NF90/ILF3 is a transcription factor that promotes proliferation over differentiation by hierarchical regulation in K562 erythroleukemia cells

Abstract

NF90 and splice variant NF110 are DNA- and RNA-binding proteins encoded by the Interleukin enhancer-binding factor 3 (ILF3) gene that have been established to regulate RNA splicing, stabilization and export. The roles of NF90 and NF110 in regulating transcription as chromatin-interacting proteins have not been comprehensively characterized. Here, chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) identified 9,081 genomic sites specifically occupied by NF90/NF110 in K562 cells. One third of NF90/NF110 peaks occurred at promoters of annotated genes. NF90/NF110 occupancy colocalized with chromatin marks associated with active promoters and strong enhancers. Comparison with 150 ENCODE ChIP-seq experiments revealed that NF90/NF110 clustered with transcription factors exhibiting preference for promoters over enhancers (POLR2A, MYC, YY1). Differential gene expression analysis following shRNA knockdown of NF90/NF110 in K562 cells revealed that NF90/NF110 activates transcription factors that drive growth and proliferation (EGR1, MYC), while attenuating differentiation along the erythroid lineage (KLF1). NF90/NF110 associates with chromatin to hierarchically regulate transcription factors that promote proliferation and suppress differentiation.

Fig 1. ChIP-seq reveals extensive NF90/NF110 occupancy along the genome.

(a) ChIP-seq signal (fold enrichment over input) of NF90/NF110 shown along chromosome one, along peaks called using Irreproducibility Discovery Rate (IDR) between biological replicates. UCSC known genes and transcripts are aligned. (b) Distribution of annotated features near NF90/NF110 binding sites. (c) Average binding profile of NF90/NF110 computed using all annotated transcription start sites (TSS). For gene bodies, all genes were scaled to a 1.5 kb length; the 0.5 kb flanking regions were not scaled. (d) Motif analysis (HOMER) based on NF90/NF110 ChIP-seq results in K562 cells showing enriched motifs in NF90/NF110 peaks. Shown: motifs ranked top 5 by statistical significance.

Fig 2. NF90/NF110 relative enrichment in different chromatin states.

IDR peaks called from NF90/NF110 ChIP-seq experiment in K562 were sorted according to the chromatin state they resided in. The segmented peaks for each of 15 chromatin states were then used to query the ChIP-seq read files to count the number of reads to obtain affinity information for each peak. The resulting distribution of NF90/NF110 occupancy frequencies in different chromatin states were plotted as a histogram. x-axis: Reads Per Kilobase of transcript per Million mapped reads (RPKM). y-axis: 8 of 15 chromatin states in which majority of NF90/NF110 peaks resided in.

Fig 3. NF90/NF110 colocalization with specific histone marks at active promoters and enhancers.

a-c. Average occupancy profile of NF90/NF110, H3K4me3, H3K9ac, and RNA pol II at (a) transcription start sites, (b) active promoters, and (c) inactive promoters. d-f. Average occupancy profile of NF90/NF110, H3K4me1, H3K27ac, and RNA pol II at (d) transcription start sites, (e) strong enhancers, and (f) poised enhancers. Transcription start sites retrieved from UCSC Known Genes database. Active promoters, inactive promoters, strong enhancers and poised enhancers from Ernst et al. x-axis: relative position near TSS. y-axis: read count frequency of tag within region.

Fig 4. Clustering of transcription factors based on relative enrichment for chromatin states.

150 datasets of transcription factor ChIP-seq peaks based on ENCODE data production centers processed through a uniform processing pipeline were retrieved. 100 unique regulatory factors, including generic and sequence-specific factors were retrieved for the K562 line to supplement the NF90/NF110 ChIP-seq data. For a given transcription factor ChIP-seq peak set, the relative enrichment in different chromatin states was computed. Enrichments were then row-normalized by the largest enrichment values for each experiment. K-means clustering with K = 4 produced the clusters graphed here using principal component analysis (PCA). The major eigenvectors for the original dataset is depicted in blue arrows.

Fig 5. NF90/NF110 occupancy at divergent transcription initiation regions is enriched for transcript stability.

(a) Average occupancy profile of NF90/NF110 ChIP-seq peaks at all transcription start site (TSS) pairs. (b) Average occupancy profile of NF90/NF110 -seq peaks at transcription start site (TSS) pairs sorted by transcript stability parity. SS = stable–stable, US = unstable–stable, UU = unstable–unstable. (c) Average occupancy profile of NF90/NF110 ChIP-seq peaks centered at TSS with 1kb flanking regions. (d) Local motif enrichment analysis of the U1 SS5 splice sequence centered at TSS with 1kb flanking regions. TSS pair coordinates from Core et al.

Fig 6. NF90/NF110 regulates transcription factors that promote K562 proliferation over differentiation.

(a) Volcano plot demonstrating statistically significant changes in gene expression between control and NF90/NF110 knockdown in K562. x-axis: log fold change in shRNA against NF90/NF110 compared to control. y-axis: posterior probability of being differentially expressed (PPDE). Each dot is a gene; red: PPDE > 0.95; orange: PPDE > 0.95 and fold change > 1.5. (b) Venn diagram analyzing overlap between NF90/NF110 ChIP-seq genomic targets and differentially expressed gene upon NF90/NF110 knockdown in K562 (P = 1.4 e-08). Venn diagram analyzing overlap between NF90/NF110 ChIP-seq genic targets, up-regulated genes upon NF90/NF110 knockdown, and down-regulated genes upon NF90/NF110 knockdown. (c) Representative ChIP-seq track alignment of NF90/NF110, RNA pol II, H3K27ac; Chromatin state annotations from ChromHMM (red: active promoter, light red: weak promoter, orange: active enhancer, yellow: weak enhancer, dark green: transcriptional transition/elongation, light green: weakly transcribed); Precision nuclear run-on sequencing (PRO-Seq, a variation of GRO-Seq) and GRO-cap reads in reads per million (RPM); RNA-seq unique reads track alignment of control shRNA, and shRNA against NF90/NF110.