In vitro models for neuropathic pain phenotypic screening in brain therapeutics.

The discovery of brain therapeutics faces a significant challenge due to the low translatability of preclinical results into clinical success. To address this gap, several efforts have been made to obtain more translatable neuronal models for phenotypic screening. These models allow the selection of active compounds without predetermined knowledge of drug targets. In this review, we present an overview of various existing models within the field, examining their strengths and limitations, particularly in the context of neuropathic pain research. We illustrate the usefulness of these models through a comparative review in three crucial areas: i) the development of novel phenotypic screening strategies specifically for neuropathic pain, ii) the validation of the models for both primary and secondary screening assays, and iii) the use of the models in target deconvolution processes.

Genomic prediction in CIMMYT maize and wheat breeding programs.

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

Genomic-enabled prediction with classification algorithms.

Pearson's correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression in the tails of the distribution, where individuals are chosen for selection. This research used 14 maize and 16 wheat data sets with different trait-environment combinations. Six different models were evaluated by means of a cross-validation scheme (50 random partitions each, with 90% of the individuals in the training set and 10% in the testing set). The predictive accuracy of these algorithms for selecting individuals belonging to the best α=10, 15, 20, 25, 30, 35, 40% of the distribution was estimated using Cohen's kappa coefficient (κ) and an ad hoc measure, which we call relative efficiency (RE), which indicates the expected genetic gain due to selection when individuals are selected based on GS exclusively. We put special emphasis on the analysis for α=15%, because it is a percentile commonly used in plant breeding programmes (for example, at CIMMYT). We also used ρ as a criterion for overall success. The algorithms used were: Bayesian LASSO (BL), Ridge Regression (RR), Reproducing Kernel Hilbert Spaces (RHKS), Random Forest Regression (RFR), and Support Vector Regression (SVR) with linear (lin) and Gaussian kernels (rbf). The performance of regression methods for selecting the best individuals was compared with that of three supervised classification algorithms: Random Forest Classification (RFC) and Support Vector Classification (SVC) with linear (lin) and Gaussian (rbf) kernels. Classification methods were evaluated using the same cross-validation scheme but with the response vector of the original training sets dichotomised using a given threshold. For α=15%, SVC-lin presented the highest κ coefficients in 13 of the 14 maize data sets, with best values ranging from 0.131 to 0.722 (statistically significant in 9 data sets) and the best RE in the same 13 data sets, with values ranging from 0.393 to 0.948 (statistically significant in 12 data sets). RR produced the best mean for both κ and RE in one data set (0.148 and 0.381, respectively). Regarding the wheat data sets, SVC-lin presented the best κ in 12 of the 16 data sets, with outcomes ranging from 0.280 to 0.580 (statistically significant in 4 data sets) and the best RE in 9 data sets ranging from 0.484 to 0.821 (statistically significant in 5 data sets). SVC-rbf (0.235), RR (0.265) and RHKS (0.422) gave the best κ in one data set each, while RHKS and BL tied for the last one (0.234). Finally, BL presented the best RE in two data sets (0.738 and 0.750), RFR (0.636) and SVC-rbf (0.617) in one and RHKS in the remaining three (0.502, 0.458 and 0.586). The difference between the performance of SVC-lin and that of the rest of the models was not so pronounced at higher percentiles of the distribution. The behaviour of regression and classification algorithms varied markedly when selection was done at different thresholds, that is, κ and RE for each algorithm depended strongly on the selection percentile. Based on the results, we propose classification method as a promising alternative for GS in plant breeding.

Direct introgression of untapped diversity into elite wheat lines.

The effective utilization of natural variation has become essential in addressing the challenges that climate change and population growth pose to global food security. Currently adopted protracted approaches to introgress exotic alleles into elite cultivars need substantial transformation. Here, through a strategic three-way crossing scheme among diverse exotics and the best historical elites (exotic/elite1//elite2), 2,867 pre-breeding lines were developed, genotyped and screened for multiple agronomic traits in four mega-environments. A meta-genome-wide association study, selective sweeps and haplotype-block-based analyses unveiled selection footprints in the genomes of pre-breeding lines as well as exotic-specific associations with agronomic traits. A simulation with a neutrality assumption demonstrated that many pre-breeding lines had significant exotic contributions despite substantial selection bias towards elite genomes. National breeding programmes worldwide have adopted 95 lines for germplasm enhancement, and 7 additional lines are being advanced in varietal release trials. This study presents a great leap forwards in the mobilization of GenBank variation to the breeding pipelines.

Correction to: High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

[This corrects the article DOI: 10.1186/s13007-018-0317-4.].

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

BACKGROUND: Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure. RESULTS: A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. CONCLUSION: The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

Use of Genomic Estimated Breeding Values Results in Rapid Genetic Gains for Drought Tolerance in Maize.

More than 80% of the 19 million ha of maize ( L.) in tropical Asia is rainfed and prone to drought. The breeding methods for improving drought tolerance (DT), including genomic selection (GS), are geared to increase the frequency of favorable alleles. Two biparental populations (CIMMYT-Asia Population 1 [CAP1] and CAP2) were generated by crossing elite Asian-adapted yellow inbreds (CML470 and VL1012767) with an African white drought-tolerant line, CML444. Marker effects of polymorphic single-nucleotide polymorphisms (SNPs) were determined from testcross (TC) performance of F families under drought and optimal conditions. Cycle 1 (C1) was formed by recombining the top 10% of the F families based on TC data. Subsequently, (i) C2[PerSe_PS] was derived by recombining those C1 plants that exhibited superior per se phenotypes (phenotype-only selection), and (ii) C2[TC-GS] was derived by recombining a second set of C1 plants with high genomic estimated breeding values (GEBVs) derived from TC phenotypes of F families (marker-only selection). All the generations and their top crosses to testers were evaluated under drought and optimal conditions. Per se grain yields (GYs) of C2[PerSe_PS] and that of C2[TC-GS] were 23 to 39 and 31 to 53% better, respectively, than that of the corresponding F population. The C2[TC-GS] populations showed superiority of 10 to 20% over C2[PerSe-PS] of respective populations. Top crosses of C2[TC-GS] showed 4 to 43% superiority of GY over that of C2[PerSe_PS] of respective populations. Thus, GEBV-enabled selection of superior phenotypes (without the target stress) resulted in rapid genetic gains for DT.

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines' performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha(-1) across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.

Pharmacological properties of S1RA, a new sigma-1 receptor antagonist that inhibits neuropathic pain and activity-induced spinal sensitization.

BACKGROUND AND PURPOSE: The sigma-1 (σ(1) ) receptor is a ligand-regulated molecular chaperone that has been involved in pain, but there is limited understanding of the actions associated with its pharmacological modulation. Indeed, the selectivity and pharmacological properties of σ(1) receptor ligands used as pharmacological tools are unclear and the demonstration that σ(1) receptor antagonists have efficacy in reversing central sensitization-related pain sensitivity is still missing. EXPERIMENTAL APPROACH: The pharmacological properties of a novel σ(1) receptor antagonist (S1RA) were first characterized. S1RA was then used to investigate the effect of pharmacological antagonism of σ(1) receptors on in vivo nociception in sensitizing conditions and on in vitro spinal cord sensitization in mice. Drug levels and autoradiographic, ex vivo binding for σ(1) receptor occupancy were measured to substantiate behavioural data. KEY RESULTS: Formalin-induced nociception (both phases), capsaicin-induced mechanical hypersensitivity and sciatic nerve injury-induced mechanical and thermal hypersensitivity were dose-dependently inhibited by systemic administration of S1RA. Occupancy of σ(1) receptors in the CNS was significantly correlated with the antinociceptive effects. No pharmacodynamic tolerance to the antiallodynic and antihyperalgesic effect developed following repeated administration of S1RA to nerve-injured mice. As a mechanistic correlate, electrophysiological recordings demonstrated that pharmacological antagonism of σ(1) receptors attenuated the wind-up responses in spinal cords sensitized by repetitive nociceptive stimulation. CONCLUSIONS AND IMPLICATIONS: These findings contribute to evidence identifying the σ(1) receptor as a modulator of activity-induced spinal sensitization and pain hypersensitivity, and suggest σ(1) receptor antagonists as potential novel treatments for neuropathic pain.

Influence of nutrition on testicular growth in Corriedale rams during spring.

To study the effect of nutrition on spring testicular growth, four adult Corriedale rams were allowed to graze enough to maintain weight (maintenance group), while another four rams, in addition to forage, received a supplemental grain-based ration (increased gradually from 100 to 400 g during the first 5 d and kept at 400 g thereafter) daily for 63 d (supplemented group). Body weight, scrotal circumference, inguinal hyperaemia and testicular consistency were assessed. Blood concentrations of LH and testosterone were measured for 24 h on the day before supplementation began, the day after the animals were fed 200 and 400 g, and 12 and 28 d after animals began to receive the supplement. On these occasions blood contents of non-esterified free fatty acid and beta-hydroxybutyrate were measured when animals were fasting. Supplemented feeding increased body weight within 21 d and scrotal circumference within 35 d (P < 0.01). Scrotal circumference also increased in rams of the maintenance group (P < 0.01) but a lower rate than the supplemented group (P < 0.001). In both groups, testicular consistency and inguinal hyperaemia increased (P < 0.01). In the supplemented group a transient increase (P < 0.01) in LH pulsatility occurred the day after rams had received the full supplement (400 g) and 5 d later (day 12). However, no difference was found in total testosterone release between groups. In conclusion, improved nutrition accelerated the testicular growth in spring, although only a transient increase in LH pulsatility was observed. The scrotal circumference of rams kept on maintenance diet did also increase, which indicates that nutrition is not the only environmental cue responsible for the vernal testicular redevelopment in Corriedale rams.

Involvement of caveolin in ligand-induced recruitment and internalization of A(1) adenosine receptor and adenosine deaminase in an epithelial cell line.

Chronic exposure of A(1) adenosine receptors (A(1)R) to A(1)R agonists leads to activation, phosphorylation, desensitization, and internalization to intracellular compartments of the receptor. Desensitization and internalization of A(1)R is modulated by adenosine deaminase (ADA), an enzyme that regulates the extracellular concentration of adenosine. ADA interacts with A(1)R on the cell surface of the smooth muscle cell line DDT1 MF-2, and both proteins are internalized following agonist stimulation of the receptor. The mechanism involved in A(1)R and ADA internalization upon agonist exposure is poorly understood in epithelial cells. In this report, we show that A(1)R and ADA interact in LLC-PK(1) epithelial cells. Exposure of LLC-PK(1) cells to A(1)R agonists induces aggregation of A(1)R and ADA on the cell surface and their translocation to intracellular compartments. Biochemical and cell biology assays were used to characterize the intracellular vesicles containing both proteins after agonist treatment. A(1)R and ADA colocalized together with the rafts marker protein caveolin. Filipin, a sterol-binding agent that disrupts rafts (small microdomains of the plasma membrane), was able to inhibit A(1)R internalization. In contrast, acid treatment of the cells, which disrupts internalization via clathrin-coated vesicles, did not inhibit agonist-stimulated A(1)R internalization. We demonstrated that A(1)R agonist N(6)-(R)-phenylisopropyl adenosine promotes the translocation of A(1)R into low-density gradient fractions containing caveolin. Furthermore, a direct interaction of the C-terminal domain of A(1)R with caveolin-1 was demonstrated by pull down experiments. These results indicate that A(1)R and ADA form a stable complex in the cell surface of LLC-PK(1) cells and that agonist-induced internalization of the A(1) adenosine receptor and ADA is mediated by clathrin-independent endocytosis.

Metabotropic glutamate 1alpha and adenosine A1 receptors assemble into functionally interacting complexes.

Recently, evidence has emerged that seven transmembrane G protein-coupled receptors may be present as homo- and heteromers in the plasma membrane. Here we describe a new molecular and functional interaction between two functionally unrelated types of G protein-coupled receptors, namely the metabotropic glutamate type 1alpha (mGlu(1alpha) receptor) and the adenosine A1 receptors in cerebellum, primary cortical neurons, and heterologous transfected cells. Co-immunoprecipitation experiments showed a close and subtype-specific interaction between mGlu(1alpha) and A1 receptors in both rat cerebellar synaptosomes and co-transfected HEK-293 cells. By using transiently transfected HEK-293 cells a synergy between mGlu(1alpha) and A1 receptors in receptor-evoked [Ca(2+)](i) signaling has been shown. In primary cultures of cortical neurons we observed a high degree of co-localization of the two receptors, and excitotoxicity experiments in these cultures also indicate that mGlu(1alpha) and A1 receptors are functionally related. Our results provide a molecular basis for adenosine/glutamate receptors cross-talk and open new perspectives for the development of novel agents to treat neuropsychiatric disorders in which abnormal glutamatergic neurotransmission is involved.