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Biology subjects

Wei, Y.

Publications and source records attributed to Wei, Y..

At least 109 records · Page 6Linked to original sources

Structural transitions in kinesin minus-end directed microtubule motility

Kinesin motor proteins hydrolyze ATP to produce force for spindle assembly and vesicle transport, performing essential functions in cell division and motility, but the structural changes required for force generation are uncertain. We now report high-resolution structures showing new transitions in the kinesin mechanochemical cycle, including power stroke fluctuations upon ATP binding and a post-hydrolysis state with bound ADP + free phosphate. We find that rate-limiting ADP release occurs upon microtubule binding, accompanied by central {beta}-sheet twisting, which triggers the power stroke - stalk rotation and neck mimic docking - upon ATP binding. Microtubule release occurs with {beta}-strand-to-loop transitions, implying that {beta}-strand refolding induces Pi release and the recovery stroke. The strained {beta}-sheet during the power stroke and strand-to-loop transitions identify the {beta}-sheet as the long-sought motor spring. TeaserStalk rotation, {beta}-sheet twisting and refolding, and neck mimic docking drive the reversed working stroke of kinesin-14 INTRODUCTIONKinesin family proteins couple ATP hydrolysis to microtubule binding, generating force to produce steps or displacements along microtubules. The mechanism by which kinesins and other cytoskeletal motor proteins produce force is not fully understood. A current hypothesis is that the motors contain a spring-like or elastic element that creates strain under load during nucleotide binding or release, followed by a strain-relieving conformational change that produces force and a working stroke of the motor. The spring has not yet been identified for any motor. The power stroke differs for different motors - it consists of neck linker docking for plus-end directed kinesin-1 or a swing of the helical stalk for minus-end directed kinesin-14. RATIONALEDespite considerable research, the molecular dynamics of the kinesin-14 power stroke are still obscure, impeded by the weak microtubule binding of the motor. We overcame the weak binding by introducing a point mutation into the motor that results in faster ATP hydrolysis than wild type and tighter microtubule binding, which enabled us to resolve the motor mode of action. We now present high-resolution cryo-electron microscopy (cryo-EM) and x-ray structures of key mechanochemical states across the full force-producing cycle of a kinesin dimeric motor. RESULTSThe new structures represent five different nucleotide states - two pre-power stroke states, a fluctuating power stroke, and two post-power stroke states. The structures are both microtubule-attached and unattached. They show the motor trapped in previously unreported transition states and reveal new conformational changes involved in energy transduction. The new transition states include a transient state in which the power stroke fluctuates during ATP binding and a new state of a kinesin motor bound to ADP and free Pi prior to phosphate release. The conformational changes include the folding of the kinesin-14 neck mimic into a structure resembling the docked kinesin-1 neck linker, accompanying the power stroke, and previously unreported {beta}-strand-to-loop transitions with stored free energy that potentially induce Pi release and drive the recovery stroke. We interpret the new structures in the context of the hypothesis that the central {beta}-sheet undergoes distortional changes during the mechanochemical cycle that store and release free energy, functioning as the elusive spring of the motors. CONCLUSIONThe new structures show that force is produced by coupled movements of the helical stalk, central {beta}-sheet, and neck mimic, and uncover structural changes during the power stroke that are conserved among kinesins and myosin. We find that kinesin-14 binds to a microtubule by one head during the mechanical cycle, undergoes rate-limiting ADP release, and changes in conformation during ATP binding and hydrolysis to produce force. Notably, kinesin-14 utilizes the same mechanical strategy for force production as other kinesins but couples the changes to a large swing of the stalk, an innovation derived from myosin that is not observed for kinesin-1 or other kinesin motors. Force is produced by rearranging the binding surfaces of the stalk, strand {beta}1, helices [a]4 and [a]6, and the neck mimic, and by twisting and shortening strands of the central {beta}-sheet. These structural changes produce a power stroke - rotation of the helical stalk accompanied by neck mimic docking - during the transition from the nucleotide-free to ATP-bound state, and a reverse stroke after phosphate release that reprimes the motor for the next microtubule binding interaction. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/605428v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1093cdforg.highwire.dtl.DTLVardef@17b6c99org.highwire.dtl.DTLVardef@16b4a5org.highwire.dtl.DTLVardef@add967_HPS_FORMAT_FIGEXP M_FIG C_FIG Kinesin-14 force productionNew transition states and structural movements in a model for motor energy transduction and force production: {beta}-sheet twisting stores free energy in the microtubule-bound nucleotide-free (NF) state. A fluctuating power stroke is produced in the ATP state with neck mimic docking in the ADP{middle dot}Pi state, resembling the kinesin-1 neck linker. This is followed by {beta}-strand-to-loop transitions in the microtubule-bound ADP + free Pi state. Finally, {beta}-sheet refolding drives the recovery stroke for reversion to the ADP state.

biophysics↗

Investigating the antimicrobial and immunomodulatory effects of honeybee venom peptide apamin in the Drosophila genetic platform.

Apamin, an 18-amino acid peptide neurotoxin, constitutes a significant portion of honeybee venom. Although traditionally recognized for its neurotoxic effects, our research demonstrates that apamin exhibits potent antimicrobial properties when genetically expressed in Drosophila. The antimicrobial efficacy of apamin is independent of its disulfide bonds and is enhanced when the peptide is membrane-tethered. This expression selectively targets and inhibits specific harmful bacterial species, such as Pseudomonas aeruginosa, Enterococcus faecalis, and Escherichia coli, while promoting beneficial bacteria like Lactobacillus plantarum thereby improving the gut microbiome. The antimicrobial activity of apamin is localized to the gut and is associated with increased proliferation of intestinal stem cells, acidification of the midgut pH, and activation of enteroendocrine cell calcium signaling. Furthermore, apamins antimicrobial function is dependent on specific peptidoglycan recognition proteins, with PGRP-LA and PGRP-SCs being essential. Apamin expression alone is sufficient to restore the integrity of the gut barrier compromised by stressed conditions. Ultimately, apamin supplementation enhances honeybee gut health, particularly in the presence of ingested bacteria. The expression of other honeybee antimicrobial peptides also significantly reduces bacterial infection in flies. Overall, our study provides a comprehensive understanding of the molecular function and regulation of honeybee venom peptides and antimicrobial peptides, utilizing the Drosophila model system to unravel their mechanisms of action and therapeutic potential.

immunology↗

Generation of High-fidelity Blastocyst-like Structures from Porcine Expanded Pluripotent stem cell via Chemically inducing cell plasticity

Understanding the mechanisms of blastocyst formation and implantation is crucial for advancing farm animal reproduction. However, research in this area is often hindered by the limited availability of embryos. In this study, we developed an efficient method to generate blastocyst-like structures (termed blastoids) from porcine expanded pluripotent stem cells (pEPSCs) through chemical induction with an efficiency up to 80%. These porcine blastoids closely resemble natural blastocysts in terms of morphology, cell composition, and single-cell transcriptomes. The tissue structures of the blastoids developed over time in culture, resembling the tissue morphology of gastrulation-stage embryos. This innovative approach not only provides a robust in vitro model for studying early embryogenesis in pigs but also holds the potential for improving reproductive efficiency and understanding the developmental processes of large farm animal species. The development of porcine blastoids represents a significant advancement in regenerative medicine and developmental biology.

cell biology↗

In a nutshell: pistachio genome and kernel development

Pistachio is a sustainable nut crop with exceptional climate resilience and nutritional value. To advance pistachios as a future food source and a model system for hard-shelled fruits, we generated a chromosome-scale reference genome of the most widely grown pistachio cultivar (Pistacia vera Kerman) and a spatiotemporal developmental study of the hull, shell, and kernel. Our study defined four distinct stages of pistachio growth and maturation by integrating tissue-level physiological and molecular data from thousands of nuts across twenty-four time points over three growing seasons. Transcriptional and metabolic changes in the kernel elucidate molecular pathways governing nutritional quality, such as the accumulation of unsaturated fatty acids, which are vital for shelf-life and dietary value. This work yields new knowledge and resources that will inform other woody crops and facilitate further improvement of pistachio as a globally significant, sustainable, and nutritious crop.

plant biology↗

One-for-all gene inactivation via PAM-independent base editing in bacteria

Base editing is preferable for bacterial gene inactivation without generating double strand breaks, requiring homology recombination or highly efficient DNA delivery capability. However, the potential of base editing is limited by the adjoined dependence on the editing window and protospacer adjacent motif (PAM). Herein, we report an unconstrained base editing system to enable the inactivation of any genes of interests (GOIs) in bacteria. We first employed a dCas9 derivative, dSpRY, as the effector to build a base editor with activation-induced cytidine deaminase, releasing the dependence on PAM. Then, we programmed the base editor to exclude the START codon of a GOI instead of introducing STOP codons to obtain a universal approach for gene inactivation, namely XSTART, with an overall efficiency approaching 100%. By using XSTART, we successfully manipulated the amino acid metabolisms in Escherichia coli, generating glutamine, arginine, and aspartate auxotrophic strains. The effectiveness of XSTART was also demonstrated in probiotic E. coli Nissle 1917 and photoautotrophic cyanobacterium Synechococcus elongatus, illustrating its potential in reprogramming clinically and industrially relevant chassis. To be noticed, we observed a relatively high frequency of off-target events as a trade-off for the efficacy and universality.

synthetic biology↗

Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster.

Neuropeptide SIFamide (SIFa) neurons in Drosophila melanogaster have been characterized by their exceptionally elaborate arborization patterns, which extend from the brain into the ventral nerve cord (VNC). SIFa neurons are equipped to receive signals that integrate both internal physiological cues and external environmental stimuli. These signals enable the neurons to regulate energy balance, sleep patterns, metabolic status, and circadian timing. These peptidergic neurons are instrumental in orchestrating the animals internal states and refining its behavioral responses, yet the precise molecular underpinnings of this process remain elusive. Here we demonstrate that SIFa neurons coordinate a range of behavioral responses by selectively integrating inputs and outputs in a context-dependent manner. These neurons engage in a feedback loop with sNPF neurons in the ventral nerve cord, modifying behaviors such as long mating duration (LMD) and shorter mating duration (SMD). Furthermore, SIFa neurons receive essential inputs from neuropeptides Dsk, sNPF, and dilp2, which regulate interval timing behaviors. Activating SIFa neurons leads to reduced mating duration and increased food intake, while deactivating them reduces food intake. Overall, these findings demonstrate the importance of SIFa neurons in absorbing inputs and turning them into behavioral outputs, shedding light on animals intricate behavioral orchestration.

neuroscience↗

Production of four-gene (GTKO/hCD55/hTBM/hCD39)-edited donor pigs and kidney xenotransplantation

BackgroundThe number of multigene-modified donor pigs for xenotransplantation is increasing with the advent of gene editing technologies. However, which gene combination is suitable for which organ transplantation remains unclear. MethodsIn this study, we utilized CRISPR/Cas9 gene editing technology, PiggyBac transposon system and somatic cell cloning to construct GTKO/hCD55/hTBM/hCD39 four-gene-edited cloned (GEC) pigs and performed kidney transplantation from pig to rhesus monkey to evaluate the effectiveness of these GEC pigs. ResultsFirst, 107 cell colonies were obtained through drug selection, of which 7 were 4-GE colonies. Two colonies were selected for somatic cell nuclear transfer, resulting in 7 fetuses, of which 4 were GGTA1 biallelic knockout. Both fetuses had higher expression of hCD55, hTBM and hCD39. Therefore, these two fetuses were selected for two consecutive rounds of cloning, resulting in a total of 97 live piglets. After phenotype identification, the GGTA1 gene of these pigs was inactivated, and hCD55, hTBM and hCD39 were expressed in cells and multiple tissues. Furthermore, the numbers of monkey IgM and IgG binding to the peripheral blood mononuclear cells (PBMCs) of the 4-GEC pigs were markedly reduced. Moreover, 4-GEC porcine PBMCs had greater survival rates than those from wild-type pigs through complement-mediated cytolysis assays. In pig-to-monkey kidney xenotransplantation, the kidney xenograft successfully survived for 11 days. All physiological and biochemical indicators were normal, and no hyperacute rejection or coagulation abnormalities were found after transplantation. ConclusionThese results indicate that the GTKO/hCD55/hTBM/hCD39 four-gene modification effectively alleviates immune rejection, and the pig kidney can functionally support the recipient monkeys life.

genetics↗

LucaOne: Generalized Biological Foundation Model with Unified Nucleic Acid and Protein Language

AbstractThe language of biology, encoded in DNA, RNA, and proteins, forms the foundation of life but remains challenging to decode due to its complexity. Traditional computational methods often struggle to integrate information across these molecules, limiting a comprehensive understanding of biological systems. Advances in Natural Language Processing (NLP) with pre-trained models offer new possibilities for interpreting biological language. Here, we introduce LucaOne, a pre-trained foundation model trained on nucleic acid and protein sequences from 169,861 species. Through large-scale data integration and semisupervised learning, LucaOne demonstrates an understanding of key biological principles, such as DNA-Protein translation. Using few-shot learning, it effectively comprehends the central dogma of molecular biology and performs competitively on tasks involving DNA, RNA, or protein inputs. Our results highlight the potential of unified foundation models to address complex biological questions, providing an adaptable framework for bioinformatics research and enhancing the interpretation of lifes complexity.

bioinformatics↗

Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer

Patients diagnosed with advanced metastatic colorectal cancer (mCRC) often exhibit heterogeneous disease progression and face poor survival prospects. In order to comprehensively analyze the varied treatment responses among individuals and the challenge of tumor recurrence resistant to drugs in advanced mRCR, we developed a novel quantitative cancer-immunity cycle (QCIC) model. The proposed model was meticulously crafted utilizing a blend of differential equations and randomized modeling techniques to quantitatively elucidate the intricate mechanisms governing the cancer-immunity cycle and forecast tumor dynamics under different treatment modalities. Furthermore, by integrating diverse clinical datasets and rigorous model analyses, we introduced two pivotal concepts: the treatment response index (TRI) and the death probability function (DPF). These concepts are crucial tools for translating model predictions into clinically relevant evaluation indexes. Using virtual patient technology, we extrapolated tumor predictive biomarkers from the model to predict survival outcomes for mCRC patients. Our findings underscore the significance of tumor-infiltrating CD8+CTL cell density as a key predictive biomarker for short-term treatment responses in advanced mCRC while emphasizing the potential predict value of the tumor-infiltrating CD4+Th1/Treg ratio in determining patient survival. This study presents a pioneering methodology bridging the divide between diverse clinical data sources and the generation of virtual patients, offering invaluable insights into understanding inter-individual treatment variations and forecasting survival outcomes in mCRC patients. Author summaryThis study introduces a sophisticated modeling approach to delineate the intricate dynamics of tumor-immune interactions within the cancer-immunity cycle. Utilizing a multi-compartmental, multi-scale, multi-dimensional differential equation model, we quantified the complex interplay between tumor cells, immune cells, cytokines, and chemokines. By integrating virtual patient technology, we have conducted in silico clinical trials that accurately predict disease progression across multiple treatment modalities for cancer patients, particularly in advanced mCRC. Through the combination of differential equations and randomized modeling, we effectively captured the diverse treatment responses and clinical manifestations of drug-resistant tumor recurrence. Furthermore, we explored the pivotal role of predictive biomarkers in determining patients survival prognosis, offering insights for tailored therapeutic strategies. Importantly, our computational framework holds promise for the extension to the investigation of patients with other tumor types, thus contributing to the personalized investigation of patients with other tumor types and cancer care.

systems biology↗

IL-2 enhances effector function but suppresses follicular localization of CD8+ T cells in chronic infection

Cytotoxic CD8+ T cells, essential in combating viral infections and cancer, become dysfunctional from prolonged antigen exposure. Precursors of exhausted T (TPEX) cells are pivotal in sustaining immune responses in chronic diseases and mediating immunotherapy efficacy. They also control viral infection within B-cell follicles, facilitated by CXCR5 expression. How cytokines regulate TPEX cell fate and follicular entry is not well understood. We reveal that IL-2 treatment enhances CD8+ T cell effector functions in chronic LCMV infection but hinders CXCR5+ TPEX cell formation and infection control within B-cell follicles. Mechanistically, IL-2 suppresses TPEX cell differentiation in a STAT5 and BLIMP1-dependent manner. Using an IL-2 fusion protein targeting CD122, we shifted the differentiation towards CX3CR1+ T cells with increased effector function. Clinical observations with low-dose IL-2 in autoimmune disease confirmed IL-2s inhibitory effect on CXCR5+ TPEX cells, underscoring IL-2s crucial regulatory role and therapeutic potential in modulating TPEX and effector T cell generation.

immunology↗

Chromosomal-level genome assembly and single-nucleotide polymorphism sites of black-faced spoonbill Platalea minor

Platalea minor, the black-faced spoonbill (Threskiornithidae) is a wading bird that is confined to coastal areas in East Asia. Due to habitat destruction, it has been classified by The International Union for Conservation of Nature (IUCN) as globally endangered species. Nevertheless, the lack of its genomic resources hinders our understanding of their biology, diversity, as well as carrying out conservation measures based on genetic information or markers. Here, we report the first chromosomal-level genome assembly of P. minor using a combination of PacBio SMRT and Omni-C scaffolding technologies. The assembled genome (1.24 Gb) contains 95.33% of the sequences anchored to 31 pseudomolecules. The genome assembly also has high sequence continuity with scaffold length N50 = 53 Mb. A total of 18,780 protein-coding genes were predicted, and high BUSCO score completeness (93.7% of BUSCO metazoa_odb10 genes) was also revealed. A total of 6,155,417 bi-allelic SNPs were also revealed from 13 P. minor individuals, accounting for [~]5% of the genome. The resource generated in this study offers the new opportunity for studying the black-faced spoonbill, as well as carrying out conservation measures of this ecologically important spoonbill species.

genomics↗

MegaKG: Toward an explainable knowledge graph for early drug development

In biomedical research, the utilization of Knowledge Graph (KG) has proven valuable in gaining deep understanding of various processes. In this study, we constructed a comprehensive biomedical KG, named as MegaKG, by integrating a total of 23 primary data sources, which finally consisted of 188, 844 nodes/entities and 9, 165, 855 edges/relations after stringent data processing. Such a massive KG can not only provide a holistic view of the entities of interest, but also generate insightful hypotheses on unknown relations by applying AI computations. We focused on the interplay of the key elements in drug development, such as genes, diseases and drugs, and aimed to facilitate practical applications that could benefit early drug development in industries. More importantly, we placed much emphasis on the exploitability of the predictions generated by MegaKG. This may greatly help researchers to assess the feasibility or design appropriate downstream validation experiments, making AI techniques more than just black-box models. In this regard, NBFNet was adopted, which combines the advantages of both traditional path-based methods and more recently developed GNN-based ones. Performance evaluation experiments indicated superior results by MegaKG. We also conducted real case studies to validate its practical utility in various scenarios, including target prediction, indication extension and drug repurposing. All these experiments highlighted the potential of MegaKG as a valuable tool in driving innovation and accelerating drug development in pharmaceutical industry.

bioinformatics↗

Genomic and transcriptomic analyses of the elite rice variety Huizhan provide insight into disease resistance and heat tolerance

Rice is an important crop and serves as a model for crop genomics and breeding studies. Here, we used Oxford Nanopore ultra-long sequencing and next-generation sequencing technologies to generate a chromosome-scale genome assembly of Huizhan, a disease-resistant and heat-tolerant indica rice variety. The final genome assembly was 395.20 Mb with a scaffold N50 of 31.87 Mb. We identified expanded gene families in Huizhan that are potentially associated with both organ growth and development, as well as stress responses. We observed that three functional rice blast resistance genes, including Pi2, Pia and Ptr, and bacterial blight resistance gene Xa27, likely contribute to disease resistance of Huizhan. In addition, integrated genomics and transcriptomics analyses show that OsHIRP1, OsbZIP60, the SOD gene family, and various transcription factors are likely involved in heat tolerance of Huizhan. Results presented in this study will serve as a valuable resource for rice functional genomics studies and breeding.

genomics↗

Exploring Pathogenic Mutation in Allosteric Proteins: the Prediction and Beyond

Allosteric regulation that triggers the functional activity of a protein through conformational changes is an inherent function of the protein in numerous physiological and pathological scenarios. In the post-genomic era, a central challenge for disease genomes is the identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes they influence during the initiation and progression of diseases. Here, we analyzed more than 38539 mutations observed in 90 human genes with 740 allosteric protein chains. We found that existing allosteric protein mutations are associated with many diseases, but the clinical significance of the majority of mutations in allosteric proteins remains unclear. Next, we developed a machine-learning-based model for pathogenic mutation prediction of allosteric proteins based on the intrinsic characteristics of proteins and the prediction results from existed methods. When tested on the benchmark allosteric protein dataset, the proposed method achieves AUCs of 0.868 and AUPR of 0.894 on allosteric proteins. Furthermore, we explored the performance of existing methods in predicting the pathogenicity of mutations at allosteric sites and identified potential significant pathogenic mutations at allosteric sites using the proposed method. In summary, these findings illuminate the significance of allosteric mutation in disease processes, and contribute a valuable tool for the identification of pathogenic mutations as well as previously unknown disease-causing allosteric-protein-encoded genes.

bioinformatics↗

Programmable marine bacteria catalyze the valorization of lignin monomers

Efficiently converting lignin, the second most abundant biopolymer on Earth, into valuable chemicals is pivotal for a circular economy and net-zero future. However, lignin is recalcitrant to bio-upcycling, demanding innovative solutions. We report here the biological valorization of lignin-derived aromatic carbon to value-added chemicals without requesting extra organic carbon and freshwater via reprogramming the marine Roseobacter clade bacterium Roseovarius nubinhibens. We discovered the unusual catalytic advantages of this strain for the oxidation of lignin monomers and implemented a CRISPR interference (CRISPRi) system with the lacI-Ptrc inducible module, nuclease-deactivated Cas9, and programmable gRNAs. This enabled precise and efficient repression of target genes. By deploying the customized CRISPRi, we reprogrammed the carbon flux from a lignin monomer, 4-hydroxybenzoate, to achieve maximum production of protocatechuate, a pharmaceutical compound, while maintaining essential carbon for cell growth and biocatalysis. As a result, we achieved a 4.89-fold increase in protocatechuate yield with a dual-targeting CRISPRi system. Our study introduces a new-to-the-field lineage of marine bacteria and underscores the potential of blue biotechnology leveraging resources from the ocean for simultaneous carbon and water conservation.

bioengineering↗

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints

Molecular simulation (MD) is an important research area in the field of life sciences, focusing on understanding the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield of life science, has frequently adopted MD for implementation, where the trajectory data play an important role in drug discovery. With the advancement of high-performance computing and deep learning technology, machine-prediction of protein properties from enormous trajectory data becomes popular and critical, which puts challenges on how to extract useful data features from the complicated simulation data and reasonably reduce the dimensionality. At the same time, in order to better study the Protein system, it is necessary to provide a meaningful explanation of biological mechanism for dimensionality reduction. In order to address this issue, a new unsupervised model RevGraphVAMP is proposed to intelligently analyze the simulation trajectory. RevGraphVAMP is based on the Markov variation method (VAMP) and innovatively integrates graph convolutional neural networks and physical constraint optimization to improve the learning performance of the model. Besides, the attention mechanism is introduced to calculate the importance of protein molecules, leading to interpretation of molecular mechanism. Compared with other VAMPNets models, the new model presented in this paper has achieved the highest VAMP scores and better state transition prediction accuracy in two public datasets. Additionally, it has higher dimensionality reduction discrimination ability for different substates and provides interpretable results for protein structural characterization.

bioinformatics↗

Development of alcoholic liver disease model for drug evaluation from human induced pluripotent stem cell derived liver organoids

Alcoholic liver disease (ALD) poses a significant health challenge, demanding comprehensive research efforts to enhance our comprehension and treatment strategies. However, the development of effective treatments is hindered by the limitations of existing liver disease models. Liver organoids, characterized by their cellular complexity and three-dimensional (3D) tissue structure closely resembling the human liver, hold promise as ideal models for liver disease research. In this study, we employ a meticulously designed protocol involving the differentiation of human induced pluripotent stem cells (hiPSCs) into liver organoids. This process incorporates a precise combination of cytokines and small molecule compounds within a 3D culture system to guide the differentiation process. Subsequently, these differentiated liver organoids are subjected to ethanol treatment to induce ALD, thus establishing a disease model. Rigorous assessment through a series of experiments reveals that this model partially replicates key pathological features observed in clinical ALD, including cellular mitochondrial damage, elevated cellular reactive oxygen species (ROS) levels, fatty liver, and hepatocyte necrosis. In addition, this model offers potential utility in screening drugs for ALD treatment. Taken together, the liver organoids model of ALD, derived from hiPSCs differentiation, emerges as an invaluable platform for advancing our understanding and management of ALD in clinical settings.

cell biology↗

Synfire Chain Dynamics Unravelling Theta-nested Gamma Oscillations for Balancing Prediction and Dodge in Navigation

Theta-nested gamma oscillations, widely observed in experiments, play a crucial role in navigation, yet their functional roles and the origin of the positive correlation between theta frequency and motion velocity remain unclear. We propose that the objects survival relies on both prediction and dodge - predicting future events and staying alert to unpredictable ones, the latter of which has seldom been considered in goal-navigation tasks. By building a biologically plausible spiking neuronal network model and reproducing experimental results, we leverage synfire chain properties - length and separation - to elucidate the functional roles of theta-nested gamma oscillations: theta oscillations for self-location awareness, gamma oscillations for predictive capabilities and their coupling for enhancing functionality. The positive correlation between theta frequency and motion velocity is demonstrated to optimally balance representing predictable events for planning and staying alert to unexpected events. Our study offers a new avenue for unravelling the neural mechanisms of navigation.

neuroscience↗