Immunological memory is a defining feature of vertebrate physiology, allowing rapid responses to repeat infections. However, the molecular mechanisms required for its establishment and maintenance remain poorly understood. Here, we demonstrated that the first steps in the acquisition of T‐cell memory occurred during the initial activation phase of naïve T cells by an antigenic stimulus. This event initiated extensive chromatin remodeling that reprogrammed immune response genes toward a stably maintained primed state, prior to terminal differentiation. Activation induced the transcription factors NFAT and AP‐1 which created thousands of new DNase I‐hypersensitive sites (DHSs), enabling ETS‐1 and RUNX1 recruitment to previously inaccessible sites. Significantly, these DHSs remained stable long after activation ceased, were preserved following replication, and were maintained in memory‐phenotype cells. We show that primed DHSs maintain regions of active chromatin in the vicinity of inducible genes and enhancers that regulate immune responses. We suggest that this priming mechanism may contribute to immunological memory in T cells by facilitating the induction of nearby inducible regulatory elements in previously activated T cells.

The expression of eukaryotic genes is regulated by cis-regulatory elements such as promoters and enhancers, which bind sequence-specific DNA-binding proteins. One of the great challenges in the gene regulation field is to characterise these elements. This involves the identification of transcription factor (TF) binding sites within regulatory elements that are occupied in a defined regulatory context. Digestion with DNase and the subsequent analysis of regions protected from cleavage (DNase footprinting) has for many years been used to identify specific binding sites occupied by TFs at individual cis-elements with high resolution. This methodology has recently been adapted for high-throughput sequencing (DNase-seq). In this study, we describe an imbalance in the DNA strand-specific alignment information of DNase-seq data surrounding protein–DNA interactions that allows accurate prediction of occupied TF binding sites. Our study introduces a novel algorithm, Wellington, which considers the imbalance in this strand-specific information to efficiently identify DNA footprints. This algorithm significantly enhances specificity by reducing the proportion of false positives and requires significantly fewer predictions than previously reported methods to recapitulate an equal amount of ChIP-seq data. We also provide an open-source software package, pyDNase, which implements the Wellington algorithm to interface with DNase-seq data and expedite analyses.

SUMMARY Oncogenic transcription factors such as RUNX1/ ETO, which is generated by the chromosomal translocation t(8;21), subvert normal blood cell development by impairing differentiation and driving malignant self-renewal. Here, we use digital footprinting and chromatin immunoprecipitation sequencing (ChIP-seq) to identify the core RUNX1/ ETO-responsive transcriptional network of t(8;21) cells. We show that the transcriptional program underlying leukemic propagation is regulated by a dynamic equilibrium between RUNX1/ETO and RUNX1 complexes, which bind to identical DNA sites in a mutually exclusive fashion. Perturbation of this equilibrium in t(8;21) cells by RUNX1/ETO depletion leads to a global redistribution of transcription factor complexes within preexisting open chromatin, resulting in the formation of a transcriptional network that drives myeloid differentiation. Our work demonstrates on a genome-wide level that the extent of impaired myeloid differentiation in t(8;21) is controlled by the dynamic balance between RUNX1/ ETO and RUNX1 activities through the repression of transcription factors that drive differentiation.

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

SummaryAcute myeloid leukemia (AML) is characterized by recurrent mutations that affect the epigenetic regulatory machinery and signaling molecules, leading to a block in hematopoietic differentiation. Constitutive signaling from mutated growth factor receptors is a major driver of leukemic growth, but how aberrant signaling affects the epigenome in AML is less understood. Furthermore, AML cells undergo extensive clonal evolution, and the mutations in signaling genes are often secondary events. To elucidate how chronic growth factor signaling alters the transcriptional network in AML, we performed a system-wide multi-omics study of primary cells from patients suffering from AML with internal tandem duplications in the FLT3 transmembrane domain (FLT3-ITD). This strategy revealed cooperation between the MAP kinase (MAPK) inducible transcription factor AP-1 and RUNX1 as a major driver of a common, FLT3-ITD-specific gene expression and chromatin signature, demonstrating a major impact of MAPK signaling pathways in shaping the epigenome of FLT3-ITD AML.