Macrophages, an important class of innate immune cells that maintain body homeostasis and ward off foreign pathogens, exhibit a high degree of plasticity and play a supportive role in different tissues and organs. Thus, dysfunction of macrophages may contribute to advancement of several diseases, including cancer. Macrophages within the tumor microenvironment are known as tumor-associated macrophages (TAMs), which typically promote cancer cell initiation and proliferation, accelerate angiogenesis, and tame anti-tumor immunity to promote tumor progression and metastasis. Massive infiltration of TAMs or enrichment of TAM-related markers usually indicates cancer progression and a poor prognosis, and consequently tumor immunotherapies targeting TAMs have gained significant attention. Here, we review the interaction between TAMs and cancer cells, discuss the origin, differentiation and phenotype of TAMs, and highlight the role of TAMs in pro-cancer functions such as tumor initiation and development, invasive metastasis, and immunosuppression. Finally, we review therapies targeting TAMs, which are very promising therapeutic strategies for malignant tumors.

Metabolic reprogramming is one of the hallmarks of cancer cells. The pentose phosphate pathway (PPP), a branch of glycolysis, is an important metabolic pathway for the survival and biosynthesis of cancer cells. Transketolase (TKT) is a key enzyme in the non-oxidative phase of PPP. The mechanistic details of TKT in hepatocellular carcinoma (HCC) development remain unclear. TKT level and subcellular location were examined in HCC cell lines and tissue samples. We established the TKT overexpression and knocking-down stable cells in HCC cell lines. Proliferation, migration, viability and enzyme activity assays in vitro, tumor growth and metastasis assays in vivo were employed to test the effects of TKT on HCC development. GFP-tagged TKT truncations and mutants were used to locate the nuclear localization sequence (NLSs) of TKT. Cross-linking co-IP/MS was applied to identify the interaction proteins of nuclear TKT. We showed that TKT increased the proliferation and migration of HCC cells, as well as the viability under oxidative stress in vitro and accelerated the growth and metastasis of HCC cells in vivo. We found as a key enzyme of PPP, TKT could promote the proliferation, cell cycle, migration and viability by regulating the metabolic flux. Moreover, it was firstly reported that unlike other key enzymes in PPP, TKT showed a strong nuclear localization in HCC cells. We found not only high TKT expression, but also its nuclear localization was a prediction for poor prognosis of HCC patients. We further identified the nuclear localization sequences (NLS) for TKT and demonstrated the NLS mutations decreased the pro-tumor function of TKT independent of the enzyme activity. Cross-linking Co-IP/MS showed that nuclear TKT interacted with kinases and transcriptional coregulators such as EGFR and MAPK3, which are associated with cell activation or stress response processes. EGF treatment significantly increased the viability and proliferation of HCC cells in the enzyme-inactivating mutation TKT-D155A overexpression cells but not in the NLS-D155A double mutant group, which could be blocked by EGFR inhibitor erlotinib treatment. Our research suggests that in addition to the metabolic manner, TKT can promote the development of HCC in a non-metabolic manner via its nuclear localization and EGFR pathway.

Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases. This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model’s reliability in discovering new knowledge by manually proofreading predicted drug–gene, gene–disease and disease–drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models. The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11. Supplementary data are available at Bioinformatics online.

Motivation: Screening new drug–target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework. Results: We developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e. Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs’ candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6766 drugs, which can be used for drug repurposing and discovery. Availability and implementation: https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544.

Background: The internal heterogeneity of breast cancer, notably the tumor microenvironment (TME) consisting of malignant and non-malignant cells, has been extensively explored in recent years. The cells in this complex cellular ecosystem activate or suppress tumor immunity through phenotypic changes, secretion of metabolites and cell-cell communication networks. Macrophages, as the most abundant immune cells within the TME, are recruited by malignant cells and undergo phenotypic remodeling. Tumor-associated macrophages (TAMs) exhibit a variety of subtypes and functions, playing significant roles in impacting tumor immunity. However, their precise subtype delineation and specific function remain inadequately defined. Methods: The publicly available single-cell transcriptomes of 49,141 cells from eight breast cancer patients with different molecular subtypes and stages were incorporated into our study. Unsupervised clustering and manual cell annotation were employed to accurately classify TAM subtypes. We then conducted functional analysis and constructed a developmental trajectory for TAM subtypes. Subsequently, the roles of TAM subtypes in cell-cell communication networks within the TME were explored using endothelial cells (ECs) and T cells as key nodes. Finally, analyses were repeated in another independent publish scRNA datasets to validate our findings for TAM characterization. Results: TAMs are accurately classified into 7 subtypes, displaying anti-tumor or pro-tumor roles. For the first time, we identified a new TAM subtype capable of proliferation and expansion in breast cancer-TUBA1B⁺ TAMs playing a crucial role in TAMs diversity and tumor progression. The developmental trajectory illustrates how TAMs are remodeled within the TME and undergo phenotypic and functional changes, with TUBA1B⁺ TAMs at the initial point. Notably, the predominant TAM subtypes varied across different molecular subtypes and stages of breast cancer. Additionally, our research on cell-cell communication networks shows that TAMs exert effects by directly modulating intrinsic immunity, indirectly regulating adaptive immunity through T cells, as well as influencing tumor angiogenesis and lymphangiogenesis through ECs. Conclusions: Our study establishes a precise single-cell atlas of breast cancer TAMs, shedding light on their multifaceted roles in tumor biology and providing resources for targeting TAMs in breast cancer immunotherapy.

Complement activation is involved in the pathogenesis of ischemia reperfusion injury (IRI), which is an inevitable process during kidney transplantation. Therefore, complement-targeted therapeutics hold great potential in protecting the allografts from IRI. We observed universal deposition of C3d and membrane attack complex in human renal allografts with delayed graft function or biopsy-proved rejection, which confirmed the involvement of complement in IRI. Using FB-, C3-, C4-, C5-, C5aR1-, C5aR2-, and C6-deficient mice, we found that all components, except C5aR2 deficiency, significantly alleviated renal IRI to varying degrees. These gene deficiencies reduced local (deposition of C3d and membrane attack complex) and systemic (serum levels of C3a and C5a) complement activation, attenuated pathological damage, suppressed apoptosis, and restored the levels of multiple local cytokines (e.g., reduced IL-1β, IL-9, and IL-12p40 and increased IL-4, IL-5, IL-10, and IL-13) in various gene-deficient mice, which resulted in the eventual recovery of renal function. In addition, we demonstrated that CRIg/FH, which is a targeted complement inhibitor for the classical and primarily alternative pathways, exerted a robust renoprotective effect that was comparable to gene deficiency using similar mechanisms. Further, we revealed that PI3K/AKT activation, predominantly in glomeruli that was remarkably inhibited by IRI, played an essential role in the CRIg/FH renoprotective effect. The specific PI3K antagonist duvelisib almost completely abrogated AKT phosphorylation, thus abolishing the renoprotective role of CRIg/FH. Our findings suggested that complement activation at multiple stages induced renal IRI, and CRIg/FH and/or PI3K/AKT agonists may hold the potential in ameliorating renal IRI.