Drosophila complement-like Mcr acts as a wound-induced inflammatory chemoattractant

Abstract

Sterile tissue injury is accompanied by an acute inflammatory response whereby innate immune cells rapidly migrate to the site of injury guided by pro-inflammatory chemotactic damage signals released at the wound. Understanding this immune response is key to improving human health, and recent advances in imaging technology have allowed researchers using different model organisms to observe this inflammatory response in vivo. Over recent decades, offering a unique combination of live time-lapse microscopy and genetics, the fruit fly Drosophila has emerged as a powerful model system to study inflammatory cell migration within a living animal.^1,2,3,4 However, we still know relatively little regarding the identity of the earliest signals that drive this immune cell recruitment and the mechanisms by which they act within the complex, in vivo setting of a multicellular organism. Here, we couple the powerful genetics and live imaging of Drosophila with mathematical modeling to identify the fly complement ortholog-macroglobulin complement-related (Mcr)-as an early, wound-induced chemotactic signal responsible for the inflammatory recruitment of immune cells to injury sites in vivo. We show that epithelial-specific knockdown of Mcr suppresses the recruitment of macrophages to wounds and combine predictive mathematical modeling with in vivo genetics to understand macrophage migration dynamics following manipulation of this chemoattractant. We propose a model whereby Mcr operates alongside hydrogen peroxide to ensure a rapid and efficient immune response to damage, uncovering a novel function for this protein that parallels the chemotactic role of the complement component C5a in mammals.

Keywords: C5a; Drosophila; Mcr; cell migration; chemoattraction; complement; hemocytes; inflammation; macrophages; wound.

Graphical abstract

Figure 1

Epithelial knockdown of Mcr suppresses macrophage recruitment to wounds (A) Schematic representation of pupae 18 h after puparium formation (APF). From top to bottom: pupal case, top view. Pupa dissected from the case, lateral view. Pupal wing, lateral view. Asterisk highlights the anatomical region where the wound is performed. Wing schematic, showing the antero-posterior compartments and wound position. (B) Confocal time-lapse microscopy of pupal wing 18 h APF, showing macrophage (magenta) recruitment upon wounding in control conditions (upper) and after knockdown (KD) of Mcr (lower) in the posterior compartment (light magenta). For all microscope images, time points refer to minutes post wounding, asterisks indicate wound site, and dashed lines show wound outline. (C) Quantification of the number of macrophages recruited at the wound site after 20 min as in (B). (D) Ratio of recruited to non-recruited macrophages following wounding as in (B). (E) Confocal time-lapse microscopy of pupal wing 18 h APF, showing macrophage nuclei (dark magenta dot) recruited to the wound site and tracking of macrophages for 25 min after wounding (right). Scale bars, 50 μm. (F–I) Behavior of tracked macrophages within 150 μm of the wound center: (F) mean distance, (G) mean velocity, (H) mean Euclidean distance, and (I) directionality. (J) Representative individual macrophage tracks relative to the center of the wound. (K) Graphical representation defining the parameters for (H) and (I). (L) Terminal displacement. (M) Δ Displacement of tracked macrophages. Data are represented as mean ± SEM. Dotted lines in (L) and (M) represent the median. Asterisks indicate significant differences (^∗p < 0.05; ^∗∗p < 0.01; ^∗∗∗p < 0.001; ^∗∗∗∗p < 0.0001; ns, not significant). See also Figures S1–S3; Videos S1, S2, and S3; and Tables S1 and S2.

Figure 2

Mathematical models and statistical inference of macrophage migration and chemoattractant production-diffusion (A) Schematic of workflow to infer cell migration parameters. Bias and persistence angles are calculated from cell-tracking data (1) and fed into biased-persistent random walk model (2) (STAR Methods). The bias parameter $b$ sets the width of a normal distribution for the bias angle through ${{\sigma }_b}^2=-2\log \left(b\right)$, and similarly for the persistence parameter $p$, whereas the weight parameter $w$ sets the balance between biased and persistent steps. A posterior probability distribution over the parameters $b,p,w$ is calculated through Bayesian inference (3). (B) Observed bias, $w\times b$, inferred from cell-tracking data vs. distance from wound center at different times after wounding in control (left) and Mcr KD (right) samples. Error bars show one standard deviation around the mean of the posterior distribution for each bin. Background color indicates the control (green) and Mcr KD (magenta) epithelium and wound area (gray). (C) Posterior distribution of chemoattractant production time, $\tau$, inferred from the spatiotemporal variation of the observed bias, assuming secretion from the wound at a constant rate from $t=0$ until $t=\tau$ (STAR Methods). The inferred distribution for the control samples is in agreement with previously published data, whereas the result for Mcr KD samples shows no evidence for chemoattractant production within the duration of the videos analyzed (25 min). (D) Posterior distribution of chemoattractant diffusivity, $D$, inferred from the spatiotemporal variation of the observed bias. The inferred distribution for the control samples is in agreement with previous published data, whereas the result for Mcr KD samples does not deviate from the uniform prior distribution, indicating no evidence for any chemoattractant signal in Mcr KD conditions. (E) Snapshots of predicted chemoattractant distribution (in arbitrary units) at 1, 10, and 20 min post wounding, using the mode of the posterior distribution as parameters and approximating the wound as a circle of point sources along the wound edge. See also Tables S1 and S2.

Figure 3

Macrophage behavior in the presence of restricted chemoattractant signal generation (A) Snapshots of predicted chemoattractant distribution (in arbitrary units) at 1, 10, and 20 min post wounding (with $D=200\mathrm{\mu }{\mathrm{m}}^2/\min ,\tau =18\min$) approximating the “half wounds” as a semicircle of point sources along the wound edge. (B) Gradient of the predicted chemoattractant concentration, which is proportional to the observed bias in the chemoattractant production-diffusion model (Figure 2; STAR Methods). Background color indicates the Mcr KD (magenta) and control (green) compartments of the epithelium and wound area (gray). The gradient was calculated as the spatial derivative of the data shown in (A) along the radial direction between azimuths $\pi /4<\varphi <3\pi /4$ for distance > 0 (green background) and $-\pi /4>\varphi >-3\pi /4$ for distance < 0 (magenta background). Solid lines show the average gradient across the azimuth range, shaded lines show the full range. (C) Observed bias toward the wound (gray), calculated separately from cell tracks binned by azimuth as in (A) in the Mcr KD (magenta) and control (green) compartments of the epithelium. (D) Confocal time-lapse microscopy of pupal wing 18 h APF and macrophage tracks (right) showing the behavior of macrophages in the presence of a wound generated across the antero-posterior boundary when both compartments are wild-type (upper) or after Mcr KD in the posterior compartment (lower). Dotted lines highlight the antero-posterior boundary across the wound. Scale bars, 50 μm. (E–H) Mean distance (E), mean velocity (F), mean Euclidean distance (G), and directionality (H) of macrophages as in (D), across the anterior (A) and posterior (P) compartments. Data are represented as mean ± SEM. Asterisks indicate significant differences (^∗p < 0.05; ns, not significant). See also Video S4 and Tables S1 and S2.

Figure 4

Macrophage behavior in the presence of two competing attractant signals (A and B) Gradient of the predicted chemoattractant concentration between two wounds. Background color indicates the posterior (magenta) and anterior (green) sides of the tissue. The gradient was calculated as the spatial derivative of the data shown in snapshots along the radial direction from the center of either wound between azimuths $\pi /4<\varphi <3\pi /4$ for distance < 0 (magenta background) and $-\pi /4>\varphi >-3\pi /4$ for distance > 0 (green background). Solid lines show the average gradient across the azimuth range, shaded lines show the full range. (C and D) Observed bias toward two competing wounds (gray), calculated separately from cell tracks binned by azimuth as in (A); control vs. control wounds (upper), control vs. Mcr KD wound (lower); shaded area represents central region of highest chemoattractant overlap that lies at the boundary of posterior (magenta) and anterior (green) compartments of the epithelium. (E) Time-lapse microscopy showing the behavior of macrophages in the presence of two competing wild-type wounds (upper) or a wild-type and a Mcr KD wound (lower) in the posterior compartment. Rectangles highlight the inter-wound regions where the chemoattractant signals overlap. Scale bars, 50 μm. (F) Macrophage tracks in the inter-wound region at 1, 10, and 20 min post wounding when subjected to two wild-type wounds (upper) or in presence of a wild-type and an MCR KD wound (lower). (G) Quantification of the macrophages in the inter-wound region toward the anterior and posterior compartment. (H) Confocal microscope projection of stage 15 embryos (dorsal view), showing macrophage nuclei (dark magenta) surrounding the bottle cells of the posterior spiracles (green) in control conditions (upper) and following bottle-cell-specific Mcr overexpression (lower). Scale bars, 50 μm (left) and 20 μm (right). (I) Quantification of the number of macrophages surrounding the spiracles. Data in (G) and (I) are represented as mean ± SEM. Asterisks indicate significant differences (^∗∗p < 0.01). See also Figures S4 and S5, Video S5, and Tables S1 and S2.