Biology subjects

Yan, C.

Publications and source records attributed to Yan, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Unstable word form representations and greater reliance on right hemisphere during word learning in adults with a history of developmental language disorder

Impairment in acquiring new vocabulary is a persistent deficit in developmental language disorder (DLD), but its cortical substrates remain unclear. We investigated learning of new words in young adults with DLD and typically developing (TD) control participants using magnetoencephalography (MEG). Participants were exposed to spoken real words and two sets of pseudowords, with shared beginnings, in two MEG sessions. One set of pseudowords was actively trained for three days, paired with fantasy animal pictures. Both groups learned new pseudowords, but adults with DLD recalled fewer items and confused similar sounding pseudowords more often than TD adults. MEG recordings revealed robust left temporal learning effects in both groups, along with left inferior frontal involvement. The DLD group additionally showed stronger right-hemispheric involvement and a significant right temporal learning effect for the newly learned words. Furthermore, only the DLD group showed changes in the left and right temporal responses for the real words after the learning phase, indicating altered lexical access for familiar words after integration of new word forms with overlapping phonology into the mental lexicon. Together, the findings suggest persisting problems in creating and accessing stable and distinctive phonological memory representations for word forms in adults with DLD, along with altered hemispheric balance in processing.

neuroscience↗

bioRxiv2026

Hybrid Semantic-Lexical Retrieval for Source-to-CDE Mapping in the NCI caDSR

Accurate mapping of local source data elements to standardized common data elements (CDEs) is critical for biomedical data integration but is challenged by short, abbreviated, and heterogeneous source descriptions. We constructed a benchmark of approximately 69,000 expert-linked source-to-CDE mappings from the National Cancer Institute\textquotesingle s caDSR and evaluated multiple bi-encoder models with two-stage fine-tuning. Here we developed a hybrid retrieval system combining semantic bi-encoder retrieval, CDE Match-inspired keyword retrieval, cross-encoder scoring, and supervised reranking. This system achieved Recall@5 ranging from 0.802 to 0.972 across internal and five distribution-shifted holdouts, outperforming the other evaluated methods on five of six datasets and approaching ceiling performance on the sixth dataset (GDC). These results demonstrate the effectiveness and robustness of integrating semantic and lexical evidence for source-to-CDE mapping, supporting improved interoperability and reuse of biomedical metadata across diverse clinical and research contexts.

bioinformatics↗

bioRxiv2026

FLASH X-rays preserve neutrophil chromatin to boost antitumor immunity

Ultrahigh dose-rate FLASH radiotherapy spares normal tissue while preserving antitumor activity, positioning it as a promising technology in radiation oncology, but its immunomodulatory mechanisms remain elusive. Here, using a bilateral subcutaneous tumor model in mice, we show that 8-Gy FLASH X-rays, versus an equal dose of conventional dose-rate (CONV) X-rays, elicit a robust abscopal antitumor effect with anti-PD-1 therapy. Single-cell transcriptome and T cell receptor (TCR) profiling reveal that FLASH X-rays plus anti-PD-1 enhance antitumor immune infiltration and TCR clonal expansion in both irradiated and abscopal tumor lesions. Mechanistically, the ultrashort temporal continuity of FLASH dose delivery conserves chromatin compaction in neutrophils during exposure and inhibits neutrophil extracellular trap (NET) formation, thereby dismantling NET-mediated CD8+ T-cell exclusion within primary tumors. Conversely, CONV or split-dose FLASH X-rays induce marked chromatin modifications prior to dose completion, leading to chromatin hyper-relaxation which promotes NETosis and consequently abrogates systemic antitumor immunity. Notably, this detrimental effect can be reversed by pharmacologically suppressing PARylation-mediated chromatin modification and decondensation in neutrophils. These findings identify radiation-induced aberrant chromatin relaxation in neutrophils as a critical determinant of radioimmunotherapy outcomes, providing a rationale for redefining the FLASH effect and advancing its clinical translation.

cancer biology↗

bioRxiv2026

Distinct Temporal Responses to Xanthomonas translucens Strains Shape Bacterial Leaf Streak Development in Triticale

Bacterial leaf streak caused by Xanthomonas translucens threatens cereal production, however, the temporal coordination of host transcriptional responses during resistant and susceptible interactions in polyploid crops remains partially understood. Here, we used time-resolved transcriptomics to characterize responses of synthetic hexaploid triticale to two X. translucens pv. undulosa strains that produce contrasting disease outcomes. The resistant interaction with non-virulent LB10 showed a strong early transcriptional response that subsequently declined, whereas responses to the virulent strain P3 progressively intensified as water-soaking symptoms developed. Analysis of syntenic A-, B-, and R-subgenome homoeologs revealed extensive regulatory asymmetry, with R-subgenome homoeologs disproportionately represented among transcriptionally suppressed genes. Despite their conserved coding sequences, homoeologs often showed divergent transcriptional responses during infection, whereas greater similarity in upstream regulatory regions was associated with more coordinated responsive trajectories. We next examined pathogen-mediated transcriptional regulation through transcription activator-like (TAL) effectors. Among eight TAL effector templates identified in LB10 and P3, TAL5-associated predicted targets showed the strongest preferential induction during P3 infection. Disruption of TAL5 in P3 predominantly reduced host gene expression, including genes involved in immune signaling, cell wall-associated defense and photosynthetic function, accompanied by reduced maximum photosystem II quantum efficiency at 72 h post-inoculation. Together, our results show that bacterial leaf streak outcomes are shaped by temporally distinct host responses and pathogen effector-associated transcriptional reprogramming, providing insight into the dynamic regulation underlying cereal-Xanthomonas interactions.

plant biology↗

bioRxiv2026

Immunizing small cell lung cancer mice with isoaspartylated Elavl4 after chemotherapy mimics improved survival of anti-ELAVL4 antibody-positive small cell lung cancer patients

Introduction: Small cell lung cancer (SCLC) patients have an ~8% 5-year survival; new therapies are urgently needed. Approximately 15% of SCLC patients have naturally-occurring low-titer antibodies against neuronal ELAVL proteins, associated with improved response to therapy and significantly improved survival. We previously determined that the anti-ELAVL4 response is triggered by isoaspartylation in the unstructured ELAVL4 N-terminal region. Methods: We used a Tp53fl/fl;Rb1fl/fl inducible SCLC mouse model to test whether 1) immunization with isoaspartylated Elavl4 (isoAsp-Elavl4) prior to SCLC induction improves survival in the absence of any other treatment, and 2) immunization with isoAsp-Elavl4 following completion of 3 rounds of cisplatin+etoposide therapy improves survival. Immunizations contained incomplete Freund's adjuvant with either a recombinant N-terminal fragment of Elavl4 (amino acids 1-117), incubated under isoaspartyl-inducing conditions, or phosphate-buffered saline (used as the negative control, since Elavl4 spontaneously isoaspartylates). Mice were monitored by blinded assessors until euthanasia was indicated. Results: IsoAsp-Elavl4-immunized animals all became immune responsive, and spontaneous anti-isoAsp-Elavl4 antibodies were observed in 7% of the control animals. Kaplan-Meier analyses revealed that pre-SCLC immunization with isoAsp-ELAVL4 in the absence of other treatments did not affect survival. In contrast, immunization of SCLC mice following chemotherapy significantly improved survival. Conclusions: An anti-isoAsp-ELAVL4 response can be actively induced in mice and significantly increases SCLC survival when given following chemotherapy. This indicates that the anti-isoAsp-ELAVL4 immune response can be leveraged to develop new therapies for SCLC patients.

cancer biology↗

bioRxiv2026

Systematic comparison of pathogenic variants of the RNA exosome gene EXOSC3/RRP40 in Saccharomyces cerevisiae reveals variant-specific functional consequences

The RNA exosome is an essential, evolutionarily conserved ribonuclease complex that processes and degrades many classes of RNA. The complex is composed of three structural cap subunits (EXOSC1-3/Csl4, Rrp4, Rrp40; H. sapiens/S. cerevisiae), six structural core subunits (EXOSC4-9/Rrp41,Rrp46,Mtr3,Rrp42,Rrp43,Rrp45), and a catalytic ribonuclease (DIS3 or DIS3L/Dis3). Cofactors that associate with the RNA exosome confer specificity to target specific RNAs for processing and/or decay. Missense mutations in genes encoding structural subunits of the RNA exosome have been linked to neurological diseases. Notably, several pathogenic mutations have been identified in EXOSC3 that are associated with pontocerebellar hypoplasia type 1b (PCH1b). These pathogenic alleles cause a broad spectrum of clinical severity, suggesting variant-specific functional consequences. Given the high degree of conservation between the human and budding yeast RNA exosome complexes, we performed a systematic analysis of eight pathogenic EXOSC3 variants modeled in budding yeast Rrp40. We find that two Rrp40 variants cause growth defects, show distinct negative genetic interactions with RNA exosome cofactor mutants, and impair RNA processing in budding yeast. One of these variants, EXOSC3-Y109N/Rrp40-Y64N, had not been previously characterized in any mechanistic studies. Computational stability predictions and immunoblot analyses indicate that most EXOSC3/Rrp40 variants display reduced steady-state protein levels, but decreased protein levels do not strictly correlate with phenotype or disease severity, suggesting that individual variants disrupt RNA exosome function through distinct mechanisms. Collectively, our studies suggest that pathogenic EXOSC3 variants alter RNA exosome function through distinct mechanisms and provide insight into the specific molecular defects that could underlie pathology.

genetics↗

bioRxiv2026

Detailed curation of biological samples and experimental designs for genomics using LLM-supported agentic workflows

We describe an automated software tool to accomplish data curation tasks previously performed by humans for the Gemma genomics data re-analysis resource. Gemma is a hand-curated database of reprocessed transcriptomic studies, currently covering over 23,000 human, mouse and rat data sets largely drawn from the Gene Expression Omnibus (GEO). We developed a pipeline that uses both traditional (mechanical) and large-language models to produce detailed ontology-anchored, sample- and experiment-level annotations in accordance with our established curation guidelines. In this report, we describe benchmarking the pipeline and investigations aimed at evaluating readiness of the v1.1 Gemma curation agent for production use. Overall, performance is near that of human curators, at approximately 1/20th the cost and at least 100 times the speed. We also present preliminary exploration of triage methods for identifying agent curations that are more likely to contain errors, and thus can be forwarded for human review. We discuss the potential place of such curation approaches in bioinformatics ecosystems. Besides the software, our deliverables include the benchmark set of 500 studies and an evaluation framework that can be used to further develop the pipeline or compare to other approaches.

bioinformatics↗

bioRxiv2026

Strict OOD Antigen-to-Antibody Retrieval with CDR-Aware Slot Late Interaction

Antigen-specific antibody retrieval aims to rank candidate antibodies for a target antigen, providing an early virtual-screening step before structural modeling or experimental validation. Existing sequence-based antibody-antigen interaction studies often formulate the problem as pairwise binding prediction, and random or non-clustered evaluations can over-estimate generalization when related antigens appear across training and test data. We study a strict antigen-cluster out-of-distribution (OOD) retrieval setting in which test antigens come from sequence clusters unseen during training. This setting is difficult because binding is driven by local epitope-CDR complementarity, while available databases mainly contain observed positive complexes and lack reliable negative labels for unlabeled candidates. We propose Ab-CASLR, an antibody CDR-aware slot late-interaction retriever that encodes antigens with ESM-2, encodes antibodies with IgBert, constrains antibody-side latent slots to complementarity-determining regions (CDRs), and scores local slot compatibility instead of single-vector global similarity. On a strict OOD benchmark with 849 antigen queries and 869 candidate antibodies, the model achieves 7.42% Hits@10, outperforming k-mer homology transfer at 5.53% Hits@10 and yielding 6.28-fold enrichment over exact random screening at K = 10. Ablations and diagnostics show that CDR-constrained antibody slots remain diverse, whereas antigen-side latent slots collapse into similar summaries. These results support CDR-aware local antibody representation as a useful inductive bias for early binder recovery under strict OOD evaluation, while antigen-side epitope grounding remains unresolved.

bioinformatics↗

bioRxiv2026

Robust Conditional Diffusion with Noisy Templates for Antibody Sequence-Structure Design

Antibodies specifically recognize antigens and play a central role in therapeutic discovery. Designing antibodies for a given antigen remains challenging because antigen-antibody complex data are limited, whereas the sequence and conformational spaces of complementarity-determining regions (CDRs) are large. Retrieved CDR templates from databases or candidate libraries can narrow the design space and improve controllability, but retrieval for novel antigens is often sparse and imperfect; treating retrieved templates as hard conditions can bias the denoising process and cause negative transfer. To address this problem, we propose Robust Conditional Diffusion with Noisy Templates for antibody sequence-structure design (NT-ABDiff), a joint diffusion framework that treats candidate CDR-only templates as optional and potentially unreliable conditions. NT-ABDiff uses reliability-aware template modulation to estimate the context-conditioned usefulness of each candidate and to adaptively reweight and fuse multiple templates during conditioning. We further train the model with mixed-quality and corrupted templates as conditional perturbation regularization, encouraging the denoiser to exploit informative templates while remaining stable when templates are uninformative. Experiments under controlled template shifts and a train-set retrieval evaluation show that NT-ABDiff improves CDR-H3 sequence recovery and structural accuracy over strong baselines, while retaining robustness to missing, mismatched, and corrupted templates. Under a stringent random-template CDR-H3 evaluation, NT-ABDiff improves amino-acid recovery (AAR) from 30.03% to 39.47% and reduces RMSD from 3.160 to 2.915 [A]; with train-set retrieval candidates, it achieves 39.50% AAR and 2.76 [A] RMSD. Code, processed splits, configuration files, and evaluation scripts are available at https://github.com/ShiDeng7rz/NT-ABDiff.

bioinformatics↗

bioRxiv2026

A Fungal Natural Product that Targets Cellulose Synthase Complex and Inhibits Plant Cellulose Biosynthesis

Cellulose, a primary component of plant cell walls, is synthesized by cellulose synthase complexes (CSCs) at the plasma membrane. Targeting this process with cellulose biosynthesis inhibitors (CBIs) has significantly advanced our understanding of plant cell wall formation and provided valuable compounds for herbicide development. Here, we identified a fungal natural product, 8-methyldichlorodiaporthin (MDD), as a broad-spectrum plant CBI. Structure-activity relationship analyses demonstrate that methylation modifications on the isocoumarin ring and chlorination of side chain are crucial for MDD-induced growth inhibition. A chemical forward genetic screen in Arabidopsis thaliana revealed two semi-dominant CESA1 mutations, causing A903T and H1024Y substitutions, that confer insensitivity to MDD. Both mutations locate to transmembrane domains of CESA1, and we show that MDD depletes CSCs from the plasma membrane and reduces cellulose content. Further genetic analyses indicate that the cesa1mddi1-1 A903T mutant also confers resistance to CBIs quinoxyphen and C17, but not to CBIs isoxaben, indaziflam, or ES20. Stacking additional point mutations conferring resistance to other CBIs, cesa3ixr1-1 G998D, and cesa6es20-r3 G935E into the cesa1mddi1-1 A903T background yields multiple-drugs resistant lines that maintain normal growth. These findings establish MDD, as a novel, natural CBI that likely targets CESA1, thereby extending our understanding of CSC regulation and abilities to develop multi-drugs resistant crop varieties. These findings offer new perspectives for weed management and plant biotechnology. Significance StatementCellulose, a fundamental structural component of plant cell walls, is synthesized by cellulose synthase complexes (CSCs) and represents a critical herbicide target. While synthetic cellulose biosynthesis inhibitors (CBIs) like isoxaben and quinoxyphen have helped in the elucidation of CSC function and aided in weed control, natural CBIs remain largely undiscovered. Here, we identify 8-methyldichlorodiaporthin (MDD), a fungal-derived isocoumarin natural product, as a CBI that inhibits plant growth by depleting CSCs from the plasma membrane. Genetic screens reveal MDD-resistant cesa1 mutations, and combining these with other CBI-resistant alleles yields multi-herbicide resistant plants that can grow normally. This research enhances our understanding of cellulose biosynthesis and paves the way for multi-herbicide resistant crops with agricultural benefits.

plant biology↗

bioRxiv2026

Dissecting the Black Box of AlphaFold in Protein-Protein Complex Assembly

AlphaFold has revolutionized protein complex structure prediction, yet how it assembles intermolecular interfaces remains poorly understood. Contrary to the prevailing view that inter-protein coevolution drives complex prediction, we uncover a geometry-over-coevolution principle governing assembly in AlphaFold-Multimer and AlphaFold3. Through systematic perturbation of evolutionary and structural inputs and development of residue-level constraint propagation mapping to trace the emergence and propagation of geometric information within the network, we show that prediction accuracy is governed primarily by monomer-derived structural geometry and interface-specific sequence-geometry compatibility, rather than direct inter-protein coevolutionary signals. Our mapping reveals a hierarchical assembly mechanism in which monomer-level geometric representations are established first and progressively propagated to constrain cross-chain interfaces. This mechanism further explains why antigen-antibody complexes are predicted less accurately, as their intrinsic interface plasticity and non-canonical architectures limit the propagation of geometric constraints across interfaces. Together, these findings establish a mechanistic framework for understanding how artificial intelligence models assemble protein complexes and provide principles for improving next-generation structure prediction.

bioinformatics↗

bioRxiv2026

Identification of a somatic H3K23me3 methyltransferase SET-19 in C. elegans

Histone methylation plays essential roles in modulating chromatin organization and gene expression. H3K23 methylation is a conserved histone modification, yet its biological roles and the enzymes responsible for its deposition remain poorly understood. Here, we show that the loss of set-19 leads to a pronounced reduction in H3K23 methylation in C. elegans, as revealed by quantitative mass spectrometry, western blotting, and immunofluorescence staining. In vitro biochemical assays show that recombinant SET-19 proteins purified from E. coli directly catalyze H3K23 methylation. Genome-wide chromatin immunoprecipitation assays reveal that H3K23me3 is enriched at heterochromatic regions and that loss of set-19 alters H3K23me3 levels, accompanied by derepression of gene expression. Genetic analyses indicate that SET-19 is dispensable for both germline and somatic RNAi as well as transgenerational epigenetic inheritance of RNAi. SET-19 is predominantly expressed in somatic cells and specifically mediates H3K23me3 deposition in somatic tissues. The loss of set-19 causes a developmental delay without affecting fertility. Together, our results identify SET-19 as a somatic H3K23 methyltransferase and link H3K23me3 to gene repression in C. elegans.

genetics↗

bioRxiv2026

β-hydroxybutyrate modulates enteric pathogen susceptibility through regulation of commensal bacteria and intestinal Th17 responses

T helper 17 (Th17) cells are a critical T lymphocyte subset involved in mucosal immunity and host defense against enteric pathogens. Although ketogenic diets (KD) and the major ketone body {beta}-hydroxybutyrate (BHB) reshape gut microbiota and suppress Th17 responses under defined diet conditions, it remains unclear whether elevation of BHB alone, independent of dietary macronutrient composition and systematic metabolic shift, is sufficient to remodel Th17-inducing commensals and alter host susceptibility to enteric infection. Here, we used 1,3-butanediol (BD), a precursor metabolized to BHB independently of KD, to elevate systemic BHB levels in mice. BD treatment significantly reduced the frequency of ileal Th17 cells, as assessed by flow cytometry for Th17 markers IL-17A and ROR{gamma}t. 16S rRNA gene sequencing revealed that BD altered gut microbial community structure, as indicated by beta-diversity analysis based on Bray-Curtis dissimilarity, and reduced Shannon diversity and evenness. Linear discriminant analysis effect size identified segmented filamentous bacteria (SFB) as significantly decreased in the ileum following BD treatment, and SFB abundance positively correlated with Th17 markers. Microbiota transplantation demonstrated that BD-shaped microbiota was sufficient to suppress Th17 responses in recipient mice, accompanied by reduced SFB abundance. In a Citrobacter rodentium infection model, BD treatment was associated with increased pathogen burden, and fecal C. rodentium levels were negatively correlated with SFB abundance. Together, these results indicate that BD-induced elevation of BHB reshapes commensal microbiota, including decreasing SFB levels, resulting in dampened Th17 responses and increased susceptibility to enteric infection. IMPORTANCEDiet is a key determinant of gut microbial composition and mucosal immune function, yet the microbial mechanisms linking how diet-mediated changes to metabolism regulate immune responses remain incompletely understood. Th17 cells play central roles in both protective mucosal immunity and inflammatory pathology, making them a critical target of immunometabolic regulation. In this study, we show that {beta}-hydroxybutyrate (BHB), generated independently of diet, suppresses intestinal Th17 responses by reshaping the gut microbiota, reducing SFB levels, a potent Th17-inducing murine commensal. We further demonstrate that BHB-associated microbiota changes are linked to increased susceptibility to enteric infection. This work provides a mechanistic framework illustrating how metabolic state can influence host immunity through selective effects on commensal microbes. These findings inform future studies of microbiota-mediated immune regulation.

microbiology↗

bioRxiv2026

A human gut-BBB-brain microphysiological model for studying neurodegenerative diseases

The gut-brain axis has emerged as a crucial factor in neurodegeneration, with growing evidence linking gut dysbiosis and metabolic dysfunction to Alzheimers disease (AD) progression. Unfortunately, the lack of human-relevant in vitro models limits our ability to effectively explore the mechanism of this axis. To address this gap, we have developed a human induced pluripotent stem cell (iPSC)-derived gut-blood-brain barrier (BBB)-brain microphysiological system that enables systematic investigation of gut-brain interaction in the context of AD under controlled conditions. Our findings reveal that the interaction between gut and brain organoids can promote the maturation of brain organoids, making them more similar to their physiological characteristics in vivo. Additionally, co-culture gut and brain organoids better recapitulates the pathological features of AD. We also discovered that gut organoids of AD can trigger neurodegenerative disease manifestations in healthy brain organoids. In summary, our microphysiological system provides a novel and versatile in vitro platform for studying the interaction between the gut and brain in neurodegenerative diseases.

neuroscience↗

bioRxiv2026

CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction

Graph foundation models have emerged as powerful tools for drug repurposing by enabling the prediction of novel drug-disease indications from large biomedical knowledge graphs. A representative example is TxGNN, which was previously developed and trained on PrimeKG, a comprehensive biomedical knowledge graph covering over 17,000 diseases. While TxGNN demonstrates strong performance, existing biomedical knowledge graphs largely lack fine-grained, cell-type-specific expression context. This limits their ability to capture disease mechanisms driven by dysregulated cellular programs, such as immune cell-specific pathways in autoimmune diseases. Moreover, prior evaluations typically test only randomly selected subsets of diseases, leaving many diseases unexamined and limiting conclusions about model performance across the full disease spectrum. To address these limitations, we first update PrimeKG to PrimeKG-U by incorporating expanded and curated biomedical knowledge and then develop TxGNN-U as a stronger graph-based baseline. Building on this foundation, we introduce CellAwareGNN, a graph foundation model that integrates single-cell genomics into PrimeKG-U. We construct a single-cell-enhanced knowledge graph, scPrimeKG, by incorporating cell-type-specific gene expression signatures from the OneK1K dataset, expanding PrimeKG from approximately 8.1 million edges and 129k nodes to over 14 million edges and 147k nodes. CellAwareGNN is pre-trained on all relation types in scPrimeKG and evaluated on drug indication prediction with explicit coverage of all diseases in the knowledge graph. CellAwareGNN consistently outperforms TxGNN and TxGNN-U. For drug indication prediction, CellAwareGNN achieves an AUPRC of 0.826, representing a 1.2% improvement over TxGNN-U (0.816) and a 3.4% improvement over TxGNN (0.799). Notably, for autoimmune diseases, CellAwareGNN attains an AUPRC of 0.864, improving by 2.0% over TxGNN-U (0.847) and 6.0% over TxGNN (0.815). Importantly, CellAwareGNN prioritizes promising repurposing candidates, including Ocrelizumab for Pemphigus via CD20-expressing B cells, Methotrexate for Pemphigus through DHFR and ATIC activity in T and B cells, and Rosiglitazone for Rheumatoid Arthritis through PPAR-{gamma} activation. These results demonstrate the value of incorporating cell-type-specific expression context to improve both predictive performance and biological interpretability in graph-based drug repurposing. CCS ConceptsO_LIApplied computing [->] Health informatics; Bioinformatics; C_LIO_LIComputing methodologies [->] Knowledge representation and reasoning; Neural networks. C_LI ACM Reference FormatXinmeng Zhang, Eugene Jeong, Chao Yan, Yubo Feng, Linshuoshuo Lyu, Xingyi Guo, and You Chen. 2026. CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 2026), August 9-13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3770855.3819012

bioinformatics↗

bioRxiv2025

Atlas of Breast Cancer in Chinese Young Women Revealed by Single-cell RNA and ATAC Sequencing

Young women with breast cancer (YBC, age[<=]40) are particularly prevalent in Asian. YBCs usually show more aggressive pathology and poorer outcomes than non-young patients. However, YBCs are underrepresented in current BC risk models, with their tumor intrinsic subtypes and microenvironments lacking a systematic elucidation at the single-cell level, thereby limiting the young-specific therapies. We established a single-cell Chinese YBC landscape baseline, including 246,659 cells, by applying scRNA-seq and scATAC-seq on untreated patients. We developed a cross-modal feature selection algorithm to construct a young-intrinsic subtype classifier BCYtype, outperforming existing classifiers in pseudobulk, cellular, and external cohorts. Comparative analyses with non-young samples revealed a direct differentiation trajectory from mammary stem cells to mature luminal cells. Pseudotemporal analysis also demonstrated that tumor cells in younger patients undergo earlier carcinogenesis. Mechanistically, we found that CDH1 interacts with pTex and NKT cells, serving as a young-specific marker and a potential therapeutic target for HR+ young patients. The interaction between exhausted T cells and antigen-presenting cells revealed NKG2A as a candidate therapeutic target for triple-negative breast cancer in young patients.

cancer biology↗

bioRxiv2025

CryoSeek identification of glycofibrils with diverse compositions and structural assemblies

Last year, we reported CryoSeek, a research strategy that employs cryo-electron microscopy (cryo-EM) to discover novel bio-entities from any accessible source, supplemented with AI-facilitated data processing and bioinformatic analyses. Here we report CryoSeek characterization of five additional glycofibrils isolated from the Tsinghua Lotus Pond (TLP), named TLP-IPT, TLP-12, TLP-3, TLP-2, and TLP-0, with overall resolutions ranging from 3.0-3.5 [A]. These five glycofibrils, all covered with dense glycoshells, have decreasing ratios of the central protein components, with TLP-0 with no protein at all. IPT (immunoglobulin-like, plexins, transcription factors) refers to the tandem domain that constitutes the central filament of this type of glycofibrils. In TLP-12, the central cylindrical stem is made of a trimer of dodecapeptide repeats that weave to {beta}-sheet ribbons. The central stem of TLP-3 is also a trimer, but of linear tripeptide repeats. TLP-2, similar to our previously reported TLP-4, only has a linear chain of dipeptide repeats, and glycosylation occurs to a phosphoserine in each repeat. Glycan-mediated interactions are essential for the assembly of all five glycofibrils. Our previous and present studies demonstrate the diversity in the high-order structure and folding of glycans.

biophysics↗

bioRxiv2025

scPhoenix: A Contrastive Learning-based Framework with Aux-Core Feature Disentanglement Enhances Sparse Cellular Multi-omics Translation

Recent advances in single-cell multi-omics co-assays and spatiotemporal sequencing technologies have provided unprecedented opportunities for systematically characterizing cellular heterogeneity. However, severe sparsity and pronounced spatial heterogeneity--hallmark features of complex diseases and tumor microenvironment--remain major obstacles in deciphering cellular multi-omics data. Here, we present scPhoenix, a contrastive learning-based framework for single-cell cross-modality translation. scPhoenix adopts a two-stage Aux-Core strategy to disentangle modality-specific feature extraction from cross-modality feature interaction. Across diverse datasets, it preserves cellular heterogeneity during translation and demonstrates significant advantages for data with high sparsity. In addition, the framework integrates a contrastive learning framework with five effective data augmentation methods tailored to single-cell data. Moreover, scPhoenixs design supports extensions to unpaired data training and spatial multi-omics translation, enabling robust performance in scenarios with high spatial heterogeneity. scPhoenix is freely available at https://github.com/liwz-lab/scPhoenix.

bioinformatics↗