Inhibition of Plk1 and Cyclin B1 expression results in panobinostat-induced G(2) delay and mitotic defects

Work by JP is funded by a Royal Society University Research Fellowship award. Experiments carried out by MBP were supported by the Department of Pathology, Albert Einstein College of Medicine / Montefiore Medical Center.The development of clinically useful histone deacetylase inhibitors has expanded greatly. In a preclinical study, we showed that panobinostat (LBH589) inhibits cell cycle progression of human head and neck squamous cell carcinoma (HNSCC) cell lines at G(2)/M and an associated decrease in expression of particular genes required for passage through G(2) and mitosis. In this study we sought to analyse the mechanistic underpinnings of panobinostat-induced growth arrest. HNSCC cell lines were synchronised and progression through mitosis monitored. We demonstrate that panobinostat causes a marked G(2) delay and mitotic defects. A loss of G(2)-specific Plk1 and Cyclin B1 expression and co-incident increase in p21(Waf1/Cip1) expression is also shown. Furthermore, we show a significant loss of E2F1 recruitment to the promoters of these genes in response to panobinostat treatment. These data provide mechanistic evidence of panobinostat-induced cell cycle arrest and highlight its potential as a chemotherapeutic agent for HNSCC.Publisher PDFPeer reviewe

A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field

Root architecture traits are a target for pre-breeders. Incorporation of root architecture traits into new cultivars requires phenotyping. It is attractive to rapidly and directly phenotype root architecture in the field, avoiding laboratory studies that may not translate to the field. A combination of soil coring with a hydraulic push press and manual core-break counting can directly phenotype root architecture traits of depth and distribution in the field through to grain development, but large teams of people are required and labour costs are high with this method. We developed a portable fluorescence imaging system (BlueBox) to automate root counting in soil cores with image analysis software directly in the field. The lighting system was optimized to produce high-contrast images of roots emerging from soil cores. The correlation of the measurements with the root length density of the soil cores exceeded the correlation achieved by human operator measurements (R 2=0.68 versus 0.57, respectively). A BlueBox-equipped team processed 4.3 cores/hour/person, compared with 3.7 cores/hour/person for the manual method. The portable, automated in-field root architecture phenotyping system was 16% more labour efficient, 19% more accurate, and 12% cheaper than manual conventional coring, and presents an opportunity to directly phenotype root architecture in the field as part of pre-breeding programs. The platform has wide possibilities to capture more information about root health and other root traits in the field

Beyond Digging: Noninvasive Root and Rhizosphere Phenotyping

A wide spectrum across physical domains has been tested to directly phenotype roots noninvasively in soils in controlled environments (CE) and fields (F). Some are established (green) in specialized groups, others are developing (pink). Magnetic resonance imaging (MRI), computed tomography (CT), and position emission tomography (PET) localize root structure and functions in containers of soil in CE. Neutron radiography, hyperspectral and multispectral instruments, and RGB (red, green, blue) cameras are applied to detect changes in root and rhizosphere chemistry through windows of rhizoboxes in CE. Field direct, noninvasive measurements are limited to ground penetrating radar (GPR) and to thick tree roots and tubers. Root system capacitance (RSC) shows promise where root thickness and the soil allow; thermoacoustic imaging is being explored

ABOVE AND BELOW GROUND PHENOTYPING TECHNOLOGIES FOR WHEAT BREEDING

Wheat varieties with greater yields arise today mainly by selecting for harvested grain yield in field environments. This approach combines multiple favourable alleles. Gains are slow because the contributions of alleles to yield are largely unknown. The contrasting “pre-breeding” approach aims to introduce a single major enhancing trait (other than yield) into a variety. This approach can take ~20 years from idea to field, and there are few successful examples in wheat (Hall and Richards 2013). Phenotyping may bridge this gap between yield based and single-trait based breeding because multiple traits (alleles) can be measured on one line. Examples are presented where phenotyping technologies quantified shoot and root traits non-destructively on wheats of different genetic and pedoclimatic origins. For early growth traits in controlled conditions: root architecture differed in response to soil water gradients, but shoots did not (Nagel et al., 2015); shoot growth declined in response to reduced N supply, but root growth did not (Gioia et al 2015); and leaf area and water use varied differentially in response to drought (Nakhforoosh et al 2016). Field phenotyping during grain filling has shown that shoot and root development vary independently depending on plant stage, genotype and environment (Severini et al. in prep), and spectral properties of wheat heads and canopy are dynamic, requiring time-lapse systems (Ahrends et al., 2014). Hence phenotyping technologies quantify the high degree of variation at important establishment and grain development stages in wheat, and may increase certainty of incorporating multiple alleles of known effect within breeding

Root phenotypes at maturity in diverse wheat and triticale genotypes grown in three field experiments: Relationships to shoot selection, biomass, grain yield, flowering time, and environment

Modelling and limited data suggest that crops with deeper and longer roots capture more soil resources and yield more when water is available deeper in soil profiles. Interest has grown in the development of new cultivars with deeper roots. This study provides data from three field experiments to help researchers and breeders continue to assess the value of selecting for deeper roots for yield and water use efficiency gains. We asked: do genotype groups with shoot phenotypes easily selectable in pre-breeding programs express predictable root depth and length at time of grain harvest in the field? Do flowering time and shoot biomass predict deep roots measured directly in the field with coring, such that deeper roots are associated with more shoot growth and yield? Does genotype, including triticale versus wheat types, vary in rooting traits? Thirty-four wheats (Triticum aestivum L.) and two triticales (× Triticosecale) were drawn from ten ‘genotype groups’; selections from breeding programs and commercial cultivars that were distinguished on the basis of height, tillering, winter habit, and early vigour. These were grown at two independent sites and soil conditions in year 1 (experiment 1 and 2), with a subset of six wheats and two triticales repeated in year 2 at year 1 site (experiment 3). Above-ground biomass, flowering date, grain yield and root length and depth were measured with a high level of replication (four replicate plots and four soil cores per plot). Root length density was predicted from root counts obtained using the core-break method on 42 mm diameter, two m deep cores. A Bayesian multivariate mixed-effects model was used with fixed effects of the environment and random effects of genotype groups, genotypes and their interactions with the environment. Variation in rooting depth and length caused by environments was much larger than that caused by genotypes. Positive relationships between biomass, yield and root depth and length were observed across experiments and genotype groups (r = 0.62 for biomass and root depth, r = 0.61 for yield and root depth; r = 0.66 for biomass and root length, r = 0.53 for yield and root length), but the largest effects were driven by differences in soil and rainfall conditions between experiments. However, the smaller genetic effects on rooting depth and yield were positively correlated (r = 0.69). We did not find that easily selectable shoot traits like early vigour, tillering, and height reliably predicted in-field deeper rooting. Notably, the two triticales were 74 % more likely to have a deeper rooting and 82 % more likely to have less total root length, than spring wheats. We conclude that deeper and longer roots at maturity are (1) challenging to pre-select using shoot phenotype prior to field evaluation; (2) depend almost entirely on environment for expression in the field with small effects of genotype; and (3) can grow at no apparent 'cost' to shoot growth or yield and as such can remain a target for breeding.EEA PergaminoFil: Severini, Alan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Ecofisiología; Argentina. CSIRO Agriculture and Food; Australia. The Australian National University. Research School of Biology. Division of Plant Sciences; AustraliaFil: Wasson, Anton P. CSIRO Agriculture and Food; AustraliaFil: Evans, John R. The Australian National University. Research School of Biology. Division of Plant Sciences; AustraliaFil: Richard, Richard A. CSIRO Agriculture and Food; AustraliaFil: Watt, Michelle. CSIRO Agriculture and Food; Australia. University of Melbourne. School of BioSciences; Australi

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today’s phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems

Hydration Behaviors Before and After an Educational and Prescribed Hydration Intervention in Adolescent Athletes

Context: The effectiveness of education in modifying hydration behaviors in adolescent athletes is unclear. Objective: To assess the hydration status and behaviors of female athletes before and after a 1-time educational intervention and prescribed hydration intervention in a warm, humid, tropical environment. Design: Cohort study. Setting: Non–air-conditioned gymnasium in a tropical environment (indoor wet bulb globe temperature  =  24.0 ± 0.2°C). Patient or Other Participants: Thirty-six female adolescent elite volleyball players (age  =  14.8 ± 0.8 years, height  =  168.2 ± 8.2 cm, mass  =  60.8 ± 9.0 kg, body mass index  =  21.7 ± 2.7, body surface area  =  1.65 ± 0.14 m2, body surface area to mass ratio  =  2.71 ± 0.18 m2·kg−1·10−2) participated. Intervention(s): Four observational periods consisting of 3 practices per observational period separated by 48 hours. The 4 periods included a control period, educational intervention, prescribed hydration intervention (PHI), and observational follow-up (OF-U). After the control period, an educational intervention consisting of a slide presentation was provided to the participants, followed by a week of observation. In the PHI, a precalculated volume of water based on individual sweat rate was consumed every 20 minutes during each 2-hour practice. During all other periods, participants consumed their fluid of choice ad libitum. The order of the treatment periods was not randomized and was the same for all participants. Main Outcome Measure(s): Prepractice to postpractice changes in body mass (ΔBM), percentage of body mass lost (%BML), urine specific gravity, urine color, urine osmolality, sweat rate, and volume of fluid consumed (Fvol). Results: The PHI was the only period during which participants maintained body mass (ΔBM  =  0.05 ± 1.3%); Fvol consumed was greatest during this time (Fvol  =  1.3 ± 0.4 L; F1,3  =  34.869, P ≤ .001). The ΔBM was less for the PHI (ΔBM  =  0.05 ± 0.9 kg, %BML  =  0.04 ± 1.3%) than the OF-U period (ΔBM  =  −0.7 ± 1.1 kg, %BML  =  −1.2 ± 1.9%; F1,3  =  6.220, P  =  .01). The Fvol (1.3 ± 0.4 L) and percentage of fluid consumed (143.7 ± 110.8%) to restore sweat loss for the PHI period were higher than for any other period (F1,3  =  34.869, P ≤ .001). None of the participants experienced serious dehydration in any of the conditions. Conclusions: A 1-time education session alone was not successful in changing hydration behaviors. However, prescribing individualized hydration protocols improved hydration for adolescents exercising in a warm, humid environment