Biology subjects

Cui, Z.

Publications and source records attributed to Cui, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

The Role of Supraoptic Hypothalamic Arginine Vasopressin Neurons in Aging-Associated Water Balance and Thermoregulatory Deficits

Aging disrupts physiological homeostasis, impairing thermoregulation, metabolism, and water balance, but the underlying neural mechanisms remain unclear. Here, we identify arginine vasopressin (AVP) neurons in the supraoptic nucleus (SON) of the hypothalamus as a critical driver of these changes. Using single-nucleus RNA-sequencing of the anterior hypothalamus in young and aged mice, we found Avp to be one of the most upregulated neuronal transcripts with age. Aged SONAVP neurons displayed enlarged size and heightened excitability, features consistent with hyperactivity. Functionally, chemogenetic activation of SONAVP neurons in young mice reproduced aging-associated phenotypes including hypothermia, reduced energy expenditure, and suppressed water intake. Conversely, knockdown of Avp in the SON of aged mice restored water balance, partially improved thermoregulation and systemic metabolism. Pharmacological inhibition of AVP receptors revealed that neuroendocrine release of AVP drives homeostatic deficits, with distinct roles for V1A and V2 receptors. Senolytic drug treatment improved systemic metabolism and reduced inflammaging but does not rescue hypothalamic AVP dysfunction, underscoring a brain autonomous mechanism of age-related physiological failure. Together, our findings establish SONAVP neuronal hyperactivity as a driver of impaired homeostasis with age and suggest that targeted modulation of neuroendocrine AVP signaling may offer a therapeutic strategy to alleviate age-associated water balance defects.

physiology↗

bioRxiv2025

Mucinolysome in gut microbiomes of farm animals and humans

Mucins are glycoproteins that create a protective barrier protecting host tissues from microbial pathogens and are instrumental for host health. Here, we provide evidence that mucin glycan degradation in the gut can be mediated by mucinolysomes, defined as extracellular multi-enzyme complexes specializing in mucin glycan degradation. We computationally predicted the presence of mucinolysomes across 63 metagenome-assembled genomes (MAGs) and two isolated genomes of anaerobic Limousia bacteria, including seven MAGs from human samples of six countries. All 65 genomes were found to display core mucinolysome components, consisting of 3[~]6 scaffoldins (containing up to 12 cohesin modules) and up to 22 dockerin-containing mucin glycan-degrading CAZymes (carbohydrate active enzymes). The organization of mucinolysomes allows the assembly of up to 24 CAZymes in the same complex. We validated that a cultivated Limousia strain ET540 from chicken cecum can support growth on mucins as its sole carbon source, triggering the expression of most mucinolysome-related genes, including both scaffoldins and CAZymes. We also modeled the assembly of proteins into a multi-enzyme complex by predicting the cohesin-dockerin interactions among most of the mucinolysome proteins using AlphaFold3. While mucinolysosome-encoding Limousia have low abundance in different animal hosts, their abundance and prevalence are higher in farm animals than in humans, highlighting a potentially important role in livestock gut ecosystems. Our findings reveal a novel mechanism of mucin glycan degradation and provide a framework to explore microbial contributions to gut health and host-microbe interactions across species.

bioinformatics↗

bioRxiv2025

Proteome constrained metabolic modeling of Sus scrofa muscle stem cells for cultured meat production

Cultured meat has recently emerged as a sustainable alternative to the traditional livestock farming and gained attention as a promising future protein source. Herein, the Sus scrofa muscle stem cell is a commonly used cell source in the cell proliferation step of cultured meat production. However, a major bottleneck of large-scale cultivation is the inhibition by secreted and accumulated lactate and ammonium in the process of S. scrofa cell proliferation. To simulate the growth and metabolism of S. scrofa muscle stem cells under different lactate and ammonium concentrations, this study constructed the first proteome constrained metabolic model for the core metabolism of S. scrofa muscle stem cells, pcPigGEM2025. The relationship of lactate and ammonium levels with cellular metabolism was derived from growth and metabolomics data of two culture conditions with low and high initial ammonium concentrations, and then incorporated into metabolic flux simulation. Metabolic flux simulations for experimental conditions, along with perturbation simulations considering stressed non-growth associated maintenance and oxygen supply, demonstrated that pcPigGEM2025 could effectively characterize the response of the S. scrofa muscle stem cells growth and metabolism to varying environmental conditions, shedding light on model-aided control and optimization of the cultured meat production process. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/679571v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@a4526aorg.highwire.dtl.DTLVardef@1ee2d1org.highwire.dtl.DTLVardef@1bd4700org.highwire.dtl.DTLVardef@18079e6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThe first proteome constrained metabolic model was built for S. scrofa myoblasts. C_LIO_LIThis model effectively simulated myoblast metabolism under lactate and NH4+ stress. C_LIO_LIPerturbation simulations showed that this model could also account for other stress. C_LIO_LIThis model enables in-silico control and optimization of cultured meat production. C_LI

bioengineering↗

bioRxiv2025

Visual-like Template Diffusion: Boosting Single-Sequence Protein Structure Prediction by Adapting Image Diffusion Models

Single-sequence protein structure prediction has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here, we propose a visual-like 2D geometric template* diffusion method, named TDFold, to generate high-quality pairwise geometries (including pairwise distances and orientations) for achieving accurate and highly efficient single-sequence 3D structure prediction for proteins. Given a protein sequence, TDFold initially generates high-quality inter-residue geometries from a probabilistic diffusion perspective. Since inter-residue geometries can be encoded as multi-channel feature matrices, analogous to image feature maps, we construct an image-level 2D geometric template diffusion module by adapting the stable diffusion (SD) model from text-vision generation to sequencegeometry diffusion for proteins. Subsequently, a lightweight sequencegeometry collaborative learning (SCL) network is constructed to facilitate accurate and efficient protein structure prediction. As a result, TDFold possesses three highlights: (i) better single-sequence prediction performance: TDFold greatly outperforms existing protein language models (PLMs, e.g. ESMFold and OmegaFold) and homology-based methods (e.g. AlphaFold2, AlphaFold3 and RoseTTAFold) on homologyinsufficient datasets such as Orphan and Orphan25, while also achieving promising results on the popular CASP14, CASP15 and CASP16 benchmarks; (ii) low resource consumption: By utilizing the lightweight SCL architecture, the GPU memory consumption of TDFold is generally lower than that of popular methods such as AlphaFold2 and ESMFold; (iii) higher efficiency in training and inference: TDFold can be trained within a week using a single NVIDIA 4090 GPU. Furthermore, the inference time of TDFold is significantly shorter (about 10x to 100x) than that of existing methods (ESMFold, AlphaFold2 and AlphaFold3) for long protein sequences. This work demonstrates the effectiveness of leveraging powerful vision diffusion models to enhance protein 2D geometric template generation, thereby establishing a new paradigm for single-sequence protein structure prediction. It also accelerates protein-related research, particularly for resource-limited universities and academic institutions. The code has been released to speed up biological research.

bioinformatics↗

bioRxiv2025

Identification of pseudotetraivprolide from Pseudomonas entomophila give novel insights into the biosynthesis of detoxin/rimosamide-like anti-antibiotics

Novel variants of known natural product (NP) classes might guide our understanding of biosynthesis, mode of action and potential application as drugs for all members of such NP classes. Here we describe a novel member of the widespread detoxine/rimosamide-like (DRL) natural products named pseudotetraivprolide from Pseudomonas strains, which has the characteristic DRL-activity of protecting Bacillus cereus against the antibiotic blasticidin S. The generation of several deletion and complemented mutants, heterologous expression experiments, identification and structure elucidation of several derivatives, chemical synthesis of main derivatives, enzymatic characterization of individual biochemical steps and detailed homology modelling of enzyme complexes were performed. This allowed us to show the primary metabolism-derived malonyl CoA-ACP transacylase (AT) FabD acting as trans-AT in the biosynthesis, suggest an order for all late-stage modifications and provide a function for the three conserved hypothetical proteins PipDFG acting as last-step acetylation complex thereby stabilizing the final product.

microbiology↗

bioRxiv2025

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

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

neuroscience↗

bioRxiv2025

Robust and Reliable de novo Protein Design: A Flow-Matching-Based Protein Generative Model Achieves Remarkably High Success Rates

Generative models have achieved significant progress in the field of protein design, particularly in the generation of tertiary structures. However, they still face several challenges, such as balancing the designability and diversity of the generated structures, generating specified structures under highly constrained conditions, and the most challenging aspect, direct functional design such as motifs and binders. We present OriginFlow, an efficient protein generative model that combines Stochastic Differential Equation (SDE) models and flow models based on flow matching. The generated structures exhibit state-of-the-art (SOTA) levels of designability, diversity, and novelty. Moreover, this model demonstrates outstanding performance in functional design aspects such as motifs, binders, and symmetric motif scaffolding. We performed binder design for multiple targets, and all results showed excellent affinity performance in AF3 complex predictions and MD experiments. Wet-lab validation was conducted on PD-L1, RBD, and VEGF targets, achieving 90% expression, solubility, and affinity. These results are significantly superior to all current AI binder design algorithms.

bioengineering↗

bioRxiv2025

Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning

The brains multi-scale hierarchical organization supports functional segregation and integration. Characterizing the hierarchy of individualized multi-scale functional networks (FNs) is crucial for understanding these fundamental brain processes. It provides promising opportunities for both basic neuroscience and translational research in neuropsychiatric illness. However, current methods typically compute individualized FNs at a single scale and are not equipped to quantify any possible hierarchical organization. To address this limitation, we present a self-supervised deep learning (DL) framework that simultaneously computes multi-scale FNs and characterizes their across-scale hierarchical structure at the individual level. Our method learns intrinsic representations of fMRI data in a low-dimensional latent space to effectively encode multi-scale FNs and their hierarchical structure by optimizing functional homogeneity of FNs across scales jointly in an end-to-end learning manner. A DL model trained on fMRI scans from the Human Connectome Project successfully identified individualized multi-scale hierarchical FNs for unseen individuals and generalized to two external cohorts. Furthermore, the individualized hierarchical structure of FNs was significantly associated with biological phenotypes, including sex, brain development, and brain health. Our framework provides an effective method to compute multi-scale FNs and to characterize the inter-scale hierarchy of FNs for individuals, facilitating a comprehensive understanding of brain functional organization and its inter-individual variation.

neuroscience↗

bioRxiv2025

Leopard Cats Occupied Human Settlements in China for 3,500 years before the Arrival of Domestic cats in 600-900 CE around the Tang Dynasty

The earliest cats in human settlements in China were not domestic cats (Felis catus), but native leopard cats (Prionailurus bengalensis). To trace when and how domestic cats arrived in East Asia, we analyzed 22 feline bones from 14 sites across China spanning 5,000 years. Nuclear and mitochondrial genomes revealed that leopard cats began occupying anthropogenic scenes around 5,400 years ago and last appeared in 150 CE. Following several centuries gap of archeological feline remains, the first known domestic cat (706-883 CE) in China was identified in Shaanxi during the Tang Dynasty. Genomic analysis suggested a white or partially white coat and a link to a contemporaneous domestic cat from Kazakhstan, indicating a likely dispersal route via the Silk Road. The two felids once independently occupied ancient anthropogenic environments in China but followed divergent paths and reached different destinations in human-animal interactions.

evolutionary biology↗

bioRxiv2025

Developmental Variations in Recurrent Spatiotemporal Brain Propagations from Childhood to Adulthood

The brain undergoes profound structural and functional transformations from childhood to adolescence. Convergent evidence suggests that neurodevelopment proceeds in a hierarchical manner, characterized by heterogeneous maturation patterns across brain regions and networks. However, the maturation of the intrinsic spatiotemporal propagations of brain activity remains largely unexplored. This study aims to bridge this gap by delineating spatiotemporal propagations from childhood to early adulthood. By leveraging a recently developed approach that captures time-lag dynamic propagations, we characterized intrinsic dynamic propagations along three axes: sensory-association (S-A), task-positive to default networks (TP-D), and somatomotor-visual (SM-V) networks, which progress towards adult-like brain dynamics from childhood to early adulthood. Importantly, we demonstrated that as participants mature, there is a prolonged occurrence of the S-A and TP-D propagation states, indicating that they spend more time in these states. Conversely, the prevalence of SM-V propagation states declines during development. Notably, top-down propagations along the S-A axis exhibited an age-dependent increase in occurrence, serving as a superior predictor of cognitive scores compared to bottom-up S-A propagation. These findings were replicated across two independent cohorts (N = 677 in total), emphasizing the robustness and generalizability of these findings. Our results provide new insights into the emergence of adult-like functional dynamics during youth and their role in supporting cognition.

neuroscience↗

bioRxiv2025

Spatial Heterogeneity and Subtypes of Functional Connectivity Development in Youth

The brain functional connectome development is fundamental to neurocognitive growth in youth. While brain age prediction has been widely used to assess connectome development at the individual level, traditional approaches providing a global index overlook the spatial variability and inter-individual heterogeneity of functional connectivity (FC) development across the cortex. In this study, we introduced a regional brain development index to assess spatially fine-grained FC development. We examined the spatial variability of FC development and stratified individuals into subtypes with distinct patterns of spatial heterogeneity in region-wise FC development across the cortex through clustering. An evaluation conducted on a sample of youths aged 8-23 years using fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) revealed three distinct FC development subtypes. Individuals with advanced FC development aligned with the hierarchical brain organization along the sensorimotor-association (S-A) axis demonstrated superior cognitive performance compared to those with other patterns. These patterns were replicated in the Human Connectome Project Development (HCP-D) cohort, confirming their robustness. Further analysis revealed associations between FC development and gene expression, with enriched genes linked to neural differentiation, synaptogenesis, and myelination. These findings suggest that spatial heterogeneity in FC development reflects underlying cortical microstructure and hierarchical cortical organization, underscoring its critical role in understanding neurocognitive maturation and individual variability during youth.

neuroscience↗

bioRxiv2024

Functional split-tRNA: a new perspective on codon decoding mechanism

The translation machinery is required to process all codon triplets without exception while maintaining high speed and accuracy, despite orders-of-magnitude differences in cognate pairing stability. For stability-based selection to be efficient, the range of pairing stabilities must be narrowed by raising the lower bound and lowering the upper bound. The constrained structure and intramolecular cooperativity of tRNA complicate understanding of how it modulates codon-anticodon stability and whether it affects selection kinetics beyond codon recognition. To address these questions, we engineered functional split-tRNAs bearing a dangling anticodon in place of the anticodon loop. Our results demonstrate that split-tRNA supports in vitro translation nearly as efficiently as intact synthetic tRNA, challenging the notion that tRNA strain is essential for triggering GTP hydrolysis in response to codon recognition. Using split-tRNA architecture, we found that codon-anticodon stability is likely modulated by the dipole moments of adjacent nucleobases. Our kinetic modeling aligns with a conformational selection mechanism, where the decoding site fluctuates between open and closed states, and the correct codon-anticodon minihelix acts as an allosteric effector that permits its spontaneous closure and stabilizes the closed state. Overall, our data challenge the notion that tRNA is an active player in the selection process.

molecular biology↗

bioRxiv2024

Structural basis for growth factor and nutrient signal integration on the lysosomal membrane by mTORC1

Mechanistic target of rapamycin complex 1 (mTORC1), which consists of mTOR, Raptor, and mLST8, receives signaling inputs from growth factor signals and nutrients. These signals are mediated by the Rheb and Rag small GTPases, respectively, which activate mTORC1 on the cytosolic face of the lysosome membrane. We biochemically reconstituted the activation of mTORC1 on membranes by physiological submicromolar concentrations of Rheb, Rags, and Ragulator. We determined the cryo-EM structure and found that Raptor and mTOR directly interact with the membrane at anchor points separated by up to 230 [A] across the membrane surface. Full engagement of the membrane anchors is required for maximal activation, which is brought about by alignment of the catalytic residues in the mTOR kinase active site. The observations show at the molecular and atomic scale how converging signals from growth factors and nutrients drive mTORC1 recruitment to and activation on the lysosomal membrane in a three-step process, consisting of (1) Rag-Ragulator-driven recruitment to within [~]100 [A] of the lysosomal membrane, (2) Rheb-driven recruitment to within [~]40 [A], and finally (3) direct engagement of mTOR and Raptor with the membrane. The combination of Rheb and membrane engagement leads to full catalytic activation, providing a structural explanation for growth factor and nutrient signal integration at the lysosome.

biochemistry↗

bioRxiv2024

TET2 contributes to gluconeogenesis and pathology of type 2 diabetes

The control of gluconeogenesis is critical for glucose homeostasis and the pathology of type 2 diabetes (T2D). Here, we uncover a novel function of TET2 in the regulation of gluconeogenesis. In mice, both fasting and a high-fat diet (HFD) stimulate the expression of TET2, and TET2 knockout impairs glucose production. Mechanistically, FBP1, a rate-limiting enzyme in gluconeogenesis, is positively regulated by TET2 in liver cells. TET2 is recruited by HNF4, contributing to the demethylation of FBP1 promoter and activating its expression in response to glucagon stimulation. Moreover, metformin treatment increases the phosphorylation of HNF4 on Ser313, which prevents its interaction with TET2, thereby decreasing the expression level of FBP1 and ameliorating the pathology of T2D. Collectively, we identify an HNF4-TET2-FBP1 axis in the control of gluconeogenesis, which contributes to the therapeutic effect of metformin on T2D and provides a potential target for the clinical treatment of T2D.

cell biology↗

bioRxiv2024

Reproducible Sex Differences in Personalized Functional Network Topography in Youth

BackgroundA key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organization at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organization of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex. AimsWe aimed to evaluate the impact of sex on the spatial organization of person-specific functional brain networks. MethodWe leveraged person-specific atlases of functional brain networks defined using non-negative matrix factorization in a sample of n = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalized additive models to uncover associations between sex and the spatial layout ("topography") of personalized functional networks (PFNs). Next, we trained support vector machines to classify participants sex from multivariate patterns of PFN topography. Finally, we leveraged transcriptomic data from the Allen Human Brain Atlas to evaluate spatial correlations between sex differences in PFN topography and gene expression. ResultsSex differences in PFN topography were greatest in association networks including the fronto-parietal, ventral attention, and default mode networks. Machine learning models trained on participants PFNs were able to classify participant sex with high accuracy. Brain regions with the greatest sex differences in PFN topography were enriched in expression of X-linked genes as well as genes expressed in astrocytes and excitatory neurons. ConclusionsSex differences in PFN topography are robust, replicate across large-scale samples of youth, and are associated with expression patterns of X-linked genes. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.

neuroscience↗

bioRxiv2024

Neural representation of vocalization self-monitoring in the bat auditory midbrain

During acoustic interaction, mammals, including humans, not only monitor sounds from external sources but also their own vocalizations, the latter of which is known as vocalization feedback monitoring. Yet, it is unclear whether subcortical auditory regions process vocalization feedback. Here, we established an experimental paradigm that allows recording chronically the single-unit activity of the auditory midbrain (inferior colliculus) in freely vocalizing bats. We report that most collicular neurons represented self-produced biosonar vocalizations distinctively from pure tones and vocalization playbacks. Some neurons showed robust excitatory responses to vocalization feedback yet lacked any excitatory response to pure tones covering the entire frequency range of the vocalizations. Surprisingly, some collicular neurons even reversed the response polarity, from vocalization-induced suppression to vocalization-playback-induced excitation. Moreover, approximately a third of the neurons responded faster to self-produced vocalizations than to both vocalization playbacks and pure tones. These findings show that the midbrain inferior colliculus is involved in vocalization feedback processing, an ability suggested to arise locally in the auditory cortex.

neuroscience↗

bioRxiv2024

Long-Range Enhancer Networks Gradually Emerge as Key Regulators During Human Cortical Neurogenesis

The chromatin in the human brain cortex shows more enhancer-enhancer contacts than in macaques and mice, yet the organization of these contacts across cellular states and their mechanisms remain unclear. Here, we developed SCOPE-C, a cost-effective technique designed for mapping DNase I Hypersensitive Sites and their spatial interactions, with the potential for scalability to single-cell resolution. Employing SCOPE-C, we generated a comprehensive chromatin map detailing cis-regulatory elements in four cell types during neurogenesis in human, macaque, and mouse fetal cortex. This map introduces a model of long-range enhancer networks that regulate gene transcription during human cortical neurogenesis. In this model, CTCF-mediated loop extrusion forms stripes of enhancer interaction hubs extending up to 10 Mb in human Excitatory Neurons. Enriched with human-specific enhancers and neuropsychiatric disorder-linked SNPs, these remarkably long-range networks predominantly control EN marker genes, suggesting their critical role in robust gene expression during human cortical neurogenesis.

genomics↗

bioRxiv2024

Programming aliphatic polyester degradation by engineered bacterial spores

Enzymatic degradation of plastics is a sustainable approach to addressing the growing issue of plastic accumulation. The primary challenges for using enzymes as catalysts are issues with their stability and recyclability, further exacerbated by their costly production and delicate structures. Here, we demonstrate an approach that leverages engineered spores that display target enzymes in high density on their surface to catalyze aliphatic polyester degradation and create self-degradable materials. Engineered spores display recombinant enzymes on their surface, eliminating the need for costly purification processes. The intrinsic physical and biological characteristics of spores enable easy separation from the reaction mixture, repeated reuse, and renewal. Engineered spores displaying lipases completely degrade aliphatic polyesters and retain activity through four cycles, with full activity recovered through germination and sporulation. Directly incorporating spores into polyesters results in robust materials that are completely degradable. Our study offers a straightforward and sustainable biocatalytic approach to plastic degradation.

bioengineering↗