Monocyte and bone marrow macrophage transcriptional phenotypes in systemic juvenile idiopathic arthritis reveal TRIM8 as a mediator of IFN-γ hyper-responsiveness and risk for macrophage activation syndrome.

OBJECTIVES: Systemic juvenile idiopathic arthritis (SJIA) confers high risk for macrophage activation syndrome (MAS), a life-threatening cytokine storm driven by interferon (IFN)-γ. SJIA monocytes display IFN-γ hyper-responsiveness, but the molecular basis of this remains unclear. The objective of this study is to identify circulating monocyte and bone marrow macrophage (BMM) polarisation phenotypes in SJIA including molecular features contributing to IFN response. METHODS: Bulk RNA-seq was performed on peripheral blood monocytes (n=26 SJIA patients) and single cell (sc) RNA-seq was performed on BMM (n=1). Cultured macrophages were used to define consequences of tripartite motif containing 8 (TRIM8) knockdown on IFN-γ signalling. RESULTS: Bulk RNA-seq of SJIA monocytes revealed marked transcriptional changes in patients with elevated ferritin levels. We identified substantial overlap with multiple polarisation states but little evidence of IFN-induced signature. Interestingly, among the most highly upregulated genes was TRIM8, a positive regulator of IFN-γ signalling. In contrast to PBMC from SJIA patients without MAS, scRNA-seq of BMM from a patient with SJIA and MAS identified distinct subpopulations of BMM with altered transcriptomes, including upregulated IFN-γ response pathways. These BMM also showed significantly increased expression of TRIM8. In vitro knockdown of TRIM8 in macrophages significantly reduced IFN-γ responsiveness. CONCLUSIONS: Macrophages with an 'IFN-γ response' phenotype and TRIM8 overexpression were expanded in the bone marrow from an MAS patient. TRIM8 is also upregulated in SJIA monocytes, and augments macrophage IFN-γ response in vitro, providing both a candidate molecular mechanism and potential therapeutic target for monocyte hyper-responsiveness to IFNγ in cytokine storms including MAS.

Mutagenesis of a conserved glutamate reveals the contribution of electrostatic energy to adenosylcobalamin co-C bond homolysis in ornithine 4,5-aminomutase and methylmalonyl-CoA mutase.

Binding of substrate to ornithine 4,5-aminomutase (OAM) and methylmalonyl-CoA mutase (MCM) leads to the formation of an electrostatic interaction between a conserved glutamate side chain and the adenosyl ribose of the adenosylcobalamin (AdoCbl) cofactor. The contribution of this residue (Glu338 in OAM from Clostridium sticklandii and Glu392 in human MCM) to AdoCbl Co-C bond labilization and catalysis was evaluated by substituting the residue with a glutamine, aspartate, or alanine. The OAM variants, E338Q, E338D, and E338A, showed 90-, 380-, and 670-fold reductions in catalytic turnover and 20-, 60-, and 220-fold reductions in k(cat)/K(m), respectively. Likewise, the MCM variants, E392Q, E392D, and E392A, showed 16-, 330-, and 12-fold reductions in k(cat), respectively. Binding of substrate to OAM is unaffected by the single-amino acid mutation as stopped-flow absorbance spectroscopy showed that the rates of external aldimine formation in the OAM variants were similar to that of the native enzyme. The decrease in the level of catalysis is instead linked to impaired Co-C bond rupture, as UV-visible spectroscopy did not show detectable AdoCbl homolysis upon binding of the physiological substrate, d-ornithine. AdoCbl homolysis was also not detected in the MCM mutants, as it was for the native enzyme. We conclude from these results that a gradual weakening of the electrostatic energy between the protein and the ribose leads to a progressive increase in the activation energy barrier for Co-C bond homolysis, thereby pointing to a key role for the conserved polar glutamate residue in controlling the initial generation of radical species.

Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles.

Modeling the relationship between chemical structure and molecular activity is a key goal in drug development. Many benchmark tasks have been proposed for molecular property prediction, but these tasks are generally aimed at specific, isolated biomedical properties. In this work, we propose a new cross-modal small molecule retrieval task, designed to force a model to learn to associate the structure of a small molecule with the transcriptional change it induces. We develop this task formally as multi-view alignment problem, and present a coordinated deep learning approach that jointly optimizes representations of both chemical structure and perturbational gene expression profiles. We benchmark our results against oracle models and principled baselines, and find that cell line variability markedly influences performance in this domain. Our work establishes the feasibility of this new task, elucidates the limitations of current data and systems, and may serve to catalyze future research in small molecule representation learning.