DdlN from Vancomycin-Producing Amycolatopsis orientalis C329.2 Is a VanA Homologue with d-Alanyl-d-Lactate Ligase Activity

Vancomycin-resistant enterococci acquire high-level resistance to glycopeptide antibiotics through the synthesis of peptidoglycan terminating in d-alanyl-d-lactate. A key enzyme in this process is a d-alanyl-d-alanine ligase homologue, VanA or VanB, which preferentially catalyzes the synthesis of the depsipeptide d-alanyl-d-lactate. We report the overexpression, purification, and enzymatic characterization of DdlN, a VanA and VanB homologue encoded by a gene of the vancomycin-producing organism Amycolatopsis orientalis C329.2. Evaluation of kinetic parameters for the synthesis of peptides and depsipeptides revealed a close relationship between VanA and DdlN in that depsipeptide formation was kinetically preferred at physiologic pH; however, the DdlN enzyme demonstrated a narrower substrate specificity and commensurately increased affinity for d-lactate in the C-terminal position over VanA. The results of these functional experiments also reinforce the results of previous studies that demonstrated that glycopeptide resistance enzymes from glycopeptide-producing bacteria are potential sources of resistance enzymes in clinically relevant bacteria.The origin of antibiotic resistance determinants is of significant interest for several reasons, including the prediction of the emergence and spread of resistance patterns, the design of new antimicrobial agents, and the identification of potential reservoirs for resistance elements. Antibiotic resistance can occur either through spontaneous mutation in the target or by the acquisition of external genetic elements such as plasmids or transposons which carry resistance genes (7). The origins of these acquired genes are varied, but it has long been recognized that potential reservoirs are antibiotic-producing organisms which naturally harbor antibiotic resistance genes to protect themselves from the actions of toxic compounds (6).High-level resistance to glycopeptide antibiotics such as vancomycin and teicoplanin in vancomycin-resistant enterococci (VRE) is conferred by the presence of three genes, vanH, vanA (or vanB), and vanX, which, along with auxiliary genes necessary for inducible gene expression, are found on transposons integrated into plasmids or the bacterial genome (1, 20). These three genes are essential to resistance and serve to change the C-terminal peptide portion of the peptidoglycan layer from d-alanyl-d-alanine (d-Ala-d-Ala) to d-alanyl-d-lactate (d-Ala-d-Lac). This change results in the loss of a critical hydrogen bond between vancomycin and the d-Ala-d-Ala terminus and in a 1,000-fold decrease in binding affinity between the antibiotic and the peptidoglycan layer, which is the basis for the bactericidal action of this class of compounds (5). The vanH gene encodes a d-lactate dehydrogenase which provides the requisite d-Lac (3, 5), while the vanX gene encodes a highly specific dd-peptidase which cleaves only d-Ala-d-Ala produced endogenously while leaving d-Ala-d-Lac intact (19, 21). The final gene, vanA or vanB, encodes an ATP-dependent d-Ala-d-Lac ligase (4, 8, 10). This enzyme has sequence homology with the chromosomal d-Ala-d-Ala ligases, which are essential for peptidoglycan synthesis but which generally lack the ability to synthesize d-Ala-d-Lac (9).We have recently cloned vanH, vanA, and vanX homologues from two glycopeptide antibiotic-synthesizing organisms: Amycolatopsis orientalis C329.2, which produces vancomycin, and Streptomyces toyocaensis NRRL 15009, which produces {"type":"entrez-nucleotide","attrs":{"text":"A47934","term_id":"2301796","term_text":"A47934"}}A47934 (14). In addition, the vanH-vanA-vanX gene cluster was identified in several other glycopeptide producers. We have also demonstrated that the VanA homologue from S. toyocaensis NRRL 15009 can synthesize d-Ala-d-Lac in vitro and in the glycopeptide-sensitive host Streptomyces lividans (15, 16). We now report the expression of the A. orientalis C329.2 VanA homologue DdlN in Escherichia coli, its purification, and its enzymatic characterization. These data reinforce the striking similarity between vancomycin resistance elements in VRE and glycopeptide-producing organisms and support the possibility of a common origin for these enzymes.

Expression, purification, and specificity of DdlN.

DdlN was overexpressed in E. coli under the control of the bacteriophage T7 promoter. The construct gave good yields of highly purified enzyme following a four-step purification procedure (Table ​(Table1;1; Fig. ​Fig.1).1). Like other dd-ligases, DdlN behaved like a dimer in solution (not shown).

TABLE 1

Purification of DdlN from E. coli BL21 (DE3)/pETDdlN

On the Classification of Epistatic Interactions

Modern genomewide association studies are characterized by the problem of “missing heritability.” Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic “subtypes,” may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum-likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes that best represent functional relationships for pairs of genes on the basis of fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (ɛ). We apply our approach to two available data sets from yeast (Saccharomyces cerevisiae) and demonstrate through overlap of our identified epistatic pairs with experimentally verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems.UNDERSTANDING the nature of genetic interactions is crucial to obtaining a more complete picture of complex biological systems and their evolution. The discovery of genetic interactions has been the goal of many researchers studying a number of model systems, including but not limited to Saccharomyces cerevisiae, Caenorhabditis elegans, and Escherichia coli (You and Yin 2002; Burch et al. 2003; Burch and Chao 2004; Tong et al. 2004; Drees et al. 2005; Sanjuán et al. 2005; Segre et al. 2005; Pan et al. 2006; Zhong and Sternberg 2006; Jasnos and Korona 2007; St. Onge et al. 2007; Decourty et al. 2008). Recently, high-throughput experimental approaches, such as epistatic mini-array profiles (E-MAPs) and genetic interaction analysis technology for E. coli (GIANT-coli), have enabled the study of epistasis on a large scale (Schuldiner et al. 2005, 2006; Collins et al. 2006, 2007; Typas et al. 2008). However, it remains unclear whether the computational and statistical methods currently in use to identify these interactions are indeed the most appropriate.The study of genetic interaction, or “epistasis,” has had a long and somewhat convoluted history. Bateson (1909) first used the term epistasis to describe the ability of a gene at one locus to “mask” the mutational influence of a gene at another locus (Cordell 2002). The term “epistacy” was later coined by Fisher (1918) to denote the statistical deviation of multilocus genotype values from an additive linear model for the value of a phenotype (Phillips 1998, 2008).These origins are the basis for the two main current interpretations of epistasis. The first, as introduced by Bateson (1909), is the “biological,” “physiological,” or “compositional” form of epistasis, concerned with the influence of an individual''s genetic background on an allele''s effect on phenotype (Cheverud and Routman 1995; Phillips 1998, 2008; Cordell 2002; Moore and Williams 2005). The second interpretation, attributed to Fisher, is “statistical” epistasis, which in its linear regression framework places the phenomenon of epistasis in the context of a population (Wagner et al. 1998; Wade et al. 2001; Wilke and Adami 2001; Moore and Williams 2005; Phillips 2008). Each of these approaches is equally valid in studying genetic interactions; however, confusion still exists about how to best reconcile the methods and results of the two (Phillips 1998, 2008; Cordell 2002; Moore and Williams 2005; Liberman and Feldman 2006; Aylor and Zeng 2008).Aside from the distinction between the statistical and the physiological definitions of epistasis, inconsistencies exist when studying solely physiological epistasis. For categorical traits, physiological epistasis is clear as a “masking” effect. When noncategorical or numerical traits are measured, epistasis is defined as the deviation of the phenotype of the multiple mutant from that expected under independence of the underlying genes.The “expectation” of the phenotype under independence, that is, in the absence of epistasis, is not defined consistently between studies. For clarity, consider epistasis between pairs of genes and, without loss of generality, consider fitness as the phenotype. The first commonly used definition of independence, originating from additivity, defines the effect of two independent mutations to be equal to the sum of the individual mutational effects. A second, motivated by the use of fitness as a phenotype, defines the effect of the two mutations as the product of the individual effects (Elena and Lenski 1997; Desai et al. 2007; Phillips 2008). A third definition of independence has been referred to as “minimum,” where alleles at two loci are independent if the double mutant has the same fitness as the less-fit single mutant. Mani et al. (2008) claim that this has been used when identifying pairwise epistasis by searching for synthetic lethal double mutants (Tong et al. 2001, 2004; Pan et al. 2004, 2006; Davierwala et al. 2005). A fourth is the “Log” definition presented by Mani et al. (2008) and Sanjuan and Elena (2006). The less-frequently used “scaled ɛ” (Segre et al. 2005) measure of epistasis takes the multiplicative definition of independence with a scaling factor.These different definitions of independence are partly due to distinct measurement “scales.” For some traits, a multiplicative definition of independence may be necessary to identify epistasis between two genes, whereas for other traits, additivity may be appropriate (Falconer and Mackay 1995; Wade et al. 2001; Mani et al. 2008; Phillips 2008). An interaction found under one independence definition may not necessarily be found under another, leading to different biological conclusions (Mani et al. 2008).Mani et al. (2008) suggest that there may be an “ideal” definition of independence for all gene pairs for identifying functional relationships. However, it is plausible that different representations of independence for two genes may reflect different biological properties of the relationship (Kupper and Hogan 1978; Rothman et al. 1980). “Two categories of general interest [the additive and multiplicative definitions, respectively] are those in which etiologic factors act interchangeably in the same step in a multistep process, or alternatively act at different steps in the process” (Rothman et al. 1980, p. 468). In some cases, the discovery of epistasis may merely be an artifact of using an incorrect null model (Kupper and Hogan 1978). It may be necessary to represent “independence” differently, resulting in different statistical measures of interactions, for different pairs of genes depending on their functions.Previous studies have suggested that different pairs of loci may have different modes of interaction and have attempted to subclassify genetic interactions into regulatory hierarchies and mutually exclusive “interaction subtypes” to elucidate underlying biological properties (Avery and Wasserman 1992; Drees et al. 2005; St. Onge et al. 2007). We suggest that epistatic relationships can be divided into several subtypes, or forms, corresponding to the aforementioned definitions of independence. As a particular gene pair may deviate from independence according to several criteria, we do not claim that these subtypes are necessarily mutually exclusive. We attempt to select the most likely epistatic subtype that is the best statistical representation of the relationship between two genes. To further subclassify interactions, epistasis among deleterious mutations can take one of two commonly used forms: positive (equivalently alleviating, antagonistic, or buffering) epistasis, where the phenotype of the double mutant is less severe than expected under independence, and negative (equivalently aggravating, synergistic, or synthetic), where the phenotype is more severe than expected (Segre et al. 2005; Collins et al. 2006; Desai et al. 2007; Mani et al. 2008).Another objective of such distinctions is to reduce the bias of the estimator of the epistatic parameter (ɛ), which measures the extent and direction of epistasis for a given gene pair. Mani et al. (2008), assuming that the overall distribution of ɛ should be centered around 0, find that inaccurately choosing a definition of independence can result in increased bias when estimating ɛ. For example, using the minimum definition results in the most severe bias when single mutants have moderate fitness effects, and the additive definition results in the largest positive bias when at least one gene has an extreme fitness defect (Mani et al. 2008). Therefore, it is important to select an optimal estimator for ɛ for each pair of genes from among the subtypes of epistatic interactions.Epistasis may be important to consider in genomic association studies, as a gene with a weak main effect may be identified only through its interaction with another gene or other genes (Frankel and Schork 1996; Culverhouse et al. 2002; Moore 2003; Cordell 2009; Moore and Williams 2009). Epistasis has also been studied extensively in the context of the evolution of sex and recombination. The mutational deterministic hypothesis proposes that the evolution of sex and recombination would be favored by negative epistatic interactions (Feldman et al. 1980; Kondrashov 1994); many other studies have also studied the importance of the form of epistasis (Elena and Lenski 1997; Otto and Feldman 1997; Burch and Chao 2004; Keightley and Otto 2006; Desai et al. 2007; MacCarthy and Bergman 2007). Indeed, according to Mani et al. (2008, p. 3466), “the choice of definition [of epistasis] alters conclusions relevant to the adaptive value of sex and recombination.”Given fitness data from single and double mutants in haploid organisms, we implement a likelihood method to determine the subtype that is the best statistical representation of the epistatic interaction for pairs of genes. We use maximum-likelihood estimation and the Bayesian information criteria (BIC) (Schwarz 1978) with a likelihood-ratio test to select the most appropriate null or epistatic model for each putative interaction. We conduct extensive simulations to gauge the performance of our method and demonstrate that it performs reasonably well under various interaction scenarios. We apply our method to two data sets with fitness measurements obtained from yeast (Jasnos and Korona 2007; St. Onge et al. 2007), whose authors assume only multiplicative epistasis for all interactions. By examining functional links and experimentally validated interactions among epistatic pairs, we demonstrate that our results are biologically meaningful. Studying a random selection of genes, we find that minimum epistasis is more prevalent than both additive and multiplicative epistasis and that the overall distribution of ɛ is not significantly different from zero (as Jasnos and Korona 2007 suggest). For genes in a particular pathway, we advise selecting among fewer epistatic subtypes. We believe that our method of epistatic subtype classification will aid in understanding genetic interactions and their properties.

St. Onge et al. (2007) data set:

St. Onge et al. (2007) examined 26 nonessential genes known to confer resistance to MMS, constructed double-deletion strains for 323 double-mutant strains (all but two of the total possible pairs), and assumed the multiplicative form of epistasis for all interactions (see Methods: Analysis of experimental data). Following these authors, we focus on single- and double-mutant fitnesses measured in the presence of MMS. (For results in the absence of MMS, see File S1 and File S1_2.)Using the resampling method described in Analysis of experimental data and File S1, 222 gene pairs pass the cutoff of having epistasis inferred in at least 900 of 1000 replicates. This does not include 5 synthetic lethal gene pairs. Hypothesis testing and a multiple-testing procedure (for 222 simultaneous hypotheses) are necessary to determine the final epistatic pairs.To select one among the three multiple-testing procedures, we follow St. Onge et al. (2007) and examine gene pairs that share specific functional links (see Analysis of experimental data). The Bonferroni method is likely too conservative, yielding only 25 significantly epistatic pairs with only one functional link among them; alternatively, the pFDR procedure appears to be too lenient in rejecting independence for all 222 pairs. Therefore, we use the FDR procedure (although the number of functional links is not significant) and detect 193 epistatic pairs, of which 5 (2.6%) are synthetic lethals, 19 (9.8%) have additive epistasis, 33 (17.1%) have multiplicative epistasis, and 136 (70.5%) have minimum epistasis (File S1_1). We find 29 gene pairs with positive (alleviating) epistasis and 159 gene pairs with negative (aggravating) epistasis.

TABLE 2

Summary of gene pairs with the indicated epistatic subtypes, inferred using the FDR procedure with the BIC method that considers all three epistatic subtypes and their corresponding null models

Peptidoglycan Fine Structure of the Radiotolerant Bacterium Deinococcus radiodurans Sark

Peptidoglycan from Deinococcus radiodurans was analyzed by high-performance liquid chromatography and mass spectrometry. The monomeric subunit was: N-acetylglucosamine–N-acetylmuramic acid–l-Ala–d-Glu-(γ)–l-Orn-[(δ)Gly-Gly]–d-Ala–d-Ala. Cross-linkage was mediated by (Gly)₂ bridges, and glycan strands were terminated in (1→6)anhydro-muramic acid residues. Structural relations with the phylogenetically close Thermus thermophilus are discussed.The gram-positive bacterium Deinococcus radiodurans is remarkable because of its extreme resistance to ionizing radiation (14). Phylogenetically the closest relatives of Deinococcus are the extreme thermophiles of the genus Thermus (4, 11). In 16S rRNA phylogenetic trees, the genera Thermus and Deinococcus group together as one of the older branches in bacterial evolution (11). Both microorganisms have complex cell envelopes with outer membranes, S-layers, and ornithine-Gly-containing mureins (7, 12, 19, 20, 22, 23). However, Deinococcus and Thermus differ in their response to the Gram reaction, having positive and negative reactions, respectively (4, 14). The murein structure for Thermus thermophilus HB8 has been recently elucidated (19). Here we report the murein structure of Deinococcus radiodurans with similar detail.D. radiodurans Sark (23) was used in the present study. Cultures were grown in Luria-Bertani medium (13) at 30°C with aeration. Murein was purified and subjected to amino acid and high-performance liquid chromatography (HPLC) analyses as previously described (6, 9, 10, 19). For further analysis muropeptides were purified, lyophilized, and desalted as reported elsewhere (6, 19). Purified muropeptides were subjected to plasma desorption linear time-of-flight mass spectrometry (PDMS) as described previously (1, 5, 16, 19). Positive and negative ion mass spectra were obtained on a short linear ²⁵²californium time-of-flight instrument (BioIon AB, Uppsala, Sweden). The acceleration voltage was between 17 and 19 kV, and spectra were accumulated for 1 to 10 million fission events. Calibration of the mass spectra was done in the positive ion mode with H⁺ and Na⁺ ions and in the negative ion mode with H⁻ and CN⁻ ions. Calculated m/z values are based on average masses.Amino acid analysis of muramidase (Cellosyl; Hoechst, Frankfurt am Main, Germany)-digested sacculi (50 μg) revealed Glu, Orn, Ala, and Gly as the only amino acids in the muramidase-solubilized material. Less than 3% of the total Orn remained in the muramidase-insoluble fraction, indicating an essentially complete solubilization of murein.Muramidase-digested murein samples (200 μg) were analyzed by HPLC as described in reference 19. The muropeptide pattern (Fig. ​(Fig.1)1) was relatively simple, with five dominating components (DR5 and DR10 to DR13 [Fig. 1]). The muropeptides resolved by HPLC were collected, desalted, and subjected to PDMS. The results are presented in Table ​Table11 compared with the m/z values calculated for best-matching muropeptides made up of N-acetylglucosamine (GlucNAc), N-acetylmuramic acid (MurNAc), and the amino acids detected in the murein. The more likely structures are shown in Fig. ​Fig.1.1. According to the m/z values, muropeptides DR1 to DR7 and DR9 were monomers; DR8, DR10, and DR11 were dimers; and DR12 and DR13 were trimers. The best-fitting structures for DR3 to DR8, DR11, and DR13 coincided with muropeptides previously characterized in T. thermophilus HB8 (19) and had identical retention times in comparative HPLC runs. The minor muropeptide DR7 (Fig. ​(Fig.1)1) was the only one detected with a d-Ala–d-Ala dipeptide and most likely represents the basic monomeric subunit. The composition of the major cross-linked species DR11 and DR13 confirmed that cross-linking is mediated by (Gly)₂ bridges, as proposed previously (20). Open in a separate windowFIG. 1HPLC muropeptide elution patterns of murein purified from D. radiodurans. Muramidase-digested murein samples were subjected to HPLC analysis, and the A₂₀₄ of the eluate was recorded. The most likely structures for each muroeptide as deduced by PDMS are shown. The position of residues in brackets is the most likely one as deduced from the structures of other muropeptides but could not be formally demonstrated. R = GlucNac–MurNac–l-Ala–d-Glu-(γ)→.

TABLE 1

Calculated and measured m/z values for the molecular ions of the major muropeptides from D. radiodurans

Molecular and Biochemical Characterization of the Protein Template Controlling Biosynthesis of the Lipopeptide Lichenysin

Lichenysins are surface-active lipopeptides with antibiotic properties produced nonribosomally by several strains of Bacillus licheniformis. Here, we report the cloning and sequencing of an entire 26.6-kb lichenysin biosynthesis operon from B. licheniformis ATCC 10716. Three large open reading frames coding for peptide synthetases, designated licA, licB (three modules each), and licC (one module), could be detected, followed by a gene, licTE, coding for a thioesterase-like protein. The domain structure of the seven identified modules, which resembles that of the surfactin synthetases SrfA-A to -C, showed two epimerization domains attached to the third and sixth modules. The substrate specificity of the first, fifth, and seventh recombinant adenylation domains of LicA to -C (cloned and expressed in Escherichia coli) was determined to be Gln, Asp, and Ile (with minor Val and Leu substitutions), respectively. Therefore, we suppose that the identified biosynthesis operon is responsible for the production of a lichenysin variant with the primary amino acid sequence l-Gln–l-Leu–d-Leu–l-Val–l-Asp–d-Leu–l-Ile, with minor Leu and Val substitutions at the seventh position.Many strains of Bacillus are known to produce lipopeptides with remarkable surface-active properties (11). The most prominent of these powerful lipopeptides is surfactin from Bacillus subtilis (1). Surfactin is an acylated cyclic heptapeptide that reduces the surface tension of water from 72 to 27 mN m⁻¹ even in a concentration below 0.05% and shows some antibacterial and antifungal activities (1). Some B. subtilis strains are also known to produce other, structurally related lipoheptapeptides (Table ​(Table1),1), like iturin (32, 34) and bacillomycin (3, 27, 30), or the lipodecapeptides fengycin (50) and plipastatin (29).

TABLE 1

Lipoheptapeptide antibiotics of Bacillus spp.

Adaptive Divergence in Experimental Populations of Pseudomonas fluorescens. IV. Genetic Constraints Guide Evolutionary Trajectories in a Parallel Adaptive Radiation

The capacity for phenotypic evolution is dependent upon complex webs of functional interactions that connect genotype and phenotype. Wrinkly spreader (WS) genotypes arise repeatedly during the course of a model Pseudomonas adaptive radiation. Previous work showed that the evolution of WS variation was explained in part by spontaneous mutations in wspF, a component of the Wsp-signaling module, but also drew attention to the existence of unknown mutational causes. Here, we identify two new mutational pathways (Aws and Mws) that allow realization of the WS phenotype: in common with the Wsp module these pathways contain a di-guanylate cyclase-encoding gene subject to negative regulation. Together, mutations in the Wsp, Aws, and Mws regulatory modules account for the spectrum of WS phenotype-generating mutations found among a collection of 26 spontaneously arising WS genotypes obtained from independent adaptive radiations. Despite a large number of potential mutational pathways, the repeated discovery of mutations in a small number of loci (parallel evolution) prompted the construction of an ancestral genotype devoid of known (Wsp, Aws, and Mws) regulatory modules to see whether the types derived from this genotype could converge upon the WS phenotype via a novel route. Such types—with equivalent fitness effects—did emerge, although they took significantly longer to do so. Together our data provide an explanation for why WS evolution follows a limited number of mutational pathways and show how genetic architecture can bias the molecular variation presented to selection.UNDERSTANDING—and importantly, predicting—phenotypic evolution requires knowledge of the factors that affect the translation of mutation into phenotypic variation—the raw material of adaptive evolution. While much is known about mutation rate (e.g., Drake et al. 1998; Hudson et al. 2002), knowledge of the processes affecting the translation of DNA sequence variation into phenotypic variation is minimal.Advances in knowledge on at least two fronts suggest that progress in understanding the rules governing the generation of phenotypic variation is possible (Stern and Orgogozo 2009). The first stems from increased awareness of the genetic architecture underlying specific adaptive phenotypes and recognition of the fact that the capacity for evolutionary change is likely to be constrained by this architecture (Schlichting and Murren 2004; Hansen 2006). The second is the growing number of reports of parallel evolution (e.g., Pigeon et al. 1997; ffrench-Constant et al. 1998; Allender et al. 2003; Colosimo et al. 2004; Zhong et al. 2004; Boughman et al. 2005; Shindo et al. 2005; Kronforst et al. 2006; Woods et al. 2006; Zhang 2006; Bantinaki et al. 2007; McGregor et al. 2007; Ostrowski et al. 2008)—that is, the independent evolution of similar or identical features in two or more lineages—which suggests the possibility that evolution may follow a limited number of pathways (Schluter 1996). Indeed, giving substance to this idea are studies that show that mutations underlying parallel phenotypic evolution are nonrandomly distributed and typically clustered in homologous genes (Stern and Orgogozo 2008).While the nonrandom distribution of mutations during parallel genetic evolution may reflect constraints due to genetic architecture, some have argued that the primary cause is strong selection (e.g., Wichman et al. 1999; Woods et al. 2006). A means of disentangling the roles of population processes (selection) from genetic architecture is necessary for progress (Maynard Smith et al. 1985; Brakefield 2006); also necessary is insight into precisely how genetic architecture might bias the production of mutations presented to selection.Despite their relative simplicity, microbial populations offer opportunities to advance knowledge. The wrinkly spreader (WS) morphotype is one of many different niche specialist genotypes that emerge when experimental populations of Pseudomonas fluorescens are propagated in spatially structured microcosms (Rainey and Travisano 1998). Previous studies defined, via gene inactivation, the essential phenotypic and genetic traits that define a single WS genotype known as LSWS (Spiers et al. 2002, 2003) (Figure 1). LSWS differs from the ancestral SM genotype by a single nonsynonymous nucleotide change in wspF. Functionally (see Figure 2), WspF is a methyl esterase and negative regulator of the WspR di-guanylate cyclase (DGC) (Goymer et al. 2006) that is responsible for the biosynthesis of c-di-GMP (Malone et al. 2007), the allosteric activator of cellulose synthesis enzymes (Ross et al. 1987). The net effect of the wspF mutation is to promote physiological changes that lead to the formation of a microbial mat at the air–liquid interface of static broth microcosms (Rainey and Rainey 2003).Open in a separate windowFigure 1.—Outline of experimental strategy for elucidation of WS-generating mutations and their subsequent identity and distribution among a collection of independently evolved, spontaneously arising WS genotypes. The strategy involves, first, the genetic analysis of a specific WS genotype (e.g., LSWS) to identify the causal mutation, and second, a survey of DNA sequence variation at specific loci known to harbor causal mutations among a collection of spontaneously arising WS genotypes. For example, suppressor analysis of LSWS using a transposon to inactivate genes necessary for expression of the wrinkly morphology delivered a large number of candidate genes (top left) (Spiers et al. 2002). Genetic and functional analysis of these candidate genes (e.g., Goymer et al. 2006) led eventually to the identity of the spontaneous mutation (in wspF) responsible for the evolution of LSWS from the ancestral SM genotype (Bantinaki et al. 2007). Subsequent analysis of the wspF sequence among 26 independent WS genotypes (bottom) showed that 50% harbored spontaneous mutations (of different kinds; see Open in a separate windowFigure 2.—Network diagram of DGC-encoding pathways underpinning the evolution of the WS phenotype and their regulation. Overproduction of c-di-GMP results in overproduction of cellulose and other adhesive factors that determine the WS phenotype. The ancestral SBW25 genome contains 39 putative DGCs, each in principle capable of synthesizing the production of c-di-GMP, and yet WS genotypes arise most commonly as a consequence of mutations in just three DGC-containing pathways: Wsp, Aws, and Mws. In each instance, the causal mutations are most commonly in the negative regulatory component: wspF, awsX, and the phosphodiesterase domain of mwsR (see text).To determine whether spontaneous mutations in wspF are a common cause of the WS phenotype, the nucleotide sequence of this gene was obtained from a collection of 26 spontaneously arising WS genotypes (WS_A-Z) taken from 26 independent adaptive radiations, each founded by the same ancestral SM genotype (Figure 1): 13 contained mutations in wspF (Bantinaki et al. 2007). The existence of additional mutational pathways to WS provided the initial motivation for this study.

TABLE 1

Mutational causes of WS

Retrograde Intraflagellar Transport Mutants Identify Complex A Proteins With Multiple Genetic Interactions in Chlamydomonas reinhardtii

The intraflagellar transport machinery is required for the assembly of cilia. It has been investigated by biochemical, genetic, and computational methods that have identified at least 21 proteins that assemble into two subcomplexes. It has been hypothesized that complex A is required for retrograde transport. Temperature-sensitive mutations in FLA15 and FLA17 show defects in retrograde intraflagellar transport (IFT) in Chlamydomonas. We show that IFT144 and IFT139, two complex A proteins, are encoded by FLA15 and FLA17, respectively. The fla15 allele is a missense mutation in a conserved cysteine and the fla17 allele is an in-frame deletion of three exons. The flagellar assembly defect of each mutant is rescued by the respective transgenes. In fla15 and fla17 mutants, bulges form in the distal one-third of the flagella at the permissive temperature and this phenotype is also rescued by the transgenes. These bulges contain the complex B component IFT74/72, but not α-tubulin or p28, a component of an inner dynein arm, which suggests specificity with respect to the proteins that accumulate in these bulges. IFT144 and IFT139 are likely to interact with each other and other proteins on the basis of three distinct genetic tests: (1) Double mutants display synthetic flagellar assembly defects at the permissive temperature, (2) heterozygous diploid strains exhibit second-site noncomplemention, and (3) transgenes confer two-copy suppression. Since these tests show different levels of phenotypic sensitivity, we propose they illustrate different gradations of gene interaction between complex A proteins themselves and with a complex B protein (IFT172).CILIA and flagella are microtubule-based organelles that are found on most mammalian cells. They provide motility to cells and participate in many sensory processes. Defects in or loss of cilia/flagella cause a variety of human diseases that include polycystic kidney disease, retinal degeneration, infertility, obesity, respiratory defects, left–right axis determination, and polydactyly (Fliegauf et al. 2007). Mouse mutants demonstrate that cilia are essential for viability, neural tube closure, and bone development (Eggenschwiler and Anderson 2007; Fliegauf et al. 2007). Cilia and flagella are also present in protists, algae, moss, and some fungi.The assembly and maintenance of cilia and flagella require intraflagellar transport (IFT) (Kozminski et al. 1995). IFT involves the movement of 100- to 200-nm-long protein particles from the basal body located in the cell body to the tip of the flagella using the heterotrimeric kinesin-2 (anterograde movement) (Kozminski et al. 1995) and movement back to the cell body (retrograde movement) using the cytoplasmic dynein complex (Pazour et al. 1999; Porter et al. 1999). IFT particles change their direction of movement as well as their size, speed, and frequency at the ends of the flagella as they switch from anterograde to retrograde movement (Iomini et al. 2001). Biochemical isolation of IFT particles reveals that they are composed of at least 16 proteins and that these particles can be dissociated into two complexes in vitro by changing the salt concentration (Cole et al. 1998; Piperno et al. 1998). Recent genetic and bioinformatics analysis adds at least 7 more proteins to the IFT particle (Follit et al. 2009) (Eggenschwiler and Anderson 2007).

TABLE 1

Proteins and gene names for the intraflagellar transport particles in Chlamydomonas, C. elegans, and mouse

A Problem With the Correlation Coefficient as a Measure of Gene Expression Divergence

The correlation coefficient is commonly used as a measure of the divergence of gene expression profiles between different species. Here we point out a potential problem with this statistic: if measurement error is large relative to the differences in expression, the correlation coefficient will tend to show high divergence for genes that have relatively uniform levels of expression across tissues or time points. We show that genes with a conserved uniform pattern of expression have significantly higher levels of expression divergence, when measured using the correlation coefficient, than other genes, in a data set from mouse, rat, and human. We also show that the Euclidean distance yields low estimates of expression divergence for genes with a conserved uniform pattern of expression.IT is now possible to measure the expression levels of thousands of genes in multiple tissues at multiple times. This has led to investigations into the evolution of gene expression and how the pattern of expression changes on a genomic scale. In some analyses, the evolution of expression is considered only within one tissue, but in many studies the evolution across multiple tissues is investigated. In this latter case, the evolution of an expression profile—a vector of expression levels of a gene across several tissues—is considered.Several different statistics have been proposed to measure the divergence between gene expression profiles. The two most popular measures are the Euclidean distance (Jordan et al. 2005; Kim et al. 2006; Yanai et al. 2006; Urrutia et al. 2008) and Pearson''s correlation coefficient (Makova and Li 2003; Huminiecki and Wolfe 2004; Yang et al. 2005; Kim et al. 2006; Liao and Zhang 2006a,b; Xing et al. 2007; Urrutia et al. 2008). The correlation coefficient is often subtracted from one, so that the statistic varies from zero, when there has been no expression divergence, to a maximum of two; we refer to this statistic as the Pearson distance. Here we describe a significant shortcoming of the Pearson distance that is not shared by the Euclidean distance.To investigate properties of these two measures of expression divergence, we compiled a data set of 2859 orthologous genes from human, mouse, and rat for which we had microarray expression data from nine homologous tissues: bone marrow, heart, kidney, large intestine, pituitary, skeletal muscle, small intestine, spleen, and thymus). The expression data for rat came from Walker et al. (2004), the mouse data from Su et al. (2004), and the human data from Ge et al. (2005). Each tissue experiment had two replicates in mouse, a varying number of replicates in rat, and one in humans; some genes were also matched by multiple probe sets. To obtain an average across experiments and probe sets we processed the data as follows:

1. Raw CEL files of gene expression levels were obtained from the NCBI Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/projects/geo/).
2. The results from the mouse, rat, and human arrays were normalized separately using both the MAS5 (Affymetrix 2001) and the RMA algorithms (Irizarry et al. 2003) as implemented in Bioconductor (Gentleman et al. 2004). The results are qualitatively similar for the two normalization procedures, although recent analyses suggest that MAS5 normalization is generally better (Ploner et al. 2005; Lim et al. 2007).
3. The expression of each gene within a tissue was averaged across experiments and probe sets.

We computed expression distances (ED) between orthologous gene expression profiles, for each of the three species comparisons, rat–mouse, rat–human, and mouse–human, according to the two different distance metrics, the Euclidean distance and the Pearson distance:(1)Here x_ij is the expression level of the gene under consideration in species i in tissue j, and is the average expression level of the gene in species i across tissues. Expression levels are known in a total of k tissues.Because expression levels are measured on different microarray platforms in the three species, we compute relative abundance (RA) values, before calculating the Euclidean distance (Liao and Zhang 2006a). The RA is the expression of a gene in a particular tissue divided by the sum of the expression values of that gene across all tissues. We calculated RA values to remove “probe” effects (the tendency for a gene to bind its probe set on one platform more efficiently than on another platform). Because of probe effects it is not easy to distinguish absolute changes in expression and differences in binding efficiency. Calculating RA values removes this problem from the Euclidean distance. Pearson''s distance does not change under such a rescaling and so this is unnecessary.In some analyses the logarithm of the expression or RA values are used (e.g., Makova and Li 2003; Kim et al. 2006; Xing et al. 2007), and in others the expression values are used without this transformation (e.g., Huminiecki and Wolfe 2004; Jordan et al. 2005; Yang et al. 2005; Liao and Zhang 2006a,b; Yanai et al. 2006; Urrutia et al. 2008). We calculated both the Pearson and the Euclidean distances on log-transformed and untransformed expression values. The results are qualitatively similar so here we present only the results obtained using the logarithm of the expression or RA values.It is natural to expect the two measures of expression divergence to be positively correlated with one another; however, the Euclidean and Pearson distances are almost completely uncorrelated (MAS5 normalization, mouse–rat correlation coefficient = 0.06, human–rat r = 0.13, human–mouse r = 0.10; RMA normalization, mouse–rat correlation coefficient = −0.12, human–rat r = −0.00, human–mouse r = −0.08; Figure 1). This could, plausibly, be because the two statistics measure different aspects of divergence. However, irrespective of this, there is a potential problem associated with the Pearson distance. Imagine that we have a gene that is expressed at identical levels in all tissues in two species (i.e., expression levels are uniform between tissues and also between species). We quite reasonably assume that measured expression levels contain noise. Thus each measured expression level (x_ij) is the sum of the (assumed) uniform expression level and an independent random number representing noise. In this case there is no real divergence in the expression profile between the species. However, the two measures of divergence may differ greatly in this case. The Euclidean distance reflects only the noise present in the data and hence will be small if the noise is small. By contrast, the Pearson distance will have a value close to 1 since the second term in PeaD in Equation 1 will be close to zero, reflecting the fact that the noise components of different expression levels are independent. Thus the Pearson distance will give the impression that expression divergence is great, but all this apparent divergence is noise. This will be a problem with Pearson''s distance whenever measurement error is of the same magnitude as the differences in expression between tissues. This will therefore tend to be a problem for lowly expressed genes, where measurement error can be large relative to the true value.Open in a separate windowFigure 1.—The correlation between the Euclidean and Pearson distances for (a) mouse–rat, (b) human–rat, and (c) human–mouse. Only the results from MAS5 normalization are shown; qualitatively similar results were obtained with RMA.The above example is unrealistic because real gene expression profiles are rarely perfectly uniform. To investigate whether this shortcoming of the Pearson distance is a problem in real data sets, we determined genes with a relatively uniform pattern of expression in all three species considered above. To do this we computed the entropy of a gene''s expression, which is a measure of uniformity in expression across tissues (Schug et al. 2005): the higher the value of the entropy, the more uniform is the expression. We calculated the entropy for each gene in each of the three species, averaged these across species, and then took those genes in the upper quartile of mean entropy values as a data set of genes with a relatively conserved pattern of uniform expression.It is natural to expect those genes with a conserved uniform pattern of expression to have relatively low expression divergence; however, on average these genes have significantly higher Pearson distances than other genes (Figure 2; supporting information, Figure S1 and Figure S2). By contrast, the Euclidean distance shows the pattern one would anticipate; all of the conserved uniform genes have low expression divergence. It therefore seems likely that the Pearson distance is sensitive to measurement error and hence may not be a good measure of expression divergence.Open in a separate windowFigure 2.—The distribution of expression divergence values for those genes with a uniform pattern of expression that is conserved across species vs. the distribution for all genes for (a) Pearson and (b) Euclidean distances for mouse–rat. We present similar values for human–mouse and human–rat in Figure S1 and Figure S2. Only the results from MAS5 normalization are shown; qualitatively similar results were obtained with RMA.

TABLE 1

The median expression divergence for genes that have a conserved uniform pattern of expression (upper quartile of mean entropy values) vs. all other genes

Surprising Fitness Consequences of GC-Biased Gene Conversion: I. Mutation Load and Inbreeding Depression

GC-biased gene conversion (gBGC) is a recombination-associated process mimicking selection in favor of G and C alleles. It is increasingly recognized as a widespread force in shaping the genomic nucleotide landscape. In recombination hotspots, gBGC can lead to bursts of fixation of GC nucleotides and to accelerated nucleotide substitution rates. It was recently shown that these episodes of strong gBGC could give spurious signatures of adaptation and/or relaxed selection. There is also evidence that gBGC could drive the fixation of deleterious amino acid mutations in some primate genes. This raises the question of the potential fitness effects of gBGC. While gBGC has been metaphorically termed the “Achilles'' heel” of our genome, we do not know whether interference between gBGC and selection merely has practical consequences for the analysis of sequence data or whether it has broader fundamental implications for individuals and populations. I developed a population genetics model to predict the consequences of gBGC on the mutation load and inbreeding depression. I also used estimates available for humans to quantitatively evaluate the fitness impact of gBGC. Surprising features emerged from this model: (i) Contrary to classical mutation load models, gBGC generates a fixation load independent of population size and could contribute to a significant part of the load; (ii) gBGC can maintain recessive deleterious mutations for a long time at intermediate frequency, in a similar way to overdominance, and these mutations generate high inbreeding depression, even if they are slightly deleterious; (iii) since mating systems affect both the selection efficacy and gBGC intensity, gBGC challenges classical predictions concerning the interaction between mating systems and deleterious mutations, and gBGC could constitute an additional cost of outcrossing; and (iv) if mutations are biased toward A and T alleles, very low gBGC levels can reduce the load. A robust prediction is that the gBGC level minimizing the load depends only on the mutational bias and population size. These surprising results suggest that gBGC may have nonnegligible fitness consequences and could play a significant role in the evolution of genetic systems. They also shed light on the evolution of gBGC itself.GC-BIASED gene conversion (gBGC) is increasingly recognized as a widespread force in shaping genome evolution. In different species, gene conversion occurring during double-strand break recombination repair is thought to be biased toward G and C alleles. In heterozygotes, GC alleles undergo a kind of molecular meiotic drive that mimics selection (reviewed in Marais 2003). This process can rapidly increase the GC content, especially around recombination hotspots (Spencer et al. 2006), and, more broadly, can affect genome-wide nucleotide landscapes (Duret and Galtier 2009a). For instance, it is thought to play a role in shaping isochore structure evolution in mammals (Galtier et al. 2001; Meunier and Duret 2004; Duret et al. 2006) and birds (Webster et al. 2006). Direct experimental evidence of gBGC mainly comes from studies in yeast (Birdsell 2002; Mancera et al. 2008; but see Marsolier-Kergoat and Yeramian 2009) and humans (Brown and Jiricny 1987). However, associations between recombination and the nucleotide landscape and frequency spectra biased toward GC alleles provide indirect evidence in very diverse organisms (

Variation in Genomic Recombination Rates Among Heterogeneous Stock Mice

We used a large panel of pedigreed, genetically admixed house mice to study patterns of recombination rate variation in a leading mammalian model system. We found considerable inter-individual differences in genomic recombination rates and documented a significant heritable component to this variation. These findings point to clear variation in recombination rate among common laboratory strains, a result that carries important implications for genetic analysis in the house mouse.THE rate of recombination—the amount of crossing over per unit DNA—is a key parameter governing the fidelity of meiosis. Recombination rates that are too high or too low frequently give rise to aneuploid gametes or prematurely arrest the meiotic cell cycle (Hassold and Hunt 2001). As a consequence, recombination rates should experience strong selective pressures to lie within the range defined by the demands of meiosis (Coop and Przeworski 2007). Nonetheless, classical genetic studies in Drosophila (Chinnici 1971; Kidwell 1972; Brooks and Marks 1986), crickets (Shaw 1972), flour beetles (Dewees 1975), and lima beans (Allard 1963) have shown that considerable inter-individual variation for recombination rate is present within populations. Recent studies examining the transmission of haplotypes in human pedigrees have corroborated these findings (Broman et al. 1998; Kong et al. 2002; Coop et al. 2008).Here, we use a large panel of heterogeneous stock (HS) mice to study variation in genomic recombination rates in a genetic model system. These mice are genetically admixed, derived from an initial generation of pseudorandom mating among eight common inbred laboratory strains (DBA/2J, C3H/HeJ, AKR/J, A/J, BALB/cJ, CBA/J, C57BL/6J, and LP/J), followed by >50 generations of pseudorandom mating in subsequent hybrid cohorts (Mott et al. 2000; Demarest et al. 2001). The familial relationships among animals in recent generations were tracked to organize the mice into pedigrees. In total, this HS panel includes ∼2300 animals comprising 85 families, 8 of which span multiple generations. The remainder consists of nuclear families (sibships) that range from 1 to 34 sibs, with an average of 9.6 sibs (Valdar et al. 2006) (Mott et al. 2000; Demarest et al. 2001; Shifman et al. 2006).

TABLE 1

Heterogeneous stock mouse pedigrees

Sequence Analysis of the GntII (Subsidiary) System for Gluconate Metabolism Reveals a Novel Pathway for l-Idonic Acid Catabolism in Escherichia coli

The presence of two systems in Escherichia coli for gluconate transport and phosphorylation is puzzling. The main system, GntI, is well characterized, while the subsidiary system, GntII, is poorly understood. Genomic sequence analysis of the region known to contain genes of the GntII system led to a hypothesis which was tested biochemically and confirmed: the GntII system encodes a pathway for catabolism of l-idonic acid in which d-gluconate is an intermediate. The genes have been named accordingly: the idnK gene, encoding a thermosensitive gluconate kinase, is monocistronic and transcribed divergently from the idnD-idnO-idnT-idnR operon, which encodes l-idonate 5-dehydrogenase, 5-keto-d-gluconate 5-reductase, an l-idonate transporter, and an l-idonate regulatory protein, respectively. The metabolic sequence is as follows: IdnT allows uptake of l-idonate; IdnD catalyzes a reversible oxidation of l-idonate to form 5-ketogluconate; IdnO catalyzes a reversible reduction of 5-ketogluconate to form d-gluconate; IdnK catalyzes an ATP-dependent phosphorylation of d-gluconate to form 6-phosphogluconate, which is metabolized further via the Entner-Doudoroff pathway; and IdnR appears to act as a positive regulator of the IdnR regulon, with l-idonate or 5-ketogluconate serving as the true inducer of the pathway. The l-idonate 5-dehydrogenase and 5-keto-d-gluconate 5-reductase reactions were characterized both chemically and biochemically by using crude cell extracts, and it was firmly established that these two enzymes allow for the redox-coupled interconversion of l-idonate and d-gluconate via the intermediate 5-ketogluconate. E. coli K-12 strains are able to utilize l-idonate as the sole carbon and energy source, and as predicted, the ability of idnD, idnK, idnR, and edd mutants to grow on l-idonate is altered.In Escherichia coli, the Entner-Doudoroff (ED) pathway serves as a metabolic “funnel” receiving intermediates formed by catabolism of several sugar acids (17). Hexuronic acids undergo rearrangement in the inducible Ashwell pathways (1) to form 2-keto-3-deoxygluconate, which is then phosphorylated to produce 2-keto-3-deoxy-6-phosphogluconate (KDPG). KDPG is cleaved by KDPG aldolase, encoded by eda, providing for entry of carbon into glycolysis. The other enzyme of the ED pathway is 6-phosphogluconate dehydratase, encoded by edd, which is induced only for catabolism of gluconate and also forms KDPG, the key intermediate of the ED pathway (7). Long considered to be of more significance than is readily obvious (9), the finding that eda and edd eda double mutants are unable to colonize the mouse large intestine underscores the possible ecological importance of ED metabolism (32). The implication from these colonization studies is that colonic mucus, which contains several sugar acids, may serve as an important source of nutrients for E. coli in the gut.Also participating in gluconate catabolism are several gluconate transporters and two gluconate kinases which appear, based upon their regulation, to comprise two distinct systems (2, 13). The GntI (main) system consists of gntT, gntU, and gntK, which code for high- and low-affinity gluconate transporters and a thermoresistant gluconate kinase, respectively (23–25, 33). Expression of the GntR regulon, that is, GntI together with the edd-eda operon, is negatively controlled by the gntR gene product. The GntII (subsidiary) system is comprised of a thermosensitive gluconate kinase and a gluconate transporter which function for gluconate catabolism in the absence of the GntI system (2, 11, 13, 22). It appears that the subsidiary gluconate transporter, which has an apparent K_m for gluconate of 60 μM (23), is encoded by a gene (idnT) which is adjacent to the gene encoding the thermosensitive gluconokinase (idnK) at 96.8 min.The DNA sequence of the GntII system genes, located at 4492 kb on the genome, was revealed by the E. coli Genome Project (5, 6). If the GntII system had evolved as a subsidiary pathway for gluconate catabolism, one would expect it to contain only a gluconate transporter and gluconate kinase. However, in addition to the divergent idnK and idnT genes, this region also encodes two “dehydrogenase-like” enzymes. The similarity of idnO to gno of Gluconobacter oxydans, which encodes d-gluconate:NADP 5-oxidoreductase (GNO) (15), led to the testing of ketogluconates as enzyme substrates for the two newly identified dehydrogenases. A process of deductive reasoning and biochemical experiments led to the conclusion that the GntII system in fact comprises a novel metabolic pathway for catabolism of l-idonic acid, in which gluconate is a key intermediate. Accordingly, the genes involved in l-idonate metabolism have been given the designation idn (see Table ​Table11 for gene nomenclature).

TABLE 1

Gene and enzyme nomenclature^a