The SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities. The captured information is represented graphically as a signed directed graph. Each signaling relationship is associated to an effect (up/down-regulation) and to the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the up/down-regulation of the target entity. Since its first release, SIGNOR has undergone a significant content increase and the number of annotated causal interactions have almost doubled. SIGNOR 2.0 now stores almost 23 000 manually-annotated causal relationships between proteins and other biologically relevant entities: chemicals, phenotypes, complexes, etc. We describe here significant changes in curation policy and a new confidence score, which is assigned to each interaction. We have also improved the compliance to the FAIR data principles by providing (i) SIGNOR stable identifiers, (ii) programmatic access through REST APIs, (iii) bioschemas and (iv) downloadable data in standard-compliant formats, such as PSI-MI CausalTAB and GMT. The data are freely accessible and downloadable at https://signor.uniroma2.it/.

Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype.

Macrophages play a critical role in shaping the immune landscape of various diseases, with secreted phosphoprotein 1 (SPP1)‐expressing macrophages emerging as a distinct subset implicated in both cancerous and non‐cancerous conditions. Leveraging recent advances in single‐cell RNA sequencing, numerous studies have identified SPP1⁺ macrophages across diverse pathological contexts, shedding light on their functional heterogeneity. In cancer, SPP1⁺ tumour‐associated macrophages contribute to tumour growth, angiogenesis, and immune evasion, often interacting with T cells and stromal components to sustain an immunosuppressive microenvironment. Conversely, in non‐cancerous diseases, these macrophages exhibit both profibrotic and disease‐promoting properties, depending on the tissue context. This review provides a comprehensive synthesis of the latest findings on SPP1⁺ macrophages, highlighting their roles in tumour progression, immune suppression, tissue remodelling, and fibrosis. By comparing their shared traits and tissue‐specific differences, we explore how SPP1⁺ macrophages adapt to distinct microenvironments and influence disease progression. Understanding their conserved and context‐dependent functions may open new avenues for therapeutic targeting.

SPP1+ macrophages, characterized by elevated expression of the osteopontin gene (secreted phosphoprotein 1, SPP1), have emerged as key players in various pathological contexts, including aging, chronic inflammatory diseases, and cancer. While frequently classified as a subclass of tumor‐associated macrophages in oncological settings, their presence in noncancer conditions, such as aging‐related disorders and muscular diseases, suggests a broader role beyond tumors. These macrophages share conserved traits, including fibrosis promotion, extracellular matrix remodeling, and immune modulation, often linked to poor clinical outcomes. This perspective explores the multifaceted roles of SPP1+ macrophages across diseases and advocates for their reclassification as a distinct macrophage subtype associated with chronic or prolonged inflammation. Recognizing their cross‐disease relevance could reshape macrophage biology and inform targeted therapeutic strategies.