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.     

Umbilical cord prostaglandins in term and preterm parturition

Objective: Prostaglandins (PGs) are considered the universal mediators of parturition. Amniotic fluid PGE₂ and PGF_2α concentrations increase before the onset of spontaneous labor at term, as well as during labor. This study was conducted to determine if the concentrations of umbilical cord PGE₂ and PGF₂α change with advancing gestational age, spontaneous labor at term, and preterm labor (with and without funisitis).

Methods: Umbilical cord (UC) tissue samples were obtained from women (N?=?158) with singleton pregnancies in the following groups: (1) term deliveries without labor (TNL; n?=?20); (2) term deliveries with labor (TIL; n?=?20); (3) spontaneous preterm deliveries (sPTD) with (n?=?20) and without acute funisitis (n?=?20); and (4) preeclampsia without labor (n?=?78). The concentrations of PGs were determined in different locations of the UC. PGE₂ and PGF_2α were measured by specific immunoassays. Non-parametric statistics were used for analysis.

Results: (1) In spontaneous preterm deliveries, the median UC PGE₂ concentration was higher in cases with funisitis than in those without funisitis (233.7?pg/µg versus 87.4?pg/µg of total protein, p?=?0.001); (2) the median UC PGE₂ concentration in sPTD with funisitis was also higher than that obtained from samples who had undergone labor at term (233.7?pg/µg versus 116.1?pg/µg of total protein, p?=?0.03); (3) the UC PGE₂ and PGF_2α concentration increased as a function of advancing gestational age before 36 weeks (PGE₂: ρ?=?0.59, p?<?0.001; PGF_2α: ρ?=?0.39, p?=?0.01), but not after 36 weeks (PGE₂: ρ?=??0.1, p?=?0.5; PGF_2α: ρ?=??0.2, p?=?0.2); (4) the median UC concentrations of PGE₂ and PGF_2α at term was similar in samples obtained from women with and without labor (PGE₂: TNL 133.7?pg/µg versus TIL 116.1?pg/µg of total protein, p?=?0.9; PGF_2α: TNL 8.4?pg/µg versus TIL 8.1?pg/µg of total protein, p?=?0.7); and (5) there was no correlation between UC PG concentration and gestational age at term pregnancy (PGE₂: ρ?=?0.01, p?=?0.9; PGF_2α: ρ?=?0.07, p?=?0.7).

Conclusions: (1) PGE₂ concentrations in the UC are higher in the presence of acute funisitis than in the absence of this lesion; (2) spontaneous labor at term was not associated with a change in the UC concentration of PGE₂ and PGF_2α; and (3) the UC concentrations of PGE₂ and PGF_2α increased as a function of gestational age. We propose that UC PGs act as inflammatory mediators generated in the context of fetal systemic inflammation.     

Placental protein 13 (galectin-13) has decreased placental expression but increased shedding and maternal serum concentrations in patients presenting with preterm pre-eclampsia and HELLP syndrome

Placental protein 13 (PP13) is a galectin expressed by the syncytiotrophoblast. Women who subsequently develop preterm pre-eclampsia have low first trimester maternal serum PP13 concentrations. This study revealed that third trimester maternal serum PP13 concentration increased with gestational age in normal pregnancies (p < 0.0001), and it was significantly higher in women presenting with preterm pre-eclampsia (p = 0.02) and hemolysis, elevated liver enzymes, and low platelet count (HELLP) syndrome (p = 0.01) than in preterm controls. Conversely, placental PP13 mRNA (p = 0.03) and protein, as well as cytoplasmic PP13 staining of the syncytiotrophoblast (p < 0.05) was decreased in these pathological pregnancies compared to controls. No differences in placental expression and serum concentrations of PP13 were found at term between patients with pre-eclampsia and control women. In contrast, the immunoreactivity of the syncytiotrophoblast microvillous membrane was stronger in both term and preterm pre-eclampsia and HELLP syndrome than in controls. Moreover, large syncytial cytoplasm protrusions, membrane blebs and shed microparticles strongly stained for PP13 in pre-eclampsia and HELLP syndrome. In conclusion, parallel to its decreased placental expression, an augmented membrane shedding of PP13 contributes to the increased third trimester maternal serum PP13 concentrations in women with preterm pre-eclampsia and HELLP syndrome.     

Analysis and correction of crosstalk effects in pathway analysis

Identifying the pathways that are significantly impacted in a given condition is a crucial step in understanding the underlying biological phenomena. All approaches currently available for this purpose calculate a P-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These P-values were previously thought to be independent. Here we show that this is not the case, and that many pathways can considerably affect each other''s P-values through a “crosstalk” phenomenon. Although it is intuitive that various pathways could influence each other, the presence and extent of this phenomenon have not been rigorously studied and, most importantly, there is no currently available technique able to quantify the amount of such crosstalk. Here, we show that all three major categories of pathway analysis methods (enrichment analysis, functional class scoring, and topology-based methods) are severely influenced by crosstalk phenomena. Using real pathways and data, we show that in some cases pathways with significant P-values are not biologically meaningful, and that some biologically meaningful pathways with nonsignificant P-values become statistically significant when the crosstalk effects of other pathways are removed. We describe a technique able to detect, quantify, and correct crosstalk effects, as well as identify independent functional modules. We assessed this novel approach on data from four experiments involving three phenotypes and two species. This method is expected to allow a better understanding of individual experiment results, as well as a more refined definition of the existing signaling pathways for specific phenotypes.The correct identification of the signaling and metabolic pathways involved in a given phenotype is a crucial step in the interpretation of high-throughput genomic experiments. Most approaches currently available for this purpose treat the pathways as independent. In fact, pathways can affect each other''s P-values through a phenomenon we refer to as crosstalk. This crosstalk may be due to the regulatory interactions among different pathways or to the gene overlap among pathways. In this work, we will use the term crosstalk to refer to the effect that pathways exercise on each other due to the presence of overlapping genes. Although it is intuitive that various pathways could influence each other, especially when they share genes, the presence and extent of this phenomenon have not been rigorously studied and, most importantly, there is no currently available technique able to quantify the amount of such crosstalk. There are three major categories of methods that aim to identify significant pathways: enrichment analysis (e.g., Fisher''s exact test–hypergeometric) (Tavazoie et al. 1999; Draghici et al. 2003); functional scoring (e.g., GSEA) (Mootha et al. 2003; Subramanian et al. 2005); and topology-based methods (e.g., impact analysis) (Draghici et al. 2007; Tarca et al. 2009). Another classification of gene set analysis methods is based on the definition of the null hypothesis and divides the methods into competitive and self-contained (Goeman and Bühlmann 2007; Nam and Kim 2008). In this work, we focus on competitive methods, and in particular on the Fisher''s exact test, although the problems identified likely apply also for self-contained methods.Here we show that the results of all these methods are affected by crosstalk effects and that this phenomenon is related to the structure of the pathways. We propose the first approach that can (1) detect crosstalk when it exists, (2) quantify its magnitude, (3) correct for it, resulting in a more meaningful ranking among pathways in a specific biological condition, and (4) identify novel functional modules that can play an independent role and have different functions than the pathway they are currently located on. This method is expected to allow a better understanding of individual experiment results, as well as a more refined definition of the existing signaling pathways for specific phenotypes.     

Relaxing and Metabolic Actions of ACTH in Rabbit Colan

Andersson , R., E. Mohme -Lundholm , N. Svedmyr and N. Vamos , Relaxing and metabolic actions of ACTH in rabbit colon. Acta physiol. scand. 1971. 81. 11–17. ACTH in the dose range of 0.15–4.60 IU/ml relaxed the circular muscular layer of rabbit colon contracted by carbacholine. The content of cyclic AMP was doubled. There was an activation of phosphorylase a which gave the same dose-effect and time-effect curves as thc relaxing action. The hexose phosphate and creatine phosphate contents of the muscle also increased. The relaxing and metabolic actions of ACTH were completely blocked by an adrenergic β-receptor blocking agent (sotalol). The type of inhibition was competive. The relaxing action of ACTH was potentiated by theophylline and puromycin, agents which inhibit the enzymatic hydrolysis of cyclic AMP. The relaxing and metabolic actions of ACTH were similar to those of isoprenaline. They could be reproduced by cyclic AMP. In contrast to cyclic AMP and isoprenaline, ACTH did not decrease the adenosine triphosphate contcnt of thc muscle. It is suggested that the relaxing action of ACTH is mediated by thc adcnyl cyclase-cyclic AMP system.     

The physical, sexual and emotional violence history of middle-aged women: a community-based prevalence study

OBJECTIVES: To determine current and lifetime rates of the experience of partner abuse and sexual violence in a community-based sample of middle-aged women and compare these to figures obtained in a general practice setting. DESIGN AND METHODS: This research was part of the Melbourne Women's Midlife Health Project (MWMHP), an observational, longitudinal, population-based study of 438 Australian-born women conducted over nine years. In 1996, during the sixth year of the study, we asked the MWMHP participants to complete a self-administered "violence questionnaire", incorporating a modified Conflict Tactics Scale and questions on sexual abuse experienced during childhood and adult life. RESULTS: Of the 395 women remaining in the sixth year of follow-up of the MWMHP, 362 (92%) completed the questionnaire. Overall, 28.5% (n = 101) of the women had experienced some form of domestic violence (physical, sexual or emotional) during their lifetime; 5.5% (n = 15) of women had experienced severe physical abuse in the past year at the hands of a partner; and 11.8% (n = 42) of the women had experienced rape or attempted rape between the age of 16 and the time of our survey. Regarding abuse in childhood, 8.9% (n = 32) of women had experienced physical abuse, 42.3% (n = 152) had experienced non-contact sexual abuse, and 35.7% (n = 128) contact sexual abuse. Compared with the general-practice-based study, rates of childhood physical abuse and penetrative sexual abuse were similar, but rates of less intrusive child sexual abuse were significantly higher in our study. CONCLUSIONS: Doctors in all areas of medicine who are dealing with middle-aged women need to be aware of the levels of violence sustained by women throughout their lives. Such experiences may have a substantial impact on women's physical and mental wellbeing.     

Placental growth hormone is increased in the maternal and fetal serum of patients with preeclampsia.

OBJECTIVES: Placental growth hormone (PGH) is a pregnancy-specific protein produced by syncytiotrophoblast and extravillous cytotrophoblast. No other cells have been reported to synthesize PGH Maternal. PGH Serum concentration increases with advancing gestational age, while quickly decreasing after delivery of the placenta. The biological properties of PGH include somatogenic, lactogenic, and lipolytic functions. The purpose of this study was to determine whether the maternal serum concentrations of PGH change in women with preeclampsia (PE), women with PE who deliver a small for gestational age neonate (PE + SGA), and those with SGA alone. STUDY DESIGN: This cross-sectional study included maternal serum from normal pregnant women (n = 61), patients with severe PE (n = 48), PE + SGA (n = 30), and SGA alone (n = 41). Fetal cord blood from uncomplicated pregnancies (n = 16) and PE (n = 16) was also analyzed. PGH concentrations were measured by ELISA. Non-parametric statistics were used for analysis. RESULTS: (1) Women with severe PE had a median serum concentration of PGH higher than normal pregnant women (PE: median 23,076 pg/mL (3473-94 256) vs. normal pregnancy: median 12 157 pg/mL (2617-34 016); p < 0.05), pregnant women who delivered an SGA neonate (SGA: median 10 206 pg/mL (1816-34 705); p < 0.05), as well as pregnant patients with PE and SGA (PE + SGA: median 11 027 pg/mL (1232-61 702); p < 0.05). (2) No significant differences were observed in the median maternal serum concentration of PGH among pregnant women with PE and SGA, SGA alone, and normal pregnancy (p > 0.05). (3) Compared to those of the control group, the median umbilical serum concentration of PGH was significantly higher in newborns of preeclamptic women (PE: median 356.1 pg/mL (72.6-20 946), normal pregnancy: median 128.5 pg/mL (21.6-255.9); p < 0.01). (4) PGH was detected in all samples of cord blood. CONCLUSIONS: (1) PE is associated with higher median concentrations of PGH in both the maternal and fetal circulation compared to normal pregnancy. (2) Patients with PE + SGA had lower maternal serum concentrations of PGH than preeclamptic patients without SGA. (3) Contrary to previous findings, PGH was detectable in the fetal circulation. The observations reported herein are novel and suggest that PGH may play a role in the mechanisms of disease in preeclampsia and fetal growth restriction.