Previous data suggested that, in a tumoural setting, somatic disruption of RAS–p110α prevents macrophage recruitment to tumours (Castellano et al., 2013; Murillo et al., 2014) and favours polarization of macrophages to a pro-inflammatory phenotype (Murillo et al., 2014), suggesting a possible role for p110α in macrophage function. To determine whether RAS-dependent activation of p110α participates in innate or adaptive immune responses to inflammation in macrophages, we used an established mouse model designed for the tamoxifen-inducible disruption of RAS binding to p110α (Castellano et al., 2013; Gupta et al., 2007; Figure 1—figure supplement 1A). This mouse model introduces two-point mutations, T208D and K227A, in the RAS-binding domain (RBD) of the endogenous Pik3ca allele (Pik3ca^RBD), enabling the selective disruption of the RAS–p110α interaction (Gupta et al., 2007). Wild-type (Pik3ca^WT) and Pik3ca^RBD mice were bred with mice containing a floxed Pik3ca allele (Zhao et al., 2006), along with a strain containing a conditional Cre recombinase (Cre-ERT2) allele targeted to the ubiquitously expressed Rosa26 locus. The resulting Pik3ca^WT/Flox and Pik3ca^RBD/Flox mice displayed no discernible phenotype and exhibited behaviour consistent with Pik3ca^WT mice (Castellano et al., 2013; Gupta et al., 2007). Activation of Cre-recombinase by tamoxifen led to the excision of the floxed Pik3ca allele, resulting in mice expressing either one Pik3ca^WT allele (Pik3ca^WT/–) or one Pik3ca^RBD allele (Pik3ca^RBD/–) (Castellano et al., 2013). This inducible genetic manipulation strategy allows us to selectively disrupt the RAS–p110α interaction in a controlled and temporally regulated manner, providing a valuable tool for dissecting the contributions of this pathway to immune responses in the context of inflammation.

Bone marrow-derived macrophages (BMDMs) were generated from tibias and femurs of Pik3ca^WT/Flox and Pik3ca^RBD/Flox mice, ensuring efficient removal of the floxed allele (Figure 1—figure supplement 1B). Subsequently, these BMDMs were induced towards a pro-inflammatory state through stimulation with lipopolysaccharide (LPS) and interferon gamma (IFN-γ). To assess the impact of RAS–p110α-binding deficiency on inflammatory intracellular signalling, we first examined Akt activation. The results revealed a decrease in Akt activation levels under inflammatory conditions in BMDMs lacking RAS–p110α binding, while no discernible change was observed in ERK activation, another well-known RAS effector (Figure 1A, Figure 1—figure supplement 1C, D). Interestingly, the decrease in Akt activation was accompanied by a decrease in the activation of NF-κB (Figure 1B).

Given the role of p65 in the expression of pro-inflammatory cytokines and chemokines (Zhao et al., 2021) we next analysed the expression of various inflammation mediators using a cytokine array in unstimulated and LPS- and IFN-γ-stimulated macrophages. Results showed that, under unstimulated conditions, RAS–PI3K disruption decreases the expression of IP-10, MIP-1α (Ccl3), JE (Ccl2/MCP1), IL-16, and IL-12p70, with no upregulated cytokines observed (Figure 1—figure supplement 1E). The downregulation of IP-10, MIP-1α, and JE indicates an impaired ability to recruit monocytes, macrophages, and T cells to sites of inflammation. This suggests that macrophages lacking RAS–PI3K interaction may have a reduced capacity to mount a robust immune response, particularly in recruiting and activating essential immune cells needed to combat pathogens or initiate inflammation.

Regarding LP + IFN-γ-stimulated macrophages, results showed a decrease in the expression of IL-1β and IL-17, key drivers of pro-inflammatory responses, and an upregulation of IL-7, Ilra, JE, BLC, I-309, Eotaxin, and G-CSF (Figure 1—figure supplement 1F). Elevated levels of these factors are associated with enhanced chemotactic signals and regulatory functions. Thus, the cytokine and chemokine expression profile observed in RAS–PI3K-deficient macrophages suggests impaired pro-inflammatory responses and altered immune cell recruitment patterns, potentially influencing the resolution of inflammation and tissue repair processes.

Next, we assessed whether the absence of RAS binding to p110α affects the ability of BMDMs to acquire a pro-inflammatory state characterized by increased expression of markers such as CD80, CD86, and MHCII (Biswas and Mantovani, 2010; Mercalli et al., 2013). To assess this, Pik3ca^RBD/– and Pik3ca^WT/– BMDMs were stimulated with LPS + IFN-γ, followed by flow cytometry analysis. Expression levels of CD80 (Figure 1—figure supplement 1G), CD86 (Figure 1—figure supplement 1H), and MCHII (Figure 1—figure supplement 1I) were examined. No significant differences were observed in the expression levels of any of these markers between the two genotypes under study.

Consequently, our next objective was to investigate whether in vivo disruption of RAS–p110α might lead to altered inflammatory responses. To address this, we administered tamoxifen to 10- to 12-week-old Pik3ca^WT/flox and Pik3ca^RBD/flox mice with tamoxifen and, after a 2-week interval, conducted a paw swelling assay. In this assay, zymosan (10 μg/μl) or PBS was injected into the hind paws of Pik3ca^RBD/– and Pik3ca^WT/– mice with paw thickness measured at regular intervals over a 5-day period. The results revealed a significant increase in paw inflammation in Pik3ca^RBD/– mice compared to Pik3ca^WT/– mice, evident from the earlier time points measured and persisting throughout the experiment (Figure 1D and E). Paws from the Pik3ca^WT/flox and Pik3ca^RBD/flox mice, which had not received tamoxifen, exhibited comparable levels of inflammation (Figure 1—figure supplement 1J). This reaffirms that the absence of p110α triggers a significant alteration in the inflammatory response and confirms that Pik3ca^WT/flox and Pik3ca^RBD/flox mice do not show any phenotype, as previously described (Castellano et al., 2013; Gupta et al., 2007). Additionally, analysis of the blood sedimentation rate, a reliable indicator of systemic inflammation levels (Paulsen et al., 2017), showed a lower basal sedimentation ratio in Pik3ca^RBD/– mice under PBS-treated conditions (Figure 1—figure supplement 1K). Following zymosan injection, both genotypes exhibited an increase in sedimentation rate, but the rise was more pronounced in Pik3ca^RBD/– mice, indicating a heightened systemic inflammatory response. These findings underscore that disruption of RAS–p110α interaction results in an exacerbated inflammatory state, reflected in both localized paw inflammation and systemic inflammatory mediator levels.

To delve deeper into the inflammatory response induced by zymosan, we collected paw samples at 2- and 5-day post-injection and conducted haematoxylin and eosin studies. This approach enabled a comprehensive analysis, allowing us not only to scrutinize the early stages of inflammation but also to monitor the subsequent resolution phase of the inflammatory process. Paws obtained from mice injected with zymosan for 2 days displayed extensive areas of damaged connective tissue in both Pik3ca^RBD/– and Pik3ca^WT/– paws (Figure 1D). Notably, the inflamed region in Pik3ca^RBD/– mice was larger compared to control samples. After 5 days of zymosan injection, there was a significant reduction in the inflamed area, although it remained comparatively larger in the paws from Pik3ca^RBD/– mice (Figure 1D). This observation suggests a prolonged and heightened inflammatory response in mice lacking RAS–p110α interaction, emphasizing the role of this interaction in the regulation of inflammatory processes.

The cellular composition within the inflammatory abscess offers crucial insights into the severity and progression of the disease. Close examination with the pathologist revealed features indicative of an acute inflammatory response, including an inflammatory abscess with elevated numbers of polymorphonuclear cells, primarily neutrophils, and macrophages displaying altered cell shape and increased cell death, accompanied by fibrin and chromatin deposition (Figure 1E, F). Notably, Pik3ca^RBD/– mice exhibited lower numbers of macrophages, a larger necrotic area with increased chromatin remnants, and reduced fibrin content compared to the paws from Pik3ca^WT/– mice (Figure 1E-G). By day 5, paws from Pik3ca^WT/– mice showed a significant increase in the number of macrophages, a decrease in polymorphonuclear cells, and a nearly complete resolution of the necrotic area, indicating the initiation of the resolution phase. Moreover, there were abundant activated fibroblasts, suggesting active production of new connective tissue. In contrast, paws from Pik3ca^RBD/– mice exhibited a delayed healing process characterized by a larger central area of polymorphonuclear cells, abundant fibrin deposits, reduced numbers of infiltrating macrophages, and limited fibroblast activity (Figure 1E-H). These findings collectively indicate an imbalance in the inflammatory response and a slower progression towards resolution in the absence of RAS–p110α interaction, emphasizing the pivotal role of this interaction in orchestrating an effective and timely resolution of inflammation.

To evaluate the presence of macrophages within the inflammatory lesion, we performed specific immunohistochemical analysis using the macrophage-specific marker CD68 in paws from day 5, where more cellular preservation and initiation of the healing process had been observed. A notable decrease in the number of CD68-positive cells was observed in the inflamed abscess region of Pik3ca^RBD/– mice (Figure 1G).

To further confirm the involvement of p110α signalling in the acute inflammatory response, Pik3ca^WT/WT mice were subjected to daily treatment with BYL719 (Alpelisib), a specific inhibitor of p110α isoform. After an initial 48-hr treatment period, the mice received injections of zymosan or PBS into their back-hind paws and were sacrificed 2 days later for analysis. Similar to what we had observed in Pik3ca^RBD/– mice, the inflamed area in BYL719-treated mice exhibited a larger extension than the inflamed are from non-treated mice (Figure 1H, Figure 1—figure supplement 1L). The central necrotic region was significantly larger in the BYL719-treated mice, and it contained large amount of apoptotic polymorphonuclear cells, lower number of macrophages, and increased deposits of chromatin (Figure 1H) resembling the observations from Pik3ca^RBD/– mice. Immunostaining for CD68 revealed a reduction in the number of macrophages present in the inflammatory abscess upon inhibition of p110α signalling with BYL719 treatment (Figure 1I).

In summary, our findings highlight that both genetic disruption of RAS–p110α interaction and pharmacological inhibition of p110α contribute to an expanded inflamed area and central necrotic region, concurrently reducing macrophage infiltration in a zymosan-induced inflammation model. These results collectively underscore the pivotal role of the p110α isoform of PI3K in orchestrating the resolution of inflammatory responses, emphasizing its significance in modulating the dynamics and outcomes of inflammatory processes.

Given the decrease in macrophages observed in the inflammatory abscess, we next set out to determine whether disruption of RAS binding to p110α had an effect on the number of monocytes circulating in the blood of adult mice. Pik3ca^WT/flox and Pik3ca^RBD/flox mice were treated with tamoxifen at 12–14 weeks of age and 4 weeks later, blood was collected by cardiac puncture and immune populations were analysed by flow cytometry. To assess the effects of RAS–p110α disruption on immune populations, we employed a flow cytometry gating strategy as described in the Methods section. Briefly, CD45+ cells were initially gated to define the overall immune cell population. Within this population, B cells were identified by CD19+ CD11b− staining, and T cells were defined by CD3+ expression. Further differentiation of T cell subtypes (CD4+, CD8+, and double-negative T cells) was conducted based on CD4 and CD8 markers within the CD3+ cell population. Additionally, myeloid cells were gated as CD11b+ cells, with further identification of granulocytes and monocytes based on Ly6G and Ly6C expression. Specifically, granulocytes/neutrophils were characterized as Ly6G+ Ly6C+, classical monocytes as Ly6G− Ly6C+, and alternative monocytes as Ly6G− Ly6C− (Figure 2—figure supplement 1A). We found a decrease in the number of circulating classical (or inflammatory) monocytes (Ly6C^Hi/Ly6G^-/CD11b⁺ cells) in Pik3ca^RBD/– mice (Figure 2A) and no changes were observed in non-classical (or non-inflammatory) monocytes (Ly6C^Lo/Ly6G^-/CD11b⁺ cells) (Figure 2B). Together with the decrease in inflammatory monocytes, we observed an increase in the number of neutrophils (Ly6C^-/Ly6G⁺/CD11b⁺ cells) (Figure 2—figure supplement 2A). We did not detect differences in the numbers of T cells (CD3⁺, CD8⁺, or CD4⁺) (Figure 2—figure supplement 2B) or B cells (CD19⁺) (Figure 2—figure supplement 2C) in the blood after the disruption of RAS binding to p110α. We also analysed these same cell populations in Pik3ca^WT/flox and Pik3ca^RBD/flox mice and no differences were found (data not shown).

Since splenic monocytes resemble their blood counterparts (Swirski et al., 2009) we next aimed at determining whether splenic monocyte population would also be altered after disruption of RAS–p110α interaction. Flow cytometry from Pik3ca^WT/– and Pik3ca^RBD/– mice was performed following the same gating strategy as described for blood samples (Figure 2—figure supplement 1B). As observed in circulating blood, the number of classical monocytes (Ly6C^Hi/Ly6G^-/CD11b⁺) in the spleen decreased after disruption of RAS–p110α interaction (Figure 2C) and no differences were found in non-classical activated monocytes (Ly6C^Lo/ Ly6G^-/CD11b⁺) (Figure 2D). We also checked the levels of resident macrophages present in the spleen and results showed that spleens from the Pik3ca^RBD/– mice had a decrease in the number of differentiated macrophages (F4/80⁺/CD11b⁺/CD45⁺) (Figure 2E). Analysis of macrophages in the white and red pulp of the spleen indicated that Pik3ca^RBD/– mice had a significant decrease in the macrophage population in the former region (Figure 2F, G). No differences were found in the number of granulocytes (Figure 2—figure supplement 2D), B cells (Figure 2—figure supplement 2E), or T cells (Figure 2—figure supplement 2F) present in the spleen between Pik3ca^RBD/– and Pik3ca^WT/– mice. There were no differences in spleen size from Pik3ca^RBD/– and Pik3ca^WT/– mice either (Figure 2—figure supplement 2G).

Given the decrease in the number of inflammatory monocytes and macrophages observed in blood and spleens of Pik3ca^RBD/– mice, we wondered if disruption of RAS activation of p110α could lead to a decrease in myeloid bone marrow precursors. Myeloid lineage descends from a common myeloid progenitor (CMP) in bone marrow and traverse into blood as mature cells (Akashi et al., 2000). CMP differentiates into the granulocyte–macrophage progenitor (GMP) and the megakaryocyte–erythroid progenitor (MEP). The CMP can generate all types of myeloid colonies, whereas the GMP or the MEP produces only granulocyte macrophage or megakaryocyte erythrocyte (MegE) lineage cells, respectively (Figure 2—figure supplement 2H). No significant differences were noted in the quantity of CMP progenitors within the bone marrow Pik3ca^RBD/– and Pik3ca^WT/– mice (Figure 2—figure supplement 2I–L). This observation suggests that the variations observed in blood and spleen parameters are not attributable to impairments in progenitor fate.

Finally, bone marrow precursors from Pik3ca^RBD/flox and Pik3ca^WT/flox mice were differentiated into macrophages in vitro, and the number of BMDMs was determined by flow cytometry analysis. No differences were found in the number of BMDMs obtained from Pik3ca^RBD/– and Pik3ca^WT/– mice (Figure 2—figure supplement 2M), suggesting that the disruption of RAS–p110a signalling do not interfere with the ability of bone marrow precursors to differentiate to macrophages.

The decrease in classical monocytes observed in the inflammatory abscess of paws from Pik3ca^RBD/– mice may indicate a decreased ability to mount an effective immune response. We carried out transwell assays since they are widely used to quantify transendothelial migration. Fibroblasts were seeded in the lower chamber to provide a continuous supply of Ccl2 (Tsuyada et al., 2012), as this cytokine is well known for its ability to drive chemotaxis of myeloid cells under inflammatory conditions (Matsushima et al., 1989). Pik3ca^RBD/– or Pik3ca^WT/– BMDMs were seeded in the upper chamber of the transwell. Additionally, we stimulated Pik3ca^RBD/– or Pik3ca^WT/– BMDMs with LPS and IFN-γ to mimic/recapitulate the pro-inflammatory phenotype typically associated with bacterial infection that causes macrophage activation (Orecchioni et al., 2019). Results showed that in both unstimulated and LPS + IFN-γ-stimulated BMDMs, a lower number of Pik3ca^RBD/– BMDMs were able to go through the trans-well pore membranes compared to Pik3ca^WT/– BMDMs (Figure 3A). As expected, when BMDMs were stimulated towards a pro-inflammatory phenotype, a decrease in their migratory ability was observed (Cui et al., 2018).

Extravasation entails the migration of monocytes through the endothelium (Auffray et al., 2009; Geissmann et al., 2010), so we conducted random migration assays of Pik3ca^RBD/– and Pik3ca^WT/– BMDMs growing in matrigel coated plates under unstimulated conditions or activated towards an inflammatory phenotype by addition of LPS and IFN-γ to the media. Analysis of the data revealed that, under pro-inflammatory conditions, Pik3ca^RBD/– BMDMs displayed a decrease in migration speed (Figure 3B). Migration was also evaluated in Pik3ca^WT/WT BMDMs treated with BYL-719. Results confirmed a decrease in migration speed of macrophages upon treatment with BYL-719 (Figure 3B) indicating that inhibition of p110α, either genetically or pharmacologically, reduces the ability of macrophages to migrate under inflammatory conditions.

To determine if disruption of RAS binding to p110α impairs monocyte ability to extravasate through the endothelium in vivo in response to an inflammatory stress, we analysed monocyte extravasation through the mesenteric vein in response to intraperitoneal Ccl2 injection. It is well established that loss of p110α function leads to significant impairment in endothelial and lymphatic system (Murillo et al., 2014; Soler et al., 2013), so in order to determine if monocytes from Pik3ca^RBD/– mice presented an alteration in extravasation, we generated chimeric mice in which only the bone marrow were defective in RAS binding to p110α (Figure 3C). For this, bone marrow from Pik3ca^RBD/– or Pik3ca^WT/– mice was injected through the tail vein of irradiated Lyz2^GFP donor mice (Faust et al., 2000). Engraftment of Pik3ca^RBD/– and Pik3ca^WT/– bone marrow could be followed by disappearance of the eGFP signal in donor Lyz2^GFP mice (Figure 3D, Figure 3—figure supplement 1A). Neutrophils (but no monocytes) were depleted using an anti-GR1 antibody (Figure 3E, Figure 3—figure supplement 1B) to avoid interference with monocyte extravasation. Intraperitoneal injection of Ccl2 was performed in the peritoneum of the chimera mice to induce extravasation of monocytes through the mesenteric vein and rolling, adhesion and extravasation was measured by intravital microscopy. Flow cytometry analysis demonstrated no differences in Ccr2 expression between Pik3ca^RBD/– and Pik3ca^WT/– monocytes (Figure 3—figure supplement 1C). We observed that blocking RAS–p110α interaction did not induce a decrease in the number of rolling monocytes (Figure 3F) or in the number of monocytes that adhere to the endothelium (Figure 3G). However, when extravasation was measured, data showed that disruption of RAS binding to p110α caused a significant decrease in the number of monocytes that were capable of extravasating through the endothelium of the mesenteric vein (Figure 3H).

Additionally, in order to determine whether the differences observed in the inflammatory response of Pik3ca^RBD/– mice were due to lack of monocyte extravasation, we repeated the zymosan paw swelling assay in the chimera mice. As previously, we observed that disruption of RAS–p110α only in the immune system led to higher inflammatory response and a delay in the initiation of the resolution phase (Figure 3I).

During transendothelial migration, leukocytes undergo cytoskeletal rearrangements that allow them to squeeze through the tight spaces between endothelial cells and enter the underlying tissue (Schwartz et al., 2021; Vicente-Manzanares and Sánchez-Madrid, 2004). Therefore, we next sought to determine whether the differences observed in Pik3ca^RBD/– BMDMs extravasation and migration were attributable to altered cytoskeletal dynamics. To do so, we first aimed at examining cell shape and spread in Pik3ca^RBD/– and Pik3ca^WT/– BMDMs in pro-inflammatory conditions after LPS + IFN-γ treatment. Treatment with LPS + IFNγ induced an increase in cell spread in both Pik3ca^RBD/– and Pik3ca^WT/– BMDMs when compared to their respective unstimulated counterparts (Figure 4A, B). However, cell spread in LPS + IFNγ activated Pik3ca^RBD/– BMDMs was significantly decreased compared to that observed Pik3ca^WT/– BMDMs (Figure 4A, B). Additionally, Pik3ca^RBD/– BMDMs were more elongated and did not acquire the typical rounded shape known to be induced in macrophages after treatment with LPS + IFN-γ (McWhorter et al., 2013; Figure 4A, C). We also analysed cell height and results showed that Pik3ca^RBD/– BMDMs are higher both in unstimulated conditions and after LPS + IFN-γ stimulation (Figure 4D).

Actin is a bona fide regulator of cell shape (Pollard and Cooper, 2009), so we next aimed at exploring the actin cytoskeleton in Pik3ca^RBD/– and Pik3ca^WT/– BMDMs. Actin fractionation assays were carried out in unstimulated or LPS + IFNγ activated Pik3ca^RBD/– and Pik3ca^WT/– BMDMs. Our results revealed that disruption of RAS–p110α binding caused a decrease in the F-actin pool in unstimulated BMDMs (Figure 4E), with no changes observed in the G-actin pool when compared to matched controls (Figure 4F). Analysis of actin dynamics after activation with LPS + IFNγ showed that, in pro-inflammatory conditions, the F-actin pool is increased, as expected (Ronzier et al., 2022; Figure 4E). However, actin polymerization was significantly active in Pik3ca^RBD/– BMDMs, leading to a striking increase in F-actin when compared to that observed in controls (Figure 4E). In parallel, a decrease in the G-actin pool in Pik3ca^RBD/– BMDMs was observed, suggesting an increase in the stabilization of F-actin after disruption of RAS binding to p110α (Figure 4F).

We next sought to investigate whether the enhanced F-actin stabilization corresponded to increased cellular rigidity. Consequently, we measured the elastic modulus of Pik3ca^RBD/– BMDMs, revealing a significant increase in cell stiffness following RAS–p110α disruption under both basal and pro-inflammatory conditions (Figure 4G). To validate that the heightened cell stiffness was attributed to p110α, we treated Pik3ca^WT/WT BMDMs with BYL-719 and observed an amplified elastic modulus in these cells when stimulated with LPS + IFN-γ (Figure 4G), confirming that the inhibition of p110α indeed results in augmented cellular rigidity.

These observations led us to hypothesize that phagocytosis, which heavily relies on actin rearrangement, might also be affected in Pik3ca^RBD/– BMDMs. To test this hypothesis, we assessed the ability of Pik3ca^RBD/– and Pik3ca^WT/– BMDMs to phagocytose fluorescent microspheres, B. burgdorferi, and apoptotic cells. The results revealed varying outcomes depending on the target. When phagocytosing 1 µm non-opsonized green-fluorescent beads, Pik3ca^RBD/– BMDMs were less efficient at engulfing the microspheres compared to their WT counterparts (Figure 4—figure supplement 1A). Similar results were obtained when Pik3ca^WT/WT BMDMs were treated with BYL-719 (Figure 4—figure supplement 1A). In contrast, no significant differences were observed between Pik3ca^RBD/– and Pik3ca^WT/– BMDMs in their ability to phagocytose Borrelia burgdorferi (Figure 4—figure supplement 1B).

Lastly, we evaluated the phagocytosis of apoptotic cells by Pik3ca^RBD/– BMDMs over extended periods. There were no differences in the initial uptake of apoptotic cells between Pik3ca^RBD/and Pik3ca^WT/– BMDMs. However, at later time points, Pik3ca^RBD/– BMDMs showed an accumulation of phagocytosed particles. Similar results were obtained when Pik3ca^WT/WT BMDMs were treated with BYL-719. These data suggest a delay in processing and degradation, indicating a potential role for RAS–p110α in the later stages of phagocytosis (Figure 4H).

In summary, our findings suggest that loss of RAS–p110α interaction leads to increased actin polymerization, resulting in stiffer and less deformable cells, which impairs phagocytic efficiency for certain targets. Specifically, RAS–p110α is important for the effective phagocytosis of non-biological particles and may also play a role in the proper processing and degradation of phagocytosed apoptotic cells. The differential effects on various phagocytic targets highlight the complexity of RAS–p110α’s role in macrophage biology and underscore the importance of cytoskeletal flexibility in efficient phagocytosis.

Given our previous observations, we hypothesized that the cytoskeletal alterations observed after disruption of RAS–p110a interaction, may significantly impact the secretory functions of macrophages. The accumulation of apoptotic material in Pik3ca^RBD/– BMDMs suggests a disruption in normal phagocytic processing, which could influence the release of cytokines and other inflammatory mediators. Thus, we analysed the secretome of Pik3ca^RBD/– ad Pik3ca^WT/– BMDMs both under steady-state condition and during phagocytosis. We utilized apoptotic LKR10 cells, a murine lung cancer cell line, as the substrate in our phagocytosis assay. LKR10 cells were exposed to cisplatin for 16 hr and apoptosis was confirmed in a cell viability assay (Figure 5—figure supplement 1A). Our goal was to create a more physiologically relevant experimental setting that more closely mimics the complex nature of the inflammatory response. For the secretome analysis, macrophages were incubated with or without apoptotic cells for 16 hr. Culture supernatants were collected, clarified, and subjected to label-free quantitative proteomics analysis.

A total of 127 peptides corresponding to 105 proteins (Supplementary file 1) showed differential expression between Pik3ca^RBD/ and Pik3ca^WT/– BMDM secretomes at steady-state conditions. Additionally, 359 peptides corresponding to 210 proteins (Supplementary file 2) were present a significantly different levels in the secretomes of Pik3ca^RBD/– and Pik3ca^WT/– BMDM during phagocytosis of apoptotic cells. Next, we compared these peptides with the list of secreted proteins available at The Human Protein Atlas, and removed those that do not correspond to secreted proteins. After this step, 18 proteins were found to be differentially secreted by Pik3ca^RBD/– and Pik3ca^WT/– BMDM in steady-state conditions (Figure 5A), and 38 by Pik3ca^RBD/– and Pik3ca^WT/– BMDM during phagocytosis (Figure 5B, Figure 5—figure supplement 1B, C). Surprisingly, most proteins were secreted at lower levels in Pik3ca^RBD/– BMDMs, independently of the conditions under study. Twelve proteins were differentially secreted in both experimental conditions under study (Figure 5C).

Functional analysis of differentially secreted proteins in unstimulated BMDMs showed no significant pathways related to these group of proteins (Figure 5D). However, differentially secreted proteins in phagocytosing conditions were involved in two main biological processes: complement activation (C1qa, C1qb, and C1qc connected through physical interaction and C3, C9, and Cfb through coexpression) and lysosome function, mainly cathepsins (Ctsd, Ctsb, Ctsz, Cst3, Psap, Anxa1, and Gsn) (Figure 5E). Together, the complement cascade and lysosome function work in concert to provide an effective defence against pathogens and promote overall maintenance of cellular homeostasis (Gordon, 2016; Hajishengallis and Lambris, 2016; Reis et al., 2019; Underhill and Goodridge, 2012) and data from the secretome analysis of Pik3ca^RBD/– and Pik3ca^WT/– BMDMs suggests that RAS activation of p110α may play a crucial role in the regulation of both response pathways. Moreover, these findings align with previous observations showing an accumulation of phagocytosed material in Pik3ca^RBD/– BMDMs, indicating a potential disruption in the processing and degradation of engulfed targets.

We next aimed to functionally validate the proteomics data suggesting that RAS–p110α activation regulates lysosomal function in macrophages. First, we performed immunofluorescence analysis of lysosomal-associated membrane protein 1 (LAMP1) to assess lysosomal biogenesis and function in Pik3ca^RBD/– and Pik3ca^WT/– BMDMs. Analysis of LAMP1 in Pik3ca^RBD/– and Pik3ca^WT/– BMDMs showed a decrease in LAMP1 expression in steady-state conditions (Figure 6A) suggesting a decrease in the number of lysosomes after disruption of RAS–p110α interaction. However, Pik3ca^RBD/– showed increased levels of Lamp1 expression when subjected to phagocytosis of apoptotic cells, suggesting an increase in the number of phagolysosomes present in these cells.

We hypothesized that the increase in Lamp1 expression in BMDMs lacking RAS–p110α interaction during phagocytosis could be attributed to aberrant lysosomal function and phagolysosome retention. Thus, we next evaluated lysosomal activity by using the lysosomotropic dye lysotracker red coupled with epifluorescence analysis, since it is specifically taken up by acidic organelles. As such, its accumulation is proportional to the number of acidic vesicles. Data analysis showed that disruption of RAS–p110α in BMDMs leads to a significant decrease in lysotracker uptake, both in unstimulated conditions and also after activation with LPS + IFN-γ (Figure 6B). Similar results were obtained with control BMDMs treated with BYL719, the p110α-specific inhibitor. Data analysis showed a decrease in the uptake of lysotracker after p110α inhibition (Figure 6B), further suggesting that loss of p110α function in BMDMs results in altered lysosomal pH. To confirm that lysosomal pH of Pik3ca^RBD/– BMDMs was less acidic, we next stained Pik3ca^RBD/– and Pik3ca^WT/– BMDMs with Green lysosensor, a pH-sensitive dye that exhibit a pH-dependent increase in fluorescent intensity upon lysosomal acidification. As shown in Figure 6C, Pik3ca^RBD/– BMDMs presented attenuated fluorescent intensity both in unstimulated and activated when compared with that in the control group, indicating that their lysosomal content is less acidic than in Pik3ca^WT/– BMDMs.

Considering that Pik3ca^RBD/– lysosomes were less acidic, we next investigated the expression level and activation of some of the cathepsins identified in the secretome analysis (Figure 5) by western blotting. Cathepsins play a critical role in lysosomal protein degradation. They are initially synthesized as inactive precursors and are activated through proteolysis in the lysosome at a low pH (Yadati et al., 2020). We found a reduction in the expression and activation of Cathepsins D and B in Pik3ca^RBD/– BMDMs upon stimulation with LPS + IFN-γ (Figure 6D). This observation suggests that the impaired lysosomal pH observed in Pik3ca^RBD/– BMDMs could potentially account for the observed decrease in cathepsin activity.

Acidification of lysosomes and cathepsin activation are critical steps for activation of the resolutive stage of the inflammatory response, so we next evaluated the ability of Pik3ca^RBD/– BMDM’s lysosomal compartment to degrade internalized particles. We set up another phagocytosis assay in which Pik3ca^RBD/– and Pik3ca^WT/– BMDMs would phagocytose apoptotic cells that were transduced with GFP (which is pH sensitive; Shinoda et al., 2018) and also labelled with Celltracker Red CMTPX (pH insensitive). Pik3ca^RBD/– and Pik3ca^WT/– BMDMs were allowed to engulf these apoptotic cells for 16 hr and after this time, apoptotic cells were eliminated by washes and BMDMs were analysed by flow cytometry at different time points to measure GFP signal. Our results showed that GFP signal is lost significantly faster in control BMDMs than in Pik3ca^RBD/– BMDMs (Figure 6E). As expected, we did not observe any decay in the red tracker signal. Collectively, these data evidence that Pik3ca^RBD/– BMDMs exhibit a significant impairment in removing engulfed particles, that can be attributed to the absence of lysosome acidification.

We have shown a delayed clearance of apoptotic cells in Pik3ca^RBD/– mice after zymosan injection (Figure 1). This, together with previous data took us to analyse the levels of Cathepsin D in the inflamed abscess from the paws of Pik3ca^RBD/– and Pik3ca^WT/– mice. Results showed a significant decrease in the levels of Cathepsin D in the inflamed area of Pik3ca^RBD/– mice (Figure 6F) and BYL719-treated mice (Figure 6G).

Lysosomal digestion plays a pivotal role in activation of resolutive programs by mediating PPAR activation (Mota et al., 2021; Qiu et al., 2023). Analysis of PPARδ and PPARγ expression in phagocyting macrophages showed a significant decrease in the expression of PPARδ, but no PPARγ in Pik3ca^RBD/– BMDMs (Figure 6—figure supplement 1A), suggesting that resolutive programs might not be activated effectively.

In summary, our findings underscore the crucial role of RAS–p110α signalling axis in maintaining the balance of the inflammatory response and promoting timely resolution, providing further evidence for the significant involvement of RAS–p110α signalling in the response to inflammatory stimuli.

Pik3ca^RBD/lox/Rosa26^CreERT2/WT/KRAS^G12D/WT mouse model was kindly given by Dr. Downward Laboratory (Castellano et al., 2013; Gupta et al., 2007).

For removal of Pik3ca- floxed allele, Pik3ca^RBD/lox and Pik3ca^WT/flox mice (Yang et al., 2015) were given 3.2 mg tamoxifen (Sigma) dissolved in 80 μl of corn oil by oral gavage once per day during 3 consecutive days. Efficiency of tamoxifen treatment was routinely performed by genotyping for the presence of the floxed allele.

For paw oedema studies Pik3ca^RBD/– and Pik3ca^WT/– mice (Yang et al., 2015) were divided into groups of four 2 weeks before oedema induction. Before inducing the paw oedema, the mice were anesthetized with 4% isofluorane. To induce the oedema, mice received ipsilateral i.pl. injection (30 µl) of either zymosan (10 μg/μl, Sigma-Aldrich) or PBS into the back-hind paw. Injection of 0.1 mg/kg Buprenorphine (NOAH, Vetergesic) was given for pain prevention. Paw thickness was measured using a calliper every hour during the first 6 hr after injection and then at 8, 10 hr and twice per day afterwards. Buprenorphine was injected twice per day during the length of the experiment.

Animals were randomly assigned to experimental groups to ensure unbiased allocation and minimize potential confounding factors. Animals were randomly assigned to groups with similar numbers, ensuring all were born on the same date to control for age-related factors. No formal sample size calculation was conducted. Blinding was implemented during data collection and analysis to reduce bias, with researchers unaware of the group allocations when assessing outcomes. Inclusion and exclusion criteria were pre-established to ensure consistency; animals showing pre-existing health conditions were excluded from the study to maintain experimental integrity; only females were included in this study. Mice were kept, managed, and sacrificed in the NUCLEUS animal facility of the University of Salamanca according to current European (2007/526/CE) and Spanish (RD 1201/2005 and RD53/2013) legislation. All experiments were approved by the Bioethics Committee of the Cancer Research Center. All animal procedures were conducted in accordance with the guidelines and humane endpoints established in our animal experimental license to minimize pain, suffering, and distress. Interventions such as Buprenorphine were employed as needed, and procedures were designed to be minimally invasive. Animals were monitored daily for expected and unexpected signs of pain or distress, with specific criteria—such as weight loss, lethargy, abnormal grooming—set as humane endpoints to ensure early intervention if required. No unexpected adverse events occurred during the study.

Bone marrow cells from tibias and femurs of 12- to 14-week-old Pik3ca^RBD/Lox mice and Pik3ca^WT/Lox littermates were cultured with DMEM supplemented with 10% FBS, 100 units/ml penicillin, 100 μg/ml streptomycin, 2 mM L-glutamine and 20 ng/ml M-CSF for 7 days. 4-hydroxytamoxifen (Sigma-Aldrich) (100 nM) was added to culture media on day 3 to eliminate Pik3ca-Lox allele. The differentiated BMDM were then detached using cell dissociation buffer (C5914-100, Merck) and cultured in DMEM supplemented with 10% FBS, 100 μg/ml streptomycin, 2 mM L-glutamine, 20 ng/ml M-CSF for unstimulated BMDMs. For macrophage polarization towards an inflammatory phenotype 20 ng/ml IFNγ (Peprotech) and 100 ng/ml LPS (Sigma-Aldrich) was added to culture media.

Chimeric mice exhibiting WT or RBD-deficient leukocytes were generated by lethal irradiation with 5.5 Gy twice, 4 hr apart of Lyz2^GFP recipient animals (mice exhibiting endogenously GFP fluorescent monocytes and neutrophils) followed by an injection of bone marrow cells (1.5 × 10⁶ cells/recipient i.v.) from C57BL/6 WT or RBD donor mice. Chimerism was then assessed 4 weeks later by flow cytometry from blood samples (reconstitution of 99.8 ± 0.2% and 99.8 ± 0.1% for WT and RBD-deficient donor cells, respectively; n = 5 mice per group). Lyz2^GFP donors were kindly given by Dr. Voisin’s laboratory.

Neutrophil depletion of chimeric mice was induced by intraperitoneal injection of anti-GR1 25 μg/mouse/day for 3 days. Numbers of blood circulating monocytes and neutrophils were quantified by flow cytometry pre- and post-depletion. Neutrophils were found to be reduced by 99.5%, while this anti GR1-depleting protocol had no effect on blood monocyte proportion (n = 5 mice/group).

Mesenteric inflammation was induced following intraperitoneal injection of mouse recombinant CCL2 (500 ng/mouse in 500 µl of PBS). Six hours later, anesthetized chimeric mice (150 mg/kg ketamine, 7.5 mg/kg xylazine, i.p.) were placed in supine position on a heating pad (37°C) for maintenance of body temperature. The mesenteric vascular bed was exteriorized, placed on a purpose-built stage of an upright brightfield microscope (Zeiss Axioskop). Mesenteries were superfused with warmed (37°C) Tyrode’s solution (Sigma). After a 5-min equilibration period, analysis of leukocyte–endothelium interactions was made in at least 9 (and up to 16) randomly selected segments (100 µm in length) of post-capillary venules (20–40 µm in diameter) for each mouse. Leukocyte rolling was quantified by counting the number of rolling cells passing a fixed transversal line in the middle of the vessel segment for 5 min. Leukocyte adhesion (stationary position of the cell for 30 s or longer) was quantified along a 100-µm vessel length and data were normalized as the number of cells per 500 µm vessel segments. Leukocyte extravasation response was quantified within 50 µm on either side of the 100 µm vessel segment in the perivenular tissue; and data were normalized as the number of extravasated leukocytes per mm2 of extravascular tissue. At the end of the analysis period, mice were humanely killed by cervical dislocation.

The elastic modulus of BMDMs was quantitatively assessed utilizing atomic force microscopy (AFM). Specifically, cells were cultured on cover glass slides, which were subsequently positioned in a specialized BIO-AFM setup integrated with an inverted optical microscope (Nikon TE2000). The BIO-AFM was equipped with a V-shaped, four-sided pyramidal silicon nitride tip (Bruker AFM Probes) to facilitate accurate force measurements. To avoid localized variations and ensure data representativeness, no more than three cells were selected from a single field of view for mechanical characterization, and these cells were never contiguous. Subsequently, the stiffness of each individual cell was characterized through measurements obtained at three perinuclear points. For each point, force–displacement (F vs. z) curves were captured (10 µm amplitude at a speed of 5 µm/s). The determination of the elastic modulus was performed based on the analysis of force–displacement curves. A total of five curves were recorded for each perinuclear point at an indentation depth of 300 nm. Data were analysed by fitting the curves to the Hertz model as previously described (Jorba et al., 2017; Rico et al., 2005). Finally, the elastic modulus for each cellular population under distinct experimental conditions was calculated based on a minimum of fifteen measurements per independent cell, with each condition comprising at least 10 independent cells (n ≥ 10).

BMDMs were cultured in 6-well plates at a density of 1 × 10⁶ cells per well. Cytokine production was analysed in unstimulated and M1 macrophages using commercial mouse cytokine array (R&D Systems, #ARY006). Cells were lysed at 4°C for 30 min using a lysis buffer containing 1% Igepal CA-630, 20 mM Tris-HCl (pH 8.0), 137 mM NaCl, 10% glycerol, 2 mM EDTA, 10 μg/ml Aprotinin, 10 μg/ml Leupeptin, and 10 μg/ml Pepstatin, following the manufacturer’s protocol. Lysates were then centrifuged at 16,100 RCF for 15 min at 4°C. The supernatant was collected, and protein concentration was determined using a Qbit 2.0 Fluorometer. A total of 1400 μg of protein was diluted to a final volume of 1 ml with Array Buffer 6, followed by the addition of 0.5 ml Array Buffer 4 and 15 μl of the reconstituted Mouse Cytokine Array Panel A Detection Antibody Cocktail. The samples were mixed and incubated for 1 hr on a shaker at room temperature. Nitrocellulose membranes from the mouse cytokine array were blocked with Array Buffer 6 for 1 hr at room temperature on a shaker. The blocking buffer was then removed, and the sample mixture was added to the arrays, which were incubated overnight at 4°C on a shaker. Membranes were washed with Wash Buffer provided by the manufacturer. Membranes were then incubated with 1× Streptavidin-HRP in Array Buffer 6 for 30 min at room temperature. After further washing, the Chemi Reagent Mix was applied, and signals were detected using an Amersham Imager 600 (GE Healthcare, #29-0981-07 AC).

BMDM were fixed using 4% paraformaldehyde, permeabilized with 0.1% Triton X-100 and blocked for 1 hr with 3% BSA in PBS before incubation with the primary antibodies, used at a 1:100 dilution: Lamp1 (#553792, BdPharmigen), Deoxyribonuclease I-Alexa Fluor 488 Conjugate (#D12371, Invitrogen, 1:2000). To stain actin cytoskeleton, Alexa Fluor 647 Phalloidin (Invitrogen, 1:10,000) was directly added to the primary antibody mixture. Alexa Fluor 488- or Alexa Fluor 555-conjugated secondary antibodies (Invitrogen) were used to detect the indicated proteins at a 1:1000 dilution. Cells were counterstained with DAPI on the mounting solution (ProLong Gold Antifade Reagent with DAPI, Invitrogen). Images were taken using a Zeiss LSM510 confocal microscope or Leica DM6 B THUNDER Imager 3D Tissue.

Transwell migration assays were carried out using the 6.5 mm Transwell with 8.0 µm Pore Polyester Membrane Insert (Corning). 9 × 10⁴ MEFs from wild-type mice were used as a chemo-attractant to encourage macrophage migration. 8 × 10⁵ BMDM were seeded in the transwell. Transwells were performed following the manufacturer’s instructions.

For random migration assays, BMDMs were seeded in 24-well plates coated with matrigel (0.5 mg/ml) and labelled using CellTracker Red CMTPX Dye 1 μM (Thermo Fisher) for 30 min. Twenty-four hours later LPS + IFN-γ was added when necessary. Triplicates of each condition and genotype were prepared. Time-lapse imaging was carried out for 24 hr. One image was taken every 10  min within the same well using a Nikon microscope driven by Metamorph (Molecular Devices, Chicago, IL, USA). A total of 80–100 cells per condition were tracked using the Fiji plugin Trackmate. Pre-processing was done using Mexican hat filter 3.0 radius to increase particle detection. Images were segmented using the fluorescence channel with the Laplacian of the Gaussian detector with a 30-μm estimated particle diameter, a 10.0 threshold and median filter option selected. Segmented objects were linked from frame to frame with a Linear Assigment Problem (LAP) tracker with 45 μm frame-to-frame linking distance and 2 frame gap closure. Criteria for track acceptance were track duration at least the 90% of the video. Tracks were visually inspected for completeness and accuracy of the tracking.

Samples for secretome analysis were prepared as previously described (Álvarez-Teijeiro et al., 2018). In brief, 100 µg of proteins were digested into peptides using trypsin and peptides were desalted using Oasis HLB extraction cartridges (Waters UK Ltd) and eluted with 50% acetonitrile (ACN) in 0.1% trifluoroacetic acid (TFA).

Dried peptides were dissolved in 0.1% TFA and analysed by nano ACQUITY liquid chromatography (Waters Corp, Milford, MA, USA) coupled on-line to a tandem LTQ Orbitrap XL, mass spectrometer (Thermo Fisher Scientific) (Casado et al., 2013). Gradient elution was from 5% to 25% buffer B in 180  min at a flow rate 300 nl/min with buffer A being used to balance the mobile phase (buffer A was 0.1% formic acid in water and B was 0.1% formic acid in ACN). The mass spectrometer was controlled by Xcalibur software and operated in the positive mode. The spray voltage was 1.95 kV and the capillary temperature was set to 200°C. The LTQ Orbitrap XL was operated in data-dependent mode with one survey MS scan followed by 5 MS/MS scans. Label-free quantitative proteomics analysis was performed using three independent biological samples per group. Additionally, each sample was analysed in technical duplicates. To ensure robust quantitative analysis, we utilized LTQ Orbitrap XL tandem mass spectrometry (MS/MS) to generate six distinct mass spectral profiles from each group.

MS raw files were converted into Mascot Generic Format using Mascot Distiller (version 2.3.0) and searched against the SwissProt database (release December 2015) restricted to human entries using the Mascot search daemon (version 2.3.1). Allowed mass windows were 10 ppm and 600 mmu for parent and fragment mass to charge values, respectively. Variable modifications included in searches were oxidation of methionine, pyro-glu (N-term) and phosphorylation of serine, threonine, and tyrosine.

Spectral counting quantification method relies on the number of times peptides are identified by tandem mass spectrometry (with expectancy value <0.05) from a given protein. Spectral counts were obtained from Mascot result (DAT) files using a python script written in house in the Mascot Parser Toolkit environment (version 2.4.x).

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD057794 and 10.6019/PXD057794.

The proteomic data obtained consisted of 6844 peptides and 30 samples: Pik3ca^WT/− and Pik3ca^RBD/− BMDMs in steady-state conditions (labelled as cell samples: WT/- and RBD/-), phagocytosing apoptotic cells (labelled as cell samples: WT/-Phag and RBD/-Phag), and the apoptotic LKR10 cells alone (labelled as LKR). For each of these samples, proteomic experiments were performed with six replicates (three biological replicates × two technical replicates), yielding a dataset of 30 samples.

The first analytical step was to remove all peptides for which there was no information contained in the proteomic raw data matrix and the peptides for which 85% or more of the signal values were missing. All these peptides were specific of mouse proteins and in many cases were unique. The corresponding proteins were annotated and labelled together with each measured peptide. Next, low-quality samples were also removed, testing the overall signal per sample to identify if there were clear outliers with very low signal or with a very different signal distribution. Comparison of the overall signal distributions of the 30 samples (comparing boxplots) and identified 3 samples that were very different were obtained and discarded (WT/-Phag_s3r2 (sample 3, replicate 2), RBD/-_s2r1 and LKRc_s1r1). These three samples showed a median signal in their distributions that deviated >20% from the median signal of the distributions of all other samples.

Differential expression analysis for each peptide of each protein was next performed. The algorithm used to carry out this analysis was limmaVoom within EdgeR R package (McCarthy et al., 2012; Robinson et al., 2010). Prior to this analysis, a Bartlett test was performed to see the homogeneity of variances, verifying that for this data we cannot consider equality of variances and this factor was included in the differential expression algorithm. With this algorithm, normalization factors to use a posteriori were calculated and, transformation and calculation of the variance weights was performed. The model to fit before using Voom as specified since it uses the variances of the model’s residuals (observed − fitted). Finally, an estimation of the contrast for each feature tested (i.e. each peptide) was carried out using the Empirical Bayes approach in limma as previously described (Campos-Laborie et al., 2019). Peptides were ordered by the p.value of the limma test considering significant peptides changed only with a p.value below 0.05 and with a log2(Fold-Change) >|2|. All these analyses were performed using the statistical computing language R and packages or libraries obtained from R-cran (https://cran.r-project.org/) or Bioconductor (https://www.bioconductor.org/).

Cytoscape software (v3.9) (Killcoyne et al., 2009) including GeneMania app (Franz et al., 2018) was then used to generate and visualize protein–protein networks of the significantly altered proteins selected in secretome analysis of unstimulated and phagocyting BMDMs. This tool provides information on protein–protein associations based in co-expression studies and also based in physical interaction studies.

Immunoblot was performed per a general western blot protocol (Abcam). Total protein was extracted using Cell Lysis Buffer (Cell Signaling Technology) supplemented with c0mplete mini protease inhibitor cocktail (Roche), 50 mM sodium fluoride and 1 mM of PMSF. Protein was quantified using Bradford Method (Bio-Rad). 20 μg of protein was separated by SDS–PAGE and transferred to 0.2 um pore-size PVDF membranes (Sigma-Aldrich). Blots were probed using the following antibodies, at a concentration 1:1000 unless otherwise stated: cathepsin B (12216-1-AP, Proteintech), cathepsin D (21327-1-AP, Proteintech), and α-tubulin (ab15246, Abcam; concentration 1:5000). Horseradish peroxidase-conjugated secondary antibodies (Amersham) were used (1:5000) and detected using an enhanced chemiluminescent substrate (Amersham). Signal was detected using an iBright 1500 System (Invitrogen).

Single-cell suspensions from cultured cell, spleen or blood monocytes were generated from mice, washed twice in staining buffer and incubated with 1:100 Fc-block (BD Biosciences, #553142) diluted in FACS buffer. Cells were subjected to surface antibody staining with labelled antibodies diluted in staining buffer for 30 min at 4°C: CD3-PE-Cy7 (#100328, Biolegend), CD4-BV605 (#100548, Biolegend), CD8-APC (#100712, Biolegend), CD19-PerCP-Cy5.5 (#115534, Biolegend), CD45-BV785 (#103149, Biolegend), Ly6C-PerCP-Cy5.5 (#128012, Biolegend), Ly6C-E450 (#48-5932-82, eBioscience), Ly6G-AF700 (#56-5931-82, eBioscience), CD11b-BV650 (#101239, Biolegend), F4/80-PE-Cy7 (#123114, Biolegend), F4/80-PE (#123110, Biolegend), and CCR2 (CD192)-PE-Vio 770 (#130-108-724, Miltenyi Biotec). After incubation, cells were washed in staining buffer and analysed immediately. For all staining, isotype controls were used.

The gating strategy for analysing distinct cellular populations began by isolating cells positive for the CD45 marker. Subsequently, B cells, identified by their positivity for CD19 and negativity for CD11b, were selected. To determine the frequency of T cells, the CD45+ cell population, devoid of both CD19 and CD11b, underwent examination for CD3 expression. CD3+ cells were further categorized based on CD4 and CD8 expression, distinguishing CD4+, CD8+, and double-negative (DN) T cells. Following the application of these gating criteria across all samples, the percentages of CD45-positive cells expressing CD19 and T cells (CD3+) within the CD45+ population were calculated. Additionally, myeloid populations were analysed by quantifying the proportion of CD11b+ cells. To distinguish between circulating monocytes and granulocytes, we assessed their expression of Ly6G (granulocytes) and Ly6C proteins (monocytes). Classical monocytes were characterized by negativity for Ly6G and positivity for Ly6C, while alternative monocytes lacked both markers, and neutrophils/granulocytes displayed weak positivity for Ly6C and positivity for Ly6G.

Samples were acquired on a BD LSR FORTESA FACS or FACS Aria III machine that uses FACS DIVA software (BD Biosciences). Compensation was performed using 1 drop of Ultracomp ebeads (eBioscience) in 300 μl of FACS buffer. 1 μl of each antibody used in the pool was mixed with 100 μl of compensation beads solution and acquired. A total of 50,000 cells per mouse were analysed. Analysis was performed with FlowJo software (FlowJo V10.4). Once the different pools were compensated samples were acquired.

For phagocytosis assay, BMDMs in suspension in DMEM without FBS were labelled with 1:200 red cell tracker (Molecular Probes) for 30 min at 37°C. Cells were then centrifuged for 5 min at 300 × g at 4°C, supernatant was removed and labelled BMDMs were washed twice with 5 ml of PBS and plated overnight in complete DMEM containing 20 ng/ml M-CSF. On the following day, 100 ng/ml LPS (Sigma-Aldrich) was added to BMDMs overnight.

LKR10 cells (murine lung cancer cell line) were stained with red cell tracker (Molecular Probes) as previously described for macrophages. Stained cells were plated in complete DMEM medium and, after 12 hr, 50 μM Cisplatin (MCE MedChemExpress) was added to the media and left overnight. BMDMs were then incubated with apoptotic cancer cells at a 1:2 ratio and cultured at 37°C for different time points in DMEM supplemented with 10% FBS.

Flow cytometry data were acquired using a BD LSR FORTESSA FACS instrument with FACS DIVA software (BD Biosciences) and analysed using FlowJo V10.4 software. A minimum of 2 × 10⁵ events were acquired and analysed. Data analysis and interpretation were done using FlowJo software (FlowJo V10.4).

Confocal images were post-processed and analysed using Fiji distribution of ImageJ version 1.53q. Cell shape descriptors such as ‘aspect ratio’ (AR), ‘circularity’ (C) and ‘cell area’ were measured using Fiji. Specifically, aspect ratio is calculated as (major axis × minor axis − 1) therefore representing solely the degree of elongation, whereas circularity is calculated as [4π*(area × perimeter − 2)], thus representing the degree of similarity to a circumference with a value ranging from 0 to 1 (perfect circle).

Tissue was fixed using 4% formaldehyde for 48 hr, dehydrated and paraffin-embedded. Sections (3 μm) were cut and stained using hematoxylin–eosin. For immunodetection, citrate pH 6 buffer was used for antigen retrieval. Staining was used using the following primary antibodies: CD68 (ab125212, Abcam, 1:200), Cathepsin B (12216-1-AP, Proteintech, 1:100), and Cathepsin D (21327-1-AP, Proteintech, 1:400). Dako EnVision+ System HRP labelled Polymer secondary antibodies (Dako) were used, and DAB+ Substrate Chromogen System (Dako) was used for colour development.

For the quantification of loose chromatin remnants in paw oedema images, the pathologist assigned a score ranging from 1 to 3 based on the chromatin content within the inflammatory abscess. A score of 1 indicates low chromatin content, occupying less than 30% of the abscess area, while a score of 3 represents high chromatin content, covering over 60% of the abscess area.

All experiments were conducted with at least three independent biological replicates, each containing technical triplicates. For statistical analysis, we used the Mann–Whitney test to compare control and RBD groups as a non-parametric approach suitable for two independent groups. Additionally, two-way ANOVA was used to evaluate interaction effects between factors in our experimental design, enabling comparisons across multiple groups and conditions.

All materials used in this study are available upon request. Researchers may contact Dr. E. Castellano (ecastellano@usal.es) for access. Most materials have no restrictions; however, access to the mouse model may require a Material Transfer Agreement (MTA) to protect intellectual property or for regulatory compliance. Dr. Castellano can provide details on terms and facilitate the transfer process.