Liposomes - Human phagocytes interplay in whole blood: effect of liposome design

Nanomedicine holds immense potential for therapeutic manipulation of phagocytic immune cells. However, in vitro studies often fail to accurately translate to the complex in vivo environment. To address this gap, we employed an ex vivo human whole-blood assay to evaluate liposome interactions with immune cells. We systematically varied liposome size, PEG-surface densities and sphingomyelin and ganglioside content. We observed differential uptake patterns of the assessed liposomes by neutrophils and monocytes, emphasizing the importance of liposome design. Interestingly, our results aligned closely with published in vivo observations in mice and patients. Moreover, liposome exposure induced changes in cytokine release and cellular responses, highlighting the potential modulation of immune system. Our study highlights the utility of human whole-blood models in assessing nanoparticle-immune cell interactions and provides insights into liposome design for modulating immune responses

The Tumor Necrosis Factor Alpha and Interleukin 6 Auto-paracrine Signaling Loop Controls Mycobacterium avium Infection via Induction of IRF1/IRG1 in Human Primary Macrophages

Macrophages sense and respond to pathogens by induction of antimicrobial and inflammatory programs to alert other immune cells and eliminate the infectious threat. We have previously identified the transcription factor IRF1 to be consistently activated in macrophages during Mycobacterium avium infection, but its precise role during infection is not clear. Here, we show that tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) autocrine/paracrine signaling contributes to controlling the intracellular growth of M. avium in human primary macrophages through activation of IRF1 nuclear translocation and expression of IRG1, a mitochondrial enzyme that produces the antimicrobial metabolite itaconate. Small interfering RNA (siRNA)-mediated knockdown of IRF1 or IRG1 increased the mycobacterial load, whereas exogenously provided itaconate was bacteriostatic at high concentrations. While the overall level of endogenous itaconate was low in M. avium-infected macrophages, the repositioning of mitochondria to M. avium phagosomes suggests a mechanism by which itaconate can be delivered directly to M. avium phagosomes in sufficient quantities to inhibit growth. Using mRNA hybridization, we further show that uninfected bystander cells actively contribute to the resolution of infection by producing IL-6 and TNF-α, which, via paracrine signaling, activate IRF1/IRG1 and strengthen the antimicrobial activity of infected macrophages. This mechanism contributes to the understanding of why patients on anti-inflammatory treatment, e.g., with tocilizumab or infliximab, can be more susceptible to mycobacterial disease

Persistent mycobacteria evade an antibacterial program mediated by phagolysosomal TLR7/8/MyD88 in human primary macrophages.

Pathogenic mycobacteria reside in macrophages where they avoid lysosomal targeting and degradation through poorly understood mechanisms proposed to involve arrest of phagosomal maturation at an early endosomal stage. A clear understanding of how this relates to host defenses elicited from various intracellular compartments is also missing and can only be studied using techniques allowing single cell and subcellular analyses. Using confocal imaging of human primary macrophages infected with Mycobacterium avium (Mav) we show evidence that Mav phagosomes are not arrested at an early endosomal stage, but mature to a (LAMP1+/LAMP2+/CD63+) late endosomal/phagolysosomal stage where inflammatory signaling and Mav growth restriction is initiated through a mechanism involving Toll-like receptors (TLR) 7 and 8, the adaptor MyD88 and transcription factors NF-κB and IRF-1. Furthermore, a fraction of the mycobacteria re-establish in a less hostile compartment (LAMP1-/LAMP2-/CD63-) where they not only evade destruction, but also recognition by TLRs, growth restriction and inflammatory host responses that could be detrimental for intracellular survival and establishment of chronic infections

Detection of Drug–Drug Interactions Inducing Acute Kidney Injury by Electronic Health Records Mining

International audienceBackground and Objective : While risk of acute kidney injury (AKI) is a well documented adverse effect of some drugs, few studies have assessed the relationship between drug–drug interactions (DDIs) and AKI. Our objective was to develop an algorithm capable of detecting potential signals on this relationship by retrospectively mining data from electronic health records.Material and methods : Data were extracted from the clinical data warehouse (CDW) of the Hôpital Européen Georges Pompidou (HEGP). AKI was defined as the first level of the RIFLE criteria, that is, an increase ≥50 % of creatinine basis. Algorithm accuracy was tested on 20 single drugs, 10 nephrotoxic and 10 non-nephrotoxic. We then tested 45 pairs of non-nephrotoxic drugs, among the most prescribed at our hospital and representing distinct pharmacological classes for DDIs.Results : Sensitivity and specificity were 50 % [95 % confidence interval (CI) 23.66–76.34] and 90 % (95 % CI 59.58–98.21), respectively, for single drugs. Our algorithm confirmed a previously identified signal concerning clarithromycin and calcium-channel blockers (unadjusted odds ratio (ORu) 2.92; 95 % CI 1.11–7.69, p = 0.04). Among the 45 drug pairs investigated, we identified a signal concerning 55 patients in association with bromazepam and hydroxyzine (ORu 1.66; 95 % CI 1.23–2.23). This signal was not confirmed after a chart review. Even so, AKI and co-prescription were confirmed for 96 % (95 % CI 88–99) and 88 % (95 % CI 76–94) of these patients, respectively.Conclusion : Data mining techniques on CDW can foster the detection of adverse drug reactions when drugs are used alone or in combination

TLR7/8/MyD88 trigger inflammatory signaling induced by Mav infection.

<p>Human MDMs were treated with siRNA against target genes or a non-targeted control before infection with Mav-CFP (10 min uptake followed by chase for the indicated times). Cell supernatants were harvested at the indicated time points post infection and secreted cytokines assessed by multiplex ELISA. Cytokine responses from Mav-infected MDMs pretreated with siMyD88 (A), siTLR7, siTLR8, siUNC93B1 or siNOD1 (B). Graphs represent average concentrations +/- SEM of TNF-α, IL-6, IL-10 and IL-8 from at least 6 donors treated with target siRNA (red) or non-targeted siRNA (black). (C) Cells were pre-treated and infected as indicated, fixed and stained for IRF-1 and NF-κB nuclear translocation before analysis using non resolution-limited confocal microscopy. Quantification graphs represent the mean value for each time point (n>700 cells per time point) of one representative donor from three independent experiments. <i>P</i> values between Ctrl and each time point were calculated using Fisher Exact Test (* <i>P</i> <0.05, ** <i>P</i> <0.01 and *** <i>P</i> <0.005.).</p

TLR7/8/MyD88 regulate intracellular growth of Mav in human primary macrophages.

<p>Human MDMs were treated with siRNA against MyD88, TLR7, TLR8 or a non-targeting control before infection with Mav-CFP (10 min uptake followed by 4h to 3d chase). (A) Representative images where infected cells treated with siNTC (left) or siTLR8 (right) are outlined for analysis (Mav-CFP: red, nuclei: blue (Hoechst)). (B, C) <i>In situ</i> quantification of Mav-CFP 4 hours (B) and 3 days (C) post infection using the Corrected Total Cell Fluorescence method on infected cells. Quantification scatter plots show individual cell measures with mean +/- 95% confident intervals from 3 donors ((B, C) left graphs, n>250 cells per time point and per donor). The bar chart in (B) shows the percentage of infected cells 4 hours post infection to compare uptake. (D) Working model. Mav (dashed line, blue) is processed in LAMP1⁺ phagolysosomes. Release of mycobacterial nucleic acids engages TLR7/8, recruitment of MyD88 to the compartment, and inflammatory signaling culminating in cytokine release and growth restriction. A fraction of live Mav actively remodels the phagolysosome and/or is sorted into a new compartment (MavC) where late endosomal/lysosomal markers are excluded, TLR7/8 are not engaged and MyD88 is not recruited, thus avoiding inflammatory signaling and destruction. The program leading to Mav destruction (red line) remains to be elucidated.</p

Mav temporarily resides in the phagolysosomal compartment.

<p>Human MDMs were infected with Mav-CFP (red) for 10 min followed by a chase for 5 minutes to 3 days, stained for EEA1 ((A), early endosomes, green) or LAMP1 ((D, E) late endosomes/lysosomes, green) using antibodies and for nuclei using Hoechst (red), and analyzed by confocal microscopy at the indicated time points. Single labeling (left and middle images) and merged images (right images) are shown. Bottom-to-top projections of 3D-stacks from boxed areas are shown in lower panels (left to right) and represent Mav, EEA1/LAMP1⁺ membranes and merged images. Quantification of Mav localization in EEA1⁺ or LAMP1⁺ compartments was performed on 3D stacks using fluorescence intensity profiles along the indicated line (<i>FAL</i>, (B) and (F), Mav: red line; EEA1/LAMP1: green line). For each time point, at least 70 cells were recorded per donor. Quantification graphs represent mean value +/- SEM of Mav localization in EEA1⁺ (C) or LAMP1⁺ (G) compartments for 3 donors. <i>P</i> values were calculated using Fisher Exact Test (* <i>P</i> <0.05, ** <i>P</i> <0.01 and *** <i>P</i> <0.005). a.u.: arbitrary unit. Scale bar represents 10 μm.</p

Activation of IRF-1 coincides with phagolysosomal localization of Mav.

<p>Human MDMs were infected with live or PFA-killed Mav-CFP (red) for 10 min, chased for 4h to 3d and stained with antibodies to IRF-1 (blue) and LAMP1 (green) before analysis of MDMs containing only single Mav using confocal microscopy. Nuclei were revealed by staining with Hoechst (red). Single and merged images are shown; bottom-to-top projections of 3D-stacks from boxed areas in lower panels represent Mav, LAMP1 and merged images (A, C). (A) Cell with live Mav in LAMP1⁺ compartment inducing nuclear translocation of IRF-1. (C) Cell with live Mav in LAMP1^- compartment and no nuclear translocation of IRF-1. (B, D) Quantification of live Mav localization in LAMP1⁺ compartments and nuclear localization of IRF-1 was performed on 3D stacks using fluorescence intensity profiles along the indicated lines of Mav phagosomes ((B) and (D), left (Mav: red trace; LAMP1: green trace) and right (Hoechst: red trace; IRF-1: green trace) graphs respectively). (E) The fraction of live Mav localized to LAMP1⁺ phagolysosomes in IRF-1-activated cells (mean percentage +/- SEM from 3 donors). For each time point, at least 70 cells were recorded for each donor. (F) IRF-1 nuclear translocation at indicated time points after challenge with live or PFA-killed Mav. Quantification graphs represent the mean value +/- SEM for each time point for live Mav (black bars) and PFA-killed Mav (red bars) (n>600 cells per time point and per donor, 3 donors). <i>P</i> values between live Mav and PFA-killed Mav were calculated using two-tailed t-test (* <i>P</i> <0.05, ** <i>P</i> <0.01 and *** <i>P</i> <0.005.). a.u.: arbitrary unit. Scale bar represents 10 μm.</p

Phagolysosomes retain PFA-killed Mav.

<p>Human MDMs were exposed to PFA-killed Mav-CFP (10 min uptake, chased for 4h to 3d), stained with anti-LAMP1 antibody and Hoechst (nucleus) and analyzed using resolution-limited confocal microscopy. (A) LAMP1⁺ late endosomes/lysosomes (green), Mav and nuclei (red). Single labeling (left and middle images) and merged images (right images) are shown. Bottom-to-top projections of 3D-stack from boxed area are shown in lower panels and represent Mav, LAMP1⁺ membranes and merged images. Quantification of Mav localization in LAMP1⁺ compartments was performed on 3D stacks using fluorescence intensity profiling of Mav phagosomes along the indicated line ((B), Mav: red line; LAMP1: green line). For each time point, at least 70 cells were recorded for each donor. (<b>c</b>) The percentage of Mav localized to LAMP1⁺ compartments (mean value +/- SEM for 3 donors). <i>P</i> values were calculated using two-tailed t-test (* <0.05, ** <0.01 and *** <0.005.). a.u.: arbitrary unit. Scale bar represents 10 μm.</p