Computational approaches and challenges in the analysis of circRNA data.

Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma.

BACKGROUND: Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD). METHODS: Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements. RESULTS: The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson's R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS. CONCLUSIONS: Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.

Genes positively regulated by Mef2c in cortical neurons are enriched for common genetic variation associated with IQ and educational attainment.

The myocyte enhancer factor 2 C (MEF2C) gene encodes a transcription factor important for neurogenesis and synapse development and contains common variants associated with intelligence (IQ) and educational attainment (EA). Here, we took gene expression data from the mouse cortex of a Mef2c mouse model with a heterozygous DNA binding-deficient mutation of Mef2c (Mef2c-het) and combined these data with MEF2C ChIP-seq data from cortical neurons and single-cell data from the mouse brain. This enabled us to create a set of genes that were differentially regulated in Mef2c-het mice, represented direct target genes of MEF2C and had elevated in expression in cortical neurons. We found this gene-set to be enriched for genes containing common genetic variation associated with IQ and EA. Genes within this gene-set that were down-regulated, i.e. have reduced expression in Mef2c-het mice versus controls, were specifically significantly enriched for both EA and IQ associated genes. These down-regulated genes were enriched for functionality in the adenylyl cyclase signalling system, which is known to positively regulate synaptic transmission and has been linked to learning and memory. Within the adenylyl cyclase signalling system, three genes regulated by MEF2C, CRHR1, RGS6, and GABRG3, are associated at genome-wide significant levels with IQ and/or EA. Our results indicate that genetic variation in MEF2C and its direct target genes within cortical neurons contribute to variance in cognition within the general population, and the molecular mechanisms involved include the adenylyl cyclase signalling system's role in synaptic function.

CCPlotR: an R package for the visualization of cell-cell interactions.

Summary: We present CCPlotR-an R package that generates visualizations of cell-cell interactions. CCPlotR is designed to work with the output of tools that predict cell-cell interactions from single-cell gene expression data and requires only a table of predicted interactions as input. The package can generate a comprehensive set of publication-ready figures such as heatmaps, dotplots, circos plots and network diagrams, providing a useful resource for researchers working on cell-cell interactions. Availability and implementation: CCPlotR is available to download and install from GitHub (https://github.com/Sarah145/CCPlotR) and comes with a toy dataset to demonstrate the different functions. Support for users will be provided via the GitHub issues tracker (https://github.com/Sarah145/CCPlotR/issues).

Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia.

The bone marrow (BM) is a complex microenvironment, coordinating the production of billions of blood cells every day. Despite its essential role and its relevance to hematopoietic diseases, this environment remains poorly characterized. Here we present a high-resolution characterization of the niche in health and acute myeloid leukemia (AML) by establishing a single-cell gene expression database of 339,381 BM cells. We found significant changes in cell type proportions and gene expression in AML, indicating that the entire niche is disrupted. We then predicted interactions between hematopoietic stem and progenitor cells (HSPCs) and other BM cell types, revealing a remarkable expansion of predicted interactions in AML that promote HSPC-cell adhesion, immunosuppression, and cytokine signaling. In particular, predicted interactions involving transforming growth factor ß1 (TGFB1) become widespread, and we show that this can drive AML cell quiescence in vitro. Our results highlight potential mechanisms of enhanced AML-HSPC competitiveness and a skewed microenvironment, fostering AML growth.

nf-core/circrna: a portable workflow for the quantification, miRNA target prediction and differential expression analysis of circular RNAs.

BACKGROUND: Circular RNAs (circRNAs) are a class of covalenty closed non-coding RNAs that have garnered increased attention from the research community due to their stability, tissue-specific expression and role as transcriptional modulators via sequestration of miRNAs. Currently, multiple quantification tools capable of detecting circRNAs exist, yet none delineate circRNA-miRNA interactions, and only one employs differential expression analysis. Efforts have been made to bridge this gap by way of circRNA workflows, however these workflows are limited by both the types of analyses available and computational skills required to run them. RESULTS: We present nf-core/circrna, a multi-functional, automated high-throughput pipeline implemented in nextflow that allows users to characterise the role of circRNAs in RNA Sequencing datasets via three analysis modules: (1) circRNA quantification, robust filtering and annotation (2) miRNA target prediction of the mature spliced sequence and (3) differential expression analysis. nf-core/circrna has been developed within the nf-core framework, ensuring robust portability across computing environments via containerisation, parallel deployment on cluster/cloud-based infrastructures, comprehensive documentation and maintenance support. CONCLUSION: nf-core/circrna reduces the barrier to entry for researchers by providing an easy-to-use, platform-independent and scalable workflow for circRNA analyses. Source code, documentation and installation instructions are freely available at https://nf-co.re/circrna and https://github.com/nf-core/circrna .

Potential Biomarkers of Acute Ischemic Stroke Etiology Revealed by Mass Spectrometry-Based Proteomic Characterization of Formalin-Fixed Paraffin-Embedded Blood Clots.

Background and Aims: Besides the crucial role in the treatment of acute ischemic stroke (AIS), mechanical thrombectomy represents a unique opportunity for researchers to study the retrieved clots, with the possibility of unveiling biological patterns linked to stroke pathophysiology and etiology. We aimed to develop a shotgun proteomic approach to study and compare the proteome of formalin-fixed paraffin-embedded (FFPE) cardioembolic and large artery atherosclerotic (LAA) clots. Methods: We used 16 cardioembolic and 15 LAA FFPE thrombi from 31 AIS patients. The thrombus proteome was analyzed by label-free quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS). MaxQuant v1.5.2.8 and Perseus v.1.6.15.0 were used for bioinformatics analysis. Protein classes were identified using the PANTHER database and the STRING database was used to predict protein interactions. Results: We identified 1,581 protein groups as part of the AIS thrombus proteome. Fourteen significantly differentially abundant proteins across the two etiologies were identified. Four proteins involved in the ubiquitin-proteasome pathway, blood coagulation or plasminogen activating cascade were identified as significantly abundant in LAA clots. Ten proteins involved in the ubiquitin proteasome-pathway, cytoskeletal remodeling of platelets, platelet adhesion or blood coagulation were identified as significantly abundant in cardioembolic clots. Conclusion: Our results outlined a set of 14 proteins for a proof-of-principle characterization of cardioembolic and LAA FFPE clots, advancing the proteome profile of AIS human thrombi and understanding the pathophysiology of ischemic stroke.

Thirteen Independent Genetic Loci Associated with Preserved Processing Speed in a Study of Cognitive Resilience in 330,097 Individuals in the UK Biobank.

Cognitive resilience is the ability to withstand the negative effects of stress on cognitive functioning and is important for maintaining quality of life while aging. The UK Biobank does not have measurements of the same cognitive phenotype at distal time points. Therefore, we used education years (EY) as a proxy phenotype for past cognitive performance and current cognitive performance was based on processing speed. This represented an average time span of 40 years between past and current cognitive performance in 330,097 individuals. A confounding factor was that EY is highly polygenic and masked the genetics of resilience. To overcome this, we employed Genomics Structural Equation Modelling (GenomicSEM) to perform a genome-wide association study (GWAS)-by-subtraction using two GWAS, one GWAS of EY and resilience and a second GWAS of EY but not resilience, to generate a GWAS of Resilience. Using independent discovery and replication samples, we found 13 independent genetic loci for Resilience. Functional analyses showed enrichment in several brain regions and specific cell types. Gene-set analyses implicated the biological process "neuron differentiation", the cellular component "synaptic part" and the "WNT signalosome". Mendelian randomisation analysis showed a causative effect of white matter volume on cognitive resilience. These results may contribute to the neurobiological understanding of resilience.

Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics.

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.

Genes influenced by MEF2C contribute to neurodevelopmental disease via gene expression changes that affect multiple types of cortical excitatory neurons.

Myocyte enhancer factor 2 C (MEF2C) is an important transcription factor during neurodevelopment. Mutation or deletion of MEF2C causes intellectual disability (ID), and common variants within MEF2C are associated with cognitive function and schizophrenia risk. We investigated if genes influenced by MEF2C during neurodevelopment are enriched for genes associated with neurodevelopmental phenotypes and if this can be leveraged to identify biological mechanisms and individual brain cell types affected. We used a set of 1055 genes that were differentially expressed in the adult mouse brain following early embryonic deletion of Mef2c in excitatory cortical neurons. Using genome-wide association studies data, we found these differentially expressed genes (DEGs) to be enriched for genes associated with schizophrenia, intelligence and educational attainment but not autism spectrum disorder (ASD). For this gene set, genes that overlap with target genes of the Fragile X mental retardation protein (FMRP) are a major driver of these enrichments. Using trios data, we found these DEGs to be enriched for genes containing de novo mutations reported in ASD and ID, but not schizophrenia. Using single-cell RNA sequencing data, we identified that a number of different excitatory glutamatergic neurons in the cortex were enriched for these DEGs including deep layer pyramidal cells and cells in the retrosplenial cortex, entorhinal cortex and subiculum, and these cell types are also enriched for FMRP target genes. The involvement of MEF2C and FMRP in synapse elimination suggests that disruption of this process in these cell types during neurodevelopment contributes to cognitive function and risk of neurodevelopmental disorders.

Molecular signatures and clinical outcomes of transplant glomerulopathy stratified by microvascular inflammation and donor-specific antibody.

BACKGROUND: We investigated clinical outcomes and molecular signatures of transplant glomerulopathy (TG) stratified by microvascular inflammation (MVI) and donor-specific antibody (DSA) status. METHODS: We performed a retrospective review of 749 kidney transplant patients who received a for-cause kidney biopsy from 2009 to 2014. We classified TG as MVI positive (MVI+) or MVI negative (MVI-), and with or without DSA. We obtained gene expression profiles for 44 biopsies by Affymetrix HuGene 1.0 ST expression arrays. RESULTS: A total of 100 patients had TG; 49 were MVI+, and 51 were MVI-. After a median post-biopsy follow-up of 2.08 years (range 0.43-4.59), Kaplan-Meier survival analysis demonstrated worse allograft survival in MVI+ TG patients compared with MVI- TG patients (P = 0.01), and time to graft failure was significantly shorter in MVI+ patients (1.08 ± 1.01 years vs 2.3 ± 1.8 years; P = 0.002). DSA status did not affect graft survival within MVI+ or MVI- groups. Analysis of pathogenesis-based transcripts (PBT) showed that MVI+ TG biopsies had increased expression of gamma interferon and rejection (GRIT) and DSA-associated transcripts (DSAST), as observed in antibody-mediated rejection. MVI- TG biopsies had increased expression of cytotoxic and regulatory T cell- and B cell-associated transcripts but not GRIT or DSAST. DSA status had no effect on expression of any PBTs studied in MVI- TG biopsies. CONCLUSIONS: Graft survival in TG is significantly worse in the presence of MVI. Gene expression profiles of MVI+ TG resemble antibody-mediated rejection while gene expression profiles of MVI- TG resemble cell-mediated rejection regardless of DSA status.

Computational Analysis of Neonatal Mouse Ultrasonic Vocalization.

Neonatal vocalization is structurally altered in mouse models of autism spectrum disorder (ASD). Our published data showed that pup vocalization, under conditions of maternal separation, contains sequences whose alterations in a genetic mouse model of ASD impair social communication between pups and mothers. We describe details of a method which reveals the statistical structure of call sequences that are functionally critical for optimal maternal care. Entropy analysis determines the degree of non-random call sequencing. A Markov model determines the actual call sequences used by pups. Sparse partial least squares discriminant analysis (sPLS-DA) identifies call sequences that differentiate groups and reveals the degrees of individual variability in call sequences between groups. These three sets of analyses can be used to identify the otherwise hidden call structure that is altered in mouse models of developmental neuropsychiatric disorders, including not only autism but also schizophrenia. © 2018 by John Wiley & Sons, Inc.

Resveratrol Improves Vascular Function and Mitochondrial Number but Not Glucose Metabolism in Older Adults.

BACKGROUND: Resveratrol, a plant-derived polyphenol, has been reported to improve glucose metabolism and vascular function and to extend life span in animal models, but studies in humans have been inconclusive. METHODS: In a randomized, double-blind crossover study, we treated older glucose-intolerant adults (n = 30) with resveratrol (2-3 g/daily) or placebo, each for 6 weeks. A standard mixed-meal test was used to assess insulin sensitivity (Matsuda index) and secretion (C-peptide deconvolution) and vascular function by reactive hyperemia peripheral arterial tonometry. Skeletal muscle samples were obtained for gene expression using RNA-Seq analysis and to assess mitochondrial morphology. RESULTS: There were no changes in glucose tolerance, insulin sensitivity, weight, blood pressure, or lipid profile following resveratrol treatment. Fasting reactive hyperemia index improved with resveratrol (2.02 ± 0.2 vs 1.76 ± 0.02, p = .002). RNA-Seq analysis yielded 140 differentially expressed transcripts (corrected p-value ≤ .05), predominantly associated with mitochondrial genes and noncoding RNA. Ingenuity Pathway Analysis confirmed that mitochondrial dysfunction (p = 2.77 × 10-12) and oxidative phosphorylation (p = 1.41 × 10-11) were the most significantly perturbed pathways. Mitochondrial number, but not size, was increased. CONCLUSIONS: Resveratrol treatment of older adults with impaired glucose regulation may have beneficial effects on vascular function, but not glucose metabolism or insulin sensitivity. Changes in gene expression suggest effects similar to those observed with caloric restriction, which has been shown to increase life and health span in animal models, although its significance for humans is uncertain. Future human studies should address the appropriate dose range and low bioavailability of resveratrol.

RNA:DNA hybrids in the human genome have distinctive nucleotide characteristics, chromatin composition, and transcriptional relationships.

BACKGROUND: RNA:DNA hybrids represent a non-canonical nucleic acid structure that has been associated with a range of human diseases and potential transcriptional regulatory functions. Mapping of RNA:DNA hybrids in human cells reveals them to have a number of characteristics that give insights into their functions. RESULTS: We find RNA:DNA hybrids to occupy millions of base pairs in the human genome. A directional sequencing approach shows the RNA component of the RNA:DNA hybrid to be purine-rich, indicating a thermodynamic contribution to their in vivo stability. The RNA:DNA hybrids are enriched at loci with decreased DNA methylation and increased DNase hypersensitivity, and within larger domains with characteristics of heterochromatin formation, indicating potential transcriptional regulatory properties. Mass spectrometry studies of chromatin at RNA:DNA hybrids shows the presence of the ILF2 and ILF3 transcription factors, supporting a model of certain transcription factors binding preferentially to the RNA:DNA conformation. CONCLUSIONS: Overall, there is little to indicate a dependence for RNA:DNA hybrids forming co-transcriptionally, with results from the ribosomal DNA repeat unit instead supporting the intriguing model of RNA generating these structures in trans. The results of the study indicate heterogeneous functions of these genomic elements and new insights into their formation and stability in vivo.

Deregulation of the Ras-Erk Signaling Axis Modulates the Enhancer Landscape.

Unrestrained receptor tyrosine kinase (RTK) signaling and epigenetic deregulation are root causes of tumorigenesis. We establish linkage between these processes by demonstrating that aberrant RTK signaling unleashed by oncogenic HRas(G12V) or loss of negative feedback through Sprouty gene deletion remodels histone modifications associated with active typical and super-enhancers. However, although both lesions disrupt the Ras-Erk axis, the expression programs, enhancer signatures, and transcription factor networks modulated upon HRas(G12V) transformation or Sprouty deletion are largely distinct. Oncogenic HRas(G12V) elevates histone 3 lysine 27 acetylation (H3K27ac) levels at enhancers near the transcription factor Gata4 and the kinase Prkcb, as well as their expression levels. We show that Gata4 is necessary for the aberrant gene expression and H3K27ac marking at enhancers, and Prkcb is required for the oncogenic effects of HRas(G12V)-driven cells. Taken together, our findings demonstrate that dynamic reprogramming of the cellular enhancer landscape is a major effect of oncogenic RTK signaling.

Intestinal microbiota-derived metabolomic blood plasma markers for prior radiation injury.

PURPOSE: Assessing whole-body radiation injury and absorbed dose is essential for remediation efforts following accidental or deliberate exposure in medical, industrial, military, or terrorist incidents. We hypothesize that variations in specific metabolite concentrations extracted from blood plasma would correlate with whole-body radiation injury and dose. METHODS AND MATERIALS: Groups of C57BL/6 mice (n=12 per group) were exposed to 0, 2, 4, 8, and 10.4 Gy of whole-body gamma radiation. At 24 hours after treatment, all animals were euthanized, and both plasma and liver biopsy samples were obtained, the latter being used to identify a distinct hepatic radiation injury response within plasma. A semiquantitative, untargeted metabolite/lipid profile was developed using gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry, which identified 354 biochemical compounds. A second set of C57BL/6 mice (n=6 per group) were used to assess a subset of identified plasma markers beyond 24 hours. RESULTS: We identified a cohort of 37 biochemical compounds in plasma that yielded the optimal separation of the irradiated sample groups, with the most correlated metabolites associated with pyrimidine (positively correlated) and tryptophan (negatively correlated) metabolism. The latter were predominantly associated with indole compounds, and there was evidence that these were also correlated between liver and plasma. No evidence of saturation as a function of dose was observed, as has been noted for studies involving metabolite analysis of urine. CONCLUSIONS: Plasma profiling of specific metabolites related to pyrimidine and tryptophan pathways can be used to differentiate whole-body radiation injury and dose response. As the tryptophan-associated indole compounds have their origin in the intestinal microbiome and subsequently the liver, these metabolites particularly represent an attractive marker for radiation injury within blood plasma.

Genomics of BK viremia in kidney transplant recipients.

BACKGROUND: This study aimed to investigate global gene expression profiles of BK viremia and nephropathy (BKVN) samples using microarrays to investigate the immunologic response to BK virus. METHODS: Patients were monitored for BK viremia in the blood monthly for 6 months, then at 9 and 12 months after kidney transplantation. BKVN and normal transplant kidney biopsy samples, and whole blood samples of patients with and without BK viremia were analyzed by Affymetrix Human Gene 1.0 ST Arrays. RESULTS: During a mean follow-up of 917±325 days, 61 of the 289 patients (21%) developed BK viremia at a median 149 (27, 1,113) days after transplantation with a median peak PCR titers of 35,900 (1,000, 2,677,000). The only significant risk factor for development of BK viremia was induction with anti-thymocyte globulin (P=0.03). Only four patients developed BKVN (1.3%). Pathogenesis-based transcript analysis revealed a significant increased expression of interferon-gamma and rejection induced (GRIT), quantitative cytotoxic T-cell (QCAT), quantitative constitutive and alternate macrophage, B-cell and natural killer cell-associated transcripts (NKAT), indicating an active inflammatory immune response in BKVN biopsies (n=3) compared to normal transplant kidney biopsies with (n=3) and without BK viremia (n=11). The whole blood gene expression profiles of 19 BK viremia patients revealed significant increased expression of GRIT, QCAT, and NKAT compared to 14 patients without viremia. CONCLUSIONS: The results showed increased activity of cytotoxic T cells and natural killer cells in BKVN and viremia samples resembling acute rejection and suggested the involvement of both adaptive and innate immunity.

The Einstein Genome Gateway using WASP - a high throughput multi-layered life sciences portal for XSEDE.

Massively-parallel sequencing (MPS) technologies and their diverse applications in genomics and epigenomics research have yielded enormous new insights into the physiology and pathophysiology of the human genome. The biggest hurdle remains the magnitude and diversity of the datasets generated, compromising our ability to manage, organize, process and ultimately analyse data. The Wiki-based Automated Sequence Processor (WASP), developed at the Albert Einstein College of Medicine (hereafter Einstein), uniquely manages to tightly couple the sequencing platform, the sequencing assay, sample metadata and the automated workflows deployed on a heterogeneous high performance computing cluster infrastructure that yield sequenced, quality-controlled and 'mapped' sequence data, all within the one operating environment accessible by a web-based GUI interface. WASP at Einstein processes 4-6 TB of data per week and since its production cycle commenced it has processed ~ 1 PB of data overall and has revolutionized user interactivity with these new genomic technologies, who remain blissfully unaware of the data storage, management and most importantly processing services they request. The abstraction of such computational complexity for the user in effect makes WASP an ideal middleware solution, and an appropriate basis for the development of a grid-enabled resource - the Einstein Genome Gateway - as part of the Extreme Science and Engineering Discovery Environment (XSEDE) program. In this paper we discuss the existing WASP system, its proposed middleware role, and its planned interaction with XSEDE to form the Einstein Genome Gateway.

An integrated genome screen identifies the Wnt signaling pathway as a major target of WT1.

WT1, a critical regulator of kidney development, is a tumor suppressor for nephroblastoma but in some contexts functions as an oncogene. A limited number of direct transcriptional targets of WT1 have been identified to explain its complex roles in tumorigenesis and organogenesis. In this study we performed genome-wide screening for direct WT1 targets, using a combination of ChIP-ChIP and expression arrays. Promoter regions bound by WT1 were highly G-rich and resembled the sites for a number of other widely expressed transcription factors such as SP1, MAZ, and ZNF219. Genes directly regulated by WT1 were implicated in MAPK signaling, axon guidance, and Wnt pathways. Among directly bound and regulated genes by WT1, nine were identified in the Wnt signaling pathway, suggesting that WT1 modulates a subset of Wnt components and responsive genes by direct binding. To prove the biological importance of the interplay between WT1 and Wnt signaling, we showed that WT1 blocked the ability of Wnt8 to induce a secondary body axis during Xenopus embryonic development. WT1 inhibited TCF-mediated transcription activated by Wnt ligand, wild type and mutant, stabilized beta-catenin by preventing TCF4 loading onto a promoter. This was neither due to direct binding of WT1 to the TCF binding site nor to interaction between WT1 and TCF4, but by competition of WT1 and TCF4 for CBP. WT1 interference with Wnt signaling represents an important mode of its action relevant to the suppression of tumor growth and guidance of development.