Defining the epigenomic signatures of myeloid skewing in aging (postdoctoral fellowship for Monica Gutierrez)

Chronic inflammation is a prominent factor driving the increased susceptibility to cardiovascular disease (CVD). Specifically, high levels pro-inflammatory cytokines and myeloid cells are strong predictors of CVD in the elderly. Despite growing evidence that chronic inflammation is a risk factor for CVD, the underlying mechanisms driving age-dependent chronic inflammation (“inflammaging”) have yet to be fully elucidated. Inflammaging is propelled by myeloid skewing, a process in which hematopoiesis is biased toward myeloid cell differentiation. The epigenome largely contributes to this myeloid skewing as work from our lab and others has demonstrated that epigenomic regulation influences HSC differentiation and myeloid cell behavior in aging models. Thus, we hypothesize that age-dependent epigenomic changes in a subset of hematopoietic stem cells result in myeloid skewing that promotes CVD. In Aim1, we will determine whether age-dependent epigenomic changes in hematopoietic progenitors increase CVD severity through myeloid skewing. We will transplant bone marrow (BM) from young and old wild type (WT) mice into a mouse model of diet-induced atherosclerosis (Ldlr-/-). To confirm that epigenomic changes are propagated after BM transplant, we will compare the chromatin profile (by ChIP-seq) of hematopoietic progenitors from the BM of chimeras to that of control young and old mice. Then, we will feed mice a high fat/high cholesterol (HFHC) diet to induce atherosclerosis. The goal is to uncover a signature of myeloid skewing in the progenitor cell population and determine that this phenomenon increases the risk of CVD. In Aim2, we will identify the HSC sub-population from aged mice that demonstrates myeloid skewing. We will compare the chromatin accessibility of individual HSCs from young, middle-aged, and old WT mice. This approach will allow us to assess the heterogeneity of the HSC population across ages. We will implement single-cell trajectory inference to deter