A Downstream CpG Island Controls Transcript Initiation and Elongation and the Methylation State of the Imprinted Airn Macro ncRNA Promoter

A CpG island (CGI) lies at the 5′ end of the Airn macro non-protein-coding (nc) RNA that represses the flanking Igf2r promoter in cis on paternally inherited chromosomes. In addition to being modified on maternally inherited chromosomes by a DNA methylation imprint, the Airn CGI shows two unusual organization features: its position immediately downstream of the Airn promoter and transcription start site and a series of tandem direct repeats (TDRs) occupying its second half. The physical separation of the Airn promoter from the CGI provides a model to investigate if the CGI plays distinct transcriptional and epigenetic roles. We used homologous recombination to generate embryonic stem cells carrying deletions at the endogenous locus of the entire CGI or just the TDRs. The deleted Airn alleles were analyzed by using an ES cell imprinting model that recapitulates the onset of Igf2r imprinted expression in embryonic development or by using knock-out mice. The results show that the CGI is required for efficient Airn initiation and to maintain the unmethylated state of the Airn promoter, which are both necessary for Igf2r repression on the paternal chromosome. The TDRs occupying the second half of the CGI play a minor role in Airn transcriptional elongation or processivity, but are essential for methylation on the maternal Airn promoter that is necessary for Igf2r to be expressed from this chromosome. Together the data indicate the existence of a class of regulatory CGIs in the mammalian genome that act downstream of the promoter and transcription start

On Path to Informing Hierarchy of Eplet Mismatches as Determinants of Kidney Transplant Loss

Introduction: To mitigate risks related to human leukocyte antigen (HLA) incompatibility, we assessed whether certain structurally defined HLA targets present in donors but absent from recipients, known as eplet mismatches (EMM), are associated with death-censored graft failure (DCGF). Methods: We studied a cohort of 118,313 American 0% panel reactive antibodies (PRA) first kidney transplant recipients (2000 to 2015) from the Scientific Registry of Transplant Recipients. Imputed allele-level donor and recipient HLA-A, -B, -C, -DRB1, and -DQB1 genotypes were converted to the repertoire of EMM. We fit survival models for each EMM with significance thresholds corrected for false discovery rate and validated those in an independent PRA > 0% cohort. We conducted network-based analyses to model relationships among EMM and developed models to select the subset of EMM most predictive of DCGF. Results: Of 412 EMM observed, 119 class I and 118 class II EMM were associated with DCGF. Network analysis showed that although 210 eplets formed profiles of 2 to 12 simultaneously occurring EMMs, 202 were singleton EMMs that were not involved in any profile. A variable selection procedure identified 55 single HLA class I and II EMMs in 70% of the dataset; of those, 15 EMMs (9 singleton and 6 involved in profiles) were predictive of DCGF in the remaining dataset. Conclusion: Our analysis distinguished increasingly smaller subsets of EMMs associated with increased risk of DCGF. Validation of these EMMs as important predictors of transplant outcomes (in contrast to acceptable EMMs) in datasets with measured allele-level genotypes will support their role as immunodominant EMMs worthy of consideration in organ allocation schemes

Image_1_Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study.jpg

IntroductionKidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients’ Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).MethodsWe studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates.ResultsA total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions.ConclusionIn this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.</p

Table_3_Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study.docx

IntroductionKidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients’ Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).MethodsWe studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates.ResultsA total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions.ConclusionIn this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.</p

Table_4_Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study.docx

IntroductionKidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients’ Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).MethodsWe studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates.ResultsA total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions.ConclusionIn this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.</p

Table_1_Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study.docx

IntroductionKidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients’ Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).MethodsWe studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates.ResultsA total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions.ConclusionIn this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.</p

Table_2_Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study.docx

IntroductionKidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients’ Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).MethodsWe studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates.ResultsA total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions.ConclusionIn this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.</p

The <i>Airn</i> CGI plays a major role in <i>Airn</i> transcription and function.

<p>(A) <i>Airn</i> expression by genome-tiling array (left axis) and strand-specific expression analysis by RNA-Seq (right axis) for differentiated <i>S12/+</i> and <i>S12/CGIΔ</i>-1A cells. Dashed arrows: sharp drop of <i>Airn</i> hybridisation signals in the <i>Airn</i>-specific region (single) and absence after 73 kb (doublet). Below: qPCR assays relative to <i>Airn</i>-TSS with colour code as (B,C). Striped box: overlapping START+RP11 assays. (B) qPCR of total+unspliced <i>Airn</i> in d0/d5/d14 differentiated <i>S12/+</i> and four <i>S12/CGIΔ</i> clones shows unspliced <i>Airn</i> is reduced by ∼40% at the 5′ end (RP11/154 bp), but when assayed downstream (<i>Airn</i>-middle/53 kb, <i>Airn</i>-end/99 kb) or at positions which include splice variants (START), is reduced by >70% in <i>S12/CGIΔ</i> cells. Shown are mean and standard deviation of three differentiation sets (details as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g003" target="_blank">Figure 3A</a>). (C) <i>Airn</i> qPCR in <i>S12/+</i> and four <i>S12/CGIΔ</i> d14 clones shows that unspliced <i>Airn</i> is reduced by 79–83% at 0.57 kb and ∼85% at 7.3 kb, while spliced <i>Airn</i> reduced by >85%. Shown are mean and standard deviation of three differentiation sets (details as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g003" target="_blank">Figure 3A</a>). (D) ChIP for Ser5P/Ser2P RNAPII in <i>S12/+</i>, <i>S12/TDRΔ</i>-1A and <i>S12/CGIΔ</i>-1A d11 cells shows unaffected <i>Airn</i> initiation and elongation (except at <i>Airn</i>-end) in <i>TDRΔ</i> and a sharp RNAPII decrease in the <i>CGIΔ</i> allele. The mean and standard deviation of three technical replicates is shown. Assay <i>Airn</i>-132 controls for background from the overlapping <i>Igf2r</i> transcript, which is 2-fold higher in <i>CGIΔ</i> that fails to repress the paternal <i>Igf2r</i> promoter. Map for qPCR assays as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g002" target="_blank">Figure 2</a>. (E) DNA blot analysing methylation of the <i>Igf2r</i> promoter NotI site (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g004" target="_blank">Figure 4A</a>). *methylated fragment in d0 cells originating from feeder-cells. This blot shows that cells carrying a paternal <i>CGIΔ</i> allele contrary to wildtype cells do not gain the methylated 5 kb band on the paternal <i>Igf2r</i> promoter. White lines: indicate the order of samples run on the same gel was changed electronically. (F) qPCR quantifying allelic expression shows absence of <i>Igf2r</i> imprinted expression (Mat∶Pat ratio is close to 1), in four <i>CGIΔ</i> (<i>S12/CGIΔ</i>) cell lines compared to wildtype (<i>S12/+</i>). Three differentiation sets are shown separately due to variability in Mat∶Pat ratios in wildtype controls for each set. Bars represent the mean, error bars the standard deviation of 3 technical replicates (details as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g004" target="_blank">Figure 4C</a>).</p

The methylation-free state of the paternal ICE depends on the CGI.

<p>(A) DNA blot assaying methylation of the <i>Airn</i> promoter MluI site as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g005" target="_blank">Figure 5A</a>, in undifferentiated ES cells carrying a paternal <i>CGIΔ</i> or wildtype (<i>+</i>) allele. The 5.0 kb band identified by probe MEi (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g005" target="_blank">Figure 5A</a> map) indicates a gain of methylation on the <i>CGIΔ</i> paternal allele. This band is weaker in cells with lower passage numbers that still retain the selection cassette (<i>S12/CGIΔ+cas</i>-1,-2) compared to cells that have been in culture for 8 more passages (<i>S12/CGIΔ</i>-1A,-1B,-2A,-2B) with a deleted selection cassette. The lower panel confirms this by showing a matching loss of the unmethylated 1.1 kb fragment specific to the paternal allele in cells with a higher passage number. Both panels were from the same blot and the intervening area lacking any hybridisation signal removed. (B) DNA blot as in (A) assaying <i>Airn</i> promoter MluI methylation during ES cell differentiation showing that the level of paternal methylation on the <i>CGIΔ</i> allele in undifferentiated ES (d0) cells (5 kb band) does not change in differentiated d5 and d14 cells. Probe MEi is a 1 kb EcoRI-MluI fragment shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g005" target="_blank">Figure 5A</a> map. (C) Bisulfite sequencing of two undifferentiated <i>S12/CGIΔ</i> ES cell clones using primers spanning the deletion that specifically amplify the paternal <i>CGIΔ</i> allele, confirms the strong gain of DNA methylation, but also shows that some alleles are more methylated than others (details as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002540#pgen-1002540-g005" target="_blank">Figure 5B</a>).</p