Biology subjects

Sun, J.

Publications and source records attributed to Sun, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Ion-Pair-Free Capillary HILIC-MS for Sensitive Nucleic Acid Analysis and RNA Modification Mapping

Mass spectrometry (MS)-based nucleic acid analysis provides direct chemical evidence for oligonucleotide sequence, composition, and modifications. However, oligonucleotide LC-MS analysis commonly relies on ion-pairing reversed-phase liquid chromatography (IP-RPLC). Although IP-RPLC provides strong retention and high-resolution separation of highly charged nucleic acids, ion-pairing reagents can contaminate LC-MS systems, suppress electrospray ionization, require extensive system cleaning, and limit the use of high-end MS platforms that are primarily dedicated to proteomics or metabolomics. Here, we developed and evaluated an ion-pair-free capillary hydrophilic interaction liquid chromatography mass spectrometry (capillary HILIC-MS) workflow for RNA modification mapping. To enable robust analysis of biologically relevant samples, we optimized sample preparation, high-organic loading conditions, chromatographic parameters, and MS source settings to overcome key challenges associated with capillary HILIC, including limited sample volume, solvent compatibility, and solvent breakthrough during injection. The optimized capillary HILIC-MS method provided effective separation of oligonucleotides below 30 nt and enabled sensitive detection of RNA modifications in the populations of tRNAs and rRNAs in biological samples. Importantly, the ion-pair-free workflow also allowed switching between nucleic acid analysis and proteomics on the same LC-MS platform without the need for extensive system decontamination. Together, this workflow provides a sensitive, robust, and MS-compatible approach for nucleic acid analysis, expanding the utility of high-end LC-MS systems for both therapeutic oligonucleotide characterization and biological RNA modification profiling.

biochemistry↗

bioRxiv2026

Myeloid-targeted RNA nanotherapeutics rewire cholesterol metabolism to unleash anti-tumor immunity in glioblastoma

Tumor-associated myeloid cells (TAMCs) dominate the glioblastoma (GBM) microenvironment and suppress anti-tumor immunity. Here, we identify cholesterol efflux via ABCA1 as a targetable metabolic checkpoint controlling TAMC immunosuppression in GBM. Reprogramming TAMC cholesterol metabolism using TAMC-targeting lipid nanoparticle encapsulating ABCA1 siRNA (ABCA1 LNP) converts TAMCs into potent antigen-presenting cells with enhanced pro-inflammatory activity and antigen-presenting capacity, thereby inducing T cell activation, expansion, and tumor infiltration. Mechanistically, ABCA1 blockade induces cholesterol accumulation in TAMC membranes, promoting lipid raft formation and enhancing MHC-I-mediated antigen presentation. In multiple preclinical GBM models, ABCA1 LNP treatment dramatically induces T cell priming, extends animal survival, and overcomes GBM resistance to radiotherapy and immune checkpoint therapy. This efficacy was well-maintained in stem-like and recurrent GBM models, GBM patient specimens, and a renal cell carcinoma model. Altogether, our work identifies cholesterol efflux as a targetable metabolic vulnerability in TAMCs to overcome therapy resistance in myeloid-rich, immunologically "cold" tumors.

immunology↗

bioRxiv2026

Genetic and stochastic basis of phenotypic discordance in 16p11.2 mouse model deletion

Recurrent copy number variations such as the 16p11.2 deletion (del/+) represent a significant genetic risk for neurodevelopmental disorders, showing incomplete penetrance and variable expressivity. Here we describe a striking discordance in a genetic mouse model of human 16p11.2 deletion. Using electrocorticography experiments, we detected that approximately 50% of the isogenic del/+ mice exhibited increased susceptibility to seizures from low doses of the convulsant drug pentylenetetrazole (PTZ). Some of these mice also displayed spontaneous epileptiform brain activity. Next, we used two-photon microscopy tomography (STPT) with c-Fos-GFP (c-Fos+) as a reporter of neuronal activity to obtain images of the entire mouse brain. We examined del/+ mice that exhibited low or high responses to PTZ. Low responders exhibited minimal c-Fos+ activation. However, they had activating circuits that suggest the presence of compensatory mechanisms that suppress seizures. These circuits include the reticular nucleus of the thalamus, the pretectal region, the pedunculopontine nucleus, and the superior colliculus. In contrast, high-responder mice exhibited high c-Fos+ expression, corresponding to a fast seizure phenotype. Using whole-brain imaging, we analyzed brain volume and detected significant differences in the cortex between low- responder and high-responder del/+ mice. In the group with higher seizure susceptibility, we observed a notable decrease in cortical volume with significant differences in several brain regions compared to low responders. Additionally, some high-responder mice showed null expansion areas compared to low-responder mice. During the longitudinal behavioral analysis, the high-responder group of mice exhibited more severe sleep disturbances and an increase in repetitive grooming. Finally, to identify transcriptional correlates of the low- versus high- response phenotypes in adulthood, we performed bulk RNA-seq on somatosensory cortex punches from adult del/+ mice stratified by seizure response. Seizure-prone del/+ mice exhibited reduced activity-dependent immediate early gene signatures and coordinated changes in regulatory programs, including decreased expression of transcriptional regulators and metabotropic glutamate receptor genes, compared to low responders. Together, these findings define two distinct adult del/+ subgroups with convergent circuit, structural, behavioral, and molecular signatures.

neuroscience↗

bioRxiv2026

Quantitative Modeling of TLR Signaling Reveals Missing Negative Feedback Guiding Identification of TANK-IKKε Checkpoint

Toll-like receptor (TLR) signaling must be activated rapidly and then terminated to support host defense without sustained inflammation. We developed a rule-based model of mouse macrophage TLR4 signaling at the molecular-interaction level using measured protein copy numbers, RNA-seq-based abundance estimates, literature- and structure-informed reaction rates, and 979 dynamic experimental constraints. The trained model reproduced much of the TLR4-induced NF-{kappa}B and MAP kinase response but consistently failed to capture deactivation of MyD88, TRAF6-associated species, and IKK/{beta}. The recurrent model failure conveyed important biological information, localizing missing regulation to the proximal MyD88-IRAK-TRAF6 module and guiding experimental evaluation of IKK{varepsilon} and its scaffold TANK. Loss of IKK{varepsilon} enhanced transcriptional, cytokine, MAP kinase, and NF-{kappa}B responses to MyD88-specific TLR ligands. TANK deficiency produced a similar cellular phenotype and abolished stimulus-induced IKK{varepsilon} phosphorylation. Deficiency of either protein increased IRAK1 and TRAF6 ubiquitination without increasing MyD88 ubiquitination, placing the inhibitory checkpoint at or immediately downstream of the IRAK1-TRAF6 ubiquitin-signaling node. Overlapping but non-identical in vivo phenotypes further supported a shared regulatory axis with additional protein-specific functions. Our study presents a model-experiment discovery cycle where quantitative pathway discordance identifies missing biology and reveals a TANK-dependent IKK{varepsilon} checkpoint that restrains MyD88-driven inflammation.

systems biology↗

bioRxiv2026

Molecular principles for graded activation of dopamine D1 receptor

G protein-coupled receptors (GPCRs) signal across a continuum of activation states, yet how ligands encode distinct signaling efficacies remains poorly understood. Here, we define the molecular mechanism underlying graded activation of the dopamine D1 receptor (D1R), a major therapeutic target for neuropsychiatric disorders. Functional analyses reveal that two widely used pharmacological tools, LE300 and SCH23390, possess intrinsic efficacy as an inverse agonist and a weak partial agonist, respectively, rather than the efficacy-silent neutral antagonists. Structural, molecular dynamics and mutagenesis analyses capture previously unrecognized inactive and intermediate receptor activation states that bridge known active conformations, and reveal that ligand efficacy is encoded through the progressive engagement of a conserved activation pathway centered on the W6.48 toggle switch. Guided by this mechanism, a single chemical modification markedly increases the agonist efficacy of SCH23390. Comparison with dopamine D2 receptor structures further reveals a conserved mechanism of inverse agonism despite distinct subtype-specific recognition. Together, these findings establish a structural framework for graded agonism at D1R and provide general principles for the rational design of efficacy-tuned therapeutics at dopamine receptors and related GPCRs.

biochemistry↗

bioRxiv2026

Protein Phosphatase 2A Activation Attenuates Acute Myocardial Injury in Takotsubo Syndrome by Modulating Ferroptosis and Mitochondrial Injury in Cardiomyocytes

BackgroundTakotsubo syndrome (TTS) is an acute stress-induced cardiomyopathy characterized by transient left ventricular dysfunction. Despite its reversible nature, TTS is associated with substantial morbidity and mortality in the acute phase, and no specific treatments are available. The precise molecular mechanisms that connect catecholamine stress to reversible myocardial injury are not fully understood. PP2A, a holoenzyme with serine/threonine phosphatase activity, plays a vital role in normal cardiac development, and its dysregulation has been associated with heart disease. Its role in TTS and its potential as a therapeutic target remain completely unknown. MethodsAnalysis of public multi-omics datasets from stress cardiomyopathy (SCM) and experimental models of TTS, along with treatment of cardiomyocytes with human TTS plasma, was used to investigate a potential role for protein phosphatase 2A (PP2A) in stress-induced myocardial injury. To clarify the functional impact of manipulating PP2A activity in TTS, we used a series of disease relevant cell based and in vivo models, leveraging both genetic and pharmacological approaches to modulate PP2A activity in cardiomyocytes and in mice. To gain mechanistic insights into how PP2A influences TTS pathology and downstream signaling pathways, RNA sequencing, stress-responsive iron handling, mitochondrial function, and cardiac phenotypes were thoroughly evaluated in both in vivo and in vitro studies. ResultsPP2A activity was markedly reduced in cardiac tissues from mice with isoprenaline-induced TTS, as well as in isoprenaline-treated cardiomyocytes. Genetic or pharmacological inhibition of PP2A worsened catecholamine-induced cardiac dysfunction and myocardial injury. Most notably, pharmacological activation of PP2A using an orally bioavailable small-molecule activator strongly mitigated myocardial damage and enhanced cardiac function in TTS models. Mechanistically, PP2A inactivation promoted JNK-MAPK signaling and dysregulated stress-responsive iron-handling pathways, leading to ferritinophagy-mediated ferroptosis and mitochondrial dysfunction. Pharmacological JNK inhibition effectively rescued myocardial injury caused by PP2A deficiency in two TTS animal models and in cardiomyocytes. ConclusionsPP2A inactivation is a key molecular event linking catecholamine stress to myocardial injury in TTS. Restoring PP2A activity or inhibiting downstream JNK attenuates ferritinophagy-dependent stress responses and mitochondrial dysfunction, providing a unifying mechanistic framework and highlighting the PP2A-JNK axis as a potential target for short-term intervention during the acute phase of TTS. CLINICAL PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABS* Protein phosphatase 2A (PP2A) activity is acutely suppressed during the early phase of Takotsubo syndrome (TTS), with preferential involvement of the apical myocardium, closely mirroring the diseases characteristic clinical phenotype. * Downstream of PP2A inactivation, catecholamine stress triggers JNK-MAPK signaling activation and dysregulated stress-responsive iron handling, including ferritinophagy-mediated ferroptosis and mitochondrial dysfunction in cardiomyocytes. * Pharmacological restoration of PP2A activity or downstream inhibition of JNK mitigates acute myocardial injury and preserves cardiac function, thereby establishing the PP2A-JNK axis as a central stress-responsive pathway in TTS. What Are the Clinical Implications?* Identification of PP2A inactivation as a key molecular event highlights a novel opportunity for targeted intervention during the acute phase. * Studies in preclinical animal TTS models provide the first evidence that illuminates the therapeutic potential of a small-molecule PP2A activator as a strategy for acute TTS and may serve as a broad cardioprotective strategy in settings where cardiac dysfunction is implicated. * Targeting downstream JNK signaling, which is activated by PP2A inactivation, emerges as an additional mechanism-based strategy for attenuating acute myocardial injury in TTS.

molecular biology↗

bioRxiv2026

Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models

Accurate predictions of transcriptomic responses to genetic perturbations could unlock our understanding of gene functions and regulatory networks. While a growing number of methods and benchmarks target this task, existing evaluations focus on mean expression accuracy alone. This overlooks differential expression (DE), which captures both mean and variance and forms the basis for biological interpretation and experimental follow-up. Here, we systematically evaluate a diverse set of deep learning and non-deep-learning methods for their ability to predict DE outcomes under two generalization regimes: unseen perturbations within the same cell line, and unseen cellular contexts across cell lines. We find that simple baselines, such as embedding-based nearest neighbors, are competitive and often outperform specialized deep learning models for DE classification across datasets and evaluation metrics. We further show that sparsity calibration, motivated by the structure of single-cell data, substantially improves DE classification for deep learning models that do not explicitly account for sparsity. Together, our findings establish practical baselines and evaluation principles for benchmarking perturbation models on DE prediction.

genomics↗

bioRxiv2026

Non-canonical ribosome organization enables translation stalling in neuronal RNA granules.

Neuronal outgrowth to support cognitive functions relies on a timely supply of proteins to distal sites in the neuron. To meet this demand, neurons transport mRNAs and ribosomes in large assemblies called "neuronal RNA granules". Ribosomes that integrate into these granules initiate protein synthesis in the soma, stall the process, and then become packaged into clusters that are transported to distal sites, where protein synthesis can be reactivated. The exact mechanisms and structure of these ribosome clusters remain unknown. We combined cryo-electron microscopy and cryo-electron tomography to examine these dense ribosome clusters purified from rat brains, which are included in neuronal RNA granules. We found that these clusters contain ribosomes stalled at various stages of elongation and that the mRNA sequences where the ribosomes stall facilitate stalling. The ribosome clusters contain multiple polysomal units, and ribosomes from nearby polysomes interact through rRNA expansion segments, only seen before in disomes of inactive or hibernating ribosomes in neurons. The ribosomes in the clusters form unique three-dimensional arrays that evade detection by the ribosome-associated quality-control and NO-GO decay responses, which normally lead to recycling of 40S and 60S subunits, and degradation of the mRNA and arrested nascent proteins. Overall, this study helps uncover specialized mechanisms in neurons that enable local protein translation at distal sites, supporting proper protein homeostasis, both crucial for neurodevelopment and optimal neuronal function. KEY POINTSO_LIRibosomes in neurons organize into unique clusters that are incorporated into "neuronal RNA granules". C_LIO_LIApproximately half of the ribosomes in these clusters are stalled in elongation. C_LIO_LIThe mRNA sequences read by these ribosomes facilitate stalling. C_LIO_LIRibosomes within the clusters interact through rRNA expansion segments. C_LIO_LIRibosomes in the clusters form unique 3D structures that are not detected by the ribosome-associated quality-control response. C_LI

neuroscience↗

bioRxiv2026

Conformational Basis of Functionally Selective Allosteric Modulation of the Angiotensin II type 1 Receptor by Small Molecules

Blockade of signaling through the angiotensin II type 1 receptor (AT1R), a prototypical G protein-coupled receptor (GPCR), by angiotensin receptor blockers (ARBs) is a major therapeutic approach to treating a wide variety of cardiovascular and renal diseases1. Like most GPCRs, the AT1R signals through two transducers, G proteins and {beta}-arrestins2,3. Previous reports have described {beta}-arrestin-biased peptide orthosteric agonists for the AT1R with potential therapeutic advantages over currently available unbiased ARBs4-6. Here we report the DNA- encoded library screening-guided isolation and pharmacological characterization of the first small molecule AT1R allosteric ligands. We use cryo-electron microscopy, double electron- electron resonance spectroscopy, molecular dynamics simulations, and targeted mutagenesis to determine their binding sites, binding modes and conformational mechanisms driving their unique and divergent modulatory effects on G protein and {beta}-arrestin pathways. Our findings uncover new mechanisms for precisely controlling the dynamic behavior of the AT1R with implications for drug development targeting this pathophysiologically important receptor family.

biochemistry↗

bioRxiv2026

OCellus: A Language-Model Framework for Single-Cell, Spatial, and Perturbation Biology with Natural-Language Reasoning

Computational modeling of cellular behavior--the virtual cell--has emerged as a stated grand challenge at the intersection of artificial intelligence and biology, yet existing foundation models remain specialized: single-cell models process dissociated transcriptomes only, spatial models require dedicated spatial-aware architectures, and perturbation predictors depend on manually curated knowledge bases that cap generalization. Here we introduce OCellus, a single nine-billion-parameter language model (Qwen3.5-9B) fine-tuned on twenty-two biological tasks that simultaneously addresses all three limitations through three coordinated technical contributions on a shared backbone. First, EvenClock encodes two-dimensional spatial coordinates as eighteen clockface sectors of text, enabling spatial reasoning on a vanilla language model without architectural modification; on ten spatial transcriptomics tasks OCellus attains 77 percent spatial-neighborhood accuracy, 96 percent spatial-cellchat accuracy, and 0.70 proportion-cosine similarity on spatial deconvolution, all without any spatial-aware architectural components. Second, per-gene language-model embeddings replace the Gene Ontology annotations that GEARS depends on, achieving Pearson correlation 0.945 on the Replogle 2022 perturbation benchmark versus 0.84 for GEARS across 457 completely unseen knockout genes. Third, OCellus-Agent provides a Planner-Router-Verifier natural-language interface that achieves 75 percent pipeline accuracy on eighty multi-task queries. Removing language-model embeddings collapses perturbation Pearson to 0.06, confirming that learned functional representations--not graph topology--drive the gain. As a cell-type encoder, OCellus ranks first among fourteen foundation models in linear-probe accuracy at 95.1 percent across four benchmark datasets, and reaches 72.6 percent average across twenty-two evaluated biological tasks--a 57-percentage-point absolute gain over the strongest baseline configuration. As a language model, OCellus uniquely generates natural-language explanations of its predictions, a capability absent from all competing methods. Code, pre-trained model weights, the graph-neural-network module, and the agent system will be made available upon publication.

bioinformatics↗

bioRxiv2026

A five-dimensional functional state space for fingerprinting disease transcriptomes

High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficult to compare across conditions or use for human-AI interpretation. We developed a five-dimensional (5-D) functional state space that represents disease transcriptomes as coordinated activity patterns across major biological systems. The framework maps transcriptomic signals onto five functional systems, 14 subcategories, and a distinct infrastructure layer, and was implemented as a reproducible pipeline for functional scoring, cross-condition profiling, benchmarking, and large language model (LLM)-assisted interpretation. Applied to wound healing, sepsis, colorectal cancer-related datasets, an extended GEO atlas of 38 complete case-control disease fingerprints spanning diverse disease contexts, and a TCGA-COAD/READ stage benchmark, the approach recovered interpretable disease-state patterns and retained progression-related information under strong compression. It also improved the quantitative grounding of LLM-generated summaries. This framework provides a compact and auditable representation for comparing disease transcriptomes and supporting human-AI biological interpretation. TeaserA five-dimensional functional framework turns complex disease transcriptomes into compact, interpretable fingerprints for human-AI analysis.

bioinformatics↗

bioRxiv2026

An allosteric pocket in KV1.3 defines a distinct chemical space for immunomodulator design

KV1.3 is a validated autoimmune drug target, yet all disclosed inhibitors converge on a narrow set of conserved binding sites, suffering from poor selectivity or clinical failure. Whether KV1.3 harbors a distinctive, druggable pocket amenable to selective targeting has remained unknown. Here we report MPiN, a highly selective, state-dependent KV1.3 inhibitor with in vivo efficacy in a mouse psoriasis model, which engages a previously uncharacterized extracellular allosteric pocket framed by the PP1-PP2 turret loops, the pore helix and the outer S5/S6 helices, acting through a bidirectional pore-to-sensor coupling that simultaneously constricts the selectivity filter and facilitates the voltage sensor toward activation. Unexpectedly, despite the extensive structural conservation of this pocket across KV1 paralogs, subtype selectivity is dictated by the peripheral residues G427 and H451, which define pocket geometry without directly contacting the ligand, thereby establishing a geometry-driven "non-contact selectivity" mechanism. By opening an unrecognized, structurally distinct chemical space on KV1.3 and redefining how selectivity is achieved within a conserved channel family, this work lays the structural and conceptual foundation for rational, structure-guided design of next-generation KV1.3 immunomodulators.

biochemistry↗

bioRxiv2026

Angiogenesis Guided by Bombyx mori Silk Proteins is Molecular Weight-Dependent

Silk proteins, including sericin and fibroin, are natural biopolymers with broad applications in tissue engineering where angiogenesis plays an essential role. However, the pro-angiogenic effects of silk proteins with varying molecular weights (MWs) remain poorly understood. Here, silk proteins with MW distributions at 40-180 kDa or less than 25 kDa were obtained through alkaline hydrolysis to evaluate their effects on angiogenesis. Structurally, reducing MW induced a conformational transition in silk proteins, accompanied by a striking morphological shift in sericin from nanofibers to nanoparticles. Functionally, high-MW sericin (SSH) suppressed, whereas low-MW sericin (SSL) and both high- and low-MW silk fibroin (SFH/SFL) directly promoted endothelial angiogenic activity. Transcriptomic analysis revealed that angiogenesis-related genes such as Id1 and Smad6/9 may underlie the angiostatic effects of SSH. Notably, both SSH and SSL enhanced angiogenesis indirectly via macrophages; however, SSH induced mixed M1/M2-like polarization, while SSL preferentially drove an M2-like phenotype. In a subcutaneous implantation model, SSH promoted angiogenesis but yielded vessels with weak integrity and increased fibrosis, whereas SSL enhanced angiogenesis with improved vascular maturity and reduced fibrotic response. These findings elucidate how the MWs of silk proteins shape angiogenic behavior and highlight the importance of MW tailoring for optimized tissue engineering applications.

Cell Biology↗

bioRxiv2026

Complex-valued representations of time-series gene expression profiles for network analysis

Time-series RNA sequencing provides a powerful framework for studying dynamic gene regulation, yet conventional analyses usually represent gene expression profiles as real-valued vectors in Euclidean space and quantify similarity using correlation or distance. Inspired by quantum information theory, we present a framework for encoding time-series gene expression profiles as complex-valued vectors comprising amplitude and phase components in Hilbert space. We designed multiple encoding models to represent gene expression in the amplitude of complex-valued vectors, encode temporal differences in the phase, and extend the phase representation to incorporate the direction of local expression changes. Gene-gene similarity was then quantified using fidelity, which measures the overlap between two encoded vectors. Evaluation using time-series RNA-seq datasets across diverse species and biological contexts showed that different encoding models produced distinct fidelity distributions that were related to, but distinct from, conventional correlation measures. We then constructed gene-gene networks using pairwise fidelity values and detected communities containing genes with similar temporal profiles. Although fidelity distributions differed across encoding models, the resulting communities captured major temporal expression programs, and functional annotations based on gene ontology and Kyoto encyclopedia of genes and genomes pathway analyses provided exploratory biological context. The detected communities were comparable to those obtained using conventional methods, including weighted correlation network analysis and fuzzy c-means clustering. Furthermore, as a proof-of-concept, we performed SWAP-test circuit simulations to mimic fidelity computation on a quantum computer; under noise-aware conditions, these simulations produced less accurate fidelity estimates with higher computational cost than classical computation. As a proof-of-concept, this study provides a complementary view of temporal transcriptome organization, rather than a uniformly superior alternative to conventional methods.

bioinformatics↗

bioRxiv2026

Dawn of segmented bilaterians unveiled by soft-bodied fossils in microbial pseudomorphs from the Basal Cambrian

It is estimated that the evolution of segmented bilaterians occurred during the Ediacaran, whereas most of their unambiguous body fossils did not appear until Cambrian Stage 3. This fossil gap hampers our understanding of the early history of body segmentation, which is a crucial evolutionary innovation for bilaterians. Trace fossils from the late Ediacaran and basal Cambrian suggest this gap is likely to represent a taphonomic bias, implying that the progenitors of segmented bilaterians that existed within this temporal span were hardly preserved. Here, we report a variety of segmented bilaterians from the lowermost Cambrian of South China. These fossils are preserved as microbial pseudomorphs, rather than as ordinary phosphatization. These findings demonstrate that microbial pseudomorphs represent a major pathway for the preservation of segmented bilaterians during this period, as well as an effective mechanism for overcoming the taphonomic bias that affects micro-animals with delicate, non-biomineralized bodies. Moreover, the newly described animals, among the earliest segmented bilaterians, reveal a high diversity of segmented bilaterians during Ediacaran-Cambrian transition, shedding new light on the evolution of body segmentation.

paleontology↗

bioRxiv2026

Large-scale prediction of transcription factor binding across human cell types informs regulatory genomics and reveals promiscuous occupancy associated with chromatin contacts

Our understanding of the mechanisms regulating gene expression has been hampered by our limited knowledge of which transcription factors (TFs) bind where in the genome, which is highly cell type-specific. While genome-wide TF binding can be experimentally assayed for individual TFs in individual cell types, profiling the full combinations of over 1600 TFs in hundreds of cell types is beyond practical reach. In this work, we developed a streamlined platform, TFBlearner, to train TF-specific models and predict bindings based on ATAC-seq data. We focused on biologically-motivated feature engineering and harnessed TF cooperativity and binding similarity across cell types to achieve state of the art binding predictions in unseen cell types in a scalable fashion. This enabled us to generate a compendium of binding predictions for 1108 Chromatin-associated proteins, of which 960 TFs, across 43 human cell types including widely-used cell lines and 36 physiological cell types representing all major human cell lineages. We show how the models additionally provide biological insights on the TFs, and show how the binding predictions can be used in downstream tasks such as TF activity inference. Our study additionally led to the observation of high promiscuity in TF occupancy. To investigate aspecific occupancy, we characterized crowded or high-occupancy (HOT) regions across cell types, providing evidence of their functionality, and reporting important cell type-specificity. Finally, we show that, across cell types, crowded regions engage in more 3D contacts, and that most TF occupancy at crowded promoters can be explained as tethered bindings from distal regulatory elements.

genomics↗

bioRxiv2026

VrySure: A Multi-Task AI Scientific Fraud Detection Platform for Identifying Manipulated and AI-Generated Biomedical Research Images

Integrity of scientific data is critical in biomedical research, where images often serve as primary evidence for experimental observations and conclusions. Advances in image-editing technologies and generative artificial intelligence (AI) have increased the accessibility and realism of visual manipulation, making detection through manual review increasingly challenging. To empower our laboratory researchers to continuously monitor and uphold scientific rigor and data integrity, and serve the global scientific community, we developed VrySure, an easy-to-deploy, AI-driven multi-task platform for automated image-integrity screening in biomedical research. VrySure integrates four detection modules: cross-image transformation detection, within-image copy-move detection, splicing detection in blot and gel images, and AI-generated image detection. The system identifies potentially manipulated images and, when possible, localizes suspicious regions using bounding-box outputs to support downstream verification. To support development and evaluation, we constructed task-specific datasets by combining public biomedical image resources, curated manipulated examples, and synthetic images generated by multiple generative AI systems. We evaluated VrySure using region-level F1 score, recall, precision, false negative rate (FNR), and false discovery rate (FDR) across multiple manipulation categories and compared its performance with two commonly used commercial image-integrity screening platforms under a predefined benchmark protocol. Under the tested conditions, VrySure achieved a higher F1 score and recall, lower FNR, and maintained a low FDR for within-image copy-move detection, splicing detection, and AI-generated image detection, while showing comparable performance in transformation detection. Beyond automated screening, VrySure is designed to support source-data comparison and evidence-based assessment in scientific integrity investigations. By integrating multiple detection capabilities into a unified and scalable workflow, VrySure provides a practical framework to improve the efficiency and consistency of image-integrity screening in biomedical research.

bioinformatics↗

bioRxiv2026

The Origin and Evolution of Protein Synthesis: A Co-Adaptation Flexible-Rigid Docking Model Based on First-Principles Reasoning

Although the "RNA world" hypothesis suggests that RNA played a crucial role in the origin of life [7], the functional framework of RNA in prebiotic protein synthesis and the mechanisms of genetic code formation during the prebiotic period remain poorly understood. Here, using the prebiotic "primordial soup" as a model, we reconstructed the detailed steps that would yield a protein with a stable ordered amino-acid sequence in the "primordial soup" at the prebiotic period. In the "primordial soup", a large number of medium- to large-sized biomolecule-like substances--such as RNA-like and protein-like molecules of various sizes and shapes, as well as related polymers like amino-acid-RNA-like etc.--did generate and accumulate. Moreover, protein-like and RNA-like molecules formed even more intricate complexes. These complexes bound free mRNA-like molecules through complementary base pairing. Subsequently, with an extremely low probability, two adjacent amino-acid-RNA-like molecules became bound to this free mRNA-like molecule, and their amino acids underwent a condensation reaction by the complexes, producing peptides and eventually proteins or polypeptides. This free mRNA-like molecule exhibits a certain flexible structure, whereas the super-large complexes formed by protein-like and RNA-like molecules (which possess certain activities) and the amino-acid-RNA molecules exhibit relatively rigid structures. Long-term evolution and mutual selection led to the emergence of proteins with stable amino acid sequences and moderate catalytic activity. In this way, the nucleotide information embedded in such mRNA-like molecules indirectly express through protein synthesis--a process we term the "A Co-Adaptation Flexible-Rigid Docking Model", where flexible mRNA-like molecules dock onto rigid complexes to enable ordered peptide formation. Finally, we show how trinucleotide codons emerge naturally from the flexible-rigid docking constraints.

evolutionary biology↗