Phenotypic Consequences of Aneuploidy in Arabidopsis thaliana

Aneuploid cells are characterized by incomplete chromosome sets. The resulting imbalance in gene dosage has phenotypic consequences that are specific to each karyotype. Even in the case of Down syndrome, the most viable and studied form of human aneuploidy, the mechanisms underlying the connected phenotypes remain mostly unclear. Because of their tolerance to aneuploidy, plants provide a powerful system for a genome-wide investigation of aneuploid syndromes, an approach that is not feasible in animal systems. Indeed, in many plant species, populations of aneuploid individuals can be easily obtained from triploid individuals. We phenotyped a population of Arabidopsis thaliana aneuploid individuals containing 25 different karyotypes. Even in this highly heterogeneous population, we demonstrate that certain traits are strongly associated with the dosage of specific chromosome types and that chromosomal effects can be additive. Further, we identified subtle developmental phenotypes expressed in the diploid progeny of aneuploid parent(s) but not in euploid controls from diploid lineages. These results indicate long-term phenotypic consequences of aneuploidy that can persist after chromosomal balance has been restored. We verified the diploid nature of these individuals by whole-genome sequencing and discuss the possibility that trans-generational phenotypic effects stem from epigenetic modifications passed from aneuploid parents to their diploid progeny.THE genome of aneuploid individuals contains incomplete chromosome sets. The balance between chromosome types, and the genes they encode, is compromised, resulting in altered expression of many genes, including genes with dosage-sensitive effects on phenotypes. In humans, only a few types of aneuploid karyotypes are viable (Hassold and Hunt 2001), highlighting the deleterious effect of chromosome imbalance. The most commonly known viable form of aneuploidy in humans is Down syndrome, which results from a trisomy of chromosome 21 in an otherwise diploid background. Down syndrome patients exhibit many specific phenotypes, sometimes visible only in a subset of patients (Antonarakis et al. 2004). For phenotypes found in all Down syndrome patients, the penetrance of each phenotype varies between patients (Antonarakis et al. 2004). Despite the increasing amount of information available about the human genome and the availability of a mouse model for Down syndrome (O''Doherty et al. 2005), the genes responsible for most of the phenotypes associated with Down syndrome are still unknown (Patterson 2007; Korbel et al. 2009; Patterson 2009). Recently, detailed phenotypic analyses of as many as 30 aneuploid patients have allowed the identification of susceptibility regions for several specific phenotypes (Patterson 2007, 2009; Korbel et al. 2009; Lyle et al. 2009), but the specific genes remain to be identified. Understanding the physiology of aneuploidy is not only relevant to those individuals with aneuploid genomes but also to understanding cancer since most cancerous cells are aneuploid (Matzke et al. 2003; Pihan and Doxsey 2003; Storchova and Pellman 2004; Holland and Cleveland 2009; Williams and Amon 2009) or the consequences of copy number variation and dosage sensitivity (Dear 2009; Henrichsen et al. 2009).Plants are more tolerant of aneuploidy than animals (Matzke et al. 2003) for reasons that remain unclear. Since the discovery of the Datura trisomic “chromosome mutants” by Blakeslee (1921, 1922), viable trisomics of each chromosome type have been described in numerous species. Trisomics exhibit phenotypes specific to the identity of the triplicated chromosome (Blakeslee 1922; Khush 1973; Koornneef and Van der Veen 1983; Singh 2003). More complex aneuploids, i.e., individuals carrying more than one additional chromosome, can be viable as well and have been observed in many plants species, especially among the progeny of triploid individuals (McClintock 1929; Levan 1942; Johnsson 1945; Khush 1973). Some species appear to be more tolerant of complex aneuploidies than others, suggesting a genetic basis for aneuploidy tolerance (Satina and Blakeslee 1938; Khush 1973; Ramsey and Schemske 2002; Henry et al. 2009). Aneuploid individuals frequently appear spontaneously within polyploid plant populations, presumably due to a failure to equally partition the multiple chromosome sets at meiosis (Randolph 1935; Doyle 1986). These aneuploids exhibit few or subtle phenotypic abnormalities and can often compete with their euploid progenitors (Ramsey and Schemske 1998). Plants therefore provide an excellent opportunity for a genome-wide investigation of aneuploid syndromes: sample size is not limited, phenotypes can be described and assessed in detail, and plant aneuploid populations provide a complex mixture of viable karyotypes.In this article, we report our investigation of the relationship between phenotype and karyotype in populations of aneuploid Arabidopsis thaliana plants. All simple trisomics of A. thaliana have been previously isolated and phenotypically characterized (Steinitz-Sears 1962; Lee-Chen and Steinitz-Sears 1967; Steinitz-Sears and Lee-Chen 1970; Koornneef and Van der Veen 1983), demonstrating that they are tolerated in A. thaliana. We previously reported that aneuploid swarms—populations of aneuploid individuals of varying aneuploid karyotypes—could be obtained from the progeny of triploid A. thaliana individuals (Henry et al. 2005, 2009). Using a combination of a quantitative PCR-based method and flow cytometry, we were able to derive the full aneuploid karyotype of each of these individuals (Henry et al. 2006). We further crossed triploid A. thaliana to diploid or tetraploid individuals and demonstrated that at least 44 of the 60 possible aneuploid karyotypes that could result from these crosses (aneuploid individuals carrying between 11 and 19 chromosomes) were viable and successfully produced adult plants. Taken together, these populations and methods make it possible to explore the basis of aneuploid syndromes in A. thaliana. In this study, we were able to phenotypically characterize at least one individual from 25 different aneuploid karyotypes falling between diploidy and tetraploidy. We demonstrated that specific phenotypes are affected by the dosage of specific chromosome types. The effect of the dosage of specific chromosome types on traits was additive and could be used to predict the observed phenotype. The availability of multiple generations of aneuploid and euploid individuals allowed us to investigate potential long-term effects of aneuploidy as well as parent-of-origin effects on aneuploid phenotypes.     

Inositol Metabolism in Plants. V. Conversion of Myo-inositol to Uronic Acid and Pentose Units of Acidic Polysaccharides in Root-tips of Zea mays

The metabolism of myo-inositol-2-¹⁴C, d-glucuronate-1-¹⁴C, d-glucuronate-6-¹⁴C, and l-methionine-methyl-¹⁴C to cell wall polysaccharides was investigated in excised root-tips of 3 day old Zea mays seedlings. From myo-inositol, about one-half of incorporated label was recovered in ethanol insoluble residues. Of this label, about 90% was solubilized by treatment, first with a preparation of pectinase-EDTA, then with dilute hydrochloric acid. The only labeled constituents in these hydrolyzates were d-galacturonic acid, d-glucuronic acid, 4-O-methyl-d-glucuronic acid, d-xylose, and l-arabinose, or larger oligosaccharide fragments containing these units. Medium external to excised root-tips grown under sterile conditions in myo-inositol-2-¹⁴C contained labeled polysaccharide.     

Identity-by-Descent Matrix Decomposition Using Latent Ancestral Allele Models

Genetic linkage and association studies are empowered by proper modeling of relatedness among individuals. Such relatedness can be inferred from marker and/or pedigree information. In this study, the genetic relatedness among n inbred individuals at a particular locus is expressed as an n × n square matrix Q. The elements of Q are identity-by-descent probabilities, that is, probabilities that two individuals share an allele descended from a common ancestor. In this representation the definition of the ancestral alleles and their number remains implicit. For human inspection and further analysis, an explicit representation in terms of the ancestral allele origin and the number of alleles is desirable. To this purpose, we decompose the matrix Q by a latent class model with K classes (latent ancestral alleles). Let P be an n × K matrix with assignment probabilities of n individuals to K classes constrained such that every element is nonnegative and each row sums to 1. The problem then amounts to approximating Q by PP^T, while disregarding the diagonal elements. This is not an eigenvalue problem because of the constraints on P. An efficient algorithm for calculating P is provided. We indicate the potential utility of the latent ancestral allele model. For representative locus-specific Q matrices constructed for a set of maize inbreds, the proposed model recovered the known ancestry.HIGH-THROUGHPUT techniques allow extensive genotyping of individuals for thousands of SNP markers (Gibbs et al. 2003) and thereby provide accurate information about the genetic diversity within a population at many chromosomal loci. If two individuals within this population carry the same DNA sequence at a locus, and this sequence can be traced to the same common ancestor, the individuals are said to be identical by descent (IBD) for this segment (Chapman and Thompson 2003). Quite often, however, the ancestral source of a chromosomal segment is ambiguous and thus IBD relationships between haplotypes are given as probabilities. Various methods have been described to estimate the IBD probability of pairs of chromosomal segments (Meuwissen and Goddard 2001; Leutenegger et al. 2003). When pedigree relationships are known, these can be included to estimate IBD probabilities (Wang et al. 1995; Heath 1997; George et al. 2000; Meuwissen and Goddard 2000; Besnier and Carlborg 2007).In quantitative genetic analysis we seek to find and characterize associations between the large number of SNPs that are now available for many organisms and phenotypic variation for traits of interest (e.g., grain yield and time to flowering). Many current methods developed for this purpose make use of IBD information. For example, a locus-specific matrix of IBD probabilities can be incorporated into restricted maximum-likelihood (REML) procedures for fine mapping quantitative trait loci (Bink and Meuwissen 2004) as well as for marker-based genetic evaluation (Fernando and Grossman 1989) using mixed models. The IBD matrix takes the role of a covariance matrix in the REML procedure.Other approaches, however, require that chromosome segments (also referred to here as haplotypes or alleles) are assigned to independent ancestors. These approaches include regression approaches with genetic predictors (Malosetti et al. 2006) and Bayesian oligo-allelic approaches that sample the ancestral origin of each chromosomal segment (Heath 1997; Uimari and Sillanpaa 2001; Bink et al. 2008a). In the IBD matrix representation the ancestral alleles and their number remain implicit. For these approaches, the locus-specific matrix of IBD probabilities must therefore be decomposed into a matrix that links the chromosomal segments to independent ancestral alleles. This decomposition is addressed in this article.The individuals that we consider in this article are inbred. For n inbred individuals the IBD matrix at a given chromosomal position is thus n × n, because there is no need to distinguish between identical chromosomes. In diploid, outbred populations, each individual would be represented by two haplotypes (alleles) and the matrix would be 2n × 2n (Fernando and Grossman 1989). This is feasible if any phase ambiguity can be resolved. From now on, the term “individual” thus means chromosomal segment or haplotype. Analogously, ancestor will be shorthand for ancestral allele (ancestral haplotype).We propose two models of IBD matrix decomposition, a simple threshold model (TIBD) and a more sophisticated latent ancestral allele model (LAAM), that provide (1) an estimate of the number of independent ancestral alleles, (2) a concise, easy-to-interpret, summary of the relatedness, (3) an explicit (probabilistic) representation of the descent of alleles, and (4) the ability to sample alleles for each individual from a set of ancestral alleles in such a way that the probability that a pair of individuals shares the same allele corresponds to their IBD probability.The last two features of the model are essential for its use in Bayesian oligo-allelic approaches to quantitative trait locus (QTL) analysis (Uimari and Sillanpaa 2001; Bink et al. 2008a).     

Polymorphic Genes of Major Effect: Consequences for Variation,Selection and Evolution in Arabidopsis thaliana

The importance of genes of major effect for evolutionary trajectories within and among natural populations has long been the subject of intense debate. For example, if allelic variation at a major-effect locus fundamentally alters the structure of quantitative trait variation, then fixation of a single locus can have rapid and profound effects on the rate or direction of subsequent evolutionary change. Using an Arabidopsis thaliana RIL mapping population, we compare G-matrix structure between lines possessing different alleles at ERECTA, a locus known to affect ecologically relevant variation in plant architecture. We find that the allele present at ERECTA significantly alters G-matrix structure—in particular the genetic correlations between branch number and flowering time traits—and may also modulate the strength of natural selection on these traits. Despite these differences, however, when we extend our analysis to determine how evolution might differ depending on the ERECTA allele, we find that predicted responses to selection are similar. To compare responses to selection between allele classes, we developed a resampling strategy that incorporates uncertainty in estimates of selection that can also be used for statistical comparisons of G matrices.THE structure of the genetic variation that underlies phenotypic traits has important consequences for understanding the evolution of quantitative traits (Fisher 1930; Lande 1979; Bulmer 1980; Kimura 1983; Orr 1998; Agrawal et al. 2001). Despite the infinitesimal model''s allure and theoretical tractability (see Orr and Coyne 1992; Orr 1998, 2005a,b for reviews of its influence), evidence has accumulated from several sources (artificial selection experiments, experimental evolution, and QTL mapping) to suggest that genes of major effect often contribute to quantitative traits. Thus, the frequency and role of genes of major effect in evolutionary quantitative genetics have been a subject of intense debate and investigation for close to 80 years (Fisher 1930; Kimura 1983; Orr 1998, 2005a,b). Beyond the conceptual implications, the prevalence of major-effect loci also affects our ability to determine the genetic basis of adaptations and species differences (e.g., Bradshaw et al. 1995, 1998).Although the existence of genes of major effect is no longer in doubt, we still lack basic empirical data on how segregating variation at such genes affects key components of evolutionary process (but see Carrière and Roff 1995). In other words, How does polymorphism at genes of major effect alter patterns of genetic variation and covariation, natural selection, and the likely response to selection? The lack of data stems, in part, from the methods used to detect genes of major effect: experimental evolution (e.g., Bull et al. 1997; Zeyl 2005) and QTL analysis (see Erickson et al. 2004 for a review) often detect such genes retrospectively after they have become fixed in experimental populations or the species pairs used to generate the mapping population. The consequences of polymorphism at these genes on patterns of variation, covariation, selection, and the response to selection—which can be transient (Agrawal et al. 2001)—are thus often unobserved.A partial exception to the absence of data on the effects of major genes comes from artificial selection experiments, in which a substantial evolutionary response to selection in the phenotype after a plateau is often interpreted as evidence for the fixation of a major-effect locus (Frankham et al. 1968; Yoo 1980a,b; Frankham 1980; Shrimpton and Robertson 1988a,b; Caballero et al. 1991; Keightley 1998; see Mackay 1990 and Hill and Caballero 1992 for reviews). However, many of these experiments report only data on the selected phenotype (e.g., bristle number) or, alternatively, the selected phenotype and some measure of fitness (e.g., Frankham et al. 1968, Yoo 1980b; Caballero et al. 1991; Mackay et al. 1994; Fry et al. 1995; Nuzhdin et al. 1995; Zur Lage et al. 1997), making it difficult to infer how a mutation will affect variation, covariation, selection, and evolutionary responses for a suite of traits that might affect fitness themselves. One approach is to document how variation at individual genes of major effect affects the genetic variance–covariance matrix (“G matrix”; Lande 1979), which represents the additive genetic variance and covariance between traits.Although direct evidence for variation at major-effect genes altering patterns of genetic variation, covariation, and selection is rare, there is abundant evidence for the genetic mechanisms that could produce these dynamics. A gene of major effect could have these consequences due to any of at least three genetic mechanisms: (1) pleiotropy, where a gene of major effect influences several traits, including potentially fitness, simultaneously, (2) physical linkage or linkage disequilibrium (LD), in which a gene of major effect is either physically linked or in LD with other genes that influence other traits under selection, and (3) epistasis, in which the allele present at a major-effect gene alters the phenotypic effect of other loci and potentially phenotypes under selection. Evidence for these three evolutionary genetic mechanisms leading to changes in suites of traits comes from a variety of sources, including mutation accumulation experiments (Clark et al. 1995; Fernandez and Lopez-Fanjul 1996), mutation induction experiments (Keightley and Ohnishi 1998), artificial selection experiments (Long et al. 1995), and transposable element insertions (Rollmann et al. 2006). For pleiotropy in particular, major-effect genes that have consequences on several phenotypic traits are well known from the domestication and livestock breeding literature [e.g., myostatin mutations in Belgian blue cattle and whippets (Arthur 1995; Grobet et al. 1997; Mosher et al. 2007), halothane genes in pigs (Christian and Rothschild 1991; Fujii et al. 1991), and Booroola and Inverdale genes in sheep (Amer et al. 1999; Visscher et al. 2000)]. While these data suggest that variation at major-effect genes could—and probably does—influence variation, covariation, and selection on quantitative traits, data on the magnitude of these consequences remain lacking.Recombinant inbred line (RIL) populations are a promising tool for investigating the influence of major-effect loci. During advancement of the lines from F₂''s to RILs, alternate alleles at major-effect genes (and most of the rest of the genome) will be made homozygous, simplifying comparisons among genotypic classes. Because of the high homozygosity, individuals within RILs are nearly genetically identical, facilitating phenotyping of many genotypes under a range of environments. In addition, because of recombination, alternative alleles are randomized across genetic backgrounds—facilitating robust comparisons between sets of lines differing at a major-effect locus.Here we investigate how polymorphism at an artificially induced mutation, the erecta locus in Arabidopsis thaliana, affects the magnitude of these important evolutionary genetic parameters under ecologically realistic field conditions. We use the Landsberg erecta (Ler) × Columbia (Col) RIL population of A. thaliana to examine how variation at a gene of major effect influences genetic variation, covariation, and selection on quantitative traits in a field setting. The Ler × Col RIL population is particularly suitable, because it segregates for an artificially induced mutation at the erecta locus, which has been shown to influence a wide variety of plant traits. The Ler × Col population thus allows a powerful test of the effects of segregating variation at a gene—chosen a priori—with numerous pleiotropic effects. The ERECTA gene is a leucine-rich receptor-like kinase (LRR-RLK) (Torii et al. 1996) and has been shown to affect plant growth rates (El-Lithy et al. 2004), stomatal patterning and transpiration efficiency (Masle et al. 2005; Shpak et al. 2005), bacterial pathogen resistance (Godiard et al. 2003), inflorescence and floral organ size and shape (Douglas et al. 2002; Shpak et al. 2003, 2004), and leaf polarity (Xu et al. 2003; Qi et al. 2004).Specifically, we sought to answer the following questions: (1) Is variation at erecta significantly associated with changes to the G matrix? (2) Is variation at erecta associated with changes in natural selection on genetically variable traits? And (3) is variation at erecta associated with significantly different projected evolutionary responses to selection?     

Reassignment of a rare sense codon to a non-canonical amino acid in Escherichia coli

The immutability of the genetic code has been challenged with the successful reassignment of the UAG stop codon to non-natural amino acids in Escherichia coli. In the present study, we demonstrated the in vivo reassignment of the AGG sense codon from arginine to l-homoarginine. As the first step, we engineered a novel variant of the archaeal pyrrolysyl-tRNA synthetase (PylRS) able to recognize l-homoarginine and l-N⁶-(1-iminoethyl)lysine (l-NIL). When this PylRS variant or HarRS was expressed in E. coli, together with the AGG-reading tRNA^Pyl_CCU molecule, these arginine analogs were efficiently incorporated into proteins in response to AGG. Next, some or all of the AGG codons in the essential genes were eliminated by their synonymous replacements with other arginine codons, whereas the majority of the AGG codons remained in the genome. The bacterial host''s ability to translate AGG into arginine was then restricted in a temperature-dependent manner. The temperature sensitivity caused by this restriction was rescued by the translation of AGG to l-homoarginine or l-NIL. The assignment of AGG to l-homoarginine in the cells was confirmed by mass spectrometric analyses. The results showed the feasibility of breaking the degeneracy of sense codons to enhance the amino-acid diversity in the genetic code.     

Age Structure,Changing Demography and Effective Population Size in Atlantic Salmon (Salmo salar)

Effective population size (N_e) is a central evolutionary concept, but its genetic estimation can be significantly complicated by age structure. Here we investigate N_e in Atlantic salmon (Salmo salar) populations that have undergone changes in demography and population dynamics, applying four different genetic estimators. For this purpose we use genetic data (14 microsatellite markers) from archived scale samples collected between 1951 and 2004. Through life table simulations we assess the genetic consequences of life history variation on N_e. Although variation in reproductive contribution by mature parr affects age structure, we find that its effect on N_e estimation may be relatively minor. A comparison of estimator models suggests that even low iteroparity may upwardly bias N_e estimates when ignored (semelparity assumed) and should thus empirically be accounted for. Our results indicate that N_e may have changed over time in relatively small populations, but otherwise remained stable. Our ability to detect changes in N_e in larger populations was, however, likely hindered by sampling limitations. An evaluation of N_e estimates in a demographic context suggests that life history diversity, density-dependent factors, and metapopulation dynamics may all affect the genetic stability of these populations.THE effective size of a population (N_e) is an evolutionary parameter that can be informative on the strength of stochastic evolutionary processes, the relevance of which relative to deterministic forces has been debated for decades (e.g., Lande 1988). Stochastic forces include environmental, demographic, and genetic components, the latter two of which are thought to be more prominent at reduced population size, with potentially detrimental consequences for average individual fitness and population persistence (Newman and Pilson 1997; Saccheri et al. 1998; Frankham 2005). The quantification of N_e in conservation programs is thus frequently advocated (e.g., Luikart and Cornuet 1998; Schwartz et al. 2007), although gene flow deserves equal consideration given its countering effects on genetic stochasticity (Frankham et al. 2003; Palstra and Ruzzante 2008).Effective population size is determined mainly by the lifetime reproductive success of individuals in a population (Wright 1938; Felsenstein 1971). Variance in reproductive success, sex ratio, and population size fluctuations can reduce N_e below census population size (Frankham 1995). Given the difficulty in directly estimating N_e through quantification of these demographic factors (reviewed by Caballero 1994), efforts have been directed at inferring N_e indirectly through measurement of its genetic consequences (see Leberg 2005, Wang 2005, and Palstra and Ruzzante 2008 for reviews). Studies employing this approach have quantified historical levels of genetic diversity and genetic threats to population persistence (e.g., Nielsen et al. 1999b; Miller and Waits 2003; Johnson et al. 2004). N_e has been extensively studied in (commercially important) fish species, due to the common availability of collections of archived samples that facilitate genetic estimation using the temporal method (e.g., Hauser et al. 2002; Shrimpton and Heath 2003; Gomez-Uchida and Banks 2006; Saillant and Gold 2006).Most models relating N_e to a population''s genetic behavior make simplifying assumptions regarding population dynamics. Chiefly among these is the assumption of discrete generations, frequently violated in practice given that most natural populations are age structured with overlapping generations. Here, theoretical predictions still apply, provided that population size and age structure are constant (Felsenstein 1971; Hill 1972). Ignored age structure can introduce bias into temporal genetic methods for the estimation of N_e, especially for samples separated by time spans that are short relative to generation interval (Jorde and Ryman 1995; Waples and Yokota 2007; Palstra and Ruzzante 2008). Moreover, estimation methods that do account for age structure (e.g., Jorde and Ryman 1995) still assume this structure to be constant. Population dynamics will, however, likely be altered as population size changes, thus making precise quantifications of the genetic consequences of acute population declines difficult (Nunney 1993; Engen et al. 2005; Waples and Yokota 2007). This problem may be particularly relevant when declines are driven by anthropogenic impacts, such as selective harvesting regimes, that can affect age structure and N_e simultaneously (Ryman et al. 1981; Allendorf et al. 2008). Demographic changes thus have broad conservation implications, as they can affect a population''s sensitivity to environmental stochasticity and years of poor recruitment (Warner and Chesson 1985; Ellner and Hairston 1994; Gaggiotti and Vetter 1999). Consequently, although there is an urgent need to elucidate the genetic consequences of population declines, relatively little is understood about the behavior of N_e when population dynamics change (but see Engen et al. 2005, 2007).Here we focus on age structure and N_e in Atlantic salmon (Salmo salar) river populations in Newfoundland and Labrador. The freshwater habitat in this part of the species'' distribution range is relatively pristine (Parrish et al. 1998), yet Atlantic salmon in this area have experienced demographic declines, associated with a commercial marine fishery, characterized by high exploitation rates (40–80% of anadromous runs; Dempson et al. 2001). A fishery moratorium was declared in 1992, with rivers displaying differential recovery patterns since then (Dempson et al. 2004b), suggesting a geographically variable impact of deterministic and stochastic factors, possibly including genetics. An evaluation of those genetic consequences thus requires accounting for potential changes in population dynamics as well as in life history. Life history in Atlantic salmon can be highly versatile (Fleming 1996; Hutchings and Jones 1998; Fleming and Reynolds 2004), as exemplified by the high variation in age-at-maturity displayed among and within populations (Hutchings and Jones 1998), partly reflecting high phenotypic plasticity (Hutchings 2004). This diversity is particularly evident in the reproductive biology of males, which can mature as parr during juvenile freshwater stages (Jones and King 1952; Fleming and Reynolds 2004) and/or at various ages as anadromous individuals, when returning to spawn in freshwater from ocean migration. Variability in life history strategies is further augmented by iteroparity, which can be viewed as a bet-hedging strategy to deal with environmental uncertainty (e.g., Orzack and Tuljapurkar 1989; Fleming and Reynolds 2004). Life history diversity and plasticity may allow salmonid fish populations to alter and optimize their life history under changing demography and population dynamics, potentially acting to stabilize N_e. Reduced variance in individual reproductive success at low breeder abundance (genetic compensation) will achieve similar effects and might be a realistic aspect of salmonid breeding systems (Ardren and Kapuscinski 2003; Fraser et al. 2007b). Little is currently known about the relationships between life history plasticity, demographic change and N_e, partly due to scarcity of the multivariate data required for these analyses.Our objective in this article is twofold. First, we use demographic data for rivers in Newfoundland to quantify how life history variation influences age structure in Atlantic salmon and hence N_e and its empirical estimation from genetic data. We find that variation in reproductive contribution by mature parr has a much smaller effect on the estimation of N_e than is often assumed. Second, we use temporal genetic data to estimate N_e and quantify the genetic consequences of demographic changes. We attempt to account for potential sources of bias, associated with (changes in) age structure and life history, by using four different analytical models to estimate N_e: a single-sample estimator using the linkage disequilibrium method (Hill 1981), the temporal model assuming discrete generations (Nei and Tajima 1981; Waples 1989), and two temporal models for species with overlapping generations (Waples 1990a,b; Jorde and Ryman 1995) that differ principally in assumptions regarding iteroparity. A comparison of results from these different estimators suggests that iteroparity may often warrant analytical consideration, even when it is presumably low. Although sometimes limited by statistical power, a quantification and comparison of temporal changes in N_e among river populations suggests a more prominent impact of demographic changes on N_e in relatively small river populations.     

Amino Acid and vitamin requirements of several bacteroides strains

Nutritional studies were performed on nine Bacteroides strains, by use of the methodology and media of anaerobic rumen microbiology. Ristella perfoetens CCI required l-arginine hydrochloride, l-tryptophan, l-leucine, l-histidine hydrochloride, l-cysteine hydrochloride, dl-valine, dl-tyrosine, and the vitamin calcium-d-pantothenate, since scant turbidity developed in media without these nutrients. R. perfoetens was stimulated by glycine, dl-lysine hydrochloride, dl-isoleucine, l-proline, l-glutamic acid, dl-alanine, dl-phenylalanine, dl-methionine, and the vitamins nicotinamide and p-aminobenzoic acid, since maximal turbidity developed more slowly in media without these nutrients than in complete medium. Medium A-23, which was devised for R. perfoetens, contained salts, 0.0002% nicotinamide and calcium d-pantothenate, 0.00001% p-aminobenzoic acid, 0.044% l-tryptophan, 0.09% l-glutamic acid, and 0.1% of the other 13 amino acids listed above. Zuberella clostridiformis and seven strains of R. pseudoinsolita did not require vitamins, and showed no absolute requirement for any one amino acid. Various strains produced maximal turbidity more slowly in media deficient in l-proline, glycine, l-glutamic acid, dl-serine, l-histidine hydrochloride, dl-alanine, or l-cysteine hydrochloride, than in complete medium. These eight strains grew optimally in medium A-23 plus 0.1% dl-serine but without vitamins.     

Studies on the Sex-Specific Lethals of DROSOPHILA MELANOGASTER . II. Further Studies on a Male-Specific Lethal Gene, Maleless

Effects of a second chromosome male-specific lethal gene, maleless (mle), of Drosophila melanogaster were further studied. It was shown that, although no maternal effect was seen with respect to the male-specific lethality, the lethal stage was influenced by whether parental females were homozygous or heterozygous for mle. Thus, in the former mle/mle males died mostly in the late third instar larval stage, while in the latter practically all males survived to the pupal stage. In the dying mle/mle male pupae complete differentiation of adult external head and thorax structures was often observed but that of abdominal structures was incomplete forming only a few segments in most cases. Imaginal discs from third instar mle/mle male larvae which were produced by mle/mle mothers and were destined to die as larvae were able to differentiate into adult structures upon transplantation into normal third instar larval hosts.——A somewhat elaborated version of the previously presented hypothesis (Fukunaga, Tanaka and Oishi 1975) was discussed as to the possible presence of a class of sex-specific lethals which are not related to the process of primary sex differentiation.     

Bacterial catabolism of threonine. Threonine degradation initiated by l-threonine acetaldehyde-lyase (aldolase) in species of Pseudomonas

1. The route of l-threonine degradation was studied in four strains of the genus Pseudomonas able to grow on the amino acid and selected because of their high l-threonine aldolase activity. Growth and manometric results were consistent with the cleavage of l-threonine to acetaldehyde+glycine and their metabolism via acetate and serine respectively. 2. l-Threonine aldolases in these bacteria exhibited pH optima in the range 8.0–8.7 and K_m values for the substrate of 5–10mm. Extracts exhibited comparable allo-l-threonine aldolase activities, K_m values for this substrate being 14.5–38.5mm depending on the bacterium. Both activities were essentially constitutive. Similar activity ratios in extracts, independent of growth conditions, suggested a single enzyme. The isolate Pseudomonas D2 (N.C.I.B. 11097) represents the best source of the enzyme known. 3. Extracts of all the l-threonine-grown pseudomonads also possessed a CoA-independent aldehyde dehydrogenase, the synthesis of which was induced, and a reversible alcohol dehydrogenase. The high acetaldehyde reductase activity of most extracts possibly resulted in the underestimation of acetaldehyde dehydrogenase. 4. l-Serine dehydratase formation was induced by growth on l-threonine or acetate+glycine. Constitutively synthesized l-serine hydroxymethyltransferase was detected in extracts of Pseudomonas strains D2 and F10. The enzyme could not be detected in strains A1 and N3, probably because of a highly active `formaldehyde-utilizing' system. 5. Ion-exchange and molecular exclusion chromatography supported other evidence that l-threonine aldolase and allo-l-threonine aldolase activities were catalysed by the same enzyme but that l-serine hydroxymethyltransferase was distinct and different. These results contrast with the specificities of some analogous enzymes of mammalian origin.     

Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions

It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.MUCH effort in genetics has been devoted to revealing the underlying genetic architecture of quantitative or complex traits. Traditionally, the polygenic model has been used extensively to estimate genetic variances and breeding values of natural and breeding populations, where an infinite number of genes is assumed to code for the trait of interest (Bulmer 1971; Falconer and Mackay 1996). The genetic variance of a quantitative trait can be decomposed into an additive part that corresponds to the effects of individual alleles and a part that is nonadditive because of interactions between alleles. Attention has generally been focused on the estimation of additive genetic variance (and heritability), since additive variation is directly proportional to the response of selection via the breeder''s equation (Falconer and Mackay 1996, Chap. 11). However, to estimate additive genetic variation and heritability accurately, it can be important to identify potential nonadditive sources in genetic evaluations (Misztal 1997; Ovaskainen et al. 2008; Waldmann et al. 2008), especially if the pedigree being analyzed contains a large proportion of full-sibs and clones, as these in particular give rise to nonadditive genetic relationships (Lynch and Walsh 1998, pp. 145). The polygenic model using pedigree and phenotypic information, i.e., the animal model (Henderson 1984), has been the model of choice for estimating genetic parameters in breeding and natural populations (Abney et al. 2000; Sorensen and Gianola 2002; O′Hara et al. 2008).Recent breakthroughs in molecular techniques have made it possible to create genome-wide, single nucleotide polymorphism (SNP) maps. These maps have helped to uncover a vast amount of new loci responsible for trait expression and have provided general insights into the genetic architecture of quantitative traits (e.g., Valdar et al. 2006; Visscher 2008; Flint and Mackay 2009). These insights can help when calculating disease risks in humans, when attempting to increase the yield from breeding programs, and when estimating relatedness in conservation programs. High-density SNPs of many species of importance to science and agriculture can now be scored quickly and relatively cheaply, for example, in mice (Valdar et al. 2006), chickens (Muir et al. 2008), and dairy cattle (VanRaden et al. 2009).In the analysis of populations of breeding stock, the inclusion of dense marker data has improved the predictive ability (i.e., reliability) of genetic evaluations compared to the traditional phenotype model, both in simulations (Meuwissen et al. 2001; Calus et al. 2008; Hayes et al. 2009) and when using real data (Legarra et al. 2008; VanRaden et al. 2009; González-Recio et al. 2009). Meuwissen et al. (2001) suggested that the effect of all markers should first be estimated, and then summed, to obtain genomic estimated breeding values (GEBVs). An alternative procedure, where all markers are used to compute the genomic relationship matrix (in place of the additive polygenic relationship matrix) has also been suggested (e.g., Villanueva et al. 2005; VanRaden 2008; Hayes et al. 2009); this matrix is then incorporated into the statistical analysis to estimate GEBVs. A comparison of both procedures (VanRaden 2008) yielded similar estimates of GEBVs in cases where the effect of an individual allele was small. In addition, if not all pedigree members have marker information, a combined relationship matrix derived from both genotyped and ungenotyped individuals could be computed; this has been shown to increase the accuracy of GEBVs (Legarra et al. 2009; Misztal et al. 2009). Another plausible option to incorporate marker information is to use low-density SNP panels within families and to trace the effect of SNPs from high-density genotyped ancestors, as suggested by Habier et al. (2009) and Weigel et al. (2009). However, fast and powerful computer algorithms, which can use the marker information as efficiently as possible in the analysis of quantitative traits, are needed to obtain accurate GEBVs from genome-wide marker data.This study describes the development of an efficient Bayesian method for incorporating general relationships into the genetic evaluation procedure. The method is based on expressing the multivariate normal prior distribution as a product of one-dimensional normal distributions, each conditioned on the descending variables. When evaluating the genetic parameters of natural and breeding populations, high-dimensional distributions are often used as prior distributions of various genetic effects, such as the additive polygenic effect (Wang et al. 1993), multivariate additive polygenic effects (Van Tassell and Van Vleck 1996), and quantitative trait loci (QTL) effects via the identical-by-decent matrix (Yi and Xu 2000). A Bayesian framework is adopted to obtain posterior distributions of all unknown parameters, estimated by using Markov chain Monte Carlo (MCMC) sampling algorithms in the software package WinBUGS (Lunn et al. 2000, 2009). By performing prior calculations in the form of the factorized product of simple univariate conditional distributions, the computational time of the MCMC estimation procedure is reduced considerably. This feature permits rapid inference for both the polygenic model and the genomic relationship model. Moreover, the decomposition allows for inbreeding of varying degree, since the correct genetic covariance structure can be inferred into the analysis. In this article, we test the method on two previously published pedigree data sets: phenotype data from a large pedigree of Scots pine, incorporation of information on both additive and dominance genetic relationships (Waldmann et al. 2008); and genomic information obtained from a genome-wide scan of a simulated animal population (Lund et al. 2009).     

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.PEDIGREE-BASED prediction of genetic values based on the additive infinitesimal model (Fisher 1918) has played a central role in genetic improvement of complex traits in plants and animals. Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) (Henderson 1984) or in a Bayesian framework (Gianola and Fernando 1986). More recently, plant breeders have incorporated pedigree information into linear mixed models for predicting breeding values (Crossa et al. 2006, 2007; Oakey et al. 2006; Burgueño et al. 2007; Piepho et al. 2007).The availability of thousands of genome-wide molecular markers has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al. 2001) in plants (e.g., Bernardo and Yu 2007; Piepho 2009; Jannink et al. 2010) and animals (Gonzalez-Recio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; de los Campos et al. 2009a). Implementing GS poses several statistical and computational challenges, such as how models can cope with the curse of dimensionality, colinearity between markers, or the complexity of quantitative traits. Parametric (e.g., Meuwissen et al. 2001) and semiparametric (e.g., Gianola et al. 2006; Gianola and van Kaam 2008) methods address these problems differently.In standard genetic models, phenotypic outcomes, , are viewed as the sum of a genetic value, , and a model residual, ; that is, . In parametric models for GS, is described as a regression on marker covariates (j = 1,  …  , p molecular markers) of the form , such that(or , in matrix notation), where is the regression of on the jth marker covariate .Estimation of via multiple regression by ordinary least squares (OLS) is not feasible when p > n. A commonly used alternative is to estimate marker effects jointly using penalized methods such as ridge regression (Hoerl and Kennard 1970) or the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani 1996) or their Bayesian counterpart. This approach yields greater accuracy of estimated genetic values and can be coupled with geostatistical techniques commonly used in plant breeding to model multienvironments trials (Piepho 2009).In ridge regression (or its Bayesian counterpart) the extent of shrinkage is homogeneous across markers, which may not be appropriate if some markers are located in regions that are not associated with genetic variance, while markers in other regions may be linked to QTL (Goddard and Hayes 2007). To overcome this limitation, many authors have proposed methods that use marker-specific shrinkage. In a Bayesian setting, this can be implemented using priors of marker effects that are mixtures of scaled-normal densities. Examples of this are methods Bayes A and Bayes B of Meuwissen et al. (2001) and the Bayesian LASSO of Park and Casella (2008).An alternative to parametric regressions is to use semiparametric methods such as reproducing kernel Hilbert spaces (RKHS) regression (Gianola and van Kaam 2008). The Bayesian RKHS regression regards genetic values as random variables coming from a Gaussian process centered at zero and with a (co)variance structure that is proportional to a kernel matrix K (de los Campos et al. 2009b); that is, , where , are vectors of marker genotypes for the ith and jth individuals, respectively, and is a positive definite function evaluated in marker genotypes. In a finite-dimensional setting this amounts to modeling the vector of genetic values, , as multivariate normal; that is, where is a variance parameter. One of the most attractive features of RKHS regression is that the methodology can be used with almost any information set (e.g., covariates, strings, images, graphs). A second advantage is that with RKHS the model is represented in terms of n unknowns, which gives RKHS a great computational advantage relative to some parametric methods, especially when p ≫ n.This study presents an evaluation of several methods for GS, using two extensive data sets. One contains phenotypic records of a series of wheat trials and recently generated genomic data. The other data set pertains to international maize trials in which different traits were measured in maize lines evaluated under severe drought and well-watered conditions.     

Selection for Chaperone-Like Mediated Genetic Robustness at Low Mutation Rate: Impact of Drift,Epistasis and Complexity

Genetic robustness is defined as the constancy of a phenotype in the face of deleterious mutations. Overexpression of chaperones, to assist the folding of proteins carrying deleterious mutations, is so far one of the most accepted molecular mechanisms enhancing genetic robustness. Most theories on the evolution of robustness have focused on the implications of high mutation rate. Here we show that genetic drift, which is modulated by population size, organism complexity, and epistasis, can be a sufficient force to select for chaperone-mediated genetic robustness. Using an exact analytical solution, we also show that selection for costly genetic robustness leads to a paradox: the decrease of population fitness on long timescales and the long-term dependency on robustness mechanisms. We suggest that selection for genetic robustness could be universal and not restricted to high mutation rate organisms such as RNA viruses. The evolution of the endosymbiont Buchnera illustrates this selection mechanism and its paradox: the increased dependency on chaperones mediating genetic robustness. Our model explains why most chaperones might have become essential even in optimal growth conditions.MUTATIONAL (or genetic) robustness is defined as the constancy of a phenotype in the face of deleterious mutations (Sanjuan et al. 2007). Selection drives populations to adapt to their environment by the fixation of successive advantageous mutations. However, in approaching a fitness optimum—i.e., a genotype that is maximally adapted—they have to cope with an increasing proportion of deleterious mutations and, when at the optimum, they experience only neutral and deleterious mutations (Silander et al. 2007). Therefore any mechanism that would reduce the effect of deleterious mutations, i.e., increase mutational robustness, could be favored by natural selection when at, or near, an optimum of fitness. Indeed, the general observation that for a large range of organisms, mutations have little effect on fitness, suggests that selection for robustness is pervasive (Melton 1994; Winzeler et al. 1999). Three main mechanisms that are not mutually exclusive could explain how genetic robustness has arisen. First, in the “intrinsic hypothesis” (de Visser et al. 2003) robustness could simply be a by-product of some biologically relevant functions. Second, mutational robustness could be a by-product of the selection for nongenetic perturbations such as environment changes or intrinsic noise (Wagner 2005). Third, mutational robustness could be selected for because it is adaptive in itself. In the following we restrict our attention to this “adaptive hypothesis” (de Visser et al. 2003).Chaperone proteins, proteins that help other proteins to fold properly, have been shown to buffer the effect of deleterious mutations in diverse organisms (Rutherford 2003). In lineages that have accumulated deleterious mutations, the overexpression of the chaperone GroESL in Escherichia coli (Fares et al. 2002) or Salmonella typhymurium (Maisnier-Patin et al. 2005) resulted in an improved fitness. However, such robustness appears to come at a cost, as the buffering was visible only in carbon-rich media (Fares et al. 2002), and it is also known that GroESL-mediated refolding of proteins is ATP dependent. Chaperones can also buffer against environmental perturbations (such as heat shock); however, the observation that groESL evolved under positive selection and is overproduced in obligate intracellular endosymbionts (Moran 1996; Fares et al. 2004), for which environmental perturbations are assumed to be very weak, suggests that genetic robustness could be the direct target of selection.Selection for a modifier of genetic robustness, i.e., a gene modulating the effect of mutations, has been mainly studied in the context of high mutation rates, as the effect of the modifier allele affects the fitness of mutants (Wagner 2005). Under some theoretical frameworks, it has been suggested that the intensity of selection acting on a modifier of robustness would be of the order of the mutation rate (Gardner and Kalinka 2006). Therefore it has been presumed that selection for genetic robustness is relevant only in very large populations having a high mutation rate, such as RNA virus populations. In agreement with these ideas, artificial life experiments (Wilke and Adami 2001; Azevedo et al. 2006) and experimental data on viruses (Montville et al. 2005; Sanjuan et al. 2007) have shown that robustness varies between organisms and that it can be selected for under high mutation rates. It has also been shown by Krakauer and Plotkin (2002) that drift, i.e., stochastic effects due to the finite size of populations, can promote selection for robustness even when more robust alleles are costly, as suggested in the case of chaperone overexpression. However, again this effect was examined only under high mutation rates.When mutations are very rare, populations experience at the most the presence of a single mutant. In such conditions, the population fitness at equilibrium does not depend on the mutation rate but only on drift (Sella and Hirsh 2005; Tenaillon et al. 2007). Two factors modulate how drift affects fitness:

1. Epistasis, defined here as a local property of the adaptive landscape, describes how the selective effects of mutations depend on the genetic background in which they arise. Epistasis is negative (positive) if two mutations have a lower (higher) fitness when simultaneously present within a genome than expected if they did not interact. Negative epistasis increases selection against mutation-loaded individuals and therefore reduces the effect of drift on population fitness (Charlesworth 1990; Tenaillon et al. 2007).
2. Phenotypic complexity, defined as the number of independent mutable traits that contribute to fitness (Orr 2000; Tenaillon et al. 2007), also affects population fitness in finite populations: complex organisms are more sensitive to the action of drift (Hartl and Taubes 1998; Poon and Otto 2000; Tenaillon et al. 2007).

In this article, we attempt to further clarify the role of drift on the evolution of chaperone-like genetic robustness and to decouple the effect of drift from the effect of the mutation rate. We use Fisher''s geometric model of adaptation (Fisher 1930), to map phenotype to fitness under an assumption of a vanishing mutation rate and extract exact analytical solutions for the genetic properties of the population at mutation–selection–drift equilibrium (MSDE). We examine how these genetic properties change under various population sizes and epistasis parameters and in organisms ranging in phenotypic complexity.     

Uptake of L-cystine via an ABC transporter contributes defense of oxidative stress in the L-cystine export-dependent manner in Escherichia coli

Intracellular thiols like L-cystine and L-cystine play a critical role in the regulation of cellular processes. Here we show that Escherichia coli has two L-cystine transporters, the symporter YdjN and the ATP-binding cassette importer FliY-YecSC. These proteins import L-cystine, an oxidized product of L-cystine from the periplasm to the cytoplasm. The symporter YdjN, which is expected to be a new member of the L-cystine regulon, is a low affinity L-cystine transporter (K _m = 1.1 μM) that is mainly involved in L-cystine uptake from outside as a nutrient. E. coli has only two L-cystine importers because ΔydjNΔyecS mutant cells are not capable of growing in the minimal medium containing L-cystine as a sole sulfur source. Another protein YecSC is the FliY-dependent L-cystine transporter that functions cooperatively with the L-cystine transporter YdeD, which exports L-cystine as reducing equivalents from the cytoplasm to the periplasm, to prevent E. coli cells from oxidative stress. The exported L-cystine can reduce the periplasmic hydrogen peroxide to water, and then generated L-cystine is imported back into the cytoplasm via the ATP-binding cassette transporter YecSC with a high affinity to L-cystine (K _m = 110 nM) in a manner dependent on FliY, the periplasmic L-cystine-binding protein. The double disruption of ydeD and fliY increased cellular levels of lipid peroxides. From these findings, we propose that the hydrogen peroxide-inducible L-cystine/L-cystine shuttle system plays a role of detoxification of hydrogen peroxide before lipid peroxidation occurs, and then might specific prevent damage to membrane lipids.     

Virulence Gene Regulation by l-Arabinose in Salmonella enterica

Invasion of the intestinal epithelium is a critical step in Salmonella enterica infection and requires functions encoded in the gene cluster known as Salmonella Pathogenicity Island 1 (SPI-1). Expression of SPI-1 genes is repressed by l-arabinose, and not by other pentoses. Transport of l-arabinose is necessary to repress SPI-1; however, repression is independent of l-arabinose metabolism and of the l-arabinose-responsive regulator AraC. SPI-1 repression by l-arabinose is exerted at a single target, HilD, and the mechanism appears to be post-translational. As a consequence of SPI-1 repression, l-arabinose reduces translocation of SPI-1 effectors to epithelial cells and decreases Salmonella invasion in vitro. These observations reveal a hitherto unknown role of l-arabinose in gene expression control and raise the possibility that Salmonella may use L-arabinose as an environmental signal.     

Biosynthesis of l-tyrosine O-sulphate from the methyl and ethyl esters of l-tyrosine

1. Rat-liver supernatant preparations are capable of achieving the biological sulphation of l-tyrosine methyl ester, the reaction proceeding maximally at a substrate concentration of 30 mm and at pH 7·0. 2. Two sulphated products are formed, one of which has been identified as l-tyrosine O-sulphate. On the basis of indirect evidence the other product can be assumed to be l-tyrosine O-sulphate methyl ester. 3. An enzyme present in rat-liver supernatant preparations is capable of converting l-tyrosine O-sulphate methyl ester into l-tyrosine O-sulphate. This enzyme is inhibited by l-tyrosine methyl ester. 4. l-Tyrosine ethyl ester also yields two sulphated products when used as an acceptor in the liver sulphating system. One of these has been identified chromatographically as l-tyrosine O-sulphate and the other may be presumed to be l-tyrosine O-sulphate ethyl ester.     

Identification of Selection Signatures in Cattle Breeds Selected for Dairy Production

Genetic correlations are expected to be high among functionally related traits and lower between groups of traits with distinct functions (e.g., reproductive vs. resource-acquisition traits). Here, we explore the quantitative-genetic and QTL architecture of floral organ sizes, vegetative traits, and life history in a set of Brassica rapa recombinant inbred lines within and across field and greenhouse environments. Floral organ lengths were strongly positively correlated within both environments, and analysis of standardized G-matrices indicates that the structure of genetic correlations is ∼80% conserved across environments. Consistent with these correlations, we detected a total of 19 and 21 additive-effect floral QTL in the field and the greenhouse, respectively, and individual QTL typically affected multiple organ types. Interestingly, QTL × QTL epistasis also appeared to contribute to observed genetic correlations; i.e., interactions between two QTL had similar effects on filament length and two estimates of petal size. Although floral and nonfloral traits are hypothesized to be genetically decoupled, correlations between floral organ size and both vegetative and life-history traits were highly significant in the greenhouse; G-matrices of floral and vegetative traits as well as floral and life-history traits differed across environments. Correspondingly, many QTL (45% of those mapped in the greenhouse) showed environmental interactions, including approximately even numbers of floral and nonfloral QTL. Most instances of QTL × QTL epistasis for floral traits were environment dependent.EVOLUTIONARY responses to selection are dependent on genetic architecture. The proportion of phenotypic variation with a heritable genetic basis affects the response to selection, as does the structure of genetic correlations among selected traits. For example, an evolutionary response will be constrained if selection favors an increase in the value of two traits that are negatively correlated; i.e., a negative correlation is antagonistic to the joint vector of selection. Alternatively, if the vector of selection is parallel to the genetic correlation, then trait covariation is reinforcing and the population mean may more rapidly approach favored trait values (Etterson and Shaw 2001; Merilä and Björklund 2004). One measure of genetic architecture is the G-matrix (Lynch and Walsh 1998), which is composed of genetic variances (diagonal matrix elements) and genetic covariances among traits (off-diagonal matrix elements). G-matrices have been shown to vary across environments (Donohue et al. 2000; Conner et al. 2003; Brock and Weinig 2007), indicating that the molecular-genetic underpinnings of matrix elements (e.g., identity and/or relative effect of additive and epistatic loci, degree of pleiotropy, etc.) and the traits'' evolutionary potential vary across environments. Few studies, however, have related matrix and QTL architectures; and, therefore, the molecular-genetic underpinnings of quantitative-genetic estimates remain unclear (but see Gardner and Latta 2007; Kelly 2009).In angiosperms, covariances between floral whorls (e.g., petal and stamen length) are frequently positive among functionally related traits. These positive correlations can arise from pollinator-mediated (or pollination-mediated) selection for specific allometric relationships among floral traits and ensuing linkage disequilibrium (LD) among causal loci (Berg 1959, 1960; also referred to as phenotypic integration, see Pigliucci 2003; Klingenberg 2008). For example, in outcrossing species, male fitness may be more dependent on the frequency and efficiency of pollinator visitation than female fitness (Bell 1985; but see Hodgins and Barrett 2008). Anther placement relative to the corolla opening can affect the efficiency of pollen dissemination (Conner and Via 1993; Morgan and Conner 2001); in addition, comparative work indicates that petal–stamen length correlations are stronger than stamen–pistil length correlations in outcrossers, whereas species that reproduce via autogamous selfing show the opposite pattern (Ushimaru and Nakata 2002). Alternatively, strong floral integration could be attributed to the developmental hypothesis that genetic correlations arise due to pleiotropic genes coregulating floral whorls (Herrera 2001; Herrera et al. 2002). Strong correlations resulting from linkage disequilibrium or from developmentally based pleiotropy may constrain the evolution of novel reproductive morphologies when biotic or abiotic factors (and selection) change (Cheverud 1984; Clark 1987; Smith and Rausher 2008; Agrawal and Stinchcombe 2009).Similar to genetic covariances among floral traits, covariances between floral and nonfloral traits could also alter the evolutionary response of reproductive traits. In contrast to hypotheses regarding the adaptive significance of floral-trait integration, genetic correlations between floral and nonfloral traits (e.g., vegetative or phenological traits) are hypothesized to be disadvantageous (Berg 1960). More specifically, floral allometry may be shaped by selection for reproductive success, as described above, whereas vegetative morphology is shaped primarily by selection to optimize other functions, such as light capture. If floral and nonfloral traits have a common genetic basis, then selection on phenological or morphological traits may result in maladaptive expression of floral organ size. As a result, functionally integrated floral traits are predicted to be genetically decoupled from vegetative and phenological traits (Berg 1960).QTL mapping provides a powerful tool to explore the genetic architecture of evolutionarily important traits. The QTL architecture of interspecific floral traits has been explored in diverse systems (Bradshaw et al. 1995; Fishman et al. 2002; Goodwillie et al. 2006; Bouck et al. 2007; Moyle 2007); however, insight into the molecular genetic basis of intraspecific floral variation comes almost exclusively from Arabidopsis thaliana (Juenger et al. 2000, 2005) and Mimulus guttatus (Hall et al. 2006). Floral traits in these intraspecific crosses are polygenic with a majority of detected QTL being of small to moderate effect size. Consistent with other quantitative-genetic studies (reviewed in Ashman and Majetic 2006), floral traits in A. thaliana and M. guttatus mapping populations exhibited moderate to high genetic correlations. In both systems, mapped QTL often affected multiple floral traits. In the few cases where QTL underlying intraspecific floral morphology have been evaluated, only a single growth environment was used; estimation of floral quantitative genetics across environments and subsequent comparison with the QTL architecture underlying observed across-environment patterns are lacking.Using a segregating progeny of Brassica rapa (recombinant inbred lines, RILs) and a small sample of crop and wild accessions, we examine the quantitative-genetic and QTL architecture of floral traits under field and greenhouse environments. Specifically, we address the following questions: (1) Does this RIL population express significant genetic (co)variation for floral traits when growing in the field or greenhouse? (2) Is there significant genetic variation for vegetative traits and days to flowering in field and greenhouse environments, and is there evidence for genetic correlations between floral and nonfloral traits? (3) Does the genetic architecture of floral and nonfloral traits, as measured by the G-matrix, differ across environments? (4) What is the number and effect size of additive and epistatic QTL in field and greenhouse environments? (5) What is the relationship between mapped QTL and quantitative genetic estimates of trait (co)variation within and between floral and nonfloral traits? And (6) what is the relationship between the quantitative-genetic architecture of floral traits in the RILs vs. in the accessions?     

Regulation of hepatic l-serine dehydratase and l-serine-pyruvate aminotransferase in the developing neonatal rat

1. The activities of l-serine dehydratase and l-serine–pyruvate aminotransferase were determined in rat liver during foetal and neonatal development. 2. l-Serine–pyruvate aminotransferase activity begins to develop in late-foetal liver, increases rapidly at birth to a peak during suckling and then decreases at weaning to the adult value. 3. l-Serine dehydratase activity is very low prenatally, but increases rapidly after birth to a transient peak. After a second transient peak around the time weaning begins, activity gradually rises to the adult value. Both of these peaks have similar isoenzyme compositions. 4. In foetal liver both l-serine dehydratase and l-serine–pyruvate aminotransferase activities are increased after injection in utero of glucagon or dibutyryl cyclic AMP. Cycloheximide or actinomycin D inhibited the prenatal induction of both enzymes and actinomycin D blocked the natural increase of l-serine dehydratase immediately after birth. Glucose or insulin administration also blocked the perinatal increase of l-serine dehydratase. 5. After the first perinatal peak of l-serine dehydratase, activity is increased by cortisol and this is inhibited by actinomycin D. After the second postnatal peak, activity is increased by amino acids or cortisol and this is insensitive to actinomycin D inhibition. Glucose administration blocks the cortisol-stimulated increase in l-serine dehydratase and also partially lowers the second postnatal peak of activity. 6. The developmental patterns of the enzymes are discussed in relation to the pathways of gluconeogenesis from l-serine. The regulation of enzyme activity by hormonal and dietary factors is discussed with reference to the changes in stimuli that occur during neonatal development and to their possible mechanisms of action.