TLR Stimulation Dynamically Regulates Heme and Iron Export Gene Expression in Macrophages

Pathogenic bacteria have evolved multiple mechanisms to capture iron or iron-containing heme from host tissues or blood. In response, organisms have developed defense mechanisms to keep iron from pathogens. Very little of the body's iron store is available as free heme; rather nearly all body iron is complexed with heme or other proteins. The feline leukemia virus, subgroup C (FeLV-C) receptor, FLVCR, exports heme from cells. It was unknown whether FLVCR regulates heme-iron availability after infection, but given that other heme regulatory proteins are upregulated in macrophages in response to bacterial infection, we hypothesized that macrophages dynamically regulate FLVCR. We stimulated murine primary macrophages or macrophage cell lines with LPS and found that Flvcr is rapidly downregulated in a TLR4/MD2-dependent manner; TLR1/2 and TLR3 stimulation also decreased Flvcr expression. We identified several candidate TLR-activated transcription factors that can bind to the Flvcr promoter. Macrophages must balance the need to sequester iron from systemic circulating or intracellular pathogens with the macrophage requirement for heme and iron to produce reactive oxygen species. Our findings underscore the complexity of this regulation and point to a new role for FLVCR and heme export in macrophages responses to infection and inflammation

Bayesian Inference By Simulation in a Stochastic Model From Hematology

A particular Markov chain Monte Carlo algorithm is constructed to allow Bayesian inference in a hidden Markov model used in hematology. The algorithm has an outer Gibbsian structure, and incorporates both Metropolis and Hastings updates to move through the space of possible hidden states. While somewhat sophisticated, this algorithm still has problems getting around the infinite-dimensional space of hidden states because of strong correlations between some of the variables. A two-step variant of the Metropolis algorithm is introduced for posterior simulation. Keywords: hidden Markov model, Metropolis algorithm, Gibbs sampler, Hastings algorithm, hematopoiesis 1. A Model Suppose that each of N people in a room is holding a coin--the probability of heads for each coin is p. Independently of one another, each person flips his/her coin at random exponentially-distributed time intervals specified by a rate parameter . Over time, X, the number of facing heads, fluctuates between 0 and N . ..