Biology subjects

Liu, X.

Publications and source records attributed to Liu, X..

At least 649 records · Page 36Linked to original sources

bioRxiv2024

Mechanical control of germ cell specification in Arabidopsis anthers

A central question in developmental biology is how the germline is established. We have studied the specification of the male germ cells (GCs) within the anther1-3. Hereby, we have focused on the potential role of mechanics, an aspect of anther development which has been very poorly characterized2,4. Using a combination of live imaging and mechanical measurements, we provide evidence that GCs originate in a special micro-mechanical niche, where inner tissues exert push on outer cell layers, placing them under compression. Mechanical perturbations significantly disrupted the GC specification and patterning. Moreover, we found that the master genetic regulator SPOROCYTELESS/NOZZLE (SPL/NZZ)5,6 is central in establishing this micro-mechanical environment by softening the cell wall. The mechanical cues, in turn, stabilize the transcription of SPL/NZZ. We propose here an intrinsic growth-derived mechano-chemical feedback loop that drives germ-cell fate acquisition.

developmental biology↗

bioRxiv2024

Multi-omics investigation of spontaneous T2DM macaque reveals gut microbiota promote T2DM by up-regulating the absorption of excess palmitic acid

Although gut microbiota and lipid metabolites have been suggested to be closely associated with type 2 diabetes mellitus (T2DM), the interactions between gut microbiota, lipid metabolites and the host in T2DM development remains unclear. Rhesus macaques may be the best animal model to investigate these relationships given their spontaneous development of T2DM. We identified eight spontaneous T2DM macaques and conducted a comprehensive study investigating the relationships using multi-omics sequencing technology. Our results from 16S rRNA, metagenome, metabolome and transcriptome analyses identified that gut microbiota imbalance, tryptophan metabolism and fatty acid {beta} oxidation disorders, long-chain fatty acid (LCFA) accumulation, and inflammation occurred in T2DM macaques. We verified the accumulation of palmitic acid (PA) and activation of inflammation in T2DM macaques. Importantly, mice transplanted with spontaneous T2DM macaque fecal microbiota and fed a high PA diet developed prediabetes within 120 days. We determined that gut microbiota mediated the absorption of excess PA in the ileum, resulting in the accumulation of PA in the serum consequently leading to T2DM in mice. In particular, we demonstrated that the specific microbiota composition was probably involved in the process. This study provides new insight into interactions between microbiota and metabolites and confirms causative effect of gut microbiota on T2DM development.

microbiology↗

bioRxiv2024

Proteolysis-targeting vaccines (PROTAVs) for robust tumor combination immunotherapy

Protein/peptide subunit vaccines are promising to promote the tumor therapeutic efficacy of immune checkpoint blockade (ICB). However, current protein/peptide vaccines elicit limited antitumor T cell responses, leading to suboptimal therapeutic efficacy. Here, we present proteolysis-targeting vaccines (PROTAVs) that facilitate antigen proteolytic processing and cross-presentation to potentiate T cell responses for robust ICB combination immunotherapy of melanoma. PROTAVs are modular conjugates of protein/peptide antigens, E3 ligase-binding ligands, and linkers. In antigen-presenting cells (APCs), PROTAVs bind to E3 ligases to rapidly ubiquitinate PROTAV antigens, facilitating antigen proteolytic processing by proteasome, and thereby promoting antigen cross-presentation to T cells and potentiating CD8+ T cell responses. We developed a melanoma PROTAV using a tandem peptide of trivalent melanoma-associated antigens. Co-delivered by lipid nanoparticles (LNPs) with bivalent immunostimulant adjuvants, this PROTAV promotes the quantity and quality of melanoma-specific CD8+ T cells in mice. Further, combining PROTAV and ICB ameliorates the immunosuppressive melanoma microenvironment. As a result, PROTAV and ICB combination enhances melanoma complete regression rates and eradicated 100% large BrafV600E melanoma without recurrence in syngeneic mice. PROTAVs hold the potential for robust tumor combination immunotherapy.

bioengineering↗

bioRxiv2024

Protein-protein interactions shape trans-regulatory impact of genetic variation on protein expression and complex traits

Most genetic variants influence complex traits by affecting gene regulation. Yet, despite comprehensive catalogs of molecular QTLs, linking trait-associated variants to biological functions remains difficult. In this study, we re-analyzed large maps of protein QTLs (pQTLs) to show that genes with trans-pQTLs but without cis-pQTLs are under strong selective constraints and are highly enriched in GWAS loci. We found that trans-pQTLs and their trans targets are highly enriched in interacting protein pairs, and trans-pQTLs in coding regions are significantly enriched at protein-protein interactions (PPI) interfaces. By leveraging existing PPI annotations for trans-pQTL mapping, we identified 26,028 trans-pQTLs influencing 1,061 PPI clusters. The trans-pQTLs of PPIs colocalized with 66% GWAS loci per trait on average for 50 complex traits, helping in many cases to link GWAS loci to cellular function. Finally, we identified trans-pQTL effects at multiple autoimmune GWAS loci that converge on the same PPIs, pinpointing protein complexes and signaling pathways that show promising therapeutic target potential.

genetics↗

bioRxiv2024

Separated attractors in neural landscape of motor cortex encoding motor learning

Animals gain motor learning via decrease of variation through repeated training. The secondary motor (M2) cortex shows an indispensable role in the learning process of the rotarod-learning task. Yet, it remains unclear how population decoding in M2 cortex guides the repetitive training to transform into motor enhancement. We recorded neuronal population activity using Ca2+ imaging during this enhancement revealing that neuronal population correlates of the persistent internal learning state evolves in the process of motor learning. With the behavioral micro-states analysis, we identify the growing periodicity, stability, and consistency with two gradually clearer point attractor in the M2 neural state space. The results show the evolution of attractors in M2 participate in decrease of training-acquisition behavior variation and provide a general framework for the mapping between arbitrary non-task motor learning and neural topological structure.

neuroscience↗

bioRxiv2024

CXXC-finger protein 1 associates with FOXP3 to stabilize homeostasis and suppressive functions of regulatory T cells

FOXP3-expressing regulatory T (Treg) cells play a pivotal role in maintaining immune homeostasis and tolerance, with their activation being crucial for preventing various inflammatory responses. However, the mechanisms governing the epigenetic program in Treg cells during their dynamic activation remain unclear. In this study, we demonstrate that CXXC finger protein 1 (CXXC1) interacts with the transcription factor FOXP3 and facilitates the regulation of target genes by modulating H3K4me3 deposition. Cxxc1 deletion in Treg cells leads to severe inflammatory disease and spontaneous T-cell activation, with impaired immunosuppressive function. As a transcriptional regulator, CXXC1 promotes the expression of key Treg functional markers under steady-state conditions, which are essential for the maintenance of Treg cell homeostasis and their suppressive functions. Epigenetically, CXXC1 binds to the genomic regulatory regions of Treg program genes in mouse Treg cells, overlapping with FOXP3 binding sites. Given its critical role in Treg cell homeostasis, CXXC1 presents itself as a promising therapeutic target for autoimmune diseases.

immunology↗

bioRxiv2024

Refined Myocardium Segmentation from CT Using a Hybrid-Fusion Transformer

1.Accurate segmentation of the left ventricle (LV) in cardiac CT images is crucial for assessing ventricular function and diagnosing cardiovascular diseases. Creating a sufficiently large training set with accurate manual labels of LV can be cumbersome. More efficient semi-automatic segmentation, however, often includes unwanted structures, such as papillary muscles, due to low contrast between the LV wall and surrounding tissues. This study introduces a two-input-channel method within a Hybrid-Fusion Transformer deep-learning framework to produce refined LV labels from a combination of CT images and semi-automatic rough labels, effectively removing papillary muscles. By leveraging the efficiency of semi-automatic LV segmentation, we train an automatic refined segmentation model on a small set of images with both refined manual and rough semi-automatic labels. Evaluated through quantitative cross-validation, our method outperformed models that used only either CT images or rough masks as input.

bioengineering↗

bioRxiv2024

Oscillatory dynamics of sustained attention states

Sustained attention allows concentration on a task over long periods of time. This ability fluctuates, with periods of effective focus ( in-the-zone) and periods of increased performance variability and susceptibility to errors ( out-of-the-zone). Little is known about the neural dynamics underlying these states and their fluctuations during sustained attention tasks. To address this, we had thirty young adults perform the gradual onset continuous performance task (gradCPT), during which their EEG and responses were recorded. States of sustained attention (out-vs. in-the-zone) were identified based on the variance time course of participants RT. Out-of-the-zone states were associated with increased errors of commission and reduced perceptual sensitivity compared to in-the-zone states, as expected. Importantly, a significant decline in theta oscillations at mid-prefrontal regions was found during out-of-the-zone (vs. in-the-zone) states over a [~]400 ms period around the transition point between stimuli, and the extent of this decline predicted commission errors and response bias. In addition, individual differences in the variability of midfrontal theta along the task were associated with RT variability. Finally, participants exhibiting greater theta variability showed a more pronounced decline in perceptual sensitivity when being out-of-the-zone and less stable RTs compared to those with lower variability. Our results suggest that states of diminished sustained attention, even during short lapses, are characterized by a reduction in midfrontal theta activity, and that fluctuations in this rhythm covary with fluctuations in attentional control.

neuroscience↗

bioRxiv2024

Diverse defense systems synergistically drive the microbe-virus arms race in deep sea cold seeps

Cold seeps host diverse microbes and viruses with numerous unexplored defense and anti-defense systems. Analysis of 3,813 microbial and 13,336 viral genomes from 191 metagenomes across 17 cold seep sites reveals extensive microbial defense repertoires, with over 60% representing candidate systems. Experimental validation confirms that several candidates protect against viral infection. These defense systems frequently co-occur, suggesting potential synergistic interactions, and are broadly distributed across sediments. In response, viruses have evolved diverse anti-defense genes, and the concurrent presence of multiple viral and microbial systems highlights intricate coevolution. Functionally critical lineages, such as anaerobic methanotrophic archaea, sulfate-reducing bacteria, and diazotrophs, appear to modify their defensive strategies under ecological and environmental pressures; for example, sulfate-reducing bacteria harbor multiple Gabija systems while corresponding viruses carry anti-Gabija genes, illustrating specific coevolutionary adaptations. Overall, these findings underscore the critical role of virus-microbe interactions in shaping microbial metabolic functions and environmental adaptation in deep-sea ecosystems.

microbiology↗

bioRxiv2024

Constructing a Norm for Children's Scientific Drawing: Distribution Features Based on Semantic Similarity of Large Language Models

The use of childrens drawings to examining their conceptual understanding has been proven to be an effective method, but there are two major problems with previous research: 1. The content of the drawings heavily relies on the task, and the ecological validity of the conclusions is low; 2. The interpretation of drawings relies too much on the subjective feelings of the researchers. To address this issue, this study uses the Large Language Model (LLM) to identify 1420 childrens scientific drawings (covering 9 scientific themes/concepts), and uses the word2vec algorithm to calculate their semantic similarity. The study explores whether there are consistent drawing representations for children on the same theme, and attempts to establish a norm for childrens scientific drawings, providing a baseline reference for follow-up childrens drawing research. The results show that the representation of most drawings has consistency, manifested as most semantic similarity>0.8. At the same time, it was found that the consistency of the representation is independent of the accuracy (of LLMs recognition), indicating the existence of consistency bias. In the subsequent exploration of influencing factors, we used Kendall rank correlation coefficient to investigate the effects of "sample size", "abstract degree", and "focus points" on drawings, and used word frequency statistics to explore whether children represented abstract themes/concepts by reproducing what was taught in class. It was found that accuracy (of LLMs recognition) is the most sensitive indicator, and data such as sample size and semantic similarity are related to it; The consistency between classroom experiments and teaching purpose is also an important factor, many students focus more on the experiments themselves rather than what they explain. In addition, most children tend to use examples they have seen in class to represent more abstract themes/concepts, indicating that they may need concrete examples to understand abstract things.

scientific communication and education↗

bioRxiv2024

BrainScale: Enabling Scalable Online Learning in Spiking Neural Networks

Spiking neural networks (SNNs) represent a promising paradigm for understanding brain functions [1, 2] and developing neuromorphic intelligence [3, 4]. However, their potential remains largely unrealized due to a fundamental limitation: the lack of a scalable online learning system capable of supporting large-scale training of complex brain dynamics over long timescales. Current approaches are either limited to offline learning [5], impeding long-time task training; constrained by high memory complexity [6, 7], hindering network scaling; or using oversimplified models [8], failing to capture complex brain dynamics. Here, we introduce Brain-Scale, a system that synergizes model universality, computational efficiency, and engineering usability to enable scalable online learning in SNNs. First, BrainScale introduces standard model abstractions to support the training of diverse spiking networks. Second, BrainScale implements an online learning algorithm with linear memory complexity by exploiting intrinsic properties of SNN dynamics. Third, BrainScale provides an online learning compiler that automates the implementation of online learning for any user-defined models. Extensive evaluations across diverse SNN dynamics and computational tasks demonstrate that BrainScale consistently delivers strong training performance while maintaining extremely low memory usage and high computational efficiency. These properties enable the online training of a whole-brain-scale SNN of the Drosophila brain, accurately capturing functional activities across brain regions. These results position BrainScale as a foundational platform for advancing large-scale brain simulation and neuromorphic computing.

neuroscience↗

bioRxiv2024

PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuron

Single-neuron axonal projections reveal the route map of neuron output and provide a key cue for understanding how information flows across the brain. Reconstruction of single-neuron axonal projections requires intensive manual operations in tens of terabytes of brain imaging data, and is highly time-consuming and labor-intensive. The main issue lies in the need for precise reconstruction algorithms to avoid reconstruction errors, yet current methods struggle with densely distributed axons, focusing mainly on skeleton extraction. To overcome this, we introduce a point assignment-based method that uses cylindrical point sets to accurately represent axons and a minimal information flow tree model to suppress the snowball effect of reconstruction errors. Our method successfully reconstructs single-neuron axonal projections across hundreds of GBs (Gigabytes) images with an average of 80% F1-score, while current methods only provide less than 40% F1-score reconstructions from a few hundred MBs (Megabytes) images. This huge improvement is helpful for high-throughput mapping of neuron projections.

neuroscience↗

bioRxiv2024

BTFBS: binding-prediction of bacterial transcription factors and binding sites based on deep learning

BackgroundThe binding of transcription factors (TFs) to TF-binding sites plays a vital role in the process of regulating gene expression and evolution. With the development of machine learning and deep learning, some successes have been achieved in predicting transcription factors and binding sites. Then a natural question arises: for a given transcription factor and a binding site, do they bind? This is the main motivation of this work. ResultsIn this paper, we develop a model BTFBS, which predicts whether the bacterial transcription factors and binding sites combine or not. The model takes both the amino acid sequences of bacterial transcription factors and the nucleotide sequences of binding sites as inputs, and extracts features through convolutional neural network and MultiheadAttention. For the model inputs, we use two negative sample sampling methods: RS and EE. On the test dataset of RS, the accuracy, sensitivity, specificity, F1-score and MCC of BTFBS are 0.91446, 0.89746, 0.93134, 0.91264 and 0.82946, respectively. And on the test dataset of EE, the accuracy, sensitivity, specificity, F1-score and MCC of BTFBS are 0.87868, 0.89354, 0.86394, 0.87996 and 0.75796, respectively. Meanwhile, our findings indicate that the optimal approach for obtaining negative samples in the context of bacterial research is to utilize the whole genome sequences of the corresponding bacteria, as opposed to the shuffling method. ConclusionsThe above results on the test dataset have shown that the proposed BTFBS model has a good performance in predicting the combination of bacterial transcription factors and their binding sites and provides an experimental guide. BTFBS is publicly available at https://github.com/Vceternal/BTFBS.

bioinformatics↗

bioRxiv2024

Distinct duration and diet dependent lipid profiles and renal complicationsin GCK-MODY

Heterozygous inactivating mutations in the glucokinase (GCK) gene cause one of the most common types of maturity-onset diabetes of the young type 2 (MODY2), also named GCK-MODY. Studies suggest that, unlike other types of diabetes, patients with GCK-MODY do not have increased risk of diabetic complications and therefore do not typically require antihyperglycemic therapy. However, long-term outcomes of GCK-MODY on lipid profiles and chronic complications under different dietary scenarios remain unclear. Herein, using a knock-in mouse model expressing a novel MODY causing mutation GCK-Q26L (GCKMut), we examined age- and diet-related lipid profiles and diabetic complications. We found that, although GCKMut mice exhibited mild elevated blood glucose, the lipid profiles, body fat composition, and diabetic kidney disease (DKD) were initially improved in high-fat-diet (HFD) fed mice at the age of 28 weeks, supporting potential beneficial effects of GCKMut on lipid metabolism and kidney health. Unexpectedly however, those protective effects diminished by 40 weeks, and became more severe dyslipidemia and kidney injury associated with renal fibrosis and inflammation at 60-week-old mice fed with normal diet (ND) or HFD. Those findings revealed distinct duration- and diet-dependent effects of inactivating GCK mutation on lipid profiles and DKD, highlighting previously unrealized long-term chronic complications in GCK-MODY.

physiology↗

bioRxiv2024

Polyacrylamide-based Antimicrobial Copolymers Immune to Typical Resistance Mechanisms to Replace or Rescue Antibiotics

Antibiotics save countless lives each year and have dramatically improved human health outcomes since their introduction in the 20th century. Unfortunately, bacteria are now developing resistance to antibiotics at an alarming rate, with many new strains of "superbugs" showing simultaneous resistance to multiple classes of antibiotics. To mitigate the global burden of antimicrobial resistance, we must develop new antibiotics that are broadly effective, safe, and highly stable for the purpose of global access. In this manuscript, we report the development of polyacrylamide-based copolymers as a novel class of broad-spectrum antibiotics with efficacy against several critical pathogens. We demonstrate that these copolymer drugs are selective for bacteria over mammalian cells, indicating a favorable safety profile. We show that they kill bacteria through a membrane disruption mechanism, which allows them to overcome traditional mechanisms of antimicrobial resistance. Finally, we demonstrate their ability to rehabilitate an existing small-molecule antibiotic that is highly subject to resistance development by improving its potency and eliminating the development of resistance in a combination treatment. This work represents a significant step towards combatting antimicrobial resistance.

bioengineering↗

bioRxiv2024

Loss of PMR4 callose synthase results in salicylic acid-independent and broad-spectrum resistance to clubroot in Arabidopsis and Brassica napus

The clubroot disease caused by protist Plasmodiophora brassicae is one of the most important diseases of Brassica crops. Growing clubroot-resistant cultivars remains the most effective and practical approach to managing clubroot disease. However, resistance gene-mediated immunity is rapidly overcome in the field when new pathotypes arise. In this study, we identified PMR4 as a potential gene target for creating a novel clubroot-resistant source. Recessive PMR4 mutations in Arabidopsis thaliana conferred broad-spectrum resistance to multiple P. brassicae pathotypes, independent of salicylic acid-mediated plant immunity. CRISPR/Cas9-mediated gene-editing was employed to create mutations in two PMR4 orthologs in the B. napus genome, and resulting homozygous mutants exhibited dual resistance to powdery mildew and clubroot. PMR4 is required for the callose deposition at wound and powdery mildew infection sites in leaves. This study reveals that callose deposition in roots is induced by P. brassicae infection and requires PMR4. It appears that the clubroot disease progression is arrested at the primary-to-secondary infection phase in pmr4-1 mutants. Together, this study demonstrates that PMR4-encoded callose synthase is a host susceptibility factor required for P. brassicae to complete its life cycle, and that PMR4 can be targeted against both powdery mildew and clubroot diseases in Brassica crops.

plant biology↗

bioRxiv2024

Rebalance the inhibitory system in the elderly brain: Influence of balance learning on GABAergic inhibition and functional connectivity

Aging involves complex processes that impact the structure, function, and metabolism of the human brain. Declines in both structural and functional integrity along with reduced local inhibitory tone in the motor areas, as indicated by reduced {gamma}-Aminobutyric acid (GABA) levels, are often associated with compromised motor performance in elderly adults. Using multi-modal neuroimaging techniques including magnetic resonance spectroscopy (MRS), diffusion magnetic resonance imaging (MRI), functional MRI as well as transcranial magnetic stimulation to assess short interval intracortical inhibition (SICI), this study explores whether these age-related changes can be mitigated by motor learning. The investigation focused on the effects of long-term balance learning (3 months) on intracortical inhibition, metabolism, structural and functional connectivity in the cortical sensorimotor network among an elderly cohort. We found that after three months of balance learning, subjects significantly improved balance performance, upregulated sensorimotor cortical GABA levels and ventral sensorimotor network functional connectivity (VSN-FC) compared to a passive control group. Furthermore, correlation analysis suggested a positive association between baseline VSN-FC and balance performance, between baseline VSN-FC and SICI, and between improvements in balance performance and upregulation in SICI in the training group, though these correlations did not survive the false discovery rate correction. These findings demonstrate that balance learning has the potential to counteract aging-related decline in functional connectivity and cortical inhibition on the tonic (MRS) and functional (SICI) level and shed new light on the close interplay between the GABAergic system, functional connectivity, and behavior.

neuroscience↗

bioRxiv2024

Structure of small HBV surface antigen reveals mechanism of dimer formation

Hepatitis B surface antigen (HBsAg), the only membrane protein on the HBV viral envelope, plays essential roles in HBV assembly, viral release, host cell attachment, and entry. Despite its functional and therapeutic importance, the detailed structure of HBsAg has remained enigmatic. Here, we present the core structure of HBsAg at 3.6 [A] resolution, determined using recombinant small spherical subviral particles (SVPs). The structure reveals how two HBsAg monomers interact to form a dimer, which is the basic building block of SVPs.

biophysics↗