NFκB and HIF display synergistic behaviour during hypoxic inflammation

Abstract

The oxygen-sensitive transcription factor Hypoxia Inducible Factor (HIF) is a key regulator of gene expression during adaptation to hypoxia. Crucially, inflamed tissue often displays regions of prominent hypoxia. Recent studies have shown HIF signalling is intricately linked to that of the pro-inflammatory transcription factor Nuclear factor kappa B (NF!B) during hypoxic inflammation. Here we describe the relative temporal contributions of each to hypoxia-induced inflammatory gene expression and investigate the level of crosstalk between the two pathways using a novel Gaussia princeps luciferase (Gluc) reporter system. Under the control of an active promoter, Gluc is expressed and secreted into the cell culture media, where it can be sampled and measured over time. Thus Gluc constructs under the control of either HIF or NF!B were used to resolve their temporal transcriptional dynamics in response to hypoxia and to cytokine stimuli respectively. We also investigated the interactions between HIF and NF!B activities using a construct containing the sequence from the promoter of the inflammatory gene cyclooxygenase 2 (COX-2), which includes functionally active binding sites for both HIF and NF!B. Finally, based on our experimental data, we constructed a mathematical model of the binding affinities of HIF and NF!B to their respective response elements to analyse transcriptional crosstalk. Taken together, these data reveal distinct temporal HIF and NF!B transcriptional activities in response to hypoxic inflammation. Furthermore, we demonstrate synergistic activity between these two transcription factors on the regulation of the COX-2 promoter, implicating a coordinated role for both HIF and NF!B in the expression of COX-2 in hypoxic inflammation.

A dynamic model of the hypoxia-inducible factor 1-alpha (HIF-1α) network

“…This crucial step has been poorly represented in previous models due to the limited amount of experimental data. Our model uses data from the Gaussia luciferase reporter which provides temporal information on the HIF-1a transcriptional activity from a population of cells (Bruning et al, 2012). Furthermore, the use of the pharmacological hydroxylase inhibitors DMOG and JNJ1935 enabled use to selectively Fig.…”

“…The readout of the HIF-1a transcriptional activity used in our model is from the Gaussia luciferase reporter under the control of HIF (Bruning et al, 2012). Our model-based study follows an integrated cycle of model-driven hypothesis generation and experimental validation strategy which has not previously been reported for the HIF pathway.…”

Catching transcriptional regulation by thermostatistical modeling

“…We fitted dose response data from a recent study [35]. HEK293 cells were transfected with a vector encoding the Gaussia luciferase under the control of NF B (pGluc-NRE) and stimulated with NF B-inducing cytokines TNF or IL-1 (0.1 to 5 ng/ml).…”

“…(9). Then, we will be able to identify the relationship between S(P)/S max and r/r(sat) because P can be calculated from r like ) sat ( (max) r P r P (35) From Figure 4A it follows that there are three possible cases. If P 0 = ·r 0 , P(max) = ·r(sat) <1/2 holds, then the thermostatistical approach predicts that the transcriptional machinery exhibits a monotonically increasing relationship between stochasticity and determinism; if P 0 <1/2 < P(max) holds, then a non-monotonic relationship is predicted; finally for P 0 , P(max) >1/2 a monotonically decreasing relationship is predicted.…”

“…While an in-depth discussion of the implementation of this theoretical framework is beyond the scope of the present manuscript, future work may be dedicated to demonstrate in more detail the application of Eqs. (29) and (35) to experimental data. (32) is shown for the best-fit estimates: q R 0, q R · =1.1 , and =1.7…”