) and salt‐sensitive (Sakha 61) wheat genotypes under three salinity levels (control, 60, and 120 mm NaCl). The effects of salinity on agro‐morphological traits were evaluated and related with forty‐five published vegetation‐ and water‐spectral reflectance indices (SRIs) taken at both the heading and grain milk growth stages for each salinity level, genotype, and growth stage. In general, the agro‐morphological traits gradually decreased as salinity levels increased; however, the reduction in these traits was more pronounced in Sakha 61 than in Sakha 93. The effect of salinity levels and their interaction with genotypes on the SRIs was only evident at the grain milk stage. The performance of the spectral reflectance indices depicted that the closest associations with agro‐morphological traits depended on salinity level, degree of salt tolerance of the genotypes, and growth stage. The SRI‐based vegetative indices correlated better with growth and yield of Sakha 93 than SRI‐based water indices and vice versa for Sakha 61. The SRI‐based vegetative and water indices are effective for assessment of agro‐morphological traits at early growth stages under high salinity level. The functional relationship between grain yield per hectare and the best SRIs was linear for the high salinity level and Sakha 61; however, the quadratic model was found to best fit this relationship for the control, moderate salinity level, and Sakha 93. The overall results indicate that the usefulness of the SRIs for assessment of traits associated with salinity tolerance is limited to salinity level and growth stage.     

Image-based root phenotyping for field-grown crops: An example under maize/soybean intercropping

Root architecture, which determines the water and nutrient uptake ability of crops, is highly plastic in response to soil environmental changes and different cultivation patterns. Root phenotyping for field-grown crops, especially topological trait extraction, is rarely performed. In this study, an image-based semi-automatic root phenotyping method for field-grown crops was developed. The method consisted of image acquisition, image denoising and segmentation, trait extraction and data analysis....     

Groundnut (Arachis hypogaea L.) improvement in sub-Saharan Africa: a review

Groundnut (Arachis hypogaea L.) is a multi-purpose legume crop widely cultivated in sub-Saharan Africa (SSA). However, yield levels of the crop has remained relatively low in SSA owing to a range of biotic, abiotic and socio-economic constraints. A dedicated groundnut improvement programme integrating new tools and methodologies to breed varieties suitable for current and emerging agro-ecologies and market needs is essential for enhanced and sustainable groundnut production in SSA. The objective of this review is to highlight breeding progress, opportunities and challenges on groundnut improvement with regard to cultivar development and deployment in SSA in order to guide future improvement of the crop. The review analysed the role of new tools in breeding such as, high-throughput and automated phenotyping techniques, rapid generation advancement, single seed descent approach, marker-assisted selection, genomic selection, next-generation sequencing, genetic engineering and genome editing for accelerated breeding and cultivar development of groundnut.     

花生基因组学在遗传育种中的研究进展

花生是我国重要的油料和经济作物,突破传统育种的盲目性和低效率仍是花生育种面临的巨大挑战.近年来花生基因组学研究取得显著进展,四倍体野生种、栽培种及其二倍体祖先种基因组序列相继发表,大量SSR和SNP标记开发利用,花生遗传图谱标记密度不断增加,高通量表型鉴定和基因分型技术广泛应用,越来越多重要农艺性状相关QTL被挖掘定位...     

Data fusion of spectral,thermal and canopy height parameters for improved yield prediction of drought stressed spring barley

Yield modelling based on visible and near infrared spectral information is extensively used in proximal and remote sensing for yield prediction of crops. Distance and thermal information contain independent information on canopy growth, plant structure and the physiological status. In a four-years′ study hyperspectral, distance and thermal high-throughput measurements were obtained from different sets of drought stressed spring barley cultivars. All possible binary, normalized spectral indices as well as thirteen spectral indices found by others to be related to biomass, tissue chlorophyll content, water status or chlorophyll fluorescence were calculated from hyperspectral data and tested for their correlation with grain yield. Data were analysed by multiple linear regression and partial least square regression models, that were calibrated and cross-validated for yield prediction. Overall partial least square models improved yield prediction (R² = 0.57; RMSEC = 0.63) compared to multiple linear regression models (R² = 0.46; RMSEC = 0.74) in the model calibration. In cross-validation, both methods yielded similar results (PLSR: R² = 0.41, RMSEV = 0.74; MLR: R² = 0.40, RMSEV = 0.78). The spectral indices R₇₈₀/R₅₅₀, R₇₆₀/R₇₃₀, R₇₈₀/R₇₀₀, the spectral water index R₉₀₀/R₉₇₀ and laser and ultrasonic distance parameters contributed favourably to grain yield prediction, whereas the thermal based crop water stress index and the red edge inflection point contributed little to the improvement of yield models. Using only more uniform modern cultivars decreased the model performance compared to calibrations done with a set of more diverse cultivars. The partial least square models based on data fusion improved yield prediction (R² = 0.62; RMSEC = 0.59) compared to the partial least square models based only on hyperspectral data (R² = 0.48; RMSEC = 0.69) in the model calibration. This improvement was confirmed by cross-validation (data fusion: R² = 0.39, RMSEV = 0.76; hyperspectral data only: R² = 0.32, RMSEV = 0.79). Thus, a combination of spectral multiband and distance sensing improved the performance in yield prediction compared to using only hyperspectral sensing.