Biology subjects

Liao, X.

Publications and source records attributed to Liao, X..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Newly identified SpoVAF/FigP complex: the role in Bacillus subtilis spore germination at moderate high pressure and influencing factors

The SpoVAF/FigP complex, a newly identified ion channel, has been shown to amplify the response of germinant receptors (GRs) to nutrient germinants. However, its contribution to high-pressure-induced germination remains unexplored. In this study, we discovered that the 5AF/FigP complex plays an important role in the GRs-dependent germination of Bacillus subtilis spores under moderate high pressure (MHP) by facilitating the release of ions such as potassium (K+), a mechanism in parallel with its role in nutrient-induced germination. Despite its predicted function as an ion channel, the 5AF/FigP complex fails to respond to MHP in the absence of GerA-type GRs. We quantitatively examined the factors that influence the 5AF/FigP complexs function in MHP-induced germination using modeling and fitting techniques. Our results indicate that the complexs amplification effect is both enhanced and accelerated as pressure levels increase from 50 to 200 MPa. However, raising the MHP treatment temperature from 22 to 30{degrees}C only speeds up the complexs function without enhancing its effectiveness. Moreover, extreme conditions of higher pressure (300 MPa) and temperature (34-37{degrees}C) can diminish the complexs functionality. Additionally, the amplification effect is weakened in spores produced at both elevated and reduced sporulation temperatures. Taken together, our findings highlight the essential role of the 5AF/FigP complex in boosting the efficiency of MHP-induced germination. This revelation has enriched our understanding of the intricate mechanisms underlying GRs-dependent germination in Bacillus spores, offering valuable insights that can be utilized to refine the germination-inactivation strategies within the food industry.

microbiology↗

bioRxiv2024

Brainstem neurons coordinate the bladder and urethra sphincter for urination

Urination, a vital and conserved process of emptying urine from the urinary bladder in mammals, requires precise coordination between the bladder and external urethral sphincter (EUS) that is tightly controlled by a complex neural network. However, the specific subpopulation of neurons that accounts for such coordination remains unidentified, limiting the development of target-specific therapies for certain urination disorders, e.g., detrusor-sphincter dyssynergia. Here, we find that cells expressing estrogen receptor 1 (ESR1+) in the pontine micturition center (PMC) initiate voiding when activated and suspend ongoing voiding when suppressed, each at 100% reliability. Transection of the pelvic nerve does not impair PMCESR1+ neurons control of the EUS via the pudendal nerve, whereas transection of the pudendal nerve does not impair their control of the bladder via the pelvic nerve. Anatomically, PMCESR1+ neurons consist of three distinct spinal-projection-based subpopulations: one targeting the sacral parasympathetic nucleus (SPN), one innervating the dorsal gray commissure (DGC), and a third that projects to both regions, thereby enforcing the coordination of bladder contraction and sphincter relaxation in a rigid temporal sequence. Thus, we identify a cell type in the brainstem that controls the bladder-urethra coordination for urination.

neuroscience↗

bioRxiv2024

Select autosomal dominant DFNA11 deafness mutations activate Myo7A in epithelial cells

Myosin-7A (Myo7A) is a motor protein crucial for the organization and function of stereocilia, specialized actin-rich protrusions on the surface of inner ear hair cells that mediate hearing. Mutations in Myo7A cause several forms of genetic hearing loss, including autosomal dominant DFNA11 deafness. Despite its importance, the structural elements of Myo7A that control its motor activity within cells are not well understood. In this study, we used cultured kidney epithelial cells to screen for mutations that activate the motor-dependent targeting of Myo7A to the tips of apical microvilli on these cells. Our findings reveal that Myo7A is regulated by specific IQ motifs within its lever arm, and that this regulation can function at least partially independent of its tail sequence. Importantly, we demonstrate that many of the DFNA11 deafness mutations reported in patients activate Myo7A targeting, providing a potential explanation for the autosomal dominant genetics of this form of deafness.

cell biology↗

bioRxiv2024

Heat Shock Factor Regulation of Antimicrobial Peptides Expression Suggests a Conserved Defense Mechanism Induced by Febrile Temperature in Arthropods

Temperature is a critical factor influencing the outbreak and progression of viral diseases in organisms. Febrile temperatures have been shown to enhance immune competence and reduce viral replication in various species. However, the underlying mechanisms remain largely unknown. In this study, we investigate the molecular mechanisms by which elevated temperatures confer resistance to viral infections, focusing on the role of heat shock factor 1 (HSF1) in regulating antimicrobial effectors rather than the traditional target genes molecular chaperones. Using shrimp Litopenaeus vannamei as a model, we demonstrate that febrile temperatures induce HSF1, which in turn upregulates antimicrobial peptides (AMPs) that target viral envelope proteins and inhibit viral replication. Importantly, this is the first to show that HSF1 directly binds to the heat shock elements (HSE) motifs of AMPs both in shrimp and Drosophila S2 cells, suggesting this may be a conserved regulatory mechanism in arthropods. Additionally, our findings highlight the role of HSF1 beyond the classical heat shock response, revealing its critical function in modulating innate immunity. These insights provide new avenues for managing viral infections in aquaculture and other settings by leveraging environmental temperature control.

immunology↗

bioRxiv2024

Synergically enhanced anti-tumor immunity of in vivo CAR by circRNA vaccine boosting

Chimeric Antigen Receptor (CAR) T cell therapy has shown promise in treating hematologic malignancies. However, it is limited to individualized cell therapy and faces challenges, including high costs, extended preparation time, and limited efficacy against solid tumors. Here, we generated circular RNAs (circRNAs) encoding Chimeric Antigen Receptor (CAR) transmembrane proteins, referred to as circRNACAR, which mediated remarkable tumor killing in both T cells and macrophages. In addition, macrophages exhibited efficient phagocytosis of tumor cells and pro-inflammatory polarization induced by circRNACAR in vitro. We demonstrated that circRNACAR, delivered with immunocyte-tropic lipid nanoparticles (LNPs), significantly inhibited tumor growth, improved survival rates and induced a pro-inflammatory tumor microenvironment in mice. Importantly, the combination of circRNAAnti-HER2-CAR and circRNA-based cancer vaccines encoding the corresponding transmembrane HER2 antigen, termed circRNAHER2, exhibited synergistically enhanced anti-tumor activity. This proof-of-concept study demonstrated that the combination of circRNA-based in vivo CAR and vaccines, termed in vivo CAR-VAC, holds the potential to become an upgraded off-the-shelf immunotherapy.

immunology↗

bioRxiv2024

Deep Learning-Based Classification of CRISPR Loci Using Repeat Sequences

With the widespread application of the CRISPR-Cas system in gene editing and related fields, the demand for detecting and classifying CRISPR-Cas systems in metagenomic data has continuously increased. The traditional classification of the CRISPR-Cas system mainly relies on identifying neighboring cas genes of repeats. However, in some cases where there is a lack of information about cas genes, such as in metagenomes and fragmented genome assemblies, traditional classification methods may become ineffective. Here, we introduce a deep learning-based method called CRISPRclassify-CNN-Att, which classifies CRISPR-Cas systems solely based on repeat sequences. CRISPRclassify-CNN-Att utilizes convolutional neural networks (CNNs) and self-attention mechanisms to extract features from repeat sequences. It employs a stacking strategy to handle sample imbalances across different subtypes and improves classification accuracy for subtypes with fewer samples through transfer learning. CRISPRclassify-CNN-Att demonstrates excellent performance in classifying multiple subtypes, particularly in subtypes with a larger number of samples. Although CRISPR loci classification primarily relies on cas genes, CRISPRclassify-CNN-Att offers a new approach as a significant complement to current methods. It can identify unclassified loci missed by traditional cas-based methods, breaking the limitations of traditional approaches, and simplifying the classification process. The proposed tool is freely accessible via https://github.com/Xingyu-Liao/CRISPRclassify-CNN-Att.

bioinformatics↗

bioRxiv2024

The continuous differentiation of multiscale structural gradients from childhood to adolescence correlates with the maturation of cortical morphology and functional specialization

From childhood to adolescence, the structural organization of the human brain undergoes dynamic and regionally heterogeneous changes across multiple scales, from synaptic pruning to the reorganization of large-scale anatomical wiring. However, during this period, the developmental process of multiscale structural architecture, its association with cortical morphological changes, and its role in the maturation of functional organization remain largely unknown. Here, we utilized a longitudinal multimodal imaging dataset including 276 children aged 6 to 14 years to investigate the developmental process of multiscale cortical wiring. We used an in vivo model of cortical wiring that combines features of white matter tractography, cortico-cortical proximity, and microstructural similarity to construct a multiscale brain structural connectome. By employing the gradient mapping method, the gradient space derived from the multiscale structural connectome effectively recapitulated the sensory-association axis and anterior-posterior axis. Our findings revealed a continuous expansion of the multiscale structural gradient space during development, with the principal gradient increasingly distinguishing between primary and transmodal regions. This age-related differentiation coincided with regionally heterogeneous changes in cortical morphology. Furthermore, our study revealed that developmental changes in coupling between multiscale structural and functional connectivity were correlated with functional specialization refinement, as evidenced by changes in the participation coefficient. We also found that the differentiation of the principal multiscale structural gradient was associated with improved cognitive abilities, such as enhanced working memory and attention performance, and potentially supported by molecular processes related to synaptic functions. These findings advance our understanding of the intricate maturation process of brain structural organization and its implications for cognitive performance.

neuroscience↗

bioRxiv2024

Skeleton-forming responses of reef-building corals under ocean acidification

Ocean acidification is increasing in frequency and is considered one of the most important causes of severe damage to global coral reefs. Therefore, there is an urgent need to study the impact of acid stress on the growth patterns of major reef-building corals. Here, we studied the skeleton forming strategies of four widely distributed coral species in a simulated acidified habitat with a pH of 7.6-7.8. We reconstructed and visualized the skeleton building process, quantified elemental calcium loss, and determined gene expression changes. The results suggest that different reef-building corals have diverse growing strategies in acidified seawater. A unique cavity-like forming process starts from the inside of the skeletons of Acropora muricata, which sacrifices skeleton density to protect its polyp-canal system. The forming patterns in Pocillopora damicornis, Montipora capricornis, and M. foliosa were characterized by osteoporosis, exhibiting disordered skeletal structures, insufficient synthesis of adhesion proteins, and low bone mass, correspondingly. In addition, we found that skeletal areas near coral polyps suffered less and had later acidified damage than other skeletal areas in the colony. These results help to understand the skeleton-forming strategies of several major coral species under acid stress, thereby laying a foundation for coral reef protection and restoration under increasing ocean acidification.

ecology↗

bioRxiv2024

Tonotopy is not preserved in a descending stage of auditory cortex

Previous studies based on layer specificity suggest that ascending signals from the thalamus to the sensory neocortex preserve spatially organized information, but it remains unknown whether sensory information descending from sensory neocortex to the thalamus also maintains such spatial organization pattern. By focusing on projection specificity, we mapped the tone response properties of two groups of cortical neurons in the primary auditory cortex (A1), based on the relationship between their specific connections to other regions and their function in ascending (thalamocortical recipient, TR neurons) or descending (corticothalamic, CT neurons) auditory information. A clear tonotopic gradient was observed among TR neurons, but not CT neurons. Additionally, CT neurons exhibited markedly higher heterogeneity in their frequency tuning and had broader bandwidth than TR neurons. These results reveal that the information flow descending from A1 to the thalamus via CT neurons is not arrange tonotopically, suggesting that the descending information flow possibly contributes to higher-order feedback processing of diverse auditory inputs.

neuroscience↗

bioRxiv2024

Development of the overlapping network modules in the human brain

Developmental connectomic studies have shown that the modular organization of functional networks in the human brain undergoes substantial reorganization with age to support cognitive growth. However, these studies implicitly assume that each brain region belongs to one and only one specific network module, ignoring the potential spatial overlap between functional modules. How the overlapping functional modular architecture develops and whether this development is related to structural signatures remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6-14 years), we investigated the development of the overlapping modular architecture of functional networks, and further explored their structural associations. Specifically, an edge-centric network model was used to identify the overlapping functional modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a remarkable regional inhomogeneity in module overlap in children, with higher entropy in the ventral attention, somatomotor, and subcortical networks and lower entropy in the visual and default-mode networks. Furthermore, the overlapping modules developed in a linear, spatially dissociable manner from childhood to adolescence, with significantly reduced entropy in the prefrontal cortex and putamen and increased entropy in the parietal lobules. Personalized overlapping modular patterns capture individual brain maturity as characterized by brain age. Finally, the overlapping functional modules can be significantly predicted by integrating gray matter morphology and white matter network properties. Our findings highlight the maturation of overlapping network modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

neuroscience↗

bioRxiv2024

The catabolism of alanine and glutamate ensure proper sporulation by respectively preventing premature germination and providing energy sources

Sporulation as a typical bacterial differentiation process has been studied for decades. However, two crucial aspects of sporulation, (i) the energy sources supporting the process, and (ii) the maintenance of spore dormancy throughout sporulation, are scarcely explored. Here, we reported the crucial role of RocG-mediated glutamate catabolism in regulating mother cell lysis, a critical step for successful sporulation, likely by providing energy metabolite ATP. Notably, rocG overexpression resulted in an excessive ATP accumulation in sporulating cells, leading to adverse effects on future spore properties, e.g. increased germination efficiency, reduced DPA content, and lowered heat resistance. Additionally, we revealed that Ald-mediated alanine metabolism decreased the typical germinant L-alanine concentration in sporulating environment, thereby preventing premature germination and maintaining spore dormancy. Our data inferred that sporulation was a highly orchestrated biological process requiring a delicate balance in diverse metabolic pathways, hence ensuring both the completion of sporulation and production of high-quality spores.

microbiology↗

bioRxiv2024

BERTE：High-precision hierarchical classification of transposable elements by a transfer learning method with BERT pre-trained model and convolutional neural network

Transposable Elements (TEs) are abundant repeat sequences found in living organisms. They play a pivotal role in biological evolution and gene regulation and are intimately linked to human diseases. Existing TE classification tools can classify classes, orders, and superfamilies concurrently, but they often struggle to effectively extract sequence features. This limitation frequently results in subpar classification results, especially in hierarchical classification. To tackle this problem, we introduced BERTE, a tool for TE hierarchical classification. BERTE encoded TE sequences into distinctive features that consisted of both attentional and cumulative k-mer frequency information. By leveraging the multi-head self-attention mechanism of the pre-trained BERT model, BERTE transformed sequences into attentional features. Additionally, we calculated multiple k-mer frequency vectors and concatenate them to form cumulative features. Following feature extraction, a parallel Convolutional Neural Network (CNN) model was employed as an efficient sequence classifier, capitalizing on its capability for high-dimensional feature transformation. We evaluated BERTEs performance on filtered datasets collected from 12 eukaryotic databases. Experimental results demonstrated that BERTE could improve the F1-score at different levels by up to 21% compared to current state-of-the-art methods. Furthermore, the results indicated that not only could BERT better characterize TE sequences in feature extraction, but also that CNN was more efficient than other popular deep learning classifiers. In general, BERTE classifies TE sequences with greater precision. BERTE is available at https://github.com/yiqichen-2000/BERTE.

bioinformatics↗

bioRxiv2024

Comprehensive Hierarchical Classification of Transposable Elements based on Deep Learning

Transposable elements (TEs) are DNA sequences capable of translocating within a genome. They constitute a substantial portion of eukaryotic genomes and play significant roles in genome evolution and gene regulation. The correct classification of these repetitive elements is essential to investigate their potential impact on genomes. Despite the existence of several tools for TE classification, they often neglect the importance of simultaneously utilizing global and local information for TE-type identification, resulting in suboptimal performance. Furthermore, these tools are not user-friendly due to the complex installation processes and numerous dependencies. In this study, we introduced a novel framework, CREATE, which leverages the strengths of Convolutional and Recurrent Neural NEtworks, combined with Attention mechanisms, for efficient TE classification. Given the tree-like structure of TE groups, we separately trained nine models within the class hierarchy. Benchmarking experiments showed that CREATE significantly outperformed other TE classification tools. The source code and demo data for CREATE are available at https://github.com/yangqi-cs/CREATE. To facilitate TE annotation for researchers, we have developed a web platform, named WebDLTE, based on the CREATE framework. This platform employs GPU-accelerated pre-trained deep learning models for real-time TE classification and offers the most comprehensive collection of TEs for download. The web interface can be accessed at https://www.webdlte.nwpu.edu.cn.

bioinformatics↗

bioRxiv2024

Topographic, cognitive, and neurobiological profiling of the interdependent structural and functional connectome in the human brain

The structural connectome (SC) is tightly coupled to the functional connectome (FC) in the human brain. Most previous related studies have modeled and analyzed SC or FC as isolated brain networks. However, challenges remain in modeling the interdependent structural-functional connectome and elucidating its cognitive implications and molecular underpinnings. Here, we present a multilayer connectome model composed of SC and FC components and further characterize their interacting topological properties. We found that the interdependent connectome is topographically heterogeneous, with the transmodal cortex exhibiting greater modular variability across layers. This spatial topography reflects cortical hierarchy and evolution and shows high test-retest reliability, reproducibility, and heritability. The interdependent connectome contributes to high-order cognitive processes and is associated with multiple neurotransmitter systems and transcriptional signatures of synaptic transmission. Our results provide insights into the nontrivial interdependencies of SC and FC, highlighting their cognitive significance and the molecular mechanisms underlying the connectome of connectomes.

neuroscience↗

bioRxiv2023

Unbiased Complete Estimation of Chloroplast Number in Plant Cells Using Deep Learning Methods

Chloroplasts are essential organelles in plants that are involved in plant development and photosynthesis. Accurate quantification of chloroplast numbers is important for understanding the status and type of plant cells, as well as assessing photosynthetic potential and efficiency. Traditional methods of counting chloroplasts using microscopy are time-consuming and face challenges such as the possibility of missing out-of-focus samples or double counting when adjusting the focal position. Here, we developed an innovative approach called Detecting- and-Counting-chloroplasts (D&Cchl) for automated detection and counting of chloroplasts. This approach utilizes a deep-learning-based object detection algorithm called You-Only-Look-Once (YOLO), along with the Intersection Over Union (IOU) strategy. The application of D&Cchl has shown excellent performance in accurately identifying and quantifying chloroplasts. This holds true when applied to both a single image and a three-dimensional (3D) structure composed of a series of images. Furthermore, by integrating Cellpose, a cell-segmentation tool, we were able to successfully perform single-cell 3D chloroplast counting. Compared to manual counting methods, this approach improved the accuracy of detection and counting to over 95%. Together, our work not only provides an efficient and reliable tool for accurately analyzing the status of chloroplasts, enhancing our understanding of plant photosynthetic cells and growth characteristics, but also makes a significant contribution to the convergence of botany and deep learning. One-sentence summaryThis deep learning-based approach enables the accurate complete detection and counting of chloroplasts in 3D single cells using microscopic image stacks, and showcases a successful example of utilizing deep learning methods to analyze subcellular spatial information in plant cells. The authors responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/) is: Zhao Dong (dongzhao@hebeu.edu.cn), Shaokai Yang, (shaokai1@ualberta.ca), Ningjing Liu (liuningjing1@yeah.net), and Qiong Zhao (qzhao@bio.ecnu.edu.cn).

cell biology↗

bioRxiv2023

Dual long-axis reorganization of hippocampus in youth

The reorganization of human hippocampus, especially its interaction with cortex, remains largely undefined in youth. The organization of a single hippocampal long-axis has been predominantly characterized as monotonic1-6, despite recent indications of nonmonotonic features in neuron density7 and geometric eigenmodes8. While the human cortical hierarchy has been well recognized for significant developmental and evolutionary advantages9-12, hippocampus has been typically considered an evolutionarily conserved brain structure1,13,14, and overlooked regarding its integrative role of cortical hierarchical processing during development. Here, we corroborated the presence and significance of a dual long-axis representation of the hippocampal connectome and geometry including both linear and quadratic gradients along its long-axis in youth. This finding was robust across two independent large-scale developmental cohorts. Charting development of the dual long-axis gradients underscored their specific contributions to the cortical hierarchy maturation from the frontoparietal and salience/ventral attention networks. The observed developmental variability in spontaneous brain activities in youth parallels the gradients of myelin content. During childhood through adolescence to early adulthood, the hippocampus reorganized the dual long-axis by gradually relaxing its geometric constraints on the intrinsic network organization of cortical spontaneous activity for refined executive functions. Molecular processes underlying such reorganization of the dual long-axis in hippocampus are linked to neural growth, stress hormone regulation, and neuroactive signaling. Our findings enrich the understanding of hippocampal-cortical reorganizational principles across structural, functional, and molecular dimensions as well as its maturation, and define the plasticity distribution within the human hippocampus at systems level, holding potentials to enhance and translate neurodevelopment and neuropsychiatric healthcare.

neuroscience↗

bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

Deep Learning Enhanced Tandem Repeat Variation Identification via Multi-Modal Conversion of Nanopore Reads Alignment

Identification of tandem repeat (TR) variations plays a crucial role in advancing our understanding of genetic diseases, forensic analysis, evolutionary studies, and crop improvement, thereby contributing to various fields of research and practical applications. However, traditional TR identification methods are often limited to processing genomes obtained through sequence assembly and cannot directly start detection from sequencing reads. Furthermore, the inflexibility of detection mode and parameters hinders the accuracy and completeness of the identification, rendering the results unsatisfactory. These shortcomings result in existing TR variation identification methods being associated with high computational cost, limited detection sensitivity, precision and comprehensiveness. Here, we propose DeepTRs, a novel method for identifying TR variations, which enables direct TR variation identification from raw Nanopore sequencing reads and achieves high sensitivity, accuracy, and completeness results through the multi-modal conversion of Nanopore reads alignment and deep learning. Comprehensive evaluations demonstrate that DeepTRs outperform existing methods.

bioinformatics↗