Biology subjects

Green, B.

Publications and source records attributed to Green, B..

4 recordsLinked to original sources

bioRxiv2026

Comparison of hiPSC-derived hepatic organoids and liver-on-a-chip systems reveal microenvironment-driven maturation

Human liver organoids (HLOs) and liver-on-a-chip (LoC) systems are emerging as physiologically relevant human models for studying liver function, disease, and drug metabolism, often in combination with human induced pluripotent stem cell (hiPSC)-derived tissues. However, hiPSC-derived models often display batch-to-batch variation and incomplete maturation, and the contribution of microfluidic flow to hepatic maturation remains insufficiently characterized. Here, we developed a cryopreservable and scalable workflow to generate hiPSC-derived hepatic organoids that can be directly matured to either static HLOs or LoC systems, enabling matched comparison of both platforms. Transcriptomic and functional characterization revealed progressive hepatic maturation during organoid differentiation, including increased expression of liver-specific metabolic pathways, enhanced albumin secretion, and increased CYP3A4 activity. Compared to mature HLOs, LoCs exposed to continuous microfluidic flow exhibited transcriptomic profiles suggesting further maturation, with increased enrichment of pathways related to lipid metabolism, xenobiotic metabolism, transport, and tissue organization. These findings demonstrate that microfluidic perfusion promotes hepatic metabolic specialization compared to static organoid culture while maintaining donor-specific characteristics. Together, this study establishes a robust hiPSC-derived LoC platform and highlights the potential of flow-based systems for improved modeling of human liver physiology, disease mechanisms, and drug responses.

cell biology↗

bioRxiv2026

Targeting GL-Lect driven endocytosis to suppress cell plasticity in breast cancer

Aberrant endocytosis has long been associated with epithelial plasticity and tumorigenesis, but direct in vivo evidence of its causal role in tumor progression and metastasis has been lacking. Here, we identify and molecularly characterize a previously unrecognized form of E-cadherin (ECAD) internalization in mammary epithelial cells. This process is mediated by the endocytic adaptor Epsin 3 (EPN3) through glycolipid-lectin (GL-Lect) driven endocytosis requiring galectin-3 and Eps15-family adaptors. Leveraging an EPN3 knock-in mouse model, we show that dysregulation of GL-Lect driven endocytosis disrupts mammary gland morphogenesis and activates epithelial-to-mesenchymal plasticity (EMP), synergizing with the ERBB2/Neu breast oncogene to drive metastasis. Pharmacologic inhibition of the GL-Lect mechanism suppresses morphogenetic and invasive phenotypes ex vivo, providing proof-of-concept for therapeutic targeting. These findings establish the GL-Lect mechanism as a driver of metastatic plasticity and uncover a tractable vulnerability in BC.

cancer biology↗

bioRxiv2025

AURKB-driven dissolution of CIZ1-RNA assemblies from the inactive X chromosome in mitosis

Cip1-interacting zinc-finger protein 1 (CIZ1) interacts with Xist lncRNA to form large RNA-protein assemblies at the inactive X-chromosome (Xi) in female mammalian nuclei, plus smaller assemblies in both sexes. CIZ1 assemblies influence underlying chromatin, and their disruption alters the expression of autosomal and X-linked gene clusters. Here, we explore the regulated dissolution of CIZ1-Xi assemblies during mitosis and show that, like Xist, CIZ1 is released in prometaphase under the regulation of Aurora Kinase B (AURKB). The part of human/mouse CIZ1 comprising 179/181 C-terminal amino-acids encodes a matrin-3 domain that facilitates dimerization to form a compact folded core with disordered C-terminal extensions. Mass spectrometry revealed 56 high-confidence interacting partners of the C-terminal fragment, predominantly chromatin, nuclear matrix and RNA-binding proteins. Phosphomimetic mutation of three conserved AURKB sites in the C-terminal extensions released CIZ1 from its nuclear anchor points, but did not affect its interaction with chromatin or nuclear matrix proteins. In contrast, the same mutations, or deletion of the C-terminal extensions, abolished interaction with RNAs including Xist. Together, the data suggest CIZ1 is a regulatable component of the protein-RNA assemblies that preserve epigenetic stability across the nucleus, and that AURKB drives their dissolution in mitosis via dissociation of CIZ1 from RNA. BulletsThe data show regulated dissolution of RNA-protein assemblies involved in protection of epigenetic state. RNA spatially constrains CIZ1 assemblies via multivalent interactions at sub-nuclear sites. CIZ1 dimerizes to present dual extensions whose phosphorylation by AURKB dissolves RNA interaction in mitosis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/676856v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@107bec2org.highwire.dtl.DTLVardef@17cbb4aorg.highwire.dtl.DTLVardef@1ec0144org.highwire.dtl.DTLVardef@1474995_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2022

Mass Dynamics 2.0: An improved modular web-based platform for accelerated proteomics insight generation and decision making

Data processing is essential to reliably generate knowledge from proteomics studies. The complexity of the proteomics data, as well as the ability of research teams to adopt complex analysis pipelines, have proven to be an obstacle to effective collaboration and more efficient biological insight generation. Here, we introduce MD 2.0, a cloud- and web-based platform for quantitative proteomics data, which implements a novel analysis workspace where statistical analyses, visualizations, and external knowledge generation are integrated into a modular framework. This modularity enables researchers the flexibility to test different hypotheses, and customize and template complex proteomics analyses, thereby expediting insight generation for complex datasets. The extensible MD 2.0 environment has been built with a scalable architecture to allow rapid development of future analysis modules and enhanced tools for remote collaboration, like experiment sharing and a live chat capability. The new drag-and-drop modules allow researchers to easily and quickly assess different aspects of an experiment, including quality control, differential expression and enrichment analysis. The modularity of MD 2.0 lays the foundation to support broader community-based analytical template generation and optimized sharing and collaboration between proteomics experts and biologists, thereby accelerating research teams abilities to extract knowledge from complex proteomics datasets.

bioinformatics↗