Secretion,blood levels and cutaneous expression of TL1A in psoriasis patients

TL1A is a TNF‐like cytokine which has been shown to co‐stimulate TH1 and TH17 responses during chronic inflammation. The expression of this novel cytokine has been investigated in inflammatory disorders like rheumatoid arthritis and inflammatory bowel disease, but little is known about expression and induction in psoriasis. Indeed, the pathogenesis in psoriasis is still not fully understood and it is speculated that cytokines other than TNF‐α are important in subsets of patients. Also, for patients with severe disease that are treated with systemic anti‐TNF‐α blockade, novel candidates to be used as disease and response biomarkers are of high interest. Here, we demonstrate TL1A expression in biopsies from psoriatic lesions. Also, we investigated spontaneous and induced TL1A secretion from PBMCs and blood levels from a cohort of psoriasis patients. Here, increased spontaneous secretion from PBMCs was observed as compared to healthy controls and a small subset of patients had highly elevated TL1A in the blood. Interestingly, activation of PBMCs with various cytokines showed a decreased sensitivity for TL1A activation in psoriasis patients compared to healthy controls.TL1A levels in blood and biopsies could not be correlated with disease activity with this patient cohort. Thus, additional large‐scale studies are warranted to investigate TL1A as a biomarker.     

IVF/ICSI outcome and serum LH concentration on day 1 of ovarian stimulation with recombinant FSH under pituitary suppression

BACKGROUND: Down-regulation with GnRH agonist has been suggested to result in a profound suppression of LH bioactivity, reduced estradiol synthesis, and thus impaired IVF and pregnancy outcome. The aims of this study were: (i) to assess the usefulness of serum LH measurement on stimulation day 1 as a predictor of ovarian response, conception and pregnancy outcome in patients treated with long-term down-regulation with GnRH agonist and recombinant FSH, and (ii) to define the best threshold LH value, if any, to discriminate between women with different outcomes of IVF. METHODS: Records of 2625 cycles in 1652 infertile women undergoing IVF (n = 1856) and/or ICSI (n = 769) treatment were reviewed. RESULTS: The range of LH concentrations on stimulation day 1 overlapped among non-conception cycles, conception cycles, ongoing pregnancies and early pregnancy losses. Receiver operating characteristic (ROC) analysis showed that serum LH concentrations on stimulation day 1 were unable to discriminate between conception and non-conception cycles (AUC(ROC) = 0.51; 95% CI: 0.49-0.54) or ongoing pregnancies versus early pregnancy loss groups (AUC(ROC) = 0.52; 95% CI: 0.47-0.57). Stratification for various low serum levels of LH did not reveal significant differences with respect to conception or pregnancy outcome among different LH levels on stimulation day 1. CONCLUSIONS: Serum LH concentration on stimulation day 1 cannot predict ovarian response, conception and pregnancy outcome in women receiving long-term down-regulation during assisted reproduction treatment.     

Identification of a new locus for autosomal dominant non-syndromic hearing impairment (DFNA7) in a large Norwegian family

Hereditary hearing impairment affects about 1 in 1000 newborns. In most cases hearing loss is non-syndromic with no other clinical features, while in other families deafness is associated with specific clinical abnormalities. Analysis of large families with non-syndromic and syndromic deafness have been used to identify genes or gene locations that cause hearing impairment. The present report describes a large Norwegian family with autosomal dominant non-syndromic, progressive high tone hearing loss with linkage to 1q21-q23. A maximum LOD score of 7.65 (theta = 0.00) was obtained with the microsatellite marker D1S196. Analysis of recombinant individuals maps the deafness gene (DFNA7) to a 22 cM region between D1S104 and D1S466. The region contains several attractive candidate genes. This report supports the idea of extensive genetic heterogeneity in hereditary hearing impairment and represents the first localization of a deafness gene in a Norwegian family.      

Transferring psychiatric specialist services to local authorities—Characteristics of the patients involved

Background: To obtain more co-ordinated services, better co-operation between the services and more efficient use of resources, a pilot project for transferring some district psychiatric centres (DPCs) to large municipalities is planned by the Norwegian government. Systematic knowledge about the patients involved is needed when clinical needs and standards, funding, and political agendas are discussed. This study identifies the clinical, socio-demographic, and behavioural characteristics of patients who need services from both the municipality and the DPC.

Method: A national mapping of patients in specialist mental health services was conducted in 2012/2013, including 65% of all inpatients (n?=?2358) and 60% of all outpatients (n?=?23?124). The need for services was assessed by each patient’s clinician.

Results: It was found that 74% of inpatients and 43% of outpatients needed one or more services from the municipality, usually involving housing, mental health treatment/therapy, or economic support according to their clinicians. These were typically patients with severe mental illness, young inpatients, older outpatients and persons with low education and weak social networks. Only small differences in the need for municipal services were found between patients in hospitals and DPCs.

Conclusions: Many of the patients in specialist mental health services, especially the inpatients, needed services from municipal social and health services. Because these patients had the most severe mental illnesses and were the most socially deprived, a stronger integration of service levels would potentially benefit these patients most. The pilot project should be evaluated to identify the consequences for patients, staff, quality of services, and costs of transferring services to a lower system level.     

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.     

B-type natriuretic peptide and echocardiographic determination of ejection fraction in the diagnosis of congestive heart failure in patients with acute dyspnea

BACKGROUND: Echocardiography and B-type natriuretic peptide (BNP) are diagnostic tests for congestive heart failure (CHF), but an emergency diagnosis can be difficult. OBJECTIVE: To assess the diagnostic performance of BNP testing and echocardiographic assessment of left ventricular systolic function, separately and combined, for the identification of CHF in patients with acute dyspnea. DESIGN: Prospective, multinational, multicenter study. SETTING: Patients presenting to emergency departments in seven hospitals between June 1999 and December 2000. PATIENTS: A total of 1,586 patients with acute dyspnea. MAIN OUTCOME MEASURES: Echocardiographic determination of ejection fraction (EF) and point-of care BNP measurement for the diagnosis of CHF. RESULTS: Seven hundred nine of the 1,586 patients underwent echocardiography; 492 patients (69.4%) had a final diagnosis of CHF. Patients with CHF were older (68.5 years vs 61.6 years, p < 0.0001), had a lower EF (39.5% vs 56.1%, p < 0.0001), and a higher BNP (683 pg/mL vs 129 pg/mL, p < 0.0001) than patients without CHF. Area under the receiver operating characteristic (ROC) curve for the diagnosis of CHF was significantly higher for BNP (0.89) than for EF (0.78; area under the ROC curve difference, 0.12; p < 0.0001). The sensitivity of BNP > or = 100 pg/mL for the diagnosis of CHF was 89%, and specificity was 73%. Values for EF < or = 50% had a sensitivity of 70% and a specificity of 77%. Multivariate logistic regression analysis showed that, in combination with clinical, ECG, and chest radiograph data, BNP > or = 100 pg/mL and EF < or = 50% remained independent predictors of CHF (odds ratios, 32.1 and 6.2, respectively). The proportions of patients who were correctly classified were 67% for BNP alone, 55% for EF alone, 82% for the two variables together, and 97.3% when clinical, ECG, and chest radiograph data were added. CONCLUSION: BNP measurement was superior to two-dimensional echocardiographic determination of EF in identifying CHF, regardless of the threshold value. The two methods combined have marked additive diagnostic value.     

Prognostic significance of N-terminal pro-atrial natriuretic factor (1-98) in acute myocardial infarction: comparison with atrial natriuretic factor (99-126) and clinical evaluation.

OBJECTIVE--To evaluate the prognostic significance of plasma N-terminal pro-atrial natriuretic factor (1-98) concentrations measured in the subacute phase after acute myocardial infarction, and to compare the predictive value of measurement of N-terminal pro-atrial natriuretic factor (1-98) with the measurement of atrial natriuretic factor (99-126) and with clinical assessment of the degree of heart failure. DESIGN--Prospective observational. SETTING--Norwegian central hospital. PATIENTS--139 patients (mean (SD) age 66.9 (11.1) years, 71.2% males) with acute myocardial infarction. Patients in cardiogenic shock or with severe heart failure (New York Heart Association class IV) were excluded. MAIN OUTCOME MEASURE--Cardiovascular death within 12 months. RESULTS--During the follow up period 15 patients died. In a univariate Cox proportional hazards model N-terminal pro-atrial natriuretic factor (1-98) was significantly related to mortality (p = 0.0003). In a multivariate model the prognostic value of N-terminal pro-atrial natriuretic factor (1-98) was better than that of atrial natriuretic factor (99-126) and clinical assessment of heart failure (N-terminal pro-atrial natriuretic factor (1-98), p = 0.0003; atrial natriuretic factor (99-126), p = 0.4513; heart failure, p = 0.0719). The odds ratio estimate of patients in whom plasma concentrations of N-terminal pro-atrial natriuretic factor (1-98) were greater than 2000 pmol/l was 25 (95% confidence interval 2.8-225.0) compared with patients with plasma concentrations less than 1000 pmol/l. CONCLUSIONS--These results suggest that determination of plasma N-terminal pro-atrial natriuretic factor (1-98) in the subacute phase of myocardial infarction may provide clinically relevant prognostic information that is superior to that obtained from atrial natriuretic factor (99-126) measurements and clinical evaluation.