Biology subjects

Sui, P.

Publications and source records attributed to Sui, P..

5 recordsLinked to original sources

bioRxiv2026

Multi-omics, organoid-based modeling reveals an SRC/mTOR-dependent fetal-like stem cell trajectory in colorectal cancer

BackgroundSingle-cell atlases have described diverse stem cell states in colorectal cancer (CRC), however, the overarching trajectories of those states and the underlying functional mechanisms, including their relevance for drug sensitivity, need better understanding. MethodsWe established 64 patient-derived organoids from microsatellite-stable colorectal cancers, characterized their transcriptomes and genomes, and performed drug screening with 62-140 clinically approved substances. We analyzed additional published transcriptome data from patient-derived organoids (72 patients from three independent datasets), TCGA-CRC data (466 patients), and single-cell transcriptomes of tumor biopsies (123,000 cells from six independent cohorts) to establish a functional and molecular landscape of CRC stem cells. We performed mechanistic follow-up analyses by mass-spectrometry-based proteomics, large-scale kinase inhibition assays and immunofluorescence analyses. ResultsWe find a continuous landscape of CRC stem cells that is characterized by distinct developmental programs: adult stem cell-to fetal-like regenerative states and transition between differentiation programs. By large-scale drug perturbations and multi-omics modeling, we identify a regenerative/fetal-like stem cell trajectory characterized by PI3K/mTOR dependency. We find the identified developmental axes conserved in organoid, clinical, as well as single-cell data, and the fetal-like PI3K/mTOR-dependent state to be associated with poor clinical prognosis. Mechanistically, PI3K/mTOR vulnerability is linked to a lack of adaptive capability due to suppressed mRNA translation and associated with an upregulated SRC signaling network. ConclusionsOur work moves beyond a molecular CRC landscape by combined functional perturbation analyses in organoids. This enables mechanistic modeling of stem cell state regulation and identifies an SRC/mTOR-dependent regenerative state in CRC, which might allow improved therapeutic targeting in the future.

cancer biology↗

bioRxiv2026

Medea: An omics AI agent for therapeutic discovery

Therapeutic hypotheses can transfer across diseases but their relevance depends on biological context. The same target, perturbation, or treatment can produce different effects across cell types, disease states, genetic backgrounds, and patients. Therapeutic reasoning therefore requires methods that preserve context, test when evidence supports transfer, and identify where context-specific effects limit it. Although AI agents can perform therapeutic analyses, existing systems often fail to preserve biological context over long workflows, verify intermediate computational steps, or reconcile conflicting evidence across datasets and literature. Here, we present Medea, an AI agent for therapeutic reasoning across biological contexts. Medea executes multi-step analyses using biological tools, machine learning models, and literature retrieval while enforcing verification during planning, execution, and evidence synthesis. We evaluate Medea across 5,673 open-ended analyses in three domains: cell type specific therapeutic target nomination in five diseases and 29 cell types, synthetic lethality prediction in 7 cancer cell lines, and immunotherapy response prediction from multimodal patient profiles. Using a previously unpublished epistatic miniarray profiling screen performed under two DNA-damaging treatments, we evaluate Medea on predicting synthetic lethality among 238,046 gene-gene pairs in yeast. Medea predicts these experimentally measured synthetic lethal interactions, indicating that its performance reflects biological relevance rather than information leakage from benchmark datasets. Across these evaluations, Medea improves performance over large language models, reasoning models, biomedical agents, and specialized machine learning models while maintaining low failure rates and calibrated abstention. These results show that verifiable AI agents can perform therapeutic analyses across biological contexts.

bioinformatics↗

bioRxiv2025

Neural circuits between nodose ganglion and pulmonary neuroendocrine cells regulate lung inflammatory responses

The lungs are organs exposed to the external environment, and the air we inhale contains various pathogens, such as endotoxins. The vagus nerve, which innervates the lungs, may play a role in detecting pathogens that invade the lungs. Through transcriptome analysis, tissue clearance imaging, electrical excitability recording, and gene- and cell-specific knockout experiments, we found that vagus nerve endings innervate pulmonary neuroendocrine cells (PNECs). These nerve endings sense bacterial endotoxins via pain receptors (TRPA1) rather than toll-like receptors (TLR4), eliciting electrical excitation and enhancing the production of neuropeptides (CGRP) in the nodose ganglia. In turn, CGRP released by sensory neurons from the nodose ganglia promotes both neuropeptide production and the proliferation of PNECs, thereby amplifying endotoxin-induced lung inflammatory responses. This reveals that the neural circuits between the nodose ganglion and PNECs play a critical role in regulating lung inflammatory responses.

neuroscience↗

bioRxiv2023

Three-dimensional chromatin landscapes in MLLr AML

Rearrangements of the mixed lineage leukemia (MLLr) gene are frequently associated with aggressive acute myeloid leukemia (AML). However, the treatment options are limited due to the genomic complexity and dynamics of 3D structure, which regulate oncogene transcription and leukemia development. Here, we carried out an integrative analysis of 3D genome structure, chromatin accessibility, and gene expression in gene-edited MLL-AF9 AML samples. Our data revealed profound MLLr-specific alterations of chromatin accessibility, A/B compartments, topologically associating domains (TAD), and chromatin loops in AML. The local 3D configuration of the AML genome was rewired specifically at loci associated with AML-specific gene expression. Together, our results show that the 3D chromatin undergoes extensive reorganization at multiple architectural levels, which underpins the remodeling of the transcriptome in MLLr AML.

genomics↗

bioRxiv2020

ATF6 aggravates angiogenesis-osteogenesis coupling during ankylosing spondylitis by mediating FGF2 expression in chondrocytes

Although angiogenesis-osteogenesis coupling is important in ankylosing spondylitis (AS), therapeutic agents targeting the vasculature remain elusive. Here, we identified activating transcription factor 6 (ATF6) as an important regulator of angiogenesis in AS patients. Firstly, we found that ATF6 and fibroblast growth factor 2 (FGF2) levels were higher in SKG mice and AS patient cartilage. The pro-angiogenic ability of human chondrocytes was enhanced through activated ATF6-FGF2 axis following long-term stimulation with inflammatory factors, e.g. TNF-, IFN-{gamma} or IL-17. Mechanistically, ATF6 interacted with the FGF2 promotor and promoted its transcription. Treatment with the ATF6 inhibitor Ceapin-A7 inhibited angiogenesis in vitro and angiogenesis-osteogenesis coupling in vivo. ATF6 may aggravate angiogenesis-osteogenesis coupling during AS by mediating FGF2 transcription in chondrocytes, implying that ATF6 represents a promising therapeutic target for AS.

molecular biology↗