Biology subjects

Tu, R.

Publications and source records attributed to Tu, R..

13 recordsLinked to original sources

bioRxiv2026

Stable large-scale network architecture and structured individual variability in the awake mouse brain

Large-scale functional networks provide a scaffold for brain activity, but their population architecture and inter-individual variability remain poorly defined in the awake mouse brain. Here we introduce the Awake Mouse Connectome Project (AMCP), a population-scale multimodal MRI resource comprising 212 sex-balanced awake mice. Using this dataset, we generated high-precision in vivo tissue probability maps, derived a reproducible whole-brain functional network atlas and quantified structured variability in network connectivity across animals. The resulting atlas defined a stable large-scale architecture aligned with structural topology, while inter-individual functional connectivity variability followed organized connectional gradients rather than random dispersion. We further benchmarked the clustering-derived scaffold against conventional group independent component analysis (GICA) and release the GICA network atlas as an accompanying resource. AMCP further provides empirical scaling relationships linking sample size, scan duration and connectome precision. These analyses indicate that mean functional connectivity and functional connectivity variability have distinct sample-size requirements, providing a protocol-specific guidance for studies focused on group-average connectivity and inter-individual variability. Individualized network parcellations derived from the AMCP scaffold improved behavioural prediction relative to group-average features for selected behavioural domains, indicating functional relevance of subject-specific network architecture. Together, AMCP establishes an open dataset, tissue-template resource, clustering-derived and GICA functional atlases, and analysis toolbox for reproducible awake mouse connectomics and for translational studies linking circuit mechanisms to large-scale brain organization.

neuroscience↗

bioRxiv2026

A microbiota-driven neuroepithelial feed-forward circuit drives gastroesophageal junction tumorigenesis

The gastroesophageal junction (GEJ) is continuously exposed to refluxed bile acids and microbial perturbations, yet how these environmental cues are converted into persistent oncogenic signaling is unknown. Here we show that bile acid remodeling enriches the oral commensal Prevotella melaninogenica at the GEJ, where bacterial succinate activates both vagal sensory neurons and SUCNR1-expressing tuft cells. Activated tuft cells release 15-deoxy-12,14-prostaglandin J2 (15-d-PGJ2), which sensitizes sensory neurons and sustains nerve remodeling, while neuronal CGRP drives expansion and tuft-cell differentiation of neuro-responsive epithelial progenitors through RAMP1. This self-reinforcing microbiota-neuron-epithelium circuit promotes dysplastic progression, and its genetic or pharmacological disruption, including with the approved CGRP antagonist rimegepant, suppresses tumor development, revealing an interceptable mechanism linking environmental dysbiosis to cancer initiation through neural circuits.

cancer biology↗

bioRxiv2026

Sensory neurons encode long-term inflammatory memory that promotes gastric regeneration and tumorigenesis

Inflammatory memory has emerged as a fundamental principle by which prior injury shapes future tissue responses, yet whether sensory neurons participate in long-term tissue memory remains unknown. Here, we show that vagal sensory neurons acquire a durable, experience-dependent state following gastric injury or Helicobacter pylori infection, leading to enhanced regeneration, metaplasia, and tumor progression upon re-injury. This neuronal program is stable, functionally transferable, and sufficient to drive epithelial responses in vivo. Mechanistically, injury-activated ILC2s establish sensory neuronal memory through IL-13-dependent epigenetic remodeling, inducing SMYD4-mediated H3K4 trimethylation and promoting CGRP-dependent activation of gastric epithelial cells. Together, our findings support a model in which tissue memory is not restricted to epithelial or immune compartments but emerges through coordinated long-term adaptations across multiple cellular systems. Within this framework, sensory neurons provide a persistent substrate for recall responses, linking prior inflammatory experience to sustained epithelial plasticity and cancer susceptibility. HIGHLIGHTSO_LISensory neurons function as a durable compartment of tissue memory. C_LIO_LICGRP-RAMP1 signaling couples neuronal memory to gastric stem cells. C_LIO_LIILC2-derived IL-13 establishes sensory neuronal memory programs. C_LIO_LISMYD4-mediated H3K4me3 stabilizes long-term neuronal memory. C_LIO_LINeuronal memory promotes gastric regeneration and tumor susceptibility. C_LI

cancer biology↗

bioRxiv2025

Open source software for tube vocal tract modeling, resonance prediction, illustration, and 3D printing

We present accessible code-free tube vocal tract modeling software. The software implements a transfer function. Applications involve exploratory speech acoustics-based basic research and education in phonetic sciences. The program has been made publicly available and presents researchers and students in speech-centric sciences with easily accessible vocal tract modelling and vowel synthesis.

scientific communication and education↗

bioRxiv2025

Netrin-1 promotes pancreatic tumorigenesis and innervation through NEO1.

Nerves have been shown to regulate cancer progression. However, a clear demonstration of a role for axon guidance molecules in pancreatic tumorigenesis, innervation, and metastasis has been lacking. Using murine KrasG12D-mutant pancreatic organoids, we screened axon guidance molecules by qRT-PCR, identified Ntn1 upregulation, and then verified its in vivo upregulation during pancreatic tumorigenesis in humans and mice. NTN1 and its receptor NEO1 were upregulated in epithelial cells by the Kras mutation and {beta}-adrenergic signaling, in part, through the MAPK pathway. Ex-vivo culture of celiac ganglia showed that NTN1 promoted the axonogenesis of sympathetic neurons through the nerve NEO1 receptor. In the Pdx1-Cre;LSL-KrasG12D/+ model, Ntn1 knockout decreased sympathetic innervation and the development of pancreatic intraepithelial neoplasia. Treatment of pancreatic tumor organoids with recombinant NTN1 enhanced cell growth, epithelial-mesenchymal transition (EMT), and cancer stemness with the upregulation of ZEB1 and SOX9 through NEO1-mediated activation of focal adhesion kinase (FAK). In Pdx1-Cre;LSL-KrasG12D/+;LSL-Trp53R172H/+mice, Ntn1 knockout reduced innervation, FAK phosphorylation, and the features of EMT and stemness to extend mouse survival. In a liver metastasis model of PDAC (pancreatic ductal adenocarcinoma), treatment with a NTN1-neutralizing antibody or tumoral knockdown of Neo1 reduced ZEB1 and SOX9 and decreased tumor progression. In contrast, Ntn1 overexpression promoted innervation and the progression of PDAC liver metastasis. These data suggest that the NTN1/NEO1 axis is a key regulator of PDAC progression, directly influencing cancer cell stemness and EMT, while indirectly promoting tumor growth through nerves. Inhibiting the NTN1/NEO1 axis could represent a potential therapeutic approach for PDAC. Statement of SignificanceNTN1 promotes pancreatic tumorigenesis and metastasis directly and indirectly through nerves, highlighting the importance of tumor cell-nerve crosstalk in cancer. NTN1 blockade could represent a promising strategy for treating PDAC liver metastasis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/666009v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@ca611corg.highwire.dtl.DTLVardef@ad9264org.highwire.dtl.DTLVardef@1661c00org.highwire.dtl.DTLVardef@b86798_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Controllable Gap Junctions by Vitamin B12 and Light

Gap junctions, mediate rapid signal transduction between contiguous cells, which are indispensable for multicellular organisms to coordinate cellular activities across numerous physiological processes. However, precise control of gap junctions remains elusive. Herein, we present CarGAP, a single-component chemo-optogenetic tool that utilizes the C-terminal adenosylcobalamin (AdoB12) binding domain of a photoreceptor protein (i.e., CarHC) to achieve reversible control over both vertebrate and invertebrate gap junctions with spatiotemporal precision. The vertebrate CarGAP (i.e., Cx-CarGAP), created by genetically fusing connexins with CarHC in mammalian cells, can efficiently block the gap junction channels through AdoB12-induced protein oligomerization, and subsequently reinstate them via green light-induced protein disassembly. We further introduced the CarGAP system (i.e., Inx-CarGAP) to the Drosophila ovary, enabling reversible control over the heterotypic gap junctions formed by innexin2 (Inx2) and innexin4 (Inx4, also known as zero population growth, Zpg), thereby uncovering the roles of gap junctions in stem cell-niche interactions. This study illustrates CarGAP as a generalizable chemo-optogenetic tool for interrogating the functions of gap junctions in various biological contexts.

bioengineering↗

bioRxiv2025

A Case-Based Explainable Graph Neural Network Framework for Mechanistic Drug Repositioning

Drug repositioning offers a cost-effective alternative to traditional drug development by identifying new uses for existing drugs. Recent advances leverage Graph Neural Networks (GNN) to model complex biological data, showing promise in predicting novel drug-disease associations. However, these frameworks often lack explainability, a critical factor for validating predictions and understanding drug mechanisms. Here, we introduce Drug-Based Reasoning Explainer (DBR-X), an explainable GNN model that combines a link prediction module and a path-identification module to generate interpretable and faithful explanations. When benchmarked against other GNN link prediction frameworks, DBR-X achieves superior performance in identifying known drug-disease associations, demonstrating higher accuracy across all evaluation metrics. The quality of DBR-X biological explanations was assessed through multiple approaches: comparison with manually-curated drug mechanisms, evaluation of explanation faithfulness through deletion and insertion studies, and measurement of stability under graph perturbations. Together, our model not only advances the state-of-the-art in drug repositioning predictions but also provides multi-hop explanations that can accelerate the translation of computational predictions into clinical applications.

bioinformatics↗

bioRxiv2025

Tff2 marks gastric corpus progenitors that give rise to pyloric metaplasia/SPEM following injury

In BriefTu et al. show that Tff2+ corpus isthmus cells are TA progenitors, and they, not chief cells, are the primary source of SPEM following injury. Upon Kras mutation, these progenitors directly progress to dysplasia, bypassing metaplasia, highlighting them as a potential origin of gastric cancer. HighlightsO_LITff2+ corpus cells are TA progenitors that give rise to secretory cells. C_LIO_LITff2+ progenitors, not chief cells, are the primary source of SPEM after injury. C_LIO_LIKras-mutant Tff2+ progenitors progress directly to dysplasia, bypassing metaplasia. C_LIO_LIMulti-omics analysis reveals distinct trajectories for SPEM and gastric cancer. C_LI O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/647847v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@48a2d1org.highwire.dtl.DTLVardef@b96642org.highwire.dtl.DTLVardef@1e89a4dorg.highwire.dtl.DTLVardef@1ea6a86_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG Pyloric metaplasia, also known as spasmolytic polypeptide-expressing metaplasia (SPEM), arises in the corpus in response to oxyntic atrophy, but its origin and role in gastric cancer remain poorly understood. Using Tff2-CreERT knockin mice, we identified highly proliferative Tff2+ progenitors in the corpus isthmus that give rise to multiple secretory lineages, including chief cells. While lacking long-term self-renewal ability, Tff2+ corpus progenitors rapidly expand to form short-term SPEM following acute injury or loss of chief cells. Genetic ablation of Tff2+ progenitors abrogated SPEM formation, while genetic ablation of GIF+ chief cells enhanced SPEM formation from Tff2+ progenitors. In response to H. pylori infection, Tff2+ progenitors progressed first to metaplasia and then later to dysplasia. Interestingly, induction of KrasG12D mutations in Tff2+ progenitors facilitated direct progression to dysplasia in part through the acquisition of stem cell-like properties. In contrast, Kras-mutated SPEM and chief cells were not able to progress to dysplasia. Tff2 mRNA was downregulated in isthmus cells during progression to dysplasia. Single-cell RNA sequencing and spatial transcriptomics of human tissues revealed distinct differentiation trajectories for SPEM and gastric cancer. These findings challenge the conventional interpretation of the stepwise progression through metaplasia and instead identify Tff2+ progenitor cells as potential cells of origin for SPEM and possibly for gastric cancer.

cell biology↗

bioRxiv2024

A CXCR4 partial agonist improves immunotherapy by targeting polymorphonuclear myeloid-derived suppressor cells and cancer-driven granulopoiesis

Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) are pathologically activated neutrophils that potently impair immunotherapy responses. The chemokine receptor CXCR4, a central regulator of hematopoiesis, represents an attractive PMN-MDSC target1. Here, we fused a secreted CXCR4 partial agonist TFF2 to mouse serum albumin (MSA) and demonstrated that TFF2-MSA peptide synergized with anti-PD-1 to induce tumor regression or eradication, inhibited distant metastases, and prolonged survival in multiple gastric cancer (GC) models. Using histidine decarboxylase (Hdc)-GFP transgenic mice to track PMN-MDSC in vivo, we found TFF2-MSA selectively reduced the immunosuppressive Hdc-GFP+ CXCR4hi tumor PMN-MDSCs while preserving proinflammatory neutrophils, thereby boosting CD8+ T cell-mediated anti-tumor response together with anti-PD-1. Furthermore, TFF2-MSA systemically reduced PMN-MDSCs and bone marrow granulopoiesis. In contrast, CXCR4 antagonism plus anti-PD-1 failed to provide a similar therapeutic benefit. In GC patients, expanded PMN-MDSCs containing a prominent CXCR4+LOX-1+ subset are inversely correlated with the TFF2 level and CD8+ T cells in circulation. Collectively, our studies introduce a strategy of using CXCR4 partial agonism to restore anti-PD-1 sensitivity in GC by targeting PMN-MDSCs and granulopoiesis.

cancer biology↗

bioRxiv2024

Weighted Ensemble Approach for Knowledge Graph completion improves performance

This study introduces a weighted ensemble method called "WeightedKgBlend" for link prediction in knowledge graphs which combines the predictive capabilities of two types of Knowledge Graph completion methods: knowledge graph embedding and path based reasoning. By dynamically assigning weights based on individual model performance, WeightedKgBlend surpasses standalone methods, demonstrating superior predictive accuracy when tested to discover drug-disease candidates over a large-scale biomedical knowledge graph called Mechanistic Repositioning Network. This research highlights the efficacy of an integrated approach combining multiple methods in drug discovery, showcasing improved performance and the potential for transformative insights in the realm of biomedical knowledge discovery.

bioinformatics↗

bioRxiv2023

Drug Repositioning using Consilience of Knowledge Graph Completion Methods

While link prediction methods in knowledge graphs have been increasingly utilized to locate potential associations between compounds and diseases, they suffer from lack of sufficient evidence to explain why a drug and a disease may be indicated. This is especially true for knowledge graph embedding (KGE) based methods where a drug-disease indication is linked only by information gleaned from a vector representation. Complementary pathwalking algorithms can increase the confidence of drug repurposing candidates by traversing a knowledge graph. However, these methods heavily weigh the relatedness of drugs, through their targets, pharmacology or shared diseases. Furthermore, these methods can rely on arbitrarily extracted paths as evidence of a compound to disease indication and lack the ability to make predictions on rare diseases. In this paper, we evaluate seven link prediction methods on a vast biomedical knowledge graph for drug repurposing. We follow the principle of consilience, and combine the reasoning paths and predictions provided by path-based reasoning approaches with those of KGE methods to identify putative drug repurposing indications. Finally, we highlight the utility of our approach through a potential repurposing indication.

bioinformatics↗

bioRxiv2021

Design and application of a knowledge network for automatic prioritization of drug mechanisms

MotivationDrug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drugs mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. ResultsIn this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNets ability to use mechanistic insight to identify a drugs mechanistic target, with a mean reciprocal rank of .525 on a test set of known drug-target interactions. Finally, we walked through a repurposing example of the anti-cancer drug imantinib for use in the treatment of asthma, to demonstrate this methods utility in providing mechanistic insight into repurposing predictions it provides. Availability and implementationThe Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet Contactasu@scripps.edu Supplementary informationSupplemental information is available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Wikidata as a FAIR knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that epitomizes the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Here, we describe the breadth and depth of biomedical knowledge contained within Wikidata, assembled from primary knowledge repositories on genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases. We built a collection of open-source tools that simplify the addition and synchronization of Wikidata with source databases. We furthermore demonstrate several use cases of how the continuously updated, crowd-contributed knowledge in Wikidata can be mined. These use cases cover a diverse cross section of biomedical analyses, from crowdsourced curation of biomedical ontologies, to phenotype-based diagnosis of disease, to drug repurposing.

bioinformatics↗