SPOP, the most commonly mutated gene in primary prostate cancer modulates DNA double strand break (DSB) repair, and that SPOP mutation is associated with genomic instability. In vivo, SPOP mutation results in a transcriptional response consistent with BRCA1 inactivation resulting in impaired homology-directed repair (HDR) of DSB. Furthermore, we found that SPOP mutation sensitizes to DNA damaging therapeutic agents such as PARP inhibitors. These results implicate SPOP as a novel participant in DSB repair, suggest that SPOP mutation drives prostate tumorigenesis in part through genomic instability, and indicate that mutant SPOP may increase response to DNA-damaging therapeutics.Genomic instability is a fundamental feature of human cancer often resulting from impaired genome maintenance. In prostate cancer, structural genomic rearrangements are a common mechanism driving tumorigenesis. However, somatic alterations predisposing to chromosomal rearrangements in prostate cancer remain largely undefined. Here, we show that DOI:http://dx.doi.org/10.7554/eLife.09207.001 Prostate cancer is the most common type of cancer in men in the UK and USA. Cancers develop when cells in the body acquire genetic mutations that allow the cells to grow rapidly and form a mass known as a tumor. Prostate cancer cells from different individuals can carry different genetic mutations, which affects whether the disease progresses and how the tumors respond to medical treatments. This genetic variety arises in cancer cells partly from a phenomenon known as genomic instability, in which DNA mutations accumulate due to defects in DNA repair.Genetic studies of biopsies taken from human prostate cancers have shown that genomic instability causes chromosomes—the structures in which the cell's DNA is organized—to break and then be stuck back together haphazardly. As a result, fragments of chromosomes can end up in the wrong position, be duplicated, or be lost altogether. All of these mutations could spur on the growth of the tumor. However, it is currently not clear why some prostate cancers are more genomically unstable than others, or what exactly causes this instability.Boysen, Barbieri et al. studied prostate cancer cells taken from patients before they started medical treatment. The experiments show that the cancer cells with high levels of genomic instability also often had mutations in a gene that encodes a protein called SPOP. These mutations occur in about 10 percent of men with prostate cancer and appear early in the development of the tumors. Next, they studied the SPOP protein in zebrafish , as well as in mouse and human cells. The experiments show that SPOP normally helps the cell to accurately repair DNA that has been damaged. Mutations in SPOP change the DNA repair process, which lead to genomic instability by increasing the likelihood that broken chromosomes will be stuck back together incorrectly.Further experiments tested drugs known as PARP inhibitors on mouse and human prostate cancer cells. The drugs, which have been recently tested successfully in patients with prostate cancer, block a different method of DNA repair that operates separately to the one that involves SPOP. When both of these pathways were inactivated—one by the SPOP mutation, the other by the drug—the cancer cells died more quickly. Therefore, men that are diagnosed with types of prostate cancer in which the gene that encodes SPOP is mutated might benefit from treatment with PARP inhibitors or other therapies that affect DNA repair.DOI:http://dx.doi.org/10.7554/eLife.09207.002 TMPRSS2-ERG) and copy number aberrations  are a key mechanism driving tumorigenesis  has allowed an unprecedented insight into the alterations underlying cancer. Recently, WGS of treatment naive, clinically localized prostate cancer revealed a striking abundance of genomic rearrangements, in some samples comparable to the number of rearrangements in metastatic prostate cancer . FurtherSPOP (Speckle-type POZ protein) occur in around 10% of prostate cancers and represent the most common non-synonymous mutations in primary prostate cancer  (Mutations in e cancer . SPOP mu (SCNAs) .SPOP mutation is an early event specifically associated with increased intrachromosomal genomic rearrangements. Mechanistically, in vitro and in vivo data suggest that SPOP participates in repair of DNA double strand breaks (DSB), and SPOP mutation impairs homology-directed repair (HDR), instead promoting error-prone non-homologous end joining (NHEJ).Here, we investigated somatic alterations associated with genomic rearrangements in prostate cancer. We show that SPOP—was significantly associated with increased rearrangements , we did not observe significant differences in SCNA burden for any one lesion  dramatically impaired brain and eye development as well as decreased overall body size (Spop knockdown and ectopic expression of wild-type SPOP (SPOP-wt) and SPOP-F133V, the most commonly mutated residue in prostate cancer  of the two major DSB repair pathways, HDR and NHEJ. RAD51 is a component of the HDR pathway and a marker for engagement of the HDR machinery . 53BP1 iV in HDR . We alsoV in HDR . The obsV in HDR .SPOP by siRNA decreased HDR competence in human epithelial cells to a similar level of BRCA1 knockdown  (Human cancers with underlying defects in HDR (such as cancers)  show sencancers) . To testcancers) . This wacancers) . We furtcancers) .10.7554/SPOP (and BRCA1) knockdown with siRNA  in prostate cells treated with olaparib. The comet assay revealed an increase of genomic instability in SPOP-F133V RWPE cells after PARP inhibition, similar to th siRNA . Similarth siRNA . Taken tIn summary, here we report that SPOP, the substrate recognition component of an E3 ubiquitin ligase, is a regulator of the HDR-based DSB repair machinery. Using functional genetic approaches, we find that prostate-specific SPOP mutants deregulate DSB repair by promoting the error-prone NHEJ pathway . ImportaGenomic rearrangements represent critical events deregulating prostate cancer genomes and driving tumorigenesis, although among individual cancer samples, there is marked variability in number and character of structural rearrangements. Here, we identified a genomically unstable subclass of primary prostate cancer characterized by dramatically increased intrachromosomal rearrangements. This subset of clinically localized, treatment-naive prostate cancers display a degree of genomic instability previously thought only to occur in late stage, metastatic tumors. Importantly, increased rearrangements likely result in higher total SCNA burden, which is associated with more aggressive clinical behavior .SPOP, encoding the substrate-recognition component of an E3 ubiquitin ligase complex. SPOP mutations are the most common point mutations in prostate cancer, but their role in promoting prostate cancer pathogenesis remains unclear. SPOP mutations occur early in the history of prostate cancer, based on clonality analysis reported here and presence in the prostate cancer precursor high grade prostatic intraepithelial neoplasia (HG-PIN) (Increased rearrangements were associated with mutations in (HG-PIN) , potentiSPOP mutant cancers, in vivo data nominated a functional role for SPOP in DSB repair, similar to BRCA1. Interestingly, SPOP loss of function induced a developmental phenotype of neuronal degeneration and apoptosis; similar effects have been reported with other genes modulating DNA repair, including BRCA1 . Cells were treated with camptothecin , olaparib , veliparib , or cisplatin (Sigma-Aldrich) at the indicated concentrations. For SPOP expression, lentiviral vectors  coding for mutant or wild-type SPOP were used. Viruses were titrated to equalize infectivity and protein expression. Viral titers were measured by a p24 ELISA . Viral transduction was performed by using Polybrene transfection reagent  at a final concentration of 8 ng/μl. Stable cell lines for WT or mutant SPOP were generated by pLenti viral transduction and subsequent selection by cell sorting for RFP using an Aria-II cell sorter . Stable selection was monitored over time by confirming RFP- and MYC-tagged SPOP expression.SPOPF133V pBTGinv (kind gift from Dr Yu Chen) was used to clone the cDNA subsequently to two inverted loxP sites, as previously described by F133V-IRES-nlsEFP was cloned into Rosa26-Pam1 (Addgene 15036). Vector was targeted into ES cells derived from albino C57B6 mice and injected into C57B6 blastocysts. Cre induction of SPOP-F133V was confirmed in ES cells. R26F133V/+ mice were generated and confirmed though standard genotyping.All mouse studies were approved by the WCMC IACUC under protocol 2012-0065. To generate mice conditionally expressing flag-myc tagged human R26F133V/+ mice and primary MPC lines were generated as described by Murine prostates were isolated from 10-week-old Stable MPC cells were transduced with a retrovirus expressing Cre-ERT2 (kind gift from Yu Chen) followed by puromycin selection. Following 48-hr treatment of the MPC cells with 1 μM 4-hydroxytamoxifen (Sigma-Aldrich) or vehicle, cells were sorted for GFP positivity using FACSAria II cell sorter (BD Biosciences). Vehicle-treated cells were sorted through a similar process.Reagents for siRNA knockdown of SPOP and BRCA1 were purchased from Dharmacon . The siGenome smartpools were used to target SPOP (M-017919-02-0005), BRCA1 (M-003461-02-0005) as well as non-targeting control (D-001206-13-05). The siRNA oligonucleotides were transfected with Lipofectamine 2000 at a final concentration of 50–100 pmol and incubated for 48 hr.Cells were cultured on gelatin (0.1%) coated coverslips in 12-well plates, treated with CPT  and then recovered for 2 hr in medium without CPT. Cells were collected at indicated time points and fixed in 4% PFA for 20 min, permeabilized in PBS with 0.5% Triton X 100  and blocked in 1% BSA in 1× PBS  for 30 min. Human prostate cells were then incubated in anti-Phospho (Ser139) H2AX , anti-MYC-tag , anti-RAD51 , or anti-53BP1  primary antibodies followed by Alexa Fluor 555 or Alexa Fluor 488 labeled anti-mouse or anti-rabbit secondary antibodies (1:500) . Mouse prostate epithelial cells were incubated in anti-phospho ATM , anti-Rad51 , anti-γH2AX , and anti-SPOP . Pictures were taken on a Zeiss LSM 510 Laser Scanning Confocal Microscope.Cells were lysed as previously described . ProteinC35006) according to the manufacturer's instructions. Data normalization and nonlinear regression to calculate IC50 values was performed with Graph Pad Prism.Human prostate and mouse prostate–epithelial cells (1000–5000) were seeded into 96-well microtiter plates . After irradiation (5GY) cells were treated with olaparib or veliparib in a concentration range of 0.0001 μM–100 μM for 72–96 hr. DMSO was used as control. Cell viability was measured by using the CyQuant NF cell proliferation assay  or olaparib . 50 cells were seeded per well of a 12-well plate, grown for 10–12 days, fixed and stained with ethanol (10%) and crystal violet (0.09%). Colonies containing 50 cells or more were counted, and the drug cytotoxicity was calculated as the ratio of colonies formed after drug treatment relative to colonies from untreated cells.5 cells/ml were mixed with molten LM agarose in a ratio of 1:10 and spread on a comet slide. Cells were subsequently lysed and subjected to electrophoresis. Cells were fixed and stained with SYBR Green. Images were collected with a 20× objective lens using a monochrome camera  fitted in an Olympus BX51 microscope . COMETscore v1.5 was used for image analysis, and 50–100 individual cells were analyzed per condition. The results are shown as comet tail moments, which is a representation of the fluorescence intensity in the tail relative to the head.Single-cell gel electrophoresis (comet assay) was performed according to the manufacturer's instructions . 10Cells were electroporated using the cell line nucleofactor kit V  with positive control plasmids (pNZE) and combinations of HDR-(pDRGFP) or NHEJ-(pPEM1-Ad2-EGFP) reporter constructs and an expression vector for the restriction enzyme I-SceI as described previously . All theCells were fixed in PFA (2% vol/vol in PBS), permeabilized in 0.5% Triton X 100, and blocked in 1% BSA. Cells were incubated overnight in mouse Alexa Fluor 647 labeled anti-MYC-Tag antibodies . SPOP (MYC-tag) and GFP expression was quantified by the LSRII flow cytometer (BD Biosciences).RWPE cells were transduced with LacZ, WT SPOP, or F133V SPOP lentiviral particles. Transduced cells were treated with Nocodazole (40 ng/ml) for 18 hr to synchronize cell cycle. Cell cycle distribution was determined by FACS 24 hr after culturing synchronized cells in normal growth media and labeling cells with propidium iodide (PI).Wild-type zebrafish (strain AB/T) were maintained as described previously . For funEmbryos (24 hpf) were fixed in 4% PFA overnight. Embryos were than washed in PBS Tween (PBT) and Methanol, rehydrated in a series of Methanol-PBT dilutions , and washed three times in PBT. Afterwards embryos were digested with PK . PK digestion was followed by two PBT washes, a refixation in 4% PFA (20 min), and a postfixation in prechilled Ethanol:Acetic acid (2:1) . TUNEL staining was performed by using the ApopTag Peroxidase in situ apoptosis detection kit  according to the manufacturer's instructions. TUNEL-positive cells were labeled by incubating the embryos in 3-3′-diaminobenzidine tetrahydrochloride (DAB).Antisense oligos (morpholinos) targeting Spop were synthesized by Gene Tools . Morpholino 5 (MO5) was targeted to the sequence flanking the border between intron 1 and exon 2. Morpholino 7 (MO7) was targeted to the sequence flanking the border between intron 2 and exon 3. Morpholino sequences are shown in For the generation of pLenti SPOP expression vectors, mutant or SPOP WT was TOPO TA-cloned from pCMV-SPOP vectors  into pLeHuman SPOP mRNA for rescue experiments in the zebrafish was generated using the mMessage mMachine SP6 kit  according to the manufacturer's instructions. RNA was analyzed quantitatively and qualitatively using the Agilent 2100 bioanalyzer.RNA was isolated from zebrafish embryos at 24 hpf using Trizol. 50 water- or morpholino-injected embryos were used. RNA libraries were created and sequenced using the GA2X or HighSeq genome analyzer  as described previously . The filSPOP mutant and SPOP wild-type tumors. Clonality analysis for selected lesions was performed from sequencing data as in Genomic analysis: SCNA profiles for 402 prostate cancers were obtained through processing of high-density oligonucleotide array data upon signal segmentation and then combined for genome-wide analysis for Differential Gene Expression (DGE) and Gene Set Enrichment Analysis: After sorting and indexing, the aligned files were used with bedtools (v2.16.1) and Ensembl Zebrafish Transcriptome (v70) to generate read counts for genes for all samples. These read counts were used with DESeq (2_1.2.10) for differential expression. Orthology was also added to the differential expression table in this step. Read counts were also counts per million (cpm) normalized using EdgeR (v3.4.2) package and then pre-ranked using Log2 Fold Change. GSEA (v2-2.0.13) was run in pre-ranked mode to identify enriched signatures. Primers used for validation of the expression of selected genes are shown in Clinically localized prostate cancers were selected for transcriptome sequencing as described . All samt-test was used to calculate the statistical significance of differences in the HR- and NHEJ-reporter assays. Fisher's exact test (two sided) was used to assess the statistical significance of differences in the foci assays. Nonlinear regression was performed to determine the IC50 values in the in vitro drug sensitivity assays.The results are represented as means of at least three experiments (standard error of the mean (s.e.m.) are indicated by error bars). Student's All RNA-seq data are deposited in the NCBI sequencing read archive (SRA) under the accession number SRP063952. review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and three reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for submitting your work entitled “The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.SPOP mutations in prostate cancers and reveals a new role for SPOP in the DNA damage repair response (DDR). While analyzing data on chromosomal rearrangements and mutational status of hundreds of prostate cancers, the authors make the interesting observation that ‘high rearrangement’ correlates with SPOP mutations. They go on to show that SPOP is involved in late steps of the DDR response, and that SPOP mutations impair homologous recombination (HR) and favor error-prone non-homologous end-joining (NEHJ), which could potentially explain the correlation between SPOP mutations and high frequency of rearrangements. This function resembles that of the DDR factor BRCA1. In fact, SPOP mutations also seem to be mutually exclusive with BRCA1 mutations. Finally, the authors show some data suggesting that SPOP mutations may sensitize cells to PARP inhibitors, which have been shown to be synthetic lethal with BRCA1 mutations.The reviewers considered this manuscript very well written, interesting, and potentially very important for a number of reasons. The manuscript describes the characterization of eLife after addressing some major concerns:The reviewers considered the manuscript to be potentially meritorious for publication in SPOP mutations sensitize cancer cells to PARP inhibitors is currently very weak, based solely on the analysis of RWPE primary prostate cells, RR2v1 prostate cancer cells and embryonic kidney cells (HEK) using artifact-prone overexpression experiments. The conclusion that SPOP mutations could be synthetic lethal with PARP inhibition could have profound implications in the clinic and therefore must be exhaustively tested by the authors. Toward this end, the following experiments are requested:1) The data supporting the conclusion that 1.1) PARP inhibitors should be tested in a dose-response experiment in prostate cancer cell lines expressing wild type or mutant SPOP. We recognize that this request may be challenging due to the limited number of prostate cancer cell lines but we would like to see if the prediction holds in whatever models may be available.1.2) The authors should establish an isogenic system using a prostate cancer cell line of choice currently expressing wild type SPOP and thus resistant to PARP inhibitors, and either knockdown or knock-out SPOP and demonstrate that SPOP depletion is sufficient to confer sensitivity.2) Can the authors account for the discrepancy between their data and the previously published work by Zhang et al. ? This paper shows that SPOP is required for the DDR. Accordingly, Boysen et al. should also test whether SPOP localizes to DSBs following IR and they should more thoroughly present their own DDR data, even though their results were negative.SPOP mutations, but also with recurrent deletions on 5q and 6q, containing the tumor suppressors CHD1 and MAP3K7. Because of clonality studies the authors pursue the role of SPOP mutations only. Though the data regarding contribution of SPOP mutation to DNA damage response and sensitivity to PARP inhibitors are convincing, no attempt is made to see whether losses at 5q and 6q contribute to this phenotype independently or cooperatively with SPOP mutation. 5q CNAs, specifically loss of CHD1, have been implicated in high chromosomal rearrangements, and MAP3K7 is also highly correlated with chromosomal rearrangements. Examining the 5q and 6q status of the patients in SPOP mutant status would be informative and provide a clearer understanding of the contributions of these genes to the DNA damage phenotype (BRCA1 mutant gene set) of this subtype. It would be informative to see how these CNAs co-segregate.3) The manuscript begins with examination of WGS data from 55 treatment naïve prostate cancers and subsequent distribution into 2 chromosomal rearrangement populations – “low-rearrangement” and “high-rearrangement”. The “high-rearrangement” group was significantly associated with SPOP, an E3 substrate binding protein, results in upregulation of specific protein substrates  via a dominant negative mechanism. But this new study by Boyser et al. does not provide a mechanism of disrupted HR. Is SPOP inhibiting the degradation of some critical substrate which is causing the transcriptional response consistent with BRCA1 inactivation? Can the authors provide any additional data in this regard?4) The paper does not provide a molecular mechanism. Specifically, recent studies by Theurillat et al. indicate that mutations in Minor comments [abridged]:SPOP mutations, but the blots in 1) The authors conclude that phosphorylation of ATR is not affected by MDM2 and CCNG1. The authors should comment on this, acknowledging that cell death could be due to p53 activation in this setting.2) In the zebrafish experiments, SPOP knockdown leads to cell death concurrently with induction of p53 mRNA and induced expression of the p53 target genes BRCA2 and ATM most commonly). The authors show some TGCA data for BRCA1. Are these mutations in CRPC (i.e. BRCA2 and ATM) mutually exclusive with SPOP mutations?3) A recent study indicates that a high percentage (15-20%) of metastatic CRPC have underlying mutations in DNA repair genes ( 1) The data supporting the conclusion that SPOP mutations sensitize cancer cells to PARP inhibitors is currently very weak, based solely on the analysis of RWPE primary prostate cells, 22Rv1 prostate cancer cells and embryonic kidney cells (HEK) using artifact-prone overexpression experiments. The conclusion that SPOP mutations could be synthetic lethal with PARP inhibition could have profound implications in the clinic and therefore must be exhaustively tested by the authors. Toward this end, the following experiments are requested:1.1) PARP inhibitors should be tested in a dose-response experiment in prostate cancer cell lines expressing wild type or mutant SPOP. We recognize that this request may be challenging due to the limited number of prostate cancer cell lines but we would like to see if the prediction holds in whatever models may be available.SPOP mutant cancers as a target for PARP inhibition is potentially important. We initially sought prostate cancer cell line models that harbor the SPOP mutation but unfortunately there are none to our knowledge. We also recognize the limitations of overexpressing mutant SPOP in the 22Rv1 prostate cancer cell lines as a model system. Therefore to address this issue, we developed a model system expressing inducible mutant SPOP in mouse prostate organoids. These are derived from a genetically engineered mouse model expressing SPOP-F133V in a Cre dependent manner, using a knock-in to the Rosa26 locus (manuscript in preparation). The protein expression levels of mutant SPOP are approximately the same as endogenous SPOP in this model, mimicking human tumors  The authors should establish an isogenic system using a prostate cancer cell line of choice currently expressing wild type SPOP and thus resistant to PARP inhibitors, and either knockdown or knock-out SPOP and demonstrate that SPOP depletion is sufficient to confer sensitivity.SPOP, similar to knockdown of BRCA1, sensitized 22Rv1 prostate cancer cells to PARP inhibition   . This efSPOP is not observed in human prostate cancer, and therefore may not phenocopy SPOP mutation in all models.While these experiments do support the proposed role of SPOP in DSB repair, they should be interpreted with caution, since deletion of 2) Can the authors account for the discrepancy between their data and the previously published work by Zhang et al. ? This paper shows that SPOP is required for the DDR. Accordingly, Boysen et al. should also test whether SPOP localizes to DSBs following IR and they should more thoroughly present their own DDR data, even though their results were negative.SPOP mutations. Their paper showed that SPOP knockdown with siRNA potentially led to prolongation of DSB repair (as measured by γH2AX foci). While these studies examining the consequences of SPOP knockdown in other cell types are interesting, and suggest a role in the DDR, they were not examined in a prostate context, and SPOP mutations are highly specific to prostate cancer. The tissue context may account for some of the differences between our results. We agree with the reviewers that determining localization of SPOP following γ-irradiation (IR) and co-localization with DNA repair complexes in a prostate context is important. Using immunofluorescence, we show that SPOP has increased nuclear foci following IR in both mouse and human prostate cells  to define the role of SPOP and impact of prostate cancer derived 3) The manuscript begins with examination of WGS data from 55 treatment naïve prostate cancers and subsequent distribution into 2 chromosomal rearrangement populations – “low-rearrangement” and “high-rearrangement”. The “high-rearrangement” group was significantly associated with SPOP mutations, but also with recurrent deletions on 5q and 6q, containing the tumor suppressors CHD1 and MAP3K7. Because of clonality studies the authors pursue the role of SPOP mutations only. Though the data regarding contribution of SPOP mutation to DNA damage response and sensitivity to PARP inhibitors are convincing, no attempt is made to see whether losses at 5q and 6q contribute to this phenotype independently or cooperatively with SPOP mutation. 5q CNAs, specifically loss of CHD1, have been implicated in high chromosomal rearrangements, and MAP3K7 is also highly correlated with chromosomal rearrangements. Examining the 5q and 6q status of the patients in, either alone or in combination with SPOP mutant status would be informative and provide a clearer understanding of the contributions of these genes to the DNA damage phenotype (BRCA1 mutant gene set) of this subtype. It would be informative to see how these CNAs co-segregate.SPOP mutations, and deletions of 5q21 (CHD1) and 6q (MAP3K7) are highly associated, and together constitute a distinct molecular class of prostate cancer. The observation that all of these alterations were associated with increased genomic rearrangements suggests that this represents a genomically unstable molecular class of prostate cancer. To prioritize alterations for functional study, we used two points of rationale:SPOP) cannot be a consequence of deregulated genomic rearrangements.A) Because the phenotype in question is structural genomic rearrangements, genomic deletions (such as CHD1 and MAP3K7) could be causative or consequences of this phenotype, since they result from structural rearrangements. Point mutations  We therefore examined clonality data to determine the earliest event in this class , which prioritized We appreciate the reviewers’ comment and understand that a better exposition of these ideas will improve the message of the manuscript. We have therefore added the following computational analyses and annotation:SPOP mutation. As originally reported, deletions on 5q21.1 and 6q15, spanning CHD1 and MAP3K7 respectively, are significantly concurrent with mutations in SPOP  To address the reviewers’ comments, we have now extended the genomic analysis to include the official TCGA-PRAD (prostate adenocarcinoma) dataset of 333 patients. The revised data for the copy number analysis include a total of 430 patients of which 47 harbor a  in SPOP . When coe lesion .SPOP mutation, CHD1 deletion and MAP3K7 deletion are 93.2%, 66.3%, and 88.1%, respectively.B) Figure panel 1F has now been updated with this larger set. The clonality fraction for SPOP mutations preceding CHD1 (C) Further, we studied the dependencies of the 3 lesions using the larger dataset. The data still supports ing CHD1 . These oing CHD1 .CHD1 and MAP3K7 deletions to the DNA damage phenotype, we included the deletion status in the annotation panel of the dendrogram from CHD1 deletion is borderline significant (in line with the strong association with SPOP mutations); no signal is detected for MAP3K7. We favor the inclusion of this panel as SPOP annotation only in D) To specifically query the contribution of 4) The paper does not provide a molecular mechanism. Specifically, recent studies by Theurillat et al. indicate that mutations in SPOP, an E3 substrate binding protein, results in upregulation of specific protein substrates  via a dominant negative mechanism. But this new study by Boyser et al. does not provide a mechanism of disrupted HR. Is SPOP inhibiting the degradation of some critical substrate which is causing the transcriptional response consistent with BRCA1 inactivation? Can the authors provide any additional data in this regard?SPOP mutations in the prostate cancer context. We agree wholeheartedly that the biochemical mechanism by which SPOP regulates DNA repair is a critical question for the field going forward. Defining the specific substrates potentially responsible for this biology will be a long process. For instance, BRCA1 is also a ubiquitin ligase clearly critical for DNA repair, yet the specific substrate of BRCA1 responsible for this function remains poorly defined over a decade after its discovery. This goal will require long-term mechanistic efforts outside the scope of the current manuscript.In this current manuscript we focus on the biologic implications of Minor comments [abridged]:1) The authors conclude that phosphorylation of ATR is not affected by SPOP mutations, but the blots inare discordant in this sense. Please repeat with several biological replicates to define this issue.The results presented originally were representative of at least three biological replicates . We foun2) In the zebrafish experiments, SPOP knockdown leads to cell death concurrently with induction of p53 mRNA and induced expression of the p53 target genes MDM2 and CCNG1. The authors should comment on this, acknowledging that cell death could be due to p53 activation in this setting.SPOP and p53 are mutual exclusive in localized prostate cancer  A recent study indicates that a high percentage (15-20%) of metastatic CRPC have underlying mutations in DNA repair genes (BRCA2 and ATM most commonly). The authors show some TGCA data for BRCA1. Are these mutations in CRPC (i.e. BRCA2 and ATM) mutually exclusive with SPOP mutations?SPOP mutations and BRCA2 and ATM mutations described in the SU2C/PCF cohort  of CRPC samples would support a continued role of SPOP mutation in compromised DNA repair in CRPC.The reviewers pose an excellent question – mutual exclusivity between SPOP mutation and ATM mutations. Two cancers showed both SPOP mutation and BRCA2 mutation, and one sample had both an ATM mutation and BRCA2 mutation. The sample size  and event rates preclude meaningful statistical analysis . We have added this analysis to As shown below , no CRPC


Donation after cardiac death (DCD) livers are marginal organs for transplant and their use is associated with a higher risk of primary non function (PNF) or early graft dysfunction (EGD). The aim was to determine if microRNA (miRNA) was able to discriminate between DCD livers of varying clinical outcome. DCD groups were categorized as PNF retransplanted within a week (n=7), good functional outcome (n=7) peak aspartate transaminase (AST) ≤ 1000 IU/L and EGD (n=9) peak AST ≥ 2500 IU/L. miRNA was extracted from archival formalin fixed post-perfusion tru-cut liver biopsies. High throughput expression analysis was performed using miRNA arrays. Bioinformatics for expression data analysis was performed and validated with real time quantitative PCR (RT-qPCR). The function of miRNA of interest was investigated using computational biology prediction algorithms. From the array analysis 16 miRNAs were identified as significantly different (p<0.05). On RT-qPCR miR-155 and miR-940 had the highest expression across all three DCD clinical groups. Only one miRNA, miR-22, was validated with marginal significance, to have differential expression between the three groups (p=0.049). From computational biology miR-22 was predicted to affect signalling pathways that impact protein turnover, metabolism and apoptosis/cell cycle. In conclusion, microRNA expression patterns have a low diagnostic potential clinically in discriminating DCD liver quality and outcome. In the climate of organ shortage the donor pool is being expanded by the use of extended criteria/marginal organs as typified by the donation after cardiac death (DCD) liver . The decThe aetiology of graft dysfunction is multifactorial with donor, recipient, preservation and operative factors contributing. The risk of graft dysfunction is highest with DCD livers and typically is associated with a significant ischemia reperfusion injury (I/R). MicroRNAs (miRNAs) are short (21–23 nucleotides) single strands of non-coding RNA that have organ specific and developmental expression with widespread influence on key cellular functions. miRNA is an important control of messenger RNA (mRNA) expression by binding to regulatory sites which are mostly located in the 3′-untranslated region (3’UTR) of mRNA. miRNA control of mRNA is produced either by translational blockade or by affecting mRNA stability leading to its degradation . The aimFrom a prospectively maintained database DCD recipients were sequentially identified and divided into 3 groups that were clinically defined, based upon peak serum aspartate transaminase (AST) in the first 5 days after transplant and the need for reLT. The three DCD groups were ‘PNF’ requiring reLT within a week (n = 7), ‘Good’ functional outcome group AST ≤ 1000IU/L (n = 7) and ‘EGD’ AST ≥ 2500 IU/L (n = 9). In total 23 trucut biopsies were analyzed. All samples had been placed in formalin at the time of transplant after perfusion and processed routinely as a formalin fixed paraffin embedded (FFPE) sample. The samples used were all archival and analyzed anonymously. The study had been approved by the Research Committee, Institute of Liver Studies, King's College Hospital. None of the transplant donors were from a vulnerable population and all donors or next of kin provided written informed consent that was freely given. Donor demographics and recipient clinical data are summarized in The RNA fraction was isolated from the FFPE biopsies using the High Pure FFPE RNA micro kit . Six curls at 10μm thickness were taken from the FFPE blocks to which 800μl Xylene was added. Following deparaffinization RNA was isolated according to manufacturer’s recommendations with an overnight incubation at 55°C to increase yields. The Nanodrop 1000 spectrophotometer  was used for quantification and the sample stored at -80°C for later analysis.Affymetrix GeneChip miRNA 2.0 Arrays  were used and the standard protocols followed. The total number of mature miRNA probes per array is 15,644 from 131 organisms of which 847 are human (miRBase v15). Probe sets for human snoRNAs were derived from snoRNABAse and Ensembl Archive. In brief, total RNA (tRNA) was enriched using microcon columns . 500ng of tRNA was diluted to 100μl with 10mM Tris pH8 and heated at 80°C for 3 minutes, then cooled on ice for 3 minutes. The microcon columns were then calibrated with 50ul of 10mM Tris pH8 and centrifuged . Diluted tRNA was then added to the column and centrifuged . RNA was labelled using the FlashTag Biotin RNA Labelling Kit  following manufacturer protocol. GeneChip miRNA 2.0 Arrays (Affymetrix) were then processed using the GeneChip Hybridization, Wash, and Stain Kit with fluidics script FS450_0003. Scanning was performed using Affymetrix Command Console Software. Microarray data generated is MIAME (minimum information about a microarray experiment) compliant and the raw data has been deposited in the gene expression omnibus (accession number: GSE67689).The array images (CEL files) were processed using Affymetrix miRNA QC Tool, using its Genisphere recommended settings. The workflow includes background correction summarization of multiple probes into an overall probe set intensity with quantile normalization. This procedure generated log2 intensities for the 15,644 probe sets on the chips for each sample. For data analysis and mining Qlucore Omics Explorer (QOE) version 2.1 was used. To determine if there was any intrinsic clustering or outliers in the data unsupervised principal component analysis (PCA) was performed initially. The statistical significance level was set at p = 0.05 with a false discovery rate q value of 0.95. Generation of a heatmap with QOE v2.1 enabled further visualization of the data. For multigroup comparison F test based on ANOVA was used. miRNAs that were significantly (p < 0.05) differentially expressed between the DCD groups was then selected for further study. As the Affymetrix GeneChip miRNA 2.0 Arrays covers 131 different organisms, if there was no miRNA sequence homology with the human counterpart it was excluded from further consideration. RT-qPCR was performed to validate the miRNA that the array analysis had identified as having significant differential expression between the DCD groups. The miRCURY LNA Universal real-time microRNA PCR system was used according to the manufacturer’s protocol (EXIQON Inc. USA). In brief, first strand cDNA was synthesized from 20ng of template RNA to create a miRNA cDNA library. Real time PCR amplification was then performed using SYBR Green master mix (EXIQON Inc. USA), forward and reverse primers for each miRNA species (EXIQON Inc. USA) and ROX reference dye (Invitrogen). The PCR reactions were performed on an Applied Biosystems 7900HT Fast Real Time PCR machine . In each assay, 1 μ L of cDNA was used for amplification PCR cycle of fast condition enzyme activation 20 seconds at 95°C, melt 3 seconds at 95°C, anneal/extend 30 seconds at 60°C, with an added dissociation stage, cycle 40 x. The PCR reactions were carried out in a final volume of 22ul and performed in triplicate.The relative quantitation data was exported for analysis. Initial data analysis was performed with manufacturer software provided with Applied Biosystems 7900HT Fast Real Time PCR machine  to obtain threshold cycle (Ct) value that was determined using default threshold settings. To correct for potential RNA input or RT efficiency biases Ct values were normalised using the average Ct of the endogenous control. Small nucleolar RNAs (snoRNAs), hsa-SNORD49A and hsa-SNORA66, were used as endogenous reference RNA. For miRNA relative expression analysis 2ΔΔCT was used and calculated as follows ΔCT (miRNA Ct—averaged endogenous control Ct) and fold-change to reference sample of normal liver. ‘Normal liver’ was a sample from a non steatotic donation after brain stem (DBD) liver. Due to the range and magnitude of relative miRNA expression, data was log transformed and presented as mean +/- SE. Intergroup comparisons were performed with ANOVA and appropriate post hoc test to compensate for multiple comparisons.A number of publically available databases were explored to characterize the miRNA and to explore putative proteins regulated by a given miRNA. For naming, annotation and published miRNA sequences mirBase database v12 was used. For target analysis and gene ontology clustering GOmir was used . GOmir iAfter normalization and data filtering, the miRNAs from the DCD livers analysed was resolved into 3 principal components as represented by the 3 dimensional scatter plot of the PCA . The PCAmiRNAs with overall high relative expression in all three clinical DCD liver groups were miR-940 and miR-155. All other miRNAs  had a low level of relative expression. On GOmir analysis 38 overlapping gene targets were identified for miR-22, of which 26 were of potential biological relevance in the transplanted liver and are listed in Despite all the advances in liver transplant assessing the donor liver for its ‘transplantability’, remains highly subjective and experience driven. Being able to objectively assess the liver in addition to gaining insight into its molecular pathophysiology has the potential to optimize the use of marginal livers and improve clinical outcome in the recipient . The premiRNAs were discovered in 1993, they are highly conserved, have ubiquitous expression and are acknowledged to be important regulators of gene expression. Each miRNA is predicted to regulate several kinds of gene and modulate/fine tune almost all biological processes ,7,8. NorThe literature on miRNAs and assessment of donor liver quality is limited . What haFrom the DCD miRNA expression data in this present work miR-940 and miR-155 were consistently found to have the highest expression, across all DCD groups of good, EGD and PNF, instead of the typical liver profile of miR-122 and let-7 family member expression dominating . miR-155Overall, the difference in the miRNA expression between the DCD groups of PNF, EGD and good was subtle, with only one miRNA miR-22) appearing to distinguish between groups. miRNA is regarded as a fine tuner of mRNA expression, so a dramatic change in miRNA expression may not necessarily be needed to produce substantial changes in biology, as a small change in miRNA expression can facilitate rapid translational repression and/or degradation of hundreds of target genes . Emergin appearinAnother aspect is most miRNA studies are on differential expression at a fixed time point, with little attention being given to miRNA temporal kinetics ,23. ThisThe finding that miR-22 expression may be of use in distinguishing a DCD liver with PNF from that of EGD, which has the potential to recover, could be biologically plausible. Emerging data from animal models of cellular hibernation show that there is an established sequence of events. When initiated cellular hibernation results in what is described as the ‘survival response’ that alters cell survival, cell cycle, glucose metabolism and protein translation . The priThe anti-proliferative effect of miR-22 has only been demonstrated in a number of cancer models . From stIn summary, microRNA expression patterns appear to have a low diagnostic potential clinically in discriminating DCD liver quality and outcome. However, more study is required to determine whether the ‘survival response’ and miR-22 are important determinants of outcome in DCD liver transplantation.S1 TableSummary of GOmir analysis of microRNA 22 (miR-22) predicted gene targets of potential biological relevance in determining outcome in donation after cardiac death (DCD) liver transplantation of early graft dysfunction (EGD) or primary non function (PNF). GOmir combines the predicted target genes of TargetScan, miRanda, RNAhybrid and PicTar using computerised prediction algorithms supplemented by the experimentally supported targets from TarBase.(DOCX)Click here for additional data file.S2 TableSummary of genes and pathways affected by a microRNA 22 using the prediction software of DIANA-TarBase 6.0 (microT-4) for KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways enrichment.(DOCX)Click here for additional data file.


Induced tolerance status index (ITSI). Naturally occurring naïve autoreactive B cells can escape the “classical” tolerogenic mechanisms of clonal deletion and receptor editing, but remain peripherally tolerized through B cell receptor (BCR) signaling inhibition . ITSI is a statistical index that numerically determines the level of homology between activation patterns of BCR signaling intermediaries in B cells that are either tolerized or activated by auto antigen exposure, and thus quantifies the level of peripheral immune tolerance. The index is based on the logistic regression analysis of phosphorylation levels in a panel of BCR signaling proteins. Our results demonstrate a new approach to identifying autoreactive B cells based on their BCR signaling features.This study examines the loss of peripherally induced B cell immune tolerance in Rheumatoid arthritis (RA) and establishes a novel signaling-based measure of activation in a subset of autoreactive B cells - the  Induced Tolerance Status Index (ITSI).We recently reported about changes in B cell antigen receptor signaling profiles associated with the loss of postdevelopmentally induced peripheral immune tolerance (anergy) in a subset of autoreactive B cells in patients with Rheumatoid arthritis Significant evidence supports the pathogenic role of autoreactive B cells in Rheumatoid arthritis (RA) BCR signaling regulation mechanisms involved in the control of peripheral immune tolerance have been under investigation from the autoimmunity perspective since the development of suitable transgenic mouse models in the 1990's PIT for short) has been identified in humans only recently +CD27−IgD+IgMlow/− peripheral blood B cells (termed NDBin vitro and produce autoreactive Abs (anti-dsDNA and anti-HEp-2). This autoreactivity appears innate, as variable Ig genes show no evidence of somatic hypermutation, indicating that it was not generated during an adaptive immune response. Two major signaling properties distinguish cells with PIT from other B cell types: reduced amplitude of protein phosphorylation and Ca2+ responses to antigen receptor ligation, and elevated BCR-related baseline protein phosphorylation activity in intact cells. These signaling features, described in both humans and mice Despite the landmark studies in mouse models ND cell subset in humans 2+ levels, and that a continuum of functional PIT persists in the mature naïve B cell repertoire After the recent definitive reports on the postdevelopmantally tolerized autoreactive BND cells as a contributing factor in the development of RA and demonstrated that inhibitory BCR signaling cascades involved in the maintenance of PIT are altered in human RA ND cells in RA are illustrated in ND cells and RA Clinical Disease Activity Index (CDAI) +CD27−IgD+IgMlow/− subset of autoreactive BND cells: nominal logistic fit demonstrated that RA predictive value of the combined phosphorylation profile of several major BCR signaling proteins and Ca2+ levels in response to BCR stimulation ND cells was highly significant , while in mature naïve CD19+IgM+ B cells this predictive value was substantially less evident . This moderate effect in CD19+IgM+ cells may be related to the recently recognized immunoregulatory role of B cells with PIT Studies in our laboratory have established the loss of PIT in autoreactive B2 = 0.12, p = 0.0518). However, RA subjects exhibited substantially increased phosphoprotein responses to BCR stimulation in the CD19+CD27−IgD+IgMllow/− B cell subset, as compared to normal controls ), suggesting that the breach of PIT in RA patients occurs regardless of the age  patterns of major BCR signaling intermediaries +CD27−IgD+IgMlow/− BND cells as described in in vitro for 10 min and baseline and phospho-response amplitude levels of individual BCR signal transduction proteins were compared between RA patients and healthy controls +CD27−IgD+IgMlow/− BND cells of RA patients (red) and healthy control subjects (blue) at baseline and in response to anti-BCR stimulation in vitro . This statistical parameter numerically determines the level of homology between BCR phosphoprotein activation patterns in BND cells (CD19+CD27−IgD+IgMlow/−) with intact PIT  vs. the cells with compromised PIT (RA patients), and thus quantifies the level of PIT. The index is based on the logistic regression analysis of phosphorylation levels in the panel of B cell signaling proteins in individual subjects. Phosphorylation of specific BCR signaling proteins was assessed with BD Phosflow assay as mean fluorescence intensity (MFI) of protein-specific phospho-Ab staining as described ND cells that were unmanipulated or stimulated with anti-BCR, and obtained from either RA patients or healthy control subjects To quantify the BCR signaling patterns  in orderThis type of statistical approach (i.e. logistic regression) has been successfully used in another study in our laboratory to compute the Cytokine Score ND cells with assumed dichotomous type of dependant variable (i.e. RA = Yes/No). Because the logistic regression analysis requires complete datasets, some subjects with missing data points for at least one of the phosphoproteins had to be excluded altogether (15 subjects with RA and 12 controls had complete datasets). We designed an algorithm (Appendix 1) to calculate logistic regression coefficients and to construct a composite index that could discriminate RA from healthy control subjects with sensitivity of 88.7% and specificity of 83.3% based on the phosphorylation pattern changes in response to anti-BCR stimulation, and with sensitivity of 73.3% and specificity of 83.3% based on the baseline protein phosphorylation levels. In both instances, the ITSI index predictably distinguished between RA and healthy control groups based on their BND BCR signaling profiles  and from −3.19 to 0.20 (no RA) for baseline MFI phosphorylation values. The mean value of the index ± standard error was 3.37±0.85 (RA) and −2.81±0.99 (no RA) for anti-BCR-stimulated BND cells, and 2.34±0.85 (RA) and −0.91±0.32 (no RA) for baseline MFI phosphorylation values  in RA patients ND cells exhibit a specific type of reversible immune tolerance – the post-developmentally induced peripheral tolerance (PIT), which is attained through selective BCR signaling inhibition  to ubiquitous autoantigens through continuous receptor tuning and BCR signaling inhibition, and our results demonstrate that this tolerogenic mechanism is compromised in RA.We have established the loss of PIT in autoreactive B2+ influx and total protein phosphorylation activity (pTyr), particularly in human B cells. The novelty of our study is in addressing the signaling pathways involved in the maintenance and pathological alterations of PIT in RA with a quantitative approach. ITSI values provide a scale to measure the level of BCR signaling inhibition which sustains the postdevelopmentally induced peripheral immune tolerance and enables future studies of correlations between ITSI and clinical RA scores in large datasets generated by comprehensive phosphoprotein microarrays. Our preliminary findings in a relatively small 8-phosphoprotein panel are encouraging. Furthermore, although murine models of autoimmunity provided important insights into peripheral tolerogenic mechanisms in B cells and suggested that the loss of PIT has clinical significance in autoimmunity, human autoreactive B cells with PIT have not been well studied due to the difficulty of identifying this subset. Recent discovery of the BND subset in humans PIT is a signaling phenomenon occurring late in the development cycle of B cells. However, the signaling aspects of this tolerogenic BCR signaling inhibition mechanism have not been studied in detail beyond the general features such as BCR-mediated CaND cells and severity of RA clinical manifestations ND cells that either have (control group) or do not have (RA group) the PIT. Authors believe this report will be of interest as it demonstrates a new approach to identify autoreactive B cells based on their BCR signaling features, and introduces a novel signaling-based biomarker associated with the loss of B cell immune tolerance in inflammatory autoimmune diseases such as Rheumatoid arthritis.In our previous studies of RA, the conclusions about specific BCR signaling pathways involved in the alterations of PIT were based on direct comparisons of baseline phosphoprotein activation levels and normalized anti-BCR response amplitudes in the context of signaling cascades in RA vs. control groups and on the empirical analysis of phosphorylation/activation levels of specific BCR signaling proteins based on their known functions in the BCR pathways i.e. , as wellThe association of the HLA region with RA was noted when the frequency of individuals with the HLA-Dw4 serotype was found to be increased among RA patients compared with healthy controls Our initial findings strongly support the prospective use of ITSI as a clinically relevant diagnostic measure of the humoral immunity/B cell component in RA, particularly in light of the recent evidence suggesting that peripherally induced immune tolerance in response to the chronic autoantigen stimulation is present in a major fraction of human naive B cells, and the scale of this tolerogenic mechanism is unique to B cells, and that peripheral tolerogenic mechanisms of selective BCR signaling inhibition are compromised in the onset of autoimmunity +CD27−IgD+IgMlow/− subset of autoreactive BND cells, recent reports suggest that another B cell subset with anergic features – IgM+CD21low may also play a role in autoimmunity. According to recent reports +CD27−IgM+CD21+ peripheral blood B cells, but may expand by a few percentage points in some  RA patients +CD21low B cell subset has not been definitively characterized after the initial reports that indicated its potential involvement in RA and in hepatitis C virus infection-related autoimmunity. IgM+CD21low anergic B cells are phenotypically different from the IgM−/low BND subset. Recent evaluations suggest that RA is best considered as a clinical syndrome spanning several disease subsets +CD21low anergic B cells are a promising target for the investigation of their pathogenic role in RA, however, such studies constitute a separate research direction and are beyond the main focus of this study - the IgM−/low BND cells.In addition to the CD19Our results demonstrate a novel approach for identifying autoreactive B cells based on intrinsic BCR signaling features and introduce the ITSI score as signaling-based biomarker associated the loss of peripheral immune tolerance in the autoreactive B cell subset in RA.ITSI discriminates between control and RA groups based on the inhibitory BCR phosphoprotein signaling patterns in autoreactive B cells, and thus confirms the loss of induced peripheral immune tolerance as a pathogenic factor in RA.ND cells.Lower ITSI values reflect increased levels of peripheral immune tolerance and suppressed responses to autoantigens in BND cells in RA.We recommend ITSI as a potential diagnostic measure of activation for innately autoreactive BAppendix S1Logistic regression algorythm (SAS code).(PDF)Click here for additional data file.Data S1Individual subject datapoins with spreads and basic statistical analysis.(PDF)Click here for additional data file.Data S2Detailed description of the logistic regression analysis with model fit statistics, maximum likelihood estimates for each individual phosphoprotein in the pattern, sensitivity and specificity calculation tables, odds ratio estimates and associations of predicted probabilities with observed responses.(PDF)Click here for additional data file.


Atrial fibrillation (AF) involves substantial electrophysiological, structural and contractile remodeling. We hypothesize that characterizing gene expression might uncover important pathways related to AF.±9 years, 55% women) from the Offspring cohort of Framingham Heart Study. The study included 177 participants with prevalent AF, 143 with incident AF during up to 7 years follow up, and 2126 participants with no AF. We identified seven genes statistically significantly up-regulated with prevalent AF. The most significant gene, PBX1 (P = 2.8×10−7), plays an important role in cardiovascular development. We integrated differential gene expression with gene-gene interaction information to identify several signaling pathways possibly involved in AF-related transcriptional regulation. We did not detect any statistically significant transcriptomic associations with incident AF.We performed genome-wide whole blood transcriptomic profiling (Affymetrix Human Exon 1.0 ST Array) of 2446 participants (mean age 66We examined associations of gene expression with AF in a large community-based cohort. Our study revealed several genes and signaling pathways that are potentially involved in AF-related transcriptional regulation. Atrial fibrillation (AF), the most common cardiac arrhythmia, is characterized by electrophysiological, structural and contractile remodeling typically in the left atrium Gene expression is a quantitative and heritable phenotype mediated by the interplay of environmental and genetic factors Identification of gene expression signatures may provide insights into AF pathogenesis and suggest targets for therapeutic intervention. Prior transcriptomic studies were generally limited to small groups of selected samples, which might not represent AF in the community. We aimed to investigate associations of AF with gene expression in whole blood using a large community-based cohort. We also investigated interactions between genes differentially expressed in AF patients.The Framingham Heart Study is a community-based observational cohort designed to investigate cardiovascular disease and its risk factors. Since the study was initiated in 1948, three generations of participants have been enrolled. A total of 5124 participants were enrolled in 1971 in the Offspring Cohort. It consists of children of the Original Cohort and their spouses At each examination, participants were asked about AF status. In addition, their AF status and cardiovascular history were solicited during surveillance interviews biennially The gene expression profiling has been described in detail by Joehanes et al www.r-project.org/).Our primary analysis used prevalent AF cases – AF diagnosed before the blood sample for RNA was drawn. The association between gene expression and AF was assessed by linear mixed effect models to account for familial correlation between Framingham Heart Study Offspring participants. We regressed gene expression on AF status, and adjusted for age and sex. Significant hits were adjusted for additional AF risk factors We applied a dense module searching strategy ±9 years, 55% women) are provided in PBX1 , encodes a transcriptional factor belonging to the PBX homeobox family. The most significant decreased expression gene was REL , which did not reach the significance cutoff. We then tested the association of these genes with prevalent AF adjusting for additional AF risk factors Table S1 in File S1, the associations of these genes were attenuated, but they still reached the nominal significance cutoff (P<0.05).Descriptive characteristics for 2446 eligible participants (mean age 66Table S2 in File S1). The most significant gene was GPCPD1 . The volcano plot of gene expression with incident AF is shown in Figure S1 in File S1.In our secondary analysis, we performed the association of gene expression with incident AF adjusted for age and sex. No genes reached the significance cutoff (FDR<0.05) , suggesting that the exon has the most significant differential expression between prevalent AF cases and controls.Since Affymetrix Exon ST1.0 Array is able to profile transcriptomic signatures at the exon level, we also studied the association of each exon expression with prevalent AF. Table S5 in File S1.We also performed additional analyses to study the correlation of gene expression with various medications, including warfarin, beta blocker, digoxin, and calcium channel blocker. We found that warfarin, beta blocker, digoxin, and calcium channel blocker have 3, 287, 0, and 166 significant genes respectively (FDR<0.05); the most significant genes for each medication are shown in the Table S6 in File S1, all seven prevalent AF-related genes were associated with both sinus rhythm and AF , although the association was attenuated due to the reduced sample size and limited power.In order to further characterize transcriptomic signatures associated with prevalent AF, we stratified samples with prevalent AF based on their status during the blood draw: sinus rhythm (n = 115) and AF (n = 62). As shown in the PBX1, was included in the subnetwork.We applied a dense module searching strategy −9) and hypoxia signaling in the cardiovascular system (FDR = 1.1×10−6) . The most significant pathways were antiproliferative role of transducer of ERBB2 (TOB) in T Cell Signaling (FDR = 3.5×10.1×10−6) Table 3.Gene expression is considered an important bridge to connect genetic variations and diseases PBX1, which encodes a homeodomain transcription factor. The gene plays an important role in cardiovascular development, and orchestrates separate transcriptional pathways to control great-artery patterning and cardiac outflow tract septation PBX1 results in persistent truncus arteriosus in mice SLC7A1, encodes a high affinity cationic amino acid transporter that participates in the total carrier-mediated uptake activity SLC7A1 was found to be associated with hypertension and endothelial dysfunction The most significant gene was Beyond the individual gene association, we also studied the combined association of differential gene expression with AF. Increasing evidence has suggested that the development of AF might result from the disruption of multiple biological pathways Table S7 in File S1, the expression of 49 out of 95 genes was correlated well between microarray and RT-PCR (Pearson’s correlation coefficient >0.5), and 91 genes have the same direction of effect, suggesting robustness of our assay, although it is not a direct validation of top hits for AF. Given that thousands of tests were conducted and the lack of external replications, it is possible that some findings might be false positives even if we used very stringent Bonferroni correction. Because of the observational study design, the associations we observed do not prove causation. Moreover, the participants were middle-age to older adults, largely of European descent, raising uncertainty regarding generalizability to other ethnicities/races. Our current study did not distinguish between atrial fibrillation and atrial flutter, nor patterns of AF, which might have unique gene expression profiles. Future analyses with stratification of rhythm types might uncover unique gene expression profiles specific to each rhythm type.There are several limitations in our study. We found no significant transcriptomic signatures associated with incident AF, although the sample size was similar to that of prevalent AF. The mechanisms contributing to the discrepancy are unclear. We speculate that the discrepancy may be due to different signaling pathways underlying the onset and perpetuation of AF: genes associated with incident AF might be responsible for the initiation of AF, whereas genes associated with prevalent AF might be responsible for the maintenance of AF. Given that RNA was only collected at the eighth examination of the Offspring cohort but not any earlier examinations, it is infeasible to examine the longitudinal changes of transcriptomic signatures for the same participants. Gene expression varies in different tissue types. The transcriptional profiles in this study were measured on whole blood, but the ideal tissue for study AF is the left atrium. However, invasive left atrial specimen collection is unfeasible in a community-based study, and would result in highly selected samples In conclusion, we examined the association of whole blood gene expression with AF in a large community-based cohort. Our study revealed that several genes and signaling pathways might be activated in prevalent AF and may be involved in AF maintenance. Future increase of sample size, longer follow up, and external validation would help to identify additional transcription signatures associated with AF and might provide valuable insights into AF pathogenesis File S1Supplemental Materials.(PDF)Click here for additional data file.


Whether codon usage fine-tunes mRNA translation in mammals remains controversial, with recent papers suggesting that production of proteins in specific Gene Ontological (GO) pathways can be regulated by actively modifying the codon and anticodon pools in different cellular conditions. In this work, we compared the sequence content of genes in specific GO categories with the exonic genome background. Although a substantial fraction of variability in codon usage could be explained by random sampling, almost half of GO sets showed more variability in codon usage than expected by chance. Nevertheless, by quantifying translational efficiency in healthy and cancerous tissues in human and mouse, we demonstrated that a given tRNA pool can equally well translate many different sets of mRNAs, irrespective of their cell-type specificity. This disconnect between variations in codon usage and the stability of translational efficiency is best explained by differences in GC content between gene sets. GC variation across the mammalian genome is most likely a result of the interplay between genome repair and gene duplication mechanisms, rather than selective pressures caused by codon-driven translational rates. Consequently, codon usage differences in mammalian transcriptomes are most easily explained by well-understood mutational biases acting on the underlying genome. Do mammalian cells employ codon usage alterations in groups of transcripts to control protein translation? Although long documented in prokaryotes, single-cell eukaryotes and protozoa, no convincing evidence has been presented to indicate this is the case in mammals. Nevertheless, many recent studies have asserted that codon usage can deviate from the genomic or transcriptomic background in a functionally impactful manner. For instance, one recent study suggests that mammalian genes associated with specific GO terms show ‘codon adaptation’ towards proliferation and differentiation. We have tested a number of closely related hypotheses by collecting and analyzing a comprehensive, genome-wide set of data that quantifies codons in mRNA as well as anticodons in tRNA transcriptomes in extreme cell conditions in human and mouse. The hypotheses our data test include suggestions that codon usage biases in mammals can be actively regulated to match: (a) most-highly differentially expressed protein coding genes, (b) condition specific GO gene sets, (c) housekeeping genes, (d) genes encoding ribosomal proteins, (e) proliferation-associated protein-coding genes. We found that of the codon usage signal that remains after accounting for gene set sizes and other genomic factors is caused largely by the underlying GC content of the genomes. In other words, codon usage biases postulated to exist within mammalian transcriptomes might be explained by small gene sets or via differences in GC content across the genome. The degeneracy of the genetic code means that often several synonymous codons encode the same amino acid. Under the neutral theory of evolution, these synonymous codons should not be affected by selection, as they do not alter the amino acid sequence. However, genome-wide and gene-specific studies have revealed the existence of non-uniform codon usage, frequently referred to as codon bias, across all domains of life –3. CodonWhether codon usage is used to fine-tune levels of protein translation in mammals is actively debated . MaBecause codons are translated into amino acids by tRNAs, the rate at which a protein can be synthesized from an mRNA is influenced by the relationship between tRNA anticodon abundance and codon frequency within the mRNA. Any computational model of this relationship makes assumptions that reflect the underlying structure of the genome. For example, in all organisms, orphan codons that lack a corresponding tRNA are decoded using non-canonical pairing between codons and anticodons, commonly referred to as wobble base pairing . PreviouFurthermore, the multi-cellularity of mammals also means that the frequency of a codon across the whole transcriptome may vary between cell types. Similarly, cell-type-specific tRNA expression can generate appreciably different pools of expressed tRNA anticodons , 26. HowWe previously evaluated the relationship between codon usage and anticodon abundance during different stages of mouse development . The traBy contrast, other recent work has suggested that functional differences do exist in codon usage in subsets of mammalian protein-coding genes—be it highly expressed genes , 31, tisin vitro and in (ii) highly differentiated, quiescent hepatocytes in vivo. If codon usage affects translational efficiency in mammalian cells, we reasoned that a proliferating cancer line and resting differentiated cells should display divergent tRNA abundance and concomitant codon bias.To resolve whether codon usage influences translation in mammalian cells, we designed experiments to quantify the codons used within cell-type specific mRNA transcriptomes, as well as the anticodons present in the corresponding pool of expressed tRNAs. Because recent publications have suggested that codon usage differs most between cells that are undergoing proliferation or differentiation , we chosWhile we had previously only investigated codon usage between whole transcriptomes , here weIn mammals, the codon usage across the entire transcriptome is highly stable, whether considered across species, diverse tissues or during development , 28. HowFirst, we estimated the stochastic variation in codon usage generated by sampling sets of genes randomly from the human genome; set sizes were chosen to match the number of genes associated with actual GO terms that range from 40 to 1 611 genes . For eacp-values to test whether the observed correlation differed from all genes. More than half  of the GO terms fell within the range expected by chance . Nevertheless, the existence of a substantial codon usage bias in many GO categories led us to design experiments to identify the underlying molecular mechanisms, as well as to test whether codon usage differences have functional implications for translation.However, for the remaining 43% of GO terms (177 out of 410), the observed correlations were smaller than expected by chance and cannot be explained by random sampling (We then asked whether codon usage has been optimized for translation: (i) across the entire transcriptome for a specific tissue , (ii) foTo explore the extent of codon adaptation, we generated data from mammalian primary tissues and cell lines with divergent cellular phenotypes. We reasoned that, if the codon usage of sets of genes that drive proliferation have been optimized, then direct comparison of codon usage in quiescent tissues and their derived cancer cell lines should be a simple and powerful model system to test this hypothesis. Therefore, we chose adult liver as a fully differentiated and highly homogeneous organ and two separate, highly proliferating liver cancer cell lines, in both mouse (Hepa1-6 and Hepa1c1c7) and human (HepG2 and Huh7) . In mousTranslational efficiency can be estimated by comparing codon usage in the mRNA transcriptome with tRNA anticodon abundance , 28. ThePrincipal components analysis (PCA) using gene expression counts revealed that our samples cluster by cell type . Using oIf codon usage is optimized for tissue-specific gene expression, then the mRNA codon usage and tRNA anticodon abundance should correspond better within a given cell type than between different cell types . For exaWe next calculated the codon–anticodon correlation of matching mRNA and tRNA transcriptomes in each cell type (hereafter named “matching” conditions) as a proxy for global translational efficiency. We also correlated the mRNA transcriptome of each cell type with the tRNA transcriptome of other cell types (hereafter named “mismatching” conditions) . If selep = 0.38; We observed that the codon–anticodon correlations computed across global transcriptomes were not significantly different between the match and the mismatch conditions   . Our resp = 0.72). We also inspected selected sets of genes in which optimized translational efficiencies could be expected either due to their basic cellular functions or due to their roles in proliferation. Similarly to the comparisons described earlier, no significant differences were observed between the matching and mismatching conditions for housekeeping, ribosomal or proliferation-driving genes in human  or genomic features at the initiation site of the transcript control the overall rate of translation, rather than mRNA codon composition , 57. By Regardless, in mammals, the available evidence suggests that mutational bias is a sufficient explanation for variations in codon usage between sets of genes. Compared to prokaryotes, the mammalian genome is better optimized for complex transcriptional and post-transcriptional regulation than for codon adaptation. Mammals have complex and multi-layered regulatory machineries and, in particular, widespread use of distal regulatory elements and epigenetic modifications. Our results argue strongly that prokaryotes and mammals differ in the extent to which translational efficiency and codon biases are relied upon as a regulatory mechanism.The investigation was approved by the Animal Welfare and Ethics Review Board and followed the Cambridge Institute guidelines for the use of animals in experimental studies under Home Office license PPL 70/7535. Human liver samples were obtained under Human Tissue Act license 08-H0308-117 from the Addenbrooke’s Hospital at the University of Cambridge with patients’ consent.2. At least two independent cell passages or biological samples were obtained for each cell line. The HepG2 and Huh7 cell lines were genotyped by short-tandem repeat genetic profiling (STR) using the PowerPlex_16HS_Cell Line panel and analyzed using Applied Biosystems Gene Mapper ID v3.2.1 software by the external provider Genetica DNA Laboratories . Healthy mouse liver were isolated from Mus musculus domesticus C57BL/6 . We used healthy adult human livers . Cells and liver tissue were either post-mortem cross-linked or fresh-frozen in liquid nitrogen.Liver hepatocellular carcinoma cell lines HepG2 and Huh7 from human and Hepa1-6 and Hepa1c1c7 from mouse were grown to 80% confluency in DMEM (Sigma) supplemented with 10% fetal bovine serum (FBS) and antibiotics (100 μg/μl penicillin and 100 μg/ml streptomycin) at 37°C and 5% COPol III ChIP-seq assays were performed as previously described . BrieflyTotal RNA was extracted using Qiazol reagent (Qiagen) from fresh-frozen cells and liver tissue and treated with DNase . RNA samples were ribosomal RNA depleted . Strand-specific libraries were prepared using d/UTPs  and multEach gene’s codon usage was determined from its canonical coding sequence. For each gene, the canonical coding sequence was defined as the longest annotated, valid coding sequence. Coding sequences were downloaded from Ensembl . We usedH. sapiens available from http://www.geneontology.org/gene-associations/submission/  to map GO terms to their gene sets. We excluded mitochondrial genes, and genes for which we could not find valid coding sequences. We then created random gene sets from the set of all annotated protein-coding genes, with sizes equal to the cardinality of actual GO term sets with at least 40 genes. For each set size, we created 10 000 random gene sets. For each sampled gene set, we calculated the aggregate codon usage as the sum of the genes’ codon frequencies. We then calculated the Spearman correlation between each sampled gene set’s codon usage and the genomic background codon usage, which we defined as the aggregate codon usage of all annotated canonical coding sequences.To assess whether gene set size has an influence on the variation in codon usage, we sampled random sets of genes and calculated the correlation between their codon usage and the genomic background. We used the Gene Ontology Association file for p-value as the fraction of random gene sets of the same size whose codon usage correlation was less than that of the GO term. In other words: the fraction of random gene sets whose codon usage correlation was smaller. We called GO terms with FDR-adjusted p < 0.05 as having a codon usage that diverges more from the genomic background than expected by chance.Next, we calculated the Spearman correlation between the aggregated codon usage of each actual GO term gene set and the exonic background codon usage . From thRNA-seq mapping and quantification was performed using the iRAP pipeline . RNA-seqChIP-seq libraries were mapped to the human reference genome GRCh38 and the mouse reference genome GRCm38, as appropriate, using BWA , writingAll subsequent analyses were performed independently on the human and mouse samples. Only genes that were expressed in at least one of the assayed conditions were used. For mRNA, we required at least one library replicate to have a nonzero count for a gene to be included. For tRNA, a stricter filter was used to account for ChIP noise: first, library size factors were estimated using the method from Anders and Huber . Next, tFor both mRNA and tRNA in human and mouse, differential expression analysis was performed using DESeq2  with thep-values of evidence for differential expression, as the test statistic for the gene set analysis. Furthermore, the log2 fold change was used to indicate the “direction” of enrichment. We then ran Piano with default parameters. We called GO terms significantly enriched if they had an adjusted p-value for directional enrichment (either “dist.dir.up” or “dist.dir.dn”) of p < 0.001.Gene set analysis was performed using Piano  using thGS, from a given mRNA transcriptome by a given tRNA pool, we calculated the gene set’s aggregate codon usage CU for codon c as follows:To estimate translational efficiency of a gene set, xg, normalized by the coding sequence length lg. We also summed the estimated tRNA gene expression of isoacceptor tRNAs to calculate anticodon frequencies. We then calculated the Spearman correlation between the codon usage and the given tRNA pool’s anticodon frequencies by pairing all codons to their corresponding anticodons. Wobble base pairings were thus ignored.That is, we sum the codon frequencies of the canonical coding sequences of each individual gene, multiplied by the estimated gene expression ij (tRNA gene copy number) in the calculation of Wi was replaced by the tRNA abundance estimated from tRNA gene expression; (ii) We used the “initial” s-values, rather than optimised ones, because the optimization procedure by dos Reis [To confirm that this method yielded reliable results, we also calculated translational efficiency in two different ways and checked that our results remained consistent. First, we proceeded as above but accounted for unpaired codons  by pairing them with their unique wobble-paired anticodon when calculating the correlation. Secondly, we calculated the tRNA adaptation index (tAI)  of each dos Reis  assumes p-values, after discarding the lower three quartiles of genes by base mean expression. (iii) The gene sets created by the union of the significantly enriched GO terms in the same contrasts as for upregulated genes. (iv) The set of 408 housekeeping genes, taken from Zhu et al. [p < 0.05. The same set was derived for mouse by translating the human gene names.To test whether an mRNA gene set could be more efficiently translated by matching tRNA pools than by mismatching tRNA pools, we compared the distribution of translation efficiencies for six different gene sets: (i) The whole transcriptome. (ii) The sets of the 200 most upregulated genes for a condition compared to all other dissimilar conditions in turn. Thus, for each condition we have established not just one gene set of upregulated genes, but several—one for each contrast. “Dissimilar” means that we compared healthy liver (and mouse E15.5) with either cancer cell line, but not the cancer cell lines between each other. To find the most upregulated genes, we performed differential expression analysis and picked the 200 genes with the lowest u et al. . For mouu et al. . (vi) Thu et al. , filteriFor each type of gene set, we performed the same analysis: For matching tRNA pools, we calculated the translational efficiency between the mRNA gene set and the tRNA pool for all pairwise combinations of replicates of the matching mRNA-seq and tRNA Pol III ChIP-seq data. For mismatching tRNA pools, we calculated the translational efficiency between the mRNA gene set and the tRNA pool for all pairwise combinations of replicates of RNA-seq data, and all replicates of dissimilar (see above) tRNA Pol III ChIP-seq data. Finally, we performed a one-tailed Mann–Whitney–Wilcoxon test between the distributions of matching and mismatching tRNA pools to determine whether we find any evidence that matching tRNA pools have higher translational efficiency.Similar to Tuller et al. , translaTo compare the variance in codon usage with the variance in translational efficiency, we performed a principal components analysis of the aggregate codon usage across GO terms. First, we calculated the aggregate codon usage for each GO term by summing over the codon frequencies of the canonical coding sequences of its genes. Next, we performed PCA on the matrix of codon usage against GO terms. We plotted the axis of largest variance (PC1) against the translational efficiency of each GO term gene set, calculated as described in the previous section. Next, for each condition we took the set of enriched GO terms and tested whether their translational efficiency was statistically higher than the translational efficiency of non-enriched GO terms using a one-tailed Mann–Whitney–Wilcoxon test. This was done for enriched GO terms in liver compared to either cancer cell line and mouse E15.5, in either cancer cell line compared to liver, and in mouse E15.5 compared to liver.GO term GC content was calculated by concatenating the canonical coding sequences of all genes within a GO term gene set, and calculating their overall GC to ACGT frequency ratio. This was plotted against the PC1 of the codon usage PCA for each GO term.S1 Fig(A) RNA-seq analysis of protein-coding gene expression, differential expression analysis, gene set analysis and codon usage analysis. (B) ChIP-seq analysis of Pol III occupancy at tRNA gene loci and anticodon isoacceptor abundance.(TIF)Click here for additional data file.S2 FigPlots present the correlation of protein-coding gene expression level (log of raw counts in 1 000) between two biological replicates in human liver cell types (A–E). Spearman’s rank correlation coefficients (ρ) are reported in bottom right of each panel.(TIF)Click here for additional data file.S3 FigPlots present the correlation of protein-coding gene expression level (log of raw counts in 1 000) between two biological replicates in mouse liver cell types (A–I). Spearman’s rank correlation coefficients (ρ) are reported in bottom right of each panel.(TIF)Click here for additional data file.S4 FigPlots present the correlation of Pol III binding intensities to tRNA genes (log of raw counts in 1 000) between two biological replicates in human liver cell types (A–C). Spearman’s rank correlation coefficients (ρ) are reported in bottom right of each panel.(TIF)Click here for additional data file.S5 FigPlots present the correlation of Pol III binding intensities to tRNA genes (log of raw counts in 1 000) between two biological replicates in mouse liver cell types (A–D). Spearman’s rank correlation coefficients (ρ) are reported in bottom right of each panel.(TIF)Click here for additional data file.S6 FigRows show cell-type specific expression of protein-coding genes in human (A–C) and mouse (D–F) as well as tRNA genes in human (G–I) and mouse (J–L). Left column: factorial map of the principal components (PC) analysis separates size factor normalized expression levels of global protein-coding genes (A and D) and tRNA genes (G and J). The proportion of variance explained by each principal component is indicated in parenthesis. Middle column: the 3-way (human) or 4-way (mouse) Venn diagram intersects the number of expressed protein-coding (B and E) and tRNA (H and K) genes. Areas are shaded according to cell-type. Right column: The intersection of the row/column for each cell type combination shows the proportion and number (in parenthesis) of differentially to all expressed protein-coding (C and F) and tRNA (I and L) genes. Top right triangle (yellow): up-regulated genes comparison from first, left to right and second, top to bottom. Bottom left triangle (blue): down-regulated genes comparison from first, top to bottom and second, left to right. Color gradient indicates proportional differences .(TIF)Click here for additional data file.S7 FigViolin plots represent the probability density of normalized expression levels (log10 transformed transcripts per million (TPM)) of protein-coding genes that are detectable in human (A) and mouse (B) liver , which are subdivided into genes that are (i) differentially expressed (“DE”) or in the top 200 fraction of those (“DEup”), (ii) associated with GO term with the most significant difference between any two conditions (“GO”) and (iii) encoding either house-keeping (“HK”), ribosomal (“RP”) or proliferation (“PP”) proteins. The median of the data is shown by a white dot, the interquartile range by a wide white line, and the first and third interquartile range by a thin white line. Gene numbers per group are shown in parenthesis.(TIF)Click here for additional data file.S8 FigThe data in this figure is the same as in (TIF)Click here for additional data file.S9 FigThe data in this plot is similar to that in (TIF)Click here for additional data file.S10 FigThe data in this plot is similar to that in (TIF)Click here for additional data file.S11 FigThe data in this plot is similar to that in (TIF)Click here for additional data file.S12 FigTranslation efficiency of each GO term for (A) all cell-types combined  or for (B) each cell-type alone is plotted against adult liver first principal component shown in  (left: h(TIF)Click here for additional data file.S1 Table(TSV)Click here for additional data file.S2 Table(TSV)Click here for additional data file.S3 Table(TSV)Click here for additional data file.S4 Table(TSV)Click here for additional data file.S5 Table(TSV)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.