Enhancers determine tissue-specific gene expression programs. Enhancers are marked by high histone H3 lysine 4 mono-methylation (H3K4me1) and by the acetyl-transferase p300, which has allowed genome-wide enhancer identification. However, the regulatory principles by which subsets of enhancers become active in specific developmental and/or environmental contexts are unknown. We exploited inducible p300 binding to chromatin to identify, and then mechanistically dissect, enhancers controlling endotoxin-stimulated gene expression in macrophages. In these enhancers, binding sites for the lineage-restricted and constitutive Ets protein PU.1 coexisted with those for ubiquitous stress-inducible transcription factors such as NF-kappaB, IRF, and AP-1. PU.1 was required for maintaining H3K4me1 at macrophage-specific enhancers. Reciprocally, ectopic expression of PU.1 reactivated these enhancers in fibroblasts. Thus, the combinatorial assembly of tissue- and signal-specific transcription factors determines the activity of a distinct group of enhancers. We suggest that this may represent a general paradigm in tissue-restricted and stimulus-responsive gene regulation.
We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.
Somatic mosaicism in the human brain may alter function of individual neurons. We analyzed genomes of single cells from the forebrains of three human fetuses (15 to 21 weeks postconception) using clonal cell populations. We detected 200 to 400 single-nucleotide variations (SNVs) per cell. SNV patterns resembled those found in cancer cell genomes, indicating a role of background mutagenesis in cancer. SNVs with a frequency of >2% in brain were also present in the spleen, revealing a pregastrulation origin. We reconstructed cell lineages for the first five postzygotic cleavages and calculated a mutation rate of ~1.3 mutations per division per cell. Later in development, during neurogenesis, the mutation spectrum shifted toward oxidative damage, and the mutation rate increased. Both neurogenesis and early embryogenesis exhibit substantially more mutagenesis than adulthood.
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