Biology subjects

Ran, X.

Publications and source records attributed to Ran, X..

12 recordsLinked to original sources

bioRxiv2026

Synaptic microRNA dysregulation orchestrates aberrant glutamatergic transmission in a human neuronal model of 22q11.2 deletion syndrome

22q11.2 Deletion Syndrome (22q11DS), the most prevalent chromosomal microdeletion disorder in humans (incidence ~1 in 4,000 live births), arises from hemizygous deletions of a 1.5-3.0 Mb region on chromosome 22. Notably, 20-30% of individuals with 22q11DS develop schizophrenia (SCZ) by early adulthood, making 22q11DS one of the strongest genetic risk factors for SCZ. However, the mechanisms by which the 22q11.2 deletion perturbs neuronal function and drive psychotic manifestations in 22q11DS remain poorly defined. In this study, we generated human 2D cortical neurons and 3D cortical organoids from induced pluripotent stem cells (iPSCs) derived from clinically well-characterized individuals with 22q11DS. Cellular and electrophysiological analyses revealed that 22q11DS neurons exhibit hyperexcitability, reduced glutamatergic synaptic transmission, and impaired homeostatic synaptic plasticity, which are pathophysiological hallmarks that may be relevant to the clinical, cognitive, and psychophysiological profiles of patients. Transcriptomic profiling of neurons and synaptosomes from 22q11DS cortical organoids uncovered dysregulated immune pathways and synaptic gene networks. Integrative mRNA-microRNA network analysis identified synergistic downregulation of hsa-miR-185-5p and hsa-miR-128-3p at synapses, driving aberrant regulation of immune and synaptic transcripts. Experimental knockdown of these microRNAs in control neurons recapitulated the molecular and functional phenotypes of 22q11DS neurons, thereby establishing a mechanistic link to synaptic dysfunction and impaired plasticity. Collectively, these findings delineate a multimodal mechanistic framework that bridges genetic vulnerability, synaptic pathology, and psychosis penetrance in 22q11DS, positioning dysregulated synaptic microRNAs as nodal regulators of neurodevelopmental networks and potential therapeutic targets.

neuroscience↗

bioRxiv2026

Coronavirus envelope protein drives iron sensing disorder by hijacking the TAp73-FDXR axis

Iron overload is increasingly recognized as a critical contributor to coronavirus pathogenesis1, yet the underlying induction mechanisms remain unclear. Here, we uncover a fundamental pathway by which coronavirus drives IRP1 RNA-binding activity to induce iron accumulation2 via targeting the TAp73-FDXR axis. Specifically, coronavirus infection represses transcription of FDXR (encoding the key rate-limiting enzyme in host iron-sulfur cluster synthesis3), thereby impairing host iron-sulfur cluster generation to trigger the functional conversion of the cytosolic aconitase 1 (ACO1) into iron-regulatory protein 1 (IRP1)4, ultimately leading to the hosts persistently false perception of iron deficiency. We identify TAp73 as the primary transcription factor governing FDXR expression, and demonstrate that the coronavirus envelope protein (CoV-E) orchestrates TAp73 nuclear export. Subsequently, CoV-E binds TAp73 through a critical valine residue within its C-terminal PBM domain, inducing the K48-linked ubiquitination and proteasomal degradation of TAp73. Furthermore, we developed a CoV-E-targeting molecule, DPTP-FC, which blocks CoV-E-TAp73 interaction via forming steric hindrance and effectively alleviates iron accumulation and tissue damage caused by PEDV, PDCoV, and SARS-CoV-2 infection. Our study reveals the central role of the TAp73-FDXR axis in CoV-induced iron accumulation, highlighting CoV-E as an attractive antiviral target and DPTP-FC as a promising therapeutic candidate.

microbiology↗

bioRxiv2025

AI-Driven Efficient De Novo design of GLP-1RAs with Extended Half-Life and Enhanced Efficacy

Peptide drug has revolutionized modern therapeutics, offering novel treatment avenues for various diseases. However, low efficacy, time consumption, and high cost hinder peptide drug design and discovery. We present an efficientin approach that integrates deep learning-based protein design with efficient functional screening, enabling the rapid design of biotechnologically important peptides with improved the stability and efficacy. We designed 10,000 de novo GLP-1 receptor agonists (GLP-1RAs), of which 60 met the stability, efficacy, and diversity criteria in the vitral functional screening. In vitro validation revealed a 62% success rate, while in vivo experiment demonstrated that two designed GLP-1RAs exhibited significantly extended half-lives, approximately three times longer than Semaglutide. In diabetic and obesity mouse models, the most competitive candidate showed superior therapeutic efficacy over Semaglutide. Our AI driven peptide design pipline integrats protein design, functional screening, and experiment validation, reducing the number of iterations required to find novel peptide candidates. The entire process, from design to screening, can be completed in a single cycle within two weeks.

bioengineering↗

bioRxiv2024

Growth-coupled continuous directed evolution by MutaT7 enables efficient and automated enzyme engineering

Traditional directed evolution is limited by labor-intensive iterative steps and low-throughput selection and screening. To address these challenges, we developed a growth-coupled continuous directed evolution (GCCDE) approach, enabling automated and efficient enzyme engineering. By linking enzyme activity to bacterial growth and utilizing the MutaT7 system, GCCDE combines in vivo mutagenesis and high-throughput selection of superior enzyme variants in a single process. To validate this approach, we evolved the thermostable enzyme CelB to enhance {beta}-galactosidase activity at lower temperatures while maintaining thermal stability. CelB activity was coupled to the growth of E. coli, allowing variants with improved activity to metabolize lactose more efficiently and promote faster bacterial growth in a minimal medium. Using a continuous culture system, we achieved automated mutagenesis and real-time selection of over 109 variants per culture. Integrating in vitro and in vivo mutagenesis further increased genetic diversity, yielding CelB variants with significantly enhanced low-temperature activity compared to the wild type while preserving thermostability. DNA sequencing identified key mutations likely responsible for improved substrate binding and catalytic turnover. This GCCDE approach is broadly applicable for optimizing diverse enzymes, demonstrating the potential of automated continuous evolution for industrial and research applications. IMPORTANCEEnzyme engineering aims to develop enzymes with improved or novel traits, but traditional methods are slow and require repetitive manual steps. This study presents a faster, automated protein engineering approach. We utilized an in vivo mutagenesis technique, MutaT7 tools, to induce mutations in living bacteria and established a direct link between enzyme activity and bacterial growth. A continuous culture setup was used to enable growth-coupled high-throughput selection of better-performing variants. Bacteria with improved enzymes grew faster, selecting superior variants without manual intervention. Using this method, we engineered CelB with better performance at lower temperatures while maintaining high-temperature stability. The approach is adaptable to many enzymes. It offers a faster and more efficient solution for enzyme engineering. This system enables high-throughput mutagenesis and selection simultaneously, showing the power of automated continuous evolution to advance enzyme engineering.

bioengineering↗

bioRxiv2024

Metabolomics unveiled the impact of alterations in nucleotide glucose metabolism on palmitic acid-induced TLR4 activation in THP-1 macrophages

OBJECTIVEThe association of palmitic acid with macrophage inflammation and its promotion of the expression of inflammatory factors through TLR4 have been demonstrated. It has been observed that immature TLR4 localizes to the endoplasmic reticulum and Golgi apparatus, necessitating glycosylation for migration to the cell membrane. The objective of this study was to identify potential biomarkers associated with N-glycosylation subsequent to the activation of TLR4 inflammatory signaling in human macrophages by palmitic acid. APPROACH AND RESULTSThe co-cultivation of palmitic acid with THP-1 macrophages was conducted for a duration of 24 hours, followed by the collection of cell extracts for subsequent metabolomic and lipidomic analyses using high performance liquid chromatography-tandem mass spectrometry. Multivariate and univariate statistical analyses were conducted to identify potential biomarkers, in accordance with established scientific protocols. The impact of palmitic acid on the TLR4 signaling pathway and macrophage N-glycosylation was assessed at various time points using Western blot analysis, immunofluorescence staining, Elisa assays, and chemical labeling techniques. The TLR4 inflammatory signaling pathway was examined in macrophages at different time points, revealing that PA induced the upregulation of MyD88 and TRAF6 expression as well as NF-{kappa}B phosphorylation, indicating the activation of classical NF-{kappa}B signaling. After 24 hours, TLR4 translocated from the cell membrane to the cytoplasm and initiated internalization, accompanied by significant colocalization with GalAz in the cytosol. In addition, the metabolites in the cell extract were found to be altered in both the control and model groups. Significantly alterations in two N-glycosylation related metabolites were observed in the model group, including guanosine diphosphate-L-fucose and uridine diphosphate-N-acetylglucosamine/uridine diphosphate-N-acetylgalactosamine. CONCLUSIONSTHP-1 macrophages incubated with palmitic acid exhibited a distinct metabolomic profile compared to the control group. Our findings suggest that metabolomics analysis holds promise in identifying disease-specific biomarkers for diagnosing fatty acid-induced inflammatory responses in macrophages.

immunology↗

bioRxiv2023

Iron acquisition by a commensal bacterium modifies host nutritional immunity during Salmonella infection

During intestinal inflammation, host nutritional immunity starves microbes of essential micronutrients such as iron. Pathogens scavenge iron using siderophores, which is counteracted by the host using lipocalin-2, a protein that sequesters iron-laden siderophores, including enterobactin. Although the host and pathogens compete for iron in the presence of gut commensal bacteria, the roles of commensals in nutritional immunity involving iron remain unexplored. Here, we report that the gut commensal Bacteroides thetaiotaomicron acquires iron in the inflamed gut by utilizing siderophores produced by other bacteria including Salmonella, via a secreted siderophore-binding lipoprotein termed XusB. Notably, XusB-bound siderophores are less accessible to host sequestration by lipocalin-2 but can be "re-acquired" by Salmonella, allowing the pathogen to evade nutritional immunity. As the host and pathogen have been the focus of studies of nutritional immunity, this work adds commensal iron metabolism as a previously unrecognized mechanism modulating the interactions between pathogen and host nutritional immunity.

microbiology↗

bioRxiv2023

Ulinastatin Ameliorates Cardiac Ischemia/Reperfusion Injury via Inhibiting the tissue kallikrein-kinin system

BackgroundEndothelial cells (ECs) are considered more sensitive to cardiac ischemia/reperfusion (I/R) injury compared to cardiomyocytes. However, current research is mainly focused on molecular mechanisms and preventive strategies targeting cardiomyocyte I/R injury, whereas insufficient attention is placed on protecting endothelial function. Methods and ResultsIn this study, we established an interlink among ulinastatin (UIT; a serine protease inhibitor), the kallikrein-kinin system (KKS), and EC injury in response to cardiac reperfusion for the first time, using in vitro and in vivo experiments, and bioinformatic analysis. Our data indicated that UTI affected I/R by inhibiting the activation of KKS and simultaneously down-regulating both bradykinin receptor 1 (Bdkrb1) and bradykinin receptor 2 (Bdkrb2) related signaling such as extracellularsignal-regulated kinase (ERK)/inducible nitric oxide synthase (iNOS) and vascular endothelial growth factor (VEGF)/endothelial nitric oxide synthase (eNOS), thereby reducing infarct size, attenuating inflammation and edema, and improving cardiac function and mortality. Interestingly, UIT significantly suppressed KLK1 activity but did not down-regulate the KKS in normal conditions, suggesting inhibition of KLK1 might be the crucial mechanism for UIT-induced cardioprotection in reperfusion injury. Moreover, knockdown of Bdkrb1 in reperfusion-induced cardiac endothelial cells (MCECs) injury significantly prevented ERK translocation into the nucleus, reducing apoptosis, junction disruption, and expression levels of cytokines, whereas Bdkrb2 deletion could not protect MCECs against I/R injury. ConclusionsOur findings imply that inhibition of KLK1/Bdkrb1 is a critical target for UIT in the treatment of reperfusion-induced cardiac endothelial inflammation, apoptosis, and leakage and might be a potential therapeutic strategy for cardiac reperfusion injury.

cell biology↗

bioRxiv2022

A High-Throughput Screen Reveals the Structure-Activity Relationship of the Antimicrobial Lasso Peptide Ubonodin

The Burkholderia cepacia complex (Bcc) is a group of bacteria including several opportunistic human pathogens. Immunocompromised individuals and cystic fibrosis patients are especially vulnerable to serious infections by these bacteria, motivating the search for compounds with antimicrobial activity against the Bcc. The natural product ubonodin is a lasso peptide with promising activity against several Bcc species, working by inhibiting RNA polymerase in susceptible bacteria. In this study, we developed a high-throughput screen using next-generation sequencing to examine the fitness of a library of over 90,000 ubonodin variants, generating the most comprehensive dataset on lasso peptide activity so far. This screen revealed information regarding the structure-activity relationship of ubonodin over a large sequence space, indicating certain residues that can tolerate amino acid substitutions and still retain activity. Remarkably, the screen identified one variant with not only improved activity compared to wild-type ubonodin but also a sub-micromolar minimum inhibitory concentration (MIC) against a clinical isolate of the Bcc member Burkholderia cenocepacia. Ubonodin and several of the variants identified in this study had a lower MIC against certain Bcc strains than many clinically approved antibiotics. Finally, the large library size enabled us to develop DeepLasso, a deep learning model that can predict the RNAP inhibitory activity of an ubonodin variant.

biochemistry↗

bioRxiv2022

Rapid modeling of experimental molecular kinetics with simple electronic circuits instead of with complex differential equations

Kinetic modeling has relied on using a tedious number of mathematical equations to describe molecular kinetics in interacting reactions. The long list of differential equations with associated abstract variables and parameters inevitably hinders readers easy understanding of the models. However, the mathematical equations describing the kinetics of biochemical reactions can be exactly mapped to the dynamics of voltages and currents in simple electronic circuits wherein voltages represent molecular concentrations and currents represent molecular fluxes. For example, we theoretically derive and experimentally verify accurate circuit models for Michaelis-Menten kinetics. Then, we show that such circuit models can be scaled via simple wiring among circuit motifs to represent more and arbitrarily complex reactions. Hence, we can directly map reaction networks to equivalent circuit schematics in a rapid, quantitatively accurate, and intuitive fashion without needing mathematical equations. We verify experimentally that these circuit models are quantitatively accurate. Examples include i) different mechanisms of competitive, noncompetitive, uncompetitive, and mixed enzyme inhibition, important for understanding pharmacokinetics; ii) product-feedback inhibition, common in biochemistry; iii) reversible reactions, important in many metabolic pathways; and iv) translation and transcription dynamics in a cell-free system, which brings insight into the functioning of all gene-protein networks. We envision that circuit modeling and simulation could become a powerful scientific communication language and tool for quantitative studies of kinetics in biology.

bioengineering↗

bioRxiv2022

SARS-CoV-2 impairs interferon production via NSP2-induced repression of mRNA translation

Viruses evade the innate immune response by suppressing the production or activity of cytokines such as type I interferons (IFNs). Here we report the discovery of a novel mechanism by which the SARS-CoV-2 virus co-opts an intrinsic cellular machinery to suppress the production of the key immunostimulatory cytokine IFN-{beta}. We reveal that the SARS-CoV-2 encoded Non-Structural Protein 2 (NSP2) directly interacts with the cellular GIGYF2 protein. This interaction enhances the binding of GIGYF2 to the mRNA cap-binding protein 4EHP, thereby repressing the translation of the Ifnb1 mRNA. Depletion of GIGYF2 or 4EHP significantly enhances IFN-{beta} production, leading to reduced viral infection. Our findings reveal a new target for rescuing the antiviral innate immune response to SARS-CoV-2 and other RNA viruses.

microbiology↗

bioRxiv2019

Unexpected higher resilience to distraction during visual working memory in schizophrenia

Visual working memory (VWM) and distractibility are two core executive functions in human cognition. It has been suggested that schizophrenia (SZ) patients exhibit worse VWM performance and lower resilience to distraction compared with healthy control (HC) subjects. Previous studies, however, have largely investigated these two functions separately. It still remains unclear what are the mechanisms of the deficits, especially the interactions between the two cognitive domains. Here we modify the standard delay-estimation task in VWM and explicitly add distractors in the task so as to examine the two domains simultaneously. We find that SZ indeed exhibit worse performance compared with HC in almost all VWM load and distraction levels, a result consistent with most prior experimental findings. But adding distractors does not selectively impose larger impacts on SZ performance. Furthermore, unlike most previous studies that only focused on behavioral performance, we use the variable precision model to disentangle the distraction effect on different computational components of VWM (resources and resources allocation variability etc.). Surprisingly, adding distractors significantly elevates resources allocation variability--a parameter describing the heterogeneity of resource allocation across different targets--in HC but not in SZ. This counterintuitive result suggests that the internal VWM process in SZ is less interfered by the distractors. However, this unexpected higher resilience to distraction might be associated with less flexible cognitive control mechanisms. In sum, our work demonstrates that multiple cognitive functions might jointly contribute to dysfunctions in SZ and their interactions might manifest differently from merely summing their independent effects.

neuroscience↗

bioRxiv2018

Atypically larger variability of resource allocation accounts for visual working memory deficits in schizophrenia

Schizophrenia patients are known to have profound deficits in visual working memory (VWM), and almost all previous studies attribute the deficits to decreased memory capacity. This account, however, ignores the potential contributions of other VWM components (e.g., memory precision). Here, we measure the VWM performance of schizophrenia patients and healthy control subjects on two classical delay-estimation tasks. Moreover, we thoroughly evaluate several established computational models of VWM to compare the performance of the two groups. We find that the model assuming variable precision across items and trials is the best model to explain the performance of both groups. According to the variable-precision model, schizophrenia subjects exhibit abnormally larger variability of allocating memory resources rather than resources per se. These results invite a rethink of the widely accepted decreased-capacity theory and propose a new perspective on the diagnosis and rehabilitation of schizophrenia.

neuroscience↗