Antiprotozoal activities of new bicyclo[2.2.2]octan-2-imines and esters of bicyclo[2.2.2]octan-2-ols.

Several bicyclo[2.2.2]octan-2-imines and esters of bicyclo[2.2.2]octan-2-ols were prepared. Their antitrypanosomal and antiplasmodial activities against Trypanosoma brucei rhodesiense (STIB 900) and the K1 strain of Plasmodium falciparum (resistant to chloroquine and pyrimethamine) were determined using microplate assays. Two of the synthesized bicyclo[2.2.2]octan-2-one 4'-phenylthiosemicarbazones showed the highest antitrypanosomal activity (IC(50)<0.3 microM) of the so far prepared 4-amino-6,7-diarylbicyclo[2.2.2]octane derivatives, but they are distinctly less active than suramine (IC(50)=0.0075 microM). Most of the 4'-phenylthiosemicarbazones and a single bicyclo[2.2.2]octan-2-yl benzoate exhibit attractive antimalarial activity (IC(50)=0.23-0.72 microM). Two bicyclooctanone oximes are even as active as chloroquine (IC(50)=0.08-0.15 microM, chloroquine: IC(50)=0.12 microM against sensitive strains).     

Coiled-coil irregularities of the M1 protein structure promote M1–fibrinogen interaction and influence group A Streptococcus host cell interactions and virulence

Group A Streptococcus (GAS) is a human pathogen causing a wide range of mild to severe and life-threatening diseases. The GAS M1 protein is a major virulence factor promoting GAS invasiveness and resistance to host innate immune clearance. M1 displays an irregular coiled-coil structure, including the B-repeats that bind fibrinogen. Previously, we found that B-repeat stabilisation generates an idealised version of M1 (M1*) characterised by decreased fibrinogen binding in vitro. To extend these findings based on a soluble truncated version of M1, we now studied the importance of the B-repeat coiled-coil irregularities in full length M1 and M1* expressed in live GAS and tested whether the modulation of M1–fibrinogen interactions would open up novel therapeutic approaches. We found that altering either the M1 structure on the GAS cell surface or removing its target host protein fibrinogen blunted GAS virulence. GAS expressing M1* showed an impaired ability to adhere to and to invade human endothelial cells, was more readily killed by whole blood or neutrophils and most importantly was less virulent in a murine necrotising fasciitis model. M1-mediated virulence of wild-type GAS was strictly dependent on the presence and concentration of fibrinogen complementing our finding that M1–fibrinogen interactions are crucial for GAS virulence. Consistently blocking M1–fibrinogen interactions by fragment D reduced GAS virulence in vitro and in vivo. This supports our conclusion that M1–fibrinogen interactions are crucial for GAS virulence and that interference may open up novel complementary treatment options for GAS infections caused by the leading invasive GAS strain M1.     

Theoretical and Experimental Evaluation of Flow Pattern of Pharmaceutical Powder Blends Discharged From Intermediate Bulk Containers (IBCs)

The purpose of this study is to assess the prevalence of funnel flow pattern for common pharmaceutical powder blends, upon discharging from modern intermediate bulk containers (IBCs) in drug product manufacturing. The estimation was built upon Jenike’s original radial stress field theory. It was modified to account for the stress-dependence of wall friction angle commonly observed in pharmaceutical powders. A total of 260 flow pattern estimations, based on 20 real-life IBCs and 13 investigational powder blends, were made. The estimated results showed that the mass flow pattern is present in less than 5% of all cases. Funnel flow pattern is clearly prevalent among pharmaceutical powder blends. The prevalence of funnel flow stems from several factors: 1) relatively shallow hopper section shared by all IBCs, 2) the common transition-type geometry, leading to even shallower hopper inclination at the edge of the hopper section, and 3) relatively high wall friction angles resulting from low wall normal stresses. This conclusion was verified through at-scale experiments, by discharging multiple pharmaceutical powder blends from a representative IBC. In general, our study suggests that, unless the powder wall friction can be substantially reduced, pharmaceutical powders are likely to discharge under funnel flow from modern IBCs.     

Cumulative Marijuana Use and Carotid Intima-Media Thickness at Middle Age: The CARDIA Study

BackgroundLong-term cardiovascular health effects of marijuana are understudied. Future cardiovascular disease is often indicated by subclinical atherosclerosis for which carotid intima-media thickness is an established parameter.MethodsUsing the data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a cohort of 5115 Black and white women and men at Year 20 visit, we studied the association between carotid intima-media thickness in midlife and lifetime exposure to marijuana (1 marijuana year = 365 days of use) and tobacco smoking (1 pack-year = 20 cigarettes/day for 365 days). We measured carotid intima-media thickness by ultrasound and defined high carotid intima-media thickness at the threshold of the 75th percentile of all examined participants. We fit logistic regression models stratified by tobacco smoking exposure, adjusting for demographics, cardiovascular risk factors, and other drug exposures.ResultsData was complete for 3257 participants; 2722 (84%) reported ever marijuana use; 374 (11%) were current users; 1539 (47%) reported ever tobacco smoking; 610 (19%) were current smokers. Multivariable adjusted models showed no association between cumulative marijuana exposure and high carotid intima-media thickness in never or ever tobacco smokers, odds ratio (OR) 0.87 (95% confidence interval [CI]: 0.63-1.21) at 1 marijuana-year among never smokers and OR 1.11 (95% CI: 0.85-1.45) among ever tobacco smokers. Cumulative exposure to tobacco was strongly associated with high carotid intima-media thickness, OR 1.88 (95%CI: 1.20-2.94) for 20 pack-years of exposure.ConclusionsThis study adds to the growing body of evidence that there might be no association between the average population level of marijuana use and subclinical atherosclerosis.     

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.