Biology subjects

Xu, R.

Publications and source records attributed to Xu, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Beyond Ornithine Metabolism in Gyrate Atrophy: Tissue-Specific Proteomic Insights into Neonatal and Adult OAT Deficiency

Ornithine aminotransferase (OAT) links the urea cycle, TCA cycle, and amino acid metabolism by interconverting ornithine to pyrroline-5-carboxylate and glutamate. Mutations in OAT cause hyperornithinemia and predominantly affect the eye, leading to gyrate atrophy of the choroid and retina (GA), a rare inherited blinding disorder. To understand the early molecular changes that make the eye susceptible to damage, we performed quantitative proteomic and metabolomic profiling of liver, retina, and retinal pigment epithelium and choroid (RPE/Cho) from OAT-deficient (Oatrhg) mice prior to detectable vision impairment. In addition to reduced OAT expression and elevated ornithine, methylation-related metabolites such as N(6)-methyl-lysine were altered in all examined tissues of Oatrhgmice. In the liver, ornithine disposal through the urea cycle was enhanced, together with altered expression of detoxification enzymes and histone H2B proteins. In contrast, the retina had minimal proteomic changes but pronounced alterations in amino acid pathways supporting glutamate homeostasis. The RPE/Cho demonstrated the most extensive proteomic changes, particularly in mitochondrial metabolism, cytoskeleton, and extracellular matrix, along with reductions in metabolites involved energy metabolism and antioxidant capacity. Together, these findings highlight common and tissue-specific impacts of OAT on the liver and ocular tissues and provide insight into early molecular changes that contribute to the selective vulnerability of the eye in GA. Proteomics data are available via ProteomeXchange (PXD063614) and metabolomics data via MassIVE repository (MSV000101103).

biochemistry↗

bioRxiv2025

BioLab: End-to-End Autonomous Life Sciences Research with Multi-Agents System Integrating Biological Foundation Models

Scientific discovery in the life sciences remains hindered by fragmented workflows, narrow-scope computational models, and inefficient links between in silico prediction and wet-lab validation. We present BioLab, a multi-agent system that integrates domain-specialized foundation models to automate end-to-end biological research. BioLab comprises eight collaborating agents, including a Planner, Reasoner, and Critic, orchestrated through a Memory Agent that enables iterative refinement via retrieval-augmented generation and a suite of 219 computational xBio-Tools spanning five biological scales (DNA, RNA, protein, cell, and chemical). These tools are built on the xTrimo Universe, a collection of 104 models derived from six foundation models (xTrimoChem, Protein, RNA, DNA, Cell, and Text), the majority of which achieve state-of-the-art (91-100% SOTA ratios) on domain benchmarks. Across standard reasoning tasks (PubMedQA, MMLU-Pro/Biology, GPQA-diamond), BioLab consistently outperformed leading large language models, including GPT-4o, Gemini-2.5, and DeepSeek-R1. Beyond benchmarks, BioLab autonomously executed a fully computational pipeline for de novo macrophage-targeting antibody design, progressing from target mining to multi-objective antibody optimization, where molecular dynamics simulations revealed structural mechanisms underlying enhanced affinity of optimized variants. Closing the computational-experimental loop, BioLab designed optimized antibodies (Pem-MOO-1, Pem-MOO-2) that achieved IC50 values of 0.01-0.016 nM, markedly surpassing the parental Pembrolizumab (0.027 nM) for PD-1. Functional assays confirmed enhanced pathway blockade and improved multi-parameter performance profiles. Together, these results establish BioLab as a generalizable framework for AI-native scientific discovery, demonstrating how multi-agent systems coupled with foundation models can autonomously generate, execute, and experimentally validate novel biological hypotheses.

bioinformatics↗

bioRxiv2025

Segmented poly(A) tails with microRNA target sites confer tissue-specific regulation for mRNA therapeutics

Targeted delivery and controlled expression of mRNA-LNPs are critical for the development of safe and effective mRNA medicines. However, efficient post-delivery regulation of mRNA-LNP expression remains challenging. In this study, we engineered segmented poly(A) tail variants that function as gene-specific regulatory elements for synthetic mRNAs. Specifically, we inserted microRNA target sites (MTS) for miR-122 or miR-142 at various positions of the poly(A) tail of synthesized luciferase reporter mRNA. These modifications significantly reduced luciferase expression in non-target tissues in vitro and in vivo, demonstrating position-dependent selective expression control. Furthermore, by incorporating triple-MTS sequences for miR-142, miR-126, and miR-148a in all possible combinations at the 5 end of the poly(A) tail, we identified triple-MTS arrangements that simultaneously decrease luciferase activities in three non-target hepatic cell types, while preserving robust expression in hepatocytes. The results highlighted the importance of MTS insertion order for optimal mRNA silencing. These triple-MTS modules significantly expanded the utility of single miRNA-responsive elements, enabling cell-type selective regulation for mRNA therapeutics. By complementing tissue-tropic delivery LNPs, our mRNA cargo regulatory elements have the potential to improve tissue and cell type selectivity as a novel platform for post-delivery regulation of mRNA-LNP.

bioengineering↗

bioRxiv2025

Stress granule sequestration of OXPHOS gene transcripts exacerbates glycolytic restriction

Stress granules (SGs) are dynamic organelles formed under cellular stress and they are generally regarded as protective entities. Meanwhile, their role in pathogenesis is becoming increasingly recognized, but the underlying mechanisms remain elusive due to the diverse nature of both stress types and biological contexts. Here we investigate SG dynamics and temporal changes in bulk and SG-associated transcriptomes under different regimens that inhibit glycolysis. We subject cells to either single assaults of glucose depletion (GD) or 2-deoxy-D-glucose addition (2DG) or a combined treatment (GD+2DG). We find that SGs formed under these conditions exhibit distinct properties, including eIF2 phosphorylation dependency, mRNA composition, and capacity to disassembly. Our results show that SGs induced by GD+2DG uniquely trap oxidative phosphorylation (OXPHOS) gene transcripts, leading to mitochondrial dysfunction. We provide evidence suggesting that the persistency of SGs formed under GD+2DG treatment is interwoven with mitochondrial dysfunction resulting in heightened apoptosis, effects that can also be recreated under single assaults when combined with mitochondrial inhibition. Our findings suggest that SG formation induced by inhibiting a single metabolic pathway can widen its impact in intensifying cellular metabolic stress under specific conditions, providing mechanistic insights into the paradoxical dual nature of SGs in stress response and pathology.

cell biology↗

bioRxiv2025

Oncogenic H-Ras Reprograms Madin-Darby Canine Kidney (MDCK) Cell-derived Midbody Remnant Proteins Following Epithelial-Mesenchymal Transition

Epithelial-mesenchymal transition (EMT) is a highly conserved morphogenic process that allows highly polarized, immotile epithelial cells to transform into motile mesenchymal cells: it is a fundamental cellular process involved in embryonic development, tumour cell metastasis, organ fibrosis and tissue regeneration. To assess the contribution of secreted midbody remnants (MBRs) - a new class of membranous extracellular vesicle (EV) molecularly distinct from exosomes/small EVs - to the EMT process, we conducted a proteomic analysis of MBRs released from Madin-Darby canine kidney (MDCK) cells, and MDCK cells transformed with oncogenic H-Ras (21D1 cells). MBRs were harvested from cell culture media in milligram quantities using a continuous culture bioreactor device and purified using sequential centrifugation and buoyant density gradient centrifugation (OptiPrep). Gel-MS/MS protein profiling showed MDCK cell-MBRs reflect their epithelial origin (e.g., enriched CDH1, DSP, THBS1, OLCN, EPCAM proteins) and 21D1 cell-MBRs their mesenchymal phenotype (e.g., HRAS, VIM, MMP14, CDH2, WNT5A and enriched invasive and cell motility proteins). Prominent findings were the unique expression of the immune checkpoint protein NT5E/CD73 (ecto-5'-nucleotidase), and ser/thr kinases LIMK1/K2 in 21D1-MBRs (not present in MDCK cell-MBRs), and enrichment in Wnt signalling network proteins. Collectively, our findings suggest MBRs might play a previously unrecognized role in the EMT process. SignificanceO_LIEpithelial-to-mesenchymal transition (EMT) is a critical cell biological process that occurs during normal embryonic development and cancer progression. Our study describes, for the first time, the large-scale sequential purification of secreted midbody remnants (MBRs) and exosomes/sEVs from the in vitro cell line EMT model Madin-Darby canine kidney (MDCK) cells and MDCK cells transformed with oncogenic H-Ras (21D1 cells): GeLC-MS/MS protein profiling identified the repertoire of enriched MDCK-MBR proteins following EMT. C_LIO_LIMBRs display a proteome profile distinct from sEVs that is enriched with factors of the centralspindlin complex (KIF23.1, KIF4A, INCENP, CEP55, PLK1) and further include components of the mitochondrial network, cytokinesis, microtubule movement, and intercellular connection. C_LIO_LIIn the context of EMT, our data reveal simultaneous activation of EMT signalling pathways in MBRs including signalling receptor binding, regulation of cell differentiation, and Wnt, VEGF and PDGF signalling. C_LIO_LIWe identify several mesenchymal enriched networks in MBRs associated with focal adhesion, cell matrix, kinase activity, and cell shape/organisation, while epithelial derived MBRs are show enriched networks predominately associated with mitochondrial (processing/transport), midbody, and plasma membrane annotation. C_LIO_LIOur study sheds light on the signalling architecture of MBRs following oncogenic H- Ras-induced EMT: collectively, our data informs ongoing efforts to delineate oncogenic drivers of cancer initiation, progression, and metastasis. C_LI

cell biology↗

bioRxiv2025

KineticGP: a computational framework for genomic prediction of leaf photosynthesis traits

Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.

systems biology↗

bioRxiv2025

Kinetic parameter prediction using neural networks identifies limitations to C4 photosynthesis

Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically-relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.

bioinformatics↗

bioRxiv2025

Thioredoxin-1 inhibits granulosa cell ferroptosis to rescue ovarian aging through mitophagy-dependent activation of BNIP3L

Ovarian aging is closely associated with a decline in fertility and an increase in reproductive dysfunction. Ovarian granulosa cells (GCs) support oocyte homeostasis and development, yet insight into GC dysfunction during aging is limited. Here, we show that aged GCs of humans and mice have indications of elevated ferroptosis, including increased ferroptosis-related metabolites, lipid peroxidation, and iron accumulation. The ferroptosis inhibitor Ferrostatin-1 reversed ovarian impairment and fertility of aged mice in vivo. We show that the age-related reduction in the expression of TXN (thioredoxin) leads to ferroptosis in human and mouse GCs by blocking BNIP3L-dependent mitophagy. Exogenous activation of TXN could promote mitophagy, thereby clearing excessive ROS and inhibiting ferroptosis. These results suggest that anti-ferroptosis-related treatments may assist in treating aging-related reproductive disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/664497v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1b97e8forg.highwire.dtl.DTLVardef@123f8d0org.highwire.dtl.DTLVardef@a44c61org.highwire.dtl.DTLVardef@92b32e_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsO_LIFerroptosis signatures are upregulated in aged GCs from human and mouse ovaries C_LIO_LITXN is deregulated in aged GCs, leading to mitochondrial and ROS metabolic dysfunction C_LIO_LITXN binds to DNA to regulate autophagy and mitophagy genes, including BNIP3L C_LIO_LIInhibition of ferroptosis can ameliorate GC dysfunction C_LI

cell biology↗

bioRxiv2025

DrugLM: A Unified Framework to Enhance Drug-Target Interaction Predictions by Incorporating Textual Embeddings via Language Models

MotivationAccurate prediction of drug-target interactions (DTIs) is central to computational drug discovery, offering the potential to reduce experimental costs and accelerate development timelines. While existing deep learning approaches such as Graph Neural Networks and Transformers have shown promise, they often overlook the rich semantic information embedded in textual descriptions of drugs and targets. These descriptions encode critical biomedical knowledge, including mechanisms of action, biological pathways involved, and therapeutic effects of drugs, which can enhance DTI prediction performance. ResultsWe introduce DrugLM, a unified framework that integrates embeddings derived from large language models (LLMs) into DTI-specific model architectures. DrugLM leverages textual descriptions of drugs and targets to generate semantic embeddings using a range of pretrained LLMs. These embeddings can be seamlessly incorporated into existing DTI models. We systematically evaluate multiple LLMs on benchmark DTI datasets and demonstrate strong performance even without fine-tuning. Moreover, supervised parameter-efficient fine-tuning of the LLMs further improves embedding quality, leading to enhanced prediction accuracy. Notably, a simple multilayer perceptron (MLP) using only LLM-derived embeddings surpasses several established DTI methods, underscoring the power of semantic features. Our findings highlight the practical value of integrating LLMs into DTI pipelines and offer a straightforward recipe for improved drug discovery: LLM embeddings of drugs and targets are both effective and easy to use. AvailabilityOur code and dataset are available at https://github.com/ShPhoebus/DrugLM

bioinformatics↗

bioRxiv2025

Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize

Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design. Significance statementWe present a comprehensive evaluation of hyperspectral reflectance for predicting 25 physiological traits, including fluorescence kinetics, in maize across three seasons using rigorous nested cross-validation. By comparing ML models, prediction scenarios, and data aggregation strategies, our study reveals trait-specific limits of generalizability and offers a robust framework for deploying hyperspectral data in breeding applications.

plant biology↗

bioRxiv2025

Single-Cell and Spatial Profiling of Tumor Microenvironment Heterogeneity in Human Osteosarcomas

Osteosarcoma, a highly heterogeneous malignant bone tumor, exhibits substantial interpatient and intratumoral heterogeneity. Utilizing integrated single-cell RNA sequencing and spatial transcriptomics, we uncovered distinct tumor microenvironment (TME) transcriptional profiles across patients, highlighting profound interpatient heterogeneity. Intratumorally, malignant cells primarily followed two differentiation trajectories, converging into osteoblastic and chondroblastic functional states, and hypoxia may be the influencer; notably, within individual patients, tumor cells exhibited greater transcriptional similarity driven by specific transcription factors, despite these divergent states. Spatial analysis revealed patient-specific TME cellular co-localization patterns, alongside conserved spatial relationships: vascular components (endothelial cells, pericytes) demonstrated strong co-localization, while immune cells (T cells, myeloid cells) clustered within shared regions. Crucially, these functional states occupied distinct microniches: osteoblastic regions were enriched with osteoclasts, vascular components, and immune cells, whereas chondroblastic regions displayed the opposite composition and were preferentially located in hypoxic, vascular-poor niches, exhibiting significant enrichment of hypoxia-related signaling pathways. Furthermore, our data suggest osteosarcoma cells may activate fibroblasts via the SPP1 signaling pathway, indicating fibroblasts act as key intermediaries in tumor-directed TME modulation. This study comprehensively delineates the intricate landscape of osteosarcoma heterogeneity, defining distinct functional cellular states, their spatially organized TME niches, and a potential SPP1-mediated tumor-fibroblast regulatory axis. This comprehensive analysis elucidates the intricate interpatient and intratumoral heterogeneity in osteosarcoma, revealing functional and spatial organization within the tumor and its microenvironment.

cancer biology↗

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

Ultrasonic rewarming of cryopreserved alginate encapsulated liver spheroids

Rapid volumetric rewarming methods are needed to enable the effective cryopreservation and recovery of large volumes of biological cells for therapy and banking of tissues and organs. Ultrasonic rewarming is currently under development, but its effect on cells and their post-rewarming viability has not yet been established. Here, we compare ultrasonic rewarming with the gold-standard 37{degrees}C water bath using cryovials containing cryopreserved alginate encapsulated liver spheroids. Mean rewarming rates are used to establish the exposure time to rewarm to 5{degrees}C for higher power (100 W) and lower power (20 W) ultrasonic rewarming. These electrical powers correspond to free-field pressures along the central cryovial axis of 2.8 MPa and 1.3 MPa, respectively. Ultrasonic rewarming is faster than the gold-standard (120 {+/-}5 s), taking 88 s (36% faster) and 34 s (350% faster) to rewarm to 5{degrees}C with the lower and higher powers. We measure post-rewarming liver spheroid viability and viable cell number across the 96-hour recovery period. The lower power improves viability by 1% and the higher power reduces viability by 2% on average, relative to the gold-standard. There were no significant differences in viable cell number between rewarming methods. Our findings will serve as a foundation for ultrasonic cryovial rewarming and demonstrates potential for scaling to larger volumes.

biophysics↗

bioRxiv2025

Deciphering the microbial landscape of lower respiratory tract in health and respiratory diseases

The microbiota plays an important role in maintaining lung homeostasis and the development of respiratory diseases. However, there have not been detailed characteristics of lung microbiome in health and respiratory diseases. Bronchoalveolar lavage fluid of 43 healthy individuals, 23 COVID patients, and 28 non-small cell lung cancer patients was conducted to characterize microbial diversity and function by metagenomic sequencing. We identified 196 species in health lung after removing low abnudance species. The most abundant species were Staphylococcus aureus, Salmonella enterica, and Klebsiella pneumoniae. Keystone species were identified through co-network analysis, such as Granulicatella adiacens and Mogibacterium diversum. To further explore the microbial function in the healthy lung, we obtained 56 non-redundant metagenome-assembled genomes (MAGs) and subsequently classified them into nine phyla. Metabolism and genetic information processing exhibit high abundance, including bacterial secretion systems, protein export, and Staphylococcus aureus infection, indicating a close correlation between microbiota and the host. Furthermore, the microbiome pairwise distances of non-small cell lung cancer (NSCLC) participants were more similar to those of the healthy group than those of individuals with COVID-19. Rothia dentocariosa, Corynebacterium kefirresidentii, Sphingobium yanoikuyae were enriched in the healthy group, while Schaalia odontolytica, Actinomyces graevenitzii, and Rothia mucilaginosa was enriched in the NSCLC group. Overall, this study provides a comprehensive overview of the diversity and gene function of the lung microbiome, which will facilitate future studies of microbiota associated with respiratory diseases in humans.

microbiology↗

bioRxiv2025

Quantitative Assessment of Protein-DNA Interactions via SYBR Green Fluorescence

Protein-DNA interactions are crucial for cellular processes, but current quantification methods lack sensitivity. We developed the Real-time PCR-based DNA Binding Assay (RP-DBA) to detect and quantify these interactions. The target protein, expressed as a Strep-tag II fusion, is purified and incubated with double-stranded DNA probes containing 4 bp 3 overhangs. Protein-DNA complexes are immobilized on Strep-Tactin beads, washed, and eluted. A complementary single-stranded DNA amplification arm is added, extended by Taq polymerase, and quantified via qPCR with SYBR Green. RP-DBA enables real-time kinetic analysis and is 4- to 10-fold more sensitive than EMSA, depending on amplification arm length (30-90 bp). Its simplicity, speed, accuracy, and high-throughput potential make it a valuable tool for advancing molecular biology research.

molecular biology↗

bioRxiv2025

Biocompatible Multi-functional Polymeric Material for Mineralized Tissue Adhesion

This study developed a biocompatible multifunctional thiol-ene resin system for adhesion to dentin mineralized tissue. Adhesive resins maintain the strength and longevity of dental composite restorations through chemophysical bonding to exposed dentin surfaces after cavity preparations. Dental pulp cells are exposed to residual monomers transported through dentinal tubules. Monomers of conventional adhesive systems may result in inhomogeneous polymer networks and the release of residual monomers that cause cytotoxicity. In this study, we develop a one-step multi-functional polymeric resin system by incorporating trimethylolpropane triacrylate (TMPTA) and bis[2-(methacryloyloxy)ethyl] phosphate (BMEP) to enhance both mechanical properties and adhesion to dentin. Molecular dynamics simulations identified an optimal triacylate:trithiol ratio of 2.5:1, which was consistent with rheological and mechanical tests that yielded a storage modulus of ~30 MPa with or without BMEP. Shear bond tests demonstrated that the addition of BMEP significantly improved dentin adhesion, achieving a shear bond strength of 10.8 MPa, comparable to the commercial primer Clearfil SE Bond. Nanoindentation modulus mapping characterized the hybrid layer and mechanical gradient of the adhesive resin system. Further, the triacrylate-BMEP resin showed biocompatibility with fibroblasts in vitro. These findings suggest the triacrylate-trithiol crosslinking and chemophysical bonding of BMEP provide enhanced bond strength and biocompatibility for dental applications.

bioengineering↗

bioRxiv2025

Controllable Gap Junctions by Vitamin B12 and Light

Gap junctions, mediate rapid signal transduction between contiguous cells, which are indispensable for multicellular organisms to coordinate cellular activities across numerous physiological processes. However, precise control of gap junctions remains elusive. Herein, we present CarGAP, a single-component chemo-optogenetic tool that utilizes the C-terminal adenosylcobalamin (AdoB12) binding domain of a photoreceptor protein (i.e., CarHC) to achieve reversible control over both vertebrate and invertebrate gap junctions with spatiotemporal precision. The vertebrate CarGAP (i.e., Cx-CarGAP), created by genetically fusing connexins with CarHC in mammalian cells, can efficiently block the gap junction channels through AdoB12-induced protein oligomerization, and subsequently reinstate them via green light-induced protein disassembly. We further introduced the CarGAP system (i.e., Inx-CarGAP) to the Drosophila ovary, enabling reversible control over the heterotypic gap junctions formed by innexin2 (Inx2) and innexin4 (Inx4, also known as zero population growth, Zpg), thereby uncovering the roles of gap junctions in stem cell-niche interactions. This study illustrates CarGAP as a generalizable chemo-optogenetic tool for interrogating the functions of gap junctions in various biological contexts.

bioengineering↗

bioRxiv2025

Single-cell alternative polyadenylation analysis reveals mechanistic insights of COVID-19-associated neurological and psychiatric effects

COVID-19 is associated with increased risks of neurological and psychiatric sequelae. Alternative polyadenylation (APA) is ubiquitous in human genes, resulting in mRNA diversity, and has been validated to play a pivotal regulatory role in the onset and progression of a variety of diseases, including viral infections. Here, we analyzed the APA usage across different cell types in frontal cortex cells from non-viral control group and COVID-19 patients, and identified functionally related APA events in COVID-19. According to our study, the poly(A) site (PAS) usage is different among cell types and following SARS-COV-2 infection. Moreover, we found the genes with significant PAS level changes affected pathways related to RNA splicing, and neuronal development and function, suggesting that survivors of COVID-19 will have a high risk of these diseases and that alternative splicing functions cause these changes. Additionally, APA usage and its correlation with gene expression levels varied across genes, some prefer short isoform that is more stable to produce more proteins, while others may be regulated by different mechanisms. A total of 267 risk genes targeted by microRNAs for common neurological and psychiatric disorders were found to undergo significant changes in APA following infection. In conclusion, our comprehensive analysis of APA in neural cells from COVID-19 patients at the single-cell level elucidated changes in APA levels in the brains of SARS-COV-2-infected patients and confirmed that these changes impair the function of the nervous system, providing important insights for COVID-19-associated sequelae. HighlightsO_LIWe analyzed the changes in single-cell APA level in the frontal cortex after SARS-COV-2 infection and found that PAS usage is different among cell types and changed after infection. C_LIO_LIWe find that APA changes are related to RNA splicing, and neuronal development and function, such as synaptic plasticity, nervous system development, learning or memory and cognition, and the brain function may be impaired due to global gene expression and APA changes. C_LIO_LIA total of 267 risk genes for common neurological and psychiatric disorders were found to undergo significant changes in APA following infection, implying that APA alterations in specific genes could be a potential therapeutic target and predictive biomarker for neurological and psychiatric disease. C_LIO_LIAPA in infection samples results in the removal/addition of miRNA target sites in the 3'-UTR of genes. A total of 2404 miRNAs were predicted which suggests that the miRNAs target site information can indicate the impact of APA events and has the potential to be used as a predictive biomarker. C_LI

bioinformatics↗