A Second Informational Suppressor, SUP-7 X, in CAENORHABDITIS ELEGANS

More than 30 independent suppressor mutations have been obtained in the nematode C. elegans through reversion analysis of two unc-13 mutants. Many of the new isolates map to the region of the previously identified informational suppressor, sup-5 III (Waterston and Brenner 1978). Several of the other suppressor mutations map to the left half of the X-linkage group and define a second suppressor gene, sup-7 X. In tests against 40 mutations in six genes, the sup-7(st5) allele was found to suppress to a greater extent the same alleles acted on by sup-5(e1464). Like sup-5(e1464), sup-7(st5) acts on null alleles of the myosin heavy-chain gene unc-54 I (MacLeod et al. 1977; MacLeod, Waterston and Brenner 1977) and the putative paramyosin gene unc-15 I (Waterston et al. 1977). Chemical analysis of unc-15(e1214); sup-7(st5) animals show that paramyosin is restored to more than 30% of the wild-type level.—As was observed for sup-5(e1464), suppression by sup-7(st5) is dose dependent and is greater in animals grown at 15° than at 25°. However, associated with this increased suppression is a decreased viability of sup-7(st5) homozygotes. Reversion of the lethality has resulted in the isolation of deficiency mutations that complement st5 lethality, but lack suppressor function. These properties of sup-7(st5) suggest that it, like sup-5(e1464), is an informational suppressor of null alleles, and its reversion via deficiencies further narrows the possible explanations of its action.     

Drosophila Transposon Insertions as Unknowns for Structured Inquiry Recombination Mapping Exercises in an Undergraduate Genetics Course

Structured inquiry approaches, in which students receive a Drosophila strain of unknown genotype to analyze and map the constituent mutations, are a common feature of many genetics teaching laboratories. The required crosses frustrate many students because they are aware that they are participating in a fundamentally trivial exercise, as the map locations of the genes are already established and have been recalculated thousands of times by generations of students. We modified the traditional structured inquiry approach to include a novel research experience for the students in our undergraduate genetics laboratories. Students conducted crosses with Drosophila strains carrying P[lacW] transposon insertions in genes without documented recombination map positions, representing a large number of unique, but equivalent genetic unknowns. Using the eye color phenotypes associated with the inserts as visible markers, it is straightforward to calculate recombination map positions for the interrupted loci. Collectively, our students mapped 95 genetic loci on chromosomes 2 and 3. In most cases, the calculated 95% confidence interval for meiotic map location overlapped with the predicted map position based on cytology. The research experience evoked positive student responses and helped students better understand the nature of scientific research for little additional cost or instructor effort.INQUIRY-BASED learning in which students are engaged in open-ended, student-centered, hands-on activities is an important tool for training undergraduates to think like scientists (Colburn 2000; Handelsman et al. 2004). With this approach, students learn scientific subjects by interpreting and discussing experimental results in a fashion similar to that used by scientific researchers (NRC 2003). There are three main approaches to instruction via inquiry. In open inquiry, students formulate their own problem, as well as the procedures to investigate the problem. In guided inquiry, the instructor provides the problem and necessary materials, but the students devise an experimental procedure to investigate the problem. Finally, in structured inquiry, the instructor provides the problem, the materials, and the procedure, but the students are required to gather and interpret the experimental data independently, coming to their own conclusions (Welch et al. 2006). In each case, the instructor does not provide “the answer” to the problem. In the ideal case, the instructor does not even know what the answer will be prior to the student experiment, forcing the students to grapple with the information themselves. Inquiry-based laboratories can even be extended so that students are participating in novel research as part of their coursework (DebBurman 2002; Buckner et al. 2007), which been shown to improve undergraduate retention and student performance in biology lecture courses (Marcus et al. 2009).The process of inquiry has been identified as central to training students to understand fundamental approaches used in the field of genetics such as the design of controlled crosses and interpretation of experimental data (Cartier and Stewart 2000). Pukkila (2004) discusses effective methods by which inquiry-based learning can be incorporated into undergraduate genetics lecture courses with large enrollments and into recitation sections. However, the implementation of inquiry-based approaches in undergraduate genetics laboratories has not been discussed extensively.Teaching laboratories offer some advantages for inquiry learning because they generally contain small groups of students, facilitating a flexible and intimate learning environment with many interactions between students and the instructor, as well as among classmates. However, teaching laboratories associated with large lecture courses also offer some challenges, in particular how to deliver substantially similar experiences to laboratory sections taught by multiple instructors, as well as how to provide inquiry-based learning in a logistically manageable and cost-effective manner. For these reasons, most inquiry-based genetics laboratory exercises have used the structured inquiry approach, for example, using many Drosophila melanogaster strains with similar mutant phenotypes (e.g., white eyes and black bodies), but a variety of genotypes, in a series of standardized genetic mapping crosses to familiarize students with the collection and interpretation of classical genetic data (MacIntyre 1974; Pye 1980). The difficulty with contrived genetic unknowns carrying well-mapped genetic mutations is that many students become frustrated that their hard work evaluating the crosses over a period of several months is devoted to a fundamentally trivial exercise, as the recombination map locations of the genes are already established in the scientific literature and have been recalculated thousands of times by generations of genetics students.We have expanded upon the structured inquiry approach to genetics to include novel research experiences for the students in our undergraduate genetics laboratories. They conduct mapping crosses with Drosophila strains carrying P-element transposon insertions in genes without documented recombination map positions. The stock centers maintain very large collections of P-element transposon stocks with known insertion sites on the cytological and genome maps (Spradling et al. 1999). However, in spite of the cytology to recombination map equivalence table available in FlyBase (2009), very few of the transposon inserts have been formally placed on the recombination map. By using the eye color phenotypes associated with many transposon inserts as visible markers in genetic crosses (Marcus 2003), it is straightforward to calculate recombination map positions for the interrupted loci. The stock collections contain many stocks with identical transposons inserted at different chromosomal locations, providing a large number of unique, but equivalent genetic unknowns that can be used for recombination mapping exercises. At the same time, this approach provides students with the opportunity to map genes that have never been mapped before, allowing them to make small but useful contributions to the field of Drosophila genetics.     

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 Genetic Screen for Suppressors of a Mutated 5′ Splice Site Identifies Factors Associated With Later Steps of Spliceosome Assembly

Many alleles of human disease genes have mutations within splicing consensus sequences that activate cryptic splice sites. In Caenorhabditis elegans, the unc-73(e936) allele has a G-to-U mutation at the first base of the intron downstream of exon 15, which results in an uncoordinated phenotype. This mutation triggers cryptic splicing at the −1 and +23 positions and retains some residual splicing at the mutated wild-type (wt) position. We previously demonstrated that a mutation in sup-39, a U1 snRNA gene, suppresses e936 by increasing splicing at the wt splice site. We report here the results of a suppressor screen in which we identify three proteins that function in cryptic splice site choice. Loss-of-function mutations in the nonessential splicing factor smu-2 suppress e936 uncoordination through changes in splicing. SMU-2 binds SMU-1, and smu-1(RNAi) also leads to suppression of e936. A dominant mutation in the conserved C-terminal domain of the C. elegans homolog of the human tri-snRNP 27K protein, which we have named SNRP-27, suppresses e936 uncoordination through changes in splicing. We propose that SMU-2, SMU-1, and SNRP-27 contribute to the fidelity of splice site choice after the initial identification of 5′ splice sites by U1 snRNP.PRE-mRNA splicing takes place in a large ribonucleoprotein complex called the spliceosome (Burge et al. 1999). Components of this splicing machinery assemble at conserved signal sequences within the pre-mRNA. The 5′ splice site consensus sequence M₋₃A₋₂G₋₁ | G₊₁U₊₂R₊₃A₊₄G₊₅U₊₆ and the 3′ splice site consensus sequence Y₋₃A₋₂G₋₁ | R₊₁ (M is either A or C; R is a purine, and Y is a pyrimidine) define the limits of the intron. Base-pairing interactions between the 5′ end of the U1 snRNA and the 5′ splice site consensus sequence occur early in spliceosome assembly. It is the nearly invariable GU dinucleotide at the first two positions of the 5′ end of the intron that defines the beginning of the intron. The 5′ consensus sequence is essential but insufficient for splice site selection, as 5′ splice sites with weaker consensus matches may require additional determinants for proper activation (Sanford et al. 2005).Mutations that disrupt the 5′ consensus splice signal can lead to genetic disease in humans (Nelson and Green 1990; Cohen et al. 1994). Approximately 15% of point mutations that cause genetic diseases affect pre-mRNA splicing consensus sequences (Krawczak et al. 1992). For some specific disease genes, as many as 50% of the known heritable alleles alter splicing (Teraoka et al. 1999; Ars et al. 2000; Roca et al. 2003; Pagenstecher et al. 2006). Among all the positions of the 5′ splice site consensus sequence, the highest proportion of human disease mutations occur at the +1G position (Buratti et al. 2007). The fidelity of pre-mRNA splice site choice is largely disrupted by this defect, since this mutation causes splicing at this site to be either abolished or outcompeted by the activation of nearby cryptic 5′ splice sites (Nelson and Green 1990; Cohen et al. 1994). Cryptic splice sites are used only when the wild-type splice donor is disrupted by mutation, as they tend to have very weak splice donor consensus sequences outside of a 5′-GU dinucleotide that defines the beginning of the intron (Roca et al. 2003). Suppression of mutations to the 5′ splice site consensus sequence in vivo has been achieved through the expression of U1 snRNAs containing compensatory base substitutions (Zhuang and Weiner 1986); however, suppression of mutations to the +1 position of the intron using reverse genetic approaches has not been successful (Newman et al. 1985; Nelson and Green 1990; Cohen et al. 1994).We have used a specific allele of the Caenorhabditis elegans unc-73 gene, e936, which contains a G-to-U mutation at the first nucleotide of intron 16 (Steven et al. 1998), as a model for studying cryptic splice site choice (Roller et al. 2000; Zahler et al. 2004). unc-73 encodes a RAC guanine nucleotide exchange factor that is expressed in neurons and is important for axon guidance (Steven et al. 1998). The e936 allele induces the use of three different cryptic 5′ splice sites (Figure 1A). Two of these 5′ splice sites, located at the −1 and +23 positions, define introns beginning with GU. The third 5′ splice site used is at the mutated wild-type (wt) position and is referred to as “wt” since splicing at this site still produces wild-type unc-73 mRNA and protein, even though the intron begins with UU (Roller et al. 2000). Use of either the −1 or the +23 cryptic site causes a shift in the reading frame and loss of gene function. In e936 animals, 90% of the stable messages of unc-73 are out-of-frame, yet the phenotype is not as severe as for other alleles in this gene. This indicates that the 10% of steady-state messages that are in frame have some functional role.Open in a separate windowFigure 1.—(A) Diagram of the unc-73 gene between exons 15 and 16. The positions of the −1 and +23 cryptic 5′ splice sites are indicated by arrows. The intronic e936 (+1G → U) point mutation is highlighted. (B) γ-³²P-labeled RT–PCR results across the cryptic splicing region of unc-73(e936) for different strains. Lanes 1, 2, and 3 are loaded with RT–PCR reactions from wild type (N2), unc-73(e936);sup-39(je5), and unc-73(e936) RNA, respectively. The lines carrying the suppressor alleles and e936 follow in lanes 4–10 as indicated. (C) The unc-73 genomic sequence from exon 15 (uppercase letters) and intron 15 (lowercase letters). The locations of the az23 and e936 mutational substitutions are indicated below. The position of the −9 cryptic splice donor activated in e936az23 is indicated by an arrow above.In a previous genetic screen for extragenic suppressors of e936 movement defects, Way and colleagues identified sup-39 (Run et al. 1996). It was subsequently shown that mutations in sup-39 alter cryptic splice site choice of e936 (Roller et al. 2000). sup-39 encodes a U1 snRNA gene with a compensatory mutation at the position that normally base pairs with the +1G. This allows sup-39 to base pair with an intron with a +1U (Zahler et al. 2004). This dominant suppressor increases usage of the mutated splice site and improves the fraction of in-frame messages from e936 from 10 to 33%, with a dramatic improvement in coordination. A similar mutant U1 snRNA suppressor with a different compensatory substitution, sup-6(st19), was found to suppress the intronic +1G to A transition of unc-13(e309) to allow for splicing at the mutated wild-type site, even though the intron begins with AU instead of GU (Zahler et al. 2004).We are interested in identifying additional factors that play a role in cryptic 5′ splice site choice. To do this, we took advantage of unc-73(e936), in which modest increases in the use of the wt splice site lead to dramatic increases in coordination, as a sensitive screen for changes in cryptic splice site choice. In this article we report that the proteins SMU-1 and SMU-2, which are nonessential factors previously shown to have a role in alternative splicing (Spartz et al. 2004), have a role in selection of cryptic 5′ splice sites. We also report the identification of a new dominant suppressor of cryptic splicing, snrp-27, which encodes a C. elegans homolog of the human tri-snRNP 27K protein.     

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.     

Deficiency in l-Serine Deaminase Interferes with One-Carbon Metabolism and Cell Wall Synthesis in Escherichia coli K-12

Escherichia coli K-12 provided with glucose and a mixture of amino acids depletes l-serine more quickly than any other amino acid even in the presence of ammonium sulfate. A mutant without three 4Fe4S l-serine deaminases (SdaA, SdaB, and TdcG) of E. coli K-12 is unable to do this. The high level of l-serine that accumulates when such a mutant is exposed to amino acid mixtures starves the cells for C₁ units and interferes with cell wall synthesis. We suggest that at high concentrations, l-serine decreases synthesis of UDP-N-acetylmuramate-l-alanine by the murC-encoded ligase, weakening the cell wall and producing misshapen cells and lysis. The inhibition by high l-serine is overcome in several ways: by a large concentration of l-alanine, by overproducing MurC together with a low concentration of l-alanine, and by overproducing FtsW, thus promoting septal assembly and also by overexpression of the glycine cleavage operon. S-Adenosylmethionine reduces lysis and allows an extensive increase in biomass without improving cell division. This suggests that E. coli has a metabolic trigger for cell division. Without that reaction, if no other inhibition occurs, other metabolic functions can continue and cells can elongate and replicate their DNA, reaching at least 180 times their usual length, but cannot divide.The Escherichia coli genome contains three genes, sdaA, sdaB, and tdcG, specifying three very similar 4Fe4S l-serine deaminases. These enzymes are very specific for l-serine for which they have unusually high K_m values (3, 32). Expression of the three genes is regulated so that at least one of the gene products is synthesized under all common growth conditions (25). This suggests an important physiological role for the enzymes. However, why E. coli needs to deaminate l-serine has been a long-standing problem of E. coli physiology, the more so since it cannot use l-serine as the sole carbon source.We showed recently that an E. coli strain devoid of all three l-serine deaminases (l-SDs) loses control over its size, shape, and cell division when faced with complex amino acid mixtures containing l-serine (32). We attributed this to starvation for single-carbon (C₁) units and/or S-adenosylmethionine (SAM). C₁ units are usually made from serine via serine hydroxymethyl transferase (GlyA) or via glycine cleavage (GCV). The l-SD-deficient triple mutant strain is starved for C₁ in the presence of amino acids, because externally provided glycine inhibits GlyA and a very high internal l-serine concentration along with several other amino acids inhibits glycine cleavage. While the parent cell can defend itself by reducing the l-serine level by deamination, this crucial reaction is missing in the ΔsdaA ΔsdaB ΔtdcG triple mutant. We therefore consider these to be “defensive” serine deaminases.The fact that an inability to deaminate l-serine leads to a high concentration of l-serine and inhibition of GlyA is not surprising. However, it is not obvious why a high level of l-serine inhibits cell division and causes swelling, lysis, and filamentation. Serine toxicity due to inhibition of biosynthesis of isoleucine (11) and aromatic amino acids (21) has been reported but is not relevant here, since these amino acids are provided in Casamino Acids.We show here that at high internal concentrations, l-serine also causes problems with peptidoglycan synthesis, thus weakening the cell wall. Peptidoglycan is a polymer of long glycan chains made up of alternating N-acetylglucosamine and N-acetylmuramic acid residues, cross-linked by l-alanyl-γ-d-glutamyl-meso-diaminopimelyl-d-alanine tetrapeptides (1, 28). The glucosamine and muramate residues and the pentapeptide (from which the tetrapeptide is derived) are all synthesized in the cytoplasm and then are exported to be polymerized into extracellular peptidoglycan (2).In this paper, we show that lysis is caused by l-serine interfering with the first step of synthesis of the cross-linking peptide, the addition of l-alanine to uridine diphosphate-N-acetylmuramate. This interference is probably due to a competition between serine and l-alanine for the ligase, MurC, which adds the first l-alanine to UDP-N-acetylmuramate (7, 10, 15). As described here, the weakening of the cell wall by l-serine can be overcome by a variety of methods that reduce the endogenous l-serine pool or counteract the effects of high levels of l-serine.     

The Genetic Signature of Conditional Expression

Conditionally expressed genes have the property that every individual in a population carries and transmits the gene, but only a fraction, φ, expresses the gene and exposes it to natural selection. We show that a consequence of this pattern of inheritance and expression is a weakening of the strength of natural selection, allowing deleterious mutations to accumulate within and between species and inhibiting the spread of beneficial mutations. We extend previous theory to show that conditional expression in space and time have approximately equivalent effects on relaxing the strength of selection and that the effect holds in a spatially heterogeneous environment even with low migration rates among patches. We support our analytical approximations with computer simulations and delineate the parameter range under which the approximations fail. We model the effects of conditional expression on sequence polymorphism at mutation–selection–drift equilibrium, allowing for neutral sites, and show that sequence variation within and between species is inflated by conditional expression, with the effect being strongest in populations with large effective size. As φ decreases, more sites are recruited into neutrality, leading to pseudogenization and increased drift load. Mutation accumulation diminishes the degree of adaptation of conditionally expressed genes to rare environments, and the mutational cost of phenotypic plasticity, which we quantify as the plasticity load, is greater for more rarely expressed genes. Our theory connects gene-level relative polymorphism and divergence with the spatial and temporal frequency of environments inducing gene expression. Our theory suggests that null hypotheses for levels of standing genetic variation and sequence divergence must be corrected to account for the frequency of expression of the genes under study.IN genetically and ecologically subdivided populations, some individuals will experience a local environment very different from others, making it difficult to evolve a single adaptation adequate for all local conditions. Phenotypic plasticity allows organisms to respond adaptively to spatially and temporally varying environments by developing alternative phenotypes that enhance fitness under local conditions (Scheiner 1993; Via et al. 1995). Examples of alternative phenotypes, i.e., polyphenisms, include the defensive morphologies in Daphnia and algae induced by the presence of predators (e.g., Lively 1986; DeWitt 1998; Harvell 1998; Hazel et al. 2004); the winged and wingless morphs of bean beetles responding to resource variation (e.g., Abouheif and Wray 2002; Roff and Gelinas 2003; Lommen et al. 2005); and bacterial genes involved in traits such as quorum sensing, antibiotic production, biofilm formation, and virulence (Fuqua et al. 1996). The developmental basis of such alternative phenotypes often lies in the inducible expression of some genes in some individuals by environmental variables. That is, all individuals carry and transmit the conditionally expressed genes but only a fraction of individuals, φ, express them when environmental conditions are appropriate.The genes underlying plastic traits should experience relaxed selection due to conditional expression. Wade and co-workers have shown that genes hidden from natural selection in a fraction of individuals in the population by X-linked (Whitlock and Wade 1995; Linksvayer and Wade 2009) or sex-limited expression (Wade 1998; Demuth and Wade 2007) experience relaxed selective constraint. In Drosophila spp., sequence data for genes with maternally limited expression quantitatively support the theoretical predictions both for within-species polymorphism (Barker et al. 2005; Cruickshank and Wade 2008) and for between-species divergence (Barker Et Al 2005; Demuth and Wade 2007; Cruickshank and Wade 2008). Furthermore, male-specific genes in the facultatively sexual pea aphid have been shown to have elevated levels of sequence variation due to relaxed selection (Brisson and Nuzhdin 2008). Genes with spatially restricted expression in a heterogeneous environment should likewise experience relaxed selection. Adaptation to the most common environment in an ecologically subdivided population (Rosenzweig 1987; Holt and Gaines 1992; Holt 1996) allows deleterious mutations to accumulate in traits expressed in rare environments (Kawecki 1994; Whitlock 1996).Here we extend these results by quantifying the consequences of relaxed selection on conditionally expressed genes. Specifically, we show that, with weak selection, spatial and temporal fluctuations in selection intensity generate approximately equivalent effects on mean trait fitness, even with low rates of migration between habitats, resulting in a great simplification of analytical results. Our analytical approximations are supported with deterministic and stochastic simulations, and we note the conditions under which the approximations fail. We then derive general expressions for (1) the expected level of sequence polymorphism within populations under mutation, migration, drift, and purifying selection with conditional gene expression; (2) the rate of sequence divergence among populations, for dominant and recessive mutations; and (3) the reduction in mean population fitness due to accumulation of deleterious mutations at conditionally expressed loci. We find that the rate of accumulation of deleterious mutations for conditionally expressed genes is accelerated and the probability of fixation of beneficial mutations is reduced, causing a reduction in the fitness of conditional traits and an inflation in sequence variation within and between species. Our results suggest that evolutionary null hypotheses must be adjusted to account for the frequency of expression of genes under study, such that signatures of elevated within- or between-species sequence variation are not necessarily evidence of the action of diversifying natural selection. Furthermore, if conditional expression is due to spatial heterogeneity, we show that the level of genetic variation in a sample will often depend on whether or not genotypes were sampled from the selective habitat, the neutral habitat, or both. In the discussion we address the scope and limitations of our theory, as well as its implications for the maintenance of genetic variation, adaptive divergence between species, constraints on phenotypic plasticity, and evolutionary inference from sequence data.     

Probing the Molecular Determinant for the Catalytic Efficiency of l-Arabinose Isomerase from Bacillus licheniformis

Bacillus licheniformis l-arabinose isomerase (l-AI) is distinguished from other l-AIs by its high degree of substrate specificity for l-arabinose and its high turnover rate. A systematic strategy that included a sequence alignment-based first screening of residues and a homology model-based second screening, followed by site-directed mutagenesis to alter individual screened residues, was used to study the molecular determinants for the catalytic efficiency of B. licheniformis l-AI. One conserved amino acid, Y333, in the substrate binding pocket of the wild-type B. licheniformis l-AI was identified as an important residue affecting the catalytic efficiency of B. licheniformis l-AI. Further insights into the function of residue Y333 were obtained by replacing it with other aromatic, nonpolar hydrophobic amino acids or polar amino acids. Replacing Y333 with the aromatic amino acid Phe did not alter catalytic efficiency toward l-arabinose. In contrast, the activities of mutants containing a hydrophobic amino acid (Ala, Val, or Leu) at position 333 decreased as the size of the hydrophobic side chain of the amino acid decreased. However, mutants containing hydrophilic and charged amino acids, such as Asp, Glu, and Lys, showed almost no activity with l-arabinose. These data and a molecular dynamics simulation suggest that Y333 is involved in the catalytic efficiency of B. licheniformis l-AI.l-Arabinose isomerase (l-AI) is an enzyme that mediates in vivo isomerization between l-arabinose and l-ribulose as well as in vitro isomerization of d-galactose and d-tagatose (20). l-Ribulose (l-erythro-pentulose) is a rare and expensive ketopentose sugar (1) that can be used as a precursor for the production of other rare sugars of high market value, such as l-ribose. Despite being a common metabolic intermediate in different organisms, l-ribulose is scarce in nature. The market for rare and unnatural sugars has been growing, especially in the sweetener and pharmaceutical industries. For example, several modified nucleosides derived from l-sugars have been shown to act as potent antiviral agents and are also useful in antigen therapy. Derivatives of rare sugars have also been used as agents against hepatitis B virus and human immunodeficiency virus (2, 22).For these reasons, interest in the enzymology of rare sugars has also been increasing. Various forms of l-AI from a variety of organisms have been characterized, and some have shown potential for industrial use. Several highly thermotolerant enzyme forms from Thermotoga maritima (12), Thermotoga neapolitana (10), Bacillus stearothermophilus (18), Thermoanaerobacter mathranii (9), and Lactobacillus plantarum (5) have been characterized previously. All of these reported l-AIs tend to have broad specificity, although a few l-AIs with high degrees of substrate specificity for l-arabinose have also been documented.The enzyme properties of l-AIs have been examined by engineering several forms by error-prone PCR and site-directed mutagenesis. Galactose conversion was reportedly enhanced 20% following site-directed introduction of a double mutation (C450S-N475K) into l-AI (16). Error-prone PCR manipulation of l-AI from Geobacillus stearothermophilus resulted in a shift in temperature specificity from 60 to 65°C and increased isomerization activity (11). All of these previously reported mutational studies have been aimed at improving enzymatic properties for industrial application. However, even though the three-dimensional (3D) structure of Escherichia coli l-AI has been determined previously (15), few new structural studies have been performed to decipher the reaction mechanism of this enzyme. Rhimi et al. (19) have reported an important role for D308, F329, E351, and H446 in catalysis, as indicated by findings from site-directed mutagenesis. Nonetheless, detailed analysis of the important molecular determinants controlling the catalytic activities of the l-AIs is still lacking.Previously, we have reported the cloning and characterization of a novel l-AI from Bacillus licheniformis (17). This enzyme can be distinguished from other l-AIs by its wide pH range, high degree of substrate specificity for l-arabinose, and extremely high turnover rate. In the present paper, we report the identification of an important amino acid residue responsible for the catalytic efficiency of l-AIs, as determined by a systematic screening process composed of sequence alignment and molecular dynamics (MD) simulation, followed by site-directed mutagenesis. Using the crystal structure of E. coli l-AI as a template, we have built a 3D model of B. licheniformis l-AI. Analysis of the 3D model of B. licheniformis l-AI docked with l-arabinose, followed by a systematic screening process, showed that Y333 interacted with the substrate, suggesting that this residue in B. licheniformis l-AI may be essential for catalysis. We further characterized the role of Y333 in B. licheniformis l-AI binding of and catalytic efficiency for l-arabinose.     

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.     

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.     

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.     

Identification of Receptor-Tyrosine-Kinase-Signaling Target Genes Reveals Receptor-Specific Activities and Pathway Branchpoints During Drosophila Development

Genetic maps provide a means to estimate the probability of the co-inheritance of linked loci as they are transmitted across generations in both experimental and natural populations. However, in the age of whole-genome sequences, physical distances measured in base pairs of DNA provide the standard coordinates for navigating the myriad features of genomes. Although genetic and physical maps are colinear, there are well-characterized and sometimes dramatic heterogeneities in the average frequency of meiotic recombination events that occur along the physical extent of chromosomes. There also are documented differences in the recombination landscape between the two sexes. We have revisited high-resolution genetic map data from a large heterogeneous mouse population and have constructed a revised genetic map of the mouse genome, incorporating 10,195 single nucleotide polymorphisms using a set of 47 families comprising 3546 meioses. The revised map provides a different picture of recombination in the mouse from that reported previously. We have further integrated the genetic and physical maps of the genome and incorporated SSLP markers from other genetic maps into this new framework. We demonstrate that utilization of the revised genetic map improves QTL mapping, partially due to the resolution of previously undetected errors in marker ordering along the chromosome.GENETIC maps exist for hundreds of different species, and genetic map construction continues to play an important role in the characterization of genomes (Tanksley et al. 1992; Kong et al. 2002; Chowdhary and Raudsepp 2006; Stapley et al. 2008). A genetic map defines the linear order and relative distances among a set of marker loci in units that correspond to the frequency of meiotic recombination between the loci. Until recently mouse genetic maps based on simple sequence length polymorphism (SSLP) markers (Lyon 1976) have been sufficient for most experimental purposes since, unlike the hundreds of thousands of markers required in human genetic association studies, a relatively small number of markers is needed to map crosses between inbred mouse strains. However, recent developments in whole-genome high-resolution mapping in the mouse (Churchill et al. 2004; Valdar et al. 2006) and interest in examining recombination rates at an ultra-fine scale (Myers et al. 2005) have reawakened the need to develop a high-resolution genetic map in the mouse.The current standard genetic map of the mouse has been compiled from a substantial body of historical data and maintained by the Mouse Genome Informatics (MGI) project at The Jackson Laboratory (Bult et al. 2008). We will refer to it as the MGI map. The primary sources of data used to construct the MGI map were two mapping panels, described here. However, the current map is based on a consensus developed by the 2000 Chromosome Committee using all available published data. The map has continued to be maintained by MGI with the addition of new genetic markers and data but, because the map is based on consensus, published errors may have been perpetuated.The Jackson Laboratory developed a genetic map based on two sets of 94 progeny obtained from reciprocal backcrosses (BSB and BSS) between the inbred strains C57BL/6J and SPRET/EiJ (Rowe et al. 1994). These strains represent two different species of mouse (Mus musculus and Mus spretus). The map provides a wealth of genetic information, but problems with male fertility restrict breeding options and thus the map is female specific. The problems with male fertility may have resulted in some multi-locus distortion in the mapping panel (Montagutelli et al. 1996). Currently, 1372 and 4913 markers have been typed on the BSB and BSS backcross panels, respectively (Broman et al. 2002). Researchers at The Whitehead Institute and the Massachusetts Institute of Technology (MIT) developed a map of 4006 SSLP markers using an intercross population of 46 mice derived from strains OB (C57BL/6J-Lep^ob/ob) and CAST (CAST/EiJ) (Dietrich et al. 1994). Both parental strains OB and CAST are derived from M. musculus, but CAST is from a distinct subspecies, M. m. castaneus. The intercross mating strategy produces observable recombination from both male and female parents, but the two cannot be distinguished. Thus the map is sex averaged and based on 92 meioses. The Whitehead/MIT map was expanded to include 7377 SSLP markers (Dietrich et al. 1996). These are denoted as, e.g., D7Mit54, where “7” indicates the chromosome to which the marker is mapped and “54” is an arbitrary index. They are commonly referred to as “Mit” markers.Map resolution is limited by the number of observable recombination events in each of these panels. With 94 meioses (in each backcross) and an average of 14 recombination events/haploid genome transmitted, limiting resolution is on the order of 1 cM. A much larger panel would be needed to achieve subcentimorgan resolution and to accurately position high-density sets of SNP markers.Here we propose a new standard genetic map of the laboratory mouse based on data from a large heterogeneous stock (HS) mouse population descended from eight inbred strains (DBA/2J, C3H/HeJ, AKR/J, A/J, BALB/cJ, CBA/J, C57BL/6J, and LP/J) representing a diverse sample of the classical inbred strains (Petkov et al. 2004). Shifman et al. (2006) calculated genetic maps based on 11,247 informative SNP markers in 2293 HS individuals. The marker set is dense with 99% of the SNP intervals <500 kb, 81.2% <250 kb, and an estimated allele inheritance-based accuracy of 99.98%. Map positions were calculated separately for male and female meioses using CRIMAP software (Green et al. 1990), and the total length of the sex-averaged map is 1630 cM, as defined by the most distal SNP markers in their panel. The MGI map is 1783 cM on the basis of the most distal available marker position for each chromosome. However, on the basis of the most distal shared markers, the original Shifman map at 1612 cM is substantially longer than the MGI map at 1445 cM. It was not immediately clear if this discrepancy was due to the nature of recombination in the HS population or to their method of map estimation.There are at least two methodological problems with the HS map reported in Shifman et al. (2006). First, the map was constructed using a sliding window of 5–15 SNPs to handle eight multi-generation families within the CRIMAP software. Ideally, one considers all markers on a chromosome simultaneously in constructing a genetic map, and we found that this could be accomplished by splitting the complex pedigrees into sibships. Although splitting the pedigree results in slightly less efficient estimates of intermarker distances, this approach should incur no bias. Maps based on the full set of markers but with the complex pedigrees split into sibships are thus arguably better than maps based on the full pedigrees but with a sliding window of 5–15 markers. Second, analysis of families with incomplete parental genotypes may have contributed to an inflated map size. Sixteen of the 72 families lack parental genotypes or have genotypes for just one parent (15 of the 72), and many of them are small (26 have six or fewer siblings). Sibships with no parental genotype data of their own can give no information about sex-specific recombination rates. In conjunction with other sibships for which parental genotypes are available, they can provide some information, but the CRIMAP software (last modified in 1990) makes some approximations that result in a large bias even in the sex-averaged genetic maps for small sibships lacking parental genotype data.For these reasons, we recomputed the mouse genetic map on the basis of the original data reported and discuss the differences between the original Shifman map and the revised Shifman map below. The revised Shifman map provides a markedly different picture of recombination in the mouse: the estimated sex-averaged chromosome lengths correspond more closely to those in the original MGI map; the sex difference in the overall recombination rate is greatly reduced; and numerous narrow regions of high recombination rate, apparent in the original Shifman map, have disappeared.We propose the revised Shifman map as a new standard genetic map for the mouse. The new genetic map represents a substantial improvement over the existing MGI map due to the large number of meioses and to the genetic diversity of strains in the HS population. We have generated male, female, and sex-averaged genetic maps with physical positions and updated locus identifiers. We have established the correspondence between physical and genetic positions of 7080 Mit markers and corrected inconsistencies in the MGI map. We provide a web-based tool for the interpolation of new marker loci into the genetic map and for converting genetic map positions to NCBI mouse build 37 coordinates. Finally, we examine the effect of changing to this revised genetic map on QTL mapping in five previously published data sets (Beamer et al. 1999, 2001; Ishimori et al. 2004, 2008; Wergedal et al. 2006).     

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).