Biology subjects

Song, L.

Publications and source records attributed to Song, L..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Glucose promotes neuron morphology abnormalities and egg-laying defects dependentofthe serotonin signaling pathway in Caenorhabditis elegans

A chronic hyperglycemic state can lead to neuropathological complications such as permanent nerve damage. Neuropathology is a debilitating ailment that affects nearly half of the individuals with diabetes, underscoring the relevance to investigate the impact of diet and glucose levels on the nervous system. Using the genetic model system Caenorhabditis elegans, we determined that a diet rich in glucose impacts biological processes dependent on neuronal function. The rate of intrauterine egg-hatching increased in animals fed a glucose diet and the inhibition of dopamine or serotonin is sufficient to counteract the impact of the glucose diet. The glucose-induced intrauterine egg-hatching phenotype is independent of the SER-1 and SER-7 receptors yet dependent on the DAF16/FOXO transcription factor. Furthermore, animals fed a glucose-supplemented diet displayed abnormal morphologies in the HSN and NSM serotonergic neurons and the HSN displayed axonal degeneration, which is a prominent pathological feature in neurological dysfunction and disease. Combined these data indicate that a glucose-diet affects nervous system and signaling pathways in C. elegans, which can be used to further model diet-induced neuropathy.

genetics↗

bioRxiv2025

Framework for analyzing MAE-derived immunopeptidomes from cell lines with shared HLA haplotypes

BACKGROUNDThe goal in vaccinology is to identify candidate antigens for clinical trials that will elicit an immune response for a significant portion of the target population. Unfortunately, promising data generated at the preclinical level often cannot be replicated in larger sample sizes. The goal of this project was to develop a methodology for processing MAE-generated data to identify MHC epitopes, minimize non-specific contaminants, find binding motifs, and utilize genetic connections among donors to determine which peptides were presented by specific MHC alleles. RESULTSOur approach demonstrated that mild acid elution of peptides from seven consanguineous B-lymphocyte lines accurately reflects the HLA genotypes within family members, highlighting the specificity of MAE. Additionally, the data successfully reproduced known MHC binding motifs and partially deconvoluted the originating HLA alleles of the epitopes. CONCLUSIONSThese findings suggest that our approach could be applied to numerous cell lines globally to evaluate a wide array of HLA haplotypes. This may help to reveal candidate vaccine antigens that induce immune protection for a wider population.

immunology↗

bioRxiv2025

Cis-delivering releasable IL-15 superagonist enhances antitumor immunity in cold tumors by invigorating preexisting CD25+CD8+ T cells

Antibody-mediated cis-delivery and trans-delivery both direct cytokines to tumors and have been extensively investigated in clinical trials. However, a comparative analysis of their differential effects on cytokine activity is still lacking. In this study, we initially verify that cis-delivery of cytokine demonstrates a markedly stronger antitumor effect than trans-delivery, but it also exhibits certain drawbacks, including severe toxicity, insufficient activation of CD25+CD8+ T cells, and enhanced stimulation of intratumoral regulatory T cells (Tregs). To further address these issues, we developed a conditionally releasable and cis-delivering IL-15 immunocytokine (termed PMIS), which can selectively release a free IL-15 superagonist within tumors rather than immobilizing IL-15 to PD-1+ Tregs, thereby potently stimulating CD25+CD8+ T cells. Mice treated with PMIS showed significantly reduced systemic toxicities while achieving notably stronger antitumor effects. Administered either alone or in combination with other therapies, PMIS exhibits great potential for inhibiting orthotopic cold tumor and its metastases. Mechanistically, the significant activation of the pre-existing intratumoral CD25+CD8+ T cells and the improved CD25+CD8/Treg ratio contribute to the enhanced antitumor response of PMIS. These findings underscore the indispensable role of CD25+CD8+ T cells in cis-delivering IL-15 and provide a promising strategy for overcoming resistance to therapies and effectively controlling advanced cold tumors.

immunology↗

bioRxiv2025

RNA reinforces condensate nucleation on chromatin to amplify oncogenic transcription

Aberrant chromatin-associated condensates have emerged as drivers of transcriptional dysregulation in cancer. Although extensive studies have elucidated intrinsic protein sequence features governing their formation, how extrinsic factors within the chromatin environment modulate their assembly and pathogenic function remains poorly understood. Gain-of-function mutations in the histone acetylation reader ENL, found in pediatric leukemia and Wilms tumor, drive oncogenesis by inducing condensate formation at highly selective genomic loci. Here, we uncover a critical role for locally produced gene transcripts in reinforcing the nucleation, chromatin engagement, and oncogenic activity of ENL mutant condensates. Mutant ENL binds RNA in part through a conserved basic patch within its YEATS domain, and this interaction enhances condensate formation both in vitro and across diverse cellular contexts. Using a chemically inducible condensate displacement and re-nucleation system, we show that blocking ENL-RNA interactions or transcription impairs condensate reformation at endogenous targets. RNA interactions preferentially enhance mutant ENL occupancy at top-bound, condensate-permissive loci, leading to increased transcriptional bursting and robust gene activation at the single-cell and single-allele level. In mouse models, disrupting ENL-RNA interactions reduces condensate formation and oncogenic transcription in hematopoietic stem and progenitor cells, thereby suppressing ENL mutant-driven leukemogenesis. Together, these findings demonstrate that locally produced RNA transcripts can promote locus-specific nucleation of pathogenic condensates on chromatin, which in turn drive persistent and hyperactive transcription of oncogenic targets and lead to tumorigenesis.

molecular biology↗

bioRxiv2025

Quality control of single-cell ATAC-seq data without peak calling using Chromap

In this work, we extend Chromap, an ultrafast method for single-cell ATAC-seq data alignment, to directly report peak-based quality control (QC) metrics, such as the fraction of reads in peaks, without calling peaks. Recent single-cell ATAC-seq analysis methods like SnapATAC2 utilize the genome-interval-based feature for data analysis, which disables filtering low-quality cells using common peak-based QC metrics. We show that Chromaps QC metrics capture additional low-quality cells missed by SnapATAC2 and improve downstream analysis results without sacrificing computational efficiency.

bioinformatics↗

bioRxiv2025

Prediction of cellular morphology change under perturbations with transcriptome-guided diffusion model

Investigating the cell morphology change after perturbations with high-throughput image-based profiling is of growing interest, considering its wide applications in phenotypic drug discovery, including MOA (Mechanism Of Action) prediction, compound bioactivity prediction, and drug repurposing. However, the vast space of chemical and genetic perturbations makes it infeasible to fully explore all the potential perturbations with image-profiling technologies. Consequently, developing a powerful in-silico method to simulate high-fidelity cell morphological response under perturbations can reduce the experiment costs and accelerate drug discovery. Motivated by this, we proposed MorphDiff, a transcriptome-guided latent diffusion model for accurately predicting the cell morphology response to perturbations. We applied MorphDiff to two large-scale datasets, including one drug perturbation and one genetic perturbation cell morphology dataset covering thousands of diverse perturbations. Extensive benchmarking and comparison with baseline methods show the remarkable accuracy and fidelity of MorphDiff in predicting cell morphological changes under unseen perturbations. Furthermore, we explored the utilities of MorphDiff in identifying and retrieving the MOAs of drugs, which is a crucial application in phenotypic drug discovery. With the designed pipeline for MOA retrieval, we demonstrated MorphDiffs capability to boost the retrieval of the drugs MOAs (Mechanism Of Actions) by generating realistic cell morphology profiles. The average MOA retrieval accuracy of MorphDiff-generated morphology is comparable with that of the ground truth cell morphology, and consistently outperforms the baseline method and gene expressionbased retrieval by 29.1% and 9.7% respectively. We also validated that complementary information provided by cell morphology generated by MorphDiff can help discover drugs with dissimilar structures but the same MOAs. In summary, with its strong capabilities in generating high-fidelity cell morphology on unseen perturbations, we envision MorphDiff as a powerful tool in phenotypic drug discovery by accelerating the phenotypic screening of vast perturbation space and improving MOA identification.

cell biology↗

bioRxiv2025

Tendon fibroblast inflammatory responses depend on NF-κβ and JAK/STAT signaling and alter mechanotransduction pathways

Tendon pathologies, including both chronic injuries and acute tendon tears, are some of the most common musculoskeletal injuries. Recent studies have suggested the importance of inflammation in the healing process in both acute and chronic tendon injury. However, there remain gaps in knowledge that hinder progress in the development of therapeutics to improve healing. A more complete characterization of the inflammatory response in tendon is needed, by defining the relative roles of different molecular pathways, and determining how these pathways interact with tendon mechanobiology. To investigate these questions, an in vitro model was developed, wherein the complexity of the in vivo healing environment was simulated by applying M1 macrophage conditioned media (M1-CM) to tendon fibroblasts (TFs). Characterization of the M1-CM and its effect on TFs revealed a robust inflammatory response, including upregulation of over 500 genes and increased secretion of several cytokines in TFs. The NF-{kappa}{beta} and JAK/STAT signaling pathways were necessary for the response to M1-CM, and each pathway was responsible for different downstream responses to inflammation in TFs. When considering the role of mechanical loading in tendon responses to inflammation, it was found that TF responses to loading were altered by the presence of an inflammatory stimulus. Analysis of the genes that responded differently to loading with inflammation present suggested changes in pathways involving extracellular matrix organization and G protein signaling. Mathematical modeling based upon these results revealed time-dependent suppression of mechanosensitivity, suggesting that therapeutic timing of inflammatory or anti-inflammatory interventions could restore or attenuate mechanical responsiveness to modulate rehabilitation outcomes. Results reveal that inflammation disrupts mechanosensitivity in tendon healing, and suggest potential pathways for therapeutic intervention.

cell biology↗

bioRxiv2025

AIDO.Tissue: Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model

Single-cell spatial transcriptomics enables high-resolution insights into tissue organization and cell-cell interactions, yet poses significant computational and modeling challenges due to its scale and complexity. Here we introduce AIDO.Tissue, a spatially-informed pretraining framework. The design employs multiple cells as input and an asymmetric encoder-decoder architecture, making it effectively encodes cross-cell dependencies while scaling to large data. Systematic evaluation shows that our method scales with neighboring size and achieves state-of-the-art performance across diverse downstream tasks, including spatial cell type classification, cell niche type prediction and cell density estimation. These results highlight the importance of spatial context in building general-purpose foundation models for tissue-level understanding.

bioinformatics↗

bioRxiv2025

Uncertainty-Aware Discrete Diffusion Improves Protein Design

Protein inverse folding involves generating amino acid sequences that adopt a specified 3D structure--a key challenge in structural biology and molecular engineering. While discrete diffusion models have demonstrated strong performance, existing methods often apply uniform denoising across residues, overlooking position-specific uncertainty. We propose an uncertainty-aware discrete denoising diffusion model that employs a prior-posterior signaling mechanism to dynamically guide the denoising process. Our approach further integrates learned priors from a pretrained protein large language model and a structure encoder within a modular framework, jointly optimized through multi-objective training. Across multiple benchmarks, our method achieves substantial improvements over state-ofthe-art baselines, offering a principled framework for structure-conditioned sequence generation in proteins and beyond.

bioinformatics↗

bioRxiv2025

Rapid and Reproducible Multimodal Biological Foundation Model Development with AIDO.ModelGenerator

Foundation models (FMs) for DNA, RNA, proteins, cells, and tissues have begun to close long-standing performance gaps in biological prediction tasks, yet each modality is usually studied in isolation. Bridging them requires software that can ingest heterogeneous data, apply large pre-trained backbones from various sources, and perform multimodal benchmarking studies at scale. We present AIDO.ModelGenerator, an open-source toolkit that turns these needs into declarative experiment recipes through a structured experimental framework. AIDO.ModelGenerator provides (i) 300+ datasets covering DNA, RNA, protein, cell, spatial, and multimodal data types; (ii) 30+ pretrained FMs ranging from 3M to 16B parameters; (iii) 10+ plug-and-play use-cases covering inference, adaptation, prediction, generation, and zero-shot evaluation; and (iv) YAML-driven experiment recipes that enable exact reproducibility. On a sequence-to-expression prediction task, AIDO.ModelGenerator systematically builds and tests unimodal and multimodal models, achieving a new SOTA by combining DNA and RNA FMs that outperforms unimodal baselines by over 10%. In a Crohns disease case-study, the frameworks simulated knockout protocol ranks the clinically implicated target SOX4 6,000 positions higher than differential-expression baselines, illustrating its utility for therapeutic target discovery. We release code, tutorials, checkpoints, datasets, and API reference to accelerate multimodal FM research in the life sciences1.

bioinformatics↗

bioRxiv2025

Multimodal Benchmarking of Foundation Model Representations for Cellular Perturbation Response Prediction

The decreasing cost of single-cell RNA sequencing (scRNA-seq) has enabled the collection of massive scRNA-seq datasets, which are now being used to train transformer-based cell foundation models (FMs). One of the most promising applications of these FMs is perturbation response modeling. This task aims to forecast how cells will respond to drugs or genetic interventions. Accurate perturbation response models could drastically accelerate drug discovery by reducing the space of interventions that need to be tested in the wet lab. However, recent studies have shown that FM-based models often struggle to outperform simpler baselines for perturbation response prediction. A key obstacle is the lack of understanding of the components driving performance in FM-based perturbation response models. In this work, we conduct the first systematic pan-modal study of perturbation embeddings, with an emphasis on those derived from biological FMs. We benchmark their predictive accuracy, analyze patterns in their predictions, and identify the most successful representation learning strategies. Our findings offer insights into what FMs are learning and provide practical guidance for improving perturbation response modeling.

bioinformatics↗

bioRxiv2025

LSH-mediated resolution of R-loops mitigates transcription-replication conflicts to preserve genomic stability in prostate cancer cells

Prostate cancer cells exhibit heightened transcriptional activity to sustain their aggressive phenotype, yet this creates DNA topological stress, leading to RNA: DNA hybrids (R-loops) and DNA damage that compromise genomic stability. LSH, an SNF2-family chromatin remodeler, is frequently dysregulated in cancers and linked to tumor progression, but its mechanistic role in genome maintenance remains unclear. Here, we demonstrate that LSH resolves R-loops in an ATP-dependent manner, mitigating transcription- replication (TR) conflicts in prostate cancer cells. LSH depletion caused R- loop accumulation due to reduced FANCD2 recruitment, exacerbating DNA damage and impairing Rad51 filament formation. Strikingly, LSH deficiency also increased R-loops at promoters of MYC and E2F target genes, stalling RNA polymerase II elongation and disrupting transcriptional programs critical for proliferation. Our findings reveal LSH is a key guardian of genome integrity, resolving R-loops to prevent TR conflicts and maintaining DNA repair fidelity. By linking LSH to R-loop resolution and transcriptional regulation, this study reveals its dual role in prostate cancer pathogenesis-- supporting malignant proliferation while safeguarding genomic stability. These insights nominate LSH as a potential therapeutic target to disrupt oncogenic transcription and induce synthetic lethality in prostate cancer.

cancer biology↗

bioRxiv2025

Amazon biodiversity is at risk from metal contamination due to mining activity

The Amazon basin hosts the most biodiverse and intact ecosystems on Earth, yet human activities are an increasing threat. Metal contamination due to mining constitutes one of these major threats, but its impacts remain poorly quantified. We provide the first quantitative assessment of biodiversity exposure to mining-associated metals--mercury [Hg], arsenic [As], copper [Cu], Zinc [Zn], and lead [Pb]--across the Amazon. Around 66% of the Amazons 38,890 species of birds, plants, mammals, reptiles, amphibians, and fishes are exposed to metal contamination, including biodiversity hotspots and Indigenous territories. Safeguarding the Amazons role as a global reservoir of biodiversity, ecosystem function, and cultural heritage requires addressing metal contamination not only as a localized issue, but as a pervasive threat to global biodiversity.

ecology↗

bioRxiv2025

Discovery of late intermediates in methylenomycin biosynthesis active against drug-resistant Gram-positive bacterial pathogens

The methylenomycins are highly functionalized cyclopentanone antibiotics produced by Streptomyces coelicolor A3(2). A biosynthetic pathway to the methylenomycins has been proposed based on sequence analysis of the proteins encoded by the methylenomycin biosynthetic gene cluster and incorporation of labelled precursors. However, the roles played by putative biosynthetic enzymes remain experimentally uninvestigated. Here, the biosynthetic functions of enzymes encoded by mmyD, mmyO, mmyF and mmyE were investigated by creating in-frame deletions in each gene and investigating the effect on methylenomycin production. No methylenomycin-related metabolites were produced by the mmyD mutant, consistent with the proposed role of MmyD in an early biosynthetic step. The production of methylenomycin A, but not methylenomycin C, was abolished in the mmyF and mmyO mutants, consistent with the corresponding enzymes catalyzing epoxidation of methylenomycin C, as previously proposed. Expression of mmyF and mmyO in a S. coelicolor M145 derivative engineered to express mmr, which confers methylenomycin resistance, enabled the resulting strain to convert methylenomycin C to methylenomycin A, confirming this hypothesis. A novel metabolite (pre-methylenomycin C), which readily cyclizes to form the corresponding butanolide (pre-methylenomycin C lactone), accumulated in the mmyE mutant, indicating the corresponding enzyme is involved in introducing the exomethylene group into methylenomycin C. Remarkably, both pre-methylenomycin C and its lactone precursor were one to two orders of magnitude more active against various Gram-positive bacteria, including antibiotic-resistant Staphylococcus aureus and Enterococcus faecium isolates, than methylenomycins A and C, providing a promising starting point for the development of novel antibiotics to combat antimicrobial resistance.

biochemistry↗

bioRxiv2025

ProGenFixer: an ultra-fast and accurate tool for correcting prokaryotic genome sequences using a mapping-free algorithm

Although prokaryotic genomes are simpler, making variation analysis more straightforward, an efficient and user-friendly tool for their rapid sequence correction is lacking. We present ProGenFixer, an ultra-fast, mapping-free tool specifically designed to efficiently identify and correct errors in prokaryotic genomes using next-generation sequencing (NGS) data. ProGenFixer compares k-mer profiles between the reference genome and reads to pinpoint discrepancies, employs a local assembly-based algorithm to estimate corrected sequences, and automatically implements corrections. Benchmarking demonstrates ProGenFixers speed and accuracy advantages. It is over >5x faster than the swiftest existing tool tested while maintaining higher accuracy. Compared to slower, highly accurate tools, ProGenFixer shows comparable accuracy but is >17x faster and demonstrates enhanced performance on long indels (>10 bp). Implemented in C, ProGenFixer is a standalone, user-friendly tool offering a comprehensive solution for prokaryotic genome correction. The software is freely available at https://github.com/Scilence2022/ProGenFixer. Additionally, a companion web-based platform offering a ready-to-use interface can be accessed at https://progenfixer.biodesign.ac.cn.

bioinformatics↗

bioRxiv2025

The Establishment of Prostate-specific, SKP2 Humanized Mice by CRISPR Knock-in Method Reveals Neoplastic Initiation and Microenvironmental Reprogramming

A recent study has shown that SKP2 inactivation can prevent cancer initiation by extension of total cell cycle duration without perturbing normal division, which suggests a new strategy for cancer prevention. However, direct in vivo evidence for human SKP2 on cancer initiation and prostatic microenvironment is still lacking and a prostate-specific SKP2 humanized mouse model is critical for developing prostate cancer immunoprevention approaches through targeting human SKP2. We therefore have established a prostate-specific human SKP2 (hSKP2) knock-in mouse model by a CRISPR knock-in approach. Overexpression of hSKP2, which is driven by an endogenous mouse probasin promoter, induces prostatic lesions including hyperplasia, mouse prostate intraepithelial neoplasia (mPIN), and low-grade carcinoma and increases prostate weights. Transcriptional profiling by RNA-sequencing analysis revealed significant gene expression alterations in epithelial to mesenchymal transition (EMT), extracellular matrix, and interferon signaling in the prostate of hSKP2 knock-in mice compared to wild-type mice. Single cell deconvolution showed an increase of fibroblasts population and a decrease of CD8+ T cell and B cell populations in the prostate of hSKP2-knock-in mice. Consistently with these results from the SKP2 humanized mouse, overexpression of hSKP2 in human prostate cancer PC3 cells markedly increased cell migration and invasion and induced the gene expression of EMT and interferon pathways, including FMOD, THY1, PFKP, USP18, IL15, etc. In addition, paired prostate organoids were derived from SKP2 humanized and wild-type mice for drug screening and validated by known SKP2 inhibitors, Flavokawain A and C1. Both of which selectively decrease the viability and alter the morphologies of organoids of hSKP2 knock-in rather than wild-type mice. Our studies provide a well-characterized prostate-specific hSKP2 knock-in mouse model and offer new mechanistic insights for understanding the oncogenic role of SKP2 in shaping the prostatic microenvironment during early carcinogenesis.

cancer biology↗

bioRxiv2025

Phased ERK function on muscle stem cell plasticity in axolotl

The precise regulation of muscle stem cell (MuSC) plasticity remains poorly understood. While studies have suggested that Pax7+ MuSCs in vertebrate limbs are committed solely to myogenic lineages during regeneration, we previously observed during axolotl tail regeneration, Pax7+ MuSCs contribute to the fibroblast and chondrocyte mesodermal lineages. However, the underlying mechanism is unclear. Here, we show that the duration of ERK signaling plays an important role in MuSCs cell fate. We observed that ERK exhibits sustained activation after tail amputation that is followed by subsequent inhibition. We found that ERK activation initiates MuSCs plasticity switch toward a trunk fibroblast fate by repressing PAX7 expression. However, subsequent inhibition of ERK activity is essential for the transition of trunk fibroblast to fin fibroblast. In addition, we found that the TGF-{beta}/SMAD2 cascade as a downstream mediator of ERK-driven regulation of MuSC plasticity towards fibroblast and chondrocyte during tail regeneration. Together, we uncovered a new phased stem cell regulatory mechanism linking injury-induced ERK signaling to stem cell plasticity regulation, offering new insights with potential implications for regenerative medicine.

developmental biology↗

bioRxiv2025

Low level of viral intra-host diversity is associated with severe SFTS disease

Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne virus that can cause severe clinical symptoms and high fatality. However, the dynamics of SFTSV intra-host diversity during infection and its association with disease severity remain unexplored. Here, we characterized SFTSV iSNVs in longitudinal clinical samples from patients with distinct clinical outcomes. Our analyses revealed that SFTSV intra-host diversity was associated with disease severity and showed distinct patterns in severe and mild cases. Severe cases exhibited paradoxically lower SFTSV intra-host diversity despite higher viral loads, likely driven by reduced selection pressure. Notably, iSNVs linked to viral replication emerged in severe cases, while iSNVs associated with immunity invasion arose in mild cases. Machine learning models showed that intra-host diversity can contribute to classification of SFTS by severity. Our findings, through the lens of within-host viral populations, highlight variations in viral-host interactions during SFTSV infection and provide potential biomarkers for predicting SFTS severity.

pathology↗