Biology subjects

Yang, M.

Publications and source records attributed to Yang, M..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Developing High-Yield and Safe Therapeutic EVs by Ablating Tissue Factor-Mediated Toxicity

The clinical translation of extracellular vesicles (EVs) as next-generation drug delivery vehicles is currently stalled by two interrelated challenges: the difficulty of manufacturing EVs at an industrial scale ("the yield problem") and the risks of toxicity associated with systemic administration of high doses ("the safety ceiling"). In this comprehensive study, we present a two-pronged genetic engineering strategy to overcome these limitations. First, we discovered that overexpression of glycosylphosphatidylinositol (GPI)-anchored proteins, such as CD55 and CD59, significantly enhances EV production in HEK293T and Expi293F cells. By engineering a truncated CD55 variant (TR3) that retains the GPI-anchor but lacks functional domains, we developed the ExoBoost cell line, which boosts EVs yield by approximately 50-fold without altering vesicle morphology. Second, we observed acute toxicity in mice following intravenous administration of high-dose ExoBoost EVs, with symptoms resembling venous thromboembolism. Through comparative analysis of EVs derived from other cell types, including keratinocyte stem cells (KSCs) and mesenchymal stem cells (MSCs), we identified Tissue Factor (TF/F3) as the critical causative agent of this lethal response. Moreover, we completely abolished this toxicity by generating a F3-knockout cell line (ExoBoost2.0) using CRISPR/Cas9, thereby raising the safe dose to as high as 1.5E12 particles per mouse, as quantified by nano flow-cytometry (nFCM). Finally, using a novel CD46-nanoluciferase reporter system, we demonstrated that high-dose administration of these safe EVs saturates hepatic and splenic clearance pathways, leading to a dramatic increase of systemic EVs accumulation, particularly in hard-to-target tissues such as the brain ([~]10,000-fold) and muscle ([~]1,000-fold). Additionally, repeated high-dose administration of ExoBoost2.0 EVs did not upregulate inflammatory cytokines or production of IgG and IgM. In summary, these findings establish a scalable, safe, and highly biocompatible EVs platform, potentially revolutionizing the drug delivery system in clinical application.

bioengineering↗

bioRxiv2025

Structural Mechanism of Specific Nucleobase Recognition by a Monoclonal Antibody in CoolMPSTM Sequencing

Massively parallel sequencing (MPS) has revolutionized genomics, yet traditional methods utilizing fluorescently labeled nucleotides suffer from "scarring" effects that limit read accuracy. The CoolMPS technology overcomes this by using unlabeled reversible terminators (RTs) with a 3-O-azidomethyl blocking group, detected by highly specific fluorescent antibodies. However, the atomic-level mechanism by which these antibodies discriminate between modified and natural nucleotides within a DNA strand remains unclear. Here, we report the crystal structures of the "A-fab" antibody fragment in complex with the monomeric antigen 3-O-azidomethyl-dATP (dATP-N3) at 2.50 [A] resolution, and with an one strand of the duplex carries an azide-modified deoxyadenosine nucleotide at its 3' terminus (dsDNA-dATP-N3) at 1.99 [A] resolution. Isothermal titration calorimetry (ITC) revealed that A-fab binds dATP-N3 with nanomolar affinity (KD{approx}17.14 nM), while binding to natural dATP is negligible (KD{approx}7.51{micro}M), representing a [~]440-fold specificity. Structural analysis reveals a conserved hydrophobic pocket formed by CDR residues (e.g., Trp79, Tyr53, Leu67) that specifically accommodates the 3-azidomethyl group. This interaction is critical for affinity and is maintained in the dsDNA-ATP-N3 complex, validating the antibodys function in a sequencing context. These findings provide the structural rationale for the high fidelity of the CoolMPS platform.

biochemistry↗

bioRxiv2025

Closing the Sim-to-Real Gap: An End-to-End Robotic Ultrasound System Leveraging In Vivo Reinforcement Learning and 3D-Prior Guided Hybrid Control

Abdominal ultrasound is a crucial first-line diagnostic tool, yet its efficacy is inherently constrained by a strong dependency on operator skill, leading to significant inter-operator variability and limiting its widespread adoption. While robotic ultrasound systems with artificial intelligence have emerged to mitigate this, current approaches face critical limitations: they often rely on simulated or phantom data for training, which hampers generalizability, and employ control strategies that lack the flexibility to adapt to complex human anatomy. To address these challenges, this paper introduces a novel, end-to-end robotic system for autonomous abdominal scanning. Our methodology is systematic: it begins with coarse scan planning via RGB and 3D point cloud-based localization of key anatomical landmarks (e.g., xiphoid process). The core of our approach involves training a reinforcement learning scanning policy directly on live human volunteers, enabling the development of a strategy that is robust to anatomical diversity. This high-level policy is executed by a sophisticated hybrid force-position controller, enhanced with real-time normal vector calibration and 3D point cloud-based respiratory phase detection to dynamically adjust contact force, particularly during inspiration. This innovation proves critical for improving scan quality in subjects with higher Body Mass Index (BMI). Extensive validation against expert sonographers demonstrates that our system achieves high precision in standard plane recognition and superior scanning efficiency. Furthermore, to holistically assess performance where standard planes are difficult to acquire, we introduce a 3D reconstruction-based coverage metric. Results confirm that our system delivers strong adaptability and significantly improved consistency, marking a substantial step toward clinically viable, operator-independent ultrasound robotics.

bioengineering↗

bioRxiv2025

A window trial in metastatic pancreatic ductal adenocarcinoma reveals resistance mechanisms to targeting the KRAS-MEK pathway

Copy number alterations of KRAS, mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), and MYC occur in 30-40% of PDAC. Here we demonstrate that KRAS and MYC are frequently co-gained and accompanied with worse prognosis in PDAC. In a Window-of-Opportunity clinical trial for metastatic PDAC, serial biopsies and deep multi-omics analyses were utilized to explore resistance mechanisms to MEK inhibition, as a surrogate for KRAS inhibition. Tumors from four of 14 patients showed Ki-67/CA19-9-based biomarker response (BR). Non-BR tumors were enriched for KRAS/MYC co-gain and KRASG12D variant. A transcriptomic signature of BR tumors was inversely correlated with KRASG12D/MYC co-gain in a large PDAC dataset and predictive for KRAS inhibitor response in multiple models. Finally, co-targeting KRAS and MYC was synergistic in KRASG12D/MYC co-gain PDAC. Together, this study provides insight into KRAS inhibitor resistance and supports MYC as an important target to improve patient outcomes in this deadly disease.

cancer biology↗

bioRxiv2025

A neuron type-specific microexon in Ank3/ankyrin-G modulates calcium activity and neuronal excitability

Recent studies have revealed many alternative exons differentially spliced across diverse neuron types in the mammalian brain, but their links to neuronal physiology remain unclear. Here we characterize a deeply conserved microexon E35a in Ank3 encoding ankyrin-G (AnkG), a multifaceted adaptor protein best known as a master organizer of the axon initial segment (AIS) and as a leading genetic risk factor for bipolar disorder. E35a is predominantly skipped in cortical glutamatergic neurons but included in cortical GABAergic neurons and cerebellar neurons, which is dictated by multiple neuronal splicing factors. In E35a-deletion mice we generated, interneurons show increased excitability and somatic Ca2+ activity, without disruption in AIS. Biochemical analyses suggest that E35a inclusion facilitates AnkG interaction with a protein complex involving inositol trisphosphate receptors (InsP3Rs) important for intracellular Ca2+ signaling. Alternative splicing therefore allows AnkG to modulate neuron type-specific excitability in addition to its ubiquitous pan-neuronal role in organizing the AIS.

neuroscience↗

bioRxiv2025

Decoding Prokaryotic Whole Genomes with a Product-Contextualized Large Language Model

Genomes encode the instructions for life, yet their interpretation requires models capable of capturing long-range genome-wide functional context at scale. Existing genomic foundation models have primarily focused on sequence-level representations, providing powerful tools for biological prediction while leaving complementary opportunities for function-level genome modeling. Here, we present GenSyntax, a function-level genome representation learning framework that models prokaryotic genomes as ordered sequences of gene-product descriptors. By treating gene products as semantic units, GenSyntax transforms annotated replicons into interpretable "genetic paragraphs" that capture genome-wide functional context. We trained GenSyntax on 49,250 annotated prokaryotic genomes and evaluated its ability across genome-scale tasks, including plasmid host prediction, gene-product disambiguation, genome contig ordering and gene essentiality prediction. Compared with general-purpose large language models and representative genomic foundation models, GenSyntax achieved competitive performance across multiple benchmarks, with independent external evaluations supporting its generalization beyond the original training datasets. GenSyntax embeddings also captured signals associated with microbial phenotypes, and gene essentiality predictions enabled exploratory design of minimal genomes. Together with the lightweight GenSyntax-Tiny model, GenSyntax provides a complementary, post-annotation framework for function-level analysis of prokaryotic genomes.

genomics↗

bioRxiv2025

ABA-responsive transcription factor CbNAC63 is involved in modulating biosynthesis of triterpenoid saponins in Conyza blinii

Conyza blinii, a traditional Chinese medicinal herb, accumulates triterpenoid saponins. NAC transcription factors (TFs) are pivotal for plant stress responses and hormone-mediated secondary metabolism, while abscisic acid (ABA) regulates terpenoid biosynthesis in medicinal plants. However, how NAC TFs mediate ABA-regulated triterpenoid saponin synthesis in C. blinii remains unclear. In order to address this gap, the focus was directed towards CbNAC63--a NAC TF identified from the nocturnal low-temperature transcriptome of C. blinii. Phylogenetic and motif analyses showed it belongs to the stress-responsive NAC subclade with conserved domains. Y2H assays revealed its self-interaction, enhancing transcriptional activity; subcellular localization confirmed nuclear localization. RT-qPCR results indicated CbNAC63 exhibits tissue-specific expression under nocturnal low-temperature stress, accompanied by increased triterpenoid saponin accumulation. ABA signaling primarily regulates this process: exogenous ABA enhanced CbNAC63 expression and saponin content, while fluridone suppressed both. Overexpression (OE) and virus-induced gene silencing (VIGS) demonstrated CbNAC63 positively regulates saponin biosynthesis. Y1H and dual-luciferase reporter assays confirmed CbNAC63 directly binds to the promoters of Cb{beta}AS and CbHMGR and activates their transcription. In summary, the study shows CbNAC63 integrates ABA and low-temperature signals, activating MVA pathway genes to regulate saponin synthesis. This unveils a novel "ABA-CbNAC63-MVA" module, enriches the classic regulatory network, and provides a target for improving medicinal plant quality. HighlightsIdentifies CbNAC63 as a novel regulator that integrates ABA and cold signals to boost saponin production in medicinal plants.

plant biology↗

bioRxiv2025

AAV-mediated regeneration of neurons in injury-induced and inherited retinal degeneration

Loss of retinal neurons is a leading cause of irreversible vision impairment, yet adult mammalian retinas lack the ability to regenerate these cells. In this study, we developed an adeno-associated virus (AAV)-based strategy to reprogram endogenous Muller glia into retinal neurons. Using stringent genetic lineage tracing, immunohistochemistry, electrophysiology and single-cell multiomic profiling, we show that AAV delivery of a stabilized, phospho-insensitive Neurogenin2 variant (Neurog2-9SA) efficiently converts Muller glia into multiple types of retinal neurons, including bipolar, starburst amacrine, and a small population of photoreceptor-like cells, in both injury-induced and inherited retinal degeneration models. The generated neurons exhibit electrical properties of retinal neurons, light-evoked responses and integrate into existing retinal circuitry. Single-cell multiomics analysis reveal that Neurog2-9SA induces reprogramming by remodeling chromatin accessibility, activating neurogenic transcriptional networks, and represses glial identity programs. Inhibiting Notch signaling markedly enhances reprogramming efficiency. Together, these findings establish Neurog2-9SA as a potent and clinically relevant factor for AAV-mediated reprogramming and provide a foundation for approaches to regenerate neuronal cells and restore function in retinal diseases.

neuroscience↗

bioRxiv2025

A unified language model bridging de novo and fragment-based 3D molecule design delivers potent CBL-B inhibitors for cancer treatment

The rational design of small molecules is central to drug discovery, yet current artificial intelligence (AI) methodologies for generating three-dimensional (3D) molecules are often siloed, focusing on either de novo design or fragment-based design. The lack of a holistic framework limits AIs application across the complex and multi-step pipeline spanning from novel scaffold identification to lead compound optimization, and prevents AI from effectively learning from the entire process. Here, we introduce UniLingo3DMol, a language model for 3D molecular generation, empowered by fragment permutation-capable molecular representation alongside multi-stage and multi-task training strategy. This integrated design enables UniLingo3DMol to seamlessly span both de novo and fragment-retained molecular design, demonstrating superior performance over existing generation models in in silico evaluations across more than 100 diverse biological targets. We further leveraged UniLingo3DMol in the design of inhibitors targeting CBL-B, a crucial immune E3 ubiquitin ligase and attractive immunotherapy target. This strategy led to a lead compound demonstrating excellent in vitro activity and robust in vivo anti-tumor efficacy. Our findings establish UniLingo3DMol as a generalized and powerful platform, showing the strong potential to advance AI-driven drug discovery.

bioinformatics↗

bioRxiv2025

MindSight: A Bio-Inspired Neural Architecture for Visual Restoration via Cortical Electrical Stimulation

Visual impairment is a common condition worldwide, and cortical electrical stimulation is one of the approaches to aid in visual restoration. However, existing methods suffer from limited precision, flexibility, and generalization in generating the desired visual perception. In this paper, we propose a novel deep learning-based algorithm for cortical electrical stimulation, named "MindSight," aimed at enhancing the clarity and accuracy of induced visual perceptions. Our framework introduces three key innovations: (1) A differentiable biophysical model simulating cortical state transitions under electrical stimulation, enabling end-to-end training; (2) A dual-path training architecture combining neural decoding fidelity with phosphene simulation constraints; (3) An attention-guided background gated network for input filtration and, a multi-channel activation constraint to ensure the effectiveness of electrical stimulation. We validated our approach through novel experiments with macaque monkeys, demonstrating superior performance in visual perception tasks. These results highlight the potential of our approach in assisting individuals with visual impairments. Codehttps://github.com/zyj9902/MindSight

bioengineering↗

bioRxiv2025

Structured Neural Variability from Repeated Naturalistic Video Watching Experiences

The brain continuously integrates perception and interpretation processes to navigate dynamic environments. It is often assumed that these processes are invariant to repeated experiences. Here we investigated how neural responses to naturalistic stimuli evolve across multiple viewings using functional magnetic resonance imaging. Twenty participants watched 24 short videos across five separate scanning sessions. Between-subject correlations (BSC) decreased progressively across all brain regions with repeated viewing, indicating increasing individual variability. Within-subject correlations (WSC) consistently exceeded BSC, demonstrating that individuals are more similar to themselves than to others. However, the structure of within-subject variability differed fundamentally between sensory and interpretive regions. Early visual cortex exhibited strong repetition effects, with brain activity becoming increasingly dissimilar with each successive viewing, accompanied by reduced coupling to low-level visual features and decreased anti-correlation with default mode regions. In contrast, ventromedial prefrontal cortex (vmPFC) showed temporal proximity effects, where sessions closer in time exhibited more similar activity patterns regardless of viewing number. This temporal structure in vmPFC aligned with fluctuations in participants video preferences across sessions. These findings suggest that repeated experiences are never processed identically: sensory systems adapt efficiently to familiar input while interpretive systems continuously reshape meaning based on current internal states.

neuroscience↗

bioRxiv2025

HEIMDALL: A Modular Framework for Tokenization in Single-Cell Foundation Models

Foundation models for single-cell RNA-sequencing (scRNA-seq) data are emerging as powerful tools for single-cell analysis, yet their performance depends critically on how cells are tokenized into model inputs. Single-cell data lack a canonical tokenization scheme, and many design choices in current single-cell foundation models (scFMs) remain heuristic, entangled, and difficult to evaluate. Here, we introduce HO_SCPLOWEIMDALLC_SCPLOW, a unified framework for dissecting and redesigning tokenizers in scFMs. By decomposing existing tokenization strategies into individual design choices, HO_SCPLOWEIMDALLC_SCPLOW enables attribution of the components that underlie robust generalization, allowing more principled design of improved tokenizers. Combining HO_SCPLOWEIMDALLC_SCPLOW with a minimal transformer backbone, we find that tokenizer design is instrumental for generalization in challenging distribution-shift settings such as cross-tissue, cross-species, and cross-gene-panel cell type classification, as well as reverse perturbation prediction. We show that, while tokenizer choice has little effect in scenarios with matched train and test data, it becomes imperative under distribution shift. Rather than identifying a single globally optimal tokenizer, HO_SCPLOWEIMDALLC_SCPLOW reveals that robust transfer depends on a small number of tokenization design axes - especially gene identity, expression encoding, and ordering - that expose different biological priors to the model. In this sense, universal transferability in scFMs still depends on a non-universal tokenizer interface. Together, these findings establish tokenization as a critical design axis in scFMs and provide design principles and reusable infrastructure for more robust scFMs.

bioinformatics↗

bioRxiv2025

Molecular evolution of octopamine receptors in Drosophila

Octopamine (OA), the insect analog of noradrenaline, plays important roles in diverse behavioral and physiological processes, from modulating fight-or-flight behavior to regulating post-mating ovulation. In Drosophila, six OA receptors have been identified: Oamb, Oct2R, Oct{beta}1R, Oct{beta}2R, Oct{beta}3R, and Oct-TyrR, and they have been linked to different behavioral and physiological processes. Here, we investigated the evolutionary characteristics of these receptors across Drosophila species. We found that OA receptors are generally found as single-copy genes. Notably, Oct{beta}2R and Oct{beta}3R exhibit positive selection within the melanogaster group, though in different structural regions from one another. The positively selected sites in Oct{beta}2R are exclusively located in regions important for ligand binding, whereas those in Oct{beta}3R are predominantly found in regions crucial for signal transduction. Interestingly, Oct{beta}2R remains highly conserved outside the melanogaster group, so the detection of positive selection in its ligand-binding related domains within this clade raises the possibility that it has evolved an additional, melanogaster-specific ligand interaction(s), among other potential reasons. These findings highlight the evolutionary flexibility of aminergic signaling and suggest lineage-specific adaptations of OA receptor function in Drosophila, likely shaped by lineage-specific selective pressures. Article summaryOctopamine is a chemical messenger in insects, analogous to noradrenaline in vertebrates, and influences many processes such as fight-or-flight responses, locomotion, and reproduction. In fruit flies and other insects, multiple octopamine receptors detect and respond to this signal. We explored how these receptors have changed over millions of years across various fly species. We found that while some receptors remain similar, others have evolved more rapidly, possibly due to changes in the insects lifestyles, environments, or other biological demands. Understanding these differences can help reveal how insects fine-tune their behaviors and adapt to diverse challenges.

evolutionary biology↗

bioRxiv2025

Mechanistic Insights into Cyclic Penta-Adenylate-Mediated Activation of Type III CRISPR Ribonuclease Csm6

Type III CRISPR systems generate cyclic oligoadenylate (cOA, 3 to 6 AMPs) messengers upon detecting viral RNA, activating downstream effectors to defend against viral infection. Although cOA-activated effectors have been extensively characterized, the cA5-specific effectors remained unexplored despite cA5 being among the most abundant cOA species produced during phage infection. Here, we report that Actinomyces procaprae Csm6 (ApCsm6) selectively employs cA5 as its activator. Unlike other characterized Csm6 proteins, ApCsm6 self-limits its ribonuclease activity by degrading cOAs via its HEPN domain, rather than relaying on the CARF domain. Cryo-EM structures of ApCsm6 and its complexes with cA5 and cA6 reveal a homotetrameric assembly, where each monomer binds a single cOA within a composite pocket formed by two tandem CARF-HEPN domains. Binding of cA5, but not cA6, enhances tetramerization and induces large conformational shifts in CARF, which in turn allosterically activates ssRNA cleavage in HEPN. These findings advance our understanding of ligand discrimination and signaling regulation in type III CRISPR immunity. HighlightsO_LIApCsm6 preferentially recognizes cA5 as its activator. C_LIO_LICryo-EM structures of ApCsm6 and its complexes with cA5 or cA6 reveal the structural basis for cA5-selective recognition and allosteric activation. C_LIO_LIApCsm6 acts as a self-limiting ribonuclease by degrading cOAs via its HEPN domain rather than relaying on the CARF domain. C_LI

biochemistry↗

bioRxiv2025

Integrating host plants and a natural enemy into MaxEnt improves global suitability predictions for Semanotus bifasciatus

BACKGROUNDSemanotus bifasciatus is a major conifer pest that causes severe wood damage. The parasitoid Sclerodermus guani is its important natural enemy. However few studies have jointly considered host plants and enemy effects when predicting pest ranges. We applied the optimized MaxEnt model and, on this basis, constructed two models: the "host-pest" model and the "host-pest-enemy" model, to predict the potential global distribution of S. bifasciatus under future scenarios and to explore the effects of climate change and the introduction of biotic interactions. RESULTSResults showed that the climate-only model projected 2.73 x 10{square} km{superscript 2} of suitable area under the historical climate condition, concentrated in Asia, North America and Europe, with expansion toward higher latitudes. The expansion of host plants further enhanced pest habitat suitability, nearly doubling the predicted range (5.46 x 10{square} km{superscript 2}) and increasing the mean suitability. Moreover, the potential distribution of S. guani overlapped extensively with S. bifasciatus, reducing the total suitable area of S. bifasciatus by up to 2.67 x 10{square} km{superscript 2}, and mean suitability declined by nearly 40%, indicating effective suppression of pest risk. Centroid shifts were consistently northward, though magnitude and fragmentation varied among models. CONCLUSIONIntegrating host availability and enemy suppression improves the realism of distribution forecasts for S. bifasciatus. The study highlights the roles of biotic factors in shaping pest suitability and identifies potential future high-risk regions of infestation. These insights provide a solid scientific foundation for targeted monitoring, and the strategic application of biological control in adaptive forest pest management.

ecology↗

bioRxiv2025

Clinical Interventions and Inflammatory Signaling Shape the Transcriptional and Cellular Architecture of the Early Postnatal Lung

The early postnatal period in human development is characterized by extensive remodeling of the distal lung to support gas exchange, but this critical period remains poorly understood. Here, we constructed a comprehensive cellular atlas of the early postnatal human lung (0 to 2 years) using single-nucleus RNA sequencing of histologically normal specimens from 23 individuals. Our analysis identified two previously unknown and mutually exclusive transcriptional states of alveolar type 2 (AT2) cells, one defined by upregulation of genes involved in lipid metabolism and identified by unique expression of FMO5, and the other defined by upregulation of inflammatory response genes and identified by unique expression of CFTR. Using spatial transcriptomics, we discovered that AT2 cell states reside in specific niches and interact with distinct alveolar fibroblast subtypes. Clinical data and organoid experiments further suggest that the environment dictates which state prevails as the pro-inflammatory/pro-regenerative signals TNF- and IL-1{beta} promoted the CFTR+ state in vitro, while patients treated with the anti-inflammatory drug dexamethasone (Dex) had more abundant FMO5+ AT2 cells, and Dex induced the FMO5+ state in vitro. These observations link inflammatory signaling and anti-inflammatory clinical interventions to shifts in transcriptional state of the alveolar epithelium. We benchmarked two neonatal lung diseases, bronchopulmonary dysplasia and pulmonary interstitial glycogenosis, revealing a profound disruption in the balance of AT2 states, a broad arrest of postnatal cellular development, and impaired cellular maturation. Our work uncovers a fundamental new understanding of early postnatal human lung biology, linking pro- and anti-inflammatory signaling to AT2 transcriptional phenotypes and providing a new framework for understanding lung disease.

developmental biology↗

bioRxiv2025

Exploring use of ozone nanobubbles for removal of cyanobacteria and co-occurring antimicrobial resistance genes in water supply and reuse systems

Harmful cyanobacterial blooms present persistent risks to both drinking water security and wastewater reuse, driving the need for advanced treatment strategies. Treatment barrier(s) need to be capable of simultaneously controlling cyanobacteria, cyanotoxins, and co-occurring contaminants like bloom-associated antimicrobial resistance genes (ARGs) particularly in the case of recycling treated wastewater. Ozone nanobubble technology has emerged as a promising innovation, offering extended oxidative stability and enhanced interfacial reactivity compared to conventional ozonation. Hence this research objectives were to (a) assess the removal performance of ozone nanobubbles in eliminating cyanobacteria and their co-occurring contaminants in comparison to conventional ozone systems, and (b) investigate the repeatability of the results in varying background water qualities, ozone decay and the potential for by-products formation Ozonation using nanobubbles enhanced oxidation performance by 19%-34% in the drinking water reservoir compared to conventional ozonation while keeping the ozone concentration below 2mg/l. Lower oxidation efficiencies were observed in treated wastewater compared to drinking water sources, reflecting the higher content of organic matter and suspended solids, and oxidant demand characteristic of recycled water systems. Despite these challenges, ozone nanobubbles consistently outperformed conventional ozonation in reducing both cyanobacterial biomass and cell viability, underscoring their potential as an advanced "polishing" step for algal management in wastewater reuse applications. By exploring fate of ARGs alongside cyanobacteria and toxin removal, this work extends beyond traditional ozonation trials. It provides valuable field-based evidence that bridges the divide between laboratory efficacy and full-scale operational performance. Future studies should build on this by exploring combined or sequential treatment barriers that enhance DNA degradation, thereby addressing both cellular and genetic risks in water supply and reuse systems. Observing the action of nanobubbles under dynamic, real-world water quality conditions is currently challenging; however, this studys novel field trials demonstrate potential nanobubble applications and provide valuable insights to guide future investigations. The results reinforce the broader applicability of ozone nanobubble technology for multi-target contaminant control in water reservoirs.

microbiology↗

bioRxiv2025

Discovery of small molecule probes targeting the 5' stem-loop in the yeast U4/U6 snRNA

The spliceosome is a large ribonucleoprotein complex that regulates pre-mRNA splicing and has been an intriguing target for drug discovery. Essential to the assembly of the spliceosome are the small nuclear RNAs (snRNAs), which form RNA-RNA and RNA-protein interactions in the intact spliceosome and during its assembly. Here, we study the yeast U4/U6 snRNA assembly and report the rapid discovery of small molecule K-turn ligands via parallel small molecule microarray (SMM) screening of multiple related RNA constructs. For hit validation, biophysical analyses were conducted to confirm the binding and effects on thermodynamic stability and of RNA structure. One analog of the hit molecule (22) exhibited improved affinity towards the yeast U4/U6 snRNA (KD = 3.9 {+/-} 2.2 {micro}M). The specific interaction between 22 and the K-turn region was studied experimentally using deltaSHAPE and in silico with MD simulations. Moreover, this molecule was found to inhibit the binding of the U4 to Snu13 in biochemical assays (IC50 = 3.2 {+/-} 0.4 {micro}M). This work reports new ligands for the U4 snRNA and reveals that a multiplexed, structure-based approach can be used to identify small molecules that bind to specific regions of complex RNAs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/681657v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@c156f4org.highwire.dtl.DTLVardef@133e90eorg.highwire.dtl.DTLVardef@109b793org.highwire.dtl.DTLVardef@f3301f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗