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.     

The Wheelchair Use Confidence Scale: Italian translation,adaptation, and validation of the short form

Objective: We developed an Italian version of the Wheelchair Use Confidence Scale for Manual Users-Short Form (WheelCon-M-I-short form) and examined its reliability and validity.

Methods: The original scale was translated from English to Italian using the “Translation and Cultural Adaptation of Patient Reported Outcomes Measures–Principles of Good Practice” guidelines. The WheelCon-M-I-short form was administered to experienced manual wheelchair users who had a variety of diagnoses. Its internal consistency and test–retest reliability were examined. Its concurrent validity was evaluated using Pearson correlation coefficients with the Italian version of the Wheelchair Outcome Measure (WhOM-I) and the Italian version of the Barthel index (BI).

Results: The WheelCon-M-I-short form was administered to 31 subjects. The mean?±?SD of the WheelCon-M-I-short form score was 7.5?±?1.9. All WheelCon-M-I-short form items were either identical or similar in meaning to the WheelCon-M-short form items. Cronbach’s α for the WheelCon-M-I-short form was 0.95 (p?p?p?p?Conclusions: The WheelCon-M-I-short form was found to be reliable and a valid outcome measure for assessing manual wheelchair confidence in the Italian population.

• Implication for Rehabilitation

• The WheelCon-M-I-short form is a valid outcome measure available for assessing wheelchair confidence, according to Bandura’s social cognitive theory, self-efficacy is a better predictor of future behavior than skill itself.

• Translation of the WheelCon-M-short form into the WheelCon-M-I-short form provides a new tool for Italian professionals.

• Clinicians now have a method to measure this invisible barrier to wheelchair use, and they will be able to make informed decisions when prescribing the use of manual wheelchairs and when training clients in their use.

• The WheelCon-M-I-short form also provides researchers with a tool in an important and relevant area of study for future research.

     

Effects on human heart valve immunogenicity in vitro by high concentration cryoprotectant treatment

It has been shown previously that cryopreservation, using an ice‐free cryopreservation method with the cryoprotectant formulation VS83, beneficially modulated immune reactions in vivo and in vitro when compared with conventionally frozen tissues. In this study, we assessed the impact of a VS83 post‐treatment of previously conventionally frozen human tissue on responses of human immune cells in vitro. Tissue punches of treated and non‐treated (control) aortic heart valve tissue (leaflets and associated aortic root) were co‐cultured for 7 days with peripheral blood mononuclear cells or enriched CD14⁺ monocytes. Effects on cellular activation markers, cytokine secretion and immune cell proliferation were analysed by flow cytometry. Flow cytometry studies showed that VS83 treatment of aortic root tissue promoted activation and differentiation of CD14⁺ monocytes, inducing both up‐regulation of CD16 and down‐regulation of CD14. Significantly enhanced expression levels for the C‐C chemokine receptor (CCR)7 and the human leukocyte antigen (HLA)‐DR on monocytes co‐cultured with VS83‐treated aortic root tissue were measured, while the interleukin (IL)‐6 and monocyte chemoattractant protein (MCP)‐1 release was suppressed. However, the levels of interferon (IFN)γ and tumour necrosis factor (TNF)α remained undetectable, indicating that complete activation into pro‐inflammatory macrophages did not occur. Similar, but non‐significant, changes occurred with VS83‐treated leaflets. Additionally, in co‐cultures with T cells, proliferation and cytokine secretion responses were minimal. In conclusion, post‐treatment of conventionally cryopreserved human heart valve tissue with the VS83 formulation induces changes in the activation and differentiation characteristics of human monocytes, and thereby may influence long‐term performance following implantation. Copyright © 2017 John Wiley & Sons, Ltd.     

Deformation strain is the main physical driver for skeletal precursors to undergo osteogenesis in earlier stages of osteogenic cell maturation

Mesenchymal stem cells play a major role during bone remodelling and are thus of high interest for tissue engineering and regenerative medicine applications. Mechanical stimuli, that is, deformation strain and interstitial fluid‐flow‐induced shear stress, promote osteogenic lineage commitment. However, the predominant physical stimulus that drives early osteogenic cell maturation is not clearly identified. The evaluation of each stimulus is challenging, as deformation and fluid‐flow‐induced shear stress interdepend. In this study, we developed a bioreactor that was used to culture mesenchymal stem cells harbouring a strain‐responsive AP‐1 luciferase reporter construct, on porous scaffolds. In addition to the reporter, mineralization and vitality of the cells was investigated by alizarin red staining and 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide. Quantification of the expression of genes associated to bone regeneration and bone remodelling was used to confirm alizarin red measurements. Controlled perfusion and deformation of the 3‐dimensional scaffold facilitated the alteration of the expression of osteogenic markers, luciferase activity, and calcification. To isolate the specific impact of scaffold deformation, a computational model was developed to derive a perfusion flow profile that results in dynamic shear stress conditions present in periodically loaded scaffolds. In comparison to actually deformed scaffolds, a lower expression of all measured readout parameters indicated that deformation strain is the predominant stimulus for skeletal precursors to undergo osteogenesis in earlier stages of osteogenic cell maturation.     

A Review of Prompt-Fading Procedures: Implications for Effective and Efficient Skill Acquisition

This paper reports a systematic review of prompt-fading research, with a focus on experiments comparing two or more prompt-fading procedures. Forty-five articles with 46 experiments met the operationally-defined inclusion criteria. For the selected articles, data on several variables were extracted and analyzed. Research demonstrated that all prompt-fading procedures were generally effective in promoting acquisition of behavior. Stimulus prompting was more effective and efficient when compared to response-prompting procedures. Comparisons of response-prompting procedures yielded variable efficiency results. These outcomes are discussed in terms of the behavioral principles that facilitate transfer of stimulus control from the prompt to the discriminative stimulus, such as blocking and overshadowing. Basic investigations of the role of these behavioral principles might help develop prompt-fading procedures that are consistently effective across participants. Implications for research include suggestions for the development of individualized assessments of stimulus control, similar to the functional analysis methodology.     

Developing research priorities for nurses working in the gynaecology setting in Western Australia

Background

Nurses working in clinical settings are instrumental to translating research into practice. The Delphi approach has been used by clinicians worldwide to set research agendas relevant to their clinical work.