Identification of large-effect QTL for kernel row number has potential for maize yield improvement

The kernel row number (KRN) per ear is an important component of maize (Zea mays L.) yield. In this study, a line with six kernel rows, MT-6, was used to investigate the genetic basis of KRN by quantitative trait locus (QTL) mapping. MT-6 was derived from a maize inbred line Mo17 and a teosinte entry X26-4 (Zea mays ssp. mexicana), with 23 % of its genome being homologous to X26-4. An MT-6/B73 F₂ segregating population consisting of 266 individuals was genotyped using 192 molecular markers spread evenly across the genome. The same F₂ population, together with its F_2:3 population, was phenotyped for KRN in three environments. Five individual QTL for KRN, including three substantially consistent major QTL detected in all environments, were identified on chromosomes 1, 2, 3, 4, and 5, respectively. These QTL accounted for 39.5–65.0 % of the KRN variation in these populations. Additionally, one pair of epistatic interaction between two loci with additive effects was detected and accounted for about 3 % of KRN variation. These results demonstrate that a few major QTL could substantially affect the evolution of maize KRNs and therefore provide valuable information for our understanding of the mechanism of KRN and the improvement in maize grain yield by molecular breeding.     

Row Ratios of Intercropping Maize and Soybean Can Affect Agronomic Efficiency of the System and Subsequent Wheat

Intercropping is regarded as an important agricultural practice to improve crop production and environmental quality in the regions with intensive agricultural production, e.g., northern China. To optimize agronomic advantage of maize (Zea mays L.) and soybean (Glycine max L.) intercropping system compared to monoculture of maize, two sequential experiments were conducted. Experiment 1 was to screening the optimal cropping system in summer that had the highest yields and economic benefits, and Experiment 2 was to identify the optimum row ratio of the intercrops selected from Experiment 1. Results of Experiment 1 showed that maize intercropping with soybean (maize || soybean) was the optimal cropping system in summer. Compared to conventional monoculture of maize, maize || soybean had significant advantage in yield, economy, land utilization ratio and reducing soil nitrate nitrogen (N) accumulation, as well as better residual effect on the subsequent wheat (Triticum aestivum L.) crop. Experiment 2 showed that intercropping systems reduced use of N fertilizer per unit land area and increased relative biomass of intercropped maize, due to promoted photosynthetic efficiency of border rows and N utilization during symbiotic period. Intercropping advantage began to emerge at tasseling stage after N topdressing for maize. Among all treatments with different row ratios, alternating four maize rows with six soybean rows (4M:6S) had the largest land equivalent ratio (1.30), total N accumulation in crops (258 kg ha^-1), and economic benefit (3,408 USD ha^-1). Compared to maize monoculture, 4M:6S had significantly lower nitrate-N accumulation in soil both after harvest of maize and after harvest of the subsequent wheat, but it did not decrease yield of wheat. The most important advantage of 4M:6S was to increase biomass of intercropped maize and soybean, which further led to the increase of total N accumulation by crops as well as economic benefit. In conclusion, alternating four maize rows with six soybean rows was the optimum row ratio in maize || soybean system, though this needs to be further confirmed by pluri-annual trials.     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.     

Genetic Architecture of Ear Fasciation in Maize (Zea mays) under QTL Scrutiny

Maize ear fasciation

Knowledge of the genes affecting maize ear inflorescence may lead to better grain yield modeling. Maize ear fasciation, defined as abnormal flattened ears with high kernel row number, is a quantitative trait widely present in Portuguese maize landraces.

Material and Methods

Using a segregating population derived from an ear fasciation contrasting cross (consisting of 149 F_2:3 families) we established a two location field trial using a complete randomized block design. Correlations and heritabilities for several ear fasciation-related traits and yield were determined. Quantitative Trait Loci (QTL) involved in the inheritance of those traits were identified and candidate genes for these QTL proposed.

Results and Discussion

Ear fasciation broad-sense heritability was 0.73. Highly significant correlations were found between ear fasciation and some ear and cob diameters and row number traits. For the 23 yield and ear fasciation-related traits, 65 QTL were identified, out of which 11 were detected in both environments, while for the three principal components, five to six QTL were detected per environment. Detected QTL were distributed across 17 genomic regions and explained individually, 8.7% to 22.4% of the individual traits or principal components phenotypic variance. Several candidate genes for these QTL regions were proposed, such as bearded-ear1, branched silkless1, compact plant1, ramosa2, ramosa3, tasselseed4 and terminal ear1. However, many QTL mapped to regions without known candidate genes, indicating potential chromosomal regions not yet targeted for maize ear traits selection.

Conclusions

Portuguese maize germplasm represents a valuable source of genes or allelic variants for yield improvement and elucidation of the genetic basis of ear fasciation traits. Future studies should focus on fine mapping of the identified genomic regions with the aim of map-based cloning.     

Fine mapping of a quantitative resistance gene for gray leaf spot of maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.) derived from teosinte (<Emphasis Type="Italic">Z. mays</Emphasis> ssp. <Emphasis Type="Italic">parviglumis</Emphasis>)

Key message

In this study we mapped the QTL Qgls8 for gray leaf spot (GLS) resistance in maize to a ~130 kb region on chromosome 8 including five predicted genes.

Abstract

In previous work, using near isogenic line (NIL) populations in which segments of the teosinte (Zea mays ssp. parviglumis) genome had been introgressed into the background of the maize line B73, we had identified a QTL on chromosome 8, here called Qgls8, for gray leaf spot (GLS) resistance. We identified alternate teosinte alleles at this QTL, one conferring increased GLS resistance and one increased susceptibility relative to the B73 allele. Using segregating populations derived from NIL parents carrying these contrasting alleles, we were able to delimit the QTL region to a ~130 kb (based on the B73 genome) which encompassed five predicted genes.

     

Life without tRNAArg–adenosine deaminase TadA: evolutionary consequences of decoding the four CGN codons as arginine in Mycoplasmas and other Mollicutes

In most bacteria, two tRNAs decode the four arginine CGN codons. One tRNA harboring a wobble inosine (tRNA^Arg_ICG) reads the CGU, CGC and CGA codons, whereas a second tRNA harboring a wobble cytidine (tRNA^Arg_CCG) reads the remaining CGG codon. The reduced genomes of Mycoplasmas and other Mollicutes lack the gene encoding tRNA^Arg_CCG. This raises the question of how these organisms decode CGG codons. Examination of 36 Mollicute genomes for genes encoding tRNA^Arg and the TadA enzyme, responsible for wobble inosine formation, suggested an evolutionary scenario where tadA gene mutations first occurred. This allowed the temporary accumulation of non-deaminated tRNA^Arg_ACG, capable of reading all CGN codons. This hypothesis was verified in Mycoplasma capricolum, which contains a small fraction of tRNA^Arg_ACG with a non-deaminated wobble adenosine. Subsets of Mollicutes continued to evolve by losing both the mutated tRNA^Arg_CCG and tadA, and then acquired a new tRNA^Arg_UCG. This permitted further tRNA^Arg_ACG mutations with tRNA^Arg_GCG or its disappearance, leaving a single tRNA^Arg_UCG to decode the four CGN codons. The key point of our model is that the A-to-I deamination activity had to be controlled before the loss of the tadA gene, allowing the stepwise evolution of Mollicutes toward an alternative decoding strategy.     

Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping

Key message

Using combined linkage and association mapping, 26 stable QTL and six stable SNPs were detected across multiple environments for eight ear and grain morphological traits in maize. One QTL, PKS2, might play an important role in maize yield improvement.

Abstract

In the present study, one bi-parental population and an association panel were used to identify quantitative trait loci (QTL) for eight ear and grain morphological traits. A total of 108 QTL related to these traits were detected across four environments using an ultra-high density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319, and 26 QTL were identified in more than two environments. Furthermore, 64 single nucleotide polymorphisms (SNPs) were found to be significantly associated with the eight ear and grain morphological traits (?log₁₀(P)?>?4) in an association panel of 240 maize inbred lines. Combining the two mapping populations, a total of 17 pleiotropic QTL/SNPs (pQTL/SNPs) were associated with various traits across multiple environments. PKS2, a stable locus influencing kernel shape identified on chromosome 2 in a genome-wide association study (GWAS), was within the QTL confidence interval defined by the RILs. The candidate region harbored a short 13-Kb LD block encompassing four SNPs (SYN11386, PHM14783.16, SYN11392, and SYN11378). In the association panel, 13 lines derived from the hybrid PI78599 possessed the same allele as Qi319 at the PHM14783.16 (GG) locus, with an average value of 0.21 for KS, significantly lower than that of the 34 lines derived from Ye478 that carried a different allele (0.25, P?<?0.05). Therefore, further fine mapping of PKS2 will provide valuable information for understanding the genetic components of grain yield and improving molecular marker-assisted selection (MAS) in maize.

     

Nonparametric Bayesian Variable Selection With Applications to Multiple Quantitative Trait Loci Mapping With Epistasis and Gene–Environment Interaction

The joint action of multiple genes is an important source of variation for complex traits and human diseases. However, mapping genes with epistatic effects and gene–environment interactions is a difficult problem because of relatively small sample sizes and very large parameter spaces for quantitative trait locus models that include such interactions. Here we present a nonparametric Bayesian method to map multiple quantitative trait loci (QTL) by considering epistatic and gene–environment interactions. The proposed method is not restricted to pairwise interactions among genes, as is typically done in parametric QTL analysis. Rather than modeling each main and interaction term explicitly, our nonparametric Bayesian method measures the importance of each QTL, irrespective of whether it is mostly due to a main effect or due to some interaction effect(s), via an unspecified function of the genotypes at all candidate QTL. A Gaussian process prior is assigned to this unknown function. In addition to the candidate QTL, nongenetic factors and covariates, such as age, gender, and environmental conditions, can also be included in the unspecified function. The importance of each genetic factor (QTL) and each nongenetic factor/covariate included in the function is estimated by a single hyperparameter, which enters the covariance function and captures any main or interaction effect associated with a given factor/covariate. An initial evaluation of the performance of the proposed method is obtained via analysis of simulated and real data.TRAITS showing continuous variation are called quantitative traits and are typically controlled by multiple genetic and nongenetic factors, which tend to have relatively small effects individually. Crosses between inbred lines produce suitable populations for quantitative trait locus (QTL) mapping and are available for agricultural plants and for animal (e.g., mouse) models of human diseases. Such crosses are often used to detect QTL. For these inbred line crosses, uniform genetic backgrounds, controlled breeding schemes, and controlled environment ensure that there is little or no confounding of uncontrolled sources of variability with genetic effects. The potential for such confounding complicates and limits the analysis and interpretation of human data. Because of the homology between humans and rodents, rodent models can be extremely useful in advancing our understanding of certain human diseases. In the past 2 decades, various statistical approaches have been developed to identify QTL in inbred line crosses (see, for example, Doerge et al. 1997 for review). To perform QTL mapping (identification), a large number of candidate positions (candidate QTL) along the genome are selected. These candidate QTL may all be located at genetic markers (positions of sequence variants in the genome where the genotypes of all individuals in a mapping population can be measured) or also in between markers if the marker density is not high. QTL mapping may then be performed by considering one candidate QTL at a time or multiple candidate QTL simultaneously. For inbred line crosses with low marker density and considering a single candidate QTL at a time, the interval-mapping method was proposed by Lander and Botstein (1989). However, these authors showed that interval mapping tends to identify a “ghost” QTL located in between two actual linked QTL if two or more closely linked QTL exist. This problem can be reduced or eliminated in two ways: (1) by using composite-interval mapping (Jansen and Stam 1994; Zeng 1994) which still performs a one-dimensional QTL search but conditional on the genotypes at a pair of markers flanking the marker interval containing the current QTL, to absorb the effects of background (nontarget QTL) outside of the target interval; or (2) by performing multiple QTL mapping, where two or more QTL are mapped simultaneously. Furthermore, if several QTL affect a quantitative trait mostly through their interactions (epistasis) while having nonexistent or weak main effects, then interval mapping or single-marker analysis will fail to detect such QTL. QTL interactions may not be limited to pairwise interactions. Marchini et al. (2005) have shown by simulation that searching for three loci jointly in the presence of a three-way interaction is more powerful than searching for a single or a pair of QTL. There are various different implementations of multiple QTL mapping. Most methods still perform only pairwise searches, with and without epistasis. The most recent methods are based on Bayesian variable selection and consider a group of candidate QTL or all candidate QTL in the genome simultaneously (e.g., Yi et al. 2007). These methods are typically still limited to pairwise interactions among QTL and do not consider gene–environment interactions.The identification of QTL can be viewed as a very large variable selection problem: for p candidate QTL, with p typically in the hundreds or thousands and sample size in the low hundreds, there are 2^p possible main-effect models, possible two-way interactions, and possible higher-order (k > 2) interactions. For inbred line crosses, where multiple-QTL mapping models can be represented as multiple linear regression models, classical variable selection methods such as forward and stepwise selection (Broman and Speed 2002) have been used in searching for main and two-way interaction effects. Bayesian analysis implemented by Markov chain Monte Carlo (MCMC) and based on the composite model space framework (Godsill 2001, 2003) has been introduced to genetic mapping (Yi 2004). Well-known Bayesian variable selection methods such as reversible jump MCMC (Green 1995) and stochastic search variable selection (SSVS) (George and McCulloch 1993) are special cases. SSVS and similar methods employ mixture priors for the regression coefficients, which specify different distributions for the coefficients under the null (effect negligible) and alternative (effect nonnegligible) hypotheses. The marginal posterior probabilities of the alternative hypotheses can be used to identify a subset of important parameters on the basis of Bayesian multiple comparison rules, including the median probability model (with a threshold of 0.5) and Bayesian false discovery rate control (e.g., Müller et al. 2006).An alternative to variable selection with mixture priors is classical and Bayesian shrinkage- or penalty-based inference. For the classical approach of penalized regression, while an L₂-based shrinkage method (ridge regression) cannot perform variable selection, other methods, in particular the L₁-based lasso of Tibshirani (1996) and later lasso extensions, are capable of performing variable selection by reducing the effects of unimportant variables effectively to zero. The lasso has been applied to parametric, regression-based QTL mapping (Yi and Xu 2008). The penalized regression methods can be interpreted as Bayesian regression models with particular sparsity priors imposed on the regression coefficients (Park and Casella 2008).Regression methods are also used for association mapping in human populations. Recently, Kwee et al. (2008) proposed a semiparametric regression-based approach for candidate regions in human association mapping, where a quantitative trait is regressed on a nonparametric function of the tagSNP genotypes within a region. They analyzed a (small) subset of the genome and tested for the joint significance of the subset. Their method potentially can be used to model interactions among SNPs and covariates. However, Kwee et al. (2008) fit their model using least-squares kernel machines, a dimension-reducing technique that is identical to an analysis based on a specific linear mixed model. Model selection for different types of kernels and different sets of variables is performed using criteria such as Akaike''s information criteria (Akaike 1974) and Bayesian information criteria (Schwarz 1978), which may not be appropriate or feasible in large-scale, sparse variable selection situations.We (Huang et al. 2010) recently developed a Bayesian semiparametric QTL mapping method, where nongenetic covariate effects are modeled nonparametrically. This method was implemented via MCMC, and a Gaussian process prior (O''Hagan 1978; Neal 1996, 1997) was placed on the unknown covariate function. The Gaussian process is particularly well suited for curve estimation due to its flexible sample path shapes. This method allows one or more nongenetic covariates to have an arbitrary (nonlinear) relationship with the phenotype. Another strong advantage of the Gaussian process is its ability to deal with high-dimensional data compared to other nonparametric techniques such as spline regression (Wahba 1984; Heckman 1986; Chen 1988; Speckman 1988; Cuzick 1992; Hastie and Loader 1993). There has been a growing interest in using Gaussian processes as a unifying framework for studying multivariate regression (Rasmussen 1996), pattern classification (Williams and Barber 1998), and hierarchical modeling (Menzefricke 2000). In this article, we build on this work and propose a nonparametric Bayesian method for multiple QTL mapping by including not only nongenetic covariates but also all candidate QTL in the unknown function. A Gaussian process prior (GPP) is again placed on the unknown function, and a variable selection approach is implemented for the hyperparameters of the GPP (one for each QTL and nongenetic covariate). Here, we rely on mixture priors and MCMC implementation, and we focus on linkage mapping in inbred line crosses, while in ongoing and future work we are considering shrinkage priors, deterministic algorithms, and association mapping. Our application of the GPP differs from “standard” applications in that the QTL covariates included in the unknown function are discrete, not continuous, with a small number (two or three) of possible values (the genotype codes). The goal of using a GPP here is not curve or response surface modeling but rather high-dimensional variable selection (QTL and nongenetic covariates) with a method requiring only a single parameter for each variable while accounting for any multiway interactions among the candidate variables.To improve current methods for linkage mapping in inbred line crosses and for association analysis of human populations, we need to be able to detect QTL irrespective of whether they act mostly through main effects, interactions with other QTL, or interactions with environment. Fitting a parametric model including all these potential effects for a genome-wide search would substantially increase the multiple-testing problem, in addition to being computationally extremely demanding. Here we offer an alternative. We show that our nonparametric Bayesian method can identify QTL irrespective of whether they act through main effects, through interactions with other QTL, or with environmental factors. This method cannot identify the source(s) of a QTL''s importance (main or interaction effects involving this QTL). Therefore, once a small number of important QTL have been identified in a genome-wide scan, then these QTL can be further analyzed with detailed parametric models to determine the source(s) of their importance.The remainder of the article is organized as follows. We first present the nonparametric multiple-QTL model and outline the MCMC sampler in the next section. Simulation results and the analysis of a real data set are presented in the section following that. And we end the article with a discussion and conclusions.     

Comparison of Mating Designs for Establishing Nested Association Mapping Populations in Maize and Arabidopsis thaliana

The nested association mapping (NAM) strategy promises to combine the advantages of linkage mapping and association mapping. The objectives of my research were to (i) investigate by computer simulations the power and type I error rate for detecting quantitative trait loci (QTL) with additive effects using recombinant inbred line (RIL) populations of maize derived from various mating designs, (ii) compare these estimates to those obtained for RIL populations of Arabidopsis thaliana, (iii) examine for both species the optimum number of inbreds used as parents of the NAM populations, and (iv) provide on the basis of the results of these two model species a general guideline for the design of NAM populations in other plant species. The computer simulations were based on empirical data of a set of 26 diverse maize inbred lines and a set of 20 A. thaliana inbreds both representing a large part of the genetic diversity of the corresponding species. I observed considerable differences in the power for QTL detection between NAM populations of the same size but created on the basis of different crossing schemes. This finding illustrated the potential to improve the power for QTL detection without increasing the total resources necessary for a QTL mapping experiment. Furthermore, my results clearly indicated that it is advantageous to create NAM populations from a large number of parental inbreds.MANY traits that are important for fitness and agricultural value of plants are quantitative traits. Such traits are affected by many genes, the environment, and interactions between genes and the environment (Holland 2007). In plants, quantitative trait locus (QTL) mapping is a key tool for studying the genetic architecture of quantitative traits (Yano 2001). This method enables the estimation of (i) the number of genome regions affecting a trait, (ii) the distribution of gene effects, and (iii) the relative importance of additive and nonadditive gene action.Until now, most of the plant QTL mapping studies have been based on linkage mapping methods using individual biparental populations. The major limitations of such approaches are a poor resolution in detecting QTL and that with biparental crosses of inbred lines only two alleles at any given locus can be studied simultaneously (Flint-Garcia et al. 2005). Association mapping methods, which are successfully applied in human genetics to detect genes coding for human diseases (e.g., Willer et al. 2008), promise to overcome these limitations (Kraakman et al. 2004). However, in comparison with linkage mapping approaches, association mapping approaches have only a low power to detect QTL in genomewide scans (Yu and Buckler 2006).The nested association mapping (NAM) strategy proposed by Yu et al. (2008) uses recombinant inbred line (RIL) populations derived from several crosses of parental inbreds. Due to diminishing chances of recombination over short genetic distance and a given number of generations, the genomes of these RILs are mosaics of chromosomal segments of their parental genomes. Consequently, within the chromosomal segments, the linkage disequilibrium (LD) information across the parental inbreds is maintained. Thus, if diverse parental inbreds are used, LD decays within the chromosomal segments of the RILs over a short physical distance (Wilson et al. 2004). Therefore, the NAM strategy allows to exploit both recent and ancient recombination and, thus, will show a high mapping resolution (Yu et al. 2008). Furthermore, due to the balanced design underlying the proposed mapping strategy as well as the systematic reshuffling of the genomes of the parental inbreds during RIL development, NAM populations are expected to show a high power to detect QTL in genomewide approaches (Buckler et al. 2009).Exploitation of the advantages of the NAM strategy requires developing, genotyping, and phenotyping of RIL populations from several crosses of diverse parental inbreds. This, however, requires large financial resources (cf. Yu et al. 2008). Therefore, it is mandatory that the available resources are spent in an optimum way.Stich et al. (2009) examined the optimum allocation of resources for NAM in maize with respect to the number of RILs derived from the reference design as well as the number of environments and replications per environment used for phenotypic evaluation. The power for QTL detection, however, is expected to be influenced not only by these factors but also by the crossing scheme from which RIL populations are derived. To my knowledge, no study has so far compared RIL populations derived from various mating designs regarding the power for detecting QTL with additive effects. Furthermore, no information is available on the optimum number of inbreds used as parents of the NAM populations.For Arabidopsis thaliana, more advanced genomic tools are available than for most other plant species (e.g., Alonso et al. 2003; Clark et al. 2007). This fact increases the prospects of success of NAM approaches. However, A. thaliana differs from maize with respect to the genome size and the allele frequency, which both have the potential to influence the power for QTL detection. Nevertheless, to my knowledge, no study has so far examined the power of NAM in A. thaliana.The objectives of my research were to (i) investigate by computer simulations the power and type I error rate for detecting QTL with additive effects using RIL populations of maize derived from various mating designs, (ii) compare these estimates to those obtained for RIL populations of A. thaliana, (iii) examine for both species the optimum number of inbreds used as parents of the NAM populations, and (iv) provide on the basis of the results of these two model species a general guideline for the design of NAM populations in other plant species.     

Correlations and comparisons of quantitative trait loci with family per se and testcross performance for grain yield and related traits in maize

Simultaneous improvement in grain yield and related traits in maize hybrids and their parents (inbred lines) requires a better knowledge of genotypic correlations between family per se performance (FP) and testcross performance (TP). Thus, to understand the genetic basis of yield-related traits in both inbred lines and their testcrosses, two F _2:3 populations (including 230 and 235 families, respectively) were evaluated for both FP and TP of eight yield-related traits in three diverse environments. Genotypic correlations between FP and TP, $ \hat{r}_{\text{g}} $ (FP, TP), were low (0–0.16) for grain yield per plant (GYPP) and kernel number per plant (KNPP) in the two populations, but relatively higher (0.32–0.69) for the other six traits with additive effects as the primary gene action. Similar results were demonstrated by the genotypic correlations between observed and predicted TP values based on quantitative trait loci positions and effects for FP, $ \hat{r}_{\text{g}} $ (M _FP, Y _TP). A total of 88 and 35 QTL were detected with FP and TP, respectively, across all eight traits in the two populations. However, the genotypic variances explained by the QTL detected in the cross-validation analysis were much lower than those in the whole data set for all traits. Several common QTL between FP and TP that accounted for large phenotypic variances were clustered in four genomic regions (bin 1.10, 4.05–4.06, 9.02, and 10.04), which are promising candidate loci for further map-based cloning and improvement in grain yield in maize. Compared with publicly available QTL data, these QTL were also detected in a wide range of genetic backgrounds and environments in maize. These results imply that effective selection based on FP to improve TP could be achieved for traits with prevailing additive effects.     

Collagen-binding Microbial Surface Components Recognizing Adhesive Matrix Molecule (MSCRAMM) of Gram-positive Bacteria Inhibit Complement Activation via the Classical Pathway

Members of a family of collagen-binding microbial surface components recognizing adhesive matrix molecules (MSCRAMMs) from Gram-positive bacteria are established virulence factors in several infectious diseases models. Here, we report that these adhesins also can bind C1q and act as inhibitors of the classical complement pathway. Molecular analyses of Cna from Staphylococcus aureus suggested that this prototype MSCRAMM bound to the collagenous domain of C1q and interfered with the interactions of C1r with C1q. As a result, C1r₂C1s₂ was displaced from C1q, and the C1 complex was deactivated. This novel function of the Cna-like MSCRAMMs represents a potential immune evasion strategy that could be used by numerous Gram-positive pathogens.     

Genetic Control of Photoperiod Sensitivity in Maize Revealed by Joint Multiple Population Analysis

Variation in maize for response to photoperiod is related to geographical adaptation in the species. Maize possesses homologs of many genes identified as regulators of flowering time in other species, but their relation to the natural variation for photoperiod response in maize is unknown. Candidate gene sequences were mapped in four populations created by crossing two temperate inbred lines to two photoperiod-sensitive tropical inbreds. Whole-genome scans were conducted by high-density genotyping of the populations, which were phenotyped over 3 years in both short- and long-day environments. Joint multiple population analysis identified genomic regions controlling photoperiod responses in flowering time, plant height, and total leaf number. Four key genome regions controlling photoperiod response across populations were identified, referred to as ZmPR1–4. Functional allelic differences within these regions among phenotypically similar founders suggest distinct evolutionary trajectories for photoperiod adaptation in maize. These regions encompass candidate genes CCA/LHY, CONZ1, CRY2, ELF4, GHD7, VGT1, HY1/SE5, TOC1/PRR7/PPD-1, PIF3, ZCN8, and ZCN19.MAIZE (Zea mays L. subsp. mays) was domesticated in southern Mexico and its center of diversity is in tropical Latin America (Goodman 1999; Matsuoka et al. 2002), where precipitation rates and day lengths cycle annually. The presumed ancestor of maize, teosinte (Zea mays L. subsp. parviglumis), likely evolved photoperiod sensitivity to synchronize its reproductive phases to the wetter, short-day growing season (Ribaut et al. 1996; Campos et al. 2006). A critical event in the postdomestication evolution of maize was its spread from tropical to temperate regions of the Americas (Goodman 1988), requiring adaptation to longer day lengths. The result of this adaptation process is manifested today as a major genetic differentiation between temperate and tropical maize (Liu et al. 2003) and substantially reduced photoperiod sensitivity of temperate maize (Gouesnard et al. 2002). Tropical maize exhibits delayed flowering time, increased plant height, and a greater total leaf number when grown in temperate latitudes with daily dark periods <11 hr (Allison and Daynard 1979; Warrington and Kanemasu 1983a,b). Identifying the genes underlying maize photoperiod sensitivity will provide insight into the postdomestication evolution of maize and may reduce barriers to the use of diverse tropical germplasm resources for improving temperate maize production (Holland and Goodman 1995; Liu et al. 2003; Ducrocq et al. 2009).Natural variation at key genes in flowering time pathways is related to adaptation and evolution of diverse plant species (Caicedo et al. 2004; Shindo et al. 2005; Turner et al. 2005; Cockram et al. 2007; Izawa 2007; Slotte et al. 2007). Identification of some of the genes controlling adaptation in numerous plant species relied on regulatory pathways elucidated in Arabidopsis (Simpson and Dean 2002). Many key genes in the Arabidopsis flowering time regulatory pathways are conserved across diverse plant species (Kojima et al. 2002; Hecht et al. 2007; Kwak et al. 2008), but their functions have diverged, resulting in unique regulatory pathways in some phylogenetic groups (Colasanti and Coneva 2009). For example, FRI and FLC control most natural variation for vernalization response in Arabidopsis (Caicedo et al. 2004; Shindo et al. 2005), but wheat and barley appear to lack homologs of these genes and regulate vernalization response with different genes (Yan et al. 2004).Maize exhibits tremendous natural variation for flowering time (Gouesnard et al. 2002; Camus-Kulandaivelu et al. 2006), for which numerous QTL have been identified (Chardon et al. 2004). In contrast, only a few flowering time mutants are known and only a handful of flowering time genes, including DWARF8 (D8), DELAYED FLOWERING1 (DLF1), VEGETATIVE TO GENERATIVE TRANSITION1 (VGT1), and INDETERMINATE GROWTH1 (ID1), have been cloned in maize (Thornsberry et al. 2001; Colasanti et al. 2006; Muszynski et al. 2006; Salvi et al. 2007; Colasanti and Coneva 2009). Variation at or near D8 and VGT1 is related to latitudinal adaptation, but these genes do not appear to regulate photoperiod responses and account for only a limited proportion of the standing flowering time variation in maize (Camus-Kulandaivelu et al. 2006, 2008; Ducrocq et al. 2008; Buckler et al. 2009).Quantitative trait loci (QTL) mapping was a key first step to identifying the genes underlying natural variation for flowering time in Arabidopsis (Koornneef et al. 2004). Photoperiodic QTL have been mapped previously in individual biparental maize mapping populations (Koester et al. 1993; Moutiq et al. 2002; Wang et al. 2008; Ducrocq et al. 2009). Such studies are informative with respect to the parents from which the populations were derived, but often do not reflect the genetic heterogeneity of broader genetic reference populations (Holland 2007).Association mapping (Thornsberry et al. 2001; Ersoz et al. 2007) and combined analysis of multiple biparental crosses (Rebaï et al. 1997; Rebaï and Goffinet 2000; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008) represent alternative approaches to understanding the variation in genetic control for complex traits among diverse germplasm. Association mapping has limited power to identify genes that affect traits closely associated with population structure, such as flowering time in maize (Camus-Kulandaivelu et al. 2006; Ersoz et al. 2007). In contrast, joint QTL analysis of multiple populations is not hindered by the associations between causal genes and population structure. Combined QTL analysis of multiple mapping populations provides improved power to detect QTL, more precise estimation of their effects and positions, and better understanding of their functional allelic variation and distribution across more diverse germplasm compared to single-population mapping (Rebaï et al. 1997; Wu and Jannink 2004; Jourjon et al. 2005; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008; Buckler et al. 2009). Joint analysis also provides a direct test of the importance of higher-order epistatic interactions between founder alleles at individual loci with genetic backgrounds (Jannink and Jansen 2001; Blanc et al. 2006). In this study, joint analysis of multiple populations was used to test directly the hypothesis that diverse tropical maize lines carry functionally similar alleles at key photoperiod loci, which would imply genetic homogeneity for a common set of mutations and a shared evolutionary pathway for photoperiod insensitivity.The objective of this study was to integrate candidate gene analyses with photoperiod QTL mapping across multiple maize populations. We tested candidate floral regulators known from other species for associations with natural variation for photoperiod response in maize. We analyzed flowering time in four interrelated recombinant inbred line (RIL) populations, each derived from crosses between temperate and tropical maize parents (Figure 1), in both long- and short-day environments to characterize their responses to distinct photoperiods. Joint population analysis provided high resolution of many QTL positions, permitting robust testing of underlying candidate genes. We directly and indirectly mapped homologs of flowering time candidates genes from Arabidopsis, rice, and barley on a dense consensus genetic map of these four populations, permitting identification of homologs that colocalize with genome regions associated with variation for photoperiod response. These mapping families are being integrated into the maize nested association mapping (NAM) population (Buckler et al. 2009; McMullen et al. 2009) because they were genotyped with the maize NAM map SNP markers, they involve the common parent B73, and their seed and genotypic information (File S1 cont.) are publicly available. Their availability further expands the genetic diversity represented by the maize NAM population and enhances this valuable public community resource.Open in a separate windowFigure 1.—Factorial mating of two temperate (B73 and B97) and two tropical (CML254 and Ki14) inbred maize lines to create four related recombinant inbred line mapping populations.     

Mutations in Grxcr1 Are The Basis for Inner Ear Dysfunction in the Pirouette Mouse

Recessive mutations at the mouse pirouette (pi) locus result in hearing loss and vestibular dysfunction due to neuroepithelial defects in the inner ear. Using a positional cloning strategy, we have identified mutations in the gene Grxcr1 (glutaredoxin cysteine-rich 1) in five independent allelic strains of pirouette mice. We also provide sequence data of GRXCR1 from humans with profound hearing loss suggesting that pirouette is a model for studying the mechanism of nonsyndromic deafness DFNB25. Grxcr1 encodes a 290 amino acid protein that contains a region of similarity to glutaredoxin proteins and a cysteine-rich region at its C terminus. Grxcr1 is expressed in sensory epithelia of the inner ear, and its encoded protein is localized along the length of stereocilia, the actin-filament-rich mechanosensory structures at the apical surface of auditory and vestibular hair cells. The precise architecture of hair cell stereocilia is essential for normal hearing. Loss of function of Grxcr1 in homozygous pirouette mice results in abnormally thin and slightly shortened stereocilia. When overexpressed in transfected cells, GRXCR1 localizes along the length of actin-filament-rich structures at the dorsal-apical surface and induces structures with greater actin filament content and/or increased lengths in a subset of cells. Our results suggest that deafness in pirouette mutants is associated with loss of GRXCR1 function in modulating actin cytoskeletal architecture in the developing stereocilia of sensory hair cells.     

KRN4 Controls Quantitative Variation in Maize Kernel Row Number

Kernel row number (KRN) is an important component of yield during the domestication and improvement of maize and controlled by quantitative trait loci (QTL). Here, we fine-mapped a major KRN QTL, KRN4, which can enhance grain productivity by increasing KRN per ear. We found that a ~3-Kb intergenic region about 60 Kb downstream from the SBP-box gene Unbranched3 (UB3) was responsible for quantitative variation in KRN by regulating the level of UB3 expression. Within the 3-Kb region, the 1.2-Kb Presence-Absence variant was found to be strongly associated with quantitative variation in KRN in diverse maize inbred lines, and our results suggest that this 1.2-Kb transposon-containing insertion is likely responsible for increased KRN. A previously identified A/G SNP (S35, also known as Ser220Asn) in UB3 was also found to be significantly associated with KRN in our association-mapping panel. Although no visible genetic effect of S35 alone could be detected in our linkage mapping population, it was found to genetically interact with the 1.2-Kb PAV to modulate KRN. The KRN4 was under strong selection during maize domestication and the favorable allele for the 1.2-Kb PAV and S35 has been significantly enriched in modern maize improvement process. The favorable haplotype (Hap1) of 1.2-Kb-PAV-S35 was selected during temperate maize improvement, but is still rare in tropical and subtropical maize germplasm. The dissection of the KRN4 locus improves our understanding of the genetic basis of quantitative variation in complex traits in maize.     

Targeted Protein Engineering Provides Insights into Binding Mechanism and Affinities of Bacterial Collagen Adhesins

The collagen-binding bacterial proteins, Ace and Cna, are well characterized on the biochemical and structural level. Despite overall structural similarity, recombinant forms of the Ace and Cna ligand-binding domains exhibit significantly different affinities and binding kinetics for collagen type I (CI) in vitro. In this study, we sought to understand, in submolecular detail, the bases for these differences. Using a structure-based approach, we engineered Cna and Ace variants by altering specific structural elements within the ligand-binding domains. Surface plasmon resonance-based binding analysis demonstrated that mutations that are predicted to alter the orientation of the Ace and Cna N₁ and N₂ subdomains significantly affect the interaction between the MSCRAMM (microbial surface components recognizing adhesive matrix molecule) and CI in vitro, including affinity, association/dissociation rates and binding ratio. Moreover, we utilized this information to engineer an Ace variant with an 11,000-fold higher CI affinity than the parent protein. Finally, we noted that several engineered proteins that exhibited a weak interaction with CI recognized more sites on CI, suggesting an inverse correlation between affinity and specificity.     

DOGS: reaction-driven de novo design of bioactive compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25''144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H₄ receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.     

Preferential Utilization of d-Lactate by Shewanella oneidensis

Shewanella oneidensis couples oxidation of lactate to respiration of many substrates. Here we report that llpR (l-lactate-positive regulator, SO_3460) encodes a positive regulator of l-lactate utilization distinct from previously studied regulators. We also demonstrate d-lactate inhibition of l-lactate utilization in S. oneidensis, resulting in preferential utilization of the d isomer.     

Evolution of the B3 DNA Binding Superfamily: New Insights into REM Family Gene Diversification

Background

The B3 DNA binding domain includes five families: auxin response factor (ARF), abscisic acid-insensitive3 (ABI3), high level expression of sugar inducible (HSI), related to ABI3/VP1 (RAV) and reproductive meristem (REM). The release of the complete genomes of the angiosperm eudicots Arabidopsis thaliana and Populus trichocarpa, the monocot Orysa sativa, the bryophyte Physcomitrella patens,the green algae Chlamydomonas reinhardtii and Volvox carteri and the red algae Cyanidioschyzon melorae provided an exceptional opportunity to study the evolution of this superfamily.

Methodology

In order to better understand the origin and the diversification of B3 domains in plants, we combined comparative phylogenetic analysis with exon/intron structure and duplication events. In addition, we investigated the conservation and divergence of the B3 domain during the origin and evolution of each family.

Conclusions

Our data indicate that showed that the B3 containing genes have undergone extensive duplication events, and that the REM family B3 domain has a highly diverged DNA binding. Our results also indicate that the founding member of the B3 gene family is likely to be similar to the ABI3/HSI genes found in C. reinhardtii and V. carteri. Among the B3 families, ABI3, HSI, RAV and ARF are most structurally conserved, whereas the REM family has experienced a rapid divergence. These results are discussed in light of their functional and evolutionary roles in plant development.