Biology subjects

Liu, J. Y.

Publications and source records attributed to Liu, J. Y..

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

Nascent extracellular matrix converts biomaterial cues into cell fate decisions

Hydrogels serve as powerful models for investigating cell-extracellular matrix (ECM) interactions. While chemical modifications are routinely used to tune hydrogel properties, it remains unclear whether these modifications mediate cell fate. Previous work has shown that cells deposit newly synthesized (nascent) ECM at the cell-hydrogel interface. Here, we demonstrate that this nascent ECM interface regulates how cells interpret chemical modifications. Using hydrogels with varied chemical modifications, we isolated the effects of chemical modification on nascent ECM and cell fate. Nascent ECM deposition increased as a function of hydrogel modification and with distinct matrisome compositions. While low modification hydrogels promoted cell differentiation, high modifications increased cell proliferation. Perturbing cell-nascent ECM interactions reversed this cell fate. Our findings reveal that nascent ECM regulates cell fate by converting hydrogel cues into signals that control cell fate. This tri-directional interplay among hydrogel chemical modifications, nascent ECM, and cell fate reframes how we interpret cell-hydrogel interactions.

bioengineering↗

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↗

bioRxiv2018

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study

Limited health literacy can be a barrier to healthcare delivery, but widespread classification of patient health literacy is challenging. We applied natural language processing and machine learning on a large sample of 283,216 secure messages sent from 6,941 patients to their clinicians for this study to develop and validate literacy profiles as indicators of patients health literacy. All patients were participants in Kaiser Permanente Northern Californias DISTANCE Study. We created three literacy profiles, comparing performance of each literacy profile against a gold standard of patient self-report. We also analyzed associations between the literacy profiles and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical data such as sex, race, continuous medication gaps and severe hypoglycemia. Literacy profiles varied in their test characteristics, with C-statistics ranging from 0.61-0.74. Relationships between literacy profiles and health outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy profiles as having limited health literacy were older and more likely minority; had poorer medication adherence and glycemic control; and higher rates of hypoglycemia, comorbidities and healthcare utilization. This research represents the first successful attempt to use natural language processing and machine learning to measure health literacy. Literacy profiles offer an automated and economical way to identify patients with limited health literacy and a greater vulnerability to poor health outcomes.

bioinformatics↗