Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro.Affymetrix microarray data were generated from A375 melanoma cells treated in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes  but not others . The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells  Malignant melanoma is a devastating form of cancer with a particularly high incidence in New Zealand (NZ) and Australia The molecular pathways that underlie melanoma are complex. The roles of twenty-five molecules strongly associated with malignant melanoma are summarised as briefly as possible below, so that when functional genomic approaches based on mRNA data are used later in this study, we can assess whether these molecules and the molecular pathways they constitute are identified.CDKN2ACDK4RB1MDM2MC1R, TYR, TYRP1 and ASIPInherited mutations cause a genetic predisposition to melanoma, including mutations in cell cycle genes such as MMP2, RACK1 and CCND1BCL2, CDKN1A, TYR, TBX2 and CDK2MITF expression is also promoted by transcription factors such as Pax3 and Sox10 MITF in melanoma is associated with clinical outcome MITF expression in melanomas is examined on a cell by cell basis, the slow-growing stem-cell-like melanoma-initiating cell population appears to have low MITF expression, and in accord with this, inhibition of MITF in B16 mouse melanomas reduces proliferation and up-regulates the stem cell marker Oct4 INK4 inactivation and BRAF mutation can accompany MITF amplification in melanoma cell lines, and ectopic MITF expression appears to work in synergy with BRAF mutation to transform primary human melanocytes Other molecules commonly involved in melanoma progression include NRASin silico models of gene-to-gene relationships known as gene networks Melanoma research was one of the earliest fields in which expression profiling was applied to tumour classification in vitro functional genomic approach. We generate microarray data from the melanoma cell line A375 exposed to a set of targeted siRNA disruptions, and used these data to identify co-expressed clusters of genes that are strongly conserved between siRNA-treated A375 cells and melanomas from patients. Several of these individual clusters encode proteins with shared cellular functions; we show that those clusters related predominantly to cellular proliferation are significantly associated with the prognosis of metastatic melanoma patients.In this study we find that the association of tumour clinical features with either individual RNAs or inferred molecular pathway activity is not consistent across published melanoma microarray datasets. Given this lack of consistency, and the consequent difficulty of using data from the diverse melanomas of patients to understand melanoma molecular pathways, we instead take an progression, metastasis and prognosis. We wished to assess whether the genes associated with these clinical features were consistent across the multiple published studies. Therefore, the raw data from several well-designed microarray studies that addressed progression, metastasis and prognosis were retrieved  Principal Coordinates and Hotelling's T2 (PCOT2) prognosis or progression. GATHER analysis only identified two gene sets consistently differentially expressed in the studies of metastasis . In summary, our analysis of published melanoma microarray studies addressing tumour progression, metastasis and prognosis identified little concordance between the different studies at the levels of individual RNAs or gene sets.We then used pathway level analysis to analyse the differential expression of functionally-linked gene sets associated with melanoma in vitro functional genomics approach. This involved multiple siRNA knockdown experiments in the A375 melanoma cell line, in which the abundance of specific target mRNAs were reduced in separate cultures of melanoma cells before Affymetrix U133plus2 microarray analysis. The principle of this study was that each siRNA experiment would alter the activity of a subset of signalling pathways and consequently the abundance of mRNAs downstream of those pathways, allowing clustering and gene network analysis to identify the strongest statistical relationships between any of the 54,000 probe sets, across the siRNA-treated cells. We selected 45 siRNAs (http://www.ingenuity.com/) to influence the expression of ≥50 downstream mRNAs, and (ii) they were relatively abundant in A375 cells (on average ≥50th percentile of abundance in the microarray data). RNA from A375 cultures transfected with these 45 siRNAs, along with control inactive fluorescently-labelled siRNAs, were analysed using Affymetrix microarrays. The distribution of target ‘knock-down’ efficacy is shown in Given the lack of consistent RNA signatures for melanoma progression, metastasis and prognosis from microarray studies of melanomas from patients, and the consequent difficulty of using data from the diverse melanomas of patients to understand melanoma molecular pathways, we took an 5 siRNAs  that tarp-values ≥0.95, 51 of which had at least one enriched GO path; of these seven had functional enrichment for cellular proliferation.To explore whether this A375 dataset contained biologically sensible information, we first attempted to identify biologically relevant clusters of RNAs that were correlated across the A375 siRNA microarray dataset. As discussed in the introduction, we would expect mRNA targets of transcription factors In one particular cluster, 61 of 67 RNAs were targets of the cell cycle-associated transcription factor E2F1. This is shown by plotting heatmaps of Spearman's correlation coefficient (ρ) and gene expression . Many ofTo evaluate the potential clinical relevance of these clusters, we assessed whether the RNAs we identified as co-expressed across the A375 siRNA microarray data were also co-expressed across microarray data from primary and metastatic melanomas from patients. A composite Affymetrix U133A microarray dataset from primary melanomas using three published studies In summary, unsupervised clustering of microarray data from siRNA-treated A375 cells identified RNA clusters that encoded proteins with common functional annotations, as well as RNA clusters that shared common transcription factor binding motifs in their gene promoters. We showed that members of four of these A375 cell-derived clusters were also correlated in both primary and metastatic melanomas, suggesting that the A375 microarray dataset did indeed contain ‘biologically sensible’ and clinically valid information.both the A375 and clinical melanoma data may represent cases where the siRNA-treated A375 data can provide a valid model for gene regulatory processes that occur in the melanomas of patients. Conversely, gene sets with high correlations between their constituent RNAs in clinical melanoma microarray data but not in the A375 data may represent cases where clinically important RNA clusters are not adequately modelled by our siRNA-treated A375 cells.We then screened pre-defined gene sets that shared common transcription factor promoter motifs (based on the TRANSFAC Pro database) or functional annotations (based on the GO database) for high correlations in any of: (i) the siRNA-treated A375 dataset, (ii) primary melanomas, and (iii) metastatic melanomas. Gene sets with high correlations between their constituent RNAs in All possible gene sets with ≥5 members were retrieved from: (i) the TRANSFAC Pro v 8.2 database  and (ii) the GO v 1.81 database . For each TRANSFAC and GO gene set, using each of the three microarray datasets, we used an R script to calculate the fraction of gene pairs that correlated so that the absolute value of Spearman's correlation coefficients (|ρ|) was ≥0.5. We found that several gene sets had a similar, albeit relatively low, frequency of correlated gene pairs in all three of the A375, primary and metastatic melanoma datasets – e.g. E2F1, PAX3, CREBP, cell cycle and DNA repair gene sets . We alsoin vitro. However, transcriptional pathways underlying some other processes such as immune response appear to be identified across the melanoma tumours (in which complex tumour cell-leukocyte interactions occur) but are not apparent in the microarray data from A375 cells cultured in the laboratory.In summary, we identified several gene sets, including gene sets associated with the cell cycle, that had similar frequencies of correlation in both siRNA-treated A375 cells and in melanomas from patients. These may represent active transcriptional pathways regulating biological processes that occur in melanomas and appear to be effectively modelled in siRNA-treated A375 cells http://www.bioinformatics.auckland.ac.nz/doc/project_data/Supplementary_FIle_1.txt).In order to predict more complex RNA-to-RNA relationships in the siRNA-treated A375 cells, including upstream regulators of the co-expressed clusters described above, and the putative direction of RNA-to-RNA relationships, we analysed the A375 cell microarray data using a whole-genome Bayesian gene network inference method SOX10, CCND1, RB1, and BCL2 all had over 50 children, while PTEN, TYR, CDKN2A, BRAF, PAX3, AKT1 and MITF had between 30 and 50 children. In line with the clustering analysis described above, several members of the E2F transcription factor family were hubs in the gene network, with E2F4 (38707_r_at), E2F7 (241725_at), and E2F1 (2028_s_at) having 181, 103 and 43 network children, respectively. Reassuringly, 226 of the 327 combined children of these three E2F transcription factors have E2F binding sites in their promoters, a significantly greater proportion than would be expected due to chance . As well as identifying hubs, Bayesian gene networks also identify clusters of co-expressed RNAs, which are downstream of the same hub. Identifying these clusters may be seen as a more conservative use of this network method than identifying directional edges, and is the primary use made of Bayesian gene networks in this paper. Reassuringly, every one of the 200 clusters identified by the hierarchical clustering method above had at least 70% of their members included among clusters identified by the gene network method.As described in the introduction, gene network nodes with large numbers of downstream “children” are putative master-regulators of biological processes, and are often known as “hubs”. 11 of the 25 molecules described in the introduction as important for melanoma pathogenesis were identified as hubs in the Bayesian gene network: We wished to determine whether the Bayesian gene network hubs and clusters identified from the A375 microarray data were associated with prognosis. Therefore, we used the ‘Survival’ package in R to generate Cox proportional Hazards models to estimate the association between the abundance of RNAs in tumours and the survival of melanoma patients. Two survival models were generated: (i) based on gene expression in metastatic melanomas using an Affymetrix microarray dataset et al., 2006 dataset et al., 2009 dataset Firstly, to establish a baseline, we considered whether the abundance of RNAs that encoded proteins with particular classes of functional annotation were significantly associated with patient survival. We hypothesised that RNAs encoding the types of proteins that perform important oncogenic functions  may be more strongly associated with the survival of patients than the abundance of RNAs that encode proteins that do not play known roles in cancer. For both primary tumours (the Winnepenninckx with children that encoded proteins of common function. We used the GATHER web tool to identify hubs with children significantly (Bayes Factor ≥5 and p≤0.05) enriched for GO paths. We found that 204 hubs had children significantly enriched for one of 60 GO paths, which covered a broad range of functions including transcription, metabolism, signal transduction, stress response, DNA repair, and cellular proliferation. Box plots were used to visualise the strength of association between the expression of these hub mRNAs in primary . All hubs with children enriched for various GO paths related to cell cycle, mitosis or proliferation with Bayes Factor ≥5 and p≤0.05 are shown in The hubs with children enriched for cell cycle functions included: DTL and its gene network children are listed in Several of these A375 gene network hubs and downstream cell cycle-associated clusters are potentially clinically relevant, since hub RNA-to-child RNA correlations were found in both the A375 and metastatic tumour  has only 8 gene network children, however all encode proteins associated with programmed cell death . (ii) TMCO1 (210768_x_at) has 92 network children, a significant subset of which encode proteins associated with the GO annotation of apoptosis , and (iii) UBE2S (202779_s_at) has 67 network children, a significant subset of which encode proteins associated with the cell cycle  and DNA damage response/repair .While many of the Bayesian gene network hubs are already well known in cancer biology, some hubs represented molecules that had not been well characterised in terms of their role in cancer cells. We subjectively selected three poorly characterised hubs for further study – all had sets of child RNAs that were significantly enriched (GATHER Bayes Factor ≥5 and p≤0.05) for functions that were easy to assess in the laboratory: (i) ELMOD1, TMCO1 and UBE2S were amplified by proof-reading PCR from an A375 cell cDNA template and ligated into the pcDNA3.3-TOPO TA expression vector. After the sequence of these plasmids was checked, they were transfected into 293T epithelial cells and Mel501 melanoma cells, along with control plasmids encoding lacZ. Initial assessment using MTT assays suggested that transfection of 293T cells with the UBE2S expression plasmid caused a significant increase in cell numbers after 2 and 4 days, relative to lacZ control plasmids and untreated cells  and Mel501 cells  appear to be significantly associated with the time to relapse in melanoma patients.In summary, three relatively uncharacterised genes, which the gene networks predicted would influence the abundance of apoptosis-associated or cell cycle-associated RNAs, were found to alter apoptosis and cell number when overexpressed progression, metastasis and prognosis, hoping to identify consistent gene signatures for these clinical features. However, we found there was very little concordance between the different studies, despite the fact they appeared to have been carried out to a high standard. We noted that each of the published studies contained different patient groups, different tumour sites, and different histopathological tumour types. These differences may be in part responsible for the distinct gene signatures produced by the different microarray studies. A recently published review of melanoma microarray studies RNA signatures from melanoma microarray studies have provided useful information about many aspects of the biology of this tumour type  were identified as hubs in the Bayesian gene networks generated from our A375 cell dataset, including: BRAF, CCND1, RB1, PTEN, TYR, CDKN2A, and SOX10. However, the interactions between these molecules that are known experimentally  were in general not identified by the gene networks. It is possible that these interactions simply do not operate in cultured A375 cells, or that the 45 siRNA disruptions used in this study did not introduce sufficient variability in the expression of these molecules to allow latent relationships between them to be identified.Given the diversity of melanomas in patients discussed above, we proposed that rather than perform a meta-analysis of patient tumours, a more effective way to obtain new insights into melanoma biology could be to generate a microarray dataset in A375 cells, in which transcription factors and signalling molecules were targeted using siRNAs. We hoped that this approach would generate a dataset with controlled differences between siRNA-treated cultures, to increase our sensitivity for revealing meaningful molecular pathways. Similar approaches have been used successfully in other cancers to understand oncogenic signalling pathways. For example, Bild in vitro cell work, our use of A375 cells, cultured in the laboratory potentially comes at the cost of losing biological validity. To assess the similarity between A375 cells and melanomas in patients at a transcriptional pathway level, we compared the RNA correlations within biologically-relevant gene sets identified across A375 cells with those identified across both primary and metastatic melanomas. We found that several gene sets (e.g. those related to the cell cycle) were approximately equivalently correlated across both the A375 cells and the clinical data. We identified other gene sets that were more frequently correlated in the clinical microarray data than in the A375 cell data, such as gene sets associated with immune response. Immune response plays a major role in melanoma biology in vitro. This limitation is not surprising, given that the immune cells that participate in these pathways in tumours are absent from the A375 cell cultures. Other gene sets that were more frequently correlated in the clinical microarray data than in the A375 cell data include heparin sulphate proteoglycan synthesis and amino acid acetylation, as well as targets of the transcription factors MYC/MAX, POU3F2 and GCNF. It is possible that these processes/transcription factors are highly active in tumour stromal cells, and therefore are not identifiable in cultured A375 cells.Like all Hub genes are predicted by the Bayesian gene network to be regulators of the expression of their children. Experimental evaluation of the directional gene expression relationships between gene network parents and their children is beyond the scope of this study. Nevertheless, we followed up three hubs from the A375 cell gene networks that were not well characterised. These were chosen based on the enrichment of their downstream network children for functions (cell cycle and apoptosis) that could be easily examined in our laboratory and that were clinically relevant. It was encouraging that in the pilot experiments presented here, overexpression of RNAs encoding these hubs appeared to alter the specific functions associated with hub children. Further studies are now needed to confirm the levels of plasmid expression achieved and to define the mechanisms by which hub overexpression alters cell biology.et al. study of this dataset We found that in metastatic tumours, the abundance of gene network hub RNAs enriched for children with cell cycle and DNA repair functions, but not several other functions, were frequently associated with patient survival. Some of the hub genes we identified had previously been associated with survival in the original Bogunovic primary tumour data Our choice of the 45 siRNAs transfected into the A375 cells was in fact biased towards RNAs encoding proteins with specific functions. The GO database indicated that of the 45 siRNAs used, 21 were associated with regulation of the cell cycle (GO:0000074), 22 with regulation of transcription (GO:0045449), and 24 with regulation of metabolism (GO:0019222). Since gene network inference depends upon variation in gene expression between the siRNA-targeted cell cultures, large numbers of siRNAs related to proliferation may have increased the resolution of our gene networks for proliferation-associated transcriptional pathways, and therefore contributed to our identification of survival-associated cell cycle clusters. However, the effect of siRNA choice on gene network results may be complex, since we did not see dominant network clusters associated with the functions of metabolism and transcription, for which our siRNA set was also enriched. We are unsure why no strong relationship was seen between abundance of hub RNAs enriched for children with cell cycle functions and patient survival in the TYMS is a target of 5-fluorouracil, a drug that has been studied in melanoma in the past TYMS in our analysis, indicating active molecular pathways involving TYMS, may be better candidates for topical 5-fluorouracil treatment than other patients.Given the clear association between cell proliferation transcriptional modules identified in this study and patient prognosis, these modules have potential clinical usefulness. For example, an 8-gene classifier developed from Bayesian gene network hubs correctly classified the prognosis of 23/26 metastatic melanoma patients in a pilot cross-validation study . While tAs described in the methods, we showed that, for one cluster of co-regulated RNAs in the A375 melanoma cell dataset, the highly correlated gene pairs were not conserved in a similar dataset generated in MCF-7 breast cancer cells A. This aand across melanomas from patients (e.g. gene sets associated with the cell cycle), as well as other gene sets that were correlated only across the clinical melanomas (e.g. gene sets associated with immune function). Several clusters enriched for cell cycle functions and the hubs upstream of these clusters in the gene networks were significantly associated with patient survival, suggesting new prognostic biomarkers, and underlining the importance of the transcriptional pathways that control the cell cycle for melanoma biology. Our analysis also illustrated the frequent co-expression of functionally-related RNAs. We hope that bioinformatic methods like those used here can work alongside traditional tumour biology studies to improve our understanding of melanoma and to derive new biomarkers and drug targets suited to the tumours of individual patients. In addition, we hope that the methods described here for estimating the correlation of genes that share the same biological functions will be useful to estimate the validity of cell culture models for specific aspects of other human diseases.In this study, we used siRNAs to knock down the abundance of 45 functionally important mRNAs in A375 melanoma cells. A variety of methods were then used to reverse engineer co-expression clusters and gene networks from this data. We identified several gene sets that were correlated both across siRNA-treated A375 cells in vitro functional genomics experiments described here since their transcriptome appears to be moderately representative of clinical melanoma tumours; for example they are positioned close to several tumours in a multidimensional scaling analysis A375 melanoma cells http://cran.r-project.org/). All microarrays passed quality control using the ‘AffyQCreport’ R package. Microarray ‘CEL’ files from the siRNA-treated A375 cells, and from previously published studies, were normalised using the Robust Multichip Averaging (RMA) algorithm http://www.ingenuity.com/).Biotin-labelled cRNA was generated and hybridised to Affymetrix Human Genome U133plus 2.0 microarrays following the manufacturer's instructions . All microarray data used in this manuscript is MIAME compliant and has been deposited in GEO. The GEO accession number for microarray data from siRNA-treated A375 cells is GSE31534. The GEO accession number for microarray data from siRNA-treated MCF-7 cells is GSE 31912. Array analysis was performed using the statistical framework ‘R’ . Class prediction was performed using shrunken centroid classifiers with the “pamr” R package Spearman rank correlation coefficients, which are based on the correlations between the ranks of variables, were generated using the ‘cor’ function in the R base package. Whole-genome Bayesian gene networks were reverse engineered from the siRNA-treated A375 cell microarray data by estimating large numbers of sub-networks in parallel that were later amalgamated, as described For all “cel” files quality control was performed using the ‘AffyQCReport’ package All 54,000 probe sets on the A375 cell microarrays were hierarchically clustered using Ward's method vice versa, suggesting that cross-hybridisation between Affymetrix probe sets was not the most significant driver of the clustering we observed across the A375 dataset.It has been suggested that a fraction of the probe sets in Affymetrix microarrays may cross-hybridise with multiple mRNA transcripts, which could lead to spurious clustering and gene network relationships. Therefore, we calculated the Spearman's correlation coefficients between all possible combinations of probe sets from the cluster shown in the neighbor node sampling & repeat (NNSR) method to estimate 100,000 overlapping small sub-networks selected from the intermediate global network structure, which is improved by the estimated sub-networks during the method. Edges that were present in at least 20% of these sub-networks were combined into a final 1,645,882 edge gene network for analysis .This was carried out using massively parallel supercomputers at Human Genome Center of the University of Tokyo as previously described To illustrate the information underlying this network, Coding regions were amplified using Platinum Taq DNA Polymerase  from cDNA that had been reverse transcribed from A375 cell RNA, then cloned directly into the pcDNA3.3-TOPO-TA expression vector. Ligation products were used to transform One-shot Top10 competent cells  and colonies selected by PCR using gene-specific primers. Plasmid DNA was extracted using a Zyppy Plasmid Miniprep Kit  and the inserts sequenced on both strands. Plasmids for transfection were prepared using a QIAGEN endo-free Plasmid Maxi Kit according to the manufacturer's instructions . The oligonucleotide primers used for cloning are described in MTT -2,5-diphenyltetrazolium bromide) colorimetric assays were used to estimate viable cell number according to the manufacturers instructions . Briefly, A375, Mel501 and 293T cells were seeded in 96-well plates at 3,000, 6,000 and 12,000 cells/well respectively. 10 µl of MTT (5 mg/ml) was added into each well; after 4 h incubation at 37°C 100µl of lysis buffer was added and plates were incubated at 37°C overnight. The next day the plates were read at OD570. Each condition was analysed in six replicate wells and all experiments were repeated at least three times.For cell cycle analysis, cells in each well of a 6-well plate were trypsinised, washed three times by ice cold PBS then fixed in 3 ml of 70% ethanol in PBS added dropwise while vortexing. Cells were stained with 50 µg/ml propidium iodine (PI), 0.1 mg/ml RNase A, 0.05% (v/v) Triton X-100 at 37°C for 40 min then assayed by Flow Cytometry, with the results analysed using ModFit LT software  according to the manufacturer's instructions. All cell cycle analysis experiments were repeated at least three times.For Western blotting to identify caspase activation during apoptosis, cells were trypsinised and washed three times with ice cold PBS, then pellets were mixed with lysis buffer  Triton X-100, 1 mM EDTA, 1 mM EGTA and 1% (v/v) Protease Inhibitor Cocktail ) and incubated for 30 min on ice. Lysates were then micro-centrifuged at 4°C at 13,000 rpm for 15 min then 20 µg loaded into each well of 10% SDS-PAGE gels, separated, then transferred to BioTrace NT nitrocellulose membranes . Membranes were blocked for 2 h at room temperature in blocking buffer  Tween-20 and 5% (w/v) skim milk powder) and then incubated with anti-Poly ADP-ribose polymerase (PARP) antibody  at 4°C overnight. The next day the membranes were washed in blocking buffer and incubated with goat anti-rabbit IgG–peroxidase antibody, washed three times in blocking buffer, then incubated with ECL plus reagents  for 5 min before scanning. All PARP Western blotting experiments were repeated at least three times.Figure S1Class prediction based on the gene network hub probe sets. (A) Shrunken centroid classifiers were developed and assessed by cross-validation using eight gene network hub probe sets. (B) For these eight probe sets, the normalised expression signals in metastatic melanoma tumours from the Bogunovic et al. Click here for additional data file.Figure S2Affymetrix probe set-to-probe set correlations are not conserved between cell types. (A) The kernel density plot shows the distribution of Spearman's correlation coefficients between all members of an E2F1-associated A375 cell-derived cluster (which was shown in (TIF)Click here for additional data file.Figure S3Relationships between gene network parents that were targeted by siRNA and their gene network children. (A) The distribution of Spearman's correlation coefficients between parents that were targeted by siRNAs and their 1,800 gene network children is shown in red. The distribution of Spearman's correlation coefficients between ten randomly chosen sets of 1,800 genes is shown in grey as a control. (B) For each of the 1,800 gene network children shown in A, the ratio of (expression after siRNA knockdown of the parent) to  was calculated. This ratio will always be < = −1 or > = 1). For all 1,800 parent-child edges, this ratio (y-axis) was plotted against Spearman's correlation (x-axis). This shows a trend for the gene network children of parents targeted by siRNAs to be down-regulated after parent knockdown when parent and child correlate positively, and to be up-regulated after parent knockdown when parent and child correlate negatively. C and D show the distributions of fold change after parent knockdown for those network edges where parent and child were positively and negatively correlated, respectively.(TIF)Click here for additional data file.Table S1The hubs in the Bayesian network with children significantly enriched (Bayes Factor ≥5 and p≤0.05) for functions related to cell cycle, mitosis or proliferation. Hub probe ID and hub official gene symbol (OGS) are given in the first two columns. The GATHER Bayes Factor and the probability of obtaining this Bayes factor due to chance are shown in columns 4 and 5. Column 6 lists the children of the gene network hub that have the enriched annotation listed in column 3. Column 7 shows the Cox proportional hazards (coxPH) survival p-value for the association of hub RNA abundance in metastatic tumours with patient survival, with those hubs with p-values ≤0.05 highlighted in yellow.(XLSX)Click here for additional data file.Table S2A375 Bayesian network hubs that have children significantly enriched for cell cycle functions. Affymetrix probe set ID (parent probe ID) and official gene symbol (parent OGS) are shown in columns 1 and 2. The number of children of these hubs in theA375 Bayesian gene network is shown in column 3. Of these children, the number and % that have |Spearman's ρ| ≥0.4 with their parents are shown in columns 4 and 5, respectively.(XLSX)Click here for additional data file.Table S3Conserved correlations between the hub DTL (probe ID 222680_s_at) and its children between the A375 in vitro siRNA dataset and metastatic tumours in patients of the Bogunovic study. The gene network parent (222680_s_at) and gene network child probe set ID and OGS are given in the first 4 column. Column 5 shows the |Spearman's ρ| between the parent and child across the A375 siRNA dataset, while column 6 shows the |Spearman's ρ| between the parent and child across the Bogunovic metastatic tumour dataset. In columns 5 and 6 |Spearman's ρ| ≥0.4 are highlighted in orange. Columns 7 and 8 show the Cox proportional hazards p-value for association between probe set abundance in metastatic tumours and patient survival for parent and child, respectively. In columns 7 and 8 p≤0.05 are highlighted in green. Click here for additional data file.


Implantation failure is the most frequent cause of pregnancy loss in couples who try to conceive, either in a natural way or using assisted reproductive techniques (ART). Identify the precise mechanisms of implantation failure can lead to identify couples at risk and also providing appropriate therapeutic options to affected couples. Despite the high prevalence of this disorder, a few causing factors are demonstrated so far. Recent studies indicate that genetic factors play an important role in the occurrence of recurrent implantation failure. Although some of these factors, such as numerical chromosomal aneuploidy are known to be causative factors, there are some other factors that solely increase susceptibility to this event. In the present review we try to list the genetic polymorphisms that are known as susceptibility factors in implantation failure. Pregnancy loss can be caused by several factors which are involved in fundamental events during human reproduction. Anatomical, immunological, hormonal and infectious factors along with known genetic factors are involved in 50% of such cases. Our previous findings have shown that 9.8% of pregnancy loss suffering couples have chromosomal balanced rearrangement . FurtherSince about 80% of pregnancies are lost during the first trimester, it has been postulated that the major cause of failed pregnancy is an error of embryo implantation .Genetic factors that lead to implantation failure have overlap with those involved in recurrent spontaneous abortion and infertility -6.Implantation failure is the most frequent cause of lack of pregnancy after in vitro fertilization (IVF) and embryo transfer, as implantation failure takes place in approximately 40% of IVF experiments . SuccessThere are increasing numbers of evidence indicating that genetic factors regulating invasion and angiogenesis processes are critical in embryo implantation. Genetic defect and even genetic polymorphisms of genes involved in these processes can lead, or at least increase susceptibility to implantation failure -6.In the present review we will attempt to provide a list of research studies performed in this area and genetic factors which are involved in implantation failure.P53 tumor suppressor geneP53 is mostly known as a “genome guardian” who has a pivotal role in genome integrity maintenance and tumor prevention. P53 becomes activated by sense of wide variety of stress signals and initiate a transcriptional program leading to apoptosis, cell cycle arrest or senescence. P53 itself is under negative control of some genes such as Mdm2. By binding to the p53 protein, Mdm2 leads to p53 polyubiquitination and sending it to the proteosome -6.Phylogenic analysis of P53 revealed that P53 is an evolutionary conserved gene and P53-like transcriptional factors exist in short - lived invertebrates that do not exhibit adult tumors. These findings suggest that P53 may have another initial role in these creatures.-/- male mice show normal reproduction rate, p53 -/- female mice show reduced pregnancy ability and litter size when mated with p53 -/-,  p53 -/+ and p53 +/+ male mice and worst pregnancy rate and litter size in mating with p53 -/- male mice  expression two fold lower than R72 allele -19. As wLeukemia inhibiting factor (LIF)−/− mutation in mice leads to defect in maternal reproduction attributable to failure of implantation . In fertile women also this polymorphism is significantly associated with a history of infertility drugs usage. These results demonstrate an association of a SNP in the LIF gene with infertility, especially in patients under the age of 35 years , 44-45. Other genes involved in p53 Pathway Recent studies suggest that several other genes involved in P53 pathway are implicated in implantation process and their polymorphisms have association with increased risk of implantation failure. Several studies on SNPs of P53 pathway, such as SNP309 in Mdm2, rs2279744 SNP of Mdm4 gene and rs1529916 SNP of Hausp gene has been performed , 46, 47.Mdm2 gene is a negative regulator of P53 and plays an important role in the regulation of mechanisms involved in implantation . EmbryosInterestingly, polymorphism of Mdm2 in Caucasian populations is evolutionary conserved. For example, the frequency of G allele in the Mdm2 gene in the Caucasian population is four times more than African ancestral race . G allelMdm4 is the other gene involved in P53 pathway which its polymorphism is related to increased risk of implantation failure. Mdm4 gene is structurally homologous of Mdm2 and in addition to regulating p53 gene negatively, regulates p73 gene . Mice wiTo date only one study has been performed on the frequency of p53 gene polymorphism in Iranian patients with infertility. In this study, 70 infertile patients with recurrent implantation failure (RIF) and 32 fertile women with at least two successful pregnancies were studied. This study showed the accumulation of P72 allele in patients. However, other polymorphisms of p53 pathway in Iranian population remain to be investigated .P53 pathway independent genesIn addition to p53 pathway members, there are several other genes which are implicated in embryo implantation. Majority of these genes are involved in invasion of embryo into endometrium and also in pregnancy hormonal homeostasis. prostaglandin-endoperoxide synthase 2 (PTGS-2) geneCox-2 enzyme encoded by PTGS-2 gene, is an inducible enzyme in prostaglandin construction pathway which is induced by a range of stimuli such as growth factors and mitogens .-/- mice indicate an impaired angiogenesis into the implantation site (Studies on the expression pattern of Cox isoforms in the preimplantation mouse uterus indicate the role of these enzymes in embryo implantation . Furtherion site . Ptgs2 mion site . In humaion site . Given tion site .MUC-1 geneTransmembrane mucin-1 (MUC-1) is a glycoprotein expressed on the endometrial cell surface and can act as a barrier to implantation. During uterine receptive period, MUC-1 expression shows a dramatic decrease -56.The gene that encodes this molecule is composed of a polymorphic tandem repeat of 60 nucleotides. Several studies indicate that MUC-1 genetic polymorphism is associated with implantation failure in patients with a history of recurrent abortion -56.Several studies indicated that smaller alleles of MUC-1 show a higher frequency in women with infertility due to embryo implantation failure when compared to patients with no history of infertility -60. HoweConsidering the increase of MUC-1 expression in response to progesterone and also the relationship of shorter MUC-1 alleles with infertility, the anti adhesive role of MUC-1 in human becomes controversial , 63.Horne et al. studies on endometrial pinopodes, using electron microscopy and immunohistochemistry confirmed that MUC-1 was linked with embryo adhesion. However they also showed that abnormal endometrial expression of MUC-1 is associated with failure of embryo implantation. This abnormal expression shows concordance with retention of nuclear progesterone receptor (PR) particularly in epithelial cells .Human progesterone receptor (hPR) gene Another genetic polymorphism which shows association with implantation failure risk, locates in human progesterone receptor gene. Human progesterone receptor (hPR) gene is a dual function gene which functionally encodes two different isoforms with different transcriptional factor activity, hPR-A and hPR-B , 65. In Isoforms deletion studies on animal models show that the imbalance between these isoforms expre--ssion leads to severe abnormalities in ovarian and uterine function and defective implantation .To date several polymorphisms in hPR gene have been identified. In 1995 a small (306 bp) insertion in G intron of hPR was found and named PROGIN . This vas investigation results surprisingly, suggest that PROGIN complex frequency increases with the number of implantation failure  geneThe most critical step in embryo implantation is adhesion of outer trophectoderm layer of the blastocyst into the luminal epithelium -77. ThisMuc-4 has an important role in invasion of human cytotrophoblasts into endometrium . Since lAccording to the presented data, implanting embryo behave as a tumor against endometrium. Invasion and angiogenesis are critical steps in this process. By genotyping of RIF suffered couples we can predict the risk of IVF failure and present appropriate therapeutic options.


Mus musculus) provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL). Many trans eQTL cluster into eleven ‘hotspots,’ seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL—but not cis eQTL—were substantially lower when mapping was restricted to a ‘fertile’ subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is applicable in a broad range of organisms and we advocate for widespread adoption of a network-centered approach in speciation genetics.Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci (“Dobzhansky-Muller incompatibilities”). The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice ( New species are created when barriers to reproduction form between groups of organisms that formerly interbred freely. Reduced fertility or viability of hybrid offspring is a common form of reproductive isolation. Hybrid defects are caused by negative interactions between genes that have undergone evolutionary change within each subgroup. Identifying genetic interactions causing disease or trait variation is very difficult, consequently there are few known hybrid incompatibility genes and even fewer cases where both interacting genes are known. Here, we combined mapping of gene expression levels in testis with previous results mapping male sterility traits in hybrid house mice. This new approach to finding genetic causes of reproductive barriers enabled us to identify a large number of hybrid incompatibilities, involving genomic regions with known roles in hybrid sterility and previously unknown regions. Understanding the number and type of genetic interactions is important for developing accurate models used to reconstruct speciation events. The genetics of hybrid sterility in mice may also contribute to understanding basic processes involved in male reproduction and causes of human infertility. Genetic dissection of reproductive isolation has proven to be an especially powerful strategy for revealing mechanisms of speciation. Many genomic regions and even specific genes that contribute to hybrid defects have been identified by genetic mapping in recombinant populations DrosophilaHybrid sterility and hybrid inviability frequently result from negative epistasis between mutations at interacting genes Mus musculus) is an excellent model for investigating speciation from a network perspective. Genomic resources are abundant for the house mouse, and reproductive processes are well characterized because the mouse is the premier model for fertility research in humans The house mouse (1 hybrids between Mus musculus domesticus and Mus musculus musculus (subsequently referred to as domesticus and musculus) revealed an important role for the X chromosome and identified several contributing autosomal loci Prdm9, a histone methyltransferase Prdm9 from domesticus show pachytene arrest of meiosis. The effects of sterile Prdm9 alleles appear to be due to mutations in the protein-coding sequence and there is evidence for downstream regulatory effects, but the incompatibility network involving Prdm9 has not been revealed.Studies of sterility in F2 hybrids between M. m. domesticus and M. m. musculus recently identified an additional set of autosomal loci, which are predominantly recessive and thus contribute to sterility in second generation and subsequent hybrids. Genetic architectures of F2 sterility traits are complex, involving a moderate number of loci with a range of phenotypic effect sizes Genetic mapping of sterility phenotypes in F1 hybrids provide evidence that sterility is associated with disrupted expression 1musculus-domesticus hybrids showed that many X-linked genes are overexpressed in sterile but not in fertile F1s 2 and later generation hybrids have not been described.Genome-wide studies of gene expression in testis of F2musculus-domesticus hybrids with results from a previous study mapping sterility phenotypes in the same individuals 1s and F2s, and we identify quantitative trait loci (QTL) that cause X chromosome-wide overexpression in hybrids. We report expression quantitative trait loci (eQTL) for a large number of transcripts. We compare the locations of eQTL with sterility QTL, and identify disrupted processes during spermatogenesis based on affected networks. Using a conditional mapping approach, we pinpoint genetic interactions affecting expression. We highlight candidate pathways, processes, and interactions for several loci, which provide insight into the mechanisms underlying their contributions to sterility.Here, we integrate analysis of genome-wide expression in testis from F1 and F2 hybrids to identify major alterations in gene expression pattern associated with sterility in M. m. domesticus  - M. m. musculus  hybrids. Sterility is asymmetric in these crosses: F1 males with musculusPWD mothers (hereafter MxD F1s) are almost always completely sterile whereas F1s with domesticusWSB mothers (hereafter DxM F1s) are fertile 1 males showed significant differences from both parents for all reproductive traits measured. By contrast, all traits in DxM F1s (except seminiferous tubule area) were within the range observed in the parental lines. Trait measurements in MxD F1s and DxM F1s provide ‘fertile’ and ‘sterile’ examples that are useful for assessing trait distributions in F2s.We measured levels of misexpression in F1s . In both F1s, levels of misexpression were higher for X-linked transcripts than autosomal transcripts. On the X chromosome, the number of overexpressed transcripts in MxD F1s was much higher than the number of underexpressed transcripts . The level of underexpression was higher on autosomes, but the difference between levels of over- and underexpression was smaller . These results are consistent with previously reported differences in expression patterns between sterile and fertile F1s Misexpression was markedly higher in testis of MxD F2s varied from 0.9–39.0% transcripts  – opposite of the pattern for autosomal transcripts, a majority of which was positively correlated with testis weight . This re1 and F2 hybrids among genes identified as specific/enriched to different spermatogenic cell types in previous studies 1s, and transcripts specific to somatic and mitotic cells are overexpressed  on a domesticus background 1 hybrid sterility have identified a key incompatibility between chromosomes 17 and X 1s, X overexpression was highest in individuals with a musculusPWD allele on the X that were heterozygous at the chromosome 17 QTL . Interestingly, high levels of overexpression were also observed in individuals with the musculusPWD allele on the X and homozygous for the musculusPWD chromosome 17 allele. These mice had the same genotype as the musculusPWD parental strain at both loci, implying the existence of a domesticusWSB allele elsewhere in the genome involved in a multilocus interaction with the X and 17 QTL.The overexpression QTL we identified on the X chromosome is in the same region associated with overexpression of a set of X-linked transcripts in a recent study using introgression lines carrying regions of the X chromosome from 2 hybrids was reduced Interpreting gene expression patterns from whole testis is complicated because differences in measured expression levels reflect changes in the relative cell-type composition of the tissue in addition to changes in per-cell expression levels trans eQTL.Next, we investigated the genetic basis of gene expression variation in individual transcripts. We identified 16,705–36,753 eQTL, depending on the significance criterion . We usedcis regulatory elements cis eQTL for convenience, although it is possible that they might not act solely in cis . We classified eQTL with peaks within 5 cM of the transcript (probe) position as cis eQTL and eQTL located on a different chromosome from the transcript as trans. We ignored eQTL>5 cM on the same chromosome, because this class might include long-distance cis eQTL in addition to trans eQTL. We identified cis eQTL for 60% of transcripts  transcripts. The number of trans eQTL identified per transcript ranged from one  to seven .The genomic positions of the eQTL and the affected transcripts are shown in  14,807;  and at lcis eQTL , underdominant (9.2%), or overdominant (8.6%). Curiously, musculusPWD alleles were more likely to be dominant among cis  and trans eQTL . We cannot think of an experimental or biological explanation for this bias. The two categories of eQTL differed in effect size (cis eQTL than for trans eQTL (t = 72.3 (d.f. = 15931), P<2.2×10−16). The difference in effect size is also apparent when comparing the peak LOD scores of cis (mean = 25.05) and trans eQTL (mean = 5.94).We next examined eQTL dominance and effect size. Most L 93.8%;  were addect size . The diftrans eQTL, which is commonly observed in eQTL analyses trans hotspots’ are visible as vertical bands in the eQTL heatmap  had eQTL mapped to the proximal X hotspot (0–42 cM). We discuss the massive effect of the X on gene expression in detail below, and relate this pattern to the known importance of the X in hybrid male sterility.We tested for clustering of  heatmap . We idencis and trans eQTL, are broadly consistent with patterns previously described in eQTL studies performed in a variety of (non-hybrid) organisms  in fertile mice was much lower than in the complete sample . Moreover, clustering of trans eQTL was dramatically reduced when only fertile individuals were included , with the effect of the musculusPWD allele consistent with the direction of misexpression. In contrast, the domesticusWSB allele was associated with the sterile pattern in the distal hotspot. Correlations between expression levels of QTT and relative right testis weight provided further corroboration for the inferred sterile alleles for the X hotspots. We expected that the sterile allele would cause lower expression of QTT that were positively correlated with testis weight (lower expression with lower testis weight) and higher expression of QTT negatively correlated with testis weight (higher expression with lower testis weight). Most QTT (83.2%) with lower expression caused by the musculusPWD allele at eQTL in the proximal hotspot were positively correlated with testis weight, and most eQTL in the distal hotspot showed the opposite pattern (64.0% negatively correlated). The converse was observed for low-expression domesticusWSB alleles .The two  pattern . For exatrans hotspots overlapped one or more sterility QTL identified previously in this cross trans hotspot and within the 1.5-LOD interval of a sterility QTL . For five of seven hotspots overlapping sterility QTL, the sterile allele identified on the basis of expression pattern matched the sterility QTL allele  was relatively far from the hotspot  that overlaps the distal-X hotspot . This region likely harbors sterility QTL from musculusPWD and domesticusWSB, providing additional evidence that the role of the X in sterility is complex.Seven of eleven L allele , suggesttrans eQTL and the unexpected association between trans hotspots and sterility. We acknowledge that cis eQTL may play an important role in hybrid sterility and we anticipate they will be useful in future studies to identify and evaluate candidate sterility genes.For the present study, we focus on the dramatic patterns of e.g. transmembrane, glycoprotein, protein kinase-C binding) and specific signaling pathways .Characterizing the QTT affected by each hotspot will provide clues about how the underlying genes disrupt fertility. We used DAVID functional annotation 1 sterility showed lower expression of broad categories, including spermatogenesis (chromosome 17), sexual reproduction (X 0–42 cM), and fertilization (X 0–42 cM), as well as classes suggesting more specific sterility-related functions, including microtubule organizing center (chromosome 17) and flagellum/microtubule-associated flagellum (X 0–42 cM). The chromosome-10 hotspot is coincident with an abnormal sperm morphology QTL  directly regulate expression of many genes, (2) indirectly affect gene regulation by disrupting an upstream pathway/process, or (3) directly/indirectly cause a change in testis cell composition resulting in altered relative expression of genes from different cell types. We were concerned that many sterility loci might cause similar changes in cell-type composition, in which case annotating hotspots might not be useful for characterizing the function and identify of specific causative genes. If there were a general ‘sterile’ expression pattern we would expect eQTL to be shared across hotspots, particularly for QTT associated with QTL for the same sterility phenotype. In contrast, most QTT with eQTL in hotspots were associated with a single hotspot . Variation among QTT associated with different trans hotspots and sterility QTL indicates that annotation of these QTT will be informative about functions of individual sterility genes with effects on gene regulation or cell composition. Candidate genes in each hotspot with known roles in male reproduction and/or gene regulation are listed in As discussed above, gene expression measures from whole testis reflect both absolute (per-cell) expression variation and relative expression variation caused by differences in cell-type composition. Genes underlying 2s analyzed here. To increase power, we treated trans eQTL hotspots as candidate hybrid sterility loci and searched for interactions involving them. We performed conditional mapping of eQTL, using genotypes at candidate loci one at a time as covariates. Genotype covariates included the marker closest to the peak of each of the nine autosomal trans eQTL hotspots, and five markers in the X chromosome trans hotspots  eQTL analysis  has effects on expression suggestive of sterility  have also been associated with male sterility in mice. Derepression is caused by unbalanced copy numbers of multicopy X- and Y-linked regulatory genes 1 studies 1 or F2 hybrids  F2s in this study have a musculusPWD Y  and overexpression/sterility is associated with the musculusPWD allele in the proximal X, which overlaps the positions of Slx/Slxl. Lack of mismatch does not explain lack of PSCR in sterile MxD F1s, however, suggesting there is polymorphism for alleles causing copy number imbalance. QTL for abnormal sperm morphology, similar to phenotypes reported for Sly-deficient males In contrast to previous F hybrids . Decoupl17@13.3 cM (likely Prdm9) and X@15 cM disrupts MSCI, either directly or indirectly. De-repression has a cascading effect on meiotic expression of autosomal genes due to activity of X-linked transcriptional activators/suppressors at stages where they are normally silenced  and the peak in number of trans eQTL within the chromosome-17 hotspot (14.69 Mb) are near Prdm9  showed defective spermiogenesis with aberrant post-meiotic gene expression Lmna (lamin A) knockouts have severely impaired spermatogenesis associated with failed chromosomal synapsis Hils1) is involved in chromatin remodeling during spermatogenesis and has evolved rapidly within rodents Rad51c alleles are infertile due to arrest of spermatogenesis in early meiotic prophase I related to failed double-strand break repair by recombination Functional annotation of QTT identifies affected pathways and processes associated with some hotspots, and provide clues about the mechanisms underlying sterility. Chromatin-related genes were overrepresented among QTT with lower expression associated with the sterile  hotspot . Mouse kPrdm9, the two loci with the most dramatic effects on expression. A gene within the chromosome-10 hotspot, Dnmt3l (DNA methyltransferase 3-like), plays a key role in epigenetic programming during spermatogenesis. Males carrying null alleles at Dnmt3l show phenotypes similar to those documented in F1s associated with the X-17 interaction, including hypogonadism, asynapsis during meiosis, abnormal formation of the sex body, and deregulation of X-linked and autosomal genes Dnmt3l does not have methyltransferase activity but shows sequence similarity to Dnmt3a and Dnmt3b, with which it interacts to promote de novo DNA methylation Dnmt3a was reported previously in sterile F1 hybrids Prdm9 is a histone methyltransferase; while speculative, an interaction between Dnmt3l and Prdm9 is a promising lead. Dnmt3l is essential for several epigenetic processes occurring at different stages of spermatogenesis, including paternal imprinting, transcriptional regulation, chromatin morphogenesis through meiosis, and the histone-protamine transition during spermiogenesis. Interestingly, Dnmt3l interacts with heterochromatin Drosophila sterility gene OdsHInteractions between novel loci and better-characterized regions point to some promising candidates. For example, the chromosome-10 hotspot interacts with the proximal X and the chromosome-17 region containing Conditional mapping revealed several genomic regions involved in the interaction network that did not have previous evidence for involvement in sterility or expression , indicatWe focused here on genome-wide patterns. Detailed characterization of individual loci, and analysis of gene co-expression networks including all related QTT, will yield additional information useful in pinpointing the disrupted pathways causing sterility and prioritizing candidates.The rate of accumulation of Dobzhansky-Muller incompatibilities and the evolution of reproductive barriers between incipient species depend on the genetic architecture of isolating traits. Theoretical models of DMI evolution assume that incompatibilities act independently on barrier traits Network characteristics are also key determinants in accurate modeling of gene-flow dynamics in zones of hybridization. Non-independence of incompatibilities due to interactions of sterility loci with multiple partners is likely to result in stronger selection and slower introgression at those loci because sterility phenotypes are expressed on a variety of genomic backgrounds. Future cline theory should incorporate epistasis with multiple partner loci.Remarkable progress in understanding the genetic basis of speciation has emerged from identification of a growing list of hybrid incompatibility genes 2 intercross can be performed and sterility or inviability phenotypes can be measured. Informative marker discovery is straight-forward and relatively low cost using RADseq The house mouse features a rich set of sophisticated genetic tools and resources, which facilitates collection of reliable genome-scale data and ultimately will enable functional characterization of candidate incompatibility genes. Although identification and characterization of reproductive barrier genes is not feasible in most species, the integrated mapping approach we employed is applicable in a broad range of organisms. For species pairs that can be crossed in the laboratory, a similar FMice were maintained at the University of Wisconsin School of Medicine and Public Health mouse facility according to animal care protocols approved by the University of Wisconsin Animal Care and Use Committee.M. m. domesticus  and M. m. musculus  were performed to generate F1 hybrids. A total of 305 F2 males were generated by mating F1 siblings . Male F2s were euthanized at 70 (±5) days of age. Five sterility phenotypes were quantified: testis weight, sperm count, sperm head shape, proportion of abnormal sperm, and seminiferous tubule area . The left testis was flash-frozen in liquid nitrogen upon dissection and stored at −80°. Testes from musculusPWD (n = 8), domesticusWSB (n = 8), musculusPWD×domesticusWSB F1s (n = 6), and domesticusWSB×musculusPWD F1s (n = 4), were dissected using the same procedure to provide controls for expression analyses. Frozen testis samples were transferred to RNAlater-ICE buffer , shipped to the Max Planck Institute in Plön and stored at −80° until processing.Reciprocal crosses of wild-derived inbred strains of e.g. caused by buffer leakage or dust particles). We used quality control metrics from Feature Extraction protocol GE1_QCMT_Dec08.We used Whole Mouse Genome Microarrays  to measure genome-wide expression in testis. This array contains 43,379 probes surveying 22,210 transcripts from 21,326 genes. We extracted RNA from testis using RNeasy kits . RNA quality (RIN>8) was verified using RNA 6000 Nano kits (Agilent) on a 2100 Bioanalyzer (Agilent). We used single-color Quick-Amp Labeling Kits (Agilent) to amplify and label RNA. The yield (>2 µg) and specific activity (>9.0 pmol Cy3/µg cRNA) of labeling reactions was verified using a NanoDrop ND-1000 UV-VIS Spectrophotometer . Arrays were scanned using a High Resolution Microarray Scanner (Agilent) and raw images processed using Feature Extraction Software (Agilent). Raw images and the distribution of non-uniformity outliers were visually inspected for large spatial artifacts  using BLAT . We used default settings for quality-control flags. Following preprocessing, we removed probes for which the 98musculusPWD, domesticusWSB and reciprocal F1s using t-tests to identify differentially expressed transcripts. To account for multiple comparisons, we used a significance threshold based on the 5% false discovery rate 1s, transcripts were classified as misexpressed if expression was lower or higher than both musculusPWD and domesticusWSB and there was a significant difference >0.5 in mean expression level (log2) between the F1 and both parental lines. For F2s, a transcript was classified as misexpressed in an individual if the expression level was greater/lower than both parental means and the difference was >two standard errors and >0.5 (log2) It is important to note that, using these criteria, some transcripts classified as misexpressed do not have aberrant expression patterns due to hybrid defects  and some transcripts with aberrant expression may fall within the parental range plus arbitrary fold cutoff.We performed pairwise comparisons of expression levels between To classify dominance of eQTL, we compared mean expression levels between genotypic classes at the peak marker/pseudomarker position. If the mean for heterozygotes was intermediate and >two standard errors from both homozygote means, the eQTL was classified as additive. If the heterozygous mean was <two standard errors from one homozygote mean and >two standard errors from the other, the eQTL was classified as dominant. If the heterozygous mean was outside the homozygote range and >two standard errors from the extreme homozygote mean, the eQTL was classified as over- or underdominant.M. m. domesticus in origin M. m. domesticusWSB is more closely related to the classical strains than M. m. musculusPWD, raising the possibility that ascertainment bias will affect expression results. 17,508 probes were differentially expressed between M. m. domesticusWSB and M. m. musculusPWD. More of these probes had higher expression in M. m. musculusPWD  than in M. m. domesticusWSB . This pattern is the opposite of what would be expected if ascertainment bias had a substantial effect. To address this issue more directly, we identified 103 probes with SNPs between the strains using the Perlegen phase 4-release of the mouse resequencing project M. m. musculusPWD (41) was also greater than those with higher expression in M. m. domesticusWSBMicroarray probes were designed using sequences from a variety of libraries including UCSC mRNA (known genes), RefSeq, and RIKEN cDNA. All sources are based on samples from classical laboratory mouse strains, which have genomes predominantly et al1 controls, segregation ratios, and proportion of missing data 2 males and females using a Carter-Falconer mapping function est.map function of R/qtl Genotyping and quality control procedures are described in White et al2 individuals (of 310) without gene expression data. Standard interval mapping was performed using the scanone function of R/qtl et alFor comparison of sterility phenotype QTL and eQTL, we repeated mapping of fertility traits from White 1 hybrids from this cross and previous studies To identify loci causing large-scale disruptions in gene expression, we performed QTL mapping using quantitative measures of misexpression as phenotypes. Misexpression of X- and autosomal transcripts were mapped separately, on the basis of misexpression patterns in sterile Fscanone and the EM method. Genotype probabilities were estimated at 2 cM intervals, assuming a genotyping error rate of 0.001. We used the normal quantile ranks of gene expression values as phenotypes. The normal quantile rank transformation left all expression traits with a shared distribution, allowing us to determine genome-wide significance thresholds by permutation of a single transcript. We performed 10,000 permutations for autosomes and 158,550 for the X. In addition, we performed 360 permutations of the entire expression dataset to identify a more conservative significance threshold based on the probability of observing a single autosomal or X eQTL by chance when mapping all traits. We permuted animal IDs associated with expression data to preserve the correlation structure among transcripts.We mapped eQTL for 24,675 expression traits using P values and determined the FDR and q values using the BioConductor package qvalue with a tuning parameter (λ) estimated by the “smoother” method To estimate the experiment-wide false positive rate, we translated the maximum LOD scores for each chromosome for each transcript into 2s without strong evidence for sterility. We identified ‘fertile’ individuals by performing a principal component analysis of four sterility phenotypes: relative right testis weight, sperm density, total abnormal sperm and seminiferous tubule area. Data for all four phenotypes were available for 286 F2s. Principal component one explains 99.9% variance. We included individuals >20 percentile for PC1 in the ‘fertile’ mapping analysis. Significance thresholds were 3.61 for autosomes, based on 10,000 single-transcript permutations, and 2.80 for the X chromosome, based on and 158,332 permutations.To assess which aspects of the eQTL patterns we observed were related to hybrid sterility vs. subspecies differences unrelated to sterility, we repeated mapping eQTL mapping in 229 Fe.g. pair-scan) are expected to be underpowered to the point of futility. However, under the Dobzhansky-Muller model, hybrid sterility phenotypes are generated by negative epistasis, and each sterility locus is expected to interact with one or more additional loci. Thus, interaction partners of a hybrid-sterility QTL can be mapped by conditioning on the genotype at the candidate locus. This hypothesis-driven approach reduces the number of tests performed to the point that meaningful results can be achieved while controlling for multiple testing. Assuming trans hotspots indeed reflect hybrid incompatibilities, we performed conditional mapping of eQTL using genotypes at a set of 14 candidate loci as covariates, including the marker closest to the peak of each of the nine autosomal trans eQTL hotspots, and five markers in X chromosome trans hotspots. We used additional X genotypes as covariates because the large size of the trans hotspots on the X suggests there may be multiple underlying causative genes.Genetic interactions (epistasis) are difficult to map using standard approaches because the sample size must accommodate both the number of individuals required to recapitulate affected two-locus genotypes as well as overcome uncertainty introduced by a substantial increase in the number of statistical tests performed. Given the large  number of traits mapped in this eQTL analysis, standard approaches for identifying interactions (i) is the difference between LOD scores under the full (LODf) and additive (LODa) models f, LODa, and LODi by permutation. We used the same seed for permutations  under the full and additive models, enabling calculation of LODi for each permutation. eQTL with both LODf and LODi above significance thresholds have significant evidence for an interaction-eQTL. Significance thresholds for each covariate are listed in We performed conditional mapping twice for each genotype covariate, first using an additive model including an effect of the covariate, and second using a full model including an effect of the covariate and a QTL x covariate interaction term. Comparison between these models is necessary to distinguish effects of gene-gene interactions from enhanced detection of eQTL due to reduced residual variation by accounting for the effect of the covariate. The LOD score for the interaction  and for models with additive covariates were similar. For simplicity, we used 3.7  and 2.9 (X) as thresholds for these analyses.trans eQTL hotspots by permutation. To preserve the distribution of eQTL across transcripts, we randomly assigned the position of each observed trans eQTL to a marker/pseudomarker  on another chromosome 10,000 times for the main eQTL analysis and 1,000 times for analyses with covariates. The maximum eQTL count in each window was recorded for each permutation. Hotspots comprise sliding windows with trans eQTL counts greater than the 95th percentile of counts for sliding windows of the same size calculated from permutations.We defined trans-eQTL hotspots with sterility phenotype QTL by permuting the positions of the trans hotspots, while keeping sterility QTL positions fixed at observed locations. For each permutation, positions of 12 non-overlapping hotspots of the same sizes (cM) as the observed data were drawn from the markers and pseudomarkers (2 cM step size) used for QTL mapping. Sterility QTL regions included all positions within the 1.5-LOD interval for any QTL for a sterility phenotype identified by single or multiple QTL mapping analyses in White et al (2011). We performed 10,000 permutations, recording the number of hotspots overlapping sterility QTL, the total number of overlapping markers/pseudomarkers, and the combined length of overlapping (cM) regions for each permutation.We tested for non-random co-localization of To identify classes of genes enriched among QTT, we used the DAVID functional annotation tool trans hotspots and among QTT that have roles in male reproduction and/or regulation of gene expression using reviews of male fertility We identified candidate genes in Figure S1cis and trans eQTL showing additive (add), dominant (dom), underdominant (under) and overdominant (over) effects. (B) Boxplots indicating median  and interquartile range (boxes) for effects of cis and trans eQTL on the autosomes (auto) and X chromosome, measured by difference in mean expression level (log2) between extreme genotypes. Whiskers indicate 1.5× interquartile range. Outliers are shown as points.Dominance and effect sizes of eQTL. (A) Proportions of (TIF)Click here for additional data file.Figure S2trans eQTL mapped in a ‘fertile’ subset of F2s and overlap of trans eQTL hotspots and sterility QTL. Graphs indicate the number of trans eQTL mapped to 4-cM sliding windows using the fertile subset (in dark gray) and full data set (blue dashed) for each chromosome. Chromosome numbers are in bold at the bottom left of their respective plots. Dark blue boxes indicate “trans hotspots,” significantly enriched for trans eQTL. Red boxes indicate positions of sterility QTL identified previously in these mice. Abbreviations indicate phenotype: TW: relative right testis weight, DBT: distal bent tail (sperm morphology), SD: sperm density, H/T: headless/tailless (sperm), ASH: abnormal sperm head morphology, STA: seminiferous tubule area, TAS: total abnormal sperm, PBT: proximal bent tail (sperm morphology), PC1: sperm head shape principal component 1.Reduced clustering of (TIF)Click here for additional data file.Figure S3trans interaction eQTL for 4 cM sliding windows is plotted across the genome for each conditional mapping analysis. The position of the marker genotype used as a covariate is indicated along the y-axis. Autosomal positions are given as chromosome number, “.”, cM position. X-linked markers are given as “X” and cM position.Clustering of ‘interaction’ eQTL identified by conditional mapping. The number of (TIF)Click here for additional data file.Figure S4trans eQTL hotspots . ‘Interaction hotspots’ identified by conditional mapping are shown as ovals above ideograms, color coded and labeled with positions of the marker genotype covariates.Overlap of interaction eQTL hotspots. Red rectangles indicate sterility QTL, with phenotype abbreviations as in (TIF)Click here for additional data file.Figure S5X chromosome interactions by region. Interaction network demonstrating distinct patterns for five X genotype covariates in four regions. Nodes/edges in purple involve only the proximal X region and those in green involve the distal X region. Nodes in gray show evidence for interaction with at least one proximal and one distal X covariate. Edge weight indicates the number of interaction eQTL and node size indicates total number of interactions.(TIF)Click here for additional data file.Table S1Misexpression of transcripts by spermatogenic cell type.(DOCX)Click here for additional data file.Table S2X Chromosome regions.(DOCX)Click here for additional data file.Table S3Significance thresholds for expression quantitative trait locus (eQTL) mapping.(DOCX)Click here for additional data file.


Epigenetic deregulation is considered a common hallmark of cancer. Nevertheless, recent publications have demonstrated its association with a large array of human diseases. Here, we explore the DNA methylation dynamics in blood samples during hematopoietic cell transplant and how they are affected by pathophysiological events during transplant evolution. We analyzed global DNA methylation in a cohort of 47 patients with allogenic transplant up to 12 months post-transplant. Recipients stably maintained the donor’s global methylation levels after transplant. Nonetheless, global methylation is affected by chimerism status. Methylation analysis of promoters revealed that methylation in more than 200 genes is altered 1 month post-transplant when compared with non-pathological methylation levels in the donor. This number decreased by 6 months post-transplant. Finally, we analyzed methylation in IFN-γ, FASL, IL-10, and PRF1 and found association with the severity of the acute graft-versus-host disease. Our results provide strong evidence that methylation changes in blood are linked to underlying physiological events and demonstrate that DNA methylation analysis is a viable strategy for the study of transplantation and for development of biomarkers. DNA methylation is an epigenetic regulatory mechanism essential for cellular differentiation processes and maintenance of cell type-specific gene expression patterns Despite the clinical relevance of epigenetic alterations in human disease, the epigenetic approach to the study of solid organ and bone marrow transplantation has been largely overlooked In this study, we sought to explore how global epigenetic dynamics in blood are affected after transplantation of hematopoietic cells and whether or not DNA methylation patterns change in response to pathophysiological events. For this purpose, we examined global DNA methylation levels and promoter specific methylation in a cohort of 47 patients before and after allogenic hematopoietic cell transplant (HCT). The results indicated that changes in DNA methylation occurred not only during the normal evolution of the transplanted cells, but also in response to pathology. These findings exposed the clinical significance of epigenetic analysis as a diagnostic tool in the field of transplantation.p-value lower than 0.001   was significantly lower than between pre-HCT recipients and post-HCT recipients (ΔMet = 17.930)  . Thus, sIt is not clear which parameters are affecting NBL2, LINE1 or D4Z4 methylation. It has been observed previously that age, gender and race/ethnicity may affect DNA methylation in repetitive elements In summary, these results showed that the recipients stably retained the global methylation status of the donors after transplantation.Because donor’s NBL2 methylation levels apparently remained stable in the host after transplantation, we wanted to analyze whether these values were affected by the transplant outcome. To this end, we focused on two clinical parameters, mixed chimerism and acute GVHD, during the first month post-transplant.p = 0.0014) (p = 0.065) (The control group comprised patients with complete chimerism that did not develop GVHD symptoms during the follow up of the transplant (n = 9). The ΔMet mean value between donors and the recipients 1 month after transplant was higher for patients with mixed chimerism  than in the control group with complete chimerism   . This re 0.0014) . On the  0.0014)  although= 0.065) .Since global DNA methylation patterns in post-transplant patients were equivalent to donor values and those values also appeared to change in response to pathophysiological events, we wanted to evaluate DNA methylation in gene promoters. For this purpose, we performed microarray-based DNA methylation profiling in blood samples from a recipient without acute or chronic GVHD (case 1) and from another recipient with grade III aGVHD that evolved into chronic GVHD (case 2). In this analysis, DNA methylation in each probe was considered to be altered when the value differed more than 20% compared to donor. Using this criterion, we found 227 CpG sites (216 genes) with altered methylation values in the case 1 and 238 CpG sites (226 genes) in the case 2  Data Se, with onp<0.05) but were not significant different between the control group and the recipients with moderate aGVHD symptoms (p = 0.0203) with an AUC value of 0.764, but it was not able to discriminate between patients with moderate and severe aGVHD   symptoms . The dissymptoms . In cont 0.1541) . On the  0.1541) . Finally 0.1541) . These rIn this study, our aims were to develop a comprehensive epigenetic approach to study hematopoietic cell transplantation. We tackled two parallel parameters: 1) global methylation levels by using pyrosequencing based analysis at repetitive DNA elements of LINE1, NBL2, and D4Z4; and 2) promoter DNA methylation profiling by bead array technology and pyrosequencing at specific gene promoters.Changes in NBL2, LINE1 and D4Z4 methylation have been reported in cancer The second question was whether global methylation can change in response to physiological events during HCT. Comparison of NBL2 methylation between patients with complete and mixed chimerism showed that patients with mixed chimerism had a much greater deviation from the donor’s methylation status. Since NBL2 methylation values post-HCT remained stable, this result is easily explained by the mixed contribution of donor and recipient to the blood samples. Global methylation analysis can accurately segregate patients according to chimerism status and therefore transplanted individuals also can be considered chimeric, not only at genetic level, but also at an epigenetic level. Therefore, intrinsic differences found in the NBL2 methylation values between individuals can be used to track down the source of the cells in transplant.On the other hand, we also observed elevated NBL2 ΔMet values in patients with severe aGVHD which could be due to a real methylation drift as a consequence of the disease. These results suggested that NBL2 methylation could be used as a surrogate marker in HCT. Nonetheless, the data did not reach statistical significance.In addition to the global DNA methylation analysis during HCT, we wanted to analyze DNA methylation in gene promoters which is often associated with modulation of gene expression. We performed microarray-based DNA methylation profiling in PB samples from two patients with allogenic cell transplant at two different times post-HCT. Methylation profiles post-HCT revealed that the methylation signatures in blood at 1 month post-transplant are markedly altered relative to their donors. Conversely, 6 months post-HCT, the methylation signatures were very similar to the donor suggesting that methylation profiles normalize overtime. Nonetheless, the difference observed between samples at 1 month post-HCT are unlikely to be due to a methylation drift post-HCT since this was not observed at global level by pyrosequencing analysis of repetitive DNA elements. Because we profiled DNA methylation in whole blood, the difference observed at promoter levels probably reflect changes in the cellular composition of the samples and thus normalization of the methylation profile is mirroring the immune reconstitution in the recipient. Each hematopoietic cell type possesses a specific methylation signature + lymphocyte activation +, NK, and Th1 cells in the blood samples in agreement with an expected expansion of alloreative cells in GVHD. Second, FASL is highly expressed on activated T cells, plays a major role in T-cell mediated cytotoxicity, and thus, it constitutes a key element of the cytolytic response during GVHD in HCT. FASL levels in serum and mRNA levels in CD4+ and CD8+ T cells are higher among patients with aGVHD. + T cells, and is partially regulated by DNA methylation. Additionally, we wanted to explore DNA methylation in gene promoters during transplant evolution in a larger sample set. For this purpose, we examined by pyrosequencing genes typically associated with the immune response to HCT. IFN-γ levels are elevated early during development of GVHD symptoms in both animal models In summary, our results demonstrated that DNA methylation analysis in HCT provides useful information that, with further studies, it may be useful as a diagnostic tool of relevant clinical parameters. The development of robust biomarkers for aGVHD using this strategy will require a much larger discovery set and validation set; nonetheless, our data support the viability of this approach as a source of biomarkers in HCT and paves the way for larger studies. Pyrosequencing-based DNA methylation analysis is relatively inexpensive, is very sensitive, and provides an interesting alternative to protein and mRNA detection in blood samples, allowing the development of novel biomarkers in a large array of human diseases. Further studies will be necessary to fully explore the potential of DNA methylation analysis in the field of hematopoietic cell and solid organ transplantation.DNA samples were obtained from the immunology department of Hospital Virgen de Arraixa in Murcia (Spain) and the Hospital Universitario in Salamanca (Spain) according to approved institutional guidelines. The DNA was obtained from peripheral blood (PB) samples collected monthly from the time of transplant up to 1 year when possible. DNA samples from donors and recipients before transplantation were also obtained. All patients received allogenic hematopoietic cell transplantation and prophylactic pharmacological treatment for graft versus host disease (GVHD). The clinical characteristic of the patients are shown in Sodium bisulfite modification of 200 ng DNA was carried out with the EZ DNA methylation kit  following the manufacturer’s protocol. Pyrosequencing was performed by using the PyroMark kit . Primers are described in 1 − q1)2+ (p2 − q2)2+ …+(pn − qn)2)0.5. By this method, each CpG site within the amplicon was treated as a spacial dimension and the methylation values in each CpG site defined a coordinate in an n-space. Therefore, n equaled the number of CpG sites in the amplicon. The distance between the coordinates obtained from two samples was the differential of methylation (ΔMet). This method allowed a very precise assessment of the methylation drift given that it was not affected by non-informative CpG sites or by opposite methylation drift between CpGs within the same amplicon. In addition, the ΔMet value comprised the methylation drift between two samples in a single value, facilitating statistical analysis and data plotting.The differential of methylation (ΔMet) was developed to allow accurate evaluation of the global methylation drift between two samples by using pyrosequencing based methylation analysis in repetitive DNA elements. The ΔMet was calculated as the Euclidian squared distance in n-space: d =  (http://www.ncbi.nlm.nih.gov/geo/) under de accession number GSE36832. Gene ontology (GO) analysis was performed with the DAVID GO Web-based tool Microarray-based DNA methylation profiling was performed on blood samples from a patient without acute or chronic GVHD and those from a patient with grade III aGVHD that evolved into chronic GVHD. Samples analyzed for each case included donor pre-HCT and recipient samples at 1 month and 6 months post-HCT. Bisulfite conversion of DNA was performed using the EZ DNA Methylation Kit  according to manufacturer’s procedures. Processed DNA samples were hybridized to the HumanMethylation27 DNA Analysis BeadChip  and the arrays were scanned on the Illumina iScan system. Raw data were imported and analyzed with the BeadStudio software  and methylation values were obtained as described www.r-project.org). Due to the asymmetry of the variables, the median was considered the measure of central tendency for statistical calculations. The Wilcoxon test was used to compare the difference between variables. The discriminative ability of the variables was described by receiver operating characteristic (ROC) curves and summarized by the area under the curve (AUC) values. P-values lower than 0.05 were considered significant.Analysis was performed with R.2.10 statistical software Click here for additional data file.Figure S2Promoter DNA methylation analysis in donors and recipients before HCT. Methylation values were measured by pyrosequencing analysis in pre-HCT samples. Significant differences were assessed by the Wilcoxon signed-rank test.(TIF)Click here for additional data file.Table S1Association between age and global DNA methylation in blood samples.(DOC)Click here for additional data file.Table S2Gene ontology analysis 1 month post-HCT.(DOC)Click here for additional data file.Table S3Correlation between DNA methylation and lymphoid levels measured by flow cytometry in blood samples 1 month post-HCT (n = 17) .(DOC)Click here for additional data file.Table S4Primer sets for pyrosequencing and sequences to analyze.(DOC)Click here for additional data file.Data Set S1Promoter DNA methylation profiling using bead arrays and differential methylation analysis after HCT.(XLS)Click here for additional data file.