Biology subjects

de Boer, C.

Publications and source records attributed to de Boer, C..

3 recordsLinked to original sources

bioRxiv2017

Deciphering cis-regulatory logic with 100 million synthetic promoters

Deciphering cis-regulation, the code by which transcription factors (TFs) interpret regulatory DNA sequence to control gene expression levels, is a long-standing challenge. Previous studies of native or engineered sequences have remained limited in scale. Here, we use random sequences as an alternative, allowing us to measure the expression output of over 100 million synthetic yeast promoters. Random sequences yield a broad range of reproducible expression levels, indicating that the fortuitous binding sites in random DNA are functional. From these data we learn models of transcriptional regulation that predict over 94% of the expression driven from independent test data and nearly 89% from sequences from yeast promoters. These models allow us to characterize the activity of TFs and their interactions with chromatin, and help refine cis-regulatory motifs. We find that strand, position, and helical face preferences of TFs are widespread and depend on interactions with neighboring chromatin. Such massive-throughput regulatory assays of random DNA provide the diverse examples necessary to learn complex models of cis-regulatory logic.

genomics↗

bioRxiv2017

Heterogeneous Responses of Hematopoietic Stem Cells to Inflammatory Stimuli are Altered with Age

Long-term hematopoietic stem cells (LT-HSCs) maintain hematopoietic output throughout an animal's lifespan. With age, however, they produce a myeloid-biased output that may lead to poor immune responses to infectious challenge and the development of myeloid leukemias. Here, we show that young and aged LT-HSCs respond differently to inflammatory stress, such that aged LT-HSCs produce a cell-intrinsic, myeloid-biased expression program. Using single-cell RNA-seq, we identify a myeloid-biased subset within the LT-HSC population (mLT-HSCs) that is much more common amongst aged LT-HSCs and is uniquely primed to respond to acute inflammatory challenge. We predict several transcription factors to regulate differentially expressed genes between mLT-HSCs and other LT-HSC subsets. Among these, we show that Klf5, Ikzf1 and Stat3 play important roles in age-related inflammatory myeloid bias. These factors may regulate myeloid versus lymphoid balance with age, and can potentially mitigate the long-term deleterious effects of inflammation that lead to hematopoietic pathologies.\n\nHighlightsO_LILT-HSCs from young and aged mice have differential responses to acute inflammatory challenge.\nC_LIO_LIHSPCs directly sense inflammatory stimuli in vitro and have a robust transcriptional response.\nC_LIO_LIAged LT-HSCs demonstrate a cell-intrinsic myeloid bias during inflammatory challenge.\nC_LIO_LISingle-cell RNA-seq unmasked the existence of two subsets within the LT-HSC population that was apparent upon stimulation but not steady-state. One of the LT-HSC subsets is more prevalent in young and the other in aged mice.\nC_LIO_LIKlf5, Ikzf1 and Stat3 regulate age- and inflammation-related LT-HSC myeloid-bias.\nC_LI\n\nOne sentence summaryMurine hematopoietic stem cells display transcriptional heterogeneity that is quantitatively altered with age and leads to the age-dependent myeloid bias evident after inflammatory challenge.

immunology↗

bioRxiv2017

Deciphering Variance In Epigenomic Regulators By k-mer Factorization

BackgroundVariation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.\n\nResultsBROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in the k-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding - and any associated epigenomic mark - is inherently more variable than non-cooperative binding.\n\nConclusionsBROCKMAN and related approaches will help gain a mechanistic understanding of the trans determinants of chromatin variability between cells, treatments, and individuals.

genomics↗