Energetics and Mechanism of Conformational Transitions of Protein‐Like NIPAM‐Sodium Styrene Sulfonate Copolymers in Aqueous Solutions

Protein‐like and random NIPAM‐sodium styrene sulfonate copolymers of similar composition have been prepared by radical polymerization in water at temperatures above and below the LCST of PNIPAM, respectively. Thermal transitions of the copolymers in aqueous solutions have been studied by means of dynamic light scattering, viscometry, and high‐sensitivity differential scanning calorimetry. The phase separation or cooperative conformational transitions without phase separation were observed for the random or the protein‐like copolymers, respectively. Transition temperature, enthalpy, and heat capacity increment of the protein‐like copolymer differed insignificantly from those of the random copolymer of similar composition. The transition heat capacity increments of the protein‐like copolymers revealed that only 10–20% of their NIPAM links participate in the formation of a dense water‐free globule core. The coil–globule transitions of the protein‐like copolymers were described by the thermodynamic three‐state model according to the scheme “random coil?condensed coil?globule”, which is known to simulate the folding mechanism of globular proteins.

     

The PCBP1 gene encoding poly(rc) binding protein i is recurrently mutated in Burkitt lymphoma

The genetic hallmark of Burkitt lymphoma is the translocation t(8;14)(q24;q32), or one of its light chain variants, resulting in IG‐MYC juxtaposition. However, these translocations alone are insufficient to drive lymphomagenesis, which requires additional genetic changes for malignant transformation. Recent studies of Burkitt lymphoma using next generation sequencing approaches have identified various recurrently mutated genes including ID3, TCF3, CCND3, and TP53. Here, by using similar approaches, we show that PCBP1 is a recurrently mutated gene in Burkitt lymphoma. By whole‐genome sequencing, we identified somatic mutations in PCBP1 in 3/17 (18%) Burkitt lymphomas. We confirmed the recurrence of PCBP1 mutations by Sanger sequencing in an independent validation cohort, finding mutations in 3/28 (11%) Burkitt lymphomas and in 6/16 (38%) Burkitt lymphoma cell lines. PCBP1 is an intron‐less gene encoding the 356 amino acid poly(rC) binding protein 1, which contains three K‐Homology (KH) domains and two nuclear localization signals. The mutations predominantly (10/12, 83%) affect the KH III domain, either by complete domain loss or amino acid changes. Thus, these changes are predicted to alter the various functions of PCBP1, including nuclear trafficking and pre‐mRNA splicing. Remarkably, all six primary Burkitt lymphomas with a PCBP1 mutation expressed MUM1/IRF4, which is otherwise detected in around 20–40% of Burkitt lymphomas. We conclude that PCBP1 mutations are recurrent in Burkitt lymphomas and might contribute, in cooperation with other mutations, to its pathogenesis. © 2015 Wiley Periodicals, Inc.     

Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data

Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution.     

Bacterial genome reduction using the progressive clustering of deletions via yeast sexual cycling

The availability of genetically tractable organisms with simple genomes is critical for the rapid, systems-level understanding of basic biological processes. Mycoplasma bacteria, with the smallest known genomes among free-living cellular organisms, are ideal models for this purpose, but the natural versions of these cells have genome complexities still too great to offer a comprehensive view of a fundamental life form. Here we describe an efficient method for reducing genomes from these organisms by identifying individually deletable regions using transposon mutagenesis and progressively clustering deleted genomic segments using meiotic recombination between the bacterial genomes harbored in yeast. Mycoplasmal genomes subjected to this process and transplanted into recipient cells yielded two mycoplasma strains. The first simultaneously lacked eight singly deletable regions of the genome, representing a total of 91 genes and ∼10% of the original genome. The second strain lacked seven of the eight regions, representing 84 genes. Growth assay data revealed an absence of genetic interactions among the 91 genes under tested conditions. Despite predicted effects of the deletions on sugar metabolism and the proteome, growth rates were unaffected by the gene deletions in the seven-deletion strain. These results support the feasibility of using single-gene disruption data to design and construct viable genomes lacking multiple genes, paving the way toward genome minimization. The progressive clustering method is expected to be effective for the reorganization of any mega-sized DNA molecules cloned in yeast, facilitating the construction of designer genomes in microbes as well as genomic fragments for genetic engineering of higher eukaryotes.Complexities of natural biological systems make it difficult to understand and define precisely the roles of individual genes and their integrated functions. The use of model organisms with a relatively small number of genes enables the isolation of core biological processes from their complex regulatory networks for extensive characterization. However, even the simplest natural microbes contain many genes of unknown function, as well as genes that can be singly or simultaneously deleted without any noticeable effect on growth rate in a laboratory setting (Hutchison et al. 1999; Glass et al. 2006; Posfai et al. 2006). Ill-defined genes and those mediating functional redundancies both compound the challenge of understanding even the simplest life forms.Toward generating a minimal cell where every gene is essential for the axenic viability of the organism, we are pursuing strategies to reduce the 1-Mb genome of Mycoplasma mycoides JCVI-syn1.0 (Gibson et al. 2010). Because we can (1) introduce this genome into yeast and maintain it as a plasmid (Benders et al. 2010; Karas et al. 2013a); and (2) “transplant” the genome from yeast into mycoplasma recipient cells (Lartigue et al. 2009), genetic tools in yeast are available for reducing this bacterial genome. Several systems offer advanced tools for bacterial genome engineering. Here we further exploit distinctive features of yeast for this purpose.Methods for serially replacing genomic regions with selectable markers are limited by the number of available markers. One effective approach is to reuse the same marker after precise and scarless marker excision (Storici et al. 2001). We have previously used a self-excising marker (Noskov et al. 2010) six times in yeast to generate a JCVI-syn1.0 genome lacking all six restriction systems (JCVI-syn1.0 ∆1-6) (Karas et al. 2013a). Despite the advantages of scarless engineering, sequential procedures are time-consuming. When applied to poorly characterized genes with the potential to interact with other genes, some paths for multigene knockout may lead to dead ends that result from synergistic mutant phenotypes. When a dead end is reached, sequentially returning to a previous genome in an effort to find a detour to a viable higher-order multimutant may be prohibitively time-consuming.An alternative approach to multigene engineering, available in yeast, is to prepare a set of single mutants and combine the deletions into a single strain via cycles of mating and meiotic recombination (Fig. 1A; Pinel et al. 2011; Suzuki et al. 2011, 2012). With a green fluorescent protein (GFP) reporter gene inserted in each deletion locus, the enrichment of higher-order yeast deletion strains in the meiotic population can be accomplished using flow cytometry. Here we apply this method to the JCVI-syn1.0 ∆1-6 exogenous, bacterial genome harbored in yeast to nonsequentially assemble deletions for genes predicted to be individually deletable based on biological knowledge or transposon-mediated disruption data. The functional identification of simultaneously deletable regions is expected to accelerate the effort to construct a minimal genome.Open in a separate windowFigure 1.Progressive clustering of deleted genomic segments. (A) Scheme of the method. Light blue oval represents a bacterial cell. Black ring or horizontal line denotes a bacterial genome, with the orange box indicating the yeast vector used as a site for linearization and recircularization. Gray shape denotes a yeast cell. Green dot in the genome indicates a deletion replaced with a GFP marker. (B) Map of deleted regions. Orange box indicates the yeast vector sequence used for genome linearization and recircularization. Green boxes indicate regions deleted in multimutant mycoplasma strains. Blue boxes denote restriction modification (RM) systems that are also deleted in the strains. (C) Pulsed-gel electrophoresis result for deleted genomes. The starting strain was the JCVI-syn1.0 ∆1–6 strain (1062 kb). Two strains were analyzed for each design of simultaneous deletion (962 kb for eight-deletion or 974 kb for seven-deletion genome). Ladder is a set of yeast chromosomes (New England BioLabs). (D) GFP-RFP ratio sorting result. Standard sorting was compared with sorting based on a GFP-RFP ratio (Methods).     

Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers

Speciation is a continuous process during which genetic changes gradually accumulate in the genomes of diverging species. Recent studies have documented highly heterogeneous differentiation landscapes, with distinct regions of elevated differentiation (“differentiation islands”) widespread across genomes. However, it remains unclear which processes drive the evolution of differentiation islands; how the differentiation landscape evolves as speciation advances; and ultimately, how differentiation islands are related to speciation. Here, we addressed these questions based on population genetic analyses of 200 resequenced genomes from 10 populations of four Ficedula flycatcher sister species. We show that a heterogeneous differentiation landscape starts emerging among populations within species, and differentiation islands evolve recurrently in the very same genomic regions among independent lineages. Contrary to expectations from models that interpret differentiation islands as genomic regions involved in reproductive isolation that are shielded from gene flow, patterns of sequence divergence (d_xy and relative node depth) do not support a major role of gene flow in the evolution of the differentiation landscape in these species. Instead, as predicted by models of linked selection, genome-wide variation in diversity and differentiation can be explained by variation in recombination rate and the density of targets for selection. We thus conclude that the heterogeneous landscape of differentiation in Ficedula flycatchers evolves mainly as the result of background selection and selective sweeps in genomic regions of low recombination. Our results emphasize the necessity of incorporating linked selection as a null model to identify genome regions involved in adaptation and speciation.Uncovering the genetic architecture of reproductive isolation and its evolutionary history are central tasks in evolutionary biology. The identification of genome regions that are highly differentiated between closely related species, and thereby constitute candidate regions involved in reproductive isolation, has recently been a major focus of speciation genetic research. Studies from a broad taxonomic range, involving organisms as diverse as plants (Renaut et al. 2013), insects (Turner et al. 2005; Lawniczak et al. 2010; Nadeau et al. 2012; Soria-Carrasco et al. 2014), fishes (Jones et al. 2012), mammals (Harr 2006), and birds (Ellegren et al. 2012) contribute to the emerging picture of a genomic landscape of differentiation that is usually highly heterogeneous, with regions of locally elevated differentiation (“differentiation islands”) widely spread over the genome. However, the evolutionary processes driving the evolution of the differentiation landscape and the role of differentiation islands in speciation are subject to controversy (Turner and Hahn 2010; Cruickshank and Hahn 2014; Pennisi 2014).Differentiation islands were originally interpreted as “speciation islands,” regions that harbor genetic variants involved in reproductive isolation and are shielded from gene flow by selection (Turner et al. 2005; Soria-Carrasco et al. 2014). During speciation-with-gene-flow, speciation islands were suggested to evolve through selective sweeps of locally adapted variants and by hitchhiking of physically linked neutral variation (“divergence hitchhiking”) (Via and West 2008); gene flow would keep differentiation in the remainder of the genome at bay (Nosil 2008; Nosil et al. 2008). In a similar way, speciation islands can arise by allopatric speciation followed by secondary contact. In this case, genome-wide differentiation increases during periods of geographic isolation, but upon secondary contact, it is reduced by gene flow in genome regions not involved in reproductive isolation. In the absence of gene flow in allopatry, speciation islands need not (but can) evolve by local adaptation, but may consist of intrinsic incompatibilities sensu Bateson-Dobzhansky-Muller (Bateson 1909; Dobzhansky 1937; Muller 1940) that accumulated in spatially isolated populations.However, whether differentiation islands represent speciation islands has been questioned. Rather than being a cause of speciation, differentiation islands might evolve only after the onset of reproductive isolation as a consequence of locally accelerated lineage sorting (Noor and Bennett 2009; Turner and Hahn 2010; White et al. 2010; Cruickshank and Hahn 2014; Renaut et al. 2014), such as in regions of low recombination (Nachman 2002; Sella et al. 2009; Cutter and Payseur 2013). In these regions, the diversity-reducing effects of both positive selection and purifying selection (background selection [BGS]) at linked sites (“linked selection”) impact physically larger regions due to the stronger linkage among sites. The thereby locally reduced effective population size (N_e) will enhance genetic drift and hence inevitably lead to increased differentiation among populations and species.These alternative models for the evolution of a heterogeneous genomic landscape of differentiation are not mutually exclusive, and their population genetic footprints can be difficult to discern. In the cases of (primary) speciation-with-gene-flow and gene flow at secondary contact, shared variation outside differentiation islands partly stems from gene flow. In contrast, under linked selection, ancestral variation is reduced and differentiation elevated in regions of low recombination, while the remainder of the genome may still share considerable amounts of ancestral genetic variation and show limited differentiation. Many commonly used population genetic statistics do not capture these different origins of shared genetic variation and have the same qualitative expectations under both models, such as reduced diversity (π) and skews toward an excess of rare variants (e.g., lower Tajima''s D) in differentiation islands relative to the remainder of the genome. However, since speciation islands should evolve by the prevention or breakdown of differentiation by gene flow in regions not involved in reproductive isolation, substantial gene flow should be detectable in these regions (Cruickshank and Hahn 2014) and manifested in the form of reduced sequence divergence (d_xy) or as an excess of shared derived alleles in cases of asymmetrical gene flow (Patterson et al. 2012). Under linked selection, predictions are opposite for d_xy (Cruickshank and Hahn 2014), owing to reduced ancestral diversity in low-recombination regions. Further predictions for linked selection include positive and negative relationships of recombination rate with genetic diversity (π) and differentiation (F_ST), respectively, and inverse correlations of the latter two with the density of targets for selection. Finally, important insights into the nature of differentiation islands may be gained by studying the evolution of differentiation landscapes across the speciation continuum. Theoretical models and simulations of speciation-with-gene-flow predict that after an initial phase during which differentiation establishes in regions involved in adaptation, differentiation should start spreading from these regions across the entire genome (Feder et al. 2012, 2014; Flaxman et al. 2013).Unravelling the processes driving the evolution of the genomic landscape of differentiation, and hence understanding how genome differentiation unfolds as speciation advances, requires genome-wide data at multiple stages of the speciation continuum and in a range of geographical settings from allopatry to sympatry (Seehausen et al. 2014). Although studies of the speciation continuum are emerging (Hendry et al. 2009; Kronforst et al. 2013; Shaw and Mullen 2014, and references therein), empirical examples of genome differentiation at multiple levels of species divergence remain scarce (Andrew and Rieseberg 2013; Kronforst et al. 2013; Martin et al. 2013), and to our knowledge, have so far not jointly addressed the predictions of alternative models for the evolution of the genomic landscape of differentiation. In the present study, we implemented such a study design encompassing multiple populations of four black-and-white flycatcher sister species of the genus Ficedula (Fig. 1A,B; Supplemental Fig. S1; for a comprehensive reconstruction of the species tree, see Nater et al. 2015). Previous analyses in collared flycatcher (F. albicollis) and pied flycatcher (F. hypoleuca) revealed a highly heterogeneous differentiation landscape across the genome (Ellegren et al. 2012). An involvement of gene flow in its evolution would be plausible, as hybrids between these species occur at low frequencies in sympatric populations in eastern Central Europe and on the Baltic Islands of Gotland and Öland (Alatalo et al. 1990; Sætre et al. 1999), although a recent study based on genome-wide markers identified no hybrids beyond the F₁ generation (Kawakami et al. 2014a). Still, gene flow from pied into collared flycatcher appears to have occurred (Borge et al. 2005; Backström et al. 2013; Nadachowska-Brzyska et al. 2013) despite premating isolation (for review, see Sætre and Sæther 2010), hybrid female sterility (Alatalo et al. 1990; Tegelström and Gelter 1990), and strongly reduced long-term fitness of hybrid males (Wiley et al. 2009). Atlas flycatcher (F. speculigera) and semicollared flycatcher (F. semitorquata) are two closely related species, which have been less studied, but may provide interesting insights into how genome differentiation evolves over time. Here, we take advantage of this system to identify the processes underlying the evolution of differentiation islands based on the population genetic analysis of whole-genome resequencing data of 200 flycatchers.Open in a separate windowFigure 1.A recurrently evolving genomic landscape of differentiation across the speciation continuum in Ficedula flycatchers. (A) Species’ neighbor-joining tree based on mean genome-wide net sequence divergence (d_A). The same species tree topology was inferred with 100% bootstrap support from the distribution of gene trees under the multispecies coalescent (Supplemental Fig. S1). (B) Map showing the locations of population sampling and approximate species ranges. (C) Population genomic parameters along an example chromosome (Chromosome 4A) (see Supplemental Figs. S2, S4 for all chromosomes). Color codes for specific–specific parameters: (blue) collared; (green) pied; (orange) Atlas; (red) semicollared. Color codes for d_xy: (green) collared-pied; (light blue) collared-Atlas; (blue) collared-semicollared; (orange) pied-Atlas; (red) pied-semicollared; (black) Atlas-semicollared. For differentiation within species, comparisons with the Italian (collared) and Spanish (pied) populations are shown. Color codes for F_ST within collared flycatchers: (cyan) Italy–Hungary; (light blue) Italy–Czech Republic; (dark blue) Italy–Baltic. Color codes for F_ST within pied flycatchers: (light green) Spain–Sweden; (green) Spain–Czech Republic; (dark green) Spain–Baltic. (D) Distributions of differentiation (F_ST) from collared flycatcher along the speciation continuum. Distributions are given separately for three autosomal recombination percentiles (33%; 33%–66%; 66%–100%) corresponding to high (>3.4 cM/Mb, blue), intermediate (1.3–3.4 cM/Mb, orange), and low recombination rate (0–1.3 cM/Mb, red), and the Z Chromosome (green). Geographically close within-species comparison: Italy–Hungary. Comparisons within species include the geographically close Italian and Hungarian populations (within [close]), and the geographically distant Italian and Baltic populations (within [far]). Geographically far within-species comparison: Italy–Baltic. (E) Differentiation from collared flycatcher along an example chromosome (Chromosome 11) (see Supplemental Fig. S3 for all chromosomes). Color codes for between-species comparisons: (green) pied; (orange) Atlas; (red) semicollared; (dark red) red-breasted; (black) snowy-browed flycatcher. Color codes for within-species comparisons: (cyan) Italy–Hungary; (blue) Italy–Baltic. Flycatcher artwork in panel A courtesy of Dan Zetterström.     

Natural 6-hydroxy-chromanols and -chromenols: structural diversity,biosynthetic pathways and health implications

We present the first comprehensive and systematic review on the structurally diverse toco-chromanols and -chromenols found in photosynthetic organisms, including marine organisms, and as metabolic intermediates in animals. The focus of this work is on the structural diversity of chromanols and chromenols that result from various side chain modifications. We describe more than 230 structures that derive from a 6-hydroxy-chromanol- and 6-hydroxy-chromenol core, respectively, and comprise di-, sesqui-, mono- and hemiterpenes. We assort the compounds into a structure–activity relationship with special emphasis on anti-inflammatory and anti-carcinogenic activities of the congeners. This review covers the literature published from 1970 to 2017.

We present the first comprehensive and systematic review on the structurally diverse toco-chromanols and -chromenols found in photosynthetic organisms, including marine organisms, and as metabolic intermediates in animals.     

Patient‐level Factors and the Quality of Care Delivered in Pediatric Emergency Departments

Objective

Quality of care delivered to adult patients in the emergency department (ED) is often associated with demographic and clinical factors such as a patient's race/ethnicity and insurance status. We sought to determine whether the quality of care delivered to children in the ED was associated with a variety of patient‐level factors.

Methods

This was a retrospective, observational cohort study. Pediatric patients (<18 years) who received care between January 2011 and December 2011 at one of 12 EDs participating in the Pediatric Emergency Care Applied Research Network (PECARN) were included. We analyzed demographic factors (including age, sex, and payment source) and clinical factors (including triage, chief complaint, and severity of illness). We measured quality of care using a previously validated implicit review instrument using chart review with a summary score that ranged from 5 to 35. We examined associations between demographic and clinical factors and quality of care using a hierarchical multivariable linear regression model with hospital site as a random effect.

Results

In the multivariable model, among the 620 ED encounters reviewed, we did not find any association between patient age, sex, race/ethnicity, and payment source and the quality of care delivered. However, we did find that some chief complaint categories were significantly associated with lower than average quality of care, including fever (–0.65 points in quality, 95% confidence interval [CI] = –1.24 to –0.06) and upper respiratory symptoms (–0.68 points in quality, 95% CI = –1.30 to –0.07).

Conclusion

We found that quality of ED care delivered to children among a cohort of 12 EDs participating in the PECARN was high and did not differ by patient age, sex, race/ethnicity, and payment source, but did vary by the presenting chief complaint.

     

A critical assessment of early warning score records in 168,000 patients

The national early warning score (NEWS) is recommended to detect deterioration in hospitalised patients. In 2013, a NEWS-based system was introduced in a hospital service with over 250,000 annual admissions, generating large amounts of NEWS data. The quality of such data has not been described. We critically assessed NEWS data recorded over 12 months. This observational study included NEWS records from adult inpatients hospitalized in the Capital Region of Denmark during 2014. Physiological variables and the use of supplementary oxygen (NEWS variables) were recorded. We identified implausible records and assessed the distributions of NEWS variable values. Of 2,835,331 NEWS records, 271,103 (10%) were incomplete with one or more variable missing and 0.2% of records containing implausible values. Digit preferences were identified for respiratory rate, supplementation oxygen flow, pulse rate, and systolic blood pressure. There was an accumulation of pulse rate records below 91 beats per minute. Among complete NEWS records, 64% had NEWS?≥?1; 29% had NEWS?≥?3; and 8% had NEWS?≥?6. In a large set of NEWS data, 10% of the records were incomplete. In a system where data were manually entered into an electronic medical record, digit preferences and the accumulation of pulse rate records below 91 beats per minute, which is the limit for NEWS point generation, showed that staff practice influenced the recorded values. This indicates a potential limitation of transferability of research results obtained in such systems to fully automated systems.