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Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection

ABSTRACT: Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. We Here we developed and validated a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyzed whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a newly developed bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

PROVIDER: EGAS00001003206 | EGA |

REPOSITORIES: EGA

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Mapping Transcription Factor Nucleosome Dynamics in Plasma

Project description:Cell-free DNA (cfDNA) contains a composite map of the epigenomes of its cells-of-origin. Tissue-specific TF binding inferred from cfDNA could enable us to track disease states in humans in a non-invasive manner. Here, we present a method to identify the subset of genome-wide transcription factor binding sites that are protected in plasma. We map binding at tissue-specific sites of constitutive factor CTCF and tissue-specific factors PU.1, LYL1, ER, and FOXA1 in plasma cfDNA. Our method also captures the chromatin structure around the factor-bound sites in their cells-of-origin. We define pure tumor TF signatures in an in vivo model by applying our method to estrogen receptor-positive (ER+) breast cancer xenografts. The tumor-specific cfDNA protections of ER-α and FOXA1 reflect TF-specific accessibility across human breast tumors, demonstrating our ability to capture tumor TF binding in plasma. By modeling cfDNA from cancer donors as a mixture of healthy plasma and pure tumor signatures, we can identify tumor TF binding in humans. Thus, our method enables non-invasive mapping of the regulatory phenotype of cancer in humans.

2021-05-24 | GSE171434 | GEO

Proteome-wide profiling of Readers for DNA modification

Project description:Abstract DNA modifications, represented by methylcytosine (5mC), and its oxidized derivatives, such as hydroxymethylcytosine (5hmC), formylcytosine (5fC), and carboxylcytosine (5caC), play important roles in epigenetic regulation of biological processes. The specific recognition of DNA modifications by the transcriptional protein machinery is thought to be a potential mechanism for epigenetic-driven gene regulation, and many modified DNA-specific binding proteins have been uncovered. However, the panoramic view of the roles of DNA modification readers at the proteome level remains largely unclear. Here, we employed a recently developed concatenated tandem array of consensus transcription factor (TF) response elements (catTFREs) approach to profile the binding activity of TFs at DNA modifications (5C, 5mC, 5hmC, and 5fC). Modified DNA-binding activity was quantified for 1,039 TFs, representing 70% of the TFs in the human genome. In addition, we monitored the modified DNA- binding activity of 600 TFs during the mouse brain development from the embryo to the adult stages. Readers of these DNA modifications were predicted, and the hierarchical networks between the transcriptional protein machinery and modified DNA were described. We further demonstrated that ZNF24 and ZSCAN21 are potential readers of 5fC-modified DNA. This study provides a landscape of TF–DNA modification interactions that can be used to elucidate the epigenetic-related transcriptional regulation mechanisms under physiological and pathological conditions.

2021-05-30 | PXD026362 | iProX

Protein−DNA binding in the absence of specific base-pair recognition

Project description:Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)−DNA binding. Contrary to this assumption, here we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TF−DNA binding preferences. We used high-throughput protein−DNA binding assays to measure the binding levels and free energies of binding for several human TFs to tens of thousands of short DNA sequences with varying re- peat symmetries. Based on statistical mechanics modeling, we iden- tify a new protein−DNA binding mechanism induced by DNA se- quence symmetry in the absence of specific base-pair recognition, and experimentally demonstrate that this mechanism indeed gov- erns protein−DNA binding preferences.

2014-10-01 | GSE61920 | GEO

Transcription factor-SNP interactome for colorectal cancer risk loci

Project description:A core task to understand the consequences of non-coding single nucleotide polymorphisms (SNP) is to identify their genotype specific binding of transcription factor (TF). Here, we generate a large-scale TF-SNP interaction map for a selection of 116 colorectal cancer (CRC) risk loci and validated TF binding to 10 putatively functional SNPs. Our data further revealed TF binding complexity adjacent to the 116 risk loci, adding an additional layer of understanding to regulatory networks associated with CRC relevant loci.

2017-03-30 | MSV000080810 | MassIVE

Synthetic genetic circuit reveals overlapping binding sites underlie TF genomic occupancy

Project description:Sequence-specific DNA binding by transcription factors (TFs) is required for accurately regulating target gene expression. However, rules governing TF-DNA interactions remain elusive because current high-throughput approaches to map them fail to identify lower-affinity interactions. To address this gap, we have developed PADIT-seq, a novel technology to detect TF-DNA interactions across the entire spectrum of affinities. By profiling the binding preferences of Egr1 and Hoxd13 to all possible 10-mers, we found that nucleotides flanking high-affinity binding sites create additional, overlapping lower-affinity sites that together modulate TF genomic occupancy. The formation of these extended recognition sequences is enabled by an inherent property of TF binding sites to interweave together. Highlighting the importance of lower-affinity binding sites, we provide mechanistic insights into their role in fine-tuning gene regulation.

2024-03-06 | GSE250601 | GEO

Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

Project description:Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF-DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF-DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods. Examine Oct4 genome-wide binding in mouse embryonic stem cells (E14)

2015-03-02 | E-GEOD-65093 | biostudies-arrayexpress

A proteomics landscape of circadian clock in mouse liver

Project description:As a circadian organ, liver executes diverse functions in different phase of the circadian clock. This process is believed to be driven by a transcription program. Here, we present a TF DNA-binding activity centered multi-dimensional proteomics landscape, including DNA-binding activity of TFs, the phosphorylation pattern, ubiquitylation pattern, the nuclear sub-proteome, the whole proteome as well as the transcriptome, to portrait the hierarchical circadian clock network of mouse liver. The TF DNA-binding activity indicates diurnal oscillation in four major pathways, immune response, glucose metabolism, fatty acid metabolism, and the cell cycle. We also isolated the mouse liver Kupffer cells and measured their proteomes in the circadian clock to reveal cell type resolved circadian clock. These are the most comprehensive datasets for circadian clock in the mouse liver and provided the richest data resource for the understanding of mouse liver physiology around the circadian clock.

2018-06-25 | PXD008771 | iProX

Supplement data of project: A proteomics landscape of circadian clock in mouse liver

2018-06-25 | PXD008862 | iProX

Characterization of sequence determinants of enhancer function using natural genetic variation [ChIP-seq]

Project description:Sequence variation in enhancers, a class of cis-regulatory elements that control cell type-specific gene transcription, contributes significantly to phenotypic variation within human populations. Enhancers are short DNA sequences (∼200 bp) composed of multiple binding sites (4-10 bp) for transcription factors (TFs). The transcriptional regulatory activity of an enhancer is encoded by the type, number, and distribution of TF binding sites that it contains. However, the sequence determinants of TF binding to enhancers and the relationship between TF binding and enhancer activity are complex, and thus it remains difficult to predict the effect of any given sequence variant on enhancer function. Here, we generate allele-specific maps of TF binding and enhancer activity in fibroblasts from a panel of F1-hybrid mice that have a high frequency of sequence variants. We identified thousands of enhancers that exhibit differences in TF binding and/or activity between alleles and use these data to define features of sequence variants that are most likely to impact enhancer function. Our data demonstrate a critical role for AP-1 TFs at many fibroblast enhancers, reveal a hierarchical relationship between AP-1 and TEAD TF binding at enhancers, and delineate the nature of sequence variants that contribute to AP-1 TF binding. These data represent one of the most comprehensive assessments to date of the impact of sequence variation on enhancer function in chromatin, with implications for identifying functional cis-regulatory variation in human populations.

2022-01-17 | GSE193727 | GEO

Characterization of sequence determinants of enhancer function using natural genetic variation [RNA-seq]

2022-01-17 | GSE193081 | GEO

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