Changes of 5-hydroxymethylcytosine distribution during myeloid and lymphoid differentiation of CD34+ cells

BACKGROUND: Hematopoietic stem cell renewal and differentiation are regulated through epigenetic processes. The conversion of 5-methylcytosine into 5-hydroxymethylcytosine (5hmC) by ten-eleven-translocation enzymes provides new insights into the epigenetic regulation of gene expression during development. Here, we studied the potential gene regulatory role of 5hmC during human hematopoiesis.

RESULTS: We used reduced representation of 5-hydroxymethylcytosine profiling (RRHP) to characterize 5hmC distribution in CD34+ cells, CD4+ T cells, CD19+ B cells, CD14+ monocytes and granulocytes. In all analyzed blood cell types, the presence of 5hmC at gene bodies correlates positively with gene expression, and highest 5hmC levels are found around transcription start sites of highly expressed genes. In CD34+ cells, 5hmC primes for the expression of genes regulating myeloid and lymphoid lineage commitment. Throughout blood cell differentiation, intragenic 5hmC is maintained at genes that are highly expressed and required for acquisition of the mature blood cell phenotype. Moreover, in CD34+ cells, the presence of 5hmC at enhancers associates with increased binding of RUNX1 and FLI1, transcription factors essential for hematopoiesis.

CONCLUSIONS: Our study provides a comprehensive genome-wide overview of 5hmC distribution in human hematopoietic cells and new insights into the epigenetic regulation of gene expression during human hematopoiesis.

Directional genetic differentiation and relative migration

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum-likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user-friendly web application called divMigrate-online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.