Cross-Species RNAi Rescue Platform in Drosophila melanogaster

RNAi-mediated gene knockdown in Drosophila melanogaster is a powerful method to analyze loss-of-function phenotypes both in cell culture and in vivo. However, it has also become clear that false positives caused by off-target effects are prevalent, requiring careful validation of RNAi-induced phenotypes. The most rigorous proof that an RNAi-induced phenotype is due to loss of its intended target is to rescue the phenotype by a transgene impervious to RNAi. For large-scale validations in the mouse and Caenorhabditis elegans, this has been accomplished by using bacterial artificial chromosomes (BACs) of related species. However, in Drosophila, this approach is not feasible because transformation of large BACs is inefficient. We have therefore developed a general RNAi rescue approach for Drosophila that employs Cre/loxP-mediated recombination to rapidly retrofit existing fosmid clones into rescue constructs. Retrofitted fosmid clones carry a selection marker and a phiC31 attB site, which facilitates the production of transgenic animals. Here, we describe our approach and demonstrate proof-of-principle experiments showing that D. pseudoobscura fosmids can successfully rescue RNAi-induced phenotypes in D. melanogaster, both in cell culture and in vivo. Altogether, the tools and method that we have developed provide a gold standard for validation of Drosophila RNAi experiments.RNAi-mediated gene knockdown, whereby an exogenous double stranded RNA (dsRNA) is used to trigger homology-dependent suppression of the target gene, is an effective loss-of-function method to interrogate gene function. The RNAi technology in Drosophila melanogaster is widely used for genomewide RNAi screens in cell culture (see review by Perrimon and Mathey-Prevot 2007a), and more recently has been extended to large scale in vivo studies (Dietzl et al. 2007; Ni et al. 2009; Mummery-Widmer et al. 2009). Gene knockdown by RNAi is achieved by the introduction of dsRNAs into cultured cells or by inducible overexpression of “hairpin” dsRNAs in transgenic flies. In the context of in vivo RNAi screening, the combination of a tissue-specific GAL4 driver with a GAL4-responsive hairpin dsRNA transgene allows knockdown of the target gene only in the desired cells, thus providing a powerful way of probing biological processes that have been so far difficult to investigate.Analysis of the specificity of long dsRNAs in Drosophila cells has revealed that these reagents, depending on their sequences and levels of expression, can knock down genes others than the intended target (Kulkarni et al. 2006; Ma et al. 2006). This phenomenon is not specific to long dsRNAs and has also been commonly observed with 21-nt long siRNAs and shRNAs used in mammalian RNAi screens. In fact the rate of false positives associated with off-target effects observed in mammalian screens is usually higher than those observed with long dsRNAs (Echeverri and Perrimon 2006). Unwanted false positives created by off-target effects are a major problem in RNAi screens and require lengthy secondary validation tests (Echeverri and Perrimon 2006; Perrimon and Mathey-Prevot 2007b; Ramadan et al. 2007). Further, false positives associated with RNAi reagents are not limited to tissue culture experiments, as they have also been reported in the context of transgenic RNAi. For example, ∼25% of the hairpins targeting nonessential genes cause lethality when driven by the constitutively expressed Act5C-GAL4 driver (Dietzl et al. 2007; Ni et al. 2009).A number of approaches can be used to validate the specificity of RNAi-induced phenotypes (Echeverri and Perrimon 2006). These include validation by multiple dsRNAs that target the same gene but that do not overlap in sequence, comparison of knockdown efficiencies of multiple dsRNAs and the phenotypic strengths, and rescue of the phenotype by either cDNAs or genomic DNAs. Rescue of RNAi phenotypes constitutes the gold standard in the field as it provides unambiguous proof that the targeted gene is indeed responsible for the phenotype observed. In Drosophila cell culture experiments, cDNAs that lack the original 3′-untranslated region (UTR) have been used to rescue phenotypes induced by dsRNAs targeting the 3′-UTR (Yokokura et al. 2004; Stielow et al. 2008). In mammalian cell culture experiments, cDNAs that have a silent point mutation in the region targeted by an siRNA are commonly used (Lassus et al. 2002). The intrinsic problem of these approaches, however, is that overexpression of cDNAs alone can evoke abnormal cellular responses on their own, complicating interpretation of the results. A cleaner method is based on cross-species rescue that uses genomic DNA from a different species whose sequence is divergent enough from the host species to make it refractory to the RNAi reagent directed against the host gene. This approach effectively addresses the issue of overexpression artifact, as the rescue transgene is expressed from its endogenous promoter, ensuring proper levels and precise spatiotemporal regulation of gene expression. Cross-species rescue methods that use bacterial artificial chromosome (BACs) retrofitted with an appropriate selection marker have been described for mammals and C. elegans (Kittler et al. 2005; Sarov et al. 2006). However, the BAC strategies are not realistic for large-scale studies, because transformation of BACs, which are typically larger than 100 kb, is inefficient, albeit not impossible, in Drosophila (Venken et al. 2006).To provide a feasible way to validate large-scale RNAi screening results, we decided to develop a universal method for cross-species RNAi rescue in Drosophila. We chose to use fosmids, which are single-copy bacterial vectors with a cloning capacity of ∼40 kb, rather than BACs because (1) transformation of plasmids around this size is relatively efficient (Venken et al. 2006) and (2) end-sequenced fosmid clones for 11 different Drosophila species generated by the Drosophila species genome project are now publicly available (Richards et al. 2005; Drosophila 12 Genomes Consortium 2007).     

Binding Pocket Optimization by Computational Protein Design

Engineering specific interactions between proteins and small molecules is extremely useful for biological studies, as these interactions are essential for molecular recognition. Furthermore, many biotechnological applications are made possible by such an engineering approach, ranging from biosensors to the design of custom enzyme catalysts. Here, we present a novel method for the computational design of protein-small ligand binding named PocketOptimizer. The program can be used to modify protein binding pocket residues to improve or establish binding of a small molecule. It is a modular pipeline based on a number of customizable molecular modeling tools to predict mutations that alter the affinity of a target protein to its ligand. At its heart it uses a receptor-ligand scoring function to estimate the binding free energy between protein and ligand. We compiled a benchmark set that we used to systematically assess the performance of our method. It consists of proteins for which mutational variants with different binding affinities for their ligands and experimentally determined structures exist. Within this test set PocketOptimizer correctly predicts the mutant with the higher affinity in about 69% of the cases. A detailed analysis of the results reveals that the strengths of PocketOptimizer lie in the correct introduction of stabilizing hydrogen bonds to the ligand, as well as in the improved geometric complemetarity between ligand and binding pocket. Apart from the novel method for binding pocket design we also introduce a much needed benchmark data set for the comparison of affinities of mutant binding pockets, and that we use to asses programs for in silico design of ligand binding.     

Accumulation of d-Glucose from Pentoses by Metabolically Engineered Escherichia coli

Escherichia coli that is unable to metabolize d-glucose (with knockouts in ptsG, manZ, and glk) accumulates a small amount of d-glucose (yield of about 0.01 g/g) during growth on the pentoses d-xylose or l-arabinose as a sole carbon source. Additional knockouts in the zwf and pfkA genes, encoding, respectively, d-glucose-6-phosphate 1-dehydrogenase and 6-phosphofructokinase I (E. coli MEC143), increased accumulation to greater than 1 g/liter d-glucose and 100 mg/liter d-mannose from 5 g/liter d-xylose or l-arabinose. Knockouts of other genes associated with interconversions of d-glucose-phosphates demonstrate that d-glucose is formed primarily by the dephosphorylation of d-glucose-6-phosphate. Under controlled batch conditions with 20 g/liter d-xylose, MEC143 generated 4.4 g/liter d-glucose and 0.6 g/liter d-mannose. The results establish a direct link between pentoses and hexoses and provide a novel strategy to increase carbon backbone length from five to six carbons by directing flux through the pentose phosphate pathway.     

Inferring Bacterial Genome Flux While Considering Truncated Genes

Bacterial gene content variation during the course of evolution has been widely acknowledged and its pattern has been actively modeled in recent years. Gene truncation or gene pseudogenization also plays an important role in shaping bacterial genome content. Truncated genes could also arise from small-scale lateral gene transfer events. Unfortunately, the information of truncated genes has not been considered in any existing mathematical models on gene content variation. In this study, we developed a model to incorporate truncated genes. Maximum-likelihood estimates (MLEs) of the new model reveal fast rates of gene insertions/deletions on recent branches, suggesting a fast turnover of many recently transferred genes. The estimates also suggest that many truncated genes are in the process of being eliminated from the genome. Furthermore, we demonstrate that the ignorance of truncated genes in the estimation does not lead to a systematic bias but rather has a more complicated effect. Analysis using the new model not only provides more accurate estimates on gene gains/losses (or insertions/deletions), but also reduces any concern of a systematic bias from applying simplified models to bacterial genome evolution. Although not a primary purpose, the model incorporating truncated genes could be potentially used for phylogeny reconstruction using gene family content.GENE content variation as a key feature of bacterial genome evolution has been well recognized (Garcia-Vallvé et al. 2000; Ochman and Jones 2000; Snel et al. 2002; Welch et al. 2002; Kunin and Ouzounis 2003; Fraser-Liggett 2005; Tettelin et al. 2005) and gained increasing attention in recent years. Various methods have been employed to study the variation of gene content in the form of gene insertions/deletions (or gene gains/losses); there are studies of population dynamics (Nielsen and Townsend 2004), birth-and-death evolutionary models (Berg and Kurland 2002; Novozhilov et al. 2005), phylogeny-dependent studies including parsimony methods (Mirkin et al. 2003; Daubin et al. 2003a,b; Hao and Golding 2004), and maximum-likelihood methods (Hao and Golding 2006, 2008b; Cohen et al. 2008; Cohen and Pupko 2010; Spencer and Sangaralingam 2009). The pattern of gene presence/absence also contains phylogenetic signals (Fitz-Gibbon and House 1999; Snel et al. 1999; Tekaia et al. 1999) and has been used for phylogenetic reconstruction (Dutilh et al. 2004; Gu and Zhang 2004; Huson and Steel 2004; Zhang and Gu 2004; Spencer et al. 2007a,b). All these studies make use of the binary information of gene presence or absence and neglect the existence of gene segments or truncated genes.Bacterial genomes are known to harbor pseudogenes. An intracellular species Mycobacterium leprae is an extreme case for both the proportion and the number of pseudogenes: estimated as 40% of the 3.2-Mb genome and 1116 genes (Cole et al. 2001). In free-living bacteria, pseudogenes can make up to 8% of the annotated genes in the genome (Lerat and Ochman 2004). Many pseudogenes result from the degradation of native functional genes (Cole et al. 2001; Mira et al. 2001). Pseudogenes could also result from the degradation of transferred genes and might even be acquired directly via lateral gene transfer. For instance, in plant mitochondrial genomes, which have an α-proteobacterial ancestry, most, if not all, of the laterally transferred genes are pseudogenes (Richardson and Palmer 2007). Furthermore, evidence has been documented that gene transfer could take place at the subgenic level in a wide range of organisms, e.g., among bacteria (Miller et al. 2005; Choi and Kim 2007; Chan et al. 2009), between ancient duplicates in archaea (Archibald and Roger 2002), between different organelles (Hao and Palmer 2009; Hao 2010), and between eukaryotes (Keeling and Palmer 2001). A large fraction of pseudogenes have been shown to arise from failed lateral transfer events (Liu et al. 2004) and most of them are transient in bacterial genomes (Lerat and Ochman 2005). Zhaxybayeva et al. (2007) reported that genomes with truncated homologs might erroneously lead to false inferences of “gene gain” rather than multiple instances of “gene loss.” This raises the question of how a false diagnosis of gene absence affects the estimation of insertion/deletion rates. Recently, we showed that the effect of a false diagnosis of gene absence on estimation of insertion/deletion rates is not systematic, but rather more complicated (Hao and Golding 2008a). To further address the problem, a study incorporating the information of truncated genes is highly desirable. This will not only yield more accurate estimates of the rates of gene insertions/deletions, but also provide a quantitative view of the effect of truncated genes on rate estimation, which has been understudied in bacterial genome evolution.In this study, we developed a model that considers the information of truncated genes and makes use of a parameter-rich time-reversible rate matrix. Rate variation among genes is allowed in the model by incorporating a discrete Γ-distribution. We also allow rates to vary on different parts of the phylogeny (external branches vs. internal branches). Consistent with previous studies, the rates of gene insertions/deletions are comparable to or larger than the rates of nucleotide substitution and the rates of gene insertions/deletions are further inflated in closely related groups and on external branches, suggesting high rates of gene turnover of recently transferred genes. The results from the new model also suggest that many recently truncated genes are in the process of being rapidly deleted from the genome. Some other interesting estimates in the model are also presented and discussed. One implication of the study, though not primary, is that the state of truncated genes could serve as an additional phylogenetic character for phylogenetic reconstruction using gene family content.     

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.     

siRNA脱靶效应研究进展

RNA干扰过程中,siRNA和mRNA特异结合能够使得靶基因沉默。但研究证实,siRNA可能与非靶基因结合而导致非靶基因沉默,这种现象称为siRNA脱靶效应。多种真核生物中的RNA干扰实验证实了脱靶效应的存在。对脱靶机制的研究发现脱靶可能与模体匹配、结构和长dsRNA等有关,很多新方法被提出来预测脱靶概率和检测脱靶基因。通过利用siRNApool、化学修饰和生物信息学方法能够尽可能地降低脱靶效应,提高RNAi实验的质量。对脱靶效应方面的研究进行了总结论述。     

Association Mapping of Quantitative Disease Resistance in a Natural Population of Loblolly Pine (Pinus taeda L.)

Genetic resistance to disease incited by necrotrophic pathogens is not well understood in plants. Whereas resistance is often quantitative, there is limited information on the genes that underpin quantitative variation in disease resistance. We used a population genomic approach to identify genes in loblolly pine (Pinus taeda) that are associated with resistance to pitch canker, a disease incited by the necrotrophic pathogen Fusarium circinatum. A set of 498 largely unrelated, clonally propagated genotypes were inoculated with F. circinatum microconidia and lesion length, a measure of disease resistance, data were collected 4, 8, and 12 weeks after inoculation. Best linear unbiased prediction was used to adjust for imbalance in number of observations and to identify highly susceptible and highly resistant genotypes (“tails”). The tails were reinoculated to validate the results of the full population screen. Significant associations were detected in 10 single nucleotide polymorphisms (SNPs) (out of 3938 tested). As hypothesized for genes involved in quantitative resistance, the 10 SNPs had small effects and proposed roles in basal resistance, direct defense, and signal transduction. We also discovered associated genes with unknown function, which would have remained undetected in a candidate gene approach constrained by annotation for disease resistance or stress response.GENETIC interactions between host and pathogen populations result in abundant natural variation in the genes involved in host disease resistance. Most of the studies leading to identification and cloning of disease resistance genes are focused on major gene disease resistance (Johal and Briggs 1992; Dangl and Jones 2001; Jones and Dangl 2006). In cases where resistance is associated with single genes, genetic effects are large in magnitude and detection is straightforward. In contrast, quantitative disease resistance is typically conditioned by many genes with relatively small effects. Quantitative resistance is generally considered to be more durable but also more difficult to investigate relative to major gene resistance, since the effects of individual genes are small and phenotyping experiments must be performed with high levels of precision. As a consequence, the genes and mechanisms of quantitative disease resistance are poorly understood, in part due to the smaller effect of individual genes on the resistance phenotype. Interactions between plants and necrotrophic pathogens often exhibit quantitative resistance (Balint-Kurti et al. 2008; Poland et al. 2009).Pitch canker disease of loblolly pine and other pine species is incited by the necrotrophic pathogen Fusarium circinatum and is manifest as resinous lesions in stems and branches (Dwinell et al. 1985; Enebak and Stanosz 2003; Carey et al. 2005; Sakamoto and Gordon 2006). There is evidence for heritable resistance to pitch canker in loblolly pine (Kayihan et al. 2005) as well as other pine species (Hodge and Dvorak 2000, 2007). In this article we report the first population-wide phenotypic screen of a clonally propagated population of loblolly pine for association testing (Eckert et al. 2010). Clonal propagation of this population enabled precise phenotyping, which was required to obtain the resolution needed to identify candidates for quantitative disease resistance loci.Pine species in general exhibit high levels of nucleotide variation and low linkage disequilibrium (LD) (Brown et al. 2004). An association genetic approach relies on the premise that historical, unrecorded recombination events over many generations have reduced LD between markers and quantitative trait loci such that only those marker-trait pairs that are tightly linked remain detectable; this may enable “fine mapping” to identify genes underlying quantitative variation (Flint-Garcia et al. 2003; Neale and Savolainen 2004). Association-based approaches have been used to identify candidate genes underlying traits in plants (Zhao et al. 2007; Stich et al. 2008; Wang et al. 2008; Yahiaoui et al. 2008; Inostroza et al. 2009; Stracke et al. 2009), based in part on applications in humans (D''alfonso et al. 2002; McGuffin et al. 2003; Easton et al. 2007; Lee et al. 2007), livestock (Martinez et al. 2006; Charlier et al. 2008; Goddard and Hayes 2009), and Drosophila (Kennington et al. 2007; Norry et al. 2007; Jiang et al. 2009). Recent association studies in tree species have evaluated single candidate genes or a modest number of candidate genes for association (Thumma et al. 2005; Gonzalez-Martinez et al. 2007, 2008; Ingvarsson et al. 2008; Eckert et al. 2009a). Association mapping has been used to identify disease resistance genes in several crop species including sugarcane, maize, barley, and potato (Flint-Garcia et al. 2005; Wei et al. 2006; Yu and Buckler 2006; Malosetti et al. 2007; Stich et al. 2008; Inostroza et al. 2009; Murray et al. 2009). The population analyzed in this study was genotyped at 3938 SNP loci that were selected without regard to the functional annotation of ESTs from which they were derived. Thus, we reasoned that the status of any particular marker as a candidate disease resistance gene would be determined by association testing, as opposed to previous studies in which markers were typically evaluated on the basis of their presumed roles in disease resistance in other species.Several different, but not mutually exclusive hypotheses have been proposed regarding the genetic origins of quantitative resistance (Poland et al. 2009), providing a useful framework for understanding evolution of resistance to necrotrophic pathogens. These six hypotheses proposed by Poland et al. (2009) predict that quantitative disease resistance is conditioned by: (1) genes regulating morphological and developmental phenotypes; (2) mutations in genes involved in basal defense causing small, incremental levels of resistance; (3) components of chemical warfare, through the action of genes producing antibiotic or antifungal compounds; (4) genes involved in defense signal transduction pathways; (5) weak forms of defeated R genes; and/or (6) genes not yet known to be involved in disease resistance.In this study, our main objective was to evaluate the genetic architecture of pitch canker disease resistance: to quantify the extent to which genes contribute to variation in the disease phenotype, to evaluate the hypothesis that disease resistance was quantitative, and to identify candidate genes for resistance as well as quantify their magnitude of effect. In the process of identifying candidate genes for resistance we were also able to evaluate support for hypotheses recently put forth by Poland et al. (2009) regarding the biological roles and origins of quantitative resistance genes.     

Utilization of d-Ribitol by Lactobacillus casei BL23 Requires a Mannose-Type Phosphotransferase System and Three Catabolic Enzymes

Lactobacillus casei strains 64H and BL23, but not ATCC 334, are able to ferment d-ribitol (also called d-adonitol). However, a BL23-derived ptsI mutant lacking enzyme I of the phosphoenolpyruvate:carbohydrate phosphotransferase system (PTS) was not able to utilize this pentitol, suggesting that strain BL23 transports and phosphorylates d-ribitol via a PTS. We identified an 11-kb region in the genome sequence of L. casei strain BL23 (LCABL_29160 to LCABL_29270) which is absent from strain ATCC 334 and which contains the genes for a GlpR/IolR-like repressor, the four components of a mannose-type PTS, and six metabolic enzymes potentially involved in d-ribitol metabolism. Deletion of the gene encoding the EIIB component of the presumed ribitol PTS indeed prevented d-ribitol fermentation. In addition, we overexpressed the six catabolic genes, purified the encoded enzymes, and determined the activities of four of them. They encode a d-ribitol-5-phosphate (d-ribitol-5-P) 2-dehydrogenase, a d-ribulose-5-P 3-epimerase, a d-ribose-5-P isomerase, and a d-xylulose-5-P phosphoketolase. In the first catabolic step, the protein d-ribitol-5-P 2-dehydrogenase uses NAD⁺ to oxidize d-ribitol-5-P formed during PTS-catalyzed transport to d-ribulose-5-P, which, in turn, is converted to d-xylulose-5-P by the enzyme d-ribulose-5-P 3-epimerase. Finally, the resulting d-xylulose-5-P is split by d-xylulose-5-P phosphoketolase in an inorganic phosphate-requiring reaction into acetylphosphate and the glycolytic intermediate d-glyceraldehyde-3-P. The three remaining enzymes, one of which was identified as d-ribose-5-P-isomerase, probably catalyze an alternative ribitol degradation pathway, which might be functional in L. casei strain 64H but not in BL23, because one of the BL23 genes carries a frameshift mutation.     

Does Gene Translocation Accelerate the Evolution of Laterally Transferred Genes?

Lateral gene transfer (LGT) and gene rearrangement are essential for shaping bacterial genomes during evolution. Separate attention has been focused on understanding the process of lateral gene transfer and the process of gene translocation. However, little is known about how gene translocation affects laterally transferred genes. Here we have examined gene translocations and lateral gene transfers in closely related genome pairs. The results reveal that translocated genes undergo elevated rates of evolution and gene translocation tends to take place preferentially in recently acquired genes. Translocated genes have a high probability to be truncated, suggesting that translocation followed by truncation/deletion might play an important role in the fast turnover of laterally transferred genes. Furthermore, more recently acquired genes have a higher proportion of genes on the leading strand, suggesting a strong strand bias of lateral gene transfer.GENE insertions and deletions, together with gene translocations play important roles in bacterial genome evolution (Garcia-Vallvé et al. 2000; Ochman and Jones 2000; Tillier and Collins 2000a; Fraser-Liggett 2005). Gene insertions and deletions, as the essential driving forces in influencing gene content (Kunin and Ouzounis 2003), have received a great deal of attention. Various methods have been employed to study gene insertions and deletions previously; for instance, there are studies of population dynamics (Nielsen and Townsend 2004), such as a birth-and-death model of evolution (Berg and Kurland 2002; Novozhilov et al. 2005), phylogeny-dependent studies including parsimony methods (Daubin et al. 2003a,b; Mirkin et al. 2003; Hao and Golding 2004), and maximum-likelihood methods (Hao and Golding 2006b, 2008b). It has been shown that recently laterally transferred genes have high evolutionary rates and high rates of gene turnover (Daubin et al. 2003b; Hao and Golding 2004, 2006b).Gene rearrangement has also been commonly studied as another important driving force that shapes bacterial genomes (for a review, see Rocha 2004). Gene order changes in genomes are history dependent; for instance, fewer gene rearrangements are expected among more closely related species. Gene order within genomes has therefore been used to reconstruct phylogeny (Sankoff et al. 2000; Tamames 2001; Rogozin et al. 2004; Belda et al. 2005). Previous studies have focused mainly on lateral gene transfer (LGT) and gene rearrangement individually, but little is known about any association between laterally transferred genes and gene rearrangements. The study of gene order of laterally acquired genes might shed some light on the understanding of the LGT process.In this study, we have examined gene translocations and lateral gene transfers in closely related genome pairs. It is shown that the proportion of translocated genes among recently acquired genes is always high, while the proportion of translocated genes is always low in ancient genes, suggesting that gene translocation tends to take place in recently transferred genes. The results also reveal that translocated genes have elevated rates of evolution compared with positionally conserved genes and gene truncation is more prevalent in translocated genes. These findings suggest that gene translocation might accelerate the gene turnover of recently transferred genes and/or that genes likely to undergo translocation are those genes more likely to be laterally transferred and dispensable for the genome. Furthermore, the proportion of recently acquired genes is higher on the leading strand, suggesting that laterally transferred genes are biased toward being on the leading strand. After lateral transfer, some genes could be translocated to the lagging strand and some translocated genes are likely to be eliminated during evolution.     

A Combined-Cross Analysis Reveals Genes With Drug-Specific and Background-Dependent Effects on Drug Sensitivity in Saccharomyces cerevisiae

Effective pharmacological therapy is often inhibited by variable drug responses and adverse drug reactions. Dissecting the molecular basis of different drug responses is difficult due to complex interactions involving multiple genes, pathways, and cellular processes. We previously found a single nucleotide polymorphism within cystathionine β-synthase (CYS4) that causes multi-drug sensitivity in a vineyard strain of Saccharomyces cerevisiae. However, not all variation was accounted for by CYS4. To identify additional genes influencing drug sensitivity, we used CYS4 as a covariate and conducted both single- and combined-cross linkage mapping. After eliminating numerous false-positive associations, we identified 16 drug-sensitivity loci, only 3 of which had been previously identified. Of 4 drug-sensitivity loci selected for validation, 2 showed replicated associations in independent crosses, and two quantitative trait genes within these regions, AQY1 and MKT1, were found to have drug-specific and background-dependent effects. Our results suggest that drug response may often depend on interactions between genes with multi-drug and drug-specific effects.RESPONSE to pharmacological therapy varies and is often highly heritable (Evans and Johnson 2001; Evans and McLeod 2003; Ingelman-Sundberg et al. 2007). Variable drug responses make it difficult to achieve optimal dosing and frequently result in adverse drug reaction, a major cause of death in hospitalized patients (Lazarou et al. 1998). In addition to impacting drug therapy, adverse drug reactions can limit or even eliminate the use of a drug (Shah 2006). Consequently, understanding the genetic basis of variable drug responses is important to both mitigating adverse drug reactions and developing new or improved pharmacological therapies. Although many pharmacogenetic variants have been identified from surveys of candidate genes and pathways (Katz and Bhathena 2009), there have been only a few studies that have conducted genomewide mapping (Dolan et al. 2004; Watters et al. 2004; Perlstein et al. 2006; Duan et al. 2007; Huang et al. 2007; Kim and Fay 2007; Perlstein et al. 2007; Bleibel et al. 2009; Shukla et al. 2009), and many of these have focused on chemotherapy-induced cytotoxicity in human lymphoblastoid cell lines, which in some instances may be susceptible to false-positive associations due to low repeatability (Choy et al. 2008). Furthermore, identification of individual genes and their causal variants in human cell lines is a significant challenge. Thus, there is still an incomplete picture of the genes, pathways, and processes responsible for both pharmacokinetic (absorption, distribution, metabolism, and excretion of a drug) and pharmacodynamic (physiological or biochemical effect of a drug) variation.Saccharomyces cerevisiae has proved to be a useful system for pharmacological research. The yeast deletion collection has been used to identify a compound''s mechanism of action as well as its indirect effects on basic biological processes (Baetz et al. 2004; Giaever et al. 2004; Lum et al. 2004). Many yeast genes that function in detoxification of xenobiotic compounds through drug transport and metabolism have been identified (Balzi and Goffeau 1995; Decottignies and Goffeau 1997; Wolfger et al. 2004; Moye-Rowley 2005; Barreto et al. 2006). In addition, many yeast genes that function in pleiotropic drug resistance are homologous to human genes involved in multi-drug resistance to chemotherapy (Kuchler and Thorner 1992; Wolfger et al. 2001; Gottesman et al. 2002). However, genes responsible for population genetic variation may be different from those identified through mutant screens since naturally occurring alleles may be neomorphic or have effects that are small or dependent on genetic background. Furthermore, many drug-sensitive phenotypes may result from the combined effects of multiple genes that show very small or no effects by themselves.Linkage mapping has generated significant insight into the genetic architecture and molecular basis of variable drug responses between different yeast strains. Two recent studies examined growth differences in the presence of 31 and 104 different compounds and found that drug sensitivity was often due to the combined effects of drug-specific as well as multi-drug-sensitive quantitative trait loci (QTL; Kim and Fay 2007; Perlstein et al. 2007). In addition to known mutations segregating at HO, URA3, HAP1, and LEU2, the two studies each identified a major-effect gene causing multi-drug sensitivity. Perlstein et al. (2007) found a nonsynonymous polymorphism within PHO84, an inorganic phosphate transporter, that caused sensitivity to 25/104 compounds. PHO84 is a member of the major facilitator superfamily of transporters, which includes human genes in the solute carrier family 22 (SLC22) that are important for hepatic and renal excretion of cationic drugs (Koepsell 2004). Kim and Fay (2007) found a nonsynonymous polymorphism within CYS4, an enzyme in the cysteine biosynthesis pathway that is required for glutathione biosynthesis. Attachment of glutathione to a drug is one of the major mechanisms by which cells detoxify xenobiotic compounds (Hayes et al. 2005). Thus, both genes affect the pharmacokinetic response to multiple drugs.QTL with small and/or drug-specific effects also contribute to variable drug responses (Kim and Fay 2007; Perlstein et al. 2007). However, identification of small-effect genes can be complicated by the simultaneous segregation of other QTL, particularly those of large effect. Studies of other quantitative traits have shown that the effects of a QTL can be small in isolation but much larger in combination with other segregating QTL (e.g., Steinmetz et al. 2002; Deutschbauer and Davis 2005; Gerke et al. 2009). Thus, identification of small-effect QTL may depend on accounting for interactions with those of large effect.One approach to identifying small or background-dependent QTL is to generate recombinants that are fixed for the major QTL through backcrosses or introgression (e.g., Sinha et al. 2008). An alternative approach, and the one implemented here, is to identify associations after statistically removing the effects of the major QTL (e.g., Brem et al. 2005). To map genes affecting drug sensitivity while controlling for the large effects of a multi-drug-sensitive allele of CYS4, we conducted both single- and combined-cross linkage scans using CYS4 as a covariate. After eliminating many false-positive associations, we identified two genes, AQY1 and MKT1, that show drug-specific and background-dependent effects. Our results show how drug sensitivity can be mediated by a combination of genes with multi-drug and drug-specific effects.     

The Fractionated Orthology of Bs2 and Rx/Gpa2 Supports Shared Synteny of Disease Resistance in the Solanaceae

Comparative genomics provides a powerful tool for the identification of genes that encode traits shared between crop plants and model organisms. Pathogen resistance conferred by plant R genes of the nucleotide-binding–leucine-rich-repeat (NB–LRR) class is one such trait with great agricultural importance that occupies a critical position in understanding fundamental processes of pathogen detection and coevolution. The proposed rapid rearrangement of R genes in genome evolution would make comparative approaches tenuous. Here, we test the hypothesis that orthology is predictive of R-gene genomic location in the Solanaceae using the pepper R gene Bs2. Homologs of Bs2 were compared in terms of sequence and gene and protein architecture. Comparative mapping demonstrated that Bs2 shared macrosynteny with R genes that best fit criteria determined to be its orthologs. Analysis of the genomic sequence encompassing solanaceous R genes revealed the magnitude of transposon insertions and local duplications that resulted in the expansion of the Bs2 intron to 27 kb and the frequently detected duplications of the 5′-end of R genes. However, these duplications did not impact protein expression or function in transient assays. Taken together, our results support a conservation of synteny for NB–LRR genes and further show that their distribution in the genome has been consistent with global rearrangements.R genes have a central role in plant disease resistance to mediate pathogen detection and response (Martin et al. 2003; Glazebrook 2005). Although R genes are only one of the components required for these responses, they are consistently identified as a critical determinant for qualitative and quantitative resistance (Fluhr 2001; Wisser et al. 2006). The structure, mechanism of action, and evolution of this gene family are still being elucidated and are critical issues for a more efficient deployment of disease resistances in agricultural crops (McDowell and Simon 2006; Takken et al. 2006; Friedman and Baker 2007; van Ooijen et al. 2007).Comparative studies of sequence similarity between plant R proteins and proteins of innate immunity in animals have made important contributions toward understanding R-protein structure, the role of individual protein domains, and the mechanism by which R proteins identify and respond to foreign proteins (Nurnberger et al. 2004; Takken et al. 2006; Rairdan and Moffett 2007). Both share a central nucleotide-binding (NB) site and a region of homology termed the “ARC” domain (collectively referred to as the NB–ARC) (van der Biezen and Jones 1998; Rairdan and Moffett 2007). The plant counterparts have a highly variable leucine-rich-repeat (LRR) domain at the C terminus and, at the N terminus, either a domain with homology to the Toll and interleukin-1 receptors (TIR) or lack this feature, instead possessing a domain that may include a coiled-coil motif. Due to uncertainty regarding the presence of a coiled-coil motif, this class of NB–LRRs is often referred to as non-TIR proteins. The LRR domains are highly variable and tend to be under diversifying selection to adapt to continually changing pathogen proteins (Meyers et al. 1998b; Michelmore and Meyers 1998; Mondragon-Palomino et al. 2002). Other conserved patterns have been identified in the N terminus of non-TIR proteins, most notably, an EDxxD motif that mediates an intramolecular interaction (Rairdan et al. 2008). The interaction with cellular factors is mediated by the N-terminal domains of NB–LRR proteins although domain-swapping experiments between closely related NB–LRR proteins have shown that recognition specificity is determined by the LRR domains (Rairdan and Moffett 2007; van Ooijen et al. 2007).The clustering of R genes has provided both insight into their ability to evolve rapidly and challenges to their identification and cloning. R genes often occur in clusters of tandem duplications that can span several megabases and include a multitude of copies of functional R genes, pseudogenes, and other genes within the clusters (Meyers et al. 1998a; Kuang et al. 2004; Smith et al. 2004). Of the various modes of evolution ascribed to these clusters, sequence exchange between R genes within the cluster by unequal crossing over or illegitimate recombination is especially noteworthy (Michelmore and Meyers 1998; Ellis et al. 2000; Hulbert et al. 2001; McDowell and Simon 2006; Friedman and Baker 2007; Wicker et al. 2007). Under stress conditions, transposon activation, recombination activation, and chromatin modifications related to small RNAs may be induced (Levy et al. 2004; Friedman and Baker 2007; Yi and Richards 2007).Two distinct models for the genomewide arrangement and distribution of NB–LRR genes and these clusters have been proposed. The first predicts rapid rearrangement of R-gene distribution during genome evolution, yielding poor conservation of R-gene locations (Leister et al. 1998; Richly et al. 2002; Meyers et al. 2003). Indeed, in monocots, extensive loss of genomewide R-gene colinearity has been attributed to frequent R-gene duplication and ectopic transposition (Gale and Devos 1998; Paterson et al. 2003). In contrast, the second model supports genomewide conservation of R-gene distribution maintained during speciation. According to this model, most duplication and recombination of R-gene sequences should occur within restricted chromosomal regions, yielding clusters of closely related R-gene sequences. The resulting orthology relationships (homologs related by speciation, not duplication) are complex due to “fractionation” (repeated cycles of duplication, deletion, and recombination) but can, as we have previously shown, be reconstructed (Grube et al. 2000b). Analysis of R genes using the complete Arabidopsis thaliana genome sequence supports this model and accounts for the consensus of NB–LRR sequences (Baumgarten et al. 2003). Resistance to a particular pathogen type is not conserved, and highly similar NB–LRR proteins may confer resistance to very different pathogens (Grube et al. 2000b).Bs2 encodes a non-TIR NB–LRR protein identified in Capsicum chacoense that confers resistance to the bacterium Xanthomonas campestris pv. vesicatoria. This R gene has greatest sequence identity to Rx and Gpa2 in potato, which confer resistance to a virus and nematode, respectively (Bendahmane et al. 1999; Tai et al. 1999b; van der Vossen et al. 2000). Despite the difference in the pathogens recognized by these genes, they are distinguishable from all other known R genes by marked sequence and structural features. In this study, we demonstrate that these three R genes are derived from syntenic regions in solanaceous genomes as predicted by our model of conservation of synteny. In performing these comparisons, we explore conserved amino acid patterns associated with proteins of the non-TIR family and the local genomic context of R genes of the Solanaceae. Finally, advances in the development of the Solanaceae as a system for comparative genomics highlight a role for chromosomal rearrangements in R-gene distribution throughout plant genomes.     

High-Level Heterologous Production of d-Cycloserine by Escherichia coli

Previously, we successfully cloned a d-cycloserine (d-CS) biosynthetic gene cluster consisting of 10 open reading frames (designated dcsA to dcsJ) from d-CS-producing Streptomyces lavendulae ATCC 11924. In this study, we put four d-CS biosynthetic genes (dcsC, dcsD, dcsE, and dcsG) in tandem under the control of the T7 promoter in an Escherichia coli host. SDS-PAGE analysis demonstrated that the 4 gene products were simultaneously expressed in host cells. When l-serine and hydroxyurea (HU), the precursors of d-CS, were incubated together with the E. coli resting cell suspension, the cells produced significant amounts of d-CS (350 ± 20 μM). To increase the productivity of d-CS, the dcsJ gene, which might be responsible for the d-CS excretion, was connected downstream of the four genes. The E. coli resting cells harboring the five genes produced d-CS at 660 ± 31 μM. The dcsD gene product, DcsD, forms O-ureido-l-serine from O-acetyl-l-serine (OAS) and HU, which are intermediates in d-CS biosynthesis. DcsD also catalyzes the formation of l-cysteine from OAS and H₂S. To repress the side catalytic activity of DcsD, the E. coli chromosomal cysJ and cysK genes, encoding the sulfite reductase α subunit and OAS sulfhydrylase, respectively, were disrupted. When resting cells of the double-knockout mutant harboring the four d-CS biosynthetic genes, together with dcsJ, were incubated with l-serine and HU, the d-CS production was 980 ± 57 μM, which is comparable to that of d-CS-producing S. lavendulae ATCC 11924 (930 ± 36 μM).