Biomedical subjects

Benedikt Brors

Publications and source records attributed to Benedikt Brors.

At least 19 recordsLinked to original sources

PubMed2026

Recurrent mechanisms of biallelic epigenetic inactivation reveal new putative tumour suppressor genes in prostate cancer.

The inactivation of tumour suppressor genes is a key step in cancer development, and is usually achieved by homozygous loss. In prostate cancer, however, large genomic regions are often hemizygously lost, which complicates the identification of putative tumour suppressors in these regions. Here, we develop Epi2Hit, an integrative computational method that leverages whole genome sequencing, epigenomic profiling and gene expression to identify biallelic inactivation of tumour suppressor genes involving DNA methylation of promoter and enhancer regions of one allele and genomic loss of the other allele. We apply Epi2Hit to a cohort of 2,021 prostate cancers to discover tumour suppressor genes. In particular, we identify epigenetic biallelic inactivation of ZFHX3 at a recurrence level similar to TP53. Biallelic inactivation of ZFHX3, a transcriptional repressor, leads to upregulation of oncogenes, including MYC and a shorter time to metastasis. Finally, we provide evidence that epigenetic silencing as 2nd hit is particularly enriched in regions with nearby essential genes, precluding homozygous loss.

Prostatic Neoplasms↗

PubMed2026

Integrated signatures define mutational processes in prostate cancer.

Prostate cancer follows a long and heterogeneous disease course with incompletely understood aetiology1. Here we dissect the mutational processes shaping the genomes of 959 donors from the Pan Prostate Cancer Group and assess their clinical relevance. By integrating de novo extracted single-base substitution, insertion-deletion and copy-number signatures with six novel complex structural variant signatures, we identify eight integrated mutational footprints (IMFs) that collectively explain the mutational processes in 85% of primary prostate cancer genomes. IMFs were strongly influenced by regional biases in the genome, most prevalently androgen receptor-mediated mutagenesis and replication stress. Four IMFs, present in 37% of primary tumours, were significantly associated with shorter time to metastasis. These included reactive oxygen-species-driven mutagenesis and both canonical and non-canonical homologous recombination deficiency, the latter being enriched in patients of African ancestry. Extending to the metastatic setting, we found that IMFs predicted sensitivity to androgen receptor pathway inhibitors. Taken together, our study delineates the aetiologies and mutational processes that drive the genomic and clinical heterogeneity of prostate cancer, introduces IMFs as a unifying framework, and highlights their potential to improve both risk stratification and biomarker-guided treatment selection.

Journal Article↗

PubMed2026

EML4-ALK Variant-Specific Genetic Interactions Shape Lung Tumorigenesis.

UNLABELLED: Diverse fusions of echinoderm microtubule-associated protein-like 4 (EML4) and anaplastic lymphoma kinase (ALK) are oncogenic drivers in lung adenocarcinoma. EML4-ALK variants have distinct breakpoints within EML4, but their functional differences remain poorly understood. In this study, we use somatic genome editing to generate autochthonous mouse models of EML4-ALK-driven lung tumors and show that variant 3 (V3) is more oncogenic than variant 1 (V1). By using multiplexed genome editing and quantifying the effects of 29 putative tumor-suppressor genes on V1- and V3-driven lung cancer growth, we show that many tumor-suppressor genes have variant-specific effects on tumorigenesis. Pharmacogenomic analyses further suggest that tumor genotype can influence therapeutic responses. Analysis of human EML4-ALK-positive lung cancers also identified variant-specific differences in their genomic landscapes. These findings suggest that EML4-ALK variants behave more like distinct oncogenes than a uniform entity and highlight the dramatic impact of oncogenic fusion partner proteins and coincident tumor-suppressor gene alterations on the biology of oncogenic fusion-driven cancers. SIGNIFICANCE: EML4-ALK-driven lung cancer is treated as a uniform disease despite the presence of distinct fusion variants in patients. Our findings show that EML4-ALK variants are functionally distinct, which may have implications for the treatment of this cancer type and highlights the need to consider differences among variants of other oncogenic fusions.

Animals↗

PubMed2024

ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers.

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

Humans↗

PubMed2006

Classification of neuroblastoma patients by published gene-expression markers reveals a low sensitivity for unfavorable courses of MYCN non-amplified disease.

Currently, Pubmed lists 385 marker genes for neuroblastoma outcome. Using a customized neuroblastoma-microarray, we evaluated the prognostic impact of the gene-expression pattern of 349 of these candidates (90.6%) in 127 neuroblastoma patients with divergent outcome. By significance analysis of microarrays (SAM) and both uncorrected and Bonferroni-corrected ANOVA, 166/349 (47.5%), 218/349 (62.5%) and 128/349 (36.4%) candidates showed significant differential expression between patients with contrasting outcome. By Prediction Analysis for Microarrays (PAM), a 38-gene-classifier was derived from all markers, which classified patients outcome with an overall accuracy of 78.5%. However, patients with unfavorable outcome of MYCN non-amplified disease were largely misclassified (accuracy: 35%), suggesting that these courses are not identified by current marker genes.

Analysis of Variance↗

PubMed2006

Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.

PURPOSE: To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease. PATIENTS AND METHODS: Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression-based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States. RESULTS: The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [favorable; n = 115] v 0.52 +/- 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P < .0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 +/- 0.04 v 0.25 +/- 0.15, P < .0001; intermediate-risk 1.00 v 0.57 +/- 0.19, P = .018; high-risk 0.81 +/- 0.10 v 0.56 +/- 0.08, P = .06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]). CONCLUSION: Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.

Biomarkers, Tumor↗

PubMed2006

Differential expression of neuronal genes defines subtypes of disseminated neuroblastoma with favorable and unfavorable outcome.

PURPOSE: Identification of molecular characteristics of spontaneously regressing stage IVS and progressing stage IV neuroblastoma to improve discrimination of patients with metastatic disease following favorable and unfavorable clinical courses. EXPERIMENTAL DESIGN: Serial analysis of gene expression profiles were generated from five stage IVS and three stage IV neuroblastoma. Differential expression of candidate genes was evaluated by real-time quantitative reverse transcription-PCR in 76 pretreatment tumor samples (stage IVS n=27 and stage IV n=49). Gene expression-based outcome prediction was determined by Prediction Analysis for Microarrays using 38 tumors as a training set and 38 tumors as a test set. RESULTS: Comparison of serial analysis of gene expression profiles from stage IV and IVS neuroblastoma revealed approximately 500 differentially expressed transcripts. Genes related to neuronal differentiation were observed more frequently in stage IVS tumors as determined by associating transcripts to Gene Ontology annotations. Forty-one candidate genes were evaluated by quantitative reverse transcription-PCR and 18 were confirmed to be differentially expressed (P<or=0.001). Classification of patients according to expression patterns of these 18 genes using Prediction Analysis for Microarrays discriminated two subgroups with significantly differing event-free survival (96+/-6% versus 40+/-8% at 3 years; P<0.0001) and overall survival (100% versus 72+/-7% at 3 years; P=0.0003). This classifier was the only independent covariate marker in a multivariate analysis considering the variables stage, age, MYCN amplification, and gene signature. CONCLUSIONS: Spontaneously regressing and progressing metastatic neuroblastoma differ by specific gene expression patterns, indicating distinct levels of neuronal differentiation and allowing for an improved risk estimation of children with disseminated disease.

Child, Preschool↗

PubMed2006

Group testing for pathway analysis improves comparability of different microarray datasets.

MOTIVATION: The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different datasets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, e.g. metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis, functional class scoring (FCS), or global tests. RESULTS: Three public prostate cancer datasets obtained with the same microarray platform (HGU95A/HGU95Av2) were analyzed. Each dataset was subjected to normalization by either variance stabilizing normalization (vsn) or mixed model normalization (MMN). Then, statistical analysis of microarrays was applied to the vsn-normalized data and mixed model analysis to the data normalized by MMN. For multiple testing adjustment the false discovery rate was calculated and the threshold was set to 0.05. Gene lists from the same method applied to different datasets showed overlaps between 42 and 52%, while lists from different methods applied to the same dataset had between 63 and 85% of genes in common. A number of six gene lists obtained by the two statistical methods applied to the three datasets was then subjected to group testing by Fisher's exact test. Group testing by GSEA and global test was applied to the three datasets, as well. Fisher's exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different datasets than the GSEA. However, all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer. Moreover, pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single dataset.

Biomarkers, Tumor↗

PubMed2006

Gene expression signature predicting pathologic complete response with gemcitabine, epirubicin, and docetaxel in primary breast cancer.

PURPOSE: Primary systemic therapy (PST) with gemcitabine (G), epirubicin (E), and docetaxel (Doc) has resulted in a pathologic complete response (pCR) in 26% of primary breast cancer patients. This study was aimed at the identification of a gene expression signature in diagnostic core biopsy tissue samples that predicts pCR. PATIENTS AND METHODS: Core biopsy samples from patients with operable primary breast cancer, T2-4N0-2M0, enrolled onto two phase I and II trials evaluating GEDoc (n = 48) and GE sequentially followed by Doc (GEsDoc; n = 52) as PST were snap frozen and subjected to RNA expression profiling. A signature predicting pCR was discovered in the training set (GEsDoc) applying a support vector machine algorithm, and performance of this classifier was validated on the independent test set (GEDoc) by receiver operator characteristics analysis. RESULTS: We identified a signature consisting of 512 genes, which was enriched in genes involved in transforming growth factor beta and RAS-mediated signaling pathways, that predicts pCR with a sensitivity of 78%, a specificity of 90%, and an overall accuracy of 88% (95% CI, 75% to 95%). Apart from our signature, only HER2 overexpression was an independent predictor of pCR in multivariate analysis. CONCLUSION: In conclusion, our gene expression signature allows prediction of pCR to PST containing G, E, and Doc with unprecedented high overall accuracy and robustness.

Adult↗

PubMed2006

Transient colocalization of X-inactivation centres accompanies the initiation of X inactivation.

The initial differential treatment of the two X chromosomes during X-chromosome inactivation is controlled by the X-inactivation centre (Xic). This locus determines how many X chromosomes are present in a cell ('counting') and which X chromosome will be inactivated in female cells ('choice'). Critical control sequences in the Xic include the non-coding RNAs Xist and Tsix, and long-range chromatin elements. However, little is known about the process that ensures that X inactivation is triggered appropriately when more than one Xic is present in a cell. Using three-dimensional fluorescence in situ hybridization (FISH) analysis, we showed that the two Xics transiently colocalize, just before X inactivation, in differentiating female embryonic stem cells. Using Xic transgenes capable of imprinted but not random X inactivation, and Xic deletions that disrupt random X inactivation, we demonstrated that Xic colocalization is linked to Xic function in random X inactivation. Both long-range sequences and the Tsix element, which generates the antisense transcript to Xist, are required for the transient interaction of Xics. We propose that transient colocalization of Xics may be necessary for a cell to determine Xic number and to ensure the correct initiation of X inactivation.

Animals↗

PubMed2006

A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials.

The use of peripheral blood mononuclear cells (PBMC) for transcriptome analysis has already been proven valuable for assessing disease-associated and drug-response-related gene signatures. While these proof-of-principle studies have been critically important, the instability of RNA within PBMC prohibits their use in large-scale multicenter trials for which samples have to be transported for a prolonged time prior to RNA isolation. Therefore, a prerequisite for transcriptome analysis of peripheral blood in clinical trials will be a standardized and valid method to stabilize the RNA profile immediately after blood withdrawal. Moreover, to be able to perform such large-scale clinical studies routinely in several hundred patients more cost-effective array technologies are required. To address these critical issues, we have combined a whole-blood RNA stabilization technology with a method to reduce globin mRNA, followed by genome-wide transcriptome analysis using a newly introduced BeadChip oligonucleotide technology. We demonstrate that the globin mRNA reduction method results in significantly improved data quality of stabilized RNA samples with low intragroup variance and a detection rate of expressed genes similar to that in PBMC. More important, even small differences in gene expression such as are observed between females and males were detected and sufficient to predict gender in whole-blood samples. We therefore propose the combination of globin mRNA reduction after whole-blood RNA stabilization with a newly introduced cost-effective BeadChip array as the preferred approach for large-scale multicenter trials, especially when establishing predictive markers for disease and treatment outcome in peripheral blood.

Blood Cells↗

PubMed2006

Argonaute--a database for gene regulation by mammalian microRNAs.

MicroRNAs (miRNAs) constitute a recently discovered class of small non-coding RNAs that regulate expression of target genes either by decreasing the stability of the target mRNA or by translational inhibition. They are involved in diverse processes, including cellular differentiation, proliferation and apoptosis. Recent evidence also suggests their importance for cancerogenesis. By far the most important model systems in cancer research are mammalian organisms. Thus, we decided to compile comprehensive information on mammalian miRNAs, their origin and regulated target genes in an exhaustive, curated database called Argonaute (http://www.ma.uni-heidelberg.de/apps/zmf/argonaute/interface). Argonaute collects latest information from both literature and other databases. In contrast to current databases on miRNAs like miRBase::Sequences, NONCODE or RNAdb, Argonaute hosts additional information on the origin of an miRNA, i.e. in which host gene it is encoded, its expression in different tissues and its known or proposed function, its potential target genes including Gene Ontology annotation, as well as miRNA families and proteins known to be involved in miRNA processing. Additionally, target genes are linked to an information retrieval system that provides comprehensive information from sequence databases and a simultaneous search of MEDLINE with all synonyms of a given gene. The web interface allows the user to get information for a single or multiple miRNAs, either selected or uploaded through a text file. Argonaute currently has information on 839 miRNAs from human, mouse and rat.

Animals↗

PubMed2006

Reduced expression of CAMTA1 correlates with adverse outcome in neuroblastoma patients.

PURPOSE: A distal portion of 1p is frequently deleted in human neuroblastomas, and it is generally assumed that this region harbors at least one gene relevant for neuroblastoma development. A 1p36.3 commonly deleted region, bordered by D1S2731 and D1S214 has been defined. The present study surveys whether expression of genes mapping to this region is associated with tumor behavior. EXPERIMENTAL DESIGN: Candidate genes localized within the deleted region were identified by sequence data analysis. Their expression was assessed in a cohort of 49 primary neuroblastomas using cDNA microarray analysis. Gene expression patterns associated with known prognostic markers and patient outcome were further evaluated by quantitative real-time reverse transcription-PCR in a cohort of 102 neuroblastomas. RESULTS: The commonly deleted region spans 261 kb and encompasses two genes, FLJ10737 and CAMTA1. We found no evidence for an association of FLJ10737 expression with established prognostic variables or outcome. In contrast, low CAMTA1 expression characterized tumors with 1p deletion, MYCN amplification, and advanced tumor stages 3 and 4. Moreover, low CAMTA1 expression was significantly associated with poor outcome (P < 0.001). In multivariate analysis of event-free survival, the prognostic information of low CAMTA1 expression was independent of 1p status, MYCN status, tumor stage, and age of the patient at diagnosis (hazard ratio, 3.52; 95% confidence interval, 1.21-10.28; P = 0.02). CONCLUSIONS: Our data suggest that assessment of CAMTA1 expression may improve the prognostic models for neuroblastoma and that it will be important to define the biological function of CAMTA1 in this disease.

Age Factors↗

PubMed2006

Oligonucleotide array-based comparative genomic hybridization (aCGH) of 90 neuroblastomas reveals aberration patterns closely associated with relapse pattern and outcome.

The study of genomic alterations in neuroblastoma is of particular importance since several cytogenetic markers proved to be closely associated with the clinical phenotype. To disclose patterns of gains and losses, we performed high-resolution oligonucleotide array-based comparative genomic hybridization (aCGH). A total cohort of 90 patients was classified into 6 subsets according to tumor stage and outcome: Stages 1-3+ (with event), Stage 1-3- (no event), Stage 4+/-, and Stage 4S+/-. The aberration patterns in Stages 1-3- and 4S- tumors differed from all other groups as they were predominantly characterized by losses (3, 4, 14, X) and gains (7, 17) of whole chromosomes. However, 59/65 (91%) tumors of Stages 1-3+ or Stage 4 revealed numerous structural copy number alterations (sCNA). While deletions in chromosomes 1, 3, and 11 discriminated outcome in Stage 4, there were no specific sCNA that distinguished tumor stage within the subgroup of unfavorable tumors. sCNA in 1p, 3p, 11q, 17q, or MYCN amplification (MNA) was seen among 22/24 patients who died, 10/12 with metastatic relapses, and 5/9 with local recurrences. Detailed breakpoint analyses on chromosomes 1, 3, 11, and 17 disclosed preferred breaking areas, although breakpoints were not identical. Amplifications were found in 18 patients and involved 2p24 (MYCN) and other segments of chromosome 2, as well as regions on chromosome arms 6q, 12q, and 17q. One single feature in 21q21.1 (BU678720, without known function yet) attracted particular attention since five patients showed a homozygous loss of this sequence.

Chromosome Aberrations↗

PubMed2005

Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling.

Neuroblastoma is a common childhood tumor comprising cases with rapid disease progression as well as spontaneous regression. Although numerous prognostic factors have been identified, risk evaluation in individual patients remains difficult. To define a reliable prognostic predictor and gene signatures characteristic of biological subgroups, we performed mRNA expression profiling of 68 neuroblastomas of all stages. Expression data were analysed using support vector machines (SVM-rbf), prediction analysis of microarrays (PAM), k-nearest neighbors (k-NN) algorithms and multiple decision trees. SVM-rbf performed best of all methods, and predicted recurrence of neuroblastoma with an accuracy of 85% (sensitivity 77%, specificity 94%). PAM identified a classifier of 39 genes reliably predicting outcome with an accuracy of 80%. In comparison, conventional risk stratification based on stage, age and MYCN-status only reached a predictive accuracy of 64%. Kaplan-Meier analysis using the PAM classifier indicated a 5-year survival of 20 versus 78% for patients with unfavorably versus favorably predicted neuroblastomas, respectively (P = 0.0001). Significance analysis of microarrays (SAM) identified additional genes differentially expressed among subgroups. MYCN-amplification and high expression of NTRK1/TrkA demonstrated a strong association with specific gene expression patterns. Our data suggest that microarray-derived data in addition to traditional clinical factors will be useful for risk assessment and defining biological properties of neuroblastoma.

Algorithms↗

PubMed2005

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes.

BACKGROUND: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. RESULTS: In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. CONCLUSION: Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance.

Animals↗

PubMed2005

Distinct molecular phenotype of malignant CD34(+) hematopoietic stem and progenitor cells in chronic myelogenous leukemia.

Chronic myelogenous leukemia (CML) is a malignant disorder of the hematopoietic stem cell characterized by the BCR-ABL oncogene. We examined gene expression profiles of highly enriched CD34(+) hematopoietic stem and progenitor cells from patients with CML in chronic phase using cDNA arrays covering 1.185 genes. Comparing CML CD34(+) cells with normal CD34(+) cells, we found 158 genes which were significantly differentially expressed. Gene expression patterns reflected BCR-ABL-induced functional alterations such as increased cell-cycle and proteasome activity. Detoxification enzymes and DNA repair proteins were downregulated in CML CD34(+) cells, which might contribute to genetic instability. Decreased expression of junction plakoglobulin and CXC chemokine receptor 4 (CXCR-4) might facilitate the release of immature precursors from bone marrow in CML. GATA-2 was upregulated in CML CD34(+) cells, suggesting an increased self-renewal in comparison with normal CD34(+) cells. Moreover, we found upregulation of the proto-oncogene SKI and of receptors for neuromediators such as opioid mu1 receptor, GABA B receptor, adenosine A1 receptor, orexin 1 and 2 receptors and corticotropine-releasing hormone receptor. Treatment of CML progenitor cells with the selective adenosine A1 receptor antagonist 8-cyclopentyl-1,3-dipropylxanthine (DPCPX) resulted in a dose-dependent significant inhibition of clonogenic growth by 40% at a concentration of 10(-5) M, which could be reversed by the equimolar addition of the receptor agonist 2-chloro-N6-cyclopentyladenosine (P<0.05). The incubation of normal progenitor cells with DPCPX resulted in an inhibition of clonogenic growth to a significantly lesser extent in comparison with CML cells (P<0.05), suggesting that the adenosine A1 receptor is of functional relevance in CML hematopoietic progenitor cells.

Adult↗

PubMed2005

Promiscuous gene expression in thymic epithelial cells is regulated at multiple levels.

The role of central tolerance induction has recently been revised after the discovery of promiscuous expression of tissue-restricted self-antigens in the thymus. The extent of tissue representation afforded by this mechanism and its cellular and molecular regulation are barely defined. Here we show that medullary thymic epithelial cells (mTECs) are specialized to express a highly diverse set of genes representing essentially all tissues of the body. Most, but not all, of these genes are induced in functionally mature CD80(hi) mTECs. Although the autoimmune regulator (Aire) is responsible for inducing a large portion of this gene pool, numerous tissue-restricted genes are also up-regulated in mature mTECs in the absence of Aire. Promiscuously expressed genes tend to colocalize in clusters in the genome. Analysis of a particular gene locus revealed expression of clustered genes to be contiguous within such a cluster and to encompass both Aire-dependent and -independent genes. A role for epigenetic regulation is furthermore implied by the selective loss of imprinting of the insulin-like growth factor 2 gene in mTECs. Our data document a remarkable cellular and molecular specialization of the thymic stroma in order to mimic the transcriptome of multiple peripheral tissues and, thus, maximize the scope of central self-tolerance.

Animals↗