Biology subjects

Zhang, D.

Publications and source records attributed to Zhang, D..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

A DNA-scaffolded Retron Ring Mediates Antiphage Immunity

Retrons are a diverse family of bacterial defence systems that couple reverse transcriptase (RT) activity to antiphage immunity by producing multicopy single-stranded DNA (msDNA). Yet, the molecular mechanisms by which most systems detect infection and execute immunity remain unknown. Here, we define the structure, immune sensing and effector mechanisms of retron type IX, composed of an RT, a non-coding RNA, a tandem KH-HTH subunit and a HEPN RNase effector. Retron IX confers robust protection against diverse bacteriophages. Cryo-EM and biochemical analyses reveal a supramolecular ring assembly composed of 8-10 repeating modules, spanning up to 230 [A] in diameter. Within this assembly, the msDNA scaffolds the outer rim inhibiting the HEPN ribonuclease in a catalytically poised state, with the active site occluded within the inner layer. We identify a phage-encoded PD-(D/E)XK nuclease as the immune trigger that cleaves the exposed msDNA stem-loop, leading to ring disassembly and HEPN effector release. Upon activation, the HEPN ribonucleases cleave tRNAs indiscriminately, inducing dormancy and blocking phage propagation. Our findings unveil how ring-shaped higher-order assembly controls enzymatic activities while allowing signal-specific activation and establish the ncRNA as an evolvable scaffold underlying the remarkable structural and defensive diversity of bacterial retron immune complexes.

microbiology↗

bioRxiv2026

Prior Context Scaffolds Sentential Semantic Integration during Noisy Speech Comprehension

Understanding speech in noise is a central challenge of everyday communication, yet listeners often succeed by using prior context. How the brain uses such context remains debated: it may refine predictions about upcoming words, or it may provide a higher-level framework that helps degraded speech cohere into meaning. Here we combined simultaneous EEG-fNIRS recording with hierarchical multivariate encoding models to track how prior context shapes speech processing from acoustics to words and sentential meaning. Participants listened to natural spoken narratives under clear speech, noisy speech, and context-supported noisy speech conditions. Context brought comprehension of noisy speech close to clear-speech levels. EEG revealed that contextual support reduced neural encoding of lexical surprisal and entropy, indicating weaker tracking of local word-level prediction demands. In contrast, when context was available, fNIRS showed enhanced encoding of sentence-level semantic integration across frontal regions and the right angular gyrus, and stronger angular gyrus encoding predicted better comprehension. By combining EEG and fNIRS to capture complementary electrophysiological and hemodynamic signals, this multimodal approach reveals a hierarchical shift in degraded speech comprehension: prior context does not simply improve word-by-word prediction, but scaffolds the integration of noisy input into coherent discourse.

neuroscience↗

bioRxiv2026

Spatial Glyco-Codes Define Human Liver Pathology and Progression

Glycosylation is a fundamental process regulating cellular function, tissue organization, and disease progression. However, comprehensive glycan profiling at single-cell spatial resolution remains largely inaccessible, particularly in clinical archival tissues. Here we develop spatial-GPT, a multimodal platform for simultaneous profiling of glycans, proteins, and/or transcripts in archival formalin-fixed paraffin-embedded (FFPE) tissues. Using a panel of 30 DNA-encoded lectins recognizing major mammalian glycan motifs and structural classes, sequencing-based spatial-GPT (DBiT-GPT) mapped the spatial glycome, proteome, and transcriptome across 16 human liver specimens encompassing steatosis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), leading to identification of spatial glyco-codes - combinatorial glycan states associated with distinct cellular identities, tissue features, and pathological processes. Unexpectedly, glyco-codes alone were sufficient to resolve major cell types, disease states, and HCC subtypes, revealing a previously unappreciated level of biological information encoded within the tissue glycome. Spatial glycomics uncovered tumor-like glyco-codes in premalignant regions, suggesting that glycan reprogramming may precede overt malignant transformation. Using imaging-based single-cell spatial glycan-protein profiling (CODEX-GP), we track glyco-codes across the whole-tissue architecture of 3 representative HCC samples. We further examined the glyco-codes across more than 300 patient specimens and quantified cell-type- and disease-specific glyco-codes as well as glycan-defined immune-evasion, T-cell-exhaustion, and steato-fibrotic niches. Together, these findings establish spatial glyco-codes as a previously unrecognized layer of tissue organization that encodes cellular identity, tissue function, and disease progression. The ability of glyco-codes to distinguish major liver pathologies across independent patient cohorts further highlights their potential as a new class of molecular histopathology biomarkers.

pathology↗

bioRxiv2026

Placental invasion mismatch underlines pregnancy disorders and cancers

Mammalian placentas vary dramatically in invasiveness, parallel aggressive cancers, and are dysregulated in pregnancy disorders, yet whether they share regulatory architecture remains unclear. We investigated single-cell transcriptomes of the maternal-fetal interface across nine mammals spanning all major placental morphotypes and integrated it with thirteen cancers and five pregnancy complications. A conserved cellular framework is deployed through three discrete regulatory programs: a cancer-like program in hemochorials, endothelial-cooperation program in endotheliochorials, and collagen-rich invasion-suppressing program in epitheliochorials. Aggressive cancers selectively converge on hemochorial program, and we functionally validated share invasion regulators including the VGLL3-TEAD1 interaction and APOE. Pregnancy disorders are partially, mismatched deployments of these programs; placental APOE knockdown in mice phenocopies preeclampsia with concurrent collapse of both M1/M2 macrophage programs. These findings unify placental diversity, cancer convergence, and obstetric disorders under a regulatory-mismatch principle, whereby evolved placental invasion programs becomes pathological when deployed outside their evolutionary context.

evolutionary biology↗

bioRxiv2026

Deoxyribonucleotide incorporation reshapes mRNA design beyond canonical ribonucleotide boundaries

Messenger RNA (mRNA) is canonically composed of ribonucleotides, with sporadic incorporation of deoxyribonucleotides into natural RNA transcripts being traditionally regarded as a rare, deleterious error arising from transcriptional infidelity. Here, we challenge this paradigm by demonstrating controlled partial substitution of ribonucleotides with deoxyribonucleotides during in vitro transcription (IVT) generates intact, stable and fully translationally competent IVT-mRNA. Unexpectedly, chimeric DNA-RNA backbone modification exhibits markedly enhanced IVT-mRNA translation several fold across multiple cell types and in vivo via diverse dosing routes relative to their ribonucleotide-based counterparts. 25% substitution of cytidine triphosphate with deoxycytidine triphosphate achieved best-performing translational output, surpassing the current gold-standard N1-methylpseudouridine (m1{Psi})-modified IVT-mRNA in a B16-OVA tumor vaccination model. These findings identify nucleotide class composition as a previously unrecognized parameter governing IVT-mRNA function and establish hybrid ribonucleotide-deoxyribonucleotide backbone engineering as a versatile strategy to expand the chemical space for next-generation mRNA therapeutics.

synthetic biology↗

bioRxiv2026

A premitotic polarity program patterns the grass leaf epidermis

Transverse asymmetric cell divisions (ACDs) in grass leaf epidermal development produce large basal pavement cells and small apical specialised cells. These "patterning divisions" generate the long-short epidermal cell pattern that is distinctive of grasses. Here, we show that patterning divisions require premitotic basal polarisation of Bd-POLAR-LIKE1 (BdPL1) in the model grass Brachypodium distachyon. Loss of BdPL1 disrupted division-plane orientation and postmitotic cell-size asymmetry in all cell files, which resulted in epidermal patterning defects. Ectopic expression analyses demonstrated that BdPL1 polarisation was independent of cellular context and sufficient to promote supernumerary transverse divisions. Furthermore, the developmental regulators BdBREVIS RADIX-solo and BdYODA1 formed a post-division polarity domain enforcing cell fate asymmetry independently of BdPL1. We propose that the premitotic BdPL1 module enforces physical cell-division asymmetry contributing to medio-lateral patterning of cell types, whereas the postmitotic BdBRX-solo/ BdYDA1 module enforces within-file cell fate asymmetry. Together, they robustly pattern the grass leaf epidermis.

plant biology↗

bioRxiv2026

Clinically derived micro- and nanoplastics uptake drives spatiotemporally confined metabolic stress revealed by bond-selective imaging

Although microplastics and nanoplastics (MP/NP) are pervasive environmental contaminants, our understanding of cellular toxicity remains incomplete, as adverse effects are often attributed to long-term intracellular accumulation, while the spatiotemporal onset of cellular damage remains poorly defined. Here, we employ chemical-bond-selective stimulated Raman scattering (SRS) microscopy and cell models that decouple continuous exposure from intracellular retention to directly visualize clinically derived MP/NP-cell interactions. Cellular stress occurs primarily during MP/NP exposure, accompanied by alterations in lipid droplet (LD) composition. In contrast, following extracellular removal, intracellularly retained MP/NP become largely inert, with recovery of lipid metabolism and cellular functions. Lipidomics identifies arachidonic acid (AA) as a key dysregulated metabolite, and SRS imaging further reveals transient, spatially confined AA enrichment in MP/NP-proximal LDs during uptake. Importantly, phospholipid coating of MP/NP attenuates LD alterations and cytotoxicity while preserving particle internalization, establishing uptake-driven metabolic stress, rather than long-term intracellular retention, as primary source of MP/NP-induced damage.

biophysics↗

bioRxiv2026

Decoding Spatial Programs in Human Glioblastoma Through Surprisal Information-Theoretical Analysis

Glioblastoma (GBM) is a highly heterogeneous and invasive brain tumor in which complex interactions among tumor cells, immune cells, and neurons shape disease progression and therapeutic resistance. Resolving spatial patterns programs in glioblastoma (GBM) requires analytical approaches that go beyond variance-driven embeddings. Here, we applied thermodynamic surprisal analysis, an information-theoretic decomposition framework, to spatial transcriptomic sequencing from human GBM specimens to identify orthogonal constraint modes that capture dominant and previously hidden spatial programs. Surprisal analysis revealed structured patterns in the data that are not highlighted by standard approaches such as PCA or cell type deconvolution, including immune-activation-associated signatures as well as spatial programs consistent with synapse remodeling. Coupling surprisal decomposition with spatial ligand-receptor interaction analysis, NICHES, along with multiplexed protein imaging connected these spatial hidden modes to reveal potential communication networks. Together, these results position surprisal analysis as a powerful, complementary strategy for interrogating spatial tumor architecture, enabling discovery of non-obvious spatial programs and interactions that are obscured by variance-based methods.

systems biology↗

bioRxiv2026

Decoding and Targeting Coordinated CDKN1A and CDKN2A Senescence Programs in ECM-Dominant Cardiovascular Pathologies

Cellular senescence is a hallmark of aging and an emerging therapeutic target; however, its role as a context-specific driver of disease remains incompletely defined, and senolytic therapies have shown inconsistent clinical benefit. Here, we identify extracellular matrix (ECM)-dominant pathologies as a major class of senescence-driven disease, characterized by inflammation, matrix degeneration, and progressive tissue dysfunction. Using integrated single-cell transcriptomics, spatial profiling, and multiplex imaging across human specimens and murine models, we demonstrate that senescent fibroblasts, rather than canonical myofibroblasts, constitute the principal disease-driving cell state in myxomatous mitral valve disease (MMVD) and related conditions. These cells exhibit coordinated CDKN1A inflammatory and CDKN2A ECM-remodeling programs that form a feed-forward circuit linking immune activation to matrix disorganization and functional decline. Senescence extends beyond fibroblasts to endothelial and immune compartments, establishing a multicellular senescent milieu that reinforces intercellular crosstalk and disease progression. Senolytic treatment (dasatinib plus quercetin or fisetin) restores ECM architecture and improves cardiac function, outperforming pathway-specific anti-inflammatory and antifibrotic approaches. Cross-disease analyses further reveal conservation of this coordinated CDKN1A/CDKN2A senescence programs across multiple ECM-dominant cardiovascular diseases, including aortic aneurysm and calcific valve disease. Notably, in vivo single-cell transcriptomic profiling following multiple senolytic treatments provides whole-transcriptome resolution of context-dependent cellular responses. Collectively, these findings establish context-specific senescence as a central organizing mechanism in ECM-dominant diseases and support a shift from generalized anti-aging strategies toward precision senolytic prevention or therapy. Given that valvular and aortic diseases affect millions and increase markedly with age to a prevalence comparable to major cancers, these results indicate a potential solution to a substantial and underrecognized clinical burden.

Cell Biology↗

bioRxiv2026

ScriptManager: a platform for scalable and reproducible high-resolution analysis of genomics datasets

BackgroundThe growing diversity of genomic and epigenomic assays has driven a parallel expansion in data formats, analysis workflows, and figure-generation tools. However, tools for analyzing data and assembling publication-quality figures are often specialized to a specific assay, dramatically limiting their interoperability and reproducibility. ResultsWe present the v1.0 release of ScriptManager, a Java-based framework for modular and reproducible analysis and visualization workflows of genomics and epigenomics data. Unlike existing tools specialized for individual assay types, ScriptManager provides a unified and extensible framework for cross-assay visualization and workflow reproducibility. The v1.0 release adds novel analytical modules, GUI session logging, automated unit and integration testing, tutorials, and expanded documentation. It also integrates with the broader reproducibility ecosystem through Singularity containers, Anaconda packaging, and Galaxy XML wrappers. We demonstrate ScriptManagers TagPileup scaling from local single-core execution to a 10,305-job analysis distributed across the Open Science Grid (OSG), with the full workload completing in <2 hours of wall-clock time. ConclusionsScriptManager v1.0 enhances workflow portability, transparency, and reproducibility across a diverse range of high-resolution genomic assays. By coupling a flexible module design with modern reproducibility standards, ScriptManager provides a bridge between exploratory data analysis and formal, publication-ready figure generation. These improvements enable researchers to build, share, and reproduce genomic analyses across diverse computational infrastructures with minimal configuration.

bioinformatics↗

bioRxiv2026

SpatialDataAgent: Autonomous Spatial Omics Data Curation at Decade Scale

Fragmented metadata in spatial omics archives has rendered large volumes of multimodal molecular-histological data inaccessible as dark data. Here, we introduce SpatialDataAgent, an agentic workflow for autonomous spatial omics data curation, combining schema-constrained evidence evaluation with a self-refining standardization agent. Applied to a decade of GEO records, SpatialDataAgent identified 769 paired H&E-spatial transcriptomics (ST) datasets, representing a 6.4-fold scale expansion over existing manually curated baselines. Within the benchmarking window, the framework achieved a 141% increase in high-confidence Class A paired datasets. We further assembled a recent high-confidence subset into HESRT, a standardized datalake containing 29.2 million spots/cells, establishing a blueprint for evidence-grounded autonomous curation of multimodal biomedical archives.

bioinformatics↗

bioRxiv2026

A graph-based pangenome reveals the genetic basis of climate-resilient and horticultural traits in pear

Climate change poses an increasing threat to the cultivation of deciduous fruit trees, placing greater demands on modern pear breeding. Using pear germplasm adapted to diverse environments, we assembled 11 chromosome-level genomes. In combination with 13 publicly accessible pear genomes, we analyzed presence-absence variations (PAVs) and constructed a graph-based pangenome for pear. By performing a PAV-eQTL analysis of the fruit of 123 pear accessions, we identified PAVs significantly associated with expression levels of genes that may be involved in regulating agronomic traits. Population analysis of 268 pear accessions revealed two stop-gained variants in DAM1 of independent origin, which may function in advancing the blooming date and reducing the chilling requirement. We detected complex PAVs at the NOR1 locus, including two copy-number variations and one deletion. These PAVs contributed to the rapid diversification of the NOR1 locus and the fruit development period through regulating ARF5 and other ripening-related genes. We revealed the selection history of the NOR1 locus and developed novel pear individuals that accumulated alleles for low chilling requirement, early blooming date, and short fruit development period. The results provide valuable resources for pear genomics research and offer a guideline for breeding modern pears with climate resilience.

plant biology↗

bioRxiv2026

A suture-specific oocyst wall protein COWP4 is essential for excystation and infectivity, while COWP6 links wall architecture to host interaction in Cryptosporidium parvum

The oocyst of Cryptosporidium is a highly resilient transmission stage that protects sporozoites in the environment and mediates infection through a specialized opening known as the suture. While recent studies have identified Cryptosporidium oocyst wall proteins (COWPs) as structural components of the oocyst wall, the functional roles of individual COWPs and the molecular basis of suture biology remain poorly understood. Here, we performed a detailed characterization of two COWP family members, COWP4 and COWP6, combining immunolocalization, ultrastructural analysis, protein interaction assays, and genetic manipulation. We show that COWP4 is strictly localized to the oocyst suture, whereas COWP6 is distributed throughout the oocyst wall inner layer and enriched at the suture. COWP6 is additionally present in sporozoites and is secreted during parasite motility and host cell invasion, where it exhibits high-affinity binding to host cells. Structural analyses indicate that both proteins are cysteine-rich and likely form disulfide-stabilized architectures consistent with roles in wall assembly. Functional analyses reveal that COWP4 is essential for proper suture formation, excystation, and parasite infectivity, establishing the suture as a genetically defined gatekeeper for parasite transmission. In contrast, COWP6 functions as a multifunctional protein linking oocyst wall architecture to host interaction. We further demonstrate that COWP4 and COWP6 interact, suggesting coordinated assembly within the oocyst wall. Together, these findings provide a functional dissection of the Cryptosporidium oocyst suture and reveal distinct, non-redundant roles for individual COWPs. This work advances our understanding of oocyst wall biology and identifies COWP4 as an essential determinant of parasite transmission that may be targeted to disrupt the infectious cycle. Author summaryCryptosporidium is a major cause of diarrheal disease worldwide and is transmitted through environmentally resistant oocysts that protect infectious stages of the parasite. A defining feature of the oocyst is a specialized seam-like structure, called the suture, through which parasites exit to initiate infection. Although oocyst wall proteins (COWPs) have been identified, how individual proteins contribute to the structure and function of the oocyst wall, particularly the suture, has remained unclear. In this study, we investigated two oocyst wall proteins, COWP4 and COWP6, and uncovered distinct roles for each. We found that COWP4 is specifically localized to the suture and is essential for its proper formation, enabling parasites to exit the oocyst and establish infection. In contrast, COWP6 is distributed more broadly in the oocyst wall and also functions beyond it, being secreted during parasite movement and interacting with host cells. These findings reveal that the oocyst wall is composed of specialized proteins with non-redundant functions and identify the suture as a critical control point for parasite transmission. Understanding these mechanisms may help guide the development of new strategies to block infection by disrupting oocyst integrity or parasite release. HighlightsO_LICOWP4 is a suture-specific protein essential for oocyst excystation and infectivity C_LIO_LIGenetic disruption of COWP4 produces non-infectious oocysts with impaired viability C_LIO_LIThe oocyst suture is a genetically defined gatekeeper for parasite transmission C_LIO_LICOWP6 localizes to the wall inner layer and is enriched at the suture C_LIO_LICOWP6 is secreted by sporozoites and binds host cells with nanomolar affinity C_LIO_LI{middle dot} COWP4 and COWP6 directly interact, linking wall architecture to host interaction C_LI

microbiology↗

bioRxiv2026

Modelling Anaerobic Co-Digestion with Agricultural Feedstock: Model Validation and Cross-Reactor Verification

As the United Kingdom (UK) targets net-zero emissions by 2050, anaerobic digestion (AD) has become a cornerstone of renewable energy infrastructure. However, mathematical models, such as the Anaerobic Digestion Model No. 1 (ADM1), often struggle with high-solids agricultural feedstocks because they rely on Chemical Oxygen Demand (COD), a metric that introduces significant experimental error. To overcome this, this study applies an established mass-based ADM1 framework tailored for the co-digestion of maize silage and cow manure sourced from a UK AD site. This study uses a parallel reactor framework, using two identical laboratory-scale reactors to physically replicate the dynamic conditions of the full-scale site. A Global Sensitivity Analysis was first conducted, identifying biomass decay and carbohydrate breakdown rates as the most influential factors affecting system stability and model accuracy. The model was calibrated using data from the first reactor and then tested against an independent second reactor subjected to significant organic loading stress. Results show high predictive capabilities, with the model achieving a R2 of 0.81 for biogas production during calibration. The model maintained high predictive accuracy during the validation test of the second physical twin, achieving an R2 of 0.85, proving that the framework is robust and not overfitted to a single dataset. While predicting rapid fluctuations in pH and alkalinity remains challenging, the mass-based approach effectively forecasts gas yields and process stability. This methodology provides a reliable foundation for robust process modelling, offering a scalable tool for the UK biogas sector to optimise AD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/721061v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@92c7e2org.highwire.dtl.DTLVardef@80d723org.highwire.dtl.DTLVardef@ac3d24org.highwire.dtl.DTLVardef@1e21a51_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

Gut microbiota-derived indole-3-propionic acid preserves dorsal hippocampal catecholamines to prevent post-stroke cognitive impairment

BackgroundGut dysbiosis has been increasingly implicated in post-stroke cognitive impairment (PSCI), yet the causal contribution and therapeutic potential of gut microbiota-derived metabolites remain unclear. This study aimed to identify key microbiota-derived metabolites involved in PSCI and to elucidate their underlying mechanisms. ResultsWe found that both PSCI patients and middle cerebral artery occlusion (MCAO) mice exhibited distinct gut microbial alterations, characterized by a marked reduction in tryptophan-metabolizing bacteria and indole-3-propionic acid (IPA), a gut microbiota-derived tryptophan metabolite. Exogenous IPA administration alleviated PSCI-like phenotypes in MCAO mice. Mechanistically, IPA preserved tyrosine hydroxylase-positive (Th) fibers and catecholamine levels in the dorsal hippocampus. Further analyses showed that IPA binds to the adaptor protein Ywhab, promotes ERK activation, and enhances neuronal survival, thereby counteracting neuronal apoptosis-associated inflammation and subsequent Th fiber degeneration. ConclusionThese findings identify IPA as a gut microbiota-derived neuromodulator that mitigates PSCI by preserving dorsal hippocampal catecholaminergic transmission. IPA may therefore serve as a promising predictive biomarker and therapeutic candidate for PSCI.

neuroscience↗

bioRxiv2026

Protein structure-informed deep learning enables species-specific codon optimization

Codon usage bias is highly species-specific, posing a major challenge for heterologous protein expression. Existing deep learning approaches to codon optimization rely primarily on DNA or protein sequence information and largely neglect constraints imposed by protein structure and folding. Here, we present Protein structure-Informed Species-specific Codon Optimization (PISCO), a Geometric Vector Perceptron (GVP)-based model that integrates protein sequence, three-dimensional protein structure, and host codon usage statistics to generate optimal, host-specific codon sequences. Compared with protein-structure-agnostic models, PISCO improves codon recovery by 6% and substantially increases similarity to natural coding sequences, reducing divergence by at least 42% in Codon Similarity Index (CSI), 50% in Codon Frequency Distribution (CFD), and 14% in Dynamic Time Warping (DTW) metrics. Ablation analyses demonstrate that incorporating protein folding kinetics and host-specific information is critical to these gains. Moreover, by leveraging host codon usage statistics, PISCO generalizes to optimize codon sequences for species absent from the training data. An autoregressive variant of PISCO further enhances concordance with natural codon usage patterns, at the cost of a modest reduction in codon recovery rate. Wet-lab validation confirms that PISCO-optimized sequences significantly enhance protein solubility and functional expression. Together, these results establish protein structure as a key determinant of species-specific codon optimization and provide a transferable framework for structure-aware gene design.

molecular biology↗

bioRxiv2026

A Unified Agent-Enabled Platform for Drug Repurposing across Molecular, Phenotypic, and Clinical Scales

Drug repurposing offers an effective path to new therapies, yet existing computational approaches rely on a single line of evidence and are rarely validated across biological scales. We present LinkD, an integrated framework that unifies diffusion-based affinity prediction, proteome-wide selectivity scoring, phenotypic validation, and population-scale clinical evidence. LinkD-Bind predicts binding across 14,981 drugs and 20,385 human targets, ranking first in 8 of 9 BindingDB, Davis, and KIBA evaluations, with the largest gains under cold-start conditions. LinkD-Select recovers 95.3% of known drug-target pairs by combining selectivity scoring and molecular docking. LinkD-Pheno integrates drug-sensitivity and CRISPR dependency data across 960 cancer cell lines, identifying 34 novel drug-gene pairs and recovering [~]85% of known targets among the top 50 candidates. Across 11.5 million individuals from Mount Sinai and UK Biobank, LinkD-prioritized {beta}-blockers propranolol (HR 0.82) and carvedilol (HR 0.92) reduced 5-year prostate cancer incidence relative to metoprolol, corroborated by ADRB2 docking and LNCaP growth inhibition. LinkD-Agent, which can effectively orchestrate all evidence layers, is served on a publicly available web platform (https://linkd-agent.onrender.com/), enabling a wide range of users to derive new drug repurposing opportunities through natural language queries.

bioinformatics↗

bioRxiv2026

Fatty acid scavenging enables cancer escape from KRAS inhibition

Although inhibitors of oncogenic KRAS have shown clinical efficacy1, resistance to KRAS inhibition is common2, and its molecular basis remains unclear. Here we show that KRASi-resistant cancer cells sustain mitochondrial bioenergetics through enhanced fatty acid (FA) metabolism, despite suppression of canonical KRAS signaling. Specifically, KRASi-resistant pancreatic cancer cells exploit macropinocytosis to scavenge FA released from adipose tissue, fueling beta-oxidation independently of KRAS-PI3K signaling. This adaptive metabolic program is driven by the adhesion G protein-coupled receptor ADGRB1, which activates non-canonical PI3K{gamma}-PAK1 signaling to stimulate macropinocytosis and maintain metabolic homeostasis under KRASi. Disruption of ADGRB1-PI3K{gamma} signaling dismantles this metabolic program and restores KRASi sensitivity. This pathway operates across multiple KRAS-mutated cancers and is associated with poor therapeutic response and outcome. These findings offer a promising strategy for overcoming KRASi resistance.

cancer biology↗