Inclusion of ACKR5 in the systematic nomenclature of atypical chemokine receptors
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Proteolysis of TAM receptors in autoimmune diseases and cancer: what does it say to us?
Proteolytic processing of Receptor Tyrosine Kinases (RTKs) leads to the release of ectodomains in the extracellular space. These soluble ectodomains often retain the ligand binding activity and dampen canonical pathways by acting as decoy receptors. On the other hand, shedding the ectodomains may initiate new molecular events and diversification of signalling. Members of the TAM (TYRO3, AXL, MER) family of RTKs undergo proteolytic cleavage, and their soluble forms are present in the extracellular space and biological fluids. TAM receptors are expressed in professional phagocytes, mediating apoptotic cell clearance, and suppressing innate immunity. Enhanced shedding of TAM ectodomains is documented in autoimmune and some inflammatory conditions. Also, soluble TAM receptors are present at high levels in the biological fluids of cancer patients and are associated with poor survival. We outline the biology of TAM receptors and discuss how their proteolytic processing impacts autoimmunity and tumorigenesis. In autoimmune diseases, proteolysis of TAM receptors likely reflects reduced canonical signalling in professional phagocytes. In cancer, TAM receptors are expressed in the immune cells of the tumour microenvironment, where they control pathways facilitating immune evasion. In tumour cells, ectodomain shedding activates non-canonical TAM pathways, leading to epithelial-mesenchymal transition, metastasis, and drug resistance.
Glucose-derived receptors for photo-controlled binding of amino acid esters in water
Selective receptors of amino acids in aqueous media are highly sought after as they may enable the creation of novel diagnostic and sensing tools. Photoswitchable receptors are particularly attractive for such purposes as their response and selectivity towards bioanalytes can be modulated using light. Herein we report glucose-based photoswitchable receptors of amino-acid methyl esters and biogenic amines in water. The tetra-ortho-fluoroazobenzene unit in the receptors structure allows to control the distance between their binding sites using light. The Z-isomers of both receptors, having these sites in closer proximity, bind lysine, ornithine and arginine esters significantly stronger compared to E-isomers, where the binding sites are further apart.
Recommendations for mitochondria transfer and transplantation nomenclature and characterization
Intercellular mitochondria transfer is an evolutionarily conserved process in which one cell delivers some of their mitochondria to another cell in the absence of cell division. This process has diverse functions depending on the cell types involved and physiological or disease context. Although mitochondria transfer was first shown to provide metabolic support to acceptor cells, recent studies have revealed diverse functions of mitochondria transfer, including, but not limited to, the maintenance of mitochondria quality of the donor cell and the regulation of tissue homeostasis and remodelling. Many mitochondria-transfer mechanisms have been described using a variety of names, generating confusion about mitochondria transfer biology. Furthermore, several therapeutic approaches involving mitochondria-transfer biology have emerged, including mitochondria transplantation and cellular engineering using isolated mitochondria. In this Consensus Statement, we define relevant terminology and propose a nomenclature framework to describe mitochondria transfer and transplantation as a foundation for further development by the community as this dynamic field of research continues to evolve.
Gestational autoantibody exposure impacts early brain development in a rat model of MAR autism
Maternal autoantibody-related autism (MARA) is a subtype of autism characterized by the maternal production of specific patterns of autoantibodies during pregnancy, which significantly increases the likelihood of an autism diagnosis in their children. Multiple patterns of MARA autoantibodies (MARA-ABS) have been identified, and differences in the severity of the autism phenotype associated with each autoantibody pattern have been described. In this study, we utilized preclinical rat models to further elucidate the differential effects of MARA-AB exposure based on the known clinical patterns, including the originally reported pattern of lactate dehydrogenase A and B (LDHA/B) + collapsin response mediator protein 1 (CRMP1) + stress-induced phosphoprotein 1 (STIP1), as well as the more recently described patterns of CRMP1+CRMP2, CRMP1 + guanine deaminase (GDA), and STIP1+ neuron-specific enolase (NSE). We induced endogenous MARA-AB production in rat dams before pregnancy to expose offspring to the ABs throughout gestation. We found that in postnatal day 2 offspring exposed to MARA-ABS, the levels of brain and serum cytokines/chemokines/growth factors were altered based on the pattern of MARA-AB exposure. Further, bulk transcriptomic profiles of coronal sections containing hippocampal formation and the adjacent cortical and subcortical structures suggested changes in cellular proliferation and differentiation following MARA exposure. These combined observations demonstrate that gestational exposure to MARA-ABS alters early gene expression and immune signaling molecules, both of which may contribute to the altered neurodevelopment and behaviors associated with MARA.
Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles
Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics.
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