Biology subjects

Daniels, K. G.

Publications and source records attributed to Daniels, K. G..

4 recordsLinked to original sources

bioRxiv2026

Programmable synthetic cytokine receptors polarize macrophages to user-defined functional states

Technology that precisely controls macrophage polarization to distinct functional states would deepen our understanding of macrophage biology and enable the development of new macrophage cell therapies. Here, we use a synthetic cytokine receptor (SCR) platform with a programmable signaling domain to control the polarization of primary human macrophages. SCRs containing signaling motifs from the interferon-gamma (IFN-{gamma}) or Interleukin-10 (IL-10) receptors mimic key features of pro-inflammatory or anti-inflammatory polarization, respectively. Random recombination of nine distinct signaling motifs to create new SCR signaling domains generates a diverse landscape of synthetic macrophage states with varied expression of inflammatory markers (CD80, CD40) and anti-inflammatory markers (CD163, CD206), and varied phagocytic capacity. SCRs programmed with multiple YLxQ motifs increase macrophage phagocytosis of E. coli and chimeric antigen receptor (CAR)-macrophage phagocytosis of cancer cells in mice, reducing tumor burden by 30-fold. The motif-dependent polarization is well-described by a two-state model, enabling quantitative prediction of macrophage polarization state from SCR signaling domain composition. Leveraging this model, we design an SCR that simultaneously enhances phagocytosis and maintains a macrophage pro-inflammatory state. Together, these findings establish a framework for synthetic programming of macrophage polarization states, with potential applications in cancer immunotherapy and other disease contexts.

synthetic biology↗

bioRxiv2026

Amino acid and codon usage explain amino acid misincorporation rates across the tree of life

Protein translation is an error-prone process resulting in a random population of altered protein sequences in every cell. Here, we analyzed thousands of publicly available mass spectrometry datasets to detect amino acid misincorporations and quantify error rates in 14 model organisms. We find that overall error rates and the patterns of codon to amino acid error rates correlate across species. We estimate that on average 1-2% of protein molecules in a cell harbor a misincorporation, whereas this proportion can reach 10% for long proteins. Highly expressed and very long proteins have lower error rates, indicating evolutionary selection on codon usage to reduce the cost of translation errors. While both codon-anticodon mispairing and tRNA mischarging contribute to misincorporations, we estimate that [~]70% of misincorporation events are due to mispairing. The more frequent an amino acid in the proteome, the more likely it is misincorporated (r = 0.53), likely because frequent amino acids are abundant in the cell, increasing the rate of mischarging, and have abundant tRNAs, leading to increased mispairing. Overall, we find that amino acid and codon usage explain error rates. The conserved patterns of amino acid misincorporations from bacteria to humans suggest universal mechanisms driving translational fidelity.

bioinformatics↗

bioRxiv2025

A continuous landscape of signaling encodes a corresponding landscape of CAR T cell phenotype

Cytokine signaling is critical to the function of natural immune cells and engineered immune cell therapies such as chimeric antigen receptor (CAR) T cells. It remains unclear how the limited set of signal transducers and activators of transcription (STATs) and other proteins activated by these cytokine receptors can encode the observed diversity of immune cell phenotypes. To understand how signaling downstream of cytokines control immune cell phenotype, we sought to map the structure of Janus kinase (JAK)/STAT signaling domains to cell signaling and resulting CAR T cell function. We recombined 14 signaling motifs to construct a library of [~]30,000 constitutively active synthetic cytokine receptors (SCRs) with intracellular domains composed of novel signaling motif combinations that activate different signaling cascades. We experimentally tested [~]450 SCRs which generated a range of CAR T cell memory, cytotoxicity, and proliferation. SCRs with pSTAT1 and 3 signaling generated effector memory CAR T cells, while SCRs with strong pSTAT5 generated effector CAR T cells with potent anti-tumor activity. To map the structure-signaling-phenotype landscape we trained models to predict signaling and CAR T cell phenotype that result from varied motif combinations. From neural network predictions we identified features, including strong STAT5 and Shc signaling, that promote unsafe autonomous CAR T cell proliferation. Models also revealed a trade-off between memory and cytotoxicity, with a Pareto front encoded by a continuous change in signaling. These results demonstrate that recombination of a limited set of signaling motifs creates a continuous spectrum of signaling that encodes a corresponding spectrum of cell phenotype. This work synthetically expands the combinatorial space of JAK/STAT signaling and provides a foundation for rational design of CAR T cells with improved cytotoxicity, memory, and safety profiles. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/658149v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@cc1498org.highwire.dtl.DTLVardef@5c2864org.highwire.dtl.DTLVardef@5fc8b3org.highwire.dtl.DTLVardef@719f8f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

Exploring the rules of chimeric antigen receptor phenotypic output using combinatorial signaling motif libraries and machine learning

Chimeric antigen receptor (CAR) costimulatory domains steer the phenotypic output of therapeutic T cells. In most cases these domains are derived from native immune receptors, composed of signaling motif combinations selected by evolution. To explore if non-natural combinations of signaling motifs could drive novel cell fates of interest, we constructed a library of CARs containing [~]2,300 synthetic costimulatory domains, built from combinations of 13 peptide signaling motifs. The library produced CARs driving diverse fate outputs, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, the non-native combination of TRAF- and PLC{gamma}1-binding motifs was found to simultaneously enhance cytotoxicity and stemness, a clinically desirable phenotype associated with effective and durable tumor killing. The neural network accurately predicts that addition of PLC{gamma}1-binding motifs improves this phenotype when combined with TRAF-binding motifs, but not when combined with other immune signaling motifs (e.g. PI3K-or Grb2-binding motifs). This work shows how libraries built from the minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/474985v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@8dbf97org.highwire.dtl.DTLVardef@1d8300org.highwire.dtl.DTLVardef@1838ba0org.highwire.dtl.DTLVardef@143d907_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗