Biology subjects

Zou, J. Y.

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

5 recordsLinked to original sources

bioRxiv2025

eIF2B Selectively Anchors and Activates Mutant KRAS

Much is known about how RAS oncoproteins regulate mRNA translation factors, but the reverse relationship, how translation factors influence RAS activity, has remained largely unexplored. At the plasma membrane (PM), Son of Sevenless (SOS) acts as the canonical guanine nucleotide exchange factor (GEF) for RAS proteins, yet mechanisms governing its specificity for individual RAS isoforms remain unknown. Here, we show that the translation initiation factor eIF2B, best known for its GEF function in translation initiation, forms a distinct complex with SOS and mutant KRAS at the PM, but not with other oncogenic RAS variants. Mechanistically, eIF2B acts as an allosteric regulator of SOS, selectively enhancing GDP-GTP exchange on mutant KRAS. This specificity arises from the translational activity of eIF2B, which upregulates glycosphingolipid (GSL) biosynthesis to remodel PM lipids and preferentially anchor mutant KRAS. Together, our results uncover an unexpected moonlighting function of eIF2B: acting both as a direct activator of SOS and as a regulator of GSL pathway that shapes the membrane landscape, both required for mutant KRAS activation. These insights redefine our understanding of eIF2B and mutant KRAS functions in cancer and have profound implications for KRAS-driven oncogenesis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/686860v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1f50934org.highwire.dtl.DTLVardef@2f55b9org.highwire.dtl.DTLVardef@1a51e39org.highwire.dtl.DTLVardef@1634aac_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIeIF2B interacts with mutant KRAS and SOS at the plasma membrane (PM). C_LIO_LIThe eIF2B:SOS complex promotes the GTP-bound active state of mutant KRAS. C_LIO_LIeIF2B enhances the translation of B4GALT5 mRNA, encoding a key enzyme of glycosphingolipid (GSL) biosynthesis. C_LIO_LIUpregulation of the GSL metabolites, ganglioside GM3 and sulfatide SM4, remodels PM lipid composition to facilitate eIF2B:SOS:KRAS complex formation and mutant KRAS nanoclustering. C_LIO_LIThrough its interaction with SOS and stimulation of GSL synthesis, eIF2B selectively activates mutant KRAS at the PM among RAS isoforms. C_LIO_LIeIF2B is required for the development of mKRAS-driven lung adenocarcinoma in mice. C_LIO_LIeIF2B is a marker of poor prognosis in mutant KRAS-driven cancers. C_LI

cancer biology↗

bioRxiv2025

Lineage plasticity of the integrated stress response is a hallmark of cancer evolution.

The link between the "stress phenotype"-a well-established hallmark of cancer-and its role in tumor progression and intratumor heterogeneity remains poorly defined. The integrated stress response (ISR) is a key adaptive pathway that enables tumor survival under oncogenic stress. While ISR has been implicated in promoting tumor growth, its precise role in driving tumor evolution and heterogeneity has not been elucidated. In this study, using a genetically engineered mouse models, we demonstrate that ISR activation--indicated by elevated levels of phosphorylated eIF2 (p-eIF2) and ATF4--is essential for the emergence of dedifferentiated, therapy-resistant cell states. ISR, through the coordinated actions of ATF4 and MYC, facilitates the development of tumor cell populations characterized by high plasticity, stemness, and an epithelial-mesenchymal transition (EMT)-prone phenotype. This process is driven by ISR-mediated expression of genes that maintain mitochondrial integrity and function, critical for sustaining tumor progression. Importantly, genetic, or pharmacological inhibition of the p-eIF2-ATF4 signaling axis leads to mitochondrial dysfunction and significantly impairs tumor growth in mouse models of lung adenocarcinoma (LUAD). Moreover, ISR-driven dedifferentiation is associated with poor prognosis and therapy resistance in advanced human LUAD, underscoring ISR inhibition as a promising therapeutic strategy to disrupt tumor evolution and counteract disease progression.

cancer biology↗

bioRxiv2024

A feedforward loop between STAT1 and YAP1 stimulates lipid biosynthesis, accelerates tumor growth, and promotes chemotherapy resistance in mutant KRAS colorectal cancer.

In tumorous conditions, the transcription factor STAT1, traditionally recognized for its anti-tumor role in immunology, exhibits pro-survival characteristics, though the underlying mechanisms remain unclear. Investigating STAT1s function in isogenic colorectal tumor cells with wild-type or mutant KRAS, we found that STAT1 specifically promotes the survival and proliferation of cells with mutant KRAS. Through gene expression profiling, we discovered a previously unknown role of STAT1 in upregulating sterol and lipid biosynthetic genes specifically in mutant KRAS cells. This effect is driven by STAT1s phosphorylation at serine 727 and its cooperation with STAT3 and STAT5 for the transcriptional upregulation of sterol regulatory element-binding proteins (SREBP) 1 and 2, which boost de novo sterol and lipid biosynthesis. In mutant KRAS cells, STAT1 amplifies the mevalonate pathway, maintaining its serine 727 phosphorylation through RHO GTPase signaling and establishing a positive feedback loop through the transcription factors YAP1 and TEAD4, further driving lipid biosynthesis and tumor growth. Through xenograft tumor assays in mice, we discovered that the STAT1-YAP1 axis plays a role in mutant KRAS tumor cells resistance to mevalonate pathway inhibitors, which can be overcome by pharmacologically targeting the YAP1-TEAD interaction. Additionally, the STAT1-YAP1 arm is essential for the intrinsic resistance to EGFR-targeting therapy in the mutant KRAS colon cancer cells. These findings indicate that the STAT1-YAP1 pathway plays a significant role in therapy resistance and presents a potential therapeutic target in mutant KRAS colorectal cancer.

cancer biology↗

bioRxiv2021

Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers

The recent development and application of methods based on the general principle of "crosslinking and proximity ligation" (crosslink-ligation) are revolutionizing RNA structure studies in living cells. However, extracting structure information from such data presents unique challenges. Here we introduce a set of computational tools for the systematic analysis of data from a wide variety of cross-link-ligation methods, specifically focusing on read mapping, alignment classification and clustering. We design a new strategy to map short reads with irregular gaps at high sensitivity and specificity. Analysis of previously published data reveals distinct properties and bias caused by the crosslinking reactions. We perform rigorous and exhaustive classification of alignments and discover 8 types of arrangements that provide distinct information on RNA structures and interactions. To deconvolve the dense and inter-twined gapped alignments, we develop a network/graph-based tool CRSSANT (Crosslinked RNA Secondary Structure Analysis using Network Techniques), which enables clustering of gapped alignments and discovery of new alternative and dynamic conformations. We discover that multiple crosslinking and ligation events can occur on the same RNA, generating multi-segment alignments to report complex high level RNA structures and multi-RNA interactions. We find that alignments with overlapped segments are produced from potential homodimers and develop a new method for their de novo identification. Analysis of overlapping alignments revealed potential new homodimers in cellular noncoding RNAs and RNA virus genomes in the Picornaviridae family. Together, this suite of computational tools enables rapid and efficient analysis of RNA structure and interaction data in living cells.

bioinformatics↗

bioRxiv2019

Deep Learning Interpretation of Echocardiograms

Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.85), normal left ventricular wall thickness (AUC = 0.75), left ventricular end systolic and diastolic volumes(R2 = 0.73 and R2 = 0.68), and ejection fraction (R2 = 0.48) as well as predicted systemic phenotypes of age (R2 = 0.46), sex (AUC = 0.88), weight (R2 = 0.56), and height (R2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlight hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, standardize interpretation in areas with insufficient qualified cardiologists, and more consistently produce echocardiographic measurements.

bioengineering↗