The PI3K-Akt-CCND2 axis orchestrates macrophage M1 polarization through metabolic reprogramming: mechanistic and therapeutic insights

The PI3K-Akt-CCND2 axis orchestrates macrophage M1 polarization through metabolic reprogramming: mechanistic and therapeutic insights | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The PI3K-Akt-CCND2 axis orchestrates macrophage M1 polarization through metabolic reprogramming: mechanistic and therapeutic insights Xiaoyu Liu, Wei Shi, Lin Chai, Jianyuan Liu, Yanqian Su, Shuxing Wei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8674632/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Sepsis is a life-threatening syndrome driven by dysregulated macrophage polarization, in which excessive M1 polarization exacerbates systemic inflammation and organ injury. However, the mechanisms underlying the interplay between metabolic reprogramming and cell-cycle regulators such as Ccnd2 during macrophage polarization in sepsis remain poorly understood. This study investigates the role of Ccnd2 and its regulatory network in LPS-induced macrophage inflammation, with a focus on the PI3K–Akt signaling axis and associated metabolic alterations. Methods RAW264.7 macrophages were divided into four experimental groups: sham, LPS, PI3K inhibitor + LPS, and M-CSF–pretreated + LPS. Transcriptomic (RNA-seq) and metabolomic (LC-MS/MS) profiling were performed to identify differentially expressed genes and metabolites. Western blotting and qRT-PCR were used to validate expression levels of Ccnd2, PI3K, Akt, and P27. Flow cytometry was employed to assess M1 polarization, and KEGG enrichment analysis was conducted to explore transcriptome–metabolome regulatory networks. Results Transcriptomic analysis indicated significant enrichment of pathways related to PI3K–Akt signaling, cell cycle, and inflammatory cascades following LPS stimulation. Ccnd2 expression was downregulated in the LPS-treated group but markedly upregulated in the M-CSF–pretreated group—an effect abolished by PI3K inhibition. Metabolomic profiling revealed distinct metabolic reprogramming in LPS-stimulated macrophages, with notable alterations in purine metabolism, glycerophospholipid metabolism, and amino acid homeostasis. Flow cytometry demonstrated that LPS enhanced M1 polarization, whereas M-CSF co-treatment reversed this effect. PI3K inhibition suppressed both Ccnd2 expression and M1 polarization, suggesting a functional connection between Ccnd2-mediated cell-cycle progression and inflammatory polarization. Conclusion This study delineates a novel regulatory network in which LPS-induced metabolic reprogramming synergizes with PI3K–Akt signaling to modulate Ccnd2 expression, thereby coordinating macrophage cell-cycle progression and M1 polarization. The PI3K–Akt–Ccnd2 axis represents a promising therapeutic target for sepsis and other inflammatory disorders, offering potential for combined metabolic and immune interventions to reprogram macrophage polarization and mitigate inflammatory injury. Biological sciences/Cell biology Health sciences/Diseases Biological sciences/Immunology Biological sciences/Molecular biology sepsis macrophage polarization Ccnd2 metabolic reprogramming PI3K-Akt signaling LPS inflammation transcriptomics metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Sepsis, defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection (Third International Consensus), remains a critical global health challenge [1] . This syndrome represents more than a simple inflammatory reaction; it is characterized by a profound immunological imbalance in which the pro-inflammatory response (aimed at pathogen clearance) and the compensatory anti-inflammatory mechanism (designed to limit tissue damage) become severely misaligned [2] . This dysregulation frequently leads to multiple organ dysfunction and high mortality. Recent epidemiological studies highlight its substantial burden, with an estimated 48.9 million cases and 11 million sepsis-related deaths worldwide annually [3] . The impact is particularly severe in low- and middle-income countries, underscoring stark disparities in healthcare resources and outcomes [4] . Despite advances in supportive care, the complex pathophysiology—often involving concurrent hyperinflammation and immunosuppression—continues to drive significant morbidity and mortality, necessitating further research into targeted therapies and improved clinical management strategies [5, 6] . The pathogenesis of sepsis is closely linked to an imbalance between pro-inflammatory and anti-inflammatory responses. Macrophages play a pivotal role in this process by dynamically polarizing into a pro-inflammatory M1 phenotype (secreting TNF-α, IL-1β) or an anti-inflammatory M2 phenotype (secreting IL-10, TGF-β) in response to microenvironmental signals [7–10] . Dysregulated macrophage polarization and sustained inflammation are key drivers of sepsis progression, making the exploration of molecular regulators in this process crucial for developing novel therapeutic strategies [8, 9, 11–13] . Cyclin D2 (Ccnd2) is a core regulator of the cell cycle [14, 15] . Beyond its canonical role in proliferation, emerging evidence indicates that Ccnd2 is also involved in immune cell function: it regulates lymphocyte activation and differentiation, and recent studies suggest its participation in macrophage-mediated inflammatory responses [15] . However, its specific role in sepsis remains unclear. Given that sepsis-associated inflammation depends heavily on macrophage activation and proliferation, investigating how Ccnd2 modulates macrophage polarization and inflammatory responses may provide novel mechanistic insights. Furthermore, growing evidence suggests that Ccnd2 participates in immunometabolic regulation. For instance, it can interact with metabolic signaling hubs such as mTOR and the PI3K-Akt pathway, potentially linking nutrient sensing to immune cell fate decisions [16, 17] . In macrophages, although inflammatory stimuli modulate Ccnd2 expression, whether and how Ccnd2 integrates metabolic reprogramming with polarization phenotypes remains largely unexplored. While recent studies hint at its involvement in macrophage-mediated inflammation [16, 18] , a systematic understanding of its role in sepsis-associated macrophage dysfunction—particularly regarding how it couples cell-cycle progression with metabolic rewiring—is still lacking. This knowledge gap motivates our investigation into Ccnd2 as a potential nexus between metabolism, signaling, and inflammatory polarization in sepsis. Notably, the interplay among Ccnd2, cell-cycle progression, and inflammatory signaling in sepsis-associated macrophages is poorly understood. Key unresolved questions include whether Ccnd2 promotes pro-inflammatory M1 polarization or favors anti-inflammatory M2 transition, and how it mechanistically links cell-cycle regulation to inflammatory cytokine production. To address these gaps, this study aims to elucidate the role of Ccnd2 in LPS-induced macrophage models by integrating functional assays, transcriptomics, and phenotypic characterization. Based on the above background, we hypothesize that in LPS-stimulated macrophages, metabolic reprogramming (e.g., in purine and glycerophospholipid metabolism) activates the PI3K-Akt pathway, which in turn upregulates Ccnd2 expression. Ccnd2 then acts as a coupling node, integrating cell-cycle progression with metabolic status to drive macrophage polarization toward the M1 phenotype, thereby forming a self-reinforcing inflammatory loop. Furthermore, we propose that M-CSF counteracts LPS-induced inflammation by promoting PI3K-Akt-dependent upregulation of Ccnd2, whereas PI3K inhibition disrupts this regulatory network. The novelty of this study lies in three aspects: (1) revealing a non-canonical, immunometabolic function of Ccnd2 in macrophage polarization, extending its role beyond cell proliferation; (2) integrating multi-omics (transcriptomics and metabolomics) with functional validation to construct a multidimensional “metabolism–signaling–cell cycle–polarization” regulatory network; and (3) identifying the PI3K-Akt-Ccnd2 axis as a druggable target for sepsis-related inflammation, providing a rationale for combined metabolic and immune interventions. 2. Materials and Methods 2.1 Cell Culture and Study Design The mouse macrophage cell line RAW264.7 (Procell Life Science & Technology Co., Ltd., Wuhan, China; Cat# CL-0190) was cultured in complete medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. Cells were maintained at 37°C in a humidified incubator with 5% CO₂ and were subcultured at a 1:3 ratio upon reaching confluence. All cell lines were authenticated by short tandem repeat (STR) profiling and confirmed to be free of mycoplasma contamination. RAW264.7 cells were divided into four experimental groups: Sham group: Untreated control; LPS group: Cells treated with 30 µg/mL lipopolysaccharide (LPS; Solarbio, Cat# L8880) for 24 hours; PI3Ki + LPS group: Cells pretreated with 10 µM of the PI3K inhibitor LY294002 (MCE, Shanghai, China; Cat# HY-10108) for 1 hour, followed by co-treatment with 30 µg/mL LPS for 24 hours. The concentration of LY294002 was optimized to 10 µM based on preliminary dose-response experiments, which demonstrated effective downregulation of PI3K/Akt signaling in our model [19] ; M-CSF + LPS group: Cells pretreated with 25 ng/mL macrophage colony-stimulating factor (M-CSF; MCE, Cat# HY-P7085) for 48 hours, followed by stimulation with 30 µg/mL LPS for 24 hours. 2.2 RNA Extraction and Quantitative Real-Time PCR (qRT-PCR) Total RNA was extracted from RAW264.7 cells using TRIzol reagent (Takara, Tokyo, Japan; Cat# 9109). Subsequently, 1 µg of RNA was reverse-transcribed into complementary DNA (cDNA) using a PrimeScript RT reagent kit (Takara; Cat# RR047A). Quantitative PCR was performed on a QuantStudio™ 5 Real-Time PCR System (Thermo Fisher Scientific, USA) using SYBR Green Premix. The expression level of the target gene Ccnd2 was analyzed using the 2−ΔΔCt method, with normalization to an appropriate housekeeping gene. All primer sequences were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China) and are listed in Table 1. Table1 Gene name Forward（5’-3’） Reverse(5’-3’) GAPDH(mouse) GGCAAATTCAACGGCACAGTCAAG TCGCTCCTGGAAGATGGTGATGG CCND2(mouse) GCCAAGATCACCCACACTGA GCGTTATGCTGCTCTTGACG 2.3 Protein Extraction and Western Blot Analysis Total protein was extracted from RAW264.7 cells using pre-cooled RIPA lysis buffer. Protein concentration was determined with a bicinchoninic acid (BCA) assay kit (Thermo Fisher Scientific, USA). Subsequently, 30 μg of protein per sample was separated by 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and transferred onto polyvinylidene fluoride (PVDF) membranes. After blocking with 5% skim milk for 2 hours at room temperature, the membranes were incubated overnight at 4 °C with the following primary antibodies listed in Table 2: Table 2 Target Supplier (Location) Catalog Number Ccnd2 Proteintech (Wuhan, China) 10934-1-AP P27 Selleck (Shanghai, China) F0170 PI3K Abmart (Shanghai, China) T40115F Phospho-PI3K Abmart (Shanghai, China) T40116F AKT Abmart (Shanghai, China) T55561F Phospho-AKT Abmart (Shanghai, China) T40067F 2.4 Flow Cytometry At 24 hours post-treatment, RAW264.7 cells were harvested and stained for surface markers to assess polarization. Cells were incubated for 60 minutes at room temperature in the dark with fluorescently conjugated antibodies: FITC anti-mouse CD86 (1:100; BioLegend, Cat# 159220) and APC anti-mouse CD206 (MMR; 1:100; BioLegend, Cat# 141708). Unstained cells served as negative controls. Data were acquired on a BD FACSAria II flow cytometer (BD Biosciences, USA). Viable single cells were gated based on forward scatter (FSC) and side scatter (SSC) properties, and analysis was performed using FlowJo software (Version 10; BD Biosciences). 2.5 Transcriptomic Sequencing and Untargeted Metabolomics For transcriptomic analysis, RAW264.7 cells were divided into two groups: a control group and a group treated with 30 μg/mL LPS for 24 hours. Subsequently, total RNA was isolated from harvested cells using TRIzol reagent (Invitrogen, USA). RNA quality was assessed with a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). Sequencing libraries were prepared and sequenced on an Illumina NovaSeq X Plus platform. All transcriptomic data processing was conducted by Majorbio Bio-pharm Technology Co., Ltd (Shanghai, China). Differentially expressed genes were identified based on thresholds of |log₂(fold change)| > 1 and adjusted p < 0.05. For untargeted metabolomics, cell metabolites were extracted using ice-cold methanol containing L-2-chlorophenylalanine as an internal standard. Samples were vortexed for 30 seconds, sonicated for 30 minutes in an ice-water bath, and then incubated at −20 °C for 30 minutes to precipitate proteins. After centrifugation at 13,000 × g and 4 °C for 15 minutes, the supernatant was collected, dried under a gentle nitrogen stream, and reconstituted in 100 µL of acetonitrile:water (1:1, v/v). The solution was sonicated at 5 °C for 5 minutes, centrifuged again (13,000 × g, 10 min, 4 °C), and the final supernatant was transferred to LC MS/MS vials for analysis. Raw LC MS/MS data were processed using Progenesis QI (Waters) for peak detection, alignment, and integration. Metabolites were annotated by matching against the HMDB, Metlin, and the Majorbio in-house databases. Data preprocessing included the removal of features with >20% missing values within any group, missing value imputation with the global minimum, and peak intensity normalization. Quality control samples with a relative standard deviation (RSD) > 30% were excluded, and data were log₁₀-transformed prior to statistical analysis. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using the ropls package (v1.6.2) in R. Metabolic pathway enrichment analysis was conducted in Python using scipy.stats . 2.6 Statistical Analysis All statistical analyses were performed using SPSS Statistics software (version 26.0, IBM, USA). Normality of data distribution was assessed with the Shapiro–Wilk test. For comparisons across multiple groups, one-way analysis of variance (ANOVA) was applied, followed by the least significant difference (LSD) post hoc test. Data are presented as mean ± standard deviation (SD). Statistical significance was defined as p< 0.05. 3. Results 3.1 Integrated Multi‑Omics Profiling Reveals LPS‑Induced Activation of PI3K‑Akt Signaling and Metabolic Reprogramming To systematically delineate the global molecular alterations underlying LPS‑induced macrophage activation, we performed integrated transcriptomic and metabolomic profiling. RNA‑sequencing analysis identified 2351 upregulated and 1974 downregulated genes in RAW264.7 macrophages after 24‑hour LPS stimulation (Fig. 1A). Unsupervised clustering showed a clear separation between the LPS‑treated and control groups (Fig. 1B), confirming extensive transcriptional reprogramming. KEGG enrichment analysis of differentially expressed genes revealed significant activation of pathways related to the cell cycle, PI3K‑Akt signaling, and MAPK signaling (Fig. 1C, D), indicating concurrent engagement of proliferative and inflammatory cascades. Parallel untargeted metabolomic profiling (LC‑MS/MS) detected 51 upregulated and 165 downregulated metabolites in LPS‑stimulated macrophages (Fig. 1E). Notably, pathways such as purine metabolism and glycerophospholipid metabolism were enriched in both transcriptomic and metabolomic datasets (Fig. 1C, F), demonstrating coordinated transcriptional and metabolic remodeling. Together, these multi‑omics profiles provide a comprehensive map of LPS‑induced macrophage activation and highlight potential regulatory hubs where inflammatory signaling, metabolic reprogramming, and cell‑cycle progression converge. 3.2 The PI3K–Akt Axis Dynamically Regulates Ccnd2 Expression in Response to LPS and M‑CSF Based on the prominent enrichment of cell‑cycle and PI3K–Akt signaling pathways in the transcriptomic data, we focused on cyclin D2 (Ccnd2), a key regulator of the G1/S phase transition. Quantitative RT‑PCR and Western blot analyses showed that LPS stimulation significantly downregulated both Ccnd2 mRNA and protein levels compared with the sham group (Fig. 2A, B). In contrast, pretreatment with macrophage colony‑stimulating factor (M‑CSF) prior to LPS challenge markedly increased Ccnd2 expression. This M‑CSF‑mediated upregulation was completely abolished by co‑treatment with the PI3K inhibitor LY294002 (Fig. 2A, B), indicating that M‑CSF enhances Ccnd2 expression in a PI3K‑Akt‑dependent manner. To investigate the upstream regulatory mechanism, we examined the protein level of P27 (also known as CDKN1B), a cyclin‑dependent kinase inhibitor whose stability is known to be regulated by Akt. Western blot analysis revealed that LPS reduced P27 abundance, while M‑CSF pretreatment restored it (Fig. 2B). PI3K inhibition attenuated this restorative effect, further supporting the role of PI3K–Akt signaling in modulating the Ccnd2/P27 axis. Together, these results identify Ccnd2 as a downstream effector of PI3K–Akt signaling in macrophages and demonstrate its divergent regulation under inflammatory (LPS) versus proliferative (M‑CSF) stimuli. 3.3 LPS Induces Distinct Metabolic Reprogramming Associated with Altered Purine and Lipid Metabolism To characterize the metabolic alterations underlying LPS-induced macrophage activation, we analyzed intracellular metabolites using orthogonal partial least squares-discriminant analysis (OPLS-DA). The score plot revealed clear separation between control and LPS-treated groups (Fig. 3A), indicating profound metabolic reprogramming. Among the most differentially abundant metabolites, xanthosine 5′-monophosphate (XMP)—a key intermediate in purine biosynthesis—showed the most pronounced upregulation (log₂FC ≈ 3) (Fig. 3B). In contrast, several lipid mediators, including prostaglandin E₂ (PGE₂) and prostaglandin J₂ (PGJ₂), as well as amino acids such as citrulline, were significantly downregulated (Fig. 3B, C). KEGG pathway enrichment analysis of these differential metabolites highlighted significant perturbations in purine metabolism, glycerophospholipid metabolism, and amino acid metabolism (Fig. 3D). These coordinated shifts reflect a systematic rewiring of biosynthetic and energy-generating pathways, aligning with the heightened anabolic and functional demands of activated macrophages. 3.4 Ccnd2‑Associated Metabolic Alterations Correlate with M1 Macrophage Polarization To determine whether the observed molecular and metabolic changes were associated with functional polarization outcomes, we assessed macrophage surface markers by flow cytometry. LPS stimulation significantly increased the proportion of M1‑polarized (CD86⁺) macrophages, whereas co‑treatment with M‑CSF reversed this shift (Fig. 4A). Notably, PI3K inhibition not only suppressed Ccnd2 expression but also attenuated M1 polarization, suggesting a functional connection between PI3K–Akt–Ccnd2 signaling and the commitment to an inflammatory phenotype. To explore potential links between metabolic rewiring and polarization, we compared metabolomic and phenotypic data. The upregulation of pyrimidine metabolism intermediates (e.g. Uridine) coincided with enhanced M1 polarization, consistent with increased nucleotide demand for proliferation and cytokine production. Conversely, the downregulation of citrulline—a substrate for anti‑inflammatory nitric oxide synthesis—may further favor a pro‑inflammatory state. Collectively, these findings position Ccnd2 as a regulatory node that, modulated by the PI3K–Akt axis and coupled to metabolic reprogramming, influences macrophage polarization fate during inflammatory challenge. 3.5 Integrated Pathway Analysis Reveals a Coordinated Network Linking PI3K–Akt Signaling, Ccnd2 Expression, and Metabolic Reprogramming To integrate the transcriptomic and metabolomic findings, we performed joint KEGG pathway enrichment and correlation analyses. The resulting circos plot (Fig. 5) illustrates that differentially expressed genes and altered metabolites were co‑enriched in key pathways including “PI3K–Akt signaling,” “Metabolic pathways,” and “Cell cycle.” Notably, metabolites associated with glycerophospholipid and purine metabolism were embedded within the PI3K–Akt signaling network, suggesting functional crosstalk between metabolic fluxes and kinase‑driven signaling cascades. This integrated model supports a coherent regulatory circuit in which LPS‑induced metabolic reprogramming—particularly in purine and glycerophospholipid metabolism—activates PI3K–Akt signaling. This signaling axis subsequently modulates Ccnd2 expression, which in turn feeds back to influence both metabolic states and cell‑cycle progression, ultimately steering macrophages toward an M1‑polarized phenotype. M‑CSF amplifies this circuit through PI3K–Akt‑mediated upregulation of Ccnd2, whereas PI3K inhibition disrupts the entire cascade. Collectively, these data delineate a novel PI3K–Akt–Ccnd2 regulatory axis that functionally integrates metabolic reprogramming with macrophage cell‑cycle progression and inflammatory polarization. This axis provides a mechanistic framework for understanding sepsis‑associated macrophage dysfunction and identifies potential targets for therapeutic intervention. The Circos plot illustrates the distribution and enrichment of differentially expressed genes (DEGs) within KEGG pathways following LPS stimulation. The outer ring annotates the enriched KEGG pathways along with the number of associated DEGs. The inner ring denotes the direction of gene expression change (red, upregulated; cyan, downregulated). The color gradient bar on the right represents the significance of pathway enrichment (−log₁₀(P value)). The analysis highlights significant enrichment of DEGs in the PI3K-Akt signaling pathway, metabolic pathways (including purine and glycerophospholipid metabolism), and pathways regulating cell cycle progression. The co-enrichment of these pathways in both transcriptomic and metabolomic data suggests functional crosstalk and synergistic regulation between metabolic reprogramming, PI3K-Akt signaling, and cell cycle progression during LPS-induced macrophage activation, which collectively drives the M1 polarization phenotype. 4. Discussion Macrophage activation and polarization play a central role in both the initiation and resolution of inflammation; their dysregulation frequently contributes to the pathogenesis of sepsis, autoimmune disorders, and cancer [23] . In this study, we integrated transcriptomic, metabolomic, and functional assays to dissect the complex regulatory networks underlying LPS-induced inflammation in RAW264.7 macrophages. Our investigation focused on the interplay among PI3K-Akt signaling, Ccnd2-mediated metabolic reprogramming, and M1 polarization, revealing a multi-layered regulatory loop (Fig. 6 ). These findings offer novel insights into macrophage biology and suggest potential therapeutic targets for LPS-driven inflammatory diseases. A principal finding of this work is the identification of the PI3K-Akt-Ccnd2 axis as a critical mediator of macrophage proliferation and inflammatory polarization, with LPS and M-CSF exerting distinct regulatory effects [17, 24] . Transcriptomic analysis demonstrated that LPS stimulation induces extensive transcriptional reprogramming, including enrichment of genes involved in cell cycle, PI3K-Akt, and MAPK signaling pathways—consistent with the activation of both proliferative and inflammatory cascades. Functional validation further showed that M-CSF, a key macrophage growth factor, upregulates Ccnd2 transcription through PI3K-Akt signaling [25] . PI3K inhibition abolished this effect, confirming a PI3K-Akt-dependent regulatory mechanism [25] . Notably, LPS alone exerted the opposite effect, reducing Ccnd2 expression relative to controls. This suggests that LPS may simultaneously trigger conflicting signals: pro-inflammatory cascades that suppress cell cycle progression, and proliferative signals that become amplified upon synergy with M-CSF [26] . Such dual regulation may represent a homeostatic mechanism through which macrophages balance inflammatory effector functions and population expansion in response to complex microenvironmental cues. Furthermore, the verification of PI3K, Akt, and P27 as upstream regulators of Ccnd2 underscores the importance of cell cycle control in macrophage activation [27–29] . Metabolic reprogramming has been established as a critical integrator of signaling and phenotypic alterations in LPS-stimulated macrophages [30, 31] . Our metabolomic profiling revealed distinct metabolic signatures between the RAW and RAW + LPS groups, characterized by pronounced changes in purine metabolism, glycerophospholipid metabolism, and amino acid homeostasis. These pathways were concurrently enriched in transcriptomic data, indicative of coordinated transcriptional and metabolic regulation. Notably, the marked upregulation of Uridine [32] —a central intermediate in pyrimidine metabolism—likely reflects enhanced purine biosynthesis and catabolism, supplying nucleotide precursors and energy essential for DNA replication (thus supporting Ccnd2-mediated proliferation) and pro-inflammatory cytokine secretion (thereby sustaining M1 polarization). Conversely, the downregulation of prostaglandins (e.g., PGE2, PGJ2) aligns with a recognized metabolic hallmark of M1 polarization: a shift toward glycolysis at the expense of lipid mediator biosynthesis, given that prostaglandin synthesis depends on fatty acid metabolism rather than glycolysis [33, 34] . This metabolic rewiring not only fulfills the elevated bioenergetic demands of inflammatory activation but also shapes the inflammatory milieu by attenuating the production of lipid mediators that exhibit context-dependent pro- or anti-inflammatory effects [35] . Furthermore, the downregulation of citrulline, a key component of the arginine–nitric oxide pathway, reinforces a pro-inflammatory phenotype, as compromised citrulline metabolism may reduce anti-inflammatory nitric oxide production, thereby favoring M1 polarization. Collectively, these metabolic shifts do not merely correlate with inflammatory activation but actively regulate the PI3K–Akt–Ccnd2 axis: purine metabolism furnishes the biosynthetic foundation for Ccnd2-driven cell-cycle progression, while glycolytic reprogramming bolsters M1 polarization, establishing a functional circuit interlinking metabolism, signaling, and cell-cycle control [36, 37] . Intriguingly, flow cytometry analysis demonstrated a close association between Ccnd2 expression and M1 polarization dynamics. LPS alone robustly enhanced M1 polarization, whereas co-treatment with M-CSF reversed this effect—mirroring the opposing effects of LPS and M-CSF on Ccnd2 expression. Moreover, the RAW+PI3K inhibitor + LPS group displayed the lowest level of M1 polarization, which correlated with diminished Ccnd2 expression and attenuated metabolic reprogramming. These observations suggest that Ccnd2 serves as a molecular nexus linking cell-cycle progression and inflammatory polarization: M-CSF-induced Ccnd2 upregulation promotes macrophage proliferation, which may dilute pro-inflammatory signals or shift cells toward a less polarized state. In contrast, PI3K inhibition suppresses both Ccnd2 expression and the metabolic support for M1 polarization, resulting in impaired inflammatory activation [38, 39] . This finding extends prior studies that have largely focused on cell-cycle regulators in cancer or tissue repair, revealing a novel role for Ccnd2 in fine-tuning macrophage polarization—a effect likely mediated through the coordination of cell-cycle progression with metabolic and signaling cascades [16] . Our findings indicate that LPS-induced transcriptional reprogramming may recapitulate certain oncogenic signaling cascades, with Ccnd2 potentially acting as a shared node connecting inflammatory proliferation and tumor-associated macrophage activation. This insight opens new avenues for investigating the interplay between inflammation and cancer [40, 41] . Despite these insights, several limitations of the present study must be acknowledged. First, our observations are derived exclusively from the murine macrophage cell line RAW264.7. Although this model offers a controlled system for mechanistic investigation, it may not fully capture the complexity of primary macrophage biology or the in vivo microenvironment of sepsis. Future studies should therefore validate the PI3K–Akt–Ccnd2 axis in primary bone marrow–derived or peritoneal macrophages, and critically, in relevant in vivo models such as LPS-induced or cecal ligation and puncture murine sepsis models. Such validation is essential to evaluate the physiological relevance and therapeutic potential of this axis. Furthermore, translational studies using clinical samples—for example, assessing CCND2 expression and associated metabolic profiles in peripheral blood mononuclear cells or tissue macrophages from sepsis patients—will be crucial to establish clinical correlations and its potential utility as a biomarker or therapeutic target. Second, our experimental design did not delineate the temporal relationship between metabolic reprogramming, Ccnd2 expression dynamics, and M1 polarization. Detailed time-course experiments are needed to determine whether these processes occur concurrently or in a causative sequence, which would help clarify the kinetic hierarchy within this regulatory network. Third, while our multi-omics approach revealed associations at the pathway level, it did not establish direct molecular interactions. Subsequent research should employ techniques such as metabolite–protein pull-down assays or spatial metabolomics to examine whether key metabolites such as Uridine physically interact with signaling components (e.g., Akt, Ccnd2) to mediate functional regulation. Addressing these limitations will enhance the mechanistic understanding and translational relevance of the PI3K–Akt–Ccnd2 axis in sepsis-associated immunometabolic dysregulation. 5. Conclusion In conclusion, our study delineates a novel regulatory network in which LPS-induced metabolic reprogramming—encompassing purine metabolism, glycerophospholipid metabolism, and amino acid homeostasis—synergizes with PI3K-Akt signaling to drive Ccnd2 expression. Ccnd2, in turn, coordinates macrophage cell cycle progression with M1 polarization. Within this network, M-CSF serves as a critical modulator by reinforcing PI3K-Akt-dependent Ccnd2 upregulation, whereas PI3K inhibition disrupts the entire cascade. These results highlight the PI3K-Akt-Ccnd2 axis as a potential therapeutic target for inflammatory disorders. Collectively, our findings enhance the understanding of the multi-dimensional regulation of macrophage activation and establish a foundation for developing metabolism-targeted strategies to control excessive inflammation in sepsis and related diseases. Beyond its mechanistic insights, our study reveals translational implications for modulating dysregulated inflammation. The PI3K-Akt-CCND2 axis presents a promising therapeutic target for sepsis and other hyperinflammatory conditions. Two strategic avenues emerge from this work: first, direct targeting of CCND2—the central cell-cycle node—using context-specific modulators (e.g., degraders during early hyperinflammation or stabilizers in subsequent repair phases); and second, the more readily applicable approach of spatially refined inhibition of the upstream PI3K-Akt pathway. The latter could be achieved through macrophage-targeted nano-delivery systems or local administration to minimize systemic toxicity. Notably, the antagonistic effect of M-CSF suggests that macrophage "re-education" via fine-tuning of this axis—rather than its simple blockade—may offer a sophisticated strategy for immune reprogramming in diseases such as cancer. Future work will focus on validating these therapeutic concepts in vivo and advancing targeted delivery technologies to translate these mechanistic insights into potential treatments. Abbreviations Ccnd2 Cyclin D2 Declarations Acknowledgements The authors express their gratitude to the administrative support provided by Medical Research Center of Capitial Medicial University. Author contributions Xiaoyu Liu and Wei Shi designed the experiments and drafted the manuscript; Lin Chai prepared the figures and analyzed the data; Jianyuan Liu verified the data;Yanqian Su served as the principal designer of the flow cytometry experiments；Shuxing Wei was responsible for the software-related work;Chunkai Jia was responsible for literature collection and data acquisition.Xiaomei Zhu and Shubin Guo performed critical revision of the work to ensure the accuracy of important academic content. Funding This work was supported by the National Natural Science Foundation of China (No. 82172123) Data availability The datasets generated during the current study are available in the NCBI repository PRJNA1416489 https://www.ncbi.nlm.nih.gov/sra/PRJNA1416489 Permission is granted to Scientific Reports of Springer Nature Ltd to publish both in print and digital under the CC BY 4.0 open access license the result of using KEGG. 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LIU X, WANG Y, SHAO P, et al. Sargentodoxa cuneata and Patrinia villosa extract inhibits LPS-induced inflammation by shifting macrophages polarization through FAK/PI3K/Akt pathway regulation and glucose metabolism reprogramming [J]. J Ethnopharmacol, 2024, 318(Pt A): 116855. DANIEL B, BELK J A, MEIER S L, et al. Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state [J]. Mol Cell, 2023, 83(1): 121-38.e7. JIANG H, FAN D, ZHOU G, et al. Phosphatidylinositol 3-kinase inhibitor(LY294002) induces apoptosis of human nasopharyngeal carcinoma in vitro and in vivo [J]. J Exp Clin Cancer Res, 2010, 29(1): 34. KANEHISA M, FURUMICHI M, SATO Y, et al. KEGG: biological systems database as a model of the real world [J]. Nucleic Acids Res, 2025, 53(D1): D672-d7. KANEHISA M. Toward understanding the origin and evolution of cellular organisms [J]. Protein Sci, 2019, 28(11): 1947-51. OGATA H, GOTO S, SATO K, et al. KEGG: Kyoto Encyclopedia of Genes and Genomes [J]. Nucleic Acids Res, 1999, 27(1): 29-34. LUO Y, JIANG Q, ZHU Z, et al. Phosphoproteomics and Proteomics Reveal Metabolism as a Key Node in LPS-Induced Acute Inflammation in RAW264.7 [J]. Inflammation, 2020, 43(5): 1667-79. DE BRITO MONTEIRO L, DAVANZO G G, DE AGUIAR C F, et al. M-CSF- and L929-derived macrophages present distinct metabolic profiles with similar inflammatory outcomes [J]. Immunobiology, 2020, 225(3): 151935. NAGEL S, FISCHER A, BENS S, et al. PI3K/AKT inhibitor BEZ-235 targets CCND2 and induces G1 arrest in breast implant-associated anaplastic large cell lymphoma [J]. Leuk Res, 2023, 133: 107377. SESTER D P, TRIEU A, BRION K, et al. LPS regulates a set of genes in primary murine macrophages by antagonising CSF-1 action [J]. Immunobiology, 2005, 210(2-4): 97-107. SEBASTIÁN C, SERRA M, YERAMIAN A, et al. Deacetylase activity is required for STAT5-dependent GM-CSF functional activity in macrophages and differentiation to dendritic cells [J]. J Immunol, 2008, 180(9): 5898-906. HAN Y, XIA G, TSANG B K. Regulation of cyclin D2 expression and degradation by follicle-stimulating hormone during rat granulosa cell proliferation in vitro [J]. Biol Reprod, 2013, 88(3): 57. COMALADA M, XAUS J, SÁNCHEZ E, et al. Macrophage colony-stimulating factor-, granulocyte-macrophage colony-stimulating factor-, or IL-3-dependent survival of macrophages, but not proliferation, requires the expression of p21(Waf1) through the phosphatidylinositol 3-kinase/Akt pathway [J]. Eur J Immunol, 2004, 34(8): 2257-67. SEIM G L, BRITT E C, JOHN S V, et al. Two-stage metabolic remodelling in macrophages in response to lipopolysaccharide and interferon-γ stimulation [J]. Nat Metab, 2019, 1(7): 731-42. MILLS E L, KELLY B, LOGAN A, et al. Succinate Dehydrogenase Supports Metabolic Repurposing of Mitochondria to Drive Inflammatory Macrophages [J]. Cell, 2016, 167(2): 457-70.e13. SCOLARO T, MANCO M, PECQUEUX M, et al. Nucleotide metabolism in cancer cells fuels a UDP-driven macrophage cross-talk, promoting immunosuppression and immunotherapy resistance [J]. Nat Cancer, 2024, 5(8): 1206-26. ZHANG K, JAGANNATH C. Crosstalk between metabolism and epigenetics during macrophage polarization [J]. Epigenetics Chromatin, 2025, 18(1): 16. CASTOLDI A, MONTEIRO L B, VAN TEIJLINGEN BAKKER N, et al. Triacylglycerol synthesis enhances macrophage inflammatory function [J]. Nat Commun, 2020, 11(1): 4107. KELLY B, O'NEILL L A. Metabolic reprogramming in macrophages and dendritic cells in innate immunity [J]. Cell Res, 2015, 25(7): 771-84. MILLS C D. Macrophage arginine metabolism to ornithine/urea or nitric oxide/citrulline: a life or death issue [J]. Crit Rev Immunol, 2001, 21(5): 399-425. BREUILLARD C, CURIS E, LE PLÉNIER S, et al. Nitric oxide production by peritoneal macrophages from aged rats: A short term and direct modulation by citrulline [J]. Biochimie, 2017, 133: 66-73. BADIA R, PUJANTELL M, RIVEIRA-MUÑOZ E, et al. The G1/S Specific Cyclin D2 Is a Regulator of HIV-1 Restriction in Non-proliferating Cells [J]. PLoS Pathog, 2016, 12(8): e1005829. LAVALETT L, RODRIGUEZ H, ORTEGA H, et al. Alveolar macrophages from tuberculosis patients display an altered inflammatory gene expression profile [J]. Tuberculosis (Edinb), 2017, 107: 156-67. DEY A, LI W. Cell cycle-independent induction of D1 and D2 cyclin expression, but not cyclin-Cdk complex formation or Rb phosphorylation, by IFNgamma in macrophages [J]. Biochim Biophys Acta, 2000, 1497(1): 135-47. LIN M, LI G, TANG X, et al. A viral Cyclin D homolog protein hijacks the metabolic stress sensor SESN2 to promote primary effusion lymphoma growth [J]. Proc Natl Acad Sci U S A, 2025, 122(45): e2520925122. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Editor invited by journal 09 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 05 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8674632","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594107611,"identity":"64b65a78-d8d2-49d4-bbd8-0e3e6892701f","order_by":0,"name":"Xiaoyu Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Liu","suffix":""},{"id":594107612,"identity":"9c387aea-3345-4a2f-b71f-3170be482449","order_by":1,"name":"Wei Shi","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shi","suffix":""},{"id":594107613,"identity":"58ac4d27-de11-44a8-9ba5-5999868d5849","order_by":2,"name":"Lin Chai","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Chai","suffix":""},{"id":594107614,"identity":"b21c9c44-0c7a-4fca-9b44-a92b91253c8b","order_by":3,"name":"Jianyuan Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianyuan","middleName":"","lastName":"Liu","suffix":""},{"id":594107616,"identity":"6624d8de-f2fc-484e-8c2b-367daa59cc54","order_by":4,"name":"Yanqian Su","email":"","orcid":"","institution":"Capital Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Zhu","suffix":""},{"id":594107625,"identity":"06ff7d55-5108-4ba7-bb29-08edb4458b15","order_by":8,"name":"Shubin Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACCQY2hgQGCR774w0MDIwNxGuxkWM4cwCqhY0YLQwMacYMNxKI1CI/u8fswcMdhxMbZ74xfPBzB0MevzwB1xncOWNukHjmcGKzdI6xYe8ZhmLJNgK2GEjkmEkkth1ObJMGMnjbGBI3HCPksBlQLT2SZ8x//gVq2U9IC8MNsJY0YwkJHjNmsC2EvG9wI63cILHNRs6AJ61YWrZNInHGsQRCDkve9vBnmwSPAfvhjR/fttkk9jcfIOQyOOAwYABFEymA/QFJykfBKBgFo2DkAAADn0RjVQ24nAAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shubin","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2026-01-23 03:39:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8674632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8674632/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103096853,"identity":"c8ae51e1-2f04-4c0b-829d-cae61b826304","added_by":"auto","created_at":"2026-02-20 18:31:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;(D) KEGG category distribution of DEGs.\u003c/strong\u003e\u0026nbsp;The annotated DEGs were distributed across major functional categories, including Metabolism, Genetic Information Processing, Environmental Information Processing, and Cellular Processes, reflecting the broad impact of LPS on macrophage biology.\u003cbr\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated transcriptomic and metabolomic profiling reveals LPS-induced activation of PI3K-Akt signaling and metabolic reprogramming in macrophages.\u003c/strong\u003e\u003cbr\u003e\n\u003cstrong\u003e(A) Statistics of differentially expressed genes (DEGs).\u003c/strong\u003e\u0026nbsp;LPS stimulation induced substantial transcriptional changes, with 2351 genes upregulated and 1974 genes downregulated, indicating widespread transcriptional reprogramming.\u003cbr\u003e\n\u003cstrong\u003e(B) Heatmap of DEG expression patterns.\u003c/strong\u003e\u0026nbsp;Unsupervised clustering clearly separated the LPS-treated group from the Sham control group, confirming distinct global transcriptional profiles upon LPS challenge.\u003cbr\u003e\n\u003cstrong\u003e(C) Comparative KEGG pathway enrichment of transcriptomic and metabolomic data\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e[20-22]\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u0026nbsp;Bars represent the number of differentially abundant molecules (genes in red, metabolites in blue) enriched in specific pathways. Notably, pathways such as “PI3K-Akt signaling pathway” and “Metabolic pathways” (e.g., purine metabolism) were co-enriched in both datasets, suggesting coordinated activation at the transcriptional and metabolic levels.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8674632/v1/5de6805e42927ef8e797f134.png"},{"id":103503979,"identity":"3e4a1fda-c89e-4c78-a97e-35aec7f92af6","added_by":"auto","created_at":"2026-02-26 13:06:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":146423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression profiles of ccn2 and P27 at mRNA and protein levels across Sham, LPS, M-CSF, and PI3K inhibitor groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Relative mRNA level of Ccnd2: This bar plot quantifies the Ccnd2 mRNA expression in four groups (Sham, LPS, M-CSF, PI3K inhibitor), with LPS as the reference comparison group. Statistical significance (P values: 0.0232, 0.0062, 0.0097, 0.0499) indicates that Ccnd2 mRNA levels vary significantly across groups, with distinct up/downregulation in M-CSF and PI3K inhibitor groups relative to LPS.\u003c/p\u003e\n\u003cp\u003e(B) Protein expression analysis of Ccnd2 and P27: Western blot bands show the protein levels of Ccnd2 (32 kDa), P27 (27 kDa), and the housekeeping protein b-actin (45 kDa, used as the loading control) across the four groups, reflecting the raw protein expression signals. In the relative protein levels of ccn2, there are significant differences among the four groups, with all p-values less than 0.05. Notably, compared with the LPS group, M-CSF treatment leads to a significant upregulation of ccn2 protein.For P27 protein expression, there are significant differences between the LPS group/M-CSF group and the Sham group (p \u0026lt; 0.05), while there is no statistically significant difference between the PI3K inhibitor group and the reference LPS group (p \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8674632/v1/5d76cbb92e7bed47057d1034.png"},{"id":103096854,"identity":"3edc4463-d922-41f4-8e09-f1b211323cee","added_by":"auto","created_at":"2026-02-20 18:31:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic reprogramming in LPS-stimulated macrophages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PLS-DA score plot. The plot demonstrates a clear separation between the metabolic profiles of the Sham (blue dots) and LPS-treated (green dots) groups, indicating substantial metabolic reprogramming upon LPS stimulation. The dashed ellipses represent the 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003e(B) Volcano plot of differential metabolites. Metabolites significantly altered by LPS treatment are displayed based on their log₂ fold-change (x-axis) and statistical significance (-log₁₀(p-value), y-axis). Red dots denote 51 significantly upregulated metabolites, blue dots denote 165 significantly downregulated metabolites, and gray dots represent metabolites with no significant change. Key metabolites discussed in the text are highlighted.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8674632/v1/0238602e3837337373e50b6a.png"},{"id":103096856,"identity":"eef19307-0fb7-40e4-b144-fcb2cf8dbcda","added_by":"auto","created_at":"2026-02-20 18:31:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinking LPS-induced M1 macrophage polarization to metabolic reprogramming.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure integrates phenotypic and metabolomic data to elucidate the regulation of metabolic reprogramming and M1 polarization by LPS via the PI3K-Akt-Ccnd2 axis.\u003cbr\u003e\n \u003cstrong\u003e(A) M1 polarization phenotype.\u003cbr\u003e\n \u003c/strong\u003eLeft: Flow cytometry scatter plots depicting the distribution of M1 macrophages (CD86⁺) across four groups (Sham, LPS, M-CSF+LPS, PI3K inhibitor+LPS).\u003cbr\u003e\nRight: Bar graph showing the proportion of M1 cells (mean ± SD; *p \u0026lt; 0.05, **p \u0026lt; 0.01 vs. the LPS group).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) Fluorescence intensity distribution histogram for M1-polarized cells (Q3 quadrant).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlue curve (Raw/Control group):\u003c/strong\u003e Untreated control cells. The fluorescence peak is located in the low-intensity region, representing the baseline expression level of the target molecule (equivalent to the Sham group).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRed curve (Raw+LPS group):\u003c/strong\u003e Cells stimulated with LPS alone. The fluorescence peak shifts markedly to the right with increased intensity, consistent with the elevated proportion of cells in the Q3 quadrant shown in the corresponding scatter plot (above), confirming the inducing effect of LPS on the target molecule.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrange curve (Raw+CSF+LPS group):\u003c/strong\u003e Cells pretreated with CSF prior to LPS stimulation. The fluorescence peak intensity is significantly reduced compared to the Raw+LPS group, indicating that CSF inhibits LPS-induced expression of the target molecule.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGreen curve (Raw+PI3K inhibitor+LPS group):\u003c/strong\u003e Cells pretreated with a PI3K inhibitor prior to LPS stimulation. The fluorescence peak shows a slight decrease in intensity relative to the Raw+LPS group, suggesting a modest attenuating effect of the PI3K inhibitor on the LPS-induced expression of the target molecule.\u003cbr\u003e\n \u003cstrong\u003e(C) Dynamics of key differentially abundant metabolites.\u003cbr\u003e\n \u003c/strong\u003eBar graph showing log₂ fold-change (FC) values of metabolites after LPS stimulation (red, upregulated; blue, downregulated). Key metabolites are annotated: Uridine (pyrimidine metabolism), Citrulline (amino acid metabolism), PGE₂ (lipid metabolism).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8674632/v1/39af14a8880d5b5250b9f446.png"},{"id":103096857,"identity":"3038b0dc-d8d9-4895-8434-ddbe911688f8","added_by":"auto","created_at":"2026-02-20 18:31:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":395424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated pathway analysis reveals coordinated involvement of PI3K-Akt signaling, metabolic reprogramming, and cell cycle pathways in LPS-induced macrophage activation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8674632/v1/f3de0d4794037b489f897b39.png"},{"id":103503993,"identity":"f36e0095-fe9f-4adc-a491-488218594ae3","added_by":"auto","created_at":"2026-02-26 13:06:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":270306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic of the mechanism underlying LPS-induced macrophage M1 polarization via metabolic reprogramming and the PI3K-Akt-CCND2 axis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8674632/v1/af4e61761db0ed25f8c1ba33.png"},{"id":103509016,"identity":"73bca8d5-ee3a-41d7-8d67-0c4900c28159","added_by":"auto","created_at":"2026-02-26 13:55:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2586166,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8674632/v1/1702791c-7b79-40f5-afef-0f16a19dff9e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The PI3K-Akt-CCND2 axis orchestrates macrophage M1 polarization through metabolic reprogramming: mechanistic and therapeutic insights","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSepsis, defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection (Third International Consensus), remains a critical global health challenge \u003csup\u003e[1]\u003c/sup\u003e. This syndrome represents more than a simple inflammatory reaction; it is characterized by a profound immunological imbalance in which the pro-inflammatory response (aimed at pathogen clearance) and the compensatory anti-inflammatory mechanism (designed to limit tissue damage) become severely misaligned\u003csup\u003e[2]\u003c/sup\u003e. This dysregulation frequently leads to multiple organ dysfunction and high mortality. Recent epidemiological studies highlight its substantial burden, with an estimated 48.9\u0026nbsp;million cases and 11\u0026nbsp;million sepsis-related deaths worldwide annually \u003csup\u003e[3]\u003c/sup\u003e. The impact is particularly severe in low- and middle-income countries, underscoring stark disparities in healthcare resources and outcomes \u003csup\u003e[4]\u003c/sup\u003e. Despite advances in supportive care, the complex pathophysiology\u0026mdash;often involving concurrent hyperinflammation and immunosuppression\u0026mdash;continues to drive significant morbidity and mortality, necessitating further research into targeted therapies and improved clinical management strategies\u003csup\u003e[5, 6]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe pathogenesis of sepsis is closely linked to an imbalance between pro-inflammatory and anti-inflammatory responses. Macrophages play a pivotal role in this process by dynamically polarizing into a pro-inflammatory M1 phenotype (secreting TNF-α, IL-1β) or an anti-inflammatory M2 phenotype (secreting IL-10, TGF-β) in response to microenvironmental signals\u003csup\u003e[7\u0026ndash;10]\u003c/sup\u003e. Dysregulated macrophage polarization and sustained inflammation are key drivers of sepsis progression, making the exploration of molecular regulators in this process crucial for developing novel therapeutic strategies\u003csup\u003e[8, 9, 11\u0026ndash;13]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCyclin D2 (Ccnd2) is a core regulator of the cell cycle\u003csup\u003e[14, 15]\u003c/sup\u003e. Beyond its canonical role in proliferation, emerging evidence indicates that Ccnd2 is also involved in immune cell function: it regulates lymphocyte activation and differentiation, and recent studies suggest its participation in macrophage-mediated inflammatory responses\u003csup\u003e[15]\u003c/sup\u003e. However, its specific role in sepsis remains unclear. Given that sepsis-associated inflammation depends heavily on macrophage activation and proliferation, investigating how Ccnd2 modulates macrophage polarization and inflammatory responses may provide novel mechanistic insights. Furthermore, growing evidence suggests that Ccnd2 participates in immunometabolic regulation. For instance, it can interact with metabolic signaling hubs such as mTOR and the PI3K-Akt pathway, potentially linking nutrient sensing to immune cell fate decisions \u003csup\u003e[16, 17]\u003c/sup\u003e. In macrophages, although inflammatory stimuli modulate Ccnd2 expression, whether and how Ccnd2 integrates metabolic reprogramming with polarization phenotypes remains largely unexplored. While recent studies hint at its involvement in macrophage-mediated inflammation\u003csup\u003e[16, 18]\u003c/sup\u003e, a systematic understanding of its role in sepsis-associated macrophage dysfunction\u0026mdash;particularly regarding how it couples cell-cycle progression with metabolic rewiring\u0026mdash;is still lacking. This knowledge gap motivates our investigation into Ccnd2 as a potential nexus between metabolism, signaling, and inflammatory polarization in sepsis.\u003c/p\u003e \u003cp\u003eNotably, the interplay among Ccnd2, cell-cycle progression, and inflammatory signaling in sepsis-associated macrophages is poorly understood. Key unresolved questions include whether Ccnd2 promotes pro-inflammatory M1 polarization or favors anti-inflammatory M2 transition, and how it mechanistically links cell-cycle regulation to inflammatory cytokine production. To address these gaps, this study aims to elucidate the role of Ccnd2 in LPS-induced macrophage models by integrating functional assays, transcriptomics, and phenotypic characterization.\u003c/p\u003e \u003cp\u003eBased on the above background, we hypothesize that in LPS-stimulated macrophages, metabolic reprogramming (e.g., in purine and glycerophospholipid metabolism) activates the PI3K-Akt pathway, which in turn upregulates Ccnd2 expression. Ccnd2 then acts as a coupling node, integrating cell-cycle progression with metabolic status to drive macrophage polarization toward the M1 phenotype, thereby forming a self-reinforcing inflammatory loop. Furthermore, we propose that M-CSF counteracts LPS-induced inflammation by promoting PI3K-Akt-dependent upregulation of Ccnd2, whereas PI3K inhibition disrupts this regulatory network.\u003c/p\u003e \u003cp\u003eThe novelty of this study lies in three aspects: (1) revealing a non-canonical, immunometabolic function of Ccnd2 in macrophage polarization, extending its role beyond cell proliferation; (2) integrating multi-omics (transcriptomics and metabolomics) with functional validation to construct a multidimensional \u0026ldquo;metabolism\u0026ndash;signaling\u0026ndash;cell cycle\u0026ndash;polarization\u0026rdquo; regulatory network; and (3) identifying the PI3K-Akt-Ccnd2 axis as a druggable target for sepsis-related inflammation, providing a rationale for combined metabolic and immune interventions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Cell Culture and Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mouse macrophage cell line RAW264.7 (Procell Life Science \u0026amp; Technology Co., Ltd., Wuhan, China; Cat# CL-0190) was cultured in complete medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. Cells were maintained at 37\u0026deg;C in a humidified incubator with 5% CO₂ and were subcultured at a 1:3 ratio upon reaching confluence. All cell lines were authenticated by short tandem repeat (STR) profiling and confirmed to be free of mycoplasma contamination.\u003c/p\u003e\n\u003cp\u003eRAW264.7 cells were divided into four experimental groups: \u003cstrong\u003eSham group:\u003c/strong\u003e Untreated control; \u003cstrong\u003eLPS group:\u003c/strong\u003e Cells treated with 30 \u0026micro;g/mL lipopolysaccharide (LPS; Solarbio, Cat# L8880) for 24 hours; \u003cstrong\u003ePI3Ki + LPS group:\u003c/strong\u003e Cells pretreated with 10 \u0026micro;M of the PI3K inhibitor LY294002 (MCE, Shanghai, China; Cat# HY-10108) for 1 hour, followed by co-treatment with 30 \u0026micro;g/mL LPS for 24 hours. The concentration of LY294002 was optimized to 10 \u0026micro;M based on preliminary dose-response experiments, which demonstrated effective downregulation of PI3K/Akt signaling in our model\u003csup\u003e[19]\u003c/sup\u003e; \u003cstrong\u003eM-CSF + LPS group:\u003c/strong\u003e Cells pretreated with 25 ng/mL macrophage colony-stimulating factor (M-CSF; MCE, Cat# HY-P7085) for 48 hours, followed by stimulation with 30 \u0026micro;g/mL LPS for 24 hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 RNA Extraction and Quantitative Real-Time PCR (qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from RAW264.7 cells using TRIzol reagent (Takara, Tokyo, Japan; Cat# 9109). Subsequently, 1 \u0026micro;g of RNA was reverse-transcribed into complementary DNA (cDNA) using a PrimeScript RT reagent kit (Takara; Cat# RR047A). Quantitative PCR was performed on a QuantStudio\u0026trade; 5 Real-Time PCR System (Thermo Fisher Scientific, USA) using SYBR Green Premix. The expression level of the target gene \u003cem\u003eCcnd2\u003c/em\u003e was analyzed using the 2\u0026minus;\u0026Delta;\u0026Delta;Ct method, with normalization to an appropriate housekeeping gene. All primer sequences were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China) and are listed in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.838%;\"\u003eGene name\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.7254%;\"\u003eForward（5\u0026rsquo;-3\u0026rsquo;）\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4366%;\"\u003eReverse(5\u0026rsquo;-3\u0026rsquo;)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.838%;\"\u003eGAPDH(mouse)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.7254%;\"\u003eGGCAAATTCAACGGCACAGTCAAG\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4366%;\"\u003eTCGCTCCTGGAAGATGGTGATGG\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.838%;\"\u003eCCND2(mouse)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.7254%;\"\u003eGCCAAGATCACCCACACTGA\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4366%;\"\u003eGCGTTATGCTGCTCTTGACG\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Protein Extraction and Western Blot Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal protein was extracted from RAW264.7 cells using pre-cooled RIPA lysis buffer. Protein concentration was determined with a bicinchoninic acid (BCA) assay kit (Thermo Fisher Scientific, USA). Subsequently, 30 \u0026mu;g of protein per sample was separated by 10% sodium dodecyl sulfate\u0026ndash;polyacrylamide gel electrophoresis (SDS\u0026ndash;PAGE) and transferred onto polyvinylidene fluoride (PVDF) membranes. After blocking with 5% skim milk for 2 hours at room temperature, the membranes were incubated overnight at 4 \u0026deg;C with the following primary antibodies listed in Table 2:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp height=\"36\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"33\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"33\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"33\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"33\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp height=\"19\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"486\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003e\u003cstrong\u003eTarget\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 191px;\"\u003e\u003cstrong\u003eSupplier (Location)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 169px;\"\u003e\u003cstrong\u003eCatalog Number\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003eCcnd2\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 191px;\"\u003eProteintech (Wuhan, China)\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 169px;\"\u003e10934-1-AP\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003eP27\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 191px;\"\u003eSelleck (Shanghai, China)\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 169px;\"\u003eF0170\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003ePI3K\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 191px;\"\u003eAbmart (Shanghai, China)\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 169px;\"\u003eT40115F\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003ePhospho-PI3K\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 191px;\"\u003eAbmart (Shanghai, China)\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 169px;\"\u003eT40116F\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003eAKT\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 191px;\"\u003eAbmart (Shanghai, China)\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 169px;\"\u003eT55561F\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 124px;\"\u003ePhospho-AKT\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 191px;\"\u003eAbmart (Shanghai, China)\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 169px;\"\u003eT40067F\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Flow Cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt 24 hours post-treatment, RAW264.7 cells were harvested and stained for surface markers to assess polarization. Cells were incubated for 60 minutes at room temperature in the dark with fluorescently conjugated antibodies: FITC anti-mouse CD86 (1:100; BioLegend, Cat# 159220) and APC anti-mouse CD206 (MMR; 1:100; BioLegend, Cat# 141708). Unstained cells served as negative controls. Data were acquired on a BD FACSAria II flow cytometer (BD Biosciences, USA). Viable single cells were gated based on forward scatter (FSC) and side scatter (SSC) properties, and analysis was performed using FlowJo software (Version 10; BD Biosciences).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Transcriptomic Sequencing and Untargeted Metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor transcriptomic analysis, RAW264.7 cells were divided into two groups: a control group and a group treated with 30 \u0026mu;g/mL LPS for 24 hours. Subsequently, total RNA was isolated from harvested cells using TRIzol reagent (Invitrogen, USA). RNA quality was assessed with a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). Sequencing libraries were prepared and sequenced on an Illumina NovaSeq X Plus platform. All transcriptomic data processing was conducted by Majorbio Bio-pharm Technology Co., Ltd (Shanghai, China). Differentially expressed genes were identified based on thresholds of |log₂(fold change)| \u0026gt; 1 and adjusted\u0026nbsp;p\u0026nbsp;\u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eFor untargeted metabolomics, cell metabolites were extracted using ice-cold methanol containing L-2-chlorophenylalanine as an internal standard. Samples were vortexed for 30 seconds, sonicated for 30 minutes in an ice-water bath, and then incubated at \u0026minus;20 \u0026deg;C for 30 minutes to precipitate proteins. After centrifugation at 13,000 \u0026times;\u0026nbsp;g\u0026nbsp;and 4 \u0026deg;C for 15 minutes, the supernatant was collected, dried under a gentle nitrogen stream, and reconstituted in 100 \u0026micro;L of acetonitrile:water (1:1, v/v). The solution was sonicated at 5 \u0026deg;C for 5 minutes, centrifuged again (13,000 \u0026times;\u0026nbsp;g, 10 min, 4 \u0026deg;C), and the final supernatant was transferred to LC MS/MS vials for analysis.\u003c/p\u003e\n\u003cp\u003eRaw LC MS/MS data were processed using Progenesis QI (Waters) for peak detection, alignment, and integration. Metabolites were annotated by matching against the HMDB, Metlin, and the Majorbio in-house databases. Data preprocessing included the removal of features with \u0026gt;20% missing values within any group, missing value imputation with the global minimum, and peak intensity normalization. Quality control samples with a relative standard deviation (RSD) \u0026gt; 30% were excluded, and data were log₁₀-transformed prior to statistical analysis. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using the \u003cem\u003eropls\u003c/em\u003e package (v1.6.2) in R. Metabolic pathway enrichment analysis was conducted in Python using \u003cem\u003escipy.stats\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS Statistics software (version 26.0, IBM, USA). Normality of data distribution was assessed with the Shapiro\u0026ndash;Wilk test. For comparisons across multiple groups, one-way analysis of variance (ANOVA) was applied, followed by the least significant difference (LSD) post hoc test. Data are presented as mean \u0026plusmn; standard deviation (SD). Statistical significance was defined as p\u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Integrated Multi‑Omics Profiling Reveals LPS‑Induced Activation of PI3K‑Akt Signaling and Metabolic Reprogramming\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo systematically delineate the global molecular alterations underlying LPS‑induced macrophage activation, we performed integrated transcriptomic and metabolomic profiling. RNA‑sequencing analysis identified 2351 upregulated and 1974 downregulated genes in RAW264.7 macrophages after 24‑hour LPS stimulation (Fig. 1A). Unsupervised clustering showed a clear separation between the LPS‑treated and control groups (Fig. 1B), confirming extensive transcriptional reprogramming. KEGG enrichment analysis of differentially expressed genes revealed significant activation of pathways related to the cell cycle, PI3K‑Akt signaling, and MAPK signaling (Fig. 1C, D), indicating concurrent engagement of proliferative and inflammatory cascades.\u003c/p\u003e\n\u003cp\u003eParallel untargeted metabolomic profiling (LC‑MS/MS) detected 51 upregulated and 165 downregulated metabolites in LPS‑stimulated macrophages (Fig. 1E). Notably, pathways such as purine metabolism and glycerophospholipid metabolism were enriched in both transcriptomic and metabolomic datasets (Fig. 1C, F), demonstrating coordinated transcriptional and metabolic remodeling. Together, these multi‑omics profiles provide a comprehensive map of LPS‑induced macrophage activation and highlight potential regulatory hubs where inflammatory signaling, metabolic reprogramming, and cell‑cycle progression converge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 The PI3K\u0026ndash;Akt Axis Dynamically Regulates Ccnd2 Expression in Response to LPS and M‑CSF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the prominent enrichment of cell‑cycle and PI3K\u0026ndash;Akt signaling pathways in the transcriptomic data, we focused on cyclin D2 (Ccnd2), a key regulator of the G1/S phase transition. Quantitative RT‑PCR and Western blot analyses showed that LPS stimulation significantly downregulated both \u003cem\u003eCcnd2\u003c/em\u003e mRNA and protein levels compared with the sham group (Fig. 2A, B). In contrast, pretreatment with macrophage colony‑stimulating factor (M‑CSF) prior to LPS challenge markedly increased Ccnd2 expression. This M‑CSF‑mediated upregulation was completely abolished by co‑treatment with the PI3K inhibitor LY294002 (Fig. 2A, B), indicating that M‑CSF enhances Ccnd2 expression in a PI3K‑Akt‑dependent manner.\u003c/p\u003e\n\u003cp\u003eTo investigate the upstream regulatory mechanism, we examined the protein level of P27 (also known as CDKN1B), a cyclin‑dependent kinase inhibitor whose stability is known to be regulated by Akt. Western blot analysis revealed that LPS reduced P27 abundance, while M‑CSF pretreatment restored it (Fig. 2B). PI3K inhibition attenuated this restorative effect, further supporting the role of PI3K\u0026ndash;Akt signaling in modulating the Ccnd2/P27 axis. Together, these results identify Ccnd2 as a downstream effector of PI3K\u0026ndash;Akt signaling in macrophages and demonstrate its divergent regulation under inflammatory (LPS) versus proliferative (M‑CSF) stimuli.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 LPS Induces Distinct Metabolic Reprogramming Associated with Altered Purine and Lipid Metabolism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the metabolic alterations underlying LPS-induced macrophage activation, we analyzed intracellular metabolites using orthogonal partial least squares-discriminant analysis (OPLS-DA). The score plot revealed clear separation between control and LPS-treated groups (Fig. 3A), indicating profound metabolic reprogramming. Among the most differentially abundant metabolites, xanthosine 5\u0026prime;-monophosphate (XMP)\u0026mdash;a key intermediate in purine biosynthesis\u0026mdash;showed the most pronounced upregulation (log₂FC \u0026asymp; 3) (Fig. 3B). In contrast, several lipid mediators, including prostaglandin E₂ (PGE₂) and prostaglandin J₂ (PGJ₂), as well as amino acids such as citrulline, were significantly downregulated (Fig. 3B, C).\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment analysis of these differential metabolites highlighted significant perturbations in purine metabolism, glycerophospholipid metabolism, and amino acid metabolism (Fig. 3D). These coordinated shifts reflect a systematic rewiring of biosynthetic and energy-generating pathways, aligning with the heightened anabolic and functional demands of activated macrophages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Ccnd2‑Associated Metabolic Alterations Correlate with M1 Macrophage Polarization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether the observed molecular and metabolic changes were associated with functional polarization outcomes, we assessed macrophage surface markers by flow cytometry. LPS stimulation significantly increased the proportion of M1‑polarized (CD86⁺) macrophages, whereas co‑treatment with M‑CSF reversed this shift (Fig. 4A). Notably, PI3K inhibition not only suppressed Ccnd2 expression but also attenuated M1 polarization, suggesting a functional connection between PI3K\u0026ndash;Akt\u0026ndash;Ccnd2 signaling and the commitment to an inflammatory phenotype.\u003c/p\u003e\n\u003cp\u003eTo explore potential links between metabolic rewiring and polarization, we compared metabolomic and phenotypic data. The upregulation of pyrimidine metabolism intermediates (e.g. Uridine) coincided with enhanced M1 polarization, consistent with increased nucleotide demand for proliferation and cytokine production. Conversely, the downregulation of citrulline\u0026mdash;a substrate for anti‑inflammatory nitric oxide synthesis\u0026mdash;may further favor a pro‑inflammatory state. Collectively, these findings position Ccnd2 as a regulatory node that, modulated by the PI3K\u0026ndash;Akt axis and coupled to metabolic reprogramming, influences macrophage polarization fate during inflammatory challenge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Integrated Pathway Analysis Reveals a Coordinated Network Linking PI3K\u0026ndash;Akt Signaling, Ccnd2 Expression, and Metabolic Reprogramming\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo integrate the transcriptomic and metabolomic findings, we performed joint KEGG pathway enrichment and correlation analyses. The resulting circos plot (Fig. 5) illustrates that differentially expressed genes and altered metabolites were co‑enriched in key pathways including \u0026ldquo;PI3K\u0026ndash;Akt signaling,\u0026rdquo; \u0026ldquo;Metabolic pathways,\u0026rdquo; and \u0026ldquo;Cell cycle.\u0026rdquo; Notably, metabolites associated with glycerophospholipid and purine metabolism were embedded within the PI3K\u0026ndash;Akt signaling network, suggesting functional crosstalk between metabolic fluxes and kinase‑driven signaling cascades.\u003c/p\u003e\n\u003cp\u003eThis integrated model supports a coherent regulatory circuit in which LPS‑induced metabolic reprogramming\u0026mdash;particularly in purine and glycerophospholipid metabolism\u0026mdash;activates PI3K\u0026ndash;Akt signaling. This signaling axis subsequently modulates Ccnd2 expression, which in turn feeds back to influence both metabolic states and cell‑cycle progression, ultimately steering macrophages toward an M1‑polarized phenotype. M‑CSF amplifies this circuit through PI3K\u0026ndash;Akt‑mediated upregulation of Ccnd2, whereas PI3K inhibition disrupts the entire cascade.\u003c/p\u003e\n\u003cp\u003eCollectively, these data delineate a novel \u003cstrong\u003ePI3K\u0026ndash;Akt\u0026ndash;Ccnd2 regulatory axis\u003c/strong\u003e that functionally integrates metabolic reprogramming with macrophage cell‑cycle progression and inflammatory polarization. This axis provides a mechanistic framework for understanding sepsis‑associated macrophage dysfunction and identifies potential targets for therapeutic intervention.\u003c/p\u003e\n\u003cp\u003eThe Circos plot illustrates the distribution and enrichment of differentially expressed genes (DEGs) within KEGG pathways following LPS stimulation. The outer ring annotates the enriched KEGG pathways along with the number of associated DEGs. The inner ring denotes the direction of gene expression change (red, upregulated; cyan, downregulated). The color gradient bar on the right represents the significance of pathway enrichment (\u0026minus;log₁₀(P value)).\u003c/p\u003e\n\u003cp\u003eThe analysis highlights significant enrichment of DEGs in the PI3K-Akt signaling pathway, metabolic pathways (including purine and glycerophospholipid metabolism), and pathways regulating cell cycle progression. The co-enrichment of these pathways in both transcriptomic and metabolomic data suggests functional crosstalk and synergistic regulation between metabolic reprogramming, PI3K-Akt signaling, and cell cycle progression during LPS-induced macrophage activation, which collectively drives the M1 polarization phenotype.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMacrophage activation and polarization play a central role in both the initiation and resolution of inflammation; their dysregulation frequently contributes to the pathogenesis of sepsis, autoimmune disorders, and cancer \u003csup\u003e[23]\u003c/sup\u003e. In this study, we integrated transcriptomic, metabolomic, and functional assays to dissect the complex regulatory networks underlying LPS-induced inflammation in RAW264.7 macrophages. Our investigation focused on the interplay among PI3K-Akt signaling, Ccnd2-mediated metabolic reprogramming, and M1 polarization, revealing a multi-layered regulatory loop (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These findings offer novel insights into macrophage biology and suggest potential therapeutic targets for LPS-driven inflammatory diseases.\u003c/p\u003e \u003cp\u003eA principal finding of this work is the identification of the PI3K-Akt-Ccnd2 axis as a critical mediator of macrophage proliferation and inflammatory polarization, with LPS and M-CSF exerting distinct regulatory effects \u003csup\u003e[17, 24]\u003c/sup\u003e. Transcriptomic analysis demonstrated that LPS stimulation induces extensive transcriptional reprogramming, including enrichment of genes involved in cell cycle, PI3K-Akt, and MAPK signaling pathways\u0026mdash;consistent with the activation of both proliferative and inflammatory cascades. Functional validation further showed that M-CSF, a key macrophage growth factor, upregulates Ccnd2 transcription through PI3K-Akt signaling \u003csup\u003e[25]\u003c/sup\u003e. PI3K inhibition abolished this effect, confirming a PI3K-Akt-dependent regulatory mechanism \u003csup\u003e[25]\u003c/sup\u003e. Notably, LPS alone exerted the opposite effect, reducing Ccnd2 expression relative to controls. This suggests that LPS may simultaneously trigger conflicting signals: pro-inflammatory cascades that suppress cell cycle progression, and proliferative signals that become amplified upon synergy with M-CSF\u003csup\u003e[26]\u003c/sup\u003e. Such dual regulation may represent a homeostatic mechanism through which macrophages balance inflammatory effector functions and population expansion in response to complex microenvironmental cues. Furthermore, the verification of PI3K, Akt, and P27 as upstream regulators of Ccnd2 underscores the importance of cell cycle control in macrophage activation \u003csup\u003e[27\u0026ndash;29]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetabolic reprogramming has been established as a critical integrator of signaling and phenotypic alterations in LPS-stimulated macrophages\u003csup\u003e[30, 31]\u003c/sup\u003e. Our metabolomic profiling revealed distinct metabolic signatures between the RAW and RAW\u0026thinsp;+\u0026thinsp;LPS groups, characterized by pronounced changes in purine metabolism, glycerophospholipid metabolism, and amino acid homeostasis. These pathways were concurrently enriched in transcriptomic data, indicative of coordinated transcriptional and metabolic regulation. Notably, the marked upregulation of Uridine\u003csup\u003e[32]\u003c/sup\u003e\u0026mdash;a central intermediate in pyrimidine metabolism\u0026mdash;likely reflects enhanced purine biosynthesis and catabolism, supplying nucleotide precursors and energy essential for DNA replication (thus supporting Ccnd2-mediated proliferation) and pro-inflammatory cytokine secretion (thereby sustaining M1 polarization). Conversely, the downregulation of prostaglandins (e.g., PGE2, PGJ2) aligns with a recognized metabolic hallmark of M1 polarization: a shift toward glycolysis at the expense of lipid mediator biosynthesis, given that prostaglandin synthesis depends on fatty acid metabolism rather than glycolysis\u003csup\u003e[33, 34]\u003c/sup\u003e. This metabolic rewiring not only fulfills the elevated bioenergetic demands of inflammatory activation but also shapes the inflammatory milieu by attenuating the production of lipid mediators that exhibit context-dependent pro- or anti-inflammatory effects\u003csup\u003e[35]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, the downregulation of citrulline, a key component of the arginine\u0026ndash;nitric oxide pathway, reinforces a pro-inflammatory phenotype, as compromised citrulline metabolism may reduce anti-inflammatory nitric oxide production, thereby favoring M1 polarization. Collectively, these metabolic shifts do not merely correlate with inflammatory activation but actively regulate the PI3K\u0026ndash;Akt\u0026ndash;Ccnd2 axis: purine metabolism furnishes the biosynthetic foundation for Ccnd2-driven cell-cycle progression, while glycolytic reprogramming bolsters M1 polarization, establishing a functional circuit interlinking metabolism, signaling, and cell-cycle control\u003csup\u003e[36, 37]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIntriguingly, flow cytometry analysis demonstrated a close association between Ccnd2 expression and M1 polarization dynamics. LPS alone robustly enhanced M1 polarization, whereas co-treatment with M-CSF reversed this effect\u0026mdash;mirroring the opposing effects of LPS and M-CSF on Ccnd2 expression. Moreover, the RAW+PI3K inhibitor\u0026thinsp;+\u0026thinsp;LPS group displayed the lowest level of M1 polarization, which correlated with diminished Ccnd2 expression and attenuated metabolic reprogramming. These observations suggest that Ccnd2 serves as a molecular nexus linking cell-cycle progression and inflammatory polarization: M-CSF-induced Ccnd2 upregulation promotes macrophage proliferation, which may dilute pro-inflammatory signals or shift cells toward a less polarized state. In contrast, PI3K inhibition suppresses both Ccnd2 expression and the metabolic support for M1 polarization, resulting in impaired inflammatory activation\u003csup\u003e[38, 39]\u003c/sup\u003e. This finding extends prior studies that have largely focused on cell-cycle regulators in cancer or tissue repair, revealing a novel role for Ccnd2 in fine-tuning macrophage polarization\u0026mdash;a effect likely mediated through the coordination of cell-cycle progression with metabolic and signaling cascades\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings indicate that LPS-induced transcriptional reprogramming may recapitulate certain oncogenic signaling cascades, with Ccnd2 potentially acting as a shared node connecting inflammatory proliferation and tumor-associated macrophage activation. This insight opens new avenues for investigating the interplay between inflammation and cancer\u003csup\u003e[40, 41]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these insights, several limitations of the present study must be acknowledged. First, our observations are derived exclusively from the murine macrophage cell line RAW264.7. Although this model offers a controlled system for mechanistic investigation, it may not fully capture the complexity of primary macrophage biology or the \u003cem\u003ein vivo\u003c/em\u003e microenvironment of sepsis. Future studies should therefore validate the PI3K\u0026ndash;Akt\u0026ndash;Ccnd2 axis in primary bone marrow\u0026ndash;derived or peritoneal macrophages, and critically, in relevant \u003cem\u003ein vivo\u003c/em\u003e models such as LPS-induced or cecal ligation and puncture murine sepsis models. Such validation is essential to evaluate the physiological relevance and therapeutic potential of this axis. Furthermore, translational studies using clinical samples\u0026mdash;for example, assessing CCND2 expression and associated metabolic profiles in peripheral blood mononuclear cells or tissue macrophages from sepsis patients\u0026mdash;will be crucial to establish clinical correlations and its potential utility as a biomarker or therapeutic target. Second, our experimental design did not delineate the temporal relationship between metabolic reprogramming, Ccnd2 expression dynamics, and M1 polarization. Detailed time-course experiments are needed to determine whether these processes occur concurrently or in a causative sequence, which would help clarify the kinetic hierarchy within this regulatory network. Third, while our multi-omics approach revealed associations at the pathway level, it did not establish direct molecular interactions. Subsequent research should employ techniques such as metabolite\u0026ndash;protein pull-down assays or spatial metabolomics to examine whether key metabolites such as Uridine physically interact with signaling components (e.g., Akt, Ccnd2) to mediate functional regulation. Addressing these limitations will enhance the mechanistic understanding and translational relevance of the PI3K\u0026ndash;Akt\u0026ndash;Ccnd2 axis in sepsis-associated immunometabolic dysregulation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study delineates a novel regulatory network in which LPS-induced metabolic reprogramming\u0026mdash;encompassing purine metabolism, glycerophospholipid metabolism, and amino acid homeostasis\u0026mdash;synergizes with PI3K-Akt signaling to drive Ccnd2 expression. Ccnd2, in turn, coordinates macrophage cell cycle progression with M1 polarization. Within this network, M-CSF serves as a critical modulator by reinforcing PI3K-Akt-dependent Ccnd2 upregulation, whereas PI3K inhibition disrupts the entire cascade. These results highlight the PI3K-Akt-Ccnd2 axis as a potential therapeutic target for inflammatory disorders. Collectively, our findings enhance the understanding of the multi-dimensional regulation of macrophage activation and establish a foundation for developing metabolism-targeted strategies to control excessive inflammation in sepsis and related diseases.\u003c/p\u003e\n\u003cp\u003eBeyond its mechanistic insights, our study reveals translational implications for modulating dysregulated inflammation. The PI3K-Akt-CCND2 axis presents a promising therapeutic target for sepsis and other hyperinflammatory conditions. Two strategic avenues emerge from this work: first, direct targeting of CCND2\u0026mdash;the central cell-cycle node\u0026mdash;using context-specific modulators (e.g., degraders during early hyperinflammation or stabilizers in subsequent repair phases); and second, the more readily applicable approach of spatially refined inhibition of the upstream PI3K-Akt pathway. The latter could be achieved through macrophage-targeted nano-delivery systems or local administration to minimize systemic toxicity. Notably, the antagonistic effect of M-CSF suggests that macrophage \u0026quot;re-education\u0026quot; via fine-tuning of this axis\u0026mdash;rather than its simple blockade\u0026mdash;may offer a sophisticated strategy for immune reprogramming in diseases such as cancer. Future work will focus on validating these therapeutic concepts in vivo and advancing targeted delivery technologies to translate these mechanistic insights into potential treatments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCcnd2 Cyclin D2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the administrative support provided by Medical Research Center of Capitial Medicial University.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eXiaoyu Liu and Wei Shi designed the experiments and drafted the manuscript; Lin Chai prepared the figures and analyzed the data; Jianyuan Liu verified the data;Yanqian Su served as the principal designer of the flow cytometry experiments；Shuxing Wei was responsible for the software-related work;Chunkai Jia was responsible for literature collection and data acquisition.Xiaomei Zhu and Shubin Guo performed critical revision of the work to ensure the accuracy of important academic content.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82172123)\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the NCBI repository PRJNA1416489\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/sra/PRJNA1416489\u003c/p\u003e\n\u003cp\u003ePermission is granted to Scientific Reports of Springer Nature Ltd to publish both in print and digital under the CC BY 4.0 open access license the result of using KEGG.\u003c/p\u003e\n\u003cp\u003eDeclarations\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSINGER M, DEUTSCHMAN C S, SEYMOUR C W, et al. 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Nitric oxide production by peritoneal macrophages from aged rats: A short term and direct modulation by citrulline [J]. Biochimie, 2017, 133: 66-73.\u003c/li\u003e\n\u003cli\u003eBADIA R, PUJANTELL M, RIVEIRA-MU\u0026Ntilde;OZ E, et al. The G1/S Specific Cyclin D2 Is a Regulator of HIV-1 Restriction in Non-proliferating Cells [J]. PLoS Pathog, 2016, 12(8): e1005829.\u003c/li\u003e\n\u003cli\u003eLAVALETT L, RODRIGUEZ H, ORTEGA H, et al. Alveolar macrophages from tuberculosis patients display an altered inflammatory gene expression profile [J]. Tuberculosis (Edinb), 2017, 107: 156-67.\u003c/li\u003e\n\u003cli\u003eDEY A, LI W. Cell cycle-independent induction of D1 and D2 cyclin expression, but not cyclin-Cdk complex formation or Rb phosphorylation, by IFNgamma in macrophages [J]. Biochim Biophys Acta, 2000, 1497(1): 135-47.\u003c/li\u003e\n\u003cli\u003eLIN M, LI G, TANG X, et al. 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Proc Natl Acad Sci U S A, 2025, 122(45): e2520925122.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sepsis, macrophage polarization, Ccnd2, metabolic reprogramming, PI3K-Akt signaling, LPS, inflammation, transcriptomics, metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-8674632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8674632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis is a life-threatening syndrome driven by dysregulated macrophage polarization, in which excessive M1 polarization exacerbates systemic inflammation and organ injury. However, the mechanisms underlying the interplay between metabolic reprogramming and cell-cycle regulators such as Ccnd2 during macrophage polarization in sepsis remain poorly understood. This study investigates the role of Ccnd2 and its regulatory network in LPS-induced macrophage inflammation, with a focus on the PI3K\u0026ndash;Akt signaling axis and associated metabolic alterations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRAW264.7 macrophages were divided into four experimental groups: sham, LPS, PI3K inhibitor\u0026thinsp;+\u0026thinsp;LPS, and M-CSF\u0026ndash;pretreated\u0026thinsp;+\u0026thinsp;LPS. Transcriptomic (RNA-seq) and metabolomic (LC-MS/MS) profiling were performed to identify differentially expressed genes and metabolites. Western blotting and qRT-PCR were used to validate expression levels of Ccnd2, PI3K, Akt, and P27. Flow cytometry was employed to assess M1 polarization, and KEGG enrichment analysis was conducted to explore transcriptome\u0026ndash;metabolome regulatory networks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTranscriptomic analysis indicated significant enrichment of pathways related to PI3K\u0026ndash;Akt signaling, cell cycle, and inflammatory cascades following LPS stimulation. Ccnd2 expression was downregulated in the LPS-treated group but markedly upregulated in the M-CSF\u0026ndash;pretreated group\u0026mdash;an effect abolished by PI3K inhibition. Metabolomic profiling revealed distinct metabolic reprogramming in LPS-stimulated macrophages, with notable alterations in purine metabolism, glycerophospholipid metabolism, and amino acid homeostasis. Flow cytometry demonstrated that LPS enhanced M1 polarization, whereas M-CSF co-treatment reversed this effect. PI3K inhibition suppressed both Ccnd2 expression and M1 polarization, suggesting a functional connection between Ccnd2-mediated cell-cycle progression and inflammatory polarization.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study delineates a novel regulatory network in which LPS-induced metabolic reprogramming synergizes with PI3K\u0026ndash;Akt signaling to modulate Ccnd2 expression, thereby coordinating macrophage cell-cycle progression and M1 polarization. The PI3K\u0026ndash;Akt\u0026ndash;Ccnd2 axis represents a promising therapeutic target for sepsis and other inflammatory disorders, offering potential for combined metabolic and immune interventions to reprogram macrophage polarization and mitigate inflammatory injury.\u003c/p\u003e","manuscriptTitle":"The PI3K-Akt-CCND2 axis orchestrates macrophage M1 polarization through metabolic reprogramming: mechanistic and therapeutic insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 18:31:24","doi":"10.21203/rs.3.rs-8674632/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T09:27:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T01:59:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T04:12:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119110759710469170127715918471845700230","date":"2026-02-23T01:01:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188385165779929726566335897103969442133","date":"2026-02-17T16:10:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T15:55:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T15:37:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-09T12:01:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T13:21:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-05T12:56:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fbd5d5fa-a8d2-465e-ad24-f33269c06140","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63223975,"name":"Biological sciences/Cell biology"},{"id":63223976,"name":"Health sciences/Diseases"},{"id":63223977,"name":"Biological sciences/Immunology"},{"id":63223978,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-04-30T02:54:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 18:31:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8674632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8674632","identity":"rs-8674632","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}