The results showed heritability were low to moderate (0.05–0.28) for number of pre-ovulatory follicles (POF), small yellow follicles (SYF) and atresia follicles (AF). The highly significant SNPs associated with SYF were mainly located on GGA17 and GGA28. Only four significant SNPs were identified for POF on GGA1. The variance partitioned across chromosomes and chromosome lengths had a linear relationship for SYF (R2 = 0.58). The enriched genes created by the closest correspondent significant SNPs were found to be involved in biological pathways related to cell proliferation, cell cycle and cell survival. Two promising candidate genes, AMH and RGS3, were suggested to be prognostic biomarkers for SYF. In conclusion, this study offers the first evidence of genetic variance and positional candidate genes which influence the number of SYF in chicken. These identified informative SNPs may facilitate selection for an improved reproductive performance of laying hens.Follicular development has a major impact on reproductive performance. Most previous researchers focused on molecular mechanisms of follicular development. The genetic architecture underlying the number of follicle, however, has yet not to be thoroughly defined in chicken. Here we report a genome-wide association study for the genetic architecture determining the numbers of follicles in a large F Therefore, thoroughly understanding the process of follicular development and its mechanisms could aid in significantly improving egg production.The reproductive performance of hens is an indicator of egg production, which occurs following a strict follicular hierarchy resulting from a process of orderly growth which begins with recruitment, followed by selection and, lastly, ovulation. During reproduction period, the majority of small yellow follicles (<8 mm in diameter) become atretic and are reabsorbed, with only 5% of follicles further developing into pre-ovulatory follicles (8–40 mm in diameter). Typically, a single SYF is selected daily to join the hierarchy POF for ovulation during peak laying periodsin vitro3, which were associated with numerous candidate genes including FSHR, ESR, PRL and PRLR4. Follicle numbers were also a key determinant of laying performance and were affected by breed5, age6, and nutrition7. For instance, researchers found a big difference in follicle numbers between old laying hens (e.g. >1 year of age) and laying hens in peak stage8. Recently, a couple of genome-wide association studies (GWAS) conducted to study follicle numbers revealed that they are associated with menopausal age9 in humans, and ovarian function and fertility in cattle10. However, no genetic variants influencing follicular numbers have been reported in chicken thus far, especially using GWAS to investigate them directly.In chicken, most previous studies focused on the molecular mechanisms of follicular growth In the present study, we conducted a GWAS to discover genomic regions explaining variations in follicle numbers of White Leghorn and a Chinese indigenous chicken, the Dongxiang Blue-shelled Chicken, using the 600 K Affymetrix Axiom Chicken Genotyping Array. The objective of this study was to investigate the genetic architecture underlying follicles and identify genetic markers of follicle numbers which would employ molecular marker-assisted selection on laying performance.The overall means, standard errors, coefficients of variation, and heritability are illustrated in Table −7) for POF (Table λ) was estimated at 1.0402, 0.9884, and 1.0446 for POF, SYF, and AF, respectively, indicating a good consistency between the observed and expected P values  between the estimated variance explained by each chromosome and the relative length of the chromosomes for SYF  of all the chromosomes. Only the SYF trait showed significant effects of individual chromosome on genetic variation Fig. . ChromosSYF Fig. .Figure 42 population and the 600 K SNP panel. To date, this is the first GWA study of follicle numbers in birds.The aims of this study were to identify the genetic architecture associated with the numbers of different types of follicles and explore the positional candidate genes for those traits using a novel F2 population, creating a unique opportunity to maximize the differences in traits, while increasing the power to identify SNPs for respective traits. Moreover, an extremely large population was used in this GWA study, as well as a higher density (600 K) SNP array, to increase the accuracy and reliability of the obtained data. Different types of follicles were selected for this GWA study, deepening our understanding of the genetic architecture underlying follicular development, from recruitment until ovulation.We used the commercial standard line White Leghorn and Chinese indigenous Dongxiang Blue-shelled Chicken to generate an F11, the mean number of POF in the current study was slightly smaller, which may have been caused by the different breeds. In this study, identified heritability were low (0.13) for POF, which could be due to unavailability, using the clutch status of hens, once follicles in the hierarchy are selected for ovulation12. The extremely low heritability (0.05) of AF suggests that unknown internal and external factors may have affected this measurement. More precise methods and control mechanism to investigate POF and AF should be explored in a subsequent study. Heritability for SYF was moderate (0.28), which was similar to previously reported heritability of follicle numbers in cattle10. The heritability of SYF was higher than the ovary weight and POF weights in previous estimates13, but similar to the heritability of egg numbers14.Compared to a previous study of commercial hens14, the predicted positional candidate genes partially agree with previous reports14.To accurately identify the potential loci affecting the number of chicken follicles, we used two different software systems –GEMMA and GenSel– to explore the observed associations for each trait. Very few SNPs associated with either POF or AF were identified using both software packages. However, a relatively high, consistent number of SNPs was identified genome-wide to significantly associate with SYF, using the two methods of analysis. Of the 55 (50.91%) most significant SNPs identified by GEMMA, 28 SNPs were also amongst the 42 (66.67%) most significant SNPs by GenSel. Both results showed evidence for association on chromosome GGA28 and GGA17 as multiple significant SNPs were located on these two chromosomes. Although most of the significant SNPs associated with SYF obtained in the current study were different from previous results of ovary weight and egg number15; OAZ1 could regulate intracellular polyamine levels16; MBD3 is an essential requirement for pluripotency of embryonic stem cells17; DOT1L is over-expression in ovarian cancer, driving cell cycle progression18. Follicle growth results from a process of cell survival, differentiation, proliferation and apoptosis. Based on the present findings, it is possible to speculate that the genes within these networks play a key role in the genetic determination of SYF.Two SNPs, rs16211213 and rs316413369, fall into non-coding regions of the genome, inferring that the function of these variations is the control of gene expression instead of variations in the gene product itself. The genes located near the significant SNPs were compiled as gene lists to perform an unbiased pathway analysis and build gene networks. All three resulting networks were tightly related to several of the genes (molecules) involved in cell development, cell cycle, cell death and survival. For example, MYO1F present in the first cluster network Fig.  has an a19. Plasma AMH level is one of the criteria used to diagnose polycystic ovary syndrome (PCOS)20, which results in an anovulation, and subsequent infertility, in women of reproductive age. Besides, AMH is an excellent marker of ovarian reserve, as a decrease in AMH level is typically accompanied by ovarian senescence22. Also, AMH can inhibit the process of primordial follicle recruitment and follicle development22.Other most promising candidate genes for SYF were AMH and RGS3. AMH, a glycoprotein, is a member of the TGF- β family, involved in tissue growth, cell differentiation and follicle development. Moreover, previous studies demonstrate that this gene is the essential in determining the number of primordial follicle23. Previous studies have demonstrated that AMH expression level in SYF, together with other genes such as BMP6, FSH, BMP15, played an essential role in follicle development25. Also, it was discovered that broiler ovaries had a higher AMH expression level than the standard commercial layer ovaries5, suggesting the inhibitory action of AMH in chicken follicle selection. The present GWAS results, along with the crucial role of AMH in chicken follicle development, indicate AMH might be an excellent candidate gene for SYF numbers and could have a similar role in mammalian ovarian folliculogenesis.In this GWA study, we identified the significant SNP rs316038837 associated with the SYF trait, which is 14 kb up-stream AMH. In chicken, the expression level of AMH is strongly related to follicular growth, especially for small follicles26. Previous studies showed that RGS3 had negative regulatory functions on FHSR and LHR signaling in rats27. In human, this gene is involved in GnRH responsiveness in granular cells28. Moreover, many research studies on chicken showed the important roles of FSHR, FSH, and GnRH in follicular growth: the expression level of FSHR is tightly related to the number of pre-hierarchy follicles; FSH is critical to the recruitment of follicles; GnRH has an established role on the FSH synthesis1. The aforementioned information, leads to concluding that RGS3 might be an essential factor in follicular growth and have a potential inhibitory effect on follicle number.Another interesting positional candidate gene, identified in the present study, is RGS3 (rs312873273 associated with SYF) on chromosome GGA17. RGS3 encodes for a regulator of G-protein signaling29. In swine, the NFκB signaling cascade is a mediator of FSH action which regulates AMH expression in ovarian cells30. In humans, AMH had an inverse relationship with breast tumor development and growth34. Based on the current results and previous studies, it is reasonable to speculate that AMH and RGS3 can regulate follicle development and growth via activation of the NFκB signaling pathway.Furthermore, the gene network analysis showed that the AMH and RGS3 genes can active NFκB  and RSG3 (rs312873273) genes could be used as prognostic biomarkers for SYF development. However, it will be important to further evaluate the role of these regions in follicular development. Since follicular development is strongly related to egg production, it will be interesting to examine whether variations in these genomic regions relates to reproductive performance. Also, these identified informative SNPs may facilitate selection for an improved productive performance of chicken.All animal care, slaughtered and experimental procedures were approved by the Institutional Animal Care and Institutional Ethic Committee of Jiangsu Institute of Poultry Science. All methods were performed in accordance with guidelines of the Jiangsu Institute of Poultry Science.1 generation. Unrelated F1 chickens were selected to breed the F2 generation: 25 males × 406 females from WL/DX parental and 24 males × 233 females from DX/WL parental. A total of 1893 F2 hens were generated. Detailed information about the resource population can be found in previously published studies36.In brief, six males from each of White Leghorn (WL) and a Chinese indigenous chicken, the Dongxiang Blue-shelled Chicken (DX), were mated to 133 and 80 females from DX and WL, respectively, to generate the F37. Heritability was estimated by ASReml38.Hens were euthanized with 60–70% carbon dioxide gas at 72 weeks of age. A total of 1456 hens were used to record the different follicle numbers, including number of pre-ovulatory hierarchical follicles (POF) (>8 mm in diameter), number of small yellow follicles (SYF) (5–8 mm), and number of atresia follicles (AF). If the initial phenotypic data were not normally distributed, they were transformed by using box-cox method in the JMP software to obtain a normal distribution for all subsequent analyses. Means, standard errors, fixed effects, and covariates were calculated using the JMP statistical software2 hens, using the 600 K Affymetrix Axiom Chicken Genotyping Array . Quality control of genotyping data was initially conducted using the Affymetrix Power Tools v1.16.0 (APT) (http://affymetrix.com/) software with dish quality control >0.82, sample call rate >97%, and SNP call rate ≥95% for the downstream analyses. Further quality control and genotyping were implemented by the PLINK v1.90 software39 with minor allele frequency (MAF) ≥5% and Hardy-Weinberg equilibrium (HWE) test P ≥ 1 × e10−6. Before the GWA analysis, an imputation was performed using the BEAGLE v4.0 procedure40 to avoid some sporadic missing genotypes. Besides, principal component analysis and independent inspection number was implemented in the PLINK package to determine the thresholds for genome-wide significant and suggestive associations. Finally, a total of 59,286 independent tests were obtained. The cutoff threshold of genome-wide significant and suggestive P-values were 8.43 × 10−7  and 1.69 × 10−5 , respectively.Blood was collected from the wing vein and genomic DNA was isolated using the standard phenol/chloroform method. Genotyping was performed on 1456 F41 and the Bayesian methods in Gensel software42. The GEMMA model can be written asy represents a vector of phenotypic values for n individuals; W is a matrix of covariates (fixed effects with a column vector of 1 and top five PCs), α is a vector of the corresponding coefficients including the intercept; x is a vector of the genotypes of the SNP marker, β is the effect size of the marker; μ is a vector of random individual effects; ε is a vector of random error. Bayesian methods can be used to genomic prediction of breeding values and GWAS45.The SNPs significantly associated with the respective phenotypic traits were identified using both genome-wide efficient mixed-model association (GEMMA)y is a vector of phenotypes; x is an incidence matrix to account for fixed effects on phenotypes; b is a vector of fixed effects; zj is a vector of genotypes covariates at SNP j ; αj is the allele substitution effect of SNP j; δj is a parameter that indicates whether SNP j was included in the Markov chain Monte Carlo (MCMC) chain; ε is the error associated with the analysis.The Bayes B model was described as47 in R project. The GenABEL package was used to calculate the genomic inflation factor λ. Linkage disequilibrium (LD) analysis was determined by Haploview v4.248 and assessed by D′ ≥0.8 and r2 ≥1/3 indicating strong LD status. The contributions to phenotypic variance (CPV) of the significant SNPs were calculated in GCTA based on the genetic relationship matrix (GRM) built by PLINK.Manhattan plots and quantile-quantile (QQ) plots of each trait were obtained by the “gap” package50. A mutual information network was generated based on current results using minet package for R50 with the Aracne algorithm. The network graphs were exported from R for visualization in the MCODE package within Cytoscape 3.2.153. Using this visualization tool, we explored networks where one or more SNP was connected via genes.Variant Effect Predictor (VEP) and BioMart tools were performed to identify the candidate genes that located the significant SNPsSupplementary Information


Jasus edwardsii) industry in Australia favours red lobsters, which are usually caught in shallow waters, over paler (brindle) lobsters. This preference is driven partly by the Chinese market, where red is associated with luck and prosperity, and additionally, by the widely held perception within the industry that brindles have greater mortality rates during out of water transport than reds. Limited scientific evidence supports these industry observations; however, these studies did not evaluate the initial condition of lobsters. This study aimed first, to determine which variables better describe condition in J. edwardsii and second, to compare condition among lobsters in several sites around Tasmania at the typical time of high transport mortality. Male lobsters were collected from the South West, South East, East and North coast of Tasmania in late November/December 2014, which correspond to the start of the Tasmanian fishing season. A comprehensive condition assessment was applied by measuring tissue proximal composition, Brix index, Total Haemocyte Count, pH, haemocyanin and another 16 haemolymph parameters of interest. A useful framework to compare condition in J. edwardsii was established by first, using Brix index as a measure of nutritional condition, second, using pH, magnesium, and bicarbonate to evaluate differences in physiological condition and finally, using THC counts as a proxy for lobster health condition. Lobsters from different sites had different nutritional, physiological and health condition, consistent with industry observations, however our results indicate that some red shallow water lobsters exhibited poorer nutritional and health condition, while some deep water brindle lobsters were in good condition. Differences in condition could not be directly associated to catch depth of lobsters and was related to other spatially discrete factors which sometimes vary over distances <3 km.The southern rock lobster ( Jasus edwardsii) from Tasmania, Victoria and South Australia to markets in Asia is currently 3,500–4,000 tonnes with a gross revenue of approximately AUD200 million [The annual export of southern rock lobsters  for measuring “nutritional condition” in lobsters –8. ProceJ. edwardsii and second, to assess differences in condition among lobsters from several sites around Tasmania at the typical time of high transport mortality. A comprehensive condition assessment was applied by measuring tissue proximal composition, Brix index, THC, pH, haemocyanin and another 16 haemolymph parameters of interest.Blood biochemistry panels developed for veterinary use provide the opportunity of analysing a wide range of haemolymph constituents from the same animal , 15 and Male lobsters were collected from the South West, South East, East and North coasts of Tasmania in late November/December 2014 with two to three sites sampled from each area . The comn = 156) from all sites in each area were sampled non-destructively for Brix index. A total of 100 μl of whole haemolymph was added directly to an automatic temperature compensated digital refractometer to measure the Brix index (%) . The refractometer was zeroed using deionised water. Biochemical composition analyses of tissues were used to calculate various biochemical indices of nutritional condition and to analyse the relationship between these and haemolymph biochemistry parameters as well as the Brix index for this species. A subsample of 54 male lobsters from all areas were destructively sampled immediately after taking the haemolymph sample by bringing them to a chill-coma on ice and were kept frozen until dissection at −40°C. Lobsters were weighed and the hepatopancreas (HP) and abdominal muscle  dissected out and weighed (± 0.01 g).In order to determine if differences in nutritional condition of male lobsters differed amongst areas around Tasmania, lobsters  was determined by the method of Bligh and Dyer (1959) using chloroform: methanol: water (1: 1: 0.9) and expressed in mg g-1 of hepatopancreas wet tissue (HPWW) for 40 lobsters.Total lipid (TL) in the HP (HP2 and three pictures of different grids were recorded using 100x magnification. THC was estimated using the software Image J (v1.48c) and calculated with the following equation:Haemolymph (2.5 ml) was collected from the base of the 5th leg of 78 lobsters collected at sites F2, O1, O2, O3, SE1, SE2, SW2, and SW3 using a 3 ml syringe and 26 G Terumo needle pre-chilled on crushed ice. Whole haemolymph pH was measured using a Testo 205 temperature compensated pH probe. A total of 200 μl of whole haemolymph was extracted from each lobster and added to 200 μl Baker’s Formol Calcium anticoagulant  to estimate total haemocyte count (THC). Samples were placed on a slide haemocytometer with grids of 0.0625 mmWhere ŷ = average count of cells per grid, dilution factor of sample , and the volume factor of a grid was defined by the haemocytometer as 80000.-1) sodium (Na), chloride (Cl), potassium (K), magnesium (Mg) and bicarbonate (bicarb); minerals (mmol l-1) calcium (Ca) and phosphorus (P); metabolites (mmol l-1) glucose (Gluc), lactate (Lact), cholesterol (Chol), triglyceride (Trig), total protein in the haemolymph , urea, and uric acid . The second aliquot was used as a backup and to determine haemocyanin. Twenty μl of plasma were diluted 30 times in deionised water for analysis of oxygenated haemocyanin (OxyHc) at 334 nm [Two aliquots of 1000 μl were extracted from a subsample of 52 lobsters originating from sites F2, O2, O3, SE2, SW2, and SW3, centrifuged at 3,000 × g for 4 min, the haemocyte-free plasma removed into clean cryovials, and snap-frozen in liquid nitrogen. Samples were then stored at − 80°C. The first aliquot was shipped frozen to Diagnostic Services at the Atlantic Veterinary College, University of Prince Edward Island, Canada, and analysed using a Cobas c501 automated biochemistry analyzer  for a full blood profile consisting of the electrolytes , respectively. Spatial differences in the Brix index , were assessed using a Nested ANOVA due to the hierarchical nature of the sampling design (sites inside areas) [To assess whether Brix index was a good indicator of nutritional condition of e areas) . The maiJ. edwardsii, based on replicability of sampling or existing literature available for discussion. These three variables were: pH, bicarb, and Mg. This step was essential as collinearity between variables is a problem when using MANOVA (see subsequent section) and not including highly correlated (redundant) variables will lessen this negative impact [Haemolymph biochemistry data were explored using factor analysis, which is a statistical method that groups variables that are highly correlated to each other and is used to simplify the interpretation of trends down to only a few factors  . Haemolye impact .Differences in physiological condition among sites were assessed using a MANOVA test. A sequential Bonferroni (Holm’s method) was used to adjust the p-values from the pairwise contrasts among the sites . The ShaTHC means were compared using an ANOVA, followed by a Tukey’s post-hoc test to determine if there were any differences amongst sites or lobster colour. Normality of residuals was assessed visually by plotting the residuals. Homogeneity of variances was assessed using the Bartlett’s test . Tukey’sLobsters from Flinders were significantly smaller and weighed less on average than lobsters collected from the other three areas . MicroscTL , AMDM,  and TP  (2 = 0.88), followed by abdominal muscle dry matter content (r2 = 0.69), and hepatopancreas lipid reserves (r2 = 0.49).The Brix index was significantly related to HPp<0.001)  and was There was significant variation in the Brix index between the replicate sites within each sampling area but there was no significant difference in the Brix index among areas . SignifiFactor analysis of the haemolymph biochemistry data resulted in four main factors explaining 75% of the cumulative variation : Factor Physiological condition was different among sites . We then used this framework to demonstrate that male lobsters originating from different sites in Tasmania differed in nutritional, physiological and health condition. Nevertheless, these differences were not always evident between lobsters of different catch depths or colouration. For example, brindle lobsters from one site at Orford  were in poorer nutritional condition than red lobsters from the other two sites at Orford , however, no such differences were detected between these two sites and brindle lobsters from South East and South West Tasmania . Similarly, while overall differences in THC between shallow water red and deep water brindle lobsters were detected, these differences were not always consistent at the level of site . Furthermore, THC for both brindle and red lobsters had overall high THC consistent with good health. THC below 4 million cells/ml have previously been reported as an indication of poor condition or health in P. cygnus lobsters [-1. Nevertheless, comparisons between studies need to be made with caution, as THC varies between species and due to a range of environmental factors  [J. edwardsii which warrants future research. Our observations strongly suggest that site-specific characteristics , are driving the differences in lobster condition in Tasmania. The presence of sites with lobsters in poor nutritional and health condition (e.g. site O1) is consistent with observations by the industry in Tasmania that lobsters from some areas have greater mortality rates during out of water transport. However our results indicate that the effect of site is complicated, and is not sufficiently explained by site depth alone.By evaluating a wide array of haemolymph constituents from the same animal, we were able to establish a useful framework to compare nutritional, physiological and health condition of the southern rock lobster, lobsters . Only onerature) . There iJ. edwardsii showed no significant change after 28 days of starvation [J. edwardsii.The Brix index correlated strongly to haemolymph total protein as well as to both hepatopancreas and abdominal muscle tissue composition in our study. Previous studies have also demonstrated refractometry  to be an easy and affordable field method to estimate nutritional condition of crustaceans including lobsters , 15, 25.arvation  suggestiHomarus americanus [Variation in nutritional condition based on the non-invasive Brix index assessment was larger between sites within broad areas of the coast than between broad areas or within pots. For example, lobsters collected from the deeper Orford site (O1), which were brindle-coloured, were in lower nutritional condition as measured by the Brix index than in other shallower sites of Orford and deeper sites in SE and SW. This suggests that if measurements were taken from lobsters to guide harvesting, data would need to be collected across small (fine) spatial scales. The close relationship between the Brix index and nutritional condition, combined with the ease of sampling suggests that further sampling of this index through the course of routine observer catch sampling for stock assessment, which is already conducted at fine spatial scales in Tasmania and samples lobsters caught in about 12000 pots per year , could be worthwhile to guide harvesting patterns. These data could conceivably also help in fisheries assessment, for example to identify years with unusually high or low growth, which would affect the productivity of the stock independently of recruitment. Linking the Brix index to condition of lobsters is complex however as the relationship may vary from year to year depending on changes in factors such as water temperature, productivity and timing of the moult cycle. For example, in our sampling, male lobsters across all areas on the whole were in better nutritional condition  in late November-December 2014 (average Brix index of 12.2) than previously noted from similar areas in November 2007 and 2008 (RI of 1.34–1.346 = Brix index of 6.5–10.0) . Additioericanus . MeasuriLobsters collected from deeper areas (South East and South West) were associated with greater values of magnesium, compared to lobsters from shallower areas (Orford or Flinders). Similarly, Chandrapavan et al  found grJ. edwardsii showed that the blood pH initial drop from 7.8 to 7.3 is associated with limited lactate build up (<5mmol l-1) but decreases in pH < 7.2 are characterised by much higher lactate levels >10 mmol l-1 linked to much greater reliance on anaerobic metabolism and are therefore of greater biological significance Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.


Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein “phosphoglycerate mutase 2” and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in  Musculus longissimus dorsi (LD) is around 1% to 5% [Pork quality is the result of complex interactions between genetic and environmental effects like rearing and slaughtering conditions, and carcass and meat processing. One important commercially interesting pork quality parameter is the ability of meat to retain water, also known as water-holding capacity (WHC). In order to characterize WHC in pork, drip loss is measured. This fluid, mainly from muscle cells, resigns from the meat surface without any mechanical force other than gravity and is influenced by shrinkage of the myofibrils, pH value, and temperature post mortem (p.m.) ,2. Avera1% to 5% . Heritab1% to 5% . This wi1% to 5% ,7,8. How1% to 5% ,10,11.Genetic studies, using standard approaches to identify candidate genes, already revealed several quantitative trait loci (QTL) and candidate genes for drip loss in pigs ,12,13. HIn recent years, the innovative technologies to record hundreds or thousands of omics profiles simultaneously and to analyze their relation to different traits were extensively developed further and established in many meat production sectors ,19.For meat scientists, the final objective is to identify meat quality genetic markers (like SNPs) or bio markers which are quantifiable on live animals or early p.m. on the carcass in order to orientate meat production towards the most adapted processes in meat processing or distribution circuits . For thiThe aim of this study is the integration of omics levels genome, proteome and metabolome to elucidate underlying functional pathways and corresponding candidate genes for drip loss. Based on the increased information density due to the consideration of proteome and metabolome, we expect that our GWAS approaches based on metabolic traits contribute to identify true candidate genes with higher accuracy.2 Duroc × Pietrain (DuPi) pigs. The mean drip loss was 1.97%, with a minimum of 0.4% and a maximum of 5.3%  due to functional connectivity (p = 0.014), that comprised four metabolites, was the most significantly enriched composition of metabolic components. Most metabolites and proteins were assigned to a single pathway. Of particular importance were across pathway components that might be indicators of key regulators with a strong impact on drip loss. As an example, the metabolites glucose and pyruvic acid are participants in six and five different pathways, respectively. The strongest overlapping induced by the metabolites glucose and pyruvic acid can be observed between glycolysis, methane and pyruvate metabolism showing that these pathways are closely linked. In contrast, the most significant pathway sphingolipid metabolism has only one link to the methane metabolism due to overlapping metabolite serine, whereas the remaining involved metabolites are exclusively members of sphingolipid metabolism.In total, 163 metabolic components (128 metabolites and 35 proteins) were assigned to 219 KEGG pathways that potentially are involved in muscle to meat conversion and manifestation of meat quality characteristics. Based on the Wilcoxon rank sum test in 10 out of 219 KEGG pathways, the metabolites and proteins were significantly enriched (ectivity . These pp ≤ 0.05) correlated  were analyzed within a GWAS. In total, 44,844 SNPs were tested for association with at least one of the 22 metabolic traits or meat quality trait drip loss itself. In order to ensure the statistical power and accuracy of GWAS possible population stratification was considered . In thisq ≤ 0.10, the GWA studies revealed 871 (without double counting) significant associations for 15 traits, including drip loss, three proteins and 11 metabolites. These SNP were distributed over almost all porcine chromosomes. Four hundred thirty one SNPs showed a chromosome-wide significance levels of q ≤ 0.05 but no SNP was detected as genome-wide significant (q ≤ 0.01).Applying a moderate significance threshold with a false discovery rate (FDR) of q ≤ 0.1). The highest proportion of explained variance was observed for SNPs that affected glucosylceramide, dihydroxyacetone phosphate (glycerone-p) and d-glycerate-3-phosphate (DG3P). The most significant SNPs were detected for metabolite hydroxylpyruvic acid (q ≤ 2.19 × 10−2).The average number of significant SNPs per trait was 66, with a minimum of two SNPs (for glucose) and a maximum of 249 SNPs for fructose-1,6-biphosphatase-2 (FBPase). The majority of the SNPs was significant at a moderate chromosome-wide level (Sus scrofa chromosome (SSC) 14, 17 and 18. For drip loss, we detected SNPs on SSC 16 and 18, which explain a maximum of variance proportion of 8.8%. Based on the distance to neighboring significant SNPs on the chromosome (1 Mb), we condensed the SNPs into 330 important QTL regions with an average of 29 QTL per chromosome  SNPs (QTL) which were significant for more than one trait. These SNPs are located on SSC 1, 7, 8, 14, 17 and 18. As presented in The functional annotation of the 871 significantly associated SNPs revealed 1430 genes that are located in a distance of ≤1 Mb to the SNPs and thereby are in linkage disequilibrium to our significant SNPs . 257 SNPq ≤ 0.1);Chromosome-wide significant  harbour more than one intronic SNPs. Based on the QTL comprising five intronic SNPs in a small range of 0.29 Mb, it might be reasonably assumed that HLCS is a very promising candidate gene for FBPase. Most significant intronic SNPs with highest proportion of explained variance in a range of 15.28% to 17.44% were detected for glucosyl-ceramide, glycerone-p and glycine  associated with drip loss were detected on SSC 16, but these SNPs do not fulfill the previously described conditions to detect potential candidate genes (LRGUK) significantly associated with drip loss and glycine. Because LRGUK is in linkage disequilibrium with EXOC4 that was associated with drip loss as well, this region ranging from 15.9 Mb to 16.1 Mb is of particular interest. From 12.2 Mb to 12.9 Mb there is a second interesting region with two candidate genes, for PGAM2 and drip loss, respectively. The Manhattan plot of SSC 18 is presented in For drip loss, five candidate genes were identified on SSC 18 . The moste genes . DistribBased on the multitude of possible post-transcriptional events, the genetic information flow from SNPs to phenotypic variations is not linearly dispersed in living organisms and samples collected p.m. . This siTo elucidate biological pathways affecting a trait, the consideration of the proteome is advantageous compared to the transcriptome. This can be assumed because the amount of proteins is not only regulated by a constant level of transcript expression but also by many possible genetic interacting mechanisms of protein regulation/modification and connected activation of other pathways . In a siIn this study, metabolite profiling was based on an untargeted metabolomics approach to uncover the whole metabolome. Compared to that, proteins were profiled more specific by means of a targeted proteomics approach using the absolute quantification of 40 proteins that have been shown as important indicators for drip loss in previous investigations. For the final enrichment analysis 128 annotated metabolites and 35 proteins were used. Five proteins were rejected because of missing entrez gene identifier. The drastic reduction of the number of metabolites from 1865 to only 128 is a severe bottleneck, so that it is highly probable that even metabolites with strong influence on drip loss were excluded. This situation is caused by the fragmentary information of biochemical functions of metabolites that is stored in metabolome databases. According to Chagoyen and Pazos , this lap ≤ 0.05) enriched components. The applied test mean-rank gene-set enrichment (MR-GSE) statistic is based on Pearson’s correlation coefficients between metabotypes and drip loss and averages the ranks of the applied statistics instead of the statistics themselves. This procedure makes the results less influenced by individual components in the set of variables [Our enrichment analysis considered all available annotated metabolome and proteome information and revealed 10 functional KEGG pathways with significant , even if the effects are very small or the amount of data is not sufficient to detect the important variables individually . This arIn our study, we observed particularly pathways and corresponding key regulators which affect muscle metabolism related to meat quality traits. Glycolysis, pyruvate and methane metabolism are strongly connected and belong to the most important energetic processes that influence the muscle to meat conversion ,39. Becawww.genome.jp). High glycine contents indicate a higher rate of glycolytic processes. A high glycolytic potential is known to be related with high drip loss. The link between drip loss and glycine was already described by Lim et al. [As a result of our enrichment analysis, the metabolite glycine is associated with drip loss. In methane metabolism the enzyme glyoxylate transaminase catalyzes the metabolization of metabolite glyoxylate into glycine or hydroxypyruvic acid . In gluconeogenesis FBPase converts fructose-1,6-biphosphate to F6P and in glycolysis phosphofructokinase catalyzes the metabolisation of F6P to fructose-1,6-bisphophate. In the following process of glycolysis, the enzyme fructose-bisphosphate aldolase converts fructose-1,6-bisphophate to glycerone-p. In the next step, glycerone-p is metabolized to glyceraldehyde-3-phosphate catalyzed by TPI1 see . Lavillep-value of 0.014, metabolic compounds in sphingolipid metabolism are the most strongly enriched metabolites and proteins in our study and thereby have an obvious effect on drip loss. According to Heidt et al. [Beneath metabolic processes whose activity directly depends on the individual energy resources, also sphingolipid metabolism is significantly associated with drip loss. With a t et al.  there ist et al. . In the t et al.  concludet et al. , the levThe metabolic processes and their involved components and overlapping are presented in Drip loss is a complex trait that is genetically controlled by a variety of different genes  and is iq ≤ 0.10.Instead of a Bonferroni correction, that favors the occurrence of false negative associations , we usedSus scrofa pleiotropic factor beta” (PTN) (q ≤ 6.26 × 10−2) is highlighted by the direct neighborhood of gene “cAMP responsive element binding protein” (CREB3L2). CREB3L2 was identified by the GWAS of the protein PGAM2, which revealed an intronic SNP (ALGA0107449) as one of the most significant marker  were found. These SNPs are ranked in the Top 10 list for drip loss as well as for glycine. The nearby gene “Exocyst complex component 4” (EXOC4) is also significantly associated with drip loss. EXOC4 is part of the exocyst complex (Exo70), which is involved in insulin-stimulated glucose transport. Due to Laramie et al. [EXOC4 and LRGUK on chromosome 7 are associated with type 2 diabetes and fasting glucose. The metabolic pathway that is regulated by the polymorphisms near EXOC4 and LRGUK potentially is also relevant for drip loss in pork, because fasting glucose also effectsthe pH decrease in muscle p.m. and drip loss. The investigations of Leheska et al. [Adenosylhomocysteinase-like 2” (AHCYL2) and “Smoothened, frizzled class receptor” (SMO) are located and significantly associated with drip loss. Just like the polymorphism between EXOC4 and LRGUK, AHCYL2 is associated with type 2 diabetes [Nuclear factor, erythroid 2-like 3” (NFE2L3) at 51 Mb, associated with protein PGAM2, fits into the same metabolic background like the previously described genes [In the second interesting region on SSC 18 from 15.9 to 16.1 Mb, two intronic SNPs located in the gene “e et al. , in humaa et al.  demonstrdiabetes . Until ned genes . In summp ≤ 2.23 × 10−5 and p ≤ 1.59 × 10−5) associated with glycerone-p and glucosylceramide, are located within the genes “Ectonucleotidepyrophosphatase/phosphodiesterase 3” (ENPP3) and “Sterile alpha motif domain containing 4a” (SAMD4A). ENPP3 is associated with lipid and fatty acid metabolism and it has been reported by to Liu et al. [SAMD4A is also associated with lipid metabolism [In this study, the most significant SNPs were detected on SSC 1. Two SNPs  is involved in the regulation of hepatic glucose output, glycogen synthase, and antilipolysis in typical insulin target cells such as those in the liver, muscle and fat tissue [PIK3C3 influences the cellular response to glucose starvation (GO term: 0042149). This biological process describes the change in state or activity of a cell  as a result of deprivation of glucose. According to Kim et al. [PIK3C3 is associated with body weight and carcass fat in Landrace and Duroc pigs.Beneath glucosylceramides, phosphoethanolamines are also key players in sphingolipid metabolism. Two genes significantly associated with this metabolite were detected on SSC 6 . “Phospht tissue . Among om et al.  and Hirom et al.  a polymoGuanylate-binding protein 4” (GBP4) and “Protein kinase N2” (PKN2) are located. Zhao et al. [PKN2 that were associated with back fat thickness. SSC 4 harbors two genes  within a region of 142–143 Mb which were significantly associated with average daily gain in Large White pigs [Moreover, we identified potential candidate genes for several metabolic components involved in glycolysis/gluconeogenesis. The protein PKM is one of the most prominent members of these pathways. The activity of PKM is decreased in the case of low glucose availability in muscle that is positive correlated with anabolic cellular processes. During the conversion of muscle to meat, the metabolic processes change into the catabolic range and if glucose is used up very early, the PKM level is significantly associated with the aberrant glycolysis leading to PSE development . In our o et al.  have ideo et al.  have repite pigs . These pProtein tyrosine phosphatase, receptor type” (PTPRT) and “VAMP (vesicle-associated membrane protein)—associated protein B and C” (VAPB) are candidate genes that affect the metabolite DG3P. The protein encoded by PTPRT is a signaling molecule that regulates a variety of cellular processes including cell growth, differentiation, mitotic cycle, and oncogenic transformation. In humans, PTPRT is strongly associated with high-fat diet-induced obesity and insulin resistance [PTPRT as candidate gene for shear force. With respect to the negative correlation between intramuscular fat content and shear force both studies came to homogeneous results. The importance of PTPRT is additionally indicated by the fact that the most important intronic SNP of PTPRT is an overlapping SNP that is also significantly associated with protein FBPase  via gas GC-MS and LC-QTOF/MS. GC-MS and LC-QTOF/MS facilitate the identification and quantification of a few hundred metabolites in a single tissue sample. Chromatography followed by mass spectrometry has a relatively broad coverage of compound classes, including organic and amino acids, sugars, sugar alcohols, phosphorylated intermediates and lipophilic compounds. With the combination of both methods it is possible to detect metabolites in a range of 50–1700 Dalton, with a precision of 1–2 part per minute (ppm) and a solution of mass/Δmass = 40.000 (Report METABOLOMIC DISCOVERIES GmbH). For details on the LC-QTOF/MS method see Lisec et al. [www.hmdb.ca), METLIN (www.metlin.scripps.edu/) and Lipid Maps (www.lipidmaps.org/). Annotation of metabolites was based on mass assignment, retention behavior and structure information. Metabolites, which could not be annotated, are characterized by their accurate mass and retention time. For details in metabolite quantification and annotation see Welzenbach et al. [https://www.bioconductor.org) based on HMDB-IDs.For metabolite profiling we choose an untargeted approach to screen the entire metabolome. The metabolite spectra in the LD samples of 97 DuPi pigs were measured by Metabolomic Discoveries GmbH  was used to determine the whole proteome (holistic approach) in LD samples of 42 DuPi pigs selected based on their extreme phenotypes of drip loss. In the second step , a set of 40 selected proteins was quantified in the 97 DuPi pigs of this study (targeted protein profiling approach).The ICPL procedure, which based on stable isotope labeling, combined with mass spectrometry has emerged as a powerful tool to identify and relatively quantify thousands of proteins within complex protein mixtures . In contBased on the holistic ICPL approach and literature research, 40 proteins with expected meaning for drip loss were selected. These proteins were validated via selected reaction monitoring (SRM) in the 97 DuPi pigs of this study. Using a triple quadrupole mass spectrometer, targeted SRM offers high selectivity, sensitivity and a wide dynamic range in the quantitative analysis of small molecules . The ICPhttps://www.bioconductor.org) were used to identify the corresponding KEGG-IDs of the proteins. In Based on the available entrez gene ID or ensemble peptide ID, R packages KEGGREST, biomaRt and AnnotationDbi of Bioconductor (http://Illumina.com). DNA (200 ng) was used for genome-wide amplification and fragmentation. A quality score for each genotype was generated. Sample preparation and genotyping has been described by Heidt et al. [DNA was extracted from LD using a Genomic DNA Purification Kit . DNA concentration was measured using a NanoDrop 8000 spectrophotometer  and concentration was adjusted to 100 ng/µL by using double-distilled RNase and DNase free water. Illumina bead array technology (Porcine SNP 60 K Bead Chip) was used for genotyping the samples  in accordance with the protocol for SNP Infinium HD assay . After checking the quality of the data, 97 animals and 44,844 SNPs remained in the data set. Quality control was performed as implemented in R package GenABEL . SNPs wePig Sscrofa 10.2   was usedIn order to investigate the overrepresentation of specific metabolite and protein sets in different KEGG pathways, an enrichment analysis was performed based on corresponding annotated metabolites and proteins and the target trait drip loss. For assignment of metabolites and proteins to relevant metabolic pathways, R package biomaRt was applied .p ≤ 0.05) enriched sets of metabolic components represent specific functional pathways that might be associated with muscle metabolism and meat quality traits. The procedure computes a p-value to test the hypothesis that a set of variables (metabolites and proteins) tends to be more highly ranked in terms of a given test statistic compared to randomly chosen variables. The calculated p-value indicates whether a set of variables is statistically independent that means that the variables are on average less or equally correlated than randomly chosen variables (H0 hypothesis), or whether a set of metabolites and proteins is enriched because of functional connectivity (H1 hypothesis). In the following step, metabolites and proteins of significant enriched functional pathways were analyzed in a GWAS.The enrichment analysis was performed as implemented in R package limma . The undijy is the phenotype of the i-th individual. Fixed effect SD and as covariable SW with regression coefficient βs are implemented in the model. Genetic effects were estimated via a fixed covariable “genotype” (ikg) and corresponding regression coefficient (βg). The significance of each SNP was tested using a fast score test. In order to verify potential stratification in our F2 DuPi population, the inflation factor λ, which depends on the squared original test statistic of the i-th SNP  [Aulchenko et al.  and PricENSTRAT) ,52, wereq-value calculation. This approach is a significance measurement based on the false discovery rate (FDR) concept [q ≤ 0.1. The variance explained by the respective SNP was calculated using following formula:χ2 is the result of the score test as implemented in GenABEL and n the number of individuals. This formula resulted from the transformation of a Student’s t-distribution into a z-distribution [The correction of phenotypes, the estimation of the PCs and the association analysis was performed with the function “egscore” as implemented in the R-package GenABEL. In order to reduce the risk of false-positive associations, the SNP significance tests were corrected for multiple testing based on the  concept . We chosribution . Based oSystems biological approaches utilize the information content of all available omic variables  in order to clarify the physiological, biochemical and genetic background of complex traits. Theoretically, across omics utilization is advantageous in comparison to classical genetic approaches, which merely investigate associations between SNPs and phenotype.In this context, Picard et al.  and van Based on the results of this study, it was possible to confirm the already known findings about the importance of energy related metabolic processes influencing meat quality and particularly drip loss. On the other hand, this study also provides novel insights into the underlying biochemical pathways of drip loss. According to our findings, the sphingolipid metabolism is of particular importance for drip loss manifestation. The involved metabolites glucosylceramide and phosphoethanolamine are promising intermediate phenotypes for drip loss and revealed promising candidate genes on SSC 1. It can be expected that such integrated omics approaches might be successfully applied to clarify the biochemical and genetic background also in more complex traits than meat quality. Against this background, our study may be considered as a model investigation to test one possible procedure to combine different omic levels.


Council of Europe data were used for HBV and HCV blood donor prevalence. HBV general population estimates were available for 13 countries, ranging from 0·1% to 4·4%. HCV general population estimates were available for 13 countries, ranging from 0·1% to 5·9%. Based on general population and blood donor estimates, the overall HBV prevalence in the EU/EEA is estimated to be 0·9% (95% CI 0·7–1·2), corresponding to almost 4·7 million HBsAg-positive cases; and the overall HCV prevalence to be 1·1% (95% CI 0·9–1·4), equalling 5·6 million anti-HCV-positive cases. We found wide variation in HCV and HBV prevalence across EU/EEA countries for which estimates were available, as well as variability between groups often considered a proxy for the general population. Prevalence estimates are essential to inform policymaking and public health practice. Comparing to other regions globally, HBV and HCV prevalence in the EU/EEA is low.This systematic review aimed at estimating chronic hepatitis B (HBV) and C virus (HCV) prevalence in the European Union (EU) and Economic Area (EEA) countries in the general population, blood donors and pregnant women. We searched PubMed Both hepatitis B (HBV) and C virus (HCV) affect the liver and can cause acute and chronic hepatitis. People with chronic HBV or HCV infection may transmit the virus to others and are at risk of developing serious liver disease such as cirrhosis or hepatocellular cancer (HCC) , 2. TranThe risk of developing chronic HBV infection depends on the age at which people are infected, with 90% of infants infected at birth developing chronic infection, compared with 1–10% of those infected at an older age or as adults , 6. GlobInitial infection with HCV is often asymptomatic or mild (70–90% of cases); however, the majority of those infected with the virus 70–80%) develop chronic infection and, over a period of 20–30 years, 10–20% on average will develop cirrhosis and 1–5% will develop liver cancer [0% develoIn 2011, the European Centre for Disease Prevention and Control (ECDC) started enhanced European Union (EU)-wide surveillance for HBV and HCV, based on annual data collection from EU and Economic Area (EEA) Member States (MS). In 2014, 22442 newly diagnosed HBV infection cases were reported from 30 MS, a rate of 4·2 cases/100 000 population [The recent development of more effective HBV and HCV treatment means that elimination of chronic viral hepatitis in Europe is now a possibility , 13. HowWe updated a previous systematic review undertaken by ECDC in 2009  with theOriginal research articles were retrieved from PubMed®, Embase® and Cochrane Library databases in March 2015. The search strategies  combined controlled (MeSH/Emtree terms) and natural vocabulary (keywords) to define disease-related (HBV/HCV infection), outcome-related  and geographic-related (EU/EEA) search parameters. The search was limited to records published from 1 January 2005 to 12 March 2015. Articles in all EU/EEA languages were included. The results of the search were shared with ECDC National Focal Points (NFP) for viral hepatitis in all 31 EU/EEA MS in May 2015 for review and to validate the list of included references for their country.Inclusion and exclusion criteria (Supplementary Table S2) encompassed time-related criteria including publishing date (2005 or later); sampling timeframe (data collection ending after 2000 or beginning from 2000 onwards); geographical coverage (EU/EEA MS and/or any of their regions/districts) and disease-specific markers (HBsAg/anti-HCV (and DNA/RNA) prevalence). Only studies reporting original data were included, although reference lists of relevant reviews were consulted for any original articles not captured by the literature search. Articles reporting prevalence in the general population or pregnant women with a sample size of <100 participants were excluded. Articles reporting only self-reported HBsAg/anti-HCV prevalence were also excluded.To ensure consistent application of the inclusion criteria, two reviewers (SHIH and AMF) independently reviewed the title and abstract of the same random selection of retrieved articles (5%). The inclusion criteria were further refined and a second round of reviewing was conducted to ensure consistent application (>95% agreement) of the criteria. Following this, title and abstract screening for all articles continued independently using Endnote. The full texts of all publications included after title/abstract screening were assessed for relevance by members of the research team where language comprehension existed  or by ECDC reviewers (other EU/EEA languages). In case of uncertainty about in- or exclusion, the two reviewers consulted each other and cases of disagreement were resolved by consultation with a third team member (IKV).Chronic HBV and HCV were defined as the presence of HBsAg and anti-HCV in serum, saliva or dry blood spot samples, respectively. The general population was defined as people  living in a defined geographical area; patients attending community/primary health care settings; and workforce/specific professional groups (e.g. workplace screening) but not healthcare workers. Pregnant women were defined as those women undergoing antenatal care screening, and blood donors were defined as first-time blood donors (Supplementary Table S3).Data extraction using a pre-defined set of variables (Supplementary Table S4) was performed simultaneously with full-text screening. The unit for data extraction was ‘study’, defined as a prevalence data report on HBsAg or anti-HCV for a defined population group, in a defined country, over a discrete period; one article may therefore include more than one study. Studies published in more than one article were extracted only once and the article with most details about the study used as the reference.ad hoc by the research team. Separate assessment frameworks were developed for the general population and pregnant women to account for differences in possible sources of selection bias. For general population studies, the domains age, gender, sampling method and response rate, and geographical coverage were considered as possible sources of selection bias (Supplementary Table S5). For pregnant women studies, potential selection bias sources included sampling method and geographical coverage (Supplementary Table S6). Points were awarded in each domain for representativeness or lower risk of bias, and a total score was calculated by summing the values in each domain. This resulted in a score between zero and six for the general population studies and between zero and three for the pregnant women studies. We refer to the total score as study quality score, since a higher score indicates a lower risk of bias. A general population estimate was considered of high quality when it reached a study quality score ⩾4. A high-quality estimate of prevalence in pregnant women was defined as a study quality score ⩾2.Each included study was evaluated for risk of selection bias using a framework developed This review reports HBsAg and anti-HCV prevalence, rather than a viraemic marker of HCV chronic infection, since information on HCV RNA and HBV DNA prevalence was reported in too few studies to conduct an analysis.©. General population estimates were reported separately for adults and children where available. All higher quality estimates of HBsAg and anti-HCV prevalence (score ⩾4) retrieved for each country for the general population are presented in forest plots (Microsoft Excel©). Higher quality prevalence estimates from pregnant women studies (score ⩾2) were also pooled where possible (using the methodology as described above) and separate forest plots prepared for HBV and HCV. Prevalence maps of Europe for pooled or single higher quality estimates were created using the ECDC Mapping and Multi-Layer Analysis (EMMA) tool [National weighted or standardized (e.g. for age and/or sex distribution) prevalence estimates, if available (Czech Republic and Belgium for HBV), were preferred over unweighted or crude estimates. Crude estimates for the general population with the highest quality (score ⩾4) were pooled at country level, where available, by summing cases and sample size. Ninety-five per cent confidence intervals (95% CI) were then calculated using the Fisher's exact method in Microsoft ExcelMA) tool .To assess the HBV and HCV prevalence among blood donors, data from 2014 collected by the Council of Europe were used [In order to estimate the current burden of chronic HBV and HCV in the EU/EEA, an algorithm combining general population and blood donor data was applied. If a pooled or single higher quality general population prevalence crude estimate was available for a country, this was used to determine the HBV and HCV prevalence in that country; if a higher quality estimate was not available, lower quality general population crude estimates were used (these were pooled when possible); if no general population estimates were available, prevalence data from blood donors were used. To determine the total number of chronic HBV and HCV cases in each country, total population size (based on Eurostat 2014 data) for each country was multiplied by the estimated HBV and HCV prevalence in each country.The literature search retrieved 9379 citations. After title/abstract screening, 142 articles for the general population and 50 articles for pregnant women were included. Seventeen MS validated the search results and/or provided additional references, adding nine additional citations for the general population and five for pregnant women. While all 55 full texts were available for pregnant women, three general population articles could not be retrieved. Following full-text screening, 48 articles for the general population and 32 articles for pregnant women were finally included .Fig. 1.From the 48 articles included, 53 HBsAg prevalence estimates and 45 anti-HCV estimates were extracted. For HBV, multiple estimates were available for 13 of 15 countries covered, with the most estimates (10) available for Italy (Supplementary Table S7). For HCV, more than one estimate was available for nine countries of 16 countries covered, with most estimates (14) again available for Italy (Supplementary Table S8).From the 53 prevalence HBsAg estimates, 18 estimates in 13 countries  were considered to be of higher quality . These data are presented in Of the 45 anti-HCV prevalence estimates, 19 higher quality  prevalence estimates from 13 countries  were available. These data are presented in To estimate the prevalence in pregnant women, 27 HBsAg estimates from 11 countries (Supplementary Table S10) and 15 anti-HCV estimates from eight countries (Supplementary Table S11) were retrieved from 32 eligible studies. Multiple estimates were available for nine countries, with the highest number of estimates (six) retrieved for Greece. Pooled estimates were available for Denmark, Italy, the Netherlands and the United Kingdom. Higher quality estimates  of HBsAg prevalence were available for seven countries, ranging from 0·1% in Norway  and SpaiOf the 15 HCV estimates for pregnant women, higher quality estimates  were available for Slovenia, Spain, Italy and Norway, with prevalence ranging from 0·1% in Slovenia  to 0·9% The prevalence of HBsAg and anti-HCV in first-time blood donors was available for 30 countries . For LatThe prevalence of anti-HCV among first-time blood donors ranged from 0·0% in Iceland to 2·2% in Latvia, and 58% of countries had an HCV prevalence that was about or below 0·1%.Using prevalence estimates for the general population and blood donors, the HBsAg prevalence in the EU/EEA as a whole is estimated to be 0·9% (95% CI 0·7–1·2), equivalent to almost 4·7 million chronic HBV cases. An overview of the estimated prevalence and data used for each country is in Supplementary Table S13. The United Kingdom has the largest estimated burden of chronic HBV in the EU/EEA with over a million cases, followed by Romania (877 682), and Spain, France and Italy (each with between 400 000 and 500 000 cases).The anti-HCV prevalence in the EU/EEA is estimated at 1·1% (95% CI 0·9–1·4) equivalent to approximately 5·6 million anti-HCV-positive cases. Of these, an estimated 70% are chronically infected, i.e. viraemic replication with detectable HCV RNA . France,In this review, we have compiled recent evidence available on the prevalence of chronic HBV and HCV infection in the general population, pregnant women and blood donors from EU/EEA countries to provide further information on the epidemiology of these infections and to identify gaps in the available evidence.The prevalence of chronic HBV and HCV in the general population varies widely across the 16 EU/EEA countries for which estimates were available, with a higher HBsAg and anti-HCV prevalence in countries in the Eastern and Southern part of the EU/EEA. There is great diversity across the region and the estimated prevalence in the country with the highest HBsAg estimate in the EU/EEA (Romania) is 44 times higher than in the country with the lowest estimated prevalence (Ireland). We also found that the highest single estimated anti-HCV prevalence  is nearly 60 times higher than estimates from countries with the lowest prevalence . Differing risk factors and transmission routes might partially explain this variation across countries, as well as different implementation of prevention and control strategies.This study updates the previous ECDC systematic review  and addsNew high-quality anti-HCV prevalence estimates were available for five countries . Again, although no statistical testing was conducted, results suggest that while the chronic HCV prevalence remained at 0·4% in Germany, it declined over time in France, the Netherlands and Spain, and increased in Greece. As with HBsAg prevalence, it is possible that the increase in Greece is mostly a reflection of the restricted geographical coverage of both the current estimate (Crete ) and theet al. [et al. [et al. reports the highest estimated prevalence found in Romania. While most HBsAg prevalence estimates are comparable, there are some notably different estimates reported by Schweitzer et al. for the 1990–2015 period, particularly for Greece (0·97% vs. 3·3% in this review) and the United Kingdom (0·01% vs. 1·74% in this review). Methodological differences, specifically the inclusion of a wider timeframe and a broader definition of the general population to include blood donors, pregnant women and health care workers, could explain these differences.Both Ott et al.  and Schw [et al.  found thet al. found the highest prevalence estimates for Romania (3·2%), Lithuania (2·9%) and Latvia (2·4%) [et al. does include 5·6%, suggesting some comparability. In our study, 14 highly heterogeneous estimates  . Howevera (2·4%) , yet thees (0·6%  to 27·6%es (0·6% ) met theet al. for France, Germany, Hungary and Romania were very similar to the estimates in this paper or the same study was identified as the most reliable estimate [et al. suggest that the best prevalence estimate for Italy is 4·4%  and 5·2% for adults, similar to the 5·9% we found. Cornberg et al. most diverges with our findings for Spain (2·6% vs. 1·1% in this review) [et al., that the prevalence in Spain is around 2·5% [HCV estimates presented in Cornberg estimate , 47. Cor review) . Althougund 2·5% , suggestThe differences in prevalence between countries and over time are difficult to interpret, because comparability between studies is limited by the use of different study designs, probabilistic and non-probabilistic sampling strategies, and use of different laboratory tests and sample types. In addition, geographical representativeness is limited as most studies were performed at sub-national level. Representative seroprevalence studies for the general population are thus needed for valid comparison.Pregnant women are commonly considered as proxy for the general population, albeit to a different extent for HBV and HCV. The majority of EU/EEA MS offer antenatal HBV screening. Estimates of HBsAg prevalence among pregnant women, although slightly higher, mostly align with observed general population estimates in most countries, except in Greece and Spain, where the prevalence among pregnant women is lower. This is consistent with results of the 2009 systematic review . The preAnti-HCV prevalence in pregnant women, where available for comparison with higher quality general population estimates, was found to be considerably lower. Chronic HCV infections in many EU/EEA countries have an age- and gender-specific prevalence distribution, with some studies from Southern Europe suggesting that 60% of the infected population is over 65 years of age . Older aHBV and HCV seroprevalence data in first-time blood donors are readily available for most EU/EEA countries and are the most complete population prevalence source. Although blood donors are often used as a proxy population, this subgroup is generally considered not to be a representative sample due to self-selection of blood donors and strict regulation by blood banks . These sThe HBV and HCV prevalence in the EU/EEA as a whole is estimated to be around 0·9% and 1·1%, respectively, resulting in an estimated total of 4·7 million chronic HBV cases and 5·6 million anti-HCV-positive cases. Considering that an estimated 70% of anti-HCV-positive cases are chronically infected , this coThe robustness of these figures is influenced not only by the intrinsic limitation of using prevalence estimates derived from an array of diverse studies, but also by the inclusion of prevalence estimates among blood donors as a proxy for the general population in the absence of other evidence. However, when taking into account both HBV and HCV data, general population estimates obtained from included studies accounted for approximately 83% of the total European population, with the remaining 17% covered by blood donor estimates.Other than perhaps the population size of the country, no clear distribution across the EU can be observed in the availability of  estimates in any of the targeted population groups. For one country, Liechtenstein, no information about HBV and HCV prevalence was available for any of the population groups. For Cyprus, Iceland and Malta, only prevalence data on first-time blood donors were available, and for Austria, Estonia, Lithuania, Poland and Sweden, only low-quality estimates were available.An important strength of this review is that publications in all EU/EEA languages were included. In addition, consultation with MS further supplemented and validated the evidence retrieved. We feel that the lower sensitivity in the literature search due to the use of a geographical filter was effectively offset by the MS consultations. Another strength is that small-sample studies were excluded, a risk of bias assessment was developed and applied, and only high-quality estimates were selected and pooled (if multiple were available) for the analyses, to ease inter- and intra-country comparisons. The risk of bias assessment tool, however, has not been previously tested. An untested assumption in the tool is the equal weight given to each domain to calculate a final quality rating. For first-time blood donors, another data source was used rather than studies identified via a systematic review and the source was not assessed for bias.This systematic literature review confirms the diversity in prevalence of chronic HBV and HCV infections across the EU/EEA, as well as the variability between groups often considered to provide a good proxy for the general population. Our findings suggest that using blood donor or pregnant women data as a proxy for HCV and, to a certain extent, HBV prevalence estimates for the general population is not desirable. Comparing to other regions globally, HBV and HCV prevalence in the EU/EEA is low, with some sign of decline, at least for HBV. The availability of studies with relatively recent data on the prevalence in the general population is limited, with data for around half of the 31 countries in the EU/EEA, reporting higher HBV and HCV prevalence in countries in the Eastern and Southern part of the region. The epidemiology of HBV and HCV is constantly changing, in part due to the impact of prevention and control programmes and changes in risk factors, but many countries lack recent robust epidemiological studies that provide reliable estimates of the burden of chronic viral hepatitis. The lack of high-quality, recent, nationwide prevalence estimates and the heterogeneity of available studies makes it challenging to gain an EU/EEA overview of the current epidemiological situation regarding chronic viral hepatitis. The need for high-quality strategic information on the burden of HBV and HCV is compelling, not only for scaling up secondary prevention services appropriately, but also to inform regional and global activities that will shape the response to these epidemics in years to come. This could be achieved by complementing case-based surveillance with alternative data sources with adequate standardization levels across the region. A standardized seroprevalence survey performed across the EU/EEA, while resource intensive, may be a well-needed intervention to consider.LT and EFD coordinated the study. LT, EFD and AJA-G developed the overall concept and framework of the study. LT, EFD, SHIH, AMF, IKV and SJMH designed the study protocol. LT developed the search strategy and performed the literature searches. SHIH and AMF acquired and analysed the data. SHIH, AMF, LT and EFD interpreted the data. SJMH and IKV provided technical expertise and advice on relevance and accuracy of studies. SHIH drafted the first version of the manuscript. AMF, EFD, SJMH, AJA-G, IKV and LT contributed to critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.


P = 0.009). Bacterial species richness (Chao1 index) and diversity (Shannon index) were significantly reduced in children with mite sensitization but not in those with food or IgE sensitization. Compared with healthy children without mite sensitization, the mite-sensitized children with rhinitis and asthma showed significantly lower Chao1 and Shannon indices. Moraxella and Leptotrichia species were significantly found in the interaction of mite sensitization with rhinitis and asthma respectively. Airway microbial diversity appears to be inversely associated with sensitization to house dust mites. A modulation between airway dysbiosis and responses to allergens may potentially cause susceptibility to rhinitis and asthma in early childhood.Microbiota plays an important role in regulating immune responses associated with atopic diseases. We sought to evaluate relationships among airway microbiota, serum IgE levels, allergic sensitization and their relevance to rhinitis and asthma. Microbial characterization was performed using Illumina-based 16S rRNA gene sequencing of 87 throat swabs collected from children with asthma (n = 32) and rhinitis (n = 23), and from healthy controls (n = 32). Data analysis was performed using QIIME  v1.8. Significantly higher abundance of Proteobacteria was found in children with rhinitis than in the healthy controls (20.1% vs. 16.1%,  Asthma is a complex disease caused by the combined effects of many genetic factors interacting with environmental factors2. Accumulating evidence suggests that exposure to environmental microbiota has a protective effect against childhood asthma4. Candidate gene-based studies in asthma patients have also identified genes such as IL-13 and FcεRI-β, which are known to modify microbial pattern recognition receptors of the innate immune system5. These studies collectively indicate the importance of bacterial microbiota in gene-environment interactions in patients with asthma and other potential allergic diseases.The occurrence of asthma and allergic diseases has increased in the past few decades and remains a major health issue for children6. The microbiota mediates mechanisms of immunological tolerance and is essential for regulating mucosal inflammation. Early exposure to microorganisms may program infant immunity to a type 2T helper (Th2) bias that is related with atopy and asthma development8. Environmental allergens also trigger Th2 cell-mediated immune responses with recruitment of immunoglobulin E (IgE) antibody-producing B cells, mast cells and eosinophils6. Early sensitization to food has been identified as a risk factor for developing allergic airway diseases, whereas sensitization to inhalant allergens appears to be more specific to the development of rhinitis and asthma in early childhood10. Although asthma and rhinitis are frequently associated with atopy with preferential sensitization to aeroallergens, their clinical manifestations present at quite different airway levels. Several studies in humans and animal models of diseases have emphasized that an altered microbiota is related to a higher prevalence of atopic diseases and asthma12. However, few studies have focused on the association between microbiota composition, especially in the airways, and allergen sensitization and allergic airway diseases such as atopic rhinitis and asthma in early childhood.Interaction between the mucosal microbiota and immune cells is important in the development of the immune systemA detailed understanding of the complexity of airway microbial profiles during early childhood will likely provide much needed clinical insights into this aspect of airway diseases. The oropharynx is constantly exposed to microbes from both the upper and lower respiratory tract, and these microbes may be related to atopic airway diseases including allergic rhinitis and bronchial asthma. The aim of this study was to examine the oropharyngeal microbial profiles in patients with childhood rhinitis, asthma and healthy controls. The relationship between bacterial composition and community diversity and atopic indices including total IgE levels and allergen sensitization were assessed; their relevance to rhinitis and asthma was also examined.P = 0.001). Atopic indices including total IgE levels and allergen sensitization to mites and food were significantly higher in children with asthma and rhinitis than in the healthy controls. There was no difference other characteristics such as sex, maternal atopy, passive smoking, older siblings and household income between the three groups.Eighty-seven subjects were enrolled into this study, including 32 children with asthma, 23 children with allergic rhinitis, and 32 healthy controls. The baseline characteristics of children with asthma, those with rhinitis, and the healthy controls are shown in Table Streptococcus (26.0%), Haemophilus (5.5%) and Prevotella (7.6%) were the most common genera in the phylum Firmicutes, Proteobacteria, and Bacteroidetes respectively.A range of reads from 38,348 to 394,673 was obtained per sample, and a total of 2,811 OTUs were obtained from all the subjects. Rarefaction curves showed that a plateau of species richness (up to 800 OTUs) was achieved in around 38,000 reads per samples . Furthermore, the abundance of members of the genera Haemophilus, Neisseria, and Moraxella of the phylum Proteobacteria in children with rhinitis was significantly increased.Differences in the abundance of members of different phyla and genera among the children with asthma, those with rhinitis, and the healthy controls are shown in Table The Chao1 index, a measure of species richness, and the Shannon index, a measure of species diversity, were calculated and analyzed. Bacterial richness and diversity were not significantly different with regard to the age, sex, maternal atopy, passive smoking, older siblings, and household income characteristics of the patients. Figure Beta diversity statistics using the Principal Coordinate Analysis (PCoA) and non-metric multidimensional scaling (NMDS) revealed no significant differences in the microbial communities cluster patterns with regard to food, IgE sensitization and allergic airway diseases. However, a good clustering was observed when comparing between with/without mite sensitization . All experiments in this study were performed in accordance with the relevant guidelines and regulations and written informed consent was obtained from the parents or guardians of all the study subjects.A case-control study was designed to investigate the airway microbiota profiles in patients with asthma or rhinitis in early childhood and in healthy controls. Children (ages: 3–5 years) who diagnosed with asthma alone or rhinitis alone for the first time, and healthy controls from August 1, 2013 to July 31, 2015 were recruited for this study. The physician-diagnosed phenotypes of atopic diseases were evaluated by the same pediatric pulmonologist at the outpatient clinics. Asthma was diagnosed as ever having asthma, with the occurrence of recurrent wheeze in the last 12 months, or current use of asthma medication, based on the guidelines of the Global Initiative for AsthmaDermatophagoides pteronyssinus and Dermatophagoides farina)31. Values of a total IgE level exceeding 100 kU/L was considered indicative of IgE sensitization32. Allergic sensitization was defined as ImmunoCAP Phadiatop Infant values ≥0.35  kU/L33.Detailed information on potential confounding variables for atopic diseases including child’s age, sex, maternal atopy, passive smoking, older siblings at birth, and household income  were recorded and analyzed. The measurement of total and allergen-specific serum IgE levels was also performed and the results were recorded. Allergen-specific IgE levels were determined using a commercial assay for IgE  for a mix of three most common food allergens  and two most common inhalant causing sensitization in more than 95% of children in Taiwan  other than the oropharynx and by using a tongue depressor. Each swab was rubbed at least three times around the oropharynx with swab rotation without interruption. After sampling, the swab was immediately placed back into the collection tube and stored at −80 °C until further use.35. The amplicons were quantified and standardized to the same concentration using a Qubit 3.0 fluorometer (Thermo Fisher Scientific). PCR products with a concentration of less than 1.0 ng/µL were excluded from the study37. Amplicon pyrosequencing was performed on the Illumina MiSeq platform  using the MiSeq reagent kit v3 at 600 cycles, according to the manufacturer’s instructions. The sequence data and mapping file for all the samples included in this study have been deposited in Figshare (http://dx.doi.org/10.6084/m9.figshare.3839838).Bacterial DNA was extracted from the throat swab using a FastDNA Spin Kit for Soil  following the manufacturer’s instructions. DNA was extracted with 70 µL of DNase/Pyrogen-Free Water and the purity was quantified by measuring the absorbance at 260 and 280 nm with a spectrophotometer . All samples had an A260-to-A280 absorbance ratio between 1.8 and 2.1. Polymerase chain reaction (PCR) was used to amplify the variable region 3 (V3) to V4 of the gene that encodes for 16 S rRNA in bacteria. The V3-V4 region of the 16S rRNA gene was amplified by complementing both Bakt_341F (CCTACGGGNGGCWGCAG) and Bakt_805R (GACTACHVGGGTATCTAATCC) with sample-specific barcodes38. Reads less than 100 nucleotides or lacking a correct primer and chimeric sequences were removed39. Data analysis was performed using the software “Quantitative Insights into Microbial Ecology” (QIIME)40. Assembled sequences were clustered into Operational taxonomic Units (OTUs) using UCLUST at 97% sequence identity41, and taxonomy classification was assigned based on full-length 16 S rRNA gene database, Greengenes42. Any sequences present once (singletons) or in only one sample were filtered out.Illumina sequencing was performed by using paired-end 300 bp reads and the entire target was assembled with a central overlap of 50 bp by using PANDASeq43 and diversity accounting for both relative abundance and evenness was evaluated with Shannon index44. Beta diversity was calculated between groups at OTU genus level using QIIME, and Phyloseq package and Adonis in R. Principal Coordinate Analysis (PCoA) plot in conjunction with unweighted UniFrac and non-metric multidimensional scaling (NMDS) plot in conjunction with RLB distance measure were produced to show clustering between groups46. Rare OTUs were defined as less than 0.01% of the reads in a given sample, and were removed if more than 50% of all samples48. Abundance differences between allergic airway diseases and healthy controls were tested using metagenomeSeq Bioconductor package for the R statistical computing environment49. A novel normalization method avoids bias due to uneven sequencing depth and a zero-inflated Gaussian distribution mixture model removes testing bias resulting from undersampling of the microbial community. A false discovery rate of 5% was applied to correct for multiple tests50.Bacteria diversity was analyzed with QIIME v1.8 software and rarefaction curves based on the number of species were generated for each sample from randomized OTU draws. Richness of each sample was calculated with the Chao1 indexP-value < 0.05 was considered significant.Differences and comparisons of the baseline characteristics among the asthma patients, rhinitis patients, and the healthy controls were performed using univariable parametric and non-parametric tests such as analysis of variance (ANOVA) and Kruskal-Wallis test respectively. All continuous variables, such as species richness and diversity were analyzed using the Mann-Whitney test for comparison between two groups and the Kruskal-Wallis test for comparisons between three groups. Repeated measures two-way analysis of variance (ANOVA) with first order autoregressive covariance structure was used to study the interaction between allergen sensitization and the relative abundance of the microorganisms for allergic airway diseases, and adjusted for confounding variables including age, sex, maternal atopy, passive smoking, older siblings, and household income. Statistical analysis was performed by using the Statistical Package for the Social Sciences . All statistical hypothesis tests were two-tailed and a Supplementary Information