Direct MR arthrography of the shoulder under axial traction: Feasibility study to evaluate the superior labrum‐biceps tendon complex and articular cartilage

Purpose:

To assess the value of adding axial traction to direct MR arthrography of the shoulder, in terms of subacromial and glenohumeral joint space widths, and coverage of the superior labrum‐biceps tendon complex and articular cartilage by contrast material.

Materials and Methods:

Twenty‐one patients investigated by direct MR arthrography of the shoulder were prospectively included. Studies were performed with a 3 Tesla (T) unit and included a three‐dimensional isotropic fat‐suppressed T1‐weighted gradient‐recalled echo sequence, without and with axial traction (4 kg). Two radiologists independently measured the width of the subacromial, superior, and inferior glenohumeral joint spaces. They subsequently rated the amount of contrast material around the superior labrum‐biceps tendon complex and between glenohumeral cartilage surfaces, using a three‐point scale: 0 = no, 1 = partial, 2 = full.

Results:

Under traction, the subacromial (Δ = 2.0 mm, P = 0.0003), superior (Δ = 0.7 mm, P = 0.0001) and inferior (Δ = 1.4 mm, P = 0.0006) glenohumeral joint space widths were all significantly increased, and both readers noted significantly more contrast material around the superior labrum‐biceps tendon complex (P = 0.014), and between the superior (P = 0.001) and inferior (P = 0.025) glenohumeral cartilage surfaces.

Conclusion:

Direct MR arthrography of the shoulder under axial traction increases subacromial and glenohumeral joint space widths, and prompts better coverage of the superior labrum‐biceps tendon complex and articular cartilage by contrast material. J. Magn. Reson. Imaging 2013;37:1228–1233. © 2012 Wiley Periodicals, Inc.     

Transforming growth factor-beta regulation of the insulin-like growth factor binding protein-4 protease system in cultured human osteoblasts.

IGFBP-4 is an inhibitor of IGF-I in bone. We show that TGF-beta regulates IGFBP-4 and enhances IGF-I-stimulated growth of cultured human bone cells through increased expression of an IGFBP-4 protease, PAPP-A. This effect of TGF-beta on IGF-I bioavailability may promote local bone formation. Insulin-like growth factor binding protein (IGFBP-4) proteolysis is implicated in the regulation of local insulin-like growth factor (IGF)-I bioavailability during bone remodeling. The IGFBP-4 protease secreted by normal adult human osteoblastic (hOB) cells in culture is a novel metalloproteinase, pregnancy-associated plasma protein-A (PAPP-A). We have recently identified an inhibitor of PAPP-A, the precursor form of major basic protein (proMBP). Very little is known about the molecular regulation of this IGFBP-4 protease system. In the present study, we determined the effect of transforming growth factor (TGF)-beta and IGF-II, the two most abundant growth factors in human bone, on PAPP-A and proMBP expression in primary cultures of hOB cells. Treatment with TGF-beta resulted in time- and dose-dependent increases in PAPP-A mRNA expression, with a maximal 12-fold increase after 24 h of stimulation with 10 ng/ml TGF-beta. Increased PAPP-A levels in hOB cell-conditioned medium paralleled PAPP-A gene expression. In addition, TGF-beta completely suppressed proMBP expression. Treatment of hOB cells with IGF-II had no effect on PAPP-A or proMBP gene expression. However, IGFBP-4 proteolysis in cell-free assay was dependent on IGF-II, and there was increased IGF-II-dependent IGFBP-4 protease activity in conditioned medium from hOB cells that were treated with TGF-beta. IGF-I stimulation of hOB cell proliferation was markedly enhanced by pretreatment with TGF-beta and [Leu27]IGF-II, and this enhancement was prevented with protease-resistant IGFBP-4. In summary, TGF-beta regulates IGFBP-4 proteolysis in hOB cells through increased expression of the protease, PAPP-A, and decreased expression of the inhibitor, proMBP. However, functional activation of the IGFBP-4 protease system is dependent on IGF-II, which acts at a post-translational level. These data support a model whereby local TGF-beta and IGF-II in the bone microenvironment coordinately amplify IGF-I bioavailability through controlled IGFBP-4 proteolysis, which may be a means to promote bone formation.     

Elastic stabilisation of proximal humeral fractures with a new percutaneous angular stable fixation device (ButtonFix®): a preliminary report

Introduction

The ButtonFix^® system represents a new angular stable percutaneous fixation device for stabilisation of fractures of the proximal humerus. The purpose of this study was to present a preliminary report of the radiological and clinical outcome after minimally invasive stabilisation of selected proximal humerus fractures with the ButtonFix^®.

Patients and methods

Seventeen patients with fractures of the proximal humerus were treated in our department with the ButtonFix^® system. The mean final follow-up was performed 19 months postoperatively. Follow-up included assessment of postoperative reposition, range of motion, the DASH score, the Constant–Murley score, and the Short Form 36 (SF36).

Results

Twelve patients showed anatomical head-to-shaft angles, four revealed minor valgus replacement, and one minor varus replacement. In one patient unplanned revision surgery was necessary due to early secondary fracture dislocation requiring ORIF with an angular stable form plate. Implant removal was performed after 6 weeks in all patients. At the final follow-up, mean range of motion was: flexion 135°, extension 45°, abduction 142°, internal rotation 51°, and external rotation 62°. The mean Constant–Murley score was 70. The mean DASH score was 26 points and the average SF36 score was 76 points. One patient showed radiological signs of avascular necrosis.

Conclusion

The ButtonFix^® system represents a valuable tool in the treatment of proximal humeral fractures with results indicating fewer complications compared to prior percutaneous fixation devices. Moreover, the ButtonFix^® seems to be able to maintain reduction even in elderly patients with potentially reduced bone mass.     

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.     

Intrauterine cytomegalovirus infection and glycoprotein B genotypes

Cytomegalovirus (CMV) strains display polymorphisms for the gene encoding glycoprotein B (gB; gpUL55). Recent data suggest that the gB genotype may influence the outcome of acquired CMV infections. To determine whether the gB genotype also contributes to the outcome of intrauterine infection, CMV strains were studied from 56 infants with culture-confirmed intrauterine CMV infections who were born in Iowa or Texas. CMV gB genotypes were compared with the neonatal clinical features and neurodevelopmental outcomes. Fifty-three strains (95%) could be assigned a gB genotype. The overall distribution of genotypes was as follows: type 1, 50%; type 2, 18%; type 3, 23%; and type 4, 4%. Strains with the gB 3 genotype were more common among the Iowa infants (P=.082). The gB 3 genotype was more common among infants with asymptomatic infections (P=.004), but geographic location and ascertainment biases may have accounted for these differences. The gB genotypes did not correlate with the neurodevelopmental outcome of intrauterine infection.