Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Systemic control and inter-organ communication of ovarian aging

Whether ovarian aging is intrinsic and unidirectional or dynamically regulated by systemic context remains an open question for fertility and women's healthspan. To address it, we developed a mouse ovarian transcriptomic aging clock (OvaAge) and applied it, together with published transcriptomic, epigenetic and pathway-resolved DNA methylation (PathAge) clocks, multi-omic analyses and reproductive outcomes, to new reciprocal heterochronic ovary transplantation and parabiosis experiments. Ovarian molecular age shifted in both directions: aged ovaries became younger in young hosts and young ovaries became older in aged hosts, and in each case the shift was partial across transcriptomic, epigenetic and pathway-resolved measures. Ovaries from 13-month-old C57BL/6J donors supported full-term pregnancies and live births at 17-18 months of age when transplanted into young hosts, but not when transplanted into old hosts, and young ovaries supported live births in 17-18-month-old recipients. Donor ovary age altered the transcriptomic age of the uterus in both directions, while young ovaries also lowered the transcriptomic age of aged liver and brain, identifying the ovary as an active node within an inter-organ aging network. Across unilateral and bilateral transplantation and parabiosis, an older environment increased inflammatory gene expression in the ovary, whereas a younger environment reduced it and increased mitochondrial and biosynthetic programs. Integration of methylation, expression and cell-type markers linked these changes to declining oocyte-associated programs and an expanding stromal compartment enriched for multinucleated giant cells (MNGCs). MNGC-rich regions formed a reversibly remodeled, focally aged niche with distinct spatial metabolic features, and a related macrophage expression signature was detected in human ovaries. Together, these findings identify ovarian aging as a modular, bidirectionally plastic process that is regulated by the systemic environment and point to tractable intervention points.

systems biology↗

bioRxiv2026

Diacylglycerol Kinases Balance DAG Signaling to Control B Cell Maturation, Germinal Center Homeostasis, and Humoral Immunity

Diacylglycerol (DAG) is a critical second messenger downstream of the B-cell receptor (BCR), but how DAG signaling is regulated during B-cell development and humoral responses remains incompletely understood. Here, complementary gain- and loss-of-function approaches revealed stage-specific requirements for diacylglycerol kinase (DGK)-regulated signaling in B cells. Expression of the gain-of-function DGK{zeta} mutant DGK{zeta}{Delta}NLS had limited effects on early BM B-cell development but progressively impaired transitional and mature B-cell populations. DGK{zeta}{Delta}NLS attenuated BCR-induced ERK1/2 and mTOR signaling, activation, and proliferation and, when expressed in AID-expressing B cells, reduced GC B-cell survival and T-dependent IgG responses without detectably impairing GC B-cell proliferation in vivo. Conversely, combined DGK/{zeta} deficiency resulted in marked spontaneous GC B-cell expansion associated with enhanced ERK1/2, Akt, and S6 signaling and increased glucose uptake. Despite this steady-state GC expansion, DGK/{zeta}-deficient mice developed spontaneous autoreactivity and mounted impaired antigen-specific antibody responses. Thus, B-cell developmental stages exhibit differential sensitivity to altered DGK activity, and opposing perturbations of DGK-regulated signaling produce reciprocal effects on GC B-cell accumulation while both compromise productive humoral immunity. These findings identify DGKs as critical regulators that tune lipid signaling for B-cell maturation, GC homeostasis, and effective antibody responses.

immunology↗

bioRxiv2026

Efficacy of Tumor Treating Fields (TTFields) in triple negative breast cancer cells and synergy with PARP inhibitor talazoparib

Breast cancer is the most diagnosed cancer worldwide. Triple-negative breast cancer (TNBC) is a subtype with a poor prognosis because of its aggressiveness and high likelihood of relapse, making its treatment particularly challenging. We explored the effect of Tumor Treating Fields (TTFields) as a novel treatment option for breast cancer, which can be used together with other therapies, such as PARP inhibitors. TTFields use alternating electric fields to inhibit cell growth and induce cellular damage. Our findings indicate that TTFields increase expression of the universal cell cycle CDK inhibitory protein p21(WAF1)/CDKN1A independently of TP53 mutational status. Moreover, when used together with the PARP inhibitor talazoparib, the combination further upregulates p21 protein. Both TTFields alone and together with talazoparib induce cell death as documented by PARP cleavage while reducing markers of cell growth and anti-apoptotic proteins, including p-STAT3, STAT3, and MCL-1. Additionally, TTFields together with talazoparib induce DNA damage, as indicated by increased {gamma}H2AX expression. The combination of talazoparib plus TTFields treatment decrease PDL-1 levels and given its utility in predicting response to PD-1/PD-L1 by mediating immune evasion, this raises the possibility of future combination for immune modulation in cancer therapy. Our preclinical results suggest TTFields alone or together with PARP inhibitors such as talazoparib as a promising therapeutic approach for further development for the treatment of breast cancer.

cancer biology↗

bioRxiv2026

DeSPA uncovers genes with cross-state spatial pattern remodeling and coordinated spatial programs across multi-slice spatial transcriptomics atlases

Spatial transcriptomics increasingly generates multi-slice atlases spanning developmental, regenerative, and disease-associated states. Identifying genes with spatial pattern remodeling across these states is essential for understanding tissue reorganization and the underlying gene programs, yet few existing methods systematically detect such changes while accounting for variation among replicate slices. Here we introduce DeSPA, a statistical framework that models gene-specific spatial variation within each slice using a linear mixed model, tests for cross-state remodeling by aggregating evidence across replicate slices, and organizes identified genes into interpretable spatial co-expression modules that reveal coordinated spatial programs. Simulations show that DeSPA achieves high power while controlling the false discovery rate under inter-slice heterogeneity. In mouse embryogenesis, it recovers programs linked to extracellular matrix organization, muscle formation, neural development, and erythrocyte development. In diabetic testis, it reveals disruption of a late-spermatid spatial program consistent with known spermatogenic dysfunction, while maintaining stable gene rankings under coordinate rotation. In axolotl brain, regeneration preserves a developmental-like biosynthetic backbone but reorganizes it into regeneration-specific modules. Across two mouse intracerebral hemorrhage models, DeSPA resolves a conserved spatial injury program alongside model-specific synaptic and tissue-response programs. Together, these findings establish DeSPA as a scalable and interpretable framework for detecting spatial pattern remodeling and resolving coordinated spatial programs across diverse biological states.

bioinformatics↗

bioRxiv2026

SORT disentangles multilevel gene programs across heterogeneous spatial transcriptomic atlases

Understanding how transcriptional programs are organized across biological contexts is essential for deciphering tissue development, disease progression, and species-specific adaptations. Spatial transcriptomic atlases spanning patients, developmental stages, and species provide an opportunity to resolve gene expression programs (GEPs) that are sample-specific, group-shared, or region-associated, yet strong sample-specific variation can obscure weaker shared programs. Here we introduce SORT, a matrix factorization framework that jointly recovers multilevel GEPs and their spatial activities, using orthogonality to reduce program redundancy and graph total variation to preserve spatial boundaries. In simulations with strong patient variation, SORT more accurately recovered patient-specific, subtype-shared, and region-associated programs than existing methods. Across 108 pancreatic ductal adenocarcinoma sections, SORT resolved tumor and stromal programs with distinct and overlapping activities and linked these activities to survival across four external cohorts. In a cross-species cerebellar atlas, SORT distinguished conserved layer-associated programs from species-specific activity variation. Across eight stages of mouse organogenesis, SORT revealed GEPs with anatomical redeployment and graded spatial activity, as well as stage-dependent local co-deployment of distinct programs.

bioinformatics↗

bioRxiv2026

Impacts of different SETD1A mutations and genetic backgrounds on human induced pluripotent stem cell-derived hippocampal neurons

Abstract Human induced pluripotent stem cells (hiPSCs) provide a translational platform to interrogate the impact of genetic risk factors for various disorders on human biology. It has been a common practice to engineer premature stop codons to model predicted loss-of-function mutations using isogenic hiPSCs, yet effects of mutation position for the same gene and genetic background have been rarely tested systematically and at multiple levels for comparison to those of patient-specific mutations. Ultra-rare, heterozygous, and predicted loss-of-function mutations in the SETD1A gene, which encodes a H3K4 methyltransferase, are highly penetrant genetic risk factors for schizophrenia. Here, we generated a large panel of isogenic heterozygous SETD1A mutant hiPSC lines, including premature stop-codon mutations in one early exon and one late exon in five genetic backgrounds and two patient-specific mutations, to gain insight into molecular, cellular, and functional changes in a disease-relevant and highly pure population of human hippocampal dentate granule cell-like neurons. Our multi-level analyses showed that different SETD1A mutations lead to divergent molecular and cellular phenotypes related to synapses, but convergent neuronal network deficits, while the genetic background influences gene expression with no substantial effects at cellular and network levels. Mechanistic analysis confirmed the early exon patient mutation as loss-of-function, but revealed that the predicted loss-of-function patient mutation in the late exon is likely gain-of-function. Consequently, while all mutations decreased global H3K4me3 levels, deficits were only partially rescued by inhibiting H3K4 demethylase LSD1 for early exon mutations, but not for late exon mutations. Together, these results suggest that highly penetrant mutations can lead to similar cellular and functional phenotypes across different genetic backgrounds, whereas different premature stop-codon mutations could have contrasting impacts due to distinct mechanisms. Our findings have practical implications for applying genome-editing in hiPSCs to model disorders, highlight the value of modeling patient-specific mutations and testing multiple mutations, and suggest the need for tailored mutation-dependent therapeutic strategies.

neuroscience↗

bioRxiv2026

Niche-dependent epistasis within the cancer genome shapes metastatic fitness

Epistasis, the genetic interaction between loci, directs non-additive genotype-to-phenotype outcomes. Within a multicellular organism, how the tissue niche (E) shapes gene-by-gene epistasis (GxG) and its phenotypic consequences remains poorly studied. Using tumor lung metastasis as an experimental model, we mapped GxGxE through combinatorial CRISPR perturbation coupled with depletion of defined host myeloid populations in mice. The metastatic niche first selects disseminated tumor cells on GxG epistatic fitness, then drives transcriptome convergence regardless of genotype. Removing anatomically distinct myeloid populations reshaped the metastatic epistasis landscape despite identical tumor genotypes. Gene-by-gene-by-environment epistasis therefore operates at the organismal level, with interaction magnitude and sign set by niche cells. Beyond metastasis, our data suggest that the tissue niche conditions how genetic interactions translate into divergent phenotypes.

genetics↗

bioRxiv2026

Defense-associated reverse transcriptases synthesize DNA in a telomerase-like manner

Defense-associated reverse transcriptases (DRTs) constitute an emerging class of bacterial defense systems that harness reverse-transcriptase activity to generate cDNA products and counter phage infection. A distinct subset of DRT10 systems evolutionarily related to telomerase generates tandem-repeat DNA to confer antiphage immunity. Here, we biochemically characterize the reverse transcription activity of DRT10 and determine cryo-EM structures of DRT10-ncRNA complex in three functional states, revealing an unanticipated 2:1 RT:ncRNA assembly mode. Two DRT10 protomers dimerize through a central pair of interlocked {beta}-hairpins and are further embraced by a single ncRNA. This architecture distinguishes DRT10 from previously characterized DRT complexes, which either lack an ncRNA component or exhibit an equivalent stoichiometry of RT:ncRNA. Strikingly, despite the nearly identical conformations of the two DRT10 protomers, cDNA product density was resolved in only one catalytic pocket, revealing different product occupancy within the dimer. Moreover, along with flanking linkers, the stem loops of the ncRNA as fixed points to supply steric barriers help define the template boundary. These findings reveal a distinctive ncRNA-mediated DRT10 dimeric assembly and establish a mechanistic framework for protein-primed reverse transcription constrained by the length of the RNA flanking linker.

biochemistry↗

bioRxiv2026

STAVelo: deciphering spatiotemporal cellular dynamics via spatial RNA velocity inference from spatial transcriptomics data

RNA velocity is a powerful tool for deciphering cellular transcriptional dynamics from transcriptomics datasets. However, existing methods have several limitations: some do not model cellular spatial location information, some rely on cell-agnostic constant transcriptional kinetic rates, and some have not been validated for generalization across a wide range of datasets. To this end, we present STAVelo, a graph attention encoder-decoder framework that models spatial information in spatial transcriptomics (ST) data and infers RNA velocity from neural representations of transcriptional kinetic parameters. In quantitative comparisons of multiple datasets from diverse species and platforms, STAVelo outperforms previous methods in capturing cellular migration and development dynamics. In human and mouse brain datasets from different platforms, STAVelo accurately inferred the inside-out migration pattern of neurons across cortical layers. Analysis of mouse kidney and chicken heart datasets captured organ-specific developmental gradients during organ development. Analysis of mouse embryo and placenta datasets with multiple time points uncovered spatiotemporal dynamics from existing states to newly established states. In short, STAVelo provides a spatial view of cellular state transitions and deeper insights into spatiotemporal cellular dynamics in complex tissue structures.

bioinformatics↗

bioRxiv2026

PPARγ brakes cytosolic DNA-induced macrophage inflammation by super-enhancer metabolic reprogramming

Cytoplasmic DNA sensing triggers intense innate immune responses that require tight regulation. PPAR{gamma} is a master metabolic regulator in macrophages, but its role in cytoplasmic DNA immunity remains unclear. Using poly(dA:dT) to mimic cytoplasmic DNA stimulation, we combined transcriptomics, epigenomics (PPAR{gamma}/H3K27ac ChIP-seq), and functional assays. Poly(dA:dT) induced a cytokine storm with MAPK activation and apoptosis/necrosis. PPAR{gamma} activation by rosiglitazone (RGZ) antagonized this response, reducing cell death and transcriptionally inhibiting MAPK genes (e.g., DUSP4/10). Mechanistically, PPAR{gamma} bound promoters/enhancers via PPRE motifs and distal intergenic regions, remodeling H3K27ac (reduced at inflammatory loci, increased at metabolic genes). RGZ induced de novo super-enhancers coupling PPAR metabolic programs (e.g., CPT2) with immune suppression by targeting TLR4, CEBPB, and PIM3. Thus, PPAR{gamma} acts as a brake in cytoplasmic DNA immunity, integrating metabolic reprogramming and inflammation suppression through a multi-level genomic/epigenetic program. This study highlights PPAR{gamma} as a key immune-metabolic integrator and therapeutic target for diseases driven by excessive cytoplasmic DNA sensing.

immunology↗

bioRxiv2026

ddkg.skill: A Compositional Agent Skill for Translating Biomedical and Bioinformatics Questions into Cypher for the Data Distillery Knowledge Graph

Biomedical knowledge graphs can connect information across genes, phenotypes, tissues, pathways, experiments, and clinical resources, but they are difficult to query correctly without detailed knowledge of the graph. Large language models can help write Cypher code for knowledge graphs, yet a query focused on a bioinformatics task that looks reasonable may still use the wrong identifier, relationship direction, source, intermediate node, or output unit if the user is not completely trained on the schema. We developed ddkg.skill, an Agent Skill that can be loaded into a compatible LLM session to support querying of the NIH Common Fund Data Ecosystem Data Distillery Knowledge Graph (DDKG), a biomedical property graph integrating more than 180 ontologies, over 40 genomics datasets, and dozens of additional cross-domain biomedical datasets. Rather than utilizing a simple markdown prompt, ddkg.skill contains a controller, a compositional library of DDKG-specific references and structured tables, primary documentation, validated query patterns, a routing table, and a Python script that checks the internal links among these materials. The skill build for the December 2025 DDKG release contains 38 bundled files and 239 routing relationships and is identified by checksum so users can state the exact build used to compose a query. The evaluation reported here was performed separately on an earlier build with 38 files and 227 routing entries. In that orthogonal nine-test evaluation against a live December 2025 DDKG instance, five of seven tests targeting sources absent from the worked examples produced correct executed results. The evaluation also exposed a failed query, a cross-source comparison that was not biologically well posed, and errors in the skills own reference material that informed later revisions. The skill guides an AI through entity resolution, graph inspection, query construction, and validation for biomedical and bioinformatics queries, while keeping the DDKG itself as the source of returned results. This design provides a portable and versioned method for giving general-purpose AI systems practical knowledge of a complex biomedical knowledge graph to empower complex bioinformatics data integration. Three executed biomedical use cases further demonstrate cross-species phenotype-to-expression querying, genomic overlap between 4DN chromatin-loop anchors and GTEx eQTLs, and integration of congenital heart phenotype associations with ClinVar and GTEx heart evidence.

bioinformatics↗

bioRxiv2026

Recomb-Mix Cloud: a cost-efficient and scalable cloud-friendly pipeline for local ancestry inference

Local ancestry inference (LAI) reveals population compositions of an individuals chromo-somes, which empowers population genetics and genetic association studies of diverse populations. The recent availability of large genomic datasets from biobanks offers unprecedented opportunities for LAI. However, such datasets are typically only accessible from cloud-based platforms, and the lack of efficient and cost-effective tooling makes LAI prohibitive for resource-constrained researchers. Here, we develop Recomb-Mix Cloud, a cloud-ready pipeline for LAI based on Recomb-Mix, a recently introduced efficient LAI method. Recomb-Mix focuses on individual variants while maintaining a lower computational burden than other LAI methods focusing on genomic windows. Recomb-Mix Cloud offers a cloud-friendly complete pipeline from panel sample selection, marker alignment, compact reference panel construction, Recomb-Mix threading, and generating LAI calls in common data formats. Recomb-Mix Cloud also offers a set of pre-computed compact population reference panels constructed based on data from the Human Genome Diversity Project and the 1000 Genomes Project. Our results showed Recomb-Mix is about one and two orders of magnitude cheaper and faster than FLARE and RFMix, two popular LAI methods, while offering largely consistent LAI calls on the All of Us Researcher Workbench. The estimated overall compute cost per All of Us sample for whole-genome LAI analysis using Recomb-Mix Cloud with a pre-computed reference panel is just $0.01, compared with $0.43 for FLARE and $4.58 for RFMix. Therefore, Recomb-Mix Cloud is well-positioned to democratize local ancestry-based genetics research on modern biobank-sized data. The Recomb-Mix Cloud pipeline and the compact reference panels are available at https://github.com/ucfcbb/Recomb-Mix.

bioinformatics↗

bioRxiv2026

Nutrient-sensitive histone propionylation regulates T cell fate and function

Tissue microenvironments differ widely in nutrient composition; yet how local nutrient conditions shape epigenetic programs that direct immune cell fate is poorly understood. We identify histone lysine propionylation (Kpr) as a chromatin mechanism linking nutrient availability to T cell fate decisions. Proteomic analysis reveals that Kpr is broadly reduced in exhausted T cells. H3K18pr is enriched at stemness-associated loci in effector T cells and depleted upon exhaustion. Isoleucine and propionate supply propionyl-CoA in T cells, and restricted nutrient availability within the tumor microenvironment limits histone Kpr, promoting T cell exhaustion. Disrupting isoleucine catabolism in T cells further suppresses Kpr, without altering histone acetylation, and impairs T cell function and antitumor immunity in a manner rescued by propionate. Dietary supplementation of isoleucine and propionate increases Kpr in tumor infiltrating lymphocytes, limits exhaustion, and improves tumor control. These findings establish a microenvironment-chromatin axis crucial for antitumor immunity and amenable to nutritional intervention.

immunology↗

bioRxiv2026

MOSAIC: Learning Graph Node Embeddings from Spectrally Isolated Dominant Subspaces of Accumulated Diffusion Operators

Graph embedding transforms complex networks into low-dimensional representations for analysis and downstream machine learning. Existing methods often rely on eigenvalue ordering, random-walk sampling, or neural optimization. We introduce MOSAIC, Multistep Operator Spectral Accumulation with Isolated Components, an unsupervised framework that constructs an accumulated diffusion operator from successive powers of a graph transition matrix. MOSAIC uses Krylov subspace analysis to identify dominant spectral components according to their convergence behavior. This approach emphasizes persistent and spectrally separated diffusion modes rather than selecting embedding coordinates solely by eigenvalue magnitude. We evaluated MOSAIC using a biomedical graph projection derived from the Data Distillery Knowledge Graph that contained 5,171 human genes and 10,147 phenotypes. Across embedding dimensions from 16 to 128, MOSAIC achieved node-type classification AUROCs from 0.9863 to 0.9926 and exceeded FastRP, Node2Vec, HashGNN, and GraphSAGE. For gene-phenotype link prediction, validation and test associations were withheld before embedding generation. Under progressive removal of all edges or specific edge classes, MOSAIC retained more node-label information than FastRP, including after extreme graph sparsification. In a focused DICER1 analysis, 106 of 137 documented phenotype associations received scores above 0.90, demonstrating concordance with known pleiotropic biology and prioritizing additional relationships for independent evaluation. These results support MOSAIC as a fully unsupervised framework for learning informative graph representations. Although demonstrated using a biomedical graph projection, MOSAIC is domain independent and can be applied to other complex networks

bioinformatics↗

bioRxiv2026

A FIT2-BLTP1 module sustains lipid droplet phosphatidic acid and restrains overgrowth

Lipid droplets (LDs) are assembled at discrete endoplasmic reticulum (ER) subdomains whose local phospholipid composition, rather than their protein content alone, determines whether a LD nucleates and how large it grows. Phosphatidic acid (PA) is the pivotal lipid of this environment, yet how PA reaches a forming LD has remained unknown. Here we show that the bridge-like lipid transfer protein BLTP1 is required to maintain PA in LD-associated membranes. BLTP1 depletion impaired LD formation and produced fewer but enlarged LDs across mammalian cell types, and lowered LD accumulation during adipogenic differentiation. At the ER, BLTP1 engaged the LD emergence factor FIT2 through its N-terminal transmembrane segment, an interaction that strengthened upon induction of LD formation and was strongly reduced by the disease-associated FIT2 G232R substitution. Co-depletion of FIT2 relieved the LD overgrowth caused by loss of BLTP1. Wild-type BLTP1, but neither a FIT2-binding-defective chimera nor a lipid-transfer-groove mutant, rescued LD formation in BLTP1-depleted cells. Lipidomics of purified LDs showed that BLTP1 loss most strongly lowered PA, most prominently the species PA 36:2, whereas PA and the major phospholipid classes of bulk ER membranes were unchanged. Collectively, we propose that local PA supply by BLTP1 coordinates LD assembly and growth.

cell biology↗

bioRxiv2026

Immune Reconstitution and Antigenic Exposure History Shape Virus-Specific Humoral Immunity in People with HIV

CD4+ T-cell recovery is widely used to assess immune reconstitution in people with HIV, but its relationship to virus-specific humoral immunity across pathogens remains unclear. We characterized neutralizing activity against SARS-CoV-2, HIV-1, and influenza viruses in ART-treated, virologically suppressed individuals spanning distinct CD4+ T-cell levels. BA.5 was the dominant SARS-CoV-2 neutralization target across CD4 strata, whereas D614G neutralization increased with greater immune reconstitution. This gradient was largely absent for the antigenically divergent NB.1.8.1 and XFG variants. HIV-1 neutralization was inversely associated with CD4+ T-cell count, while influenza showed strain-dependent patterns. Neutralization-to-protection bridging preserved a CD4-associated gradient for D614G but not for highly immune-evasive variants. These findings show that immune reconstitution does not translate uniformly into antiviral humoral function. Instead, antibody profiles reflect host immune status, prior antigenic exposure, and viral antigenic evolution, supporting pathogen-specific functional assessment alongside conventional immune monitoring.

immunology↗

bioRxiv2026

Single-point mutation alters odorant receptor sensitivity associated with host plant specialization in Spodoptera moths

Host specialization in herbivorous insects is often associated with divergence in chemosensory abilities. Here, we investigated the possible contribution of odorant receptors (ORs) in host plant restriction in the lily moth Spodoptera picta, a species specialized on Amaryllidaceae. Manual annotation of S. picta ORs in its genome revealed a repertoire similar in size and composition to those of its polyphagous sister species, S. littoralis and S. litura, suggesting that specialization did not involve major gene loss or expansion in the lily moth. To assess functional divergence beyond gene number, we applied a large scaled structure-based virtual screening approach to the entire OR repertoires of these three Spodoptera species, generating ligand-binding profiles for 120,591 volatile compounds. Among 69 1:1:1 OR orthologs, 24 exhibited divergent predicted binding spectra. We pinpointed OR29 that we also found to be highly expressed in both male and female antennae of S. picta through a RNAseq approach. Functional assays demonstrated that S. picta OR29 acquired heightened sensitivity to limonene enantiomers, volatiles emitted by host Amaryllidaceae inflorescences. Site-directed mutagenesis revealed that a single amino acid substitution within the predicted binding region underlies this shift in sensitivity. These results show that host specialization in S. picta has not been accompanied by significant OR repertoire remodeling, but rather by subtle molecular changes that fine-tune receptor sensitivity to host-derived volatiles.

molecular biology↗

bioRxiv2026

Deciphering Mechanistic Signatures in Drug-Drug Interactions with Dual Topology Graphs

Drug-drug interactions (DDIs) represent a critical challenge in drug development and clinical practice, as they can lead to severe adverse effects, including toxicity and reduced therapeutic efficacy. Deep learning methods have shown promise in large-scale, rapid DDI prediction; however, current approaches suffer from significant limitations in providing mechanistic insights into these interactions. Here, we propose DualTopoDDI, a dual-topology-enhanced interpretable deep learning model for DDI prediction. We applied DualTopoDDI to predict 9.2 billion potential interactions among approved drugs, achieving 97.99% high-confidence predictions. The model demonstrates molecular-level interpretability, identifying key substructures responsible for drug actions in terms of both atom-centric and bond-centric views. DualTopoDDI also excels in elucidating particular DDI toxicity mechanisms, which is validated by its successful explanation of the controversial cardiac toxicity in two COVID-19 drug combination regimens at the time. Evaluations across 11 benchmark datasets demonstrates that DualTopoDDI not only achieves state-of-the-art performance but also showcases robust generalizability and good interpretability. Overall, DualTopoDDI offers a powerful, interpretable tool for understanding and predicting drug-drug interactions, providing critical insights for drug safety and design.

bioinformatics↗