Ophélie Arnaud

ABCG2 Transports and Transfers Heme to Albumin through Its Large Extracellular Loop

ABCG2 is an ATP-binding cassette (ABC) transporter preferentially expressed by immature human hematopoietic progenitors. Due to its role in drug resistance, its expression has been correlated with a protection role against protoporhyrin IX (PPIX) accumulation in stem cells under hypoxic conditions. We show here that zinc mesoporphyrin, a validated fluorescent heme analog, is transported by ABCG2. We also show that the ABCG2 large extracellular loop ECL3 constitutes a porphyrin-binding domain, which strongly interacts with heme, hemin, PPIX, ZnPPIX, CoPPIX, and much less efficiently with pheophorbide a, but not with vitamin B12. Kd values are in the range 0.5–3.5 μm, with heme displaying the highest affinity. Nonporphyrin substrates of ABCG2, such as mitoxantrone, doxo/daunorubicin, and riboflavin, do not bind to ECL3. Single-point mutations H583A and C603A inside ECL3 prevent the binding of hemin but hardly affect that of iron-free PPIX. The extracellular location of ECL3 downstream from the transport sites suggests that, after membrane translocation, hemin is transferred to ECL3, which is strategically positioned to release the bound porphyrin to extracellular partners. We show here that human serum albumin could be one of these possible partners as it removes hemin bound to ECL3 and interacts with ABCG2, with a Kd of about 3 μm.

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.

Understanding polyspecificity within the substrate‐binding cavity of the human multidrug resistance P‐glycoprotein

Human P‐glycoprotein (P‐gp) controls drugs bioavailability by pumping structurally unrelated drugs out of cells. The X‐ray structure of the mouse P‐gp ortholog has been solved, with two SSS enantiomers or one RRR enantiomer of the selenohexapeptide inhibitor QZ59, found within the putative drug‐binding pocket (Aller SG, Yu J, Ward A, Weng Y, Chittaboina S, Zhuo R, Harrell PM, Trinh YT, Zhang Q, Urbatsch IL et al. (2009). Science 323, 1718–1722). This offered the first opportunity to localize the well‐known H and R drug‐binding sites with respect to the QZ59 inhibition mechanisms of Hoechst 33342 and daunorubicin transports, characterized here in cellulo. We found that QZ59‐SSS competes efficiently with both substrates, with KI,app values of 0.15 and 0.3 μm, which are 13 and 2 times lower, respectively, than the corresponding Km,app values. In contrast, QZ59‐RRR non‐competitively inhibited daunorubicin transport with moderate efficacy (KI,app = 1.9 μm); it also displayed a mixed‐type inhibition of the Hoechst 33342 transport, resulting from a main non‐competitive tendency (Ki2,app = 1.6 μm) and a limited competitive tendency (Ki1,app = 5 μm). These results suggest a positional overlap of QZ59 and drugs binding sites: full for the SSS enantiomer and partial for the RRR enantiomer. Crystal structure analysis suggests that the H site overlaps both QZ59‐SSS locations while the R site overlaps the most embedded location.

The acridone derivative MBLI-87 sensitizes breast cancer resistance protein-expressing xenografts to irinotecan

The breast cancer resistance protein ABCG2 confers cellular resistance to irinotecan (CPT-11) and its active metabolite SN-38. We utilised ABCG2-expressing xenografts as a model to evaluate the ability of a non-toxic ABCG2 inhibitor to increase intracellular drug accumulation. We assessed the activity of irinotecan in vivo in SCID mice: irinotecan completely inhibited the development of control pcDNA3.1 xenografts, whilst only delaying the growth of ABCG2-expressing xenografts. Addition of MBLI-87, an acridone derivative inhibitor, significantly increased the irinotecan effect against the growth of ABCG2-expressing xenografts. In vitro, MBLI-87 was as potent as GF120918 against ABCG2-mediated irinotecan efflux, and additionally was specific for ABCG2. A significant sensitisation to irinotecan was achieved despite the fact that doses remained well below the maximum tolerated dose (due to the rather limited solubility of MBLI-87). This suggested that MBLI-87 is an excellent candidate to prevent drug efflux by ABCG2, without altering plasma concentrations of irinotecan and SN-38 after IP (intra-peritoneal) injections. This could constitute a useful strategy to improve drug pharmacology, to facilitate drug penetration into normal tissue compartments protected by ABCG2, and potentially to reverse drug resistance in cancer cells.

The multidrug resistance half-transporter ABCG2 is purified as a tetramer upon selective extraction from membranes☆

ABCG2 is a human membrane ATP-binding cassette half-transporter that hydrolyzes ATP to efflux a large number of chemotherapeutic agents. Several oligomeric states of ABCG2 from homodimers to dodecamers have been reported depending on the overexpression systems and/or the protocols used for purification. Here, we compared the oligomeric state of His(6)-ABCG2 expressed in Sf9 insect cells and in human Flp-In-293/ABCG2 cells after solubilization in mild detergents. His(6)-ABCG2 was purified through a new approach involving its specific recognition onto a functionalized lipid layer containing a Ni-NTA lipid. This approach allowed the purification of His-ABCG2 in presence of all solubilized membrane components that might be involved in the stabilisation of native oligomers and without requiring any additional washing or concentration passages. ABCG2 purified onto the NiNTA lipid surfaces were directly analyzed by electron microscopy and by biochemical assays. Altogether, our data are consistent with a tetrameric organization of ABCG2 when expressed in either heterologous Sf9 insect cells or in human homologous cells.

Potent and Fully Noncompetitive Peptidomimetic Inhibitor of Multidrug Resistance P-Glycoprotein

N(α)-Boc-l-Asp(OBn)-l-Lys(Z)-OtBu (reversin 121, 1), an inhibitor of the P-gp ABC transporter, was used to conceive compounds inhibiting the drug efflux occurring through the Hoechst 33342 and daunorubicin transport sites of P-gp, respectively H and R sites. Replacement of the aspartyl residue by trans-4-hydroxy-l-proline (4(R)Hyp) gave compounds 11 and 15 characterized by half-maximal inhibitory concentrations (IC(50)) of 0.6 and 0.2 μM, which are 2- and 7-fold lower than that of the parent molecule. The difference in IC(50) between 11 and 15 rests on the carbonyl group of the peptidyl bond, reduced in 15. Those compounds are rather specific of P-gp, having no or limited activity on MRP1 and BCRP. 15 displayed no marked cytotoxicity up to 10-fold its IC(50). Importantly, 15 equally inhibited the Hoechst 33342 and daunorubicin effluxes through a typical noncompetitive inhibition mechanism, suggesting its binding to a site different from the H and R drug-transport sites.

Stochastic Fluctuations and Distributed Control of Gene Expression Impact Cellular Memory

Despite the stochastic noise that characterizes all cellular processes the cells are able to maintain and transmit to their daughter cells the stable level of gene expression. In order to better understand this phenomenon, we investigated the temporal dynamics of gene expression variation using a double reporter gene model. We compared cell clones with transgenes coding for highly stable mRNA and fluorescent proteins with clones expressing destabilized mRNA-s and proteins. Both types of clones displayed strong heterogeneity of reporter gene expression levels. However, cells expressing stable gene products produced daughter cells with similar level of reporter proteins, while in cell clones with short mRNA and protein half-lives the epigenetic memory of the gene expression level was completely suppressed. Computer simulations also confirmed the role of mRNA and protein stability in the conservation of constant gene expression levels over several cell generations. These data indicate that the conservation of a stable phenotype in a cellular lineage may largely depend on the slow turnover of mRNA-s and proteins.

Erratum to: Temperature-induced variation in gene expression burst size in metazoan cells.

© 2016 Arnaud et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 InternationalLicense (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction inany medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commonslicense, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Erratum to: BMC Molecular Biol (2015) 16:20 DOI 10.1186/s12867‐015‐0048‐2 Unfortunately, the original version of this article [1] contained two mistakes. The figure order was incorrectly captured during the production process with Figure 1 intended as Figure 6. All other figures are renumbered to accommodate this. The reference to the incorrect Figure 1 in the text should also have read, “(2) the RNA half-life, and it is further validated by the good agreement of the model with experimental curves (see below).” The original version of the article has been updated to rectify these errors.