Biology subjects

Steach, H. R.

Publications and source records attributed to Steach, H. R..

3 recordsLinked to original sources

bioRxiv2024

IL-4 Licenses B Cell Activation Through Cholesterol Synthesis

Lymphocyte activation involves a transition from quiescence and associated catabolic metabolism to a metabolic state with noted similarities to cancer cells such as heavy reliance on aerobic glycolysis for energy demands and increased nutrient requirements for biomass accumulation and cell division1-3. Following antigen receptor ligation, lymphocytes require spatiotemporally distinct "second signals". These include costimulatory receptor or cytokine signaling, which engage discrete programs that often involve remodeling of organelles and increased nutrient uptake or synthesis to meet changing biochemical demands4-6. One such signaling molecule, IL-4, is a highly pleiotropic cytokine that was first identified as a B cell co-mitogen over 30 years ago7. However, how IL-4 signaling mechanistically supports B cell proliferation is incompletely understood. Here, using single cell RNA sequencing we find that the cholesterol biosynthetic program is transcriptionally upregulated following IL-4 signaling during the early B cell response to influenza virus infection, and is required for B cell activation in vivo. By limiting lipid availability in vitro, we determine cholesterol to be essential for B cells to expand their endoplasmic reticulum, progress through cell cycle, and proliferate. In sum, we demonstrate that the well-known ability of IL-4 to act as a B cell growth factor is through a previously unknown rewiring of specific lipid anabolic programs, relieving sensitivity of cells to environmental nutrient availability.

immunology↗

bioRxiv2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

bioinformatics↗

bioRxiv2018

Skin inflammation driven by differentiation of quiescent tissue-resident ILCs into a spectrum of pathogenic effectors

Psoriasis pathology is driven by the type 3 cytokines IL-17 and Il-22, but little is understood about the dynamics that initiate alterations in tissue homeostasis. Here, we use mouse models, single-cell RNA-seq (scRNA-seq), computational inference and cell lineage mapping to show that psoriasis induction reconfigures the functionality of skin-resident ILCs to initiate disease. Tissue-resident ILCs amplified an initial IL-23 trigger and were sufficient, without circulatory ILCs, to drive pathology, indicating that ILC tissue remodeling initiates psoriasis. Skin ILCs expressed type 2 cytokines IL-5 and IL-13 in steady state, but were epigenetically poised to become ILC3-like cells. ScRNA-seq profiles of ILCs from psoriatic and naive skin of wild type (WT) and Rag1-/- mice form a dense continuum, consistent with this model of fluid ILC states. We inferred biological \"topics\" underlying these states and their relative importance in each cell with a generative model of latent Dirichlet allocation, showing that ILCs from untreated skin span a spectrum of states, including a naive/quiescent-like state and one expressing the Cd74 and Il13 but little Il5. Upon disease induction, this spectrum shifts, giving rise to a greater proportion of classical Il5- and Il13- expressing \"ILC2s\" and a new, mixed ILC2/ILC3-like subset, expressing Il13, Il17, and Il22. Using these key topics, we related the cells through transitions, revealing a quiescence-ILC2-ILC3s state trajectory. We demonstrated this plasticity in vivo, combining an IL-5 fate mouse with IL-17A and IL-22 reporters, validating the transition of IL-5-producing ILC2s to IL-22- and IL-17A-producing cells during disease initiation. Thus, steady-state skin ILCs are actively repressed and cued for a plastic, type 2 response, which, upon induction, morphs into a type 3 response that drives psoriasis. This suggests a general model where specific immune activities are primed in healthy tissue, dynamically adapt to provocations, and left unchecked, drive pathological remodeling.

immunology↗