Genetic recombination pathways and their application for genome modification of human embryonic stem cells

Human embryonic stem cells are pluripotent cells derived from early human embryo and retain a potential to differentiate into all adult cell types. They provide vast opportunities in cell replacement therapies and are expected to become significant tools in drug discovery as well as in the studies of cellular and developmental functions of human genes. The progress in applying different types of DNA recombination reactions for genome modification in a variety of eukaryotic cell types has provided means to utilize recombination-based strategies also in human embryonic stem cells. Homologous recombination-based methods, particularly those utilizing extended homologous regions and those employing zinc finger nucleases to boost genomic integration, have shown their usefulness in efficient genome modification. Site-specific recombination systems are potent genome modifiers, and they can be used to integrate DNA into loci that contain an appropriate recombination signal sequence, either naturally occurring or suitably pre-engineered. Non-homologous recombination can be used to generate random integrations in genomes relatively effortlessly, albeit with a moderate efficiency and precision. DNA transposition-based strategies offer substantially more efficient random strategies and provide means to generate single-copy insertions, thus potentiating the generation of genome-wide insertion libraries applicable in genetic screens.     

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

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

Effect of Glycosylation and Additional Domains on the Thermostability of a Family 10 Xylanase Produced by Thermopolyspora flexuosa

The effects of different structural features on the thermostability of Thermopolyspora flexuosa xylanase XYN10A were investigated. A C-terminal carbohydrate binding module had only a slight effect, whereas a polyhistidine tag increased the thermostability of XYN10A xylanase. In contrast, glycosylation at Asn26, located in an exposed loop, decreased the thermostability of the xylanase. The presence of a substrate increased stability mainly at low pH.The thermophilic actinomycete Thermopolyspora flexuosa, previously named Nonomuraea flexuosa and before that Actinomadura flexuosa or Microtetraspora flexuosa (15), produces family 11 and family 10 xylanases, which show high thermostability (16, 17, 22). T. flexuosa xylanase XYN10A has a C-terminal family 13 carbohydrate binding module (CBM) (22). Many xylanases have an additional CBM, which can be a cellulose binding domain (CBD) or a xylan binding domain (XBD) (1, 5, 7, 22, 25, 28). XBD typically increases activity against insoluble xylan (1, 5, 24), although some XBDs also bind soluble xylans (21, 25).We studied the thermostability of T. flexuosa xylanase XYN10A and how CBM and other additional groups affect its thermostability. In addition to confirming the previously described importance of terminal regions, our study identified a loop that is important for the thermostability of T. flexuosa XYN10A. In general, identification of sites important for protein stability is necessary for targeted mutagenesis attempts to increase thermostability.The T. flexuosa xyn10A gene (GenBank accession no. {"type":"entrez-nucleotide","attrs":{"text":"AJ508953","term_id":"57897980","term_text":"AJ508953"}}AJ508953) (22), which encodes the full-length XYN10A xylanase (1-AAST… SYNA-448) containing the catalytic domain and CBM, and a truncated gene, which encodes the catalytic domain only (1-AAST… DALN-301) were expressed in Trichoderma reesei as 3′ fusions to a sequence that encodes the Cel6A CBD (A+B) carrier polypeptide and a Kex2 cleavage site (RDKR) (27). In this article, the catalytic domain and the full-length enzyme are referred to as XYN10A and XYN10A-CBM, respectively. The catalytic domain was also produced in Escherichia coli. For production in E. coli, the sequence encoding the catalytic domain was cloned into a pKKtac vector (33) with and without an additional 3′ sequence encoding a 6×His tag at the protein C terminus (… DALNHHHHHH).The proteins were purified by hydrophobic interaction chromatography using a Phenyl Sepharose column and by ion-exchange chromatography using a DEAE Sepharose FF column (Amersham Pharmacia Biotech). The 6×His-tagged XYN10A xylanase produced in E. coli was purified by affinity chromatography using Ni-nitrilotriacetic acid (Ni-NTA) agarose beads (Qiagen).Mass spectrometric (MS) analyses were performed on a high-resolution 4.7-T hybrid quadrupole-Fourier transform ion cyclotron resonance (FT-ICR) instrument (APEX-Qe; Bruker Daltonics), which employs electrospray ionization (ESI) (see supplemental material for details).Xylanase activity was measured with a 3,5-dinitrosalicylic acid assay by using 1% solubilized birchwood xylan as a substrate (33). The optimum temperature, residual activity, and half-life assays were performed as described earlier (36). SWISS-MODEL (4) was used to automatically model T. flexuosa XYN10A and XYN10A-CBM (PDB codes for the modeling templates are 1v6w and 1e0w, respectively [12, 14]).The results of sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis indicated that the masses of XYN10A xylanase and XYN10A-CBM produced in Trichoderma reesei were ∼37 kDa and ∼50 kDa, respectively (Fig. ​(Fig.1A).1A). MS analysis of the 6×His-tagged XYN10A produced in E. coli (SDS-PAGE not shown) indicated the presence of a single protein form (Fig. ​(Fig.1B),1B), with a measured mass of 34,943.25 Da. This is consistent with the theoretical mass of 6×His-tagged XYN10A (34,942.93 Da). In contrast, XYN10A produced in T. reesei was heterogeneously modified, and six protein forms (numbered 1 to 6) were detected (Fig. ​(Fig.1B).1B). The mass of form 1 (34,120.76 Da) is in excellent agreement with the calculated mass of XYN10A (34120.73 Da). The masses of forms 2 and 3, with mass increments of ∼203 and ∼162 Da, respectively, suggested protein glycosylation (+203 Da = GlcNAc; +162 Da = Man). There are two potential sites for N-glycosylation in XYN10A, Asn26 and Asn95. These six protein forms were resolved only by the high-resolution FT-ICR MS technique, not by SDS-PAGE (eluted as a single band [Fig. ​[Fig.1A1A]).Open in a separate windowFIG. 1.(A) SDS-PAGE of purified XYN10A and XYN10A-CBM produced in Trichoderma reesei. Lane 1, molecular weight markers; lane 2, catalytic domain (XYN10A); lane 3, full-length enzyme (XYN10A-CBM). (B) ESI FT-ICR mass spectra of XYN10A with a 6×His tag produced in E. coli (bottom) and XYN10A produced in T. reesei (top). Only the expanded view at m/z 1260 to 1300, with the signals representing the most abundant protein ion charge state z = 27+, is presented. For the measured and calculated masses of the protein forms identified, see the supplemental material.In order to locate the glycosylation site or sites, XYN10A proteins produced in E. coli and T. reesei were subjected to on-line pepsin digestion (see supplemental material for details). The sequence coverage for XYN10A xylanase produced in E. coli was 62%. For XYN10A produced in T. reesei, a lower sequence coverage was obtained, but three glycopeptides (residues 20 to 44, 20 to 46, and 20 to 59), carrying one GlcNAc residue, were detected (glycopeptides A to C in Fig. S1B in the supplemental material). A triply charged glycopeptide A was further analyzed by collision-induced dissociation (CID) measurement (see inset in Fig. S1B in the supplemental material). A ladder of b-type fragment ions further identified this peptide and verified Asn26 as the N-glycosylation site in XYN10A, carrying GlcNAc(Man) as a glycan core structure.The additional sequences attached to the catalytic domain affected the thermostability of XYN10A xylanase. The deletion of the native C-terminal CBM domain (XYN10A produced in T. reesei) slightly decreased (∼2°C) the apparent temperature optimum in the region of 70 to 75°C (Table ​(Table11 and Fig. ​Fig.2A).2A). However, at 80°C, the deletion of the CBM domain increased the activity (Fig. ​(Fig.2A).2A). Furthermore, the half-life in the presence of the substrate at 80°C was lower when the CBM was present (Table ​(Table22).Open in a separate windowFIG. 2.Enzyme activity and stability profiles. (A) Enzyme activity as a function of temperature. The enzymes were incubated for 30 min at each temperature at pH 7. (B) Enzyme inactivation as a function of temperature. The enzyme samples were incubated without the substrate for 30 min at each temperature (pH 7), and the residual activity was measured at 70°C. Values are means ± standard deviations (error bars) for three experiments. Symbols: ⧫, XYN10A xylanase produced in T. reesei; ⋄, XYN10A-CBM produced in T. reesei; ▪, XYN10A produced in E. coli; □, XYN10A-6×His produced in E. coli.

TABLE 1.

Peaks of the optimum temperatures (30-min assay)^a

Cold-sensitive ompB mutants affecting expression of ompC in Escherichia coli K12

The regulatory locus ompB, consisting of 2 genes, ompR and envZ, is required for the expression of ompC and ompF genes encoding the major outer membrane porin proteins OmpC and OmpF in Escherichia coli K12. We utilized localized mutagenesis to isolate cold-sensitive mutants in the ompB operon. The isolated mutants exhibited a cold-sensitive OmpC⁻ phenotype, but remained OmpF⁺. Furthermore, ompC expression was still regulated by medium osmolarity. The cold-sensitive OmpC⁻ phenotype was complemented by plasmids carrying the wild-type ompB operon, but not by plasmids containing either envZ or ompR genes alone. This suggests that the mutations are in the ompB promotor. We show that the mutations can be used to control expression vectors based on the ompC promotor.     

Stochastic search variable selection based on two mixture components and continuous‐scale weighting

Stochastic search variable selection (SSVS) is a Bayesian variable selection method that employs covariate‐specific discrete indicator variables to select which covariates (e.g., molecular markers) are included in or excluded from the model. We present a new variant of SSVS where, instead of discrete indicator variables, we use continuous‐scale weighting variables (which take also values between zero and one) to select covariates into the model. The improved model performance is shown and compared to standard SSVS using simulated and real quantitative trait locus mapping datasets. The decision making to decide phenotype‐genotype associations in our SSVS variant is based on median of posterior distribution or using Bayes factors. We also show here that by using continuous‐scale weighting variables it is possible to improve mixing properties of Markov chain Monte Carlo sampling substantially compared to standard SSVS. Also, the separation of association signals and nonsignals (control of noise level) seems to be more efficient compared to the standard SSVS. Thus, the novel method provides efficient new framework for SSVS analysis that additionally provides whole posterior distribution for pseudo‐indicators which means more information and may help in decision making.     

Zur Abnahme des Stars(Sturnus vulgaris) in Skandinavien

Zusammenfassung In vielen Teilen Skandinaviens sind übereinstimmend in den letzten Jahren Rückgänge im Bestand des Stars festgestellt worden. Die Gründe hierfür sind unbekannt. Vor allem bleibt zu prüfen, ob auch in südlich gelegenen Teilen des Areals Änderungen in der Siedlungsdichte, Reproduktionsrate usw. des Stars festgestellt wurden. Anregungen zu Untersuchungen der Bestandsentwicklung des Stars in Mittel- und Westeuropa bzw. zur Veröffentlichung bisher noch nicht publizierter Befunde werden gegeben.

---

Decrease in Skandinavian starling populations

Summary After an imitial large-scale increase in all parts of Europe the populations of Starlings have been seriously decreased in Scandinavia, northern parts of the species range.Data from 8 populations from Finland, 2 from Sweden (Tab.) and notices from Norway show, that population densities have decreased to the level of 0–50% of their previous size. Many reasons have been proposed to be responsible for this: low productivity, pesticides, actions against Starlings in their wintering areas and shortening of suitable breeding habitats, but non of these gives a sufficient explanation.

     

Qualitative geographical variation in interspecific interactions

We explore geographical variation in the density relationship between potential competitor forest bird groups, resident Parus spp. and migrant Fringilla spp., across Europe using published bird census results. In addition, we summarized results from three experimental studies from northern Europe on their density associations. Based on anticipated changes in the relative intensity of positive and competitive interactions we predicted a unimodal density association between Parus and Fringilla : at low and intermediate densities the two groups are positively associated (positive interaction), whereas high densities promote interspecific competition. In central Europe where densities are high, densities were unimodally related to each other. In northern and southern Europe linear and positive associations appeared. Experimental studies provided consistent support for positive interspecific interactions in the north. The results suggest that species interactions may indeed vary in relation to the density of potential competitor and switch from positive to negative along environmental gradients.