Biology subjects

Zhang, K.

Publications and source records attributed to Zhang, K..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

SimSpace: a comprehensive in-silico spatial omics data simulation framework

Spatial omics technologies provide rich measurements of molecular states in their native tissue context, but the development and benchmarking of computational methods remain constrained by limited access to datasets with known ground truth. We present SimSpace, a flexible framework for simulating spatial omics data for method development, stress-testing, and controlled in silico experiments. SimSpace uses a hierarchical generative model that couples tissue-scale spatial organization, local cell-type interactions, and modular omics-profile generation. Its Markov random field-based spatial model enables controllable simulation of niche structure, cell-type co-localization, phenotype-dependent density, and spatially mediated molecular interactions. SimSpace supports both reference-free simulations under user-defined generative assumptions and reference-guided simulations that calibrate spatial parameters to real datasets while generating new spatial realizations. Across Xenium, MERFISH, and CODEX examples, SimSpace reproduced multiple evaluated spatial statistics, preserved key domain-level structure in reference-guided settings, and supported benchmarking of cell-type deconvolution and spatially variable gene detection methods. We further show that inferred interaction parameters can be perturbed to generate interpretable counterfactual spatial proteomics scenarios. SimSpace is implemented as an open-source Python package with reproducible workflows for rigorous benchmarking and methodological development in spatial omics.

bioinformatics↗

bioRxiv2025

Comparison of the 2-CT method and the 2-ΔΔCT method for real-time qPCR data analysis

[purpose]The [Formula] method proposed by Livak and Schmittgen in 2001 is used for real-time quantitative polymerase chain reaction (RT-qPCR) data analysis. This methods fundamental logic involves normalizing all data against the control group to achieve relative quantification of gene expression. During our practical calculations, we identified an inherent bias in this method: the practice of directly taking arithmetic means of a set of C (or {Delta}C) values during [Formula] calculations fails to fully account for the exponential nature of CT values (where CT values serve as exponents in power-of-2 operations), leading to deviations in computational results. In this study, we elucidate this systematic bias and propose an innovative approach to circumvent it. [Methods]We propose the [Formula] method, which bases calculations on [Formula] values and computes the fold-change of target genes relative to control group target genes. The computational logic of the [Formula] method strictly adheres to the exponential characteristics of CT values, avoiding the use of arithmetic averaging at CT and {Delta}CT levels, thereby more accurately reflecting gene expression levels in datasets. In this paper, we detail the computational process of the [Formula] method and compare results from both methodologies. [Results]Calculations based on Livak and Schmittgens published data show differences between the two methods, though the discrepancies are relatively small. In calculations from our recent cadmium exposure experiments, the [Formula] method indicates that 8-hour cadmium exposure increases irg-6 gene expression in C. elegans from 1.314 to 7.125-fold, while the [Formula] method shows an increase from 1 to 4.124-fold. The two methods exhibit nearly 70% discrepancy in irg-6 gene fold-change quantification, with statistical p-values of 0.0002 and 0.0015 respectively. In all calculations presented in the paper, the [Formula] method fails to produce an exact unit value (mean fold-change = 1) for control groups, whereas the [Formula] method consistently yields control group results of exactly 1, demonstrating its capability for precise normalization. [Conclusions]In summary, the [Formula] method demonstrates superior computational rigor, and we recommend its adoption in RT-qPCR data analysis. This improvement proves particularly valuable for experimental datasets with substantial CT value variability among different samples, establishing a more reliable computational paradigm for RT-qPCR analysis.

molecular biology↗

bioRxiv2025

Effect of Caenorhabditis elegans U6 Promoters on CRISPR/Cas9-Mediated Gene Editing Efficiency

ObjectiveThis study investigated the effects of endogenous C. elegans U6 promoters on dpy-10 gene editing efficiency. MethodsWe screened endogenous U6 snRNA genes from the WormBase database and constructed 14 editing plasmids targeting dpy-10 by replacing the U6r07e5.16 promoter in pSX524 (Peft-3::cas9::tbb-2 terminator::U6 r07e5.16::dpy-10 sgRNA) through molecular cloning. Following standardized microinjection protocols, we quantified gene editing efficiency and high-efficiency gene editing index based on dpy-10 mutant phenotypes in F1 progeny. ResultsFifteen U6 snRNA genes (r07e5.16, f35c11.9, t20d3.13, k09b11.15, k09b11.16, w05b2.8, c28a5.7, f54c8.9, k09b11.11, k09b11.12, k09b11.14, t20d3.12, f54c8.8, f54c8.10, k09b11.13) were identified from WormBase database. Comparative analysis revealed four U6 promoters (w05b2.8, c28a5.7, f54c8.9, and k09b11.11) significantly enhanced gene editing compared to other U6 promoters, including commonly used U6r07e5.16 and U6k09b11.12 promoters in C. elegans community. Notably, the gRNAF+E scaffold showed no improvement over the gRNA scaffold when paired with the optimal U6w05b2.8 promoter. ConclusionThis study identifies superior U6 promoters for C. elegans gene editing and demonstrates the critical role of promoter optimization in CRISPR systems, providing novel insights for technical refinement.

molecular biology↗

bioRxiv2025

Genes on the Move: Exploring the Resistome of Viable Isolates from a First Nation Community in Manitoba

Despite wastewater treatment plants (WWTPs) and oxidation lagoons (OL) being designed for pollutant and pathogen removal, these systems can also serve as environments conducive to the exchange of mobile genetic elements (MGEs) and antibiotic resistance genes (ARGs) among microorganisms. This context facilitates the dissemination of antimicrobial resistance (AMR) among both pathogenic and non-pathogenic environmental bacteria, as well as phage populations, within these facilities. To enhance understanding of AMR dynamics in wastewater, our study examined the resistome of bacterial isolates recovered from a OL in a First Nation (FN) community in Manitoba, Canada. Thirty-five samples were collected from all OL stages, including raw sewage, lagoons, submerged attached growth reactor (SAGR), UV-treated effluent, and an upstream control site, from September 2022 to April 2023. Whole genome sequencing of 58 viable isolates was performed using Oxford Nanopore MinION, with genomes reconstructed via de novo assembly. The isolates corresponded to the following genera: Aeromonas 50.87% , Serratia 15.78%, Pantoea and Escherichia 7.01% each, Lelliottia 5.26%, Rahnella, Enterobacter and Buttiauxella 3.50% each, and finally Acinetobacter, Yersinia and Citrobacter 1.75%. Furthermore, a total of 32,559 elements were classified as ARGs. MGEs carrying ARGs were detected in 72.72% of the isolates. ARGs conferring resistance to macrolides, tetracyclines, and fluoroquinolones demonstrated a notable presence within MGEs (plasmids and bacteriophages) identified throughout the facilitys stages. This research represents a non-invasive approach, enabling an in-depth exploration of AMR dynamics by discerning the resistome profiles of bacteria residing within a rural OL facility from a FN community. ImportanceOLs commonly found in rural and Indigenous communities in Canada, are crucial for mitigating environmental hazards and safeguarding public health. Paradoxically, these systems can also serve as hotspots for the dissemination of ARGs and MGEs, thereby exacerbating AMR. The persistence of ARGs within both pathogenic and non-pathogenic bacteria, often carried on MGEs including phages, in treated effluents raises significant concerns about the environmental impact and public health. This study offers fundamental insights into the unique dynamics of AMR within OLs serving a First Nation community in Manitoba. Understanding these specific dynamics is crucial for developing robust monitoring frameworks and implementing targeted, sustainable strategies to mitigate the public health and environmental risks posed by AMR in these often-underserved remote settings.

microbiology↗

bioRxiv2025

Reporting quality, effect sizes, and biases for aging interventions: a methodological appraisal of the DrugAge database

Though interest has grown significantly over the past decades in interventions that may slow the aging process, most evidence for these interventions still comes from experiments in non-human animals. These studies may suffer from design, quality and reporting issues. The quality and reporting of preclinical studies have not yet been studied systematically in anti-aging research. Here we analyzed the DrugAge database, assessing reporting study quality, bias and effect sizes across 667 anti-aging preclinical studies. We found significant shortcomings in reporting of crucial design features such as randomization and blinding, as well as large variation in reporting quality and effects across species. Non-mammal findings typically did not translate to mammals. Although anti-aging interventions may have different effects depending on when they are started, most studies began giving the intervention under investigation very early in the organisms lifespan. Our findings suggest there is substantial room for improvement in preclinical anti-aging research.

physiology↗

bioRxiv2025

Microbial Tryptophan Metabolism Activates Host Lysosomal Activity to Facilitate Lipid Breakdown and Ameliorate Hepatic Steatosis

Lysosomes are central to lipid metabolism, yet how gut microbiota-derived metabolites regulate lysosomal function to influence host lipid homeostasis remains unknown. Here, we identify an evolutionarily conserved mechanism in which bacterial tryptophan metabolism activates lysosomal activity to promote lipid breakdown. By developing a lysosomal-responsive lipid reporter in C. elegans to screen for bacterial metabolic states that modulate host lipid storage, we discover that E. coli tryptophan catabolism via tryptophanase TnaA induces lysosomal lipid chaperone LBP-8, driving lipid mobilization. Moreover, tryptophan metabolites enhanced lysosomal acidification and degradation capacity, while genetic disruption of lysosomal regulators reversed these effects. Strikingly, bacterial tryptophan metabolism further promoted mitochondrial {beta}-oxidation through lysosomal lipase activity. This pathway was conserved in mammalian hepatocytes, where E. coli-derived tryptophan metabolites enhance lysosomal function and reduce lipid accumulation. In high-fat diet mice, restoring gut bacterial tryptophan metabolism alleviated hepatic steatosis. Our work uncovers microbiota-regulated lysosomal activation as a critical axis in lipid homeostasis, highlighting its potential as a therapeutic target for metabolic disorders linked to lysosomal dysfunction. SignificanceWe uncover a conserved mechanism by which microbial tryptophan metabolism enhances lysosomal function to maintain host lipid homeostasis. Specifically, we demonstrate that bacterial tryptophan catabolism--via the enzyme TnaA--promotes lysosomal acidification, proteolytic capacity, and structural remodeling in C. elegans, driving lipid breakdown through the lysosomal chaperone LBP-8. This activation boosts mitochondrial {beta}-oxidation and reduces lipid storage. Importantly, the same pathway operates in mammalian hepatocytes and in a high-fat diet mouse model, where restoring bacterial tryptophan metabolism markedly alleviates hepatic steatosis. Our findings bridge microbial metabolism and lysosomal dynamics, offering fresh insights into host-microbe crosstalk and metabolic regulation. HighlightsTnaA-mediated bacterial tryptophan catabolism promotes lipid mobilization via lysosomal chaperone. Bacterial tryptophan metabolites boost lysosomal function, lipid breakdown, and mitochondrial {beta}-oxidation. Conserved microbiota-lysosome-lipid axis from worms to mammalian liver. Restoring gut bacterial tryptophan metabolism alleviates hepatic steatosis in high-fat diet mice.

cell biology↗

bioRxiv2025

Genomic Touchstone: Benchmarking Genomic Language Models in the Context of the Central Dogma

The emergence of genomic language models (gLMs) has revolutionized the analysis of genomic sequences, enabling robust capture of biologically meaningful patterns from DNA sequences for an improved understanding of human genome-wide regulatory programs, variant pathogenicity and therapeutic discovery. Given that DNA serves as the foundational blueprint within the central dogma, the ultimate evaluation of a gLM is its ability to generalize across this entire biological cascade. However, existing evaluations lack this holistic and consistent framework, leaving researchers uncertain about which model best translates sequence understanding into downstream biological prediction. Here we present Genomic Touchstone, a comprehensive benchmark designed to evaluate gLMs across 36 diverse tasks and 88 datasets structured along the central dogmas modalities of DNA, RNA, and protein, encompassing 5.34 billion base pairs of genomic sequences. We evaluate 34 representative models encompassing transformers and convolutional neural networks, as well as emerging efficient architectures such as Hyena and Mamba. Our analysis yield four key insights. First, gLMs achieve comparable or superior performance on RNA and protein tasks compared to models pretrained on these molecules. Second, transformer-based models continue to lead in overall performance, yet efficient sequence models show promising task-specific capabilities and deserve further exploration. Third, the scaling behavior of gLMs remains incompletely understood. While longer input sequences and more diverse pretraining data consistently improve performance, increases in model size do not always translate into better results. Fourth, pretraining strategies, including the choice of training objectives and the composition of pretraining corpora, exert substantial influence on downstream generalization across different genomic contexts. Genomic Touchstone establishes a unified evaluation framework tailored to human genomics. By spanning multiple molecular modalities and biological tasks, it offers a valuable foundation for guiding future gLM design and understanding model generalizability in complex biological contexts.

bioinformatics↗

bioRxiv2025

Evaluation of the nano Liquid Chromatography coupled with Zeno TOF 7600 Mass Spectrometer for Cell Type-Resolved Spatial Proteomics

In this study, we systematically evaluated the nanoLC-Zeno TOF platform using data-independent acquisition (DIA) and identified 3,142 to 7,059 protein groups from 1 to 200 ng of standard K562 digest. Additionally, a targeted acquisition strategy detected 184 peptides with enhanced signal using Zeno activation. To achieve spatial cell-type resolved proteomics, we developed a workflow integrating image-guided laser capture microdissection for analysis of gastric cancer tissue. A 30-minute nanoLC gradient facilitated the quantification of 2,514 proteins from 0.2 mm{superscript 2} regions representing cancer cells, cancer-associated fibroblasts (CAFs), and immune cells, as defined by multiplex immunohistochemistry. This spatial proteomics approach revealed cellular heterogeneity within the tumor microenvironment of a clinical gastric cancer sample, and targeted validation identified differential expression of 12 cancer-associated proteins comprising 103 peptides between cancer cell-enriched and CAFs-enriched regions. This workflow offers a practical strategy for spatial visual proteomic analysis in clinical tissue samples.

cancer biology↗

bioRxiv2025

Structural insights into type I and type II Lamassu anti-phage systems

Bacteria have developed a variety of immune systems to combat phage infections. The Lamassu system is a prokaryotic immune system with a core conserved Structural Maintenance of Chromosomes (SMC) superfamily protein LmuB and diverse effectors named LmuA, whose mechanism remains unclear. Here, we present a series of cryo-electron microscopy structures of type I Lamassu complex from Bacillus cellulasensis and type II Lamassu complex from Vibrio cholerae, both in apo- and dsDNA-bound states, which reveal an unexpected stoichiometry and topological architecture, distinct from canonical SMC complexes. Combined structural and biochemical analyses show how the nuclease effector LmuA is sequestered in an inactive monomeric form within the Lamassu complex, and upon sensing foreign DNA ends, dissociates and assembles into an active tetramer capable of DNA cleavage. Our findings elucidate the mechanism by which Lamassu systems detect viral replication and implement anti-phage defense, highlighting the unprecedented roles of SMC proteins in prokaryotic immunity.

microbiology↗

bioRxiv2025

Biomechanical simplification of the motor control of whisking

1.Animal nervous systems must coordinate the sequence and timing of numerous muscles - a challenging control problem. The challenge is particularly acute for highly mobile sensing structures with many degrees of freedom, such as eyes, pinnae, hands, forepaws, and whiskers, because these low-mass, distal sensors require complex muscle coordination. This work examines how the geometry of the rat whisker array simplifies coordination required for "whisking" behavior 1-3. During whisking, 33 intrinsic ("sling") muscles are the primary drivers 4-12 of the rapid, rhythmic protractions of the large mystacial vibrissae (whiskers), which vary more than sixfold in length and threefold in base diameter 13-16. Although whisking is a rhythmic, centrally-patterned behavior 17-24, rodents can change the position, shape, and size of the whisker array, indicating considerable voluntary control 25-34. To begin quantifying how the arrays biomechanics contribute to whisking movements, we used three-dimensional anatomical reconstructions of follicle and sling muscle geometry to simulate the movement resulting from a "uniform motor command," defined as equal firing rates across all sling muscle motor neurons. This simulation provides a baseline profile of protraction under anatomically realistic but uniformly driven conditions. It does not isolate neural from biomechanical contributions but helps identify deviations that suggest active control. Simulations reveal that all follicles rotate through approximately equal angles, regardless of size. The angular fanning of the whiskers at their bases increases monotonically throughout protraction, while maximum distance between whisker tips occurs at approximately 90% of resting muscle length, after which whisker tips converge and sensing resolution increases monotonically.

neuroscience↗

bioRxiv2025

Contrastive Learning-Enhanced Drug Metabolite Prediction Drives the Discovery of an Orally Bioavailable RSK4 Inhibitor for Esophageal Cancer

Drug metabolism liabilities are a major bottleneck in drug development, necessitating accurate metabolite prediction tools. While AI-driven methods have advanced, challenges remain in achieving high-precision, robustness, broad reaction coverage and seamless integration into the drug development workflow. To address this, we first constructed the most comprehensive human-specific drug metabolism database to date, encompassing 11,665 human metabolic reactions and 2,497 high-quality rules. Leveraging this resource, we developed ConMeter (Contrastive learning and feature interaction based Metabolic Reaction prediction), a novel model integrating chemical feature interaction and contrastive learning. ConMeter demonstrates significant improvements, achieving [~]80% accuracy in identifying at least one metabolite within the top 5 predictions and showing an average 25% relative enhancement in ranking true positives compared to state-of-the-art methods. The comprehensive database also expands the range of predictable metabolic reactions. To demonstrate the utility of ConMeter, we integrated it into a metabolite prediction-guided drug design strategy aimed at addressing the low oral bioavailability of a metabolically vulnerable RSK4 lead compound -- a pervasive issue among known RSK inhibitors. This strategy successfully yielded R636, a novel RSK4 inhibitor exhibiting remarkably improved bioavailability (a 63-fold increase, reaching 63% absolute bioavailability) while maintaining its potent activity. Notably, R636 showed favorable safety profile and significant anti-tumor efficacy against esophageal squamous cell carcinoma (ESCC) in vitro and in two distinct patient-derived xenograft (PDX) mouse models. This work introduces ConMeter as a powerful and practical tool for high-accuracy metabolite prediction, demonstrating its potential to accelerate lead optimization, exemplified by the discovery of R636 as a promising orally bioavailable candidate for ESCC.

cancer biology↗

bioRxiv2025

Towards Unmanned Proteomics Data Generation: A Fully Automated Sample-to-Data System for Proteomic Experiments

The {pi}-HuB project requires proteomic measurements of millions of low-input samples that are challenging with current technology. Here, we introduce a pilot {pi}-HuB data factory that features a fully automated sample-to-data system to enable high-throughput digitization of human samples. Our platform, as a proof-of-concept towards unmanned proteomics, was demonstrated to show longitudinal robustness and applied for spatial proteomics and the profiling of plasma and cell lines, thereby serving as the workhorse to drive {pi}-HuB.

biochemistry↗

bioRxiv2025

A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography

Developing high-resolution reference maps of disease-susceptible spatial niches is a critical step to mitigating the profound effects of lung disease. Here, we present an integrated multimodal single-nucleus human lung atlas (snHLA) profiling 746,047 nuclei from 49 mapped lung blocks spanning clinically relevant distal airways, alveoli, and interstitium across 11 healthy adults. Integrating snRNA-seq and SNARE-seq2, which co-assays chromatin accessibility and gene expression from the same nucleus, we resolved 70 molecularly distinct populations and captured 332,846 accessible chromatin regions, nominating new transcriptional regulators of human lung cell diversity. Spatial transcriptomics using MERFISH mapped 25 cell populations across 7 structural neighborhoods and multiplexed immunofluorescence localized cell subtypes and distal airway-defining protein markers, expanding and validating distinct lung structure-specific cell populations. This open access snHLA and companion Cell Type and Marker Gene Dictionary with anatomically aligned nomenclature delivers a foundational resource at an unprecedented resolution to interrogate the origins of lung pathophysiology.

cell biology↗

bioRxiv2025

Integrated Classification of Cortical Cells and Quantitative Projectomic Mapping Unveil Organizational Principles of Brain-Wide Connectomes at Single Cell Level

Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small tufted SPC, and stellate SSC) are based primarily on local dendrosomatic and axonal structures, especially apical dendrites. This study bridges these perspectives by establishing a new neuronal taxonomy, analyzing the connectomes of defined cortical cell types, and comparing them with those obtained from bulk anterograde injections. Neurons were sparsely labeled via tamoxifen-inducible Cre lines with GFP reporters, and 1,419 WNM cells were comprehensively reconstructed with Vaa3D-TeraVR from [~]15 areas across six functional regions of molecularly labeled brains imaged with 2p-fMOST. These cells were newly classified by integrating current molecular-WNM and classical morphological perspectives, with sample size augmented by 1,455 publicly available WNM cells reconstructed from the Mouse-Light project and CEBSIT. This effort defined ten combined molecular-WNM-classical morphological cell types: L5ET_TPC, L6CT_NPC, L6b_HPC, and seven IT types--L2/3IT_TPC, L4IT_SSC, L4IT_UPC, L4IT_TPC, L5IT_SPC, L6IT_IPC, and L6IT_car3PC. Clustering, quantitative analyses and random Forest classifier objectively validated these types and revealed their distinct connectomes, along with convergent, topographic, and hierarchical organizations across their projection brain regions. At the single-cell level, multiple organizational principles governing cortico-cortical (C-C) and cortico-subcortical (C-subC) connectomes emerged with unprecedented detail, offering a precise GPS-like tool for in vivo recordings and robust datasets for neuronal network modeling. Comparisons with bulk anterograde injection data underscored the limitations of traditional methods in identifying projection targets. Overall, our approach provides significant insights into cortical circuitry and elucidates the complex interplay between neuronal molecular identity, whole morphology, and classical morphological classification.

neuroscience↗

bioRxiv2025

PhysDock: A Physics-Guided All-Atom Diffusion Model for Protein-Ligand Complex Prediction

Accurate prediction of protein-ligand complexes remains a central challenge in structural biology. Traditional methods are computationally inefficient and prone to local minima, whereas deep learning approaches struggle to capture structural flexibility and physical plausibility. We introduce PhysDock, a physics-guided diffusion model that uniquely integrates (i) all-atom diffusion to model ligand flexibility and protein precision-flexibility (i.e., subtle conformational adjustments); (ii) physical priors as diffusion conditioning, alongside two-phase physics guidance during the denoising diffusion to ensure physical plausibility. PhysDock demonstrates state-of-the-art performance in redocking benchmarks and excels in the more challenging cross-docking assessments. For practical utility, PhysDock (i) resolves cannabinoid receptor selectivity across diverse molecules, achieving accuracy comparable to experiments; (ii) distinguishes most drug candidates from weak binders in virtual screening of NTRK3 kinase, while uncovering novel candidates with structural insights. PhysDock serves as a versatile tool for protein-ligand complex prediction, with substantial potential to accelerate structure-based drug discovery.

bioinformatics↗

bioRxiv2025

Effect of Transcranial Light Stimulation on the Neurovascular Unit in the Human Brain

Transcranial light stimulation (tLS) is emerging as a non-invasive approach for enhancing brain function and treating neurological disorders; however, its impact on the human neurovascular unit (NVU) remains poorly understood. Herein, we combined photon transport modeling with multimodal neuroimaging to reveal how light influences vascular and neuronal responses in the human brain. Simulations of photon propagation through transcranial tissue captured key scattering and attenuation patterns, guiding the localization of light effects in vivo. Using simultaneous functional magnetic resonance imaging and arterial spin labeling, we showed that tLS significantly increased blood oxygenation level-dependent signals and cerebral blood flow in the light-affected regions. These hemodynamic changes co-occurred with a reduction in cortical excitability, as revealed by electroencephalographic source reconstruction and transcranial magnetic stimulation-evoked potentials. To probe the underlying mechanism, we incorporated inhibitory neural inputs into the computational NVU model. The model predicted that tLS enhances inhibitory neuronal activity and nitric oxide release, driving vasodilation and elevating metabolic support. These findings revealed that transcranial photons can differentially modulate neuronal and vascular components of the NVU--suppressing excitability while promoting perfusion--thereby suggesting a novel therapeutic avenue for targeting neurovascular dynamics in cognitive and clinical applications.

neuroscience↗

bioRxiv2025

BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature

Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction remains challenging because it requires not only interpreting biochemical semantics distributed across text, tables, and figures, but also reconstructing chemically exact ligand structures (e.g., Markush structures). To address this bottleneck, we introduce BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW, a multi-modal extraction framework that explicitly separates bioactivity semantic interpretation from ligand structure construction. Within BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW, bioactivity semantics are inferred through direct reasoning, while chemical structures are resolved via a chemical-structure-grounded visual semantic reasoning paradigm, in which multi-modal large language models operate on chemically grounded visual representations to infer inter-structure relationships, and exact molecular construction is delegated to domain chemistry tools. For rigorous evaluation and method development, we further establish BO_SCPLOWIOC_SCPLOWVO_SCPLOWISTAC_SCPLOW, a comprehensive benchmark comprising 16,457 bioactivity entries curated from 500 publications. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW validates its extraction ability and provides a quantitative baseline, achieving an F1 score of 0.32 for bioactivity triplets. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOWs practical utility is demonstrated via three applications: (1) extracting 82,262 data from 11,683 papers to build a pre-training database that improves downstream models performance by 3.9%; (2) enabling a human-in-the-loop workflow that doubles the number of high-quality NLRP3 bioactivity data, helping 38.6% improvement over 28 QSAR models and identification of 16 hit candidates with novel scaffolds; and (3) accelerating protein-ligand complex bioactivity annotation, achieving a 5.59-fold speed increase and 5.75% accuracy improvement over manual workflows in PoseBusters dataset. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW and BO_SCPLOWIOC_SCPLOWVO_SCPLOWISTAC_SCPLOW provide a scalable extraction methodology and a rigorous benchmark, paving the way to unlock bioactivity data that previously required extensive human effort. All data and code are available at GitHub.

bioinformatics↗

bioRxiv2025

Resolving Oligomeric States of Photoactivatable Proteins in Live Cells via Photon Counting Histogram Analysis

Oligomerization of photoactivatable proteins is widely used in optogenetics to modulate protein activity and regulate biological processes. However, their oligomerization states remain challenging to quantify in living cells. We applied photon counting histogram (PCH) analysis to quantify the oligomerization of two commonly used photoactivatable proteins, Vaucheria frigida Aureochrome light-oxygen-voltage (VfAuLOV) and Arabidopsis Thaliana cryptochrome 2 (AtCRY2), under dark and light conditions, providing a direct measurement of oligomerization states in live cells. Under blue light stimulation, VfAuLOV primarily forms dimers, whereas AtCRY2 forms higher order multimers in HEK293T cells. At intracellular concentrations above 1000 nM, AtCRY2 transitions into tetramers upon light stimulation, consistent with structural data obtained from cryo-electron microscopy. Unexpectedly, AtCRY2 (W374), a constitutively active mutant, still exhibits light-induced oligomerization. Human cryptochrome 2, in contrast, shows light-independent oligomerization at the intracellular concentration above 100 nM, below which the monomeric state dominates. Optogenetic signaling outcomes, demonstrated by light-induced lytic cell death, align with the oligomerization state of tested proteins. This study presents a quantitative framework for elucidating protein oligomerization in live cells, thereby enhancing the understanding of optogenetic mechanisms and their role in cellular signaling. Significance StatementPhotoactivatable proteins enable the development of optical actuators that modulate cell signaling through light-mediated protein-protein interaction. Light-oxygen-voltage (LOV) and cryptochrome 2 (CRY2) are commonly used photoactivable protein modules that undergo blue light-mediated oligomerization, and the latter has been used to create synthetic biomolecular condensates in live cells. Although in vitro structural studies suggested their oligomerization states, their behavior in live cells is unclear. Here, we applied photon counting histogram analysis to determine the dark and light-activated oligomerization states of these photoactivatable proteins in live cells. Insights gained will provide better guidance in designing optical actuators, leading to more quantitative interpretation of the light-mediated signaling outcomes in cells.

biophysics↗