ISG15, OAS1, and OAS2 (P<0.05). The promoter region methylation status of these three genes was assessed for each birth weight group, and no differences were found. These expression data are consistent with our previous finding that LBW pigs had less severe lesion scores and a trend towards lower viral titres in lung than the HBW cohort. The SUS v RES comparison identified 91 differentially expressed genes (FDR<0.05) that were enriched with functional annotation terms and pathways associated with inflammation. The cytokine genes IL6, IL8, and CCL2 were all upregulated in SUS pigs, and may have driven disease severity in these animals. In conclusion, this study found no evidence that the transcriptional immune response to influenza was adversely affected by low litter birth weight, but did identify several candidate genes for driving disease pathology.Influenza viruses are a common cause of respiratory disease in swine. Infections range in severity from asymptomatic to causing significant morbidity. The main objective of this study was to compare lung transcriptomic and epigenetic responses to influenza infection in pigs from high or low birth weight litters. The latter is a potential indicator of intrauterine growth restriction, a significant risk factor for prenatal programming effects. Individual pigs from high (HBW) or low birth weight (LBW) litters (n = 17) were inoculated with influenza A virus and euthanized 48 hours later. Lesion severity and viral loads were assessed as previously described. The transcriptional response to infection in LBW and HBW groups (n = 16) was assessed by microarray. A separate analysis of pigs classified as ‘Resilient’ (RES) or ‘Susceptible’ (SUS) (n = 6) on the basis of severity of lung pathology was also conducted. Eight genes were confirmed as differentially expressed for the birth weight comparison, including three antiviral genes with lower expression in LBW:  Resilience in the face of infectious disease is an increasingly desirable trait in today’s intensive pig farming industry. However, it is rare to find naïve individuals that exhibit complete resistance to a particular infectious disease within a population. For most diseases, the reality is a range of variable susceptibility to infection, disease pathology, or both that is governed by multiple genetic and environmental factors. Influenza is a highly contagious disease that can rapidly infect all naïve pigs on a farm. However, the severity of clinical signs and pathology can vary significantly among individuals within a population. The infection can be subclinical and asymptomatic in some individuals, but cause acute respiratory distress and morbidity, and occasionally death, in others .in utero environment, and the developing fetus can adaptively adjust its development in response to the environmental cues it receives. This ‘programming’ of biological systems can have adverse consequences after birth, as adaptive changes made to these systems during development have the potential to persist into adulthood, and even across generations [Multiple host factors have an impact on susceptibility to infectious disease. One factor that has not previously been studied in swine is that of ‘prenatal programming’ of postnatal disease susceptibility. Specific periods of prenatal development are critically important in determining the postnatal functional attributes of the tissues and hence biological systems of an individual. Fetal development is constrained by the erations .in utero, have found a reduction in cellular and adaptive immune responses to vaccination [An extensive body of literature has been published on the prenatal programming of chronic conditions in adulthood such as diabetes and cardiovascular disease , but relcination ,5, dimincination , and inccination . In addicination ,9.In the pig, intrauterine crowding (IUC) during gestation is a major risk factor for prenatal programming. Experimental models have shown that the degree of IUC during the early period of gestation restricts placental development, with subsequent knock-on effects on embryonic and fetal development –12. In cThe main objective of this study was to use transcriptomic and epigenetic approaches to compare the responses in lung tissue following an influenza A virus challenge to piglets originating from either ‘high’ or ‘low’ birth weight litters. Phenotypic differences in the response to infection between these high and low birth weight pigs have previously been described . The epiA detailed description of the animal experiment from which lung tissue was obtained for the present study has been published previously . Briefly6.3 TCID50/ml. Rectal temperatures and clinical signs of respiratory disease were recorded twice daily during the challenge period. Pigs were euthanized 48 hours post-inoculation by intravenous pentobarbital injection. A humane intervention point protocol (HIP) was in place for this study requiring that animals with any of the following clinical signs were to be euthanized immediately: body temperatures >41°C or <35°C for a 24 h period, in lateral recumbency and non-responsive, severe dyspnea, severe depression, severe dehydration, painful or life-threatening fracture or injury. No pigs, however, met these criteria. Lungs and trachea were removed and the percentage of lung with lesions typical of influenza A  was evaluated. These data were used to evaluate apparent differences among birth weight phenotypes in disease severity and also to identify two groups (n = 6 each) with divergent severity of influenza pneumonia, classified based on percentage lung area affected by consolidation, as ‘Resilient’  and ‘Susceptible’ . Tissue samples from each cranial and middle lobe were snap-frozen in liquid nitrogen within 15 minutes of death and stored at -80°C for gene expression studies. Similar samples were fixed in 10% (v/v) formalin and processed routinely for haematoxylin and eosin staining to score microscopic lesion severity and immunohistochemistry with anti-influenza A ribonucleoprotein antibody  to quantify immunoreactivity to influenza antigen [Pigs were inoculated intratracheally with 2 ml of influenza strain A/swine/Texas/4199-2/98 H3N2 (TX98) at a titre of 1x10 antigen . This woTotal RNA and genomic DNA were extracted from cranial lung tissue using the All Prep DNA/RNA mini kit  according to the manufacturer’s instructions. Nucleic acids were quantified and assessed for purity by spectrophotometry using a Nanodrop ND 2000 . All samples had 260/280 nm absorbance ratios of ~2. RNA quality was assessed using an Agilent Bioanalyzer . The mean RNA Integrity Number (RIN) sample value was 8.2 ±0.09.in vitro transcription. Two micrograms of aRNA were then labeled in an ozone-free environment with either Cy3 or Cy5 using the Universal Linkage System (ULSTM). Reverse transcription, aRNA synthesis, and labeling were all performed using the RNAampULSe kit according to the manufacturer’s instructions . Labeled aRNAs were purified using the PicoPure RNA Isolation kit  and 825 ng of each dye-labeled aRNA was hybridized for 17 h to Porcine V2 Gene Expression 4x44 Microarrays (Agilent Technologies). Microarray slides were washed  and then scanned on an Axon AL4200 scanner . Hybridization signal intensities were quantified using GenePixPro v7.0 software (Molecular Devices). No flagging criteria were applied to the spots.One pig from each of 32 litters (16 HBW and 16 LBW) was randomly selected for microarray analysis. RNA samples from 2 litters were excluded on the basis of a RIN <7. Lung RNA of individual animals, and a common reference that included RNA from 3 swine tissues pooled from experiment animals , were hybridized to each array . A 1μg amount of total RNA was reverse transcribed into cDNA, which was then used to synthesize antisense RNA (aRNA) by http://www.gqinnovationcenter.com/services/bioinformatics/flexarray/index.aspx?l=e). Firstly, background subtraction was performed on the raw expression data from each array using a ‘norm exp’ method. Next, the background-subtracted expression data were normalized both within array  and between arrays . The ‘limma simple’ algorithm was then used to assess differential gene expression between the HBW and LBW groups, and also between the RES and SUS groups. P values were corrected for multiple-testing error using the False Discovery Rate (FDR) method. Only genes with a ≥1.75 fold expression difference, and an appropriate P value, were considered as differentially expressed. For the LBW v HBW comparison, no genes were found to be differentially expressed after application of the FDR correction, so an uncorrected P value cutoff of ≤0.05 was adopted. For the SUS v RES comparison, a ≥1.75 fold expression difference and an FDR cutoff of ≤0.05 were used. All data have been submitted to the NCBI Geo database (series identifiers GSE62765 for the LBW v HBW contrast and GSE62768 for the SUS v RES contrast).Statistical analysis of the microarray data to identify differentially expressed genes (DEGs) was carried out using the ‘limma’ package  in the program ‘FlexArray’ , C2 (Curated), C5 (Gene Ontology), and C7 (Immunologic) collections of gene sets in the Molecular Signatures Database (MSigDB), which together comprise over 8000 individual gene sets [P values <0.01 were considered to exhibit a tendency for significance. Potentially important genes were identified by leading edge analysis of multiple significant gene sets within MSigDB collections. This method identifies those genes with large phenotype correlations, which contribute most to the enrichment score, in multiple gene sets within an MSigDB collection.Normalized expression ratio data for the LBW v HBW and SUS v RES contrasts were further analyzed using the Gene Set Enrichment Analysis (GSEA) method . This apene sets . The nulRT-qPCR was carried out to validate the differential expression status of putative DEGs from the microarray experiments, and also to assess the expression of other genes of interest for this study, selected on the basis of known involvement in influenza response. Primer sequences were designed either from the pig genome sequence (Sscrofa 10.2 assembly) or from porcine ‘Refseq’ mRNA sequences (for unmapped genes) using the ‘Primer3’ program (bioinfo.ut.ee/primer3). Where possible, the forward primer was designed over an intron/exon boundary. A list of gene specific primer and RT-qPCR assay information that satisfy the ‘Minimal Information for the publication of Quantitative PCR Experiments’ (MIQE) requirements  is proviReverse transcription reactions were carried out on samples from all 64 pigs . One microgram of total RNA was reversed transcribed using a ‘High capacity cDNA reverse transcription’ kit . PCR assays were performed in duplicate using 1 μl of a 1/25 dilution of each cDNA as template. Reactions were carried out using Kappa SYBR Fast Master Mix  and 200 nmol of each primer in 20 μl volume on a 7900 HT Fast Real-Time PCR System (Life Technologies). A no-template negative control reaction was included for each gene. The PCR conditions were 95°C for 20 s, then 40 cycles of 95°C for 3 s and 60°C for 20 s. A melt curve analysis was carried out for each assay to validate its specificity. Also, PCR products were run on a 3% (w/v) agarose gel with a 50 bp ladder to check that the product size matched that predicted by the Primer3 software.q) values were determined using a manual ΔRn threshold of 0.2. Reaction efficiencies were calculated for each assay from a standard curve generated from a 5-fold serial dilution series spanning 5 orders of magnitude. Expression data for DEGs of interest were normalized using values for the reference gene 18S rRNA, chosen for the stability of its expression across the sample set and a previous report of its reliability as a reference gene for RT-qPCR based on influenza-infected cells [http://www.gene-quantification.de/rest-2009.html) [P<0.05) was calculated by the randomization test in the software.Quantification cycle (Ced cells . RT-qPCR09.html) . Normali09.html)  and statISG15, OAS1, and OAS2. Sequences were obtained for the promoter region of each gene  from the pig genome sequence (S.scrofa 10.2 assembly), and for a CpG island in the first intron of OAS2. It was only possible to obtain 274 bp of promoter sequence for OAS2 due to a gap in the current pig genome assembly. Primer pairs that work on bisulfite-converted DNA were then designed to span each region using MethPrimer (www.urogene.org/methprimer/). DNA samples (see section 2.3 for isolation protocol) were bisulfite-converted using the EZ DNA Methylation kit (Zymo Research) and fragments were amplified using the Complete PCR Reagent Set (Agenta Biosciences). The methylation percentage at each CpG dinucleotide was determined by mass spectrometry (Sequenom EpiTyper Assay). Mean values were calculated for each fragment and for each region as a whole for individual animals, and a comparison of the difference in % methylation between groups was performed using the Mann-Whitney U test in STATA 13 (Stata Corporation).Three genes that were differentially expressed in both the LBW v HBW and SUS v RES comparison, were selected for DNA methylation analyses: P<0.05), and corresponded to Z-scores of 1.0 and -1.9 respectively (P<0.05). The mean growth rate during lactation was the same for both groups: 0.214 ±0.043 kg/day. Although no clinical parameter differed by group, the percentage of total lung affected by lesions was significantly higher in the HBW (14.2 ±9.2%) compared to the LBW (10.1 ±8.3%) group (P = 0.03). Similarly, microscopic lung lesion severity (P = 0.009) and influenza A immunoreactivity in middle lobes were also significantly higher in the HBW compared to LBW pigs (P<0.05). The virus concentration in lung tissue homogenates trended higher in HBW .Detailed phenotypic results were published previously , but perCCL17 and CCL25, and two other genes expressed in lymphocytes: CD3E and LEF1. The downregulated genes included the three antiviral genes ISG15, OAS1 and OAS2, and CCL28, a chemokine with antimicrobial activity.A total of 63 probes that correspond to 45 genes were identified as differentially expressed between the lung of HBW and LBW groups following microarray analysis .. Of theP<0.01)  signaling.No gene sets were identified as being significantly associated with the LBW phenotype, although 24 gene sets exhibited a tendency for association. For the HBW phenotype, 8 gene sets had a significant association and a further 16 gene sets had a tendency for association (P<0.01) . The topP value of <0.01). Three of the C2 gene sets contain genes activated in response to immune signaling pathways: interleukin-22, JNK, and myeloid cell CEBPA signaling. The enrichment result for the CEBPA signaling pathway is shown in C3 and factor CFB, and monocyte/macrophage cell membrane proteins CD14 and CD74. Only two GO terms were enriched in HBW pigs, ‘Peptidyl tyrosine modification’ and ‘Peptidyl tyrosine phosphorylation’. Two tyrosine kinases appear in both gene sets, EGFR and ABL2. Other genes of interest include two antiviral cytokines, IFNL1 and IL12A, the integrin ITGB2, which is the receptor for complement component 3, and coagulation factor F2. Four of the top 5 immunological signature gene sets correspond to transcriptional analyses using cells of the myeloid lineage, and the three most enriched gene sets contain genes that are more highly expressed in these cells (monocytes or dendritic cells) than lymphocytes (cytotoxic or helper T cells or B cells lymphocytes). Eleven genes appear in three of these gene sets. They include the chemokine receptor CCR1, which encodes the receptor for macrophage inflammatory protein 1 alpha and monocyte chemoattractant protein 3. Two phagolysosomal enzyme genes, CTSB and CTSH, and two pattern recognition receptor (PRR) pathway genes, CLEC7A and LY96, were also among these genes.CCL25, CCL28, IL1RN, ISG15, OAS1, OAS2, TCN1, and TNNT1), of which 6 genes were less highly expressed in the LBW compared to the HBW group , but no expression difference between groups were observed for the other cytokines , coagulation , and tissue re-modeling  were also upregulated. Several genes involved in the negative regulation of inflammation were also upregulated, such as the interleukin-1 beta antagonist IL1RN, and SFN, a negative regulator of several Toll-like Receptor (TLR) signaling cascades [OASL was found to be upregulated in SUS animals.A total of 131 probes that correspond to 91 genes were identified as being differentially expressed in the lung of ‘SUS’ and ‘RES’ pigs following influenza infection . Of thescascades . FinallyCYTB) and NADH dehydrogenase subunits 4 and 5 (ND4 and ND5). In addition, several genes whose products are components of the protein translation machinery were also upregulated: RPL5, RPL11, and EEF1A1. Only two genes with obvious immune functions were upregulated in RES animals, and both are involved in the allergic response: the immunoglobulin epsilon receptor gene FCER1A was the most up-regulated gene, and ENPP3 encodes an enzyme expressed in basophils and mast cells.Among the 51 genes that were downregulated in SUS group (upregulated in RES) were several components of the electron transport chain: the mitochondrial-encoded cytochrome B  such as collagens COL15A1, COL4A5, and COL5A2 and other structural components of collagen fibril matrices such as FBN1 and LUM. Forty C7 immunological signature gene sets were enriched in RES pigs. Four out of the Top 25 sets contain genes that are more highly expressed in lymphocytes than cells of the myeloid lineage. Genes common to three or more of these sets include TCR complex genes CD3G, CD3E, CD3G, and CD247, membrane receptor BCL2 and the transcription factor LEF1.In total, 172 gene sets were significantly enriched in RES pigs . SelecteAPOA1, C1R, C1S, CCL2, CP, CRABP1, CXCL2, FCER1A, IL1RN, IVNSABP1, ND4, ND5, OASL, SFN, and TCN1. For the other 3 genes , the direction of change in gene expression was consistent with what was observed by microarray, but results were not statistically significant. The Pearson correlation coefficient for gene expression changes measured by microarray and RT-qPCR for this set of genes was 0.956 , but no significant expression difference between groups was observed for the other cytokines  and 11.87 ±3.62%) respectively, whereas that of OAS1 was much more methylated (75.25 ±10.80%). The % methylation at the putative CpG island in intron 1 of OAS2 was very low (3.47 ±0.54%), and confirms that this region of the genome is relatively unmethylated. For each gene, the % methylation within the promoter region is lower in the vicinity of the putative transcriptional start site (TSS)  and another site 34 bp upstream of the OAS2 TSS . However, no significant differences between HBW and LBW animals were identified at any of the individual CpG-containing fragments spanning the regions of interest  within the promoter regions of three differentially expressed antiviral genes: te (TSS) . Howeverte (TSS)  or the Rte (TSS) . Two indinterest .Previous epidemiological studies and animal experiments have shown that gestational insults such as stress and undernutrition can have a detrimental effect on post-natal immune function and susceptibility to infectious diseases. The epigenetic modification of gene transcription has been proposed as the mechanism responsible for mediating the effects of these environmental factors, but very few studies have looked for the causative changes at the DNA and RNA level that underpin these effects on immune function. The principal aim of this study was to investigate whether there was any evidence for such ‘prenatal programming’ effects on the expression and epigenetic regulation of genes in commercial pig populations, using litter mean birth weight as a proxy for intrauterine growth restriction and severity of influenza pathology as our response trait of interest. The second aim of the study was to compare the transcriptome of pigs classified as ‘resilient’ or ‘susceptible’ to influenza on the basis of lung pathology, and irrespective of birth weight phenotype. This was done to help inform the results from the main birth weight analysis, and to obtain a greater understanding of the molecular pathways involved in the pathology of swine influenza.Only minor differences were observed in the transcriptome of HBW and LBW groups of pigs in this study, in contrast to the larger differences between the RES and SUS groups, and the results should be interpreted with caution due to the less stringent significance thresholds adopted. Nevertheless, the expression profile of HBW pigs resembled that of SUS pigs to an extent, and likewise gene expression in LBW pigs had similarities with that of RES pigs. The HBW and SUS groups shared a higher expression of antiviral and leukocyte genes from cells of the myeloid lineage, including some pro-inflammatory cytokines. In LBW and RES pigs there was an enrichment of T cell associated genes. These results are consistent with the experiment dataset as a whole, in which the LBW group were not immunologically compromised or more susceptible to influenza infection than HBW pigs, contrary to our original hypothesis . In factISG15, OAS1, and OAS2. ISG15 is an ubiquitin-like modifier that can bind to and modify both host and viral proteins and antagonize virus replication [The type I interferon pathway is central to the innate immune response to viral infection in mammals . Three ilication . The OASlication . Both ISlication ,28. Howelication , and so CCR1, whose expression was found to correlate with HBW phenotype by GSEA, is expressed on monocytes and is the receptor for the pro-inflammatory chemokines MCP3 and CCL3 (MIP1-α) [CTSB and CTSH were also identified. Phagocytosis is clearly important in limiting the spread of virus in the lungs during the early stages of influenza infection, but prolonged release of pro-inflammatory cytokines such as TNF-α results in excessive inflammation and pulmonary damage [Cells of the myeloid lineage also play important roles in the host response to influenza, in particular alveolar macrophages and dendritic cells. Alveolar macrophages are the predominant immune cell type in the lung under normal conditions, while dendritic cells are located in smaller numbers throughout the respiratory tract. Following activation by influenza virus, both cell types phagocytose influenza virions and infected, apoptotic epithelial cells, and produce a number of pro-inflammatory cytokines and chemokines that recruit and activate additional monocytes from local blood vessels. (MIP1-α) . Indicaty damage ,32.IFNA and IFNB, were not screened as neither of these genes was differentially expressed between birth weight groups. It is possible other non-tested DEGs were epigenetically regulated, or that epigenetic mechanisms such as histone acetylation and microRNA expression were responsible for the gene expression differences we found. The epigenetic alteration of gene expression in response to a low-protein nutritional model of prenatal programming has been demonstrated in rat [This study did not find any evidence for the epigenetic alteration of gene expression as a consequence of prenatal programming of birth weight phenotype. None of the promoter regions of the three ISGs downregulated in LBW pigs were differentially methylated. In addition, it seems unlikely that their lower expression is due to epigenetic silencing of their upstream regulators. The promoter regions of their regulators, type I interferons d in rat ,34. It hd in rat ,36. In td in rat , but no et al. reported that IUGR piglets had a reduced immune competence in comparison to their normal weight littermates [Several studies in swine have demonstrated some long-term detrimental effects of low birth weight on glucose tolerance, cardiovascular function, HPA axis function, muscle development, and lean muscle growth ,14,38–40termates . HoweverOther mechanisms of prenatal programming in swine have given inconsistent results with regard to effects on the immune system. For example, maternal stress during gestation has been shown to either increase, decrease, or have no effect on the proliferative response of lymphocytes to mitogens in their offspring after birth in different models ,43. AlsoFrom a genetic standpoint, it is interesting to note that intensive selection for production traits such as body weight and lean growth in livestock species tends to have a negative impact on immunocompetence. For example, broiler chickens selected for a high birth weight exhibited lower antibody responses than control lines, and turkeys selected for high body weight exhibited higher mortality when challenged with a bacterial or viral pathogen ,46. SimiIL6 and IL8, and chemokine genes CCL2 and CXCL2. All four genes are good candidates for driving disease pathology during influenza infection. Levels of both IL-6 and IL-8 are known to correlate with virus titers in lung of H3N2 influenza A-infected swine, and IL-6 levels also correlate with disease severity [IL1RN, IL6, CCL2, and CXCL10 were all associated with SUS pigs in this study and pigs infected with H1N2 influenza reported by Skovgaard et al [IL8 expression was expressed differently between experiments . CXCL10 mRNA was predicted to be the target of multiple microRNAs that are upregulated following infection, which could indicate that endogenous regulation of CXCL10 expression is required for the resolution of infection without excessive tissue damage.Irrespective of litter birth weight phenotype, a range in severity of disease following influenza challenge was found, particularly in regard to lung pathology . The infseverity . Elevateseverity , and IL-severity . CCL2, aseverity . CCL2 haseverity . Finallyseverity . The cytseverity –56. For IL8 and CXCL2 both attract neutrophils to the infection site. Also TCN1, which encodes a major constituent of the secondary granules of neutrophils, is the most upregulated genes in the lung of SUS pigs. Another feature that can be associated with severe influenza infection in the lung is thrombosis [SERPINE1 and THBS1, were more highly expressed in SUS pigs. Apoptosis of alveolar epithelial cells and leukocytes causes significant damage to the respiratory epithelium in severe cases of influenza-based pneumonia. CCL2 also has a central role in this process by attracting CCR2+ macrophages from the blood. These cells express the tumour necrosis factor-related apoptosis inducing ligand (TRAIL) and induce apoptosis of airway epithelial cells [TNFRSF12A, DDIT3, and CDKN1A were upregulated in SUS animals, indicating that they may also contribute to apoptotic events during influenza infection in swine.Other pathologic features of influenza infection in the lung were also represented in the gene expression profile of SUS pigs. Infiltration of the lung by neutrophils is part of the early innate immune response to influenza, and an important contributor to the overall pathology. The previously mentioned rombosis . Endotheal cells . SeveralISG15, OAS1, OAS2, and OASL were upregulated in SUS pigs. In addition, GSEA identified interferon-associated gene sets and additional ISGs whose expression correlated with SUS phenotype, such as MX1 and IFI35. Type I interferons inhibit protein synthesis in infected and neighbouring cells, which probably explains why ribosomal gene expression is greater in RES than SUS pigs. Many of the ISGs identified have antiviral functions, but the magnitude of their expression is positively associated with increased disease severity in this study rather than disease resilience. It may be that their elevated expression in SUS compared to RES pigs was in response to increased viral load in the lung of these animals at the 48-hour time-point. Clearly, if these and other antiviral genes do contribute to a disease resistance phenotype in swine influenza then it must be during the earlier stages of infection before the virus is well established in the lung tissue, and before it is possible to categorize animals into different susceptibility groups.Finally, a number of innate antiviral genes including IL6 and CCL2.In this study, the effect of a litter birth weight phenotype on the transcriptomic and epigenetic responses in the lung following experimental influenza infection in swine was investigated. The transcriptomic response of immune genes to infection was lower in the LBW than HBW group of pigs, consistent with the lower disease burden previously reported in the low birth weight group. This was contrary to our original hypothesis that the low birth weight group would be more susceptible to influenza due to the detrimental effects of ‘prenatal programming’ on the immune system. Furthermore, no evidence was found for epigenetic changes in the promoter regions of genes that were differentially expressed between LBW and HBW groups. A second analysis using a subset of animals classified into divergent susceptibility groups on the basis of lung pathology found that susceptible pigs mounted a more robust inflammatory response to infection that was likely driven by a group of pro-inflammatory cytokine genes that include S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(XLSX)Click here for additional data file.S3 Appendix(XLSX)Click here for additional data file.


Evidence suggests that epigenetics plays a role in osteoarthrits (OA). The aim of the study was to describethe genome wide DNA methylation changes in hip and knee OA and identify novel genes and pathwaysinvolved in OA by comparing the DNA methylome of the hip and knee osteoarthritic cartilage tissues withthose of OA-free individuals.p value <0.0005 were defined as differentially methylated regions (DMRs) for OA. A sub-analysis was also done for hip and knee OA separately. DAVID v6.7 was used for the functional annotation clustering of the DMR genes. Clustering analysis was done using multiple dimensional scaling and hierarchical clustering methods.Cartilage samples were collected from hip or knee joint replacement patients either due to primary OA or hip fractures as controls. DNA was extracted from the collected cartilage and assayed by Illumina Infinium HumanMethylation450 BeadChip array, which allows for the analysis of >480,000 CpG sites. Student T-test was conducted for each CpG site and those sites with at least 10 % methylation difference and a The study included 5 patients with hip OA, 6 patients with knee OA and 7 hip cartilage samples from OA-free individuals. The comparisons of hip, knee and combined hip/knee OA patients with controls resulted in 26, 72, and 103 DMRs, respectively. The comparison between hip and knee OA revealed 67 DMRs. The overall number of the sites after considering the overlaps was 239, among which 151 sites were annotated to 145 genes. One-fifth of these genes were reported in previous studies. The functional annotation clustering of the identified genes revealed clusters significantly enriched in skeletal system morphogenesis and development. The analysis revealed significant difference among OA and OA-free cartilage, but less different between hip OA and knee OA.We found that a number of CpG sites and genes across the genome were differentially methylated in OA patients, a remarkable portion of which seem to be involved in potential etiologic mechanisms of OA. Genes involved in skeletal developmental pathways and embryonic organ morphogenesis may be a potential area for further OA studies.The online version of this article (doi:10.1186/s12891-015-0745-5) contains supplementary material, which is available to authorized users. Osteoarthritis (OA), affecting 250 million people worldwide, is the most common form of arthritis . It is cOA is a multifactorial condition resulting from the combination and interaction of natural and environmental factors . Age, geMMP9), Matrix metallopeptidase 13 (MMP13), Leptin receptor (LEPR), and A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) [COL9A1) [NOS) gene is shown to increase transcription through elevated binding of the transcription factor NF-κB, which leads to suppression of the synthesis of cartilage matrix [GDF5 -- the most replicated genetic association locus in OA - is thought to be caused by the methylation level variability of the CpG dinucleotide created at the location of the SNP, leading to altered expression of the gene [RUNX1, RUNX2), transforming growth factor beta 1 (TGFB1), micro RNA 128 (miR-128) and collagen, type XI, alpha 2 COL11A2 [To date, only few studies have been undertaken to examine the role of DNA methylation in OA. Candidate gene studies have shown the up-regulation of catabolic factors of Matrix metallopeptidase 9 (ADAMTS4) , as well[COL9A1) , caused e matrix . DNA metthe gene . The han COL11A2 . DespiteIn the present study, we conducted a genome wide DNA methylation analysis in OA-free and OA-affected cartilage from human hips and knees using the Illumina Infinium HumanMethylation450 BeadChip in the hope of providing novel insights into the pathogenesis and treatment of OA.The study was part of the ongoing Newfoundland Osteoarthritis Study (NFOAS) that was initiated in 2011, aiming at identifying novel genetic, epigenetic, and biochemical markers for OA –17. OA pDemographic information was obtained by a self-administered questionnaire with the help of the research staff if necessary. Anthropometric data including height and weight was retrieved from their hospital admission and medical records and body mass index (BMI) was calculated by dividing weight in kilograms by squared height in meters. Age was calculated at the time of the surgery.Four pieces (~200 mg each) of cartilage tissues were retained from either tibial plateau or femoral heads during the surgery. The samples were then flash frozen and stored in liquid nitrogen until the experiment. Up to 200 mg frozen cartilage tissue was transferred to the homogenizing cylinder together with 1 ml TRIzole lysis reagent and 200 μl guanidine thiocyanate and homogenized using a cryogenic mill  with the following parameters: two cycles of 2 min grinding at maximum frequency with 10 min cooling down between grinding cycles. The homogenate was then transferred to a new 2 ml RNase free tube and incubated for 5 min at room temperature. Then, 200 μl chloroform was added and the mix was vortexed vigorously, before being incubated for 2–3 min, followed by a centrifugation at 12,000xg at −4 °C for 15 min. Following centrifugation, the sample separated into 3 phases: the aqueous phase containing RNA, the interphase, and the organic phase containing DNA. The DNA was extracted using Phenol-Chloroform method from the interphase and organic phase.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73626 with accession number GSE73626.DNA methylation assay was conducted using the HumanMethylation 450 Bead-Chip microarray , which analyzes the methylation status of 485,000 methylation sites throughout the genome, covering 99 % of RefSeq genes at an average of 17 CpG sites per gene across the 5-UTR, gene promoter regions, first exon, gene body, and 3-UTR, and covering 96 % of University of California, Santa Cruz-defined CpG islands and their flanking regions. Briefly, DNA is first bisulfite converted, which results in unmethylated cytosines being converted to uracils, whereas methylated cytosines are not converted. The bisulfite-converted DNA is amplified, fragmented and hybridized to the arrays. For each CpG site, methylation levels are measured by probes attached to beads, one each for unmethylated and methylated DNA, followed by allele-specific base extension that includes a fluorescent label. Different labels are used for the T (unmethylated) or C (methylated) alleles. The array is fluorescently stained and scanned, and the intensities of the unmethylated and methylated bead types are measured. DNA methylation values, described as “beta values (β)”, are recorded for each locus in each sample and represent the ratio of the intensity of the methylated bead type to the combined locus intensity. The array has several features that make it a powerful option for genome-wide DNA methylation profiling: (i) multi sample format allows for interrogation of 12 samples on a single BeadChip i.e. it is high-throughout and cost-effective; (ii) low sample input (500 ng genomic DNA); (iii) reproducibility  , 21. TheR packages minfi (version 1.6.0)  and minfp-value above 0.01, those located in sex chromosomes and at SNPs, and those with deviation from bimodal distribution were removed from further analysis. In order to eliminate the difference caused by two type design probes (type I and type II), beta-mixture quantile normalization (BMIQ) method [For the purpose of quality control, CpG probes with detection ) method  was usedp-value ≤ 0.0005. The identified loci were examined using a linear regression model to determine if they were associated with age. Genomic annotation of DMRs was carried out using the Infinium HumanMethylation450 BeadChip annotation file (http://www.illumina.com/products/methylation_450_beadchip_kits.html).To identify DMRs, the average beta values were compared between the groups of interest using a student T-test assuming equal variances . Given tp-value of less than 0.05 were reported.DAVID bioinformatics database functional tool  was usedHierarchical clustering and multiple dimensional scaling were performed on all individuals after genome wide methylation distance reduction. The distance was calculated between samples taking into account the genome wide methylation levels of each sample. Then a matrix containing the pairwise distances between the samples was created. Using singular value decomposition, the matrix was transformed into three matrixes, two of which were orthogonal (U and V) and one was diagonal (D). A new matrix was generated using the multiplication of V and D, containing 12 rows representing each sample and 24 columns representing each dimension. Every two dimensions were plotted against each other. Clustering were further illustrated using heat maps of the top 800 loci with the greatest variations in methylation levels across the entire study population after the same methodology of dimension reduction. To obtain these 800 sites, the cross population variance for each CpG site was calculated, the clustering was performed for the top 200, 400, 600, 800, and above sites with the highest variance. The top 800 samples resulted in the best visual grouping of the three phenotypes while the numbers beyond this figure did not change the pattern.The study protocol was approved by the Health Research Ethics Authority (HREA) of Newfoundland and Labrador and a written informed consent was obtained from all the participants.The study included cartilage samples from 5 patients with hip OA, 6 patients with knee OA, and 7 OA-free hip controls. All subjects were females. The OA-free population were on average about 10 years older than the affected group and had a lower BMI. Table p <0.0005. Methylation levels of these sites were not associated with age. Almost half of the sites (53 %) showed hypo-methylation and the remainders represented hyper-methylation in OA compared to controls. This was, however, reversed among the sites with the highest methylation difference, since most of them were hypo-methylated in OA as seen in Table A total of 384,266 CpG sites were included in the final analysis after quality control. A total of 72, 26, and 103 CpG sites was identified from the comparison of knee OA, hip OA, and combined knee/hip OA versus hip controls, respectively. The comparison of hip and knee OA resulted in 67 CpG sites. After removing the overlaps between these analyses, a total of 239 CpG sites showed more than 10 % difference in β values among the comparison groups with all p-value < 0.05. The most significant terms are related to skeletal and embryonic organ system development and homeobox (HOX). By increasing the classification stringency, the GO terms were classified into 31 clusters, among which only two yielded significant Bonferroni corrected p-values including Embryoinic and skeletal system development and HOX genes . The analysis was repeated after the removal of 33 genes which were only identified in the comparison of knee OA and hip OA. Similar to previous clustering, the GO terms included skeletal and embryonic system development, but no HOX genes.The annotation clustering was conducted for the 145 genes. Table nd dimension was not informative (not shown). Similar patterns are observed from the heat map and dendogram as shown in Fig. We used multiple dimensional scaling and hierarchical clustering to classify the three phenotypes in the study, i.e. hip OA, knee OA, and OA-free hip cartilage. Due to the small sample size, the classification was not perfect; however, some trends were observed which are worthy of consideration. As it is seen in the plots Fig. , samplesOur study is one of the few reports on the status of genome wide methylation of DNA from OA-free and OA affected human cartilage. We found a number of CpG sites differentially methylated in hip and knee OA, identified the pathways enriched in the sites, and attempted to classify hip and knee OA and OA-free cartilage according to their genome wide DNA methylation profiling.RUNX2, involved in the regulation of matrix metallopeptidase 13 in OA cartilage [LEPR, associated with knee OA [CLCN7, associated with bone marrow density [ESRRG, associated with multiple bone disease phenotypes [IL21, being upregulated in synovial biopsies of rheumatoid arthritis patients [MTHFD1, associated with response to osteosarcoma chemotherapy [PAX1, associated with congenital scoliosis [COL4A1).The majority of the CpG sites we identified were novel and only about one fifth of them were reported by the previous epigenetic studies of OA (Additional file artilage , cg03050 knee OA , bone ma knee OA , cg05516 density , cg17279enotypes , cg14340patients , cg18637otherapy , cg10629Although most of the DMR genes in our study were of unknown significance in OA, the functional analysis revealed their enrichment in relevant pathways; i.e. skeletal and embryonic organ system development and homeobox. The latter was only found from the genes differentially methylated in the comparison of knee OA and hip OA methylation. Since homeobox genes are responsible for the body segmentation procedure and specification of lower limbs from upper limbs, it is likely that the DMRs from knee OA and hip OA comparison represent the processes required for body segment specifications rather than the underlying genetic difference between hip OA and knee OA. The same gene ontology term has also been reported by Den Hollander et al. who made a comparison between genome wide methylation of articular cartilage DNA from hip OA and knee OA . The smaOur clustering analysis of the three phenotypes in the study clearly shows that OA-affected cartilage has a distinct methylation profiling compared to OA-free cartilage. The hip OA and knee OA clustering, however, is not perfect and is suggesting that although hip OA and knee OA could have different epigenomic landscape, they are very similar to each other. The only minor differences observed in hip OA and knee OA might only be joint specific differences that could have been observed if OA-free knee and hip cartilage were studied. The only comparison of the hip OA and knee OA was done by Den Hollander et al.  who succOur study is limited by several factors. Due to technical issues we studied a small sample size, and as the result, none of the DMRs reported reached Bonferroni corrected significance. The cases and controls were different in age, which might influence the status of DNA methylation. The controls were selected from the population of patients with possible osteoporosis who may not necessarily represent healthy cartilage epigenome. In addition, we did not have information on the pathological scoring of the OA joints, which could partially explain why the clustering was not perfect. Due to financial issues we did not validate the methylation levels using alternative methods such as pyrosequencing and did not perform functional experiments to add more to the mechanism of involvement. These limitations will likely tackled by future studies attempting to replicate and further studying our findings. Despite these limitations, the trends observed in the study were informative and add to the current knowledge on the pathogenesis of OA.Through a genome wide methylation study of OA-free and OA-affected human cartilage, we were able to identify a number of CpG sites with methylation changes in OA. We also reported that genes involved in skeletal system morphogenesis are differentially methylated in OA and might be candidate genes for further OA studies. We found a small difference between the overall landscapes of hip OA and knee OA; however, OA-free hip cartilage samples were significantly different from the OA-affected ones. Our findings shed light on the pathophysiology of OA and can pave the road for further research in the field.


Children and adolescents are at high risk of sustaining fractures during growth. Therefore, epidemiological assessment is crucial for fracture prevention. The AO Comprehensive Injury Automatic Classifier (AO COIAC) was used to evaluate epidemiological data of pediatric long bone fractures in a large cohort.Data from children and adolescents with long bone fractures sustained between 2009 and 2011, treated at either of two tertiary pediatric surgery hospitals in Switzerland, were retrospectively collected. Fractures were classified according to the AO Pediatric Comprehensive Classification of Long Bone Fractures (PCCF).Age, sex, BMI, injury and treatment data were recorded. Children were classified into four age classes and five BMI classes were applied. Seven major accident categories were established. Study parameters were tabulated using standard descriptive statistics. The relationship of categorical variables was tested using the chi-square test. The Children’s BMI was compared to WHO reference data and Swiss population data.For a total of 2716 patients (60% boys), 2807 accidents with 2840 long bone fractures  were documented. Children’s mean age (SD) was 8.2 (4.0) years . Adolescent boys sustained more fractures than girls (p < 0.001). The leading cause of fractures was falls (27%), followed by accidents occurring during leisure activities (25%), at home (14%), on playgrounds (11%), and traffic (11%) and school accidents (8%). There was boy predominance for all accident types except for playground and at home accidents. The distribution of accident types differed according to age classes (p < 0.001). Twenty-six percent of patients were classed as overweight or obese — higher than data published by the WHO for the corresponding ages — with a higher proportion of overweight and obese boys than in the Swiss population (p < 0.0001).Overall, differences in the fracture distribution were sex and age related. Overweight and obese patients seemed to be at increased risk of sustaining fractures. Our data give valuable input into future development of prevention strategies. The AO PCCF proved to be useful in epidemiological reporting and analysis of pediatric long bone fractures. Children are at a high risk of injury with up to one of every four children sustaining an injury annually ,2. FractIn 2007, the AO Pediatric Comprehensive Classification of Long Bone Fractures (PCCF)  was deveDifferent risk factors for sustaining fractures in children have been reported, such as age, sex, season, risk-taking behavior, bone mineral density (BMD), sports, but also violence and race/ethnicity and socioeconomic status ,11,17-22As the amount of data is still limited, further epidemiological data are needed to better understand the occurrence of pediatric long bone fractures and for the planning of future prevention strategies. The aim of this retrospective cohort study was to review the demographic data of all recorded pediatric long bone fractures at the University Children’s Hospitals in Bern and Lausanne and to evaluate any differences between these data. This is the first time that a large patient cohort was classified according to the AO PCCF using the AO COIAC software to collect epidemiological data on pediatric long bone fractures.The present study was a retrospective cohort study to survey fractures with open physes in children and adolescents younger than 17 years of age in Switzerland. All fractures were sustained between January 2009 and December 2011. Inclusion criteria were documented pediatric long bone fractures in children (in- and outpatient cases) treated at the University Children’s Hospitals in Bern  and Lausanne. Ethical approval from the regional ethic committees  was obtained for both clinics. The Children’s Hospital in Lausanne is a tertiary care university hospital, serving as a primary care center for the city of Lausanne, which has approximately 160′000 inhabitants. The Children’s Hospital in Bern is also a tertiary care university hospital, serving as a primary care center for the city of Bern, with a population of 170′000, and the adjacent cities. Being the only children’s hospitals in the aforementioned cities, a majority of children up to the age of 16 are treated in these hospitals (approx. 80%). Twenty-four-hour in- and outpatient primary emergency service is provided to patients for both cities, but also for the entire cantons of Bern and Vaud and the adjoining cantons.All long bone fractures were classified by an experienced pediatric trauma surgeon in each clinic using the AO PCCF system  on the bPatients were classified into four age groups including: 1) infants (< 2 years); 2) preschool children (2 to < 6 years); 3) school children (6 to < 11 years); and 4) adolescents (11 to < 17 years). The type of accident was divided into seven major categories: 1) home accidents ; 2) school accidents (including accidents occurring at school or kindergarten); 3) playground accidents ; 4) leisure activities; 5) traffic accidents ; 6) falls and 7) others . Furthermore, differentiations were made between boys and girls, upper and lower limbs and the time at which fractures were sustained.For most of the patients undergoing a conventional radiograph at the Children’s Hospital in Bern, height and weight measurements were documented prior to the examination to adapt the individual’s radiation exposure. Therefore, retrospective BMI calculations were possible for these patients. Using this baseline characteristic, the BMI distribution of this subpopulation was compared to the World Health Organization (WHO) BMI-for-age percentiles for boys and girls  to furthth percentile), respectively. The chi-square test was used to assess the relationship between two categorical variables (e.g. BMI classes and age classes).All data collected with AO COIAC were transferred into Intercooled Stata version 12 . Study parameters were analyzed and tabulated using standard descriptive statistics. The one-sample test of proportions was used to compare the proportions of overweight and obese children in boys and girls aged between 6 and 12 years in this study with those in boys (18.7%) and girls (17.0%) in the same age category in Switzerland  who experienced 2807 accidents were included into the study. While a single accident was documented for 2630 patients, multiple accidents were recorded for 86 patients. Fifty-one patients sustained more than one fracture during the same accident, either in the same bone (n = 20) or different bones (n = 31) , 699 preschool children (26%), 1074 school children (40%) and 767 adolescents (28%) sustained a fracture. The overall mean age was 8.2 years .More boys sustained a fracture . In infants, the sex distribution was equal , but changed towards a distinct predominance of boys in the adolescent subgroup  Figure . Among pCommon concomitant diseases including osteogenesis imperfecta, dysraphism, and neuromuscular or metabolic diseases were reported for 73 patients (2.7%).2 . According to the WHO BMI-for-age percentile curves, 210 children (27%) were categorized as either overweight or obese in this study. Whereas the proportion of children with normal weight remained stable (61/62%), the proportion of thin children decreased with age (9% to 3%) while the proportion of overweight children increased with age . Accordingly, the highest proportion of overweight patients were adolescents  followed by school children  and preschool children . Obesity was most frequently seen in school children  followed by adolescents  and preschool children   fractures, the injury mechanism was not evaluated due to insufficient documentation in the patient charts. The leading cause for long bone fractures was falls 27%) , with those activities pursued in organized sports clubs, representing 5% n = 153) of all accidents. Out of the 153 accidents sustained in sports clubs, 90% (n = 138) involved boys and almost three-quarters  occurred while playing soccer. Overall, playing soccer was the main reason for sustaining a long bone fracture during leisure activities  , followed by playground accidents (11%), traffic accidents (11%) and school accidents (8%). Playground accidents occurred mainly whilst playing on swings 32%), slides (29%) and trampolines (22%)  and preschool children (25%), were significantly less frequent in school children and adolescents  (p < 0.001). Fractures sustained from a fall increased in preschool and school children, but became less frequent in adolescents Figure .Figure 4A high proportion of fractures caused by undertaking leisure-activities (33%) was observed during the winter months (i.e. December to March); 232 fractures were sustained from skiing, snowboarding, ice skating or sledding, with 50% caused by skiing accidents. For all skiing accident-related fractures, there was an approximately equal distribution among preschool children (n = 35), school children (n = 40) and adolescents (n = 38). Fractures caused by playground accidents occurred mainly between March and October, while no seasonal distribution was observed for school accidents.The most commonly reported long bone fractures involved those of the forearm  followed by fractures of the upper arm , the lower leg  and the femur  .Only 3 long bone fractures were documented as related to non-accidental injuries.In the presented study we were able to describe distinct age and sex related differences in the fractured bone distribution and different accident types. Furthermore, compared with the overall Swiss population of similar age, we identified a higher percentage of overweight and obese children in our study population. This observation suggests that overweight and obese children are at increased risk of sustaining a fracture.While the overall predominance of fractures sustained by boys over girls with odds of 1.5:1 was consistent with current knowledge ,20, thesIn accordance with published data, boys were more likely to sustain femoral fractures ,39. Howeth BMI-for-age related percentile [Petersen et al. reported a doubling in the prevalence of overweight and obese children and adolescents from 1986 to 2001, reaching 23.1% (18.3% overweight and 4.8% obese children) at or above the 85rcentile . Our sturcentile , but morNational Swiss surveys found a decrease in the number of overweight and obese children between 2002 and 2007 following a stabilization in the prevalence of childhood obesity in 2009; the prevalence of overweight and obesity in children from 6 to 12 years was 19.9% for boys and 18.9% for girls in 2002 compared to 18.7% for boys and 17.0% for girls in 2009 ,44. CompKessler et al.  reportedThough categorized differently, our data confirmed recently published data reporting falls being the leading cause for fractures in children and adolescents (27% in this study) with most of the fractures resulting from a fall from low height (57% within this group) .Children older than 5 years sustain more sport-related injuries as they start to pursue more organized sports activities (i.e. in sports clubs) around this age . There wDuring the winter months a seasonal peak (33%) of winter leisure activity-related fractures was observed at both Children’s Hospitals, Bern and Lausanne.Consistent with previous reports ,47, foreThe rising popularity of trampolines in yards and playgrounds has been followed by an increase in trampoline-related injuries impacting the physical health of children and adolescents . Our stuThe increased popularity of non-motorized scooters since the beginning of this century , especiaFinally, we have to acknowledge that our rate of non-accidental injuries is rather low with only three patients. Most likely, this is due to underreporting and our study focusing on long-bone fractures, but not on non-accidental injuries in general.This study has its limitations, in particular, its retrospective study design. Data quality was dependent on the completeness of the patient charts, specifically regarding the accident type. The categorization of pediatric accident data is often prone to error or misclassification because younger children, in particular, may have difficulties in explaining the correct course of events or the accident occurred unobserved. The fact that approximately 80% of all pediatric long bone fractures are treated in the Children’s hospitals in Bern and Lausanne could lead to potential publication biaz assuming that the remaining 20% of long bone fractures could present differently. As the two hospitals do not cover 100% of patients of the respective regions, epidemiological data such as incidences could not be provided. Nonetheless, the large cohort is a strength of this study.In conclusion, our retrospective cohort study provided valuable epidemiological data about pediatric long bone fractures in a large Swiss cohort of children and adolescents and may contribute to the development of appropriate prevention strategies in the future. Comprehensive epidemiological data could also serve as a future teaching aid to orthopedic and trauma surgeons in training.The AO PCCF system was successfully used to classify all encountered fractures; the detailed fracture patterns however were beyond the scope of this paper. The AO COIAC software proved to be practical and useful in the clinical context, providing the basis for a standardized, multicenter documentation of pediatric long bone fractures. We encourage any hospital treating pediatric fractures to adopt the AO PCCF system and use the freely available AO COIAC software .


Genes located within 50 kbp of SNPs significantly associated with RFI and RFI2  were subsequently used for pathway analyses. These analyses were performed by assigning genes to biological pathways and then testing the association of individual pathways with RFI using a Fisher’s exact test. Metabolic pathway was significantly associated with both RFIs. Other biological pathways regulating phagosome, tight junctions, olfactory transduction, and insulin secretion were significantly associated with both RFI traits when relaxed threshold for cut-off p-value was used (p ≤ 0.05). These results implied porcine RFI is regulated by multiple biological mechanisms, although the metabolic processes might be the most important. Olfactory transduction pathway controlling the perception of feed via smell, insulin pathway controlling food intake might be important pathways for RFI. Furthermore, our study revealed key genes and genetic variants that control feed efficiency that could potentially be useful for genetic selection of more feed efficient pigs.Residual feed intake (RFI) is a complex trait that is economically important for livestock production; however, the genetic and biological mechanisms regulating RFI are largely unknown in pigs. Therefore, the study aimed to identify single nucleotide polymorphisms (SNPs), candidate genes and biological pathways involved in regulating RFI using Genome-wide association (GWA) and pathway analyses. A total of 596 Yorkshire boars with phenotypes for two different measures of RFI (RFI1 and 2) and 60k genotypic data was used. GWA analysis was performed using a univariate mixed model and 12 and 7 SNPs were found to be significantly associated with RFI1 and RFI2, respectively. Several genes such as  FCR is currently the only available measure of feed efficiency that is included in the selection index for the Danish pig breeds. However, ratio traits such as FCR are not ideal for statistical and biological reasons  and supplied by the Pig Research Centre of the Danish Agriculture and Food Council. A total of 596 Yorkshire pigs had both phenotypic (RFI) and genotypic records (based on PorcineSNP60 Illumina iSelect BeadChip). The method of calculation of RFI has been previously discussed in detail . In summc was the part of the genetic variance that was assumed to be not explained by markers (c = 0.1), h2 was the heritability of the trait, and ith animal.in which p < 0.0001) and SNPs with a MAF < 0.05.The details of the resource population used and DNA collection were described in A univariate linear mixed model was implemented to test the association between each SNP and RFI. The model was similar with previous GWA analysis in Duroc pigs . In summy is the vector of dEBVs for RFI, 1is a vector of 1s with length equal to number of observations, μ is the general mean, Z is an incidence matrix relating phenotypes to the corresponding random polygenic effect, a is a vector of the random polygenic effect ∼N using the DMU software , (n - 3), log = FALSE, lower.tail = FALSE; where β is regression coefficient, SE is standard error estimated based on the inverse of the mixed model coefficient matrix and n is number of SNPs in the genotype data]. Bonferroni corrected significance threshold, used to account for multiple comparisons, was estimated at a p = 1.31e-06. However, Bonferroni correction is known to be overly conservative especially when genetic data exhibits high LD, which could produce false negative results  ≤0.2 were used to identify putative candidate genes. Based on previous studies, we also included pathway annotations associated with genes within 50 kb of SNPs associated with RFI at a nominal significance thresold of 0.05, in pathway analyses (2 for R program (r2 = 0.5) was around 72 kb in the current population  was located either inside the genic region or within 50 kb of the genes.Assigning genes close to a few SNPs with high statistical significance, and ignoring many SNPs with lower significance levels could result in missing out on key putative candidates and associated pathways. Hence, we used the procedure of controlling false discovery rate FDR;  to selecanalyses . Positio program . The dispulation . Therefo3 (KEGG) was used for getting pathways. To assign genes to pathways, we used the function GetPathways in NCBI2R package; and to get number and names of genes in each pathway, the mapPathwaytoname function4 was used. Because RFI was production trait in pigs, the assigned pathways belonging to human diseases and drug development categories were removed. To determine whether a pathway term was significantly associated with RFI, we tested if genes significantly associated with RFI were overrepresented amongst all the genes of any given pathway. This association analysis was performed using a Fisher’s exact test via the fisher.test function in R.For functional annotation, Kyoto Encyclopedia of Genes and GenomesFigure 1), and seven were associated with RFI2 ; two SNPs (MARC0027992 and ASGA0039145) being associated with both traits [p ≤ 1e-04 (0.0001)]. The most significant associations were found between ASGA0039145 and RFI1 (p = 7.76e-06) and between MARC0027992 and RFI2 (p = 2.47e-05). Significant SNPs associated with RFI1 were located on SSC 3, 7, 8, 9, 15, and 17 meanwhile those for RFI2 were located in SSC 1, 7, 8, 10, 14, and 15 (Table 1). A search for genes located in the immediate 50 kbp vicinity of these SNPs revealed XIRP2,TTC29,SOGA1,MAS1,GRK5,PROX1,GPR155, and ZFYVE26 as putative candidates. Two LD blocks were detected on a candidate region (86–88 Mb on SSC8) which associated with both RFI .After data quality control, a total of 37,192 SNPs and 596 pigs remained in the final dataset for GWA analyses. Eleven SNPs were significantly associated with RFI1  were used to locate 339 and 304 genes, respectively, within 50 kb of these SNPs for pathway analyses. A total of 21,296 genes in 50 kb flanking regions of SNPs which passed QC was used as background for enrichment test. Pathway analysis tests for KEGG pathways revealed a metabolic pathway to be associated with both RFI1 (p = 0.008) and RFI2 (p = 0.002); and an additional olfactory transduction pathway only associated with RFI2 (p = 0.03). Repeating pathway analyses after relaxing the significance threshold to p ≤ 0.05, revealed 15 and 12 pathways associated with RFI1 and RFI2, respectively  that adjusts the nominal significance threshold only for the number of independent tests that are possible given a particular dataset. The Bonferroni correction that was used in the previous study  gene that is also located on SSC 7. The gene encodes a protein containing a FYVE zinc finger binding domain which helps target the protein to membrane lipids  and 8 (ALGA0039145) were found in the present study. Here, re-mRNAs . Anotherre-mRNAs  reportede lipids . While Zn humans , the preENSSSCG00000009034 is a gene closest to this SNP; however, the gene has not been functionally characterized yet. Moreover, this SNP is tightly linked with five other SNPs to form the LD block 1 . This LD block spans 487 kb region and consists three significant SNPs for RFI1. This LD block also covers a TTC29 gene which encodes a testis development protein that could also be an interesting candidate for further investigation. Also known as NYD-SP14, TTC29 is a component of axonemal dyneins , and is located in 5UTR′ region of SOGA1 gene. This gene encodes a SOGA1 protein that plays a role in reducing glucose production  as possible candidate genes for RFI2. For instance, the GRK5 regulates the GPCR signaling pathway and GRK5 deficiency led to insulin resistance and hepatic steatosis, or decreases diet-induced obesity and adipogenesis in mice  is associated with higher feed efficiency in chicken while RPS18, encoding the 40S ribosomal protein S18, is involved in regulation of development  is perhaps most well-known candidate gene for feed efficiency or/and feed intake in pigs  significantly reduced the numbers of SNP, and consequently the number of genes in the gene-set, for pathway annotation. Therefore, by setting the at a q-value ≤0.2 , we had a reasonable number of SNPs for gene-set enrichment analyses.Results from gene-set enrichment analyses are largely dependent on how gene-sets are identified or defined. In the current study, our gene-set was determined by the significance threshold that was used to declare SNPs significantly associated with RFI. Consequently, our enrichment analyses was very dependent on our choice of significance threshold. The choice of significance threshold also influences the degree of confidence that can be ascribed to results from gene-set enrichment analyses. Choosing a stringent threshold like Bonferroni will likely yield very few results with higher confidence as opposed to a lenient threshold that will likely yield more results with lower confidence. In our analyses, we decided to use a FDR based 8 was significantly associated with both RFI traits. Many previous studies have shown that variation in mediators of metabolic processes contribute to the variation of RFI  was related to olfactory transduction. Olfactory transduction pathways are responsible for the perception of odor via olfactory receptors and downstream biochemical signaling events that ultimately get transformed into electrical impulses sent to the brain  was the insulin secretion pathway  that were found to be associated with RFI based on a lenient nominal significance threshold might be of some interest. However, more studies are required to determine their role in regulating RFI.In summary, the present study describes SNPs, candidate genes and biological pathways putatively influencing RFI in Yorkshire pigs. Important candidate genes such as Duy N. Do did the analysis with the help of Haja N. Kadarmideen, Anders B. Strathe, Tage Ostersen, and Sameer D. Pant. Duy N. Do wrote the first draft of the manuscript. Haja N. Kadarmideen conceived and designed this project and provided supervision for Duy N. Do in all aspects of this research, including GWAS and biological interpretations. All authors contributed to writing of this manuscript, read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


However, disruptions in these genes converge on key molecular pathways early in development. In particular, functional enrichment analyses have found that there is a bias toward genes involved in transcriptional regulation, such as chromatin modifiers. Here we review recent genetic, animal model, co-expression network, and functional genomics studies relating to the high confidence ASD risk gene, CHD8. CHD8, a chromatin remodeling factor, may serve as a “master regulator” of a common ASD etiology. Individuals with a CHD8 mutation show an ASD subtype that includes similar physical characteristics, such as macrocephaly and prolonged GI problems including recurrent constipation. Similarly, animal models of CHD8 disruption exhibit enlarged head circumference and reduced gut motility phenotypes. Systems biology approaches suggest CHD8 and other candidate ASD risk genes are enriched during mid-fetal development, which may represent a critical time window in ASD etiology. Transcription and CHD8 binding site profiles from cell and primary tissue models of early development indicate that CHD8 may also positively regulate other candidate ASD risk genes through both direct and indirect means. However, continued study is needed to elucidate the mechanism of regulation as well as identify which CHD8 targets are most relevant to ASD risk. Overall, these initial studies suggest the potential for common ASD etiologies and the development of personalized treatments in the future.Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder with a strong but complex genetic component. Recent family based exome-sequencing strategies have identified recurrent  Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder characterized by restricted, repetitive behaviors and impaired communication and social interactions  factors that range in size, from single base changes to large chromosomal deletions/duplications or other rearrangements, (3) impacts that include necessary and sufficient single variants, to small (oligogenic) and large (polygenic) sets of factors, (4) the relative contributions of these factors are likely different in subpopulations, such as simplex  vs. multiplex , or other demographics yet to be elucidated. These realities have hampered traditional gene discovery methods.Many studies have indicated a strong genetic contribution for ASD risk . We review the discovery of CHD8 as one of the most mutated genes in simplex ASD, its molecular function, associated ASD subtype, and its potential role as a master regulator of other candidate ASD risk genes. Although CHD8 is still just one of many ASD risk genes, these data point toward at least one converging neuromolecular mechanism in ASD etiology and the potential stratification of ASD into distinct genetic/biologic subtypes. This promises to have major implications for the future as we strive to develop personalized therapies and precision treatments for individuals with ASD.Despite these challenges, recent advances in sequencing technology and novel approaches have begun to unlock the genetics of idiopathic ASD and de novo mutations related to the condition, thereby allowing the identification of novel genetic events with major biologic effect in an unbiased genomwide fashion. These initial studies demonstrated the feasibility of obtaining exome data of sufficient quality across the trios (>90% jointly covered) and filtering strategies to identify the ~1 true de novo event expected per generation , as substantial contributors to the risk of developing neurodevelopmental disorders , providing additional evidence for CHD8 as an ASD risk factor  dn], which directly disrupted only one gene, CHD8 at the 14q11.1 breakpoint. The proband was diagnosed with ASD, ID, and had dysmorphic facial features. Large de novo deletions of 14q11.2 were previously observed in three other subjects that all exhibited developmental delay (DD) and cognitive impairment and similar dysmorphic features including widely spaced eyes, short nose with broad nasal tip, and unusual helical root formation of the ear  were applied to larger cohorts of ASD and DD probands. In the first such study, the protein coding regions of 44 genes were successfully sequenced in 2446 ASD probands from the SSC . Including exome data available at the time, 9/2573 (0.35%) SSC probands carried a CHD8 mutation, making this locus one of the most frequently mutated in sporadic/simplex ASD.In an effort to identify additional mutations and firmly implicate ng Table . This fide novo LoF variants, four de novo missense variants, and one inherited LoF variant. LoF mutations were not seen in unaffected siblings or in an additional 6503 general population control  were identified in the SSC and TASC cohorts, both of which required probands to meet ASD criteria on the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule . The ASC identified two CHD8 de novo missense mutations from the family and three splice site variants from case only exome data , that integrates family-based (de novo and transmitted) and case-control data  of < 0.01. In summation, both exome and targeted resequening data have firmly demonstrated that de novo CHD8 mutations play an important role in ASD risk.In late 2014, two large-scale exome sequence studies were also published, including ~2500 families from the SSC  as opposed to those without. Additionally, about 2% of SSC probands with macrocephay had CHD8 mutations. Macrocephaly has been recognized in other genetic etiologies of ASD, mainly loss of PTEN function and deletions in 16p11.2 , phenotypic data on 15 total patients with disruptive CHD8 variants were included in the evaluation  showed an initial orbital overgrowth within the first 2 months post birth and a continued trajectory of large head growth at or above the 97th percentile throughout early childhood . There were also many similarities in facial features among the group including: prominent forehead, wide set eyes, broad nose with full nasal tip, and pointed chin  domains , a marker for active transcription, and is present at active promoters  and in-situ hybridization data sets  in both progenitor and post mitotic neocortical layers and then gradually declines , an unbiased, genomewide method for constructing networks based on pairwise correlations of gene expression  and M3 (p = 0.0011). M2 peaks at 12–22 PCW and M3 is highly upregulated until PCW 12. The overall set of genes in these modules, including non-ASD risk genes, were highly enriched for chromatin modifiers, DNA binding proteins, and transcriptional regulators (p < 1 × 10−4). Modules M2 and M3 seemed specific to the superficial cortical layers L2–L4 and cells expressing glutamatergic neurons.They identified 17 modules and mapped sets of candidate ASD risk genes onto proteins within the modules. These gene sets included: “asdM12” which was derived from a WGCNA expression module from ASD patient postmortem cerebral cortex and “SFARI ASD” which was a subset of 155 genes from the larger AutDB gene list filtered by gene evidence score , M16 , and M17 . The overall set of genes in these modules had a concentration of synaptic proteins (p < 1 × 10−4) and peaks late in fetal development, starting at 16 PCW and into birth. An enrichment for fragile-X mental retardation protein (FMRP) targets was seen in the M2 module, suggesting a common mechanism between ASD and fragile-X syndrome, in line with the enrichment of FMRP targets first observed by Iossifov et al.  and 13–24 (p = 0.05) PCW . These networks were specific to the prefrontal and primary motor-somatosensory cortex, but in contrast to Parikshak et al., the networks were specific to the deep layers, L5-6. Immunostaining revealed that CHD8 and other ASD genes like SCN2A, DYRK1A, and TBR1 were highly expressed in cortical projection neurons, particularly glutamatergic cell types.Willsey and colleagues took an alternative approach by focusing on nine high-confidence ASD risk genes, defined as having two or more LoF mutations, and determining their co-expression networks  derived neuron progenitor cells (NPCs) using lentiviral delivery of six independent shRNA achieving a range of 38–69% reduction in CHD8 mRNA expression , most of which were upregulated  were enriched for roles in neuronal development. Intriguingly, this same set of downregulated genes was also enriched for candidate ASD risk genes from a large set of 628 genes from AutDB . CHD8 preferentially bound to regions that in ES cell-derived neuroprogenitor lines from the Roadmap Epigenomics are marked with histone H3 trimethyl Lysine4 (H3K4me3), an indicator of active TSSs . This set of candidate ASD risk genes was enriched for pathways involved in neurodevelopment such as axon guidance and neurotransmitter regulation. Enrichment of the more restricted set of AutDB genes from Parikshak et al., “SFARI ASD” was also observed in the downregulated, non-CHD8 bound genes (p = 2.26 × 10−2). However, genes from Willsey et al., “Willsey set” were enriched as CHD8 bound genes (p = 4.34 × 10−3), but were not associated with any changes in gene expression. CHD8 bound genes in the “Willsey set” were enriched for chromatin and transcription. Though these data sets are not independent, they do have different gene representations, with the “Willsey set” more biased toward rare de novo variants. CHD8 binding sites with or without differential expression were explored in the modules described in Parikshak et al. The downregulated genes with no CHD8 binding site were enriched in modules M13, M16, and M17 which correspond to mid-late fetal develop whereas CHD8 bound genes were enriched in modules M2 and M3 corresponding to early-midfetal development. CHD8 bound genes were also highly enriched in TGCA cancer associated genes and also showed no specific enrichment in either up or downregulated genes. No other disease gene sets from the 184 lists for complex disease and traits available from the National Human Genome Research Institute were as significantly enriched as the ASD or cancer gene sets for CHD8 targets or genes affected by CHD8 knockdown. These data suggest both direct and indirect roles of CHD8 in gene regulation, particularly in the development of ASD and cancer.Combining the differentially expressed (DE) set of genes with CHD8 binding data, Sugathan and colleagues further explored a number of enrichments using functional classifications, candidate ASD risk gene sets, and other disease gene sets. Overall, only 9.2% of genes with CHD8 binding sites were DE in the nominal P gene set . Candidate ASD risk genes from AutDB were enriched in the set of nominally downregulated genes, but not targeted by CHD8  and the “Willsey set” , further suggesting a conserved mechanism for CHD8 regulation in development. Of the candidate ASD risk genes from the “Willsey set” found to contain CHD8 binding sites, 25 were shared between Cotney et al. and Sugathan et al. and were also conserved in mouse  as compared to genes with binding sites only found in hNSC (46%) or hNSC and human brain (54%). Cell cycle, Hippo, and p53 pathways were among the pathways most affected. Candidate ASD risk genes actually exhibited the most significant dysregulation compared to any other set of CHD8 targeted genes (~60% for both gene sets). Additionally, those candidate ASD risk genes most DE, tended to be downregulated. While Sugathan et al. did find that candidate ASD risk genes from the Willsey set were enriched as CHD8 targets, they were not significantly DE in either direction. In contrast, Cotney et al.'s data suggest that CHD8 directly activates this same set of candidate ASD risk genes.In their knockdown of CHD8 reduction had a global impact on gene expression. Interestingly, the top DE genes in this study were noncoding RNAs, the majority of which were upregulated. Noncoding RNAs have only recently been appreciated as having a potential role in neurodevelopment and may be yet another layer in transcriptional control. Enrichment of candidate ASD risk genes Parikshak et al. “SFARI ASD” was only seen in the set of downregulated genes, similar to Cotney et al. as were processes involved in regulation of neuron projection, differentiation and neurogenesis (Parikshak et al., In line with the other studies, Wilkinson et al. found CHD8 appears to act as a master regulator in the foundational pathways of the developing brain, particularly those that may also be implicated in ASD development. Yet the mechanism for regulation remains unclear. Many of the candidate ASD risk genes are direct targets of CHD8 protein and are also involved in chromatin regulation (Figure In summation, n Figure . Cotney n Figure . ContinuCHD8 emerging as particularly strong. Both human case studies and animal models suggest a common phenotype for CHD8 mutation including a significant association with ASD diagnosis and behavior, macrocephaly, and dysfunction in the enteric nervous system. Though ASD is highly heterogeneous, CHD8 systems biology and functional genomics studies give some hint as to the complex nature of the disorder. The CHD8 protein and those of many of the other candidate genes serve as master regulators, influencing the expression of a large network of genes and pathways that control neuron formation, proliferation, and differentiation. The timing of proper neural development is a highly regulated phenomenon, requiring a concerted effort by many different genes. While it's clear that CHD8 controls expression of genes involved in these developmental processes, the manner in which CHD8 regulates them is still unclear. Initial studies suggest that CHD8 may act as a direct repressor by modifying chromatin to make it less accessible. However, the loss of function studies point toward a role for CHD8 as an activator in the emerging ASD gene network. One of the consistent findings from these and previous studies is that CHD8 is associated with actively transcribed genes and has affinity for H3K4me3 labeled promoters suggesting an influential role in global gene expression. CHD8 may function to make promoters more accessible through chromatin remodeling, promote transcription factor binding, or indirectly enhance expression through as of yet, unknown mechanisms that may involve interactions with H3K4me3 itself. The mechanism of CHD8 regulation is likely to be gene dependent as well.From exome sequencing studies, a number of high-confident ASD risk genes have been identified with CHD8 affects neural circuitry and connect these affects to autistic associated behaviors. Lastly, development of co-expression modules have proved useful in identifying relevant ASD developmental time periods and interactions between candidate ASD risk genes, including CHD8. However, developing methods that integrate multiple types of data such as co-expression and protein-protein interactions may improve identification of ASD and other neurodevelopmental disorder specific networks, as proposed recently (Hormozdiari et al., It's also still uncertain which CHD8 targets may be relevant for ASD. Though the studies indicate a number of candidate ASD risk genes contain CHD8 binding sites, further functional studies will be required to demonstrate if these genes interact in a physiologic setting. Additionally, although enrichment for candidate ASD risk genes may be seen in specific neuron types, it is still unclear how the alterations in these neurons ultimately relate to behavioral phenotypes. It will be important to determine how RAB: writing, editing, and figure conception, MBP: writing and figure design, BJO: writing, editing, and figure conception.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Brian J. O'Roak is an inventor on patent PCT/US2009/30620: mutations in contactin associated protein 2 are associated with increased risk for idiopathic autism.