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Huang, D.

Publications and source records attributed to Huang, D..

At least 37 records · Page 2Linked to original sources
bioRxiv2025

Graded BMP signals modulate yellow and red color in fishes impacting adult pigment pattern and behavior

Among the most interesting adult traits are those with roles in animal communication. Yet developmental mechanisms by which genes drive cell behaviors in building the final forms of such traits are rarely known. In this context, pigmentation is useful because colors and patterns often provide signals in mate choice, predation avoidance and other behaviors and pigmentation is unusually accessible to observation and manipulation. Here we focus on some of the most prominent signaling colors--red, orange and yellow--and show how BMP signaling at the cellular level allows for a very different kind of signal at the organismal level. Using pearl danio, Danio albolineatus, we find that spatially and temporally graded BMP signals promote development of yellow/orange xanthophores over red erythrophores in the fin of this species and a distantly related minnow, Tanichthys albonubes, and that conserved mechanisms, involving BMP co-receptor Rgmb, regulate differentiation of other pigment cell types in corresponding locations of zebrafish, D. rerio. We further use mutants of D. albolineatus with more red or more yellow cells than wild-type to demonstrate female responsiveness to carotenoid-based color differences between males in shoaling preference assays, and we show the existence of polygenic standing variation for this pigmentary trait. Our findings illustrate a chain of function spanning hierarchical levels and provide a deeper understanding of pigmentary form and function and its evolution. HIGHLIGHTSO_LIFate specification of alternative red or yellow pigment cell types in the fin of a minnow, pearl danio, depends on thresholds and gradients in BMP signaling during fin outgrowth. C_LIO_LIGenetic loss of BMP signaling leads to production of red over yellow carotenoids with resulting "super-red" fish preferred by females in shoaling assays. C_LIO_LIBMP-dependence of pigmentary traits is conserved across species with standing, polygenic variation for fin pattern and color in pearl danio. C_LI

developmental biology↗
bioRxiv2025

Rapid ecosystem collapse and biofilter formation following seabed methane leakage

Globally, vast amounts of methane are trapped within subseafloor gas hydrates. Recent evidence suggests that climate- and human-induced perturbations can destabilize gas hydrates and trigger methane release, yet the response of deep-sea ecosystems to these changes remains poorly understood. Here we show multi-year in situ monitoring of seabed ecosystem following methane leakage induced by hydrate exploration activities. Within just two years, benthic microbial and eukaryotic diversity in the affected areas declined significantly, while microbial and macrofaunal abundance increased. Integrated geochemical and omics analyses reveal the rapid successional shift of seabed ecosystem to a novel chemosynthetic system. Aerobic methanotrophs (Methyloprofundus) co-established with unexpectedly fast growing anaerobic methanotrophic archaea (ANME-3), accompanied by a rapid recruitment of opportunistic polychaetes that bioirrigated the seabed to >50 cm depths. The active animal-microbe interactions sustained exceptionally high rates of methane oxidation that utilized diverse electron acceptors. We demonstrate that methane hydrate destabilization can trigger rapid collapse of native seabed ecosystem while driving the formation of an effective methane biofilter consuming this potent greenhouse gas much faster than previously estimated. Understanding seabed ecosystem response and feedback is critical for predicting benthic methane cycling under ongoing global change and for developing sustainable strategies for methane hydrate resource management.

ecology↗
bioRxiv2025

IgG-Bridging-Seeded Synergistic Aggregation of SARS-CoV-2 Spikes Underlies Potent Neutralization by A Low-Affinity Antibody

Mechanistic studies of viral neutralization typically prioritize high-affinity antibodies, relegating low-affinity binders to the sidelines. We report P5-1C8, a Class 1 SARS-CoV-2 antibody that exemplifies this underexplored "low-affinity yet high-potency" phenotype, retaining strong neutralization of Omicron JN.1 despite markedly weakened trimer binding (KD = 225 nM; IC50 = 0.06 nM). Structural and biophysical analyses reveal that P5-1C8 engages WT and BA.1 spikes through canonical intra-spike bivalency, but with JN.1 it induces aggregation. Using virion-like nanoparticles displaying multiple spikes, we show that IgG remains bound with no detectable dissociation and triggers pronounced aggregation. Coarse-grained molecular dynamics delineate the stepwise pathway in which weak IgG-spike contacts seed aggregation via transient inter-spike bridging. Together, these findings establish the first mechanistic framework demonstrating how weak-binding antibodies can nonetheless achieve potent neutralization through higher-order aggregation, thereby expanding the conceptual landscape of antibody function and opening new directions for antibody evaluation and design. Graphical AbstractLow-affinity antibodies are frequently disregarded in discovery pipelines. This work reports P5-1C8, a Class 1 SARS-CoV-2 antibody with weak trimer binding (KD-to-IC50 > 3,700-fold) yet potent neutralization of Omicron JN.1. Structural, biophysical, functional and coarse-grained simulations collectively demonstrate that transient inter-spike IgG bridging seeds higher-order aggregation, which in turn drives neutralization and provides a mechanistic framework. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/691454v1_ufig1.gif" ALT="Figure 1000"> View larger version (79K): org.highwire.dtl.DTLVardef@1d4f132org.highwire.dtl.DTLVardef@1274400org.highwire.dtl.DTLVardef@e186a6org.highwire.dtl.DTLVardef@4f1870_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗
bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗
bioRxiv2025

Fixing Hybrid Rice: >99% Efficient Apomixis with Near-Normal Seed Set

Apomixis, a form of clonal seed reproduction, offers a transformative approach to agriculture by enabling the stable fixation of hybrid vigor and elite heterozygosity across generations. However, the practical implementation of synthetic apomixis has been hindered by highly variable clonal efficiency or significant yield penalties. In this study, through integrated transcriptomic analyses, we identified a sperm-specific transcription factor in rice that likely functions as a key initiator of embryogenesis. Ectopic expression of this factor in egg cells effectively induces parthenogenesis and produces haploid progeny. When combined with clonal gametogenesis, this system achieved nearly complete synthetic apomixis, with clonal seed production rates exceeding 99% across all derived hybrid rice lines. Moreover, we generated apomictic hybrid lines that not only consistently produced over 99% clonal seeds but also exhibited seed yields comparable to conventional F1 hybrids. These findings establish a scalable and agriculturally viable platform for synthetic apomixis, achieving stable fixation of heterosis through clonal seeds and paving the way for the commercial deployment of self-perpetuating hybrid crops.

plant biology↗
bioRxiv2025

A universal law of thermodynamics-kinetics coupling shapes enzyme allocation and glycolytic efficiency across species

Efficient use of limited cellular resources is fundamental to metabolism. Although flux optimization is widely recognized as a central objective of metabolic networks, how flux efficiency influences the allocation of the metabolic proteome remains unclear and lacks direct validation. Here, we derive a simple analytical relationship linking the equilibrium constant and the catalytic-abundance quotient of reactions within a pathway that defines the condition for maximal efficiency. We refer to this principle as the law of thermodynamics-kinetics coupling. By integrating reaction thermodynamics, enzyme kinetics, transcriptomic, and proteomic data, we show that glycolytic enzyme allocation consistently obeys this law across evolutionarily distant species. Moreover, the drive to optimize glycolytic efficiency is strengthened under oncogenic signaling and limited cellular budget for glycolytic enzymes. These findings establish a universal principle governing enzyme allocation in metabolic pathways and reveal key determinants of efficiency optimality in glycolysis. Significance statementCurrent models of metabolic optimality either rely on coarse-grained proteome allocation or constraint-based simulations, largely neglecting the fundamental constraints imposed by enzyme kinetics and thermodynamics on pathway efficiency. Here, we derive and validate a universal law of thermodynamics-kinetics coupling that robustly governs the allocation of glycolytic enzymes across species under optimal efficiency. This study has three major advances: it reveals universal economic principles of metabolism with simple, generalized mathematical form, uncovers determinants underlying the heterogeneity of metabolic efficiency optimization, and provides a practical blueprint for efficiency optimization of engineered pathways.

systems biology↗
bioRxiv2025

Mapping the Subtype-Specific PARP1 ADP-ribosylated Proteome in Breast Cancer Cells

Breast cancers are molecularly heterogeneous, with subtype-specific differences in transcriptional programs, chromatin architecture, and therapeutic responses. While PARP1 has been extensively studied in the context of DNA repair, emerging evidence implicates its catalytic activity in a broader set of cellular processes, including the regulation of gene expression. Here, we employed an NAD analog-sensitive PARP1 (asPARP1) chemical genetics approach combined with mass spectrometry to map the ADP-ribosylated proteome across six human breast cancer cell lines representing luminal and basal/triple negative subtypes. We identified thousands of PARP1 substrates and hundreds of Glu/Asp ADPRylation sites, revealing both shared and subtype-specific modifications in cell lines maintained under basal growth conditions. Luminal-specific substrates were enriched in chromatin and transcriptional regulators, whereas basal-specific substrates were preferentially linked to translation and RNA processing, highlighting lineage-dependent PARP1 activity. Transcription factors emerged as major substrates, with TFAP2A serving as a proof-of-concept; it is selectively ADPRylated in luminal cells and inhibition of PARP1-mediated ADPRylation modulates its promoter occupancy in a subtype-specific manner. Our data provide a new resource for studying subtype-specific PARP1-mediated ADPRylation in breast cancer cells. Collectively, our findings expand the conceptual framework for PARP1 function beyond DNA repair, offering mechanistic insights into subtype-specific gene regulation and potential determinants of PARP inhibitor sensitivity in breast cancer.

cancer biology↗
bioRxiv2025

OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine

Real-world biomedical scientific discovery operates as an iterative lifecycle integrating four core pillars: the targeted identification and analysis of question-specific omics datasets; the context-aware interpretation of molecular data using rich biomedical prior knowledge database; comprehensive literature reviews; and the subjective creativity and expert intuition of human scientists. Together, these pillars drive robust evidence synthesis and novel hypothesis generation. The recently reported AI agents support automated omics analysis and literature review, which typically require users to predefine and curate disease-specific datasets, which is a process that remains challenging and time-consuming. In this study, we present OmniCellAgent, a novel multi-agent AI framework built on large-scale single-cell RNA sequencing (scRNA-seq) datasets, and can autonomously retrieve and analyze disease and control-related scRNAseq datasets of diverse cell types across tissues and conditions. Moreover, it incorporates a biomedical prior knowledge and literature review agents, and disease domain-specific expert agents to systematically annotate omic data-derived targets. By aggregating evidence across agents, the framework generates structured analytical reports and potential scientific hypotheses. We evaluated OmniCellAgent across multiple disease settings, demonstrating its ability to identify relevant datasets, generate omic data analysis results, and produce structured reports and scientific hypotheses. Our findings demonstrate that multi-agent AI systems lower the technical barriers to omics-driven research, thereby accelerating scientific discovery and hypothesis generation in biomedical research and precision medicine. The source code is publicly available at: https://github.com/FuhaiLiAiLab/OmniCellAgent.

bioinformatics↗
bioRxiv2025

Structural and functional basis of PU.1-BAF interaction enables targeting of lineage-specific transcription

Chromatin remodeling complexes like BAF finely regulate transcriptional programs by working in concert with transcription factors. However, evidence is lacking as to whether TFs interact directly with BAF and if so, what the mechanistic and structural principles governing these critical interactions are. Here, we establish direct engagement between a crucial and therapeutically relevant full-length human TF, PU.1 (SPI1), and BAF. Within this 1MDa+ complex, we precisely map the binding site of PU.1 to a YEATS-like domain on BAF60A and elucidate the structure of the PU.1-BAF60A complex. This work reveals that upon binding to BAF, a disordered region within the TF adopts a helical conformation, and that disruption of this functionally critical interface via knockdown abrogates the ability of PU.1 to rescue cell viability. To explore the druggability of TF-BAF protein-protein interactions (PPIs), we conducted a high-throughput screen that identified small molecules capable of disrupting the PU.1-BAF60A PPI by binding to BAF60A. Co-crystal structures reveal distinct compound binding modes that converge on a critical PU.1-BAF60A interaction hotspot. These findings define, for the first time, the structural interface between a human TF and a chromatin remodeling complex and establish a platform that enables the targeting of these interactions, a novel mechanism in cancer therapeutics.

biochemistry↗
bioRxiv2025

Single-Cell Profiling Reveals Altered Endometrial Cellular Features Across the Menstrual Cycle in Endometriosis Patients

Endometriosis is a chronic, estrogen-dependent condition affecting over 190 million women globally, characterized by the ectopic presence of endometrial-like tissue that leads to inflammation, pain, and infertility. Despite its prevalence, the pathogenesis of endometriosis remains poorly understood. Here, we present a comprehensive single-cell transcriptomic atlas comprising 228,000 cells derived from 43 eutopic endometrial biopsies from patients with endometriosis, fibroid controls and healthy controls, sampled across the menstrual cycle. This analysis reveals previously uncharacterized subpopulations of endometrial fibroblasts and epithelial cells undergoing epithelial-mesenchymal transition, alongside disrupted immune cell communication networks. Comparative gene expression profiling implicates oxidative stress, aberrant cell migration, and dysregulated apoptosis as central features of the disease state. These findings suggest that endometriosis alters eutopic endometrial homeostasis, with potential consequences for fertility, regeneration, and disease progression. Our dataset provides a valuable resource for biomarker discovery and identifies candidate therapeutic targets aimed at restoring endometrial function and alleviating symptoms in affected individuals.

bioinformatics↗
bioRxiv2025

A scalable computational framework for predicting gene expression from candidate cis-regulatory elements

Deciphering the relationships between cis-regulatory elements (CREs) and target gene expression has been a long-standing unsolved problem in molecular biology, and the dynamics of CREs in different cell types make this problem more challenging. To address this challenge, we propose a scalable computational framework for predicting gene expression (ScPGE) from discrete candidate CREs (cCREs). ScPGE assembles DNA sequences, transcription factor (TF) binding scores, and epigenomic tracks from discrete cCREs into 3-dimensional tensors, and then models the relationships between cCREs and genes by combining convolutional neural network with transformer. Compared with current state-of-the-art models, ScPGE exhibits superior performance in predicting gene expression and yields higher accuracy in identifying active enhancer-gene interactions through attention mechanisms. By comprehensively analyzing ScPGEs predictions, we find a pattern in true positives (TPs) that the regulatory effect of cCREs on genes decreases with distance. Inspired by the pattern, we design two methods to enhance the ability to capture distal cCRE-gene interactions by incorporating chromatin loops into the ScPGE model. Furthermore, ScPGE accurately discovers some crucial TF motifs within prioritized cCREs and reveals the different regulatory types of these cCREs.

bioinformatics↗
bioRxiv2025

Functional screening of TCR-like antibodies for targeted cancer immunotherapy

Although hybridomas and display technologies have long been used in antibody discovery, identifying TCR-like antibodies that recognize major histocompatibility (MHC)-presented neoantigens remains an inefficient process. Here, we present a functional screening approach for antibody discovery based on T cell-specific functionality. By constructing Synthetic T cell receptor and Antigen Receptor (STAR)-T cells expressing antibody libraries and co-culturing them with target cells, we observed distinct antigen-specific STAR endocytosis and T cell activation. These two indicators are combined into an E-A functional index, which we demonstrate as an effective screening strategy for identifying antibodies with strict antigen sensitivity and specificity. Using this E-A functional screening method, we successfully discovered antibodies or nanobodies targeting cell-surface tumor antigens and neoantigens presented by human leukocyte antigen (HLA). These antibodies exhibited potent anti-tumor efficacy both in vitro and in vivo. Moreover, converting these antibodies into chimeric antigen receptors (CARs) and bispecific antibodies also induced T cell activation and tumor killing. In summary, our study establishes a robust functional screening strategy for identifying antibodies or TCR-like nanobodies targeting tumor-specific neoantigens, offering significant potential for advancing targeted cancer therapies.

immunology↗
bioRxiv2025

Pluripotent stem cell-derived siTNK cells attack tumors via a synthetic CD8-CD28 TCR complex

Tumor-associated antigen-specific T cell receptor (TCR)-engineered T cells offer a promising strategy for cancer therapy. Natural killer (NK) cells exhibit broad anti-tumor activity with low side effects but lack the capacity to recognize intracellular antigens. Here, we found that the human pluripotent stem cell (hPSC)-derived iNK cells, unlike tissue-isolated NK cells, expressed all four CD3 subunits at the transcriptome level. We introduced a synthetic gene-expressing complex (SCOTR), encoding a tumor antigen- specific TCR, CD8 coreceptor, and CD28 costimulatory molecule, into hPSCs to generate SCOTR-hPSCs. The SCOTR-hPSCs gave rise to abundant synthetic TCR complex-expressing iNK (siTNK) cells via an organoid induction method. These siTNK cells showed HLA-dependent, antigen-specific cytotoxicity against tumor cells and significantly suppressed tumor growth in tumor xenograft animal models, while also preserving universal non-specific tumor-killing activity. Collectively, siTNK cells, as hPSC-derived hybrid cells with dual features of adaptive T and inherent NK cells, offer an artificial cell source for human immunotherapies.

immunology↗
bioRxiv2025

Super-silencers are crucial for development and carcinogenesis in B cells.

The strength of the repressive histone H3 lysine 27 trimethylation modification signal varies drastically at individual silencers. Focusing on cases of an unusually strong repressive signal in regions that we refer to as super-silencers, we demonstrate that the regions that become B-cell super-silencers are originally associated with gene upregulation during development, and their target genes are highly expressed in stem cells, especially during early developmental stages. About 13% of B-cell super-silencers transmute to super-enhancers in B-cell lymphoma and 22% of these conversions recur across more than half of patients. Notably, genes associated with these conversions, like BCL6 and BACH2, are downregulated more swiftly than others when subjected to JQ1, a super-enhancer-disrupting bromodomain and extra-terminal domain inhibitor utilized in cancer chemotherapy. Furthermore, super-silencers are characterized by an over-representation of B-cell-cancer-associated mutations, both somatic and germline, and B-cell-cancer translocation breakpoints. This surpasses the prevalence found in other regulatory elements, such as CTCF binding sites, underlining the crucial role of super-silencers in forming and stabilizing regulatory topologies in standard B cells. For example, over 80% of cases involving the B-cell-lymphoma translocation t(3;14)(q27;q32) fuse super-silencers in the BCL6 locus with enhancer-rich domains. Finally, we demonstrate that the repressive mechanisms of super-silencers are partially governed by the CpG content in their sequences. While CpG-rich super-silencers often prevent promoters from interacting with enhancers, CpG-depleted super-silencers typically suppress the chromatin looping of nearby enhancers. In summary, our findings accentuate the critical role super-silencers play in the normal function of B-cells, suggesting that sequence mutations and activity modifications in these elements could be primary factors in B-cell carcinogenesis.

genomics↗
bioRxiv2025

IL-6 is a Key Factor in the Formation of Gut Tissue Resident Memory T Cells from Naïve T cells

Tissue resident memory CD4+ T cells (TRMs) populate mucosal sites and play a critical role in local immune responses. Gut TRM cells persist for extended periods in the gut mucosa where they rapidly respond to invading pathogens and provide long lasting protection. This study investigates the factors that mediate differentiation of naive CD4+ T cells into cells presenting a gut TRM phenotype. Naive CD4+ T cells were cultured under conditions that mimicked mucosal environments. This included signaling through MAdCAM-1 in the presence of Retinoic Acid (RA) and TGF-{beta}. This combination of stimuli primed naive CD4+ T cells to adopt a TRM phenotype. However, to fully differentiate into TRMs an additional soluble factor provided by memory T cells was required. Our results identified IL-6 as a key factor that induces the expression of TRM -associated markers, including CD69, CD103 and CCR5. This unique combination of stimuli promoted TRM differentiation despite low level proliferation. TRM differentiation was mediated through JAK/STAT signaling, and antagonists that target JAK/STAT pathways suppressed MAdCAM-1 mediated TRM cell formation. Our findings revealed that MAdCAM-1 works together with TGF-{beta}, RA and IL-6 in this process. Such information may aid in the design of next generation adjuvants and effective mucosal vaccines. Additionally, each of these factors may be targeted to treat excessive gut inflammation associated with conditions like inflammatory bowel disease. Overall, these findings provide new strategies aimed at modulating immune responses to invading pathogens and identify therapeutic approaches toward regulating gut inflammation. Author SummaryImmunologists and vaccinologists have long been interested in strategies aimed at boosting immune responses in mucosal tissues where pathogens are first encountered. Gut Tissue resident memory T cells (TRMs) play a central role in gut homeostasis and immunity. They provide a rapid long-lasting protection against pathogens. TRMs must target harmful pathogens and at the same time tolerate harmless commensal bacteria. This balance between immunity and tolerance in the complex environment of gut tissues is essential to the maintenance of gut homeostasis. In this study we have identified key factors present in the gut tissue milieu that are involved in TRM differentiation from naive T cells. These findings point to new therapeutic approaches that can potentially target immune responses in gut tissues and may help in developing effective mucosal vaccines. Conversely, these factors may be targeted in excessive gut inflammation involved in conditions like inflammatory bowel disease (IBD). Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/660664v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@14e6015org.highwire.dtl.DTLVardef@860b93org.highwire.dtl.DTLVardef@61f8c8org.highwire.dtl.DTLVardef@142b48_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗
bioRxiv2025

Role of Macrophage PARP1 in the Regulation of Crosstalk between Adipose Immune Cells and Adipocytes during Diet-induced Obesity

Adipose tissue consists of heterogeneous cell populations, including macrophages, which play a key role in maintaining adipose tissue homeostasis. We previously identified PARP1 as a critical regulator of proadipogenic gene expression in preadipocytes and proinflammatory gene expression in macrophages. To investigate the role of macrophage PARP1 in regulating adipose tissue homeostasis, we generated myeloid lineage-specific Parp1 knockout mice (Parp1 KOLysM). When subjected to a high fat diet for 12 weeks, the Parp1 KOLysM mice exhibited an obese phenotype accompanied by white adipose tissue (WAT) dysfunction, characterized by altered metabolite profile, pronounced adipocyte hypertrophy, and increased macrophage infiltration. Coculture of primary preadipocytes with the conditioned medium from bone marrow-derived macrophages (BMDMs) isolated from Parp1 KOLysM or control mice demonstrated that macrophage PARP1 depletion inhibited LPS-induced proinflammatory gene expression in BMDMs, but enhanced differentiation of preadipocytes into mature adipocytes. Single cell RNA-sequencing using CD45+-sorted WAT resident immune cells showed that macrophage PARP1 depletion increased the fraction of macrophages and NK cells, altered gene expression in both cell populations, and promoted intercellular communications. Taken together, our studies demonstrate a key role for macrophage PARP1 in the maintenance of adipose tissue inflammatory and metabolic homeostasis. Macrophage PARP1 depletion promotes cell-cell crosstalk among macrophages, fat cells, and other immune cell populations in adipose tissue, which cooperatively drives the development of obesity. SignificanceIn this study, we characterized a role for macrophage PARP1 in regulating adipose tissue homeostasis. Depletion of PARP1 in macrophages enhances cell-cell crosstalk among immune cells and fat cells, which exacerbates adipose dysfunction and drives obesity and adverse metabolic outcomes.

molecular biology↗
bioRxiv2025

A map of Non-translated RNA (nt-RNA) junctions in cancer genomes: a database resource of unproductive splicing

BackgroundNon-translated transcripts (nt-RNAs) with frame-shifts or premature termination codons resulting from alternative splicing events (ASE), have been recently found at unexpectedly abundant in transcriptomes of cancer tissue. However, their full genomic spectrum has not yet been fully elucidated. This study comprehensively characterised the expression of signature junctions of these nt-RNA (termed "toxic junctions" here) of both known and novel nt-RNA across multiple cancer types and investigated their potential as biomarkers. MethodsRNA-seq data of [~]6,000 samples, including the tumor and normal samples for 13 cancer types were retrieved from The Cancer Genome Atlas database (TCGA) together with data from Cancer Cell Line Encyclopedia (CCLE) project. Due to the difficulty in quantifying the entire transcript isoform of nt-RNA, we pioneered an algorithm to focus exclusively on the expression of junctional reads, which also circumvented the limitation of non-directional RNA- seq of TCGA data. We showed that the majority of nt-RNA is associated with at least one toxic junction. We built a comprehensive catalogue of known nt-RNA toxic junctions from genome databases. And novel toxic junctions were also identified by a new junction-focused algorithm from the higher quality discovery subsets of TCGA data. Splicing in Ratio (SiR) was used to quantify ASE leading to nt-RNA, enabling: Differential expression analysis between cancer and normal tissue and across cancer types. Identification of different profiles of nt-RNA abundance and various factor which may be the causes of differential nt-RNA abundance and SiR results Identification of specific nt-RNA and toxic junctions that were expressed in various cancer (and/or normal tissue) types. Assessment of nt-RNA and their toxic junction expression as biomarkers or prognosis indicators. ResultsWe profiled the expressed known nt-RNA (toxic) junctions of known transcripts and discovered [~]22,000 novel toxic junctions out of [~]250,000 novel junctions found in the transcriptome data. The expression of nt-RNA was as high as 10% of all transcripts of the corresponding gene in cancer transcriptomes. Interestingly, some signature toxic junctions of nt-RNA are expressed in even higher quantities, e.g. up to 50% or more, which is reminiscent of a heterozygous mutation. We identified distinct patterns between cancer and normal samples, including example of nt-RNA expressing toxic junctions exclusively in normal or tumor samples. Clinically relevant examples included ANXA6 in breast cancer, where the nt-RNA isoform showed significantly higher expression in tumors (p=1.8e-15). In kidney renal clear cell carcinoma (KIRC), a significant isoform switch of ESYT2 based on the RNA-seq data was confirmed. The Kaplan-Meier survival curves showed that samples with the higher expression ratio of ESYT2-L are associated with better survival (p=2.0e-06). Unsupervised clustering showed that SiR results of 150 toxic signatures defined 4 subgroups of patients with different prognosis. Through principal component analysis (PCA), PC1 and PC2 can be used as an independent prognosis biomarkers. nt-RNA accounting for these PCs included splicing factors SRSF3 and CLK1, where CLK1 phosphorylates SRSF3 to promote exon 4 inclusion in both genes. ConclusionsIn summary, the expression profiles of all known and novel toxic junctions were explored using pan-cancer RNA-seq data. A dual 10% rule emerged from this study: [~]10% of novel junctions were toxic junctions associated with nt-RNA, and up to 10% of RNA transcripts inside a cell were also nt-RNA. The SiR metric enables accurate quantification of unproductive splicing and identification of cancer biomarkers. Our findings reveal that unproductive splicing represents functionally important post-transcriptional regulation in cancer. These expression profiles allow researchers to study the expression of nt-RNA signature junctions or novel signature junctions in or near the genes they are interested in, which could provide a new direction for their research. The SRSF3-CLK1 regulatory mechanism provides insights into splicing dysregulation. Our comprehensive toxic junction catalogue serves as a valuable resource, suggesting that targeting unproductive splicing pathways may offer novel therapeutic strategies for cancer treatment. Data availabilityThe catalogue is available on GitHub and UCSC browser. https://github.com/danhuang0909/nt_database for GitHub overview https://genome.ucsc.edu/s/dandan_0909/hg38_all_new_nr for genome browsing of all novel (unannotated) toxic junctions https://genome.ucsc.edu/s/dandan_0909/hg38_5_26 for toxic junctions in known (annotated) nt-RNA.

cancer biology↗
bioRxiv2025

Advancing the Discovery of Phage-Host Interactions and Disease Classification from Metagenomic Profiles Using Deep Learning

Microbial communities are essential for sustaining ecosystem functions in diverse environments, including the human gut. Phages interact dynamically with their prokaryotic hosts and play a crucial role in shaping the structure and function of microbial communities. Previous approaches for inferring phage-host interactions (PHIs) from metagenomic data are constrained by low sensitivity and the inability to accurately capture ecological relationships. To overcome these limitations, we developed PHILM (Phage-Host Interaction Learning from Metagenomic profiles), a deep learning framework that predicts PHIs directly from the taxonomic profiles of metagenomic data. We validated PHILM on both synthetic datasets generated by ecological models and real-world data, finding that it consistently outperformed the co-abundance-based approach for inferring PHIs. When applied to a large-scale metagenomic dataset comprising 7,016 stool samples from healthy individuals, PHILM identified 90% more genus-level PHIs than the traditional assembly-based approach. In a longitudinal dataset tracking PHI dynamics, PHILMs latent representations recapitulated microbial succession patterns originally described using taxonomic abundances. Furthermore, we demonstrated that PHILMs latent representations served as more discriminative features than taxonomic abundance-based features for disease classifications. In summary, PHILM represents a novel computational framework for predicting phage-host interactions from metagenomic data, offering valuable insights for both microbiome science and translational medicine.

bioinformatics↗