Biology subjects

Morris, A. H.

Publications and source records attributed to Morris, A. H..

7 recordsLinked to original sources

bioRxiv2026

Engineered cells that sense and respond to interleukin-23 with neutralizing therapy for autoimmunity

Engineered cell sensors are powerful tools for monitoring biomarkers and delivering timely therapy. Here we create an engineered cell sensor of interleukin-23 (IL-23), a critical biomarker in many autoimmune diseases, and a therapeutic delivery platform for autoimmune disease management. We utilized Modular Extracellular Sensor Architecture (MESA), a promising platform given its modularity and previously developed cell sensors for immune targets such as IL-10. First, we developed a library of IL-23 MESA receptors by incorporating IL-23-specific nanobody binders and performed high-throughput functional screening. We achieved further receptor optimization by modifying the extracellular linker length and the ratio of receptor chain expression. Receptor performance was highly correlated with predicted geometric features such as binder pair distance and degree of transmembrane domain dimerization. The receptors were limited by poor surface expression and functioned most effectively as sensors for intracellular IL-23. Finally, we modified the IL-23 sensing circuit to output an IL-23 neutralizing nanobody with the potential to reduce inflammation triggered by IL-23 secretion. This sense-and-respond architecture can be utilized for dynamic therapeutic treatment to meet the dynamic relapse-remitting pattern of many autoimmune diseases.

synthetic biology↗

bioRxiv2026

Synthetic Immunological Niche Reveals Early Immune Dysregulationand Stratifies Therapeutic Response in Type 1 Diabetes

In Type 1 Diabetes (T1D), disease onset and response to immunotherapy vary widely among individuals, reflecting heterogeneous stage-specific immune dysregulation that remains undefined. To investigate this heterogeneity, we engineered a microporous polycaprolactone scaffold that forms a synthetic immunological niche (IN) upon subcutaneous implantation, enabling in vivo capture of systemic immune dysregulation. In non-obese diabetic (NOD) mice, longitudinal transcriptomic profiling of IN-infiltrating cells identified early-stage genes relatively enriched for myeloid cells, followed by progressive increases in T cell-associated dysregulation at later stages that distinguished T1D progressors from non-progressors. We derived an early-stage IN-based T1D gene signature capturing immune alterations. The signature stratified NOD progressors from non-progressors as early as 6 weeks of age and was conserved across human T1D datasets, distinguishing T1D from non-diabetic individuals in spleen and pancreatic lymph node samples, but not peripheral blood. Signature-based stratification further revealed enrichment of macrophage-associated TNF- pathways in NOD progressors, validated in human T1D islets. Given heterogeneous response to anti-TNF- therapy, IN profiling identified resistance-associated mechanisms and enabled derivation of a pathway score that prospectively distinguished treatment-sensitive from resistant mice prior to therapy, establishing the IN as a minimally invasive platform for detecting stage-wise immune dysregulation and stratifying immunotherapy response in T1D.

bioengineering↗

bioRxiv2024

The Evolution of Microbiome-Mediated Traits

The role of host-associated microbiomes in shaping host fitness is becoming increasingly recognized, and may have important evolutionary consequences. The degree to which microbiomes contribute to host evolution depends on how they are inherited. To understand the conditions under which host evolution is mediated by microbiome inheritance, we introduce a simple mathematical model. Our model tracks the evolution of a quantitative host trait that is mediated by host genotype and host microbiome. Unlike host genotype, the host microbiome may have several routes of transmission, including social contact and environmental acquisition. Our model takes a breeding-value approach to formally generalize classical quantitative genetic theory to account for these various modes of microbiome inheritance. A key prediction of our model is that, as long as microbiomes are transmitted between host generations, their mode of transmission has no effect on host trait dynamics. To understand the conditions required for this result, we analyze microbiome-explicit simulations that break important model assumptions. We then discuss avenues to generalize our model and connections with related concepts. Taken together, this work provides novel insights into the dynamics of non-Mendelian populations, and makes first steps towards establishing a theory of microbiome-mediated trait evolution.

evolutionary biology↗

bioRxiv2024

Conversion of natural cytokine receptors into orthogonal synthetic biosensors

Synthetic receptors enable bioengineers to build cell-based therapies that perform therapeutic functions in a targeted or conditional fashion to enhance specificity and efficacy. Although many synthetic receptors exist, it remains challenging to generate new receptors that sense soluble cues and relay that detection through orthogonal mechanisms independent of native pathways. Towards this goal, we investigated co-opting natural cytokine receptor ectodomains into Modular Extracellular Sensor Architecture receptors (yielding natural ectodomain, NatE MESA receptors). We generated multiple high-performing, orthogonal synthetic cytokine receptors, identified design principles and constraints, and propose guidance for extending this approach to other natural receptors. We demonstrate utility of NatE MESA by engineering T cells to sense an immunosuppressive cue and respond with customized transcriptional output to support CAR T-cell activity. Finally, we multiplex NatE MESA to logically evaluate multiple cues associated with the tumor microenvironment. These technologies and learnings will enable engineering cellular functions for new applications.

synthetic biology↗

bioRxiv2023

Response of soil microbiome composition to selection on methane oxidation rate

Microbiomes mediate important ecosystem functions, yet it has proven difficult to determine the relationship between microbiome composition and the rate of ecosystem functions. This challenge remains because it is difficult to manipulate microbiome composition directly, we often cannot know a priori which microbiome members influence the rate of an ecosystem function, and microbiomes can covary strongly with other drivers of ecosystem function, such as the environment. To address these challenges, we imposed artificial selection on whole soil ecosystems over multiple generations to select for microbial communities with a high rate of CH4 oxidation. This approach is potentially powerful because it is biologically "agnostic" in that it makes few assumptions about which taxa are important to function, and repeated passaging with fresh substrate weakens the covariance between microbes and the environment. As a response to selection, we observed a 50.7% increase in CH4 oxidation rate per passage relative to a control that experienced random selection. We estimated that 31.5% of the variation in CH4 oxidation rate in these soils can be attributed to microbiome variation (though this was not significant). We also found that selection did not enrich for known CH4 oxidizers; instead, 12 families not known to oxidize CH4, including Fimbriimonadaceae, Cytophagaceae, and Diplorickettsiaceae, were enriched by selection. This result is in contrast to the typical assumption that the rate of an ecosystem function is limited by the final step in the associated microbial pathway. Our study demonstrates that variation in microbiome composition can contribute to variation in the rate of ecosystem function independent of the environment and that this may not always be limited by the final step in a pathway. This suggests that manipulating microbiome composition directly without altering the environment could be a viable strategy for managing ecosystem functions.

ecology↗

bioRxiv2019

Cargo-free scaffold implant recruits metastatic cancer cells via lung-mimicking myeloid cell S100A8/A9 axis

Pre-metastatic niches in distant tissue facilitate metastasis from the primary tumor. Cargo-free porous polymer scaffolds implanted in tumor-bearing mice act as synthetic metastatic niches recruiting metastasizing cancer cells. Herein, we investigated the mechanisms by which these implants attract cancer cells from circulation. Scaffolds attract cancer cells in part via S100A8/A9 secreted by Gr1+ myeloid cells in a mechanism that mimics lung metastasis. Further, cancer cells attracted to the scaffold have a lung-tropic gene expression signature regardless of their tissue of origin. The scaffold implant reduces metastasis to the lung suggesting otherwise lung-tropic cancer cells are diverted to the scaffold. The suppression of metastatic spread by the scaffold suggests this mechanism may be exploited for novel therapies, and may broadly influence the design of scaffold-based drug delivery system for anti-cancer therapy.

bioengineering↗

bioRxiv2019

Linking microbial communities to ecosystem functions: what we can learn from genotype-phenotype mapping in organisms

Microorganisms mediate many important ecosystem functions, yet it remains unclear to what extent microbial diversity or community composition is important for determining the rates of ecosystem-scale functions. This uncertainty limits our ability to predict and manage crucial microbially-mediated processes, such as nutrient loss and greenhouse gas emissions. Our lack of understanding stems from the relatively large diversity of microorganisms, the difficulty in directly identifying functional groups, and our limited ability to manipulate microbial community attributes. For this reason, we propose that integrating traditional biodiversity-ecosystem function research with ideas from genotype-phenotype mapping could provide the new perspective our discipline needs. We identify three insights from genotype-phenotype mapping that could be useful for microbial biodiversity-ecosystem function studies: the concept of \"agnostic\" mapping, the use of more powerful ways to account for multiple comparisons, and the incorporation of covariates into models of ecosystem function. We illustrate the potential for these approaches to elucidate microbial biodiversity-ecosystem function relationships by analyzing a subset of published data measuring methane oxidation rates from incubations of tropical soil. We assert that combining the approaches of traditional biodiversity-ecosystem function research with ideas from genotype-phenotype mapping will not only generate novel hypotheses about how complex microbial communities drive ecosystem function, but also help scientists predict and manage changes to ecosystem functions resulting from human activities.

ecology↗