Biology subjects

Zeng, X.

Publications and source records attributed to Zeng, X..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Chromosomal rearrangements and instability caused by the LINE-1 retrotransposon

LINE-1 (L1) retrotransposition is widespread in many cancers, especially those with a high burden of chromosomal rearrangements. However, whether and to what degree L1 activity directly impacts genome integrity is unclear. Here, we apply whole-genome sequencing to experimental models of L1 expression to comprehensively define the spectrum of genomic changes caused by L1. Combining the analyses of experimental models of L1 induction and of cancer genomes, we demonstrate that L1 retrotransposition can directly generate reciprocal translocations, genomic DNA inversions, and foldback rearrangements resulting from illegitimate recombination of double-strand DNA ends generated by L1-encoded ORF2p. We further show that L1-induced rearrangements can produce unstable chromosomes that fuel the acquisition of complex rearrangements, large segmental copy-number alterations, and genetic heterogeneity through breakage-fusion bridge cycles or DNA fragmentation. Together, these findings suggest L1 as a potent mutagenic force capable of driving genome evolution in cancers.

genomics↗

bioRxiv2024

A CRISPR/SpCas9M-reporting system for efficient and rapid genome editing in Caulobacter crescentus

As members of the -proteobacteria group, Caulobacter crescentus and its relatives are known for their asymmetric life cycle and comprehensive applications in gene delivery, agricultural biotechnology, and the production of high-value compounds. However, genetic manipulations of these bacteria are often time-consuming and labor-intensive due to the lack of efficient genome editing tools. Here, we report a practical CRISPR/SpCas9M-reporting system that overcomes the limitations of SpCas9 expression, enabling efficient, markerless, and rapid genome editing in C. crescentus. As a demonstration, we successfully knocked out two genes encoding the scaffold proteins, achieving apparent editing efficiencies up to 80%. Key components, including the Cas protein, Cas inducer, sgRNA, homologous arms, and reporter, were systematically analyzed and optimized to enhance the editing efficiency or decrease the cell lethality. A nearly zero off-target ratio was observed after the curing of the editor plasmid in editing strains. Furthermore, we applied the CRISPR/SpCas9M-reporting system to two C. crescentus relatives, Agrobacterium fabrum and Sinorhizobium meliloti, establishing it as an efficient and reliable editing strategy. We anticipate that this system could be applied to other hard-to-edit organisms, accelerating both basic and applied research in -proteobacteria.

bioengineering↗

bioRxiv2024

A novel RyR2-selective stabilizer prevents and ameliorates severe arrhythmias in stress-induced ventricular tachycardia

Background and PurposeAberrant activation of the type 2 ryanodine receptor (RyR2) causes lethal arrhythmias, such as catecholaminergic polymorphic ventricular tachycardia (CPVT). Developing drugs that suppress RyR2 hyperactivation may be key to novel arrhythmia treatments. This study evaluated the antiarrhythmic potential of Ryanozole, a recently developed novel RyR2 modulator with high affinity and selectivity, using CPVT mouse models harboring mutant RyR2s. Experimental approachIn vitro effects of Ryanozole were evaluated by ER Ca2+-based assay and [3H]Ryanodine binding assay using RyR2-expressing HEK293 cells. Two lines of mice with different arrhythmia severity, RyR2-R420W and -K4750Q, were employed for in vivo assessments. Intracellular Ca2+ signals were analyzed in isolated cardiomyocytes using Cal520. Antiarrhythmic effects were evaluated by ECG under catecholaminergic challenge in anesthetized mice and during spontaneous arrhythmias in conscious mice. ECG and echocardiographic parameters were evaluated before and after drug administration. Key resultsRyanozole inhibited both wild type and mutant RyR2s with similar IC50 of 15-40 nM. The inhibition was more potent at lower cytosolic Ca2+ concentrations. It suppressed Ca2+ waves and Ca2+ sparks without affecting action potential-evoked Ca2+ transients. Ryanozole effectively prevented adrenaline-induced arrhythmias and rapidly terminated spontaneous arrhythmias during daily activity. Importantly, Ryanozole did not impair cardiac conduction or contractility, unlike conventional antiarrhythmic drugs. Conclusions and implicationsRyanozole strongly suppresses RyR2 under diastolic Ca2+ conditions, thereby preventing the arrhythmogenic trigger of aberrant Ca2+ release. This mechanism likely provides potent antiarrhythmic effects while preserving cardiac function. Ryanozole is a promising therapeutic candidate treating RyR2-mediated arrhythmias, such as CPVT.

pharmacology and toxicology↗

bioRxiv2024

A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design

Antimicrobial peptides (AMPs) play a crucial role in developing novel antiinfective drugs due to their broad-spectrum antimicrobial activity and lower likelihood of causing bacterial resistance. However, laboratory synthesis of AMPs is tedious and time-consuming. Existing computational methods have limited capability in optimizing multiple desired properties simultaneously. Here, we propose a Multi-Property Optimizing Generative Adversarial Network (MPOGAN), a feedback-loop framework that iteratively learns from data with multiple desired properties. This approach enables de novo design of AMPs with potent antimicrobial activity, reduced cytotoxicity, and diversity. Through extensive computational tests, MPOGAN exhibits superior performance in optimizing multiple desired properties of generated AMPs. Ten of the most promising candidates are chemically synthesized, with nine showing potent antimicrobial activity against three bacterial strains and low cytotoxicity against eukaryotic cells. MPOGAN offers a powerful computational approach for effective multi-property optimization of AMPs, thereby advancing the field of AI-aided drug discovery.

bioinformatics↗

bioRxiv2024

Spatiotemporal transcriptomic analyses reveal molecular gradient patterning during development and the tonotopic organization along cochlear axis

While cochlear tonotopic organization is essential for sound frequency discrimination, the spatiotemporal molecular programs shaping this architecture remain unresolved. Here, we integrate spatiotemporal transcriptomics (E17.5-P8-adult) with single-cell RNA-seq to construct a single-cell-resolution atlas of mouse cochlear development. As marked by differential gradient expression genes, distinct spatially and molecularly defined hair cell (HC) subtypes along the apical-basal axis were identified: such as the apical Myo7a+++/Calb1+++ outer hair cell (OHC) subtype, the basal Myo7a+/Calb1{square} OHC subtype, the apical vGlut3+++/Calb2+ inner hair cell (IHC) subtype, and the basal vGlut3+/Calb2+++ IHC subtype. In addition, the spatial data also demonstrate the developmental reversal of Myo7a/Calb2Nefh gradient patterns in HCs and spiral ganglion neurons (SGNs), revealing dynamic plasticity and complexity in frequency coding during development. Spatial analyses further demonstrate regional heterogeneity in cell communication intensity between HCs and SGNs, and also reveal core signaling hubs coordinating tonotopic organization, such as Fgf10-Fgfr2 pathway in the basal region and Ptn-Sdc pathway in the apical region HC or SGNs at P8 stage. Our study provides an open-access spatial database and reveals the morphological and molecular foundations underlying cochlear tonotopic organization, linking molecular and developmental mechanisms to auditory pathophysiology.

developmental biology↗

bioRxiv2024

OfWRKY33 binds to the key linalool synthase gene OfTPS7 to promote linalool synthesis in Osmanthus fragrans flowers

Volatile aroma compounds make significant contributions to human perception of flowers. Osmanthus fragrans is a famous aroma plant and linalool along with its oxides are proved to be the dominant aroma active compounds. Although some terpene synthases (TPSs) have been characterized, a comprehensive study of the hub metabolic gene and its transcriptional regulation remain to be revealed. Here, we selected a specific cultivar Boyeyingui (BBYG) with the highest content of linalool among 20 wide-cultivated cultivars for genome and transcriptome sequencings. Among the 25 new putative OfTPSs, only OfTPS6, OfTPS7 could exclusively produce linalool in planta. Biochemical analysis demonstrated that OfTPS6, OfTPS7 were able to catalyze geranyl diphosphate (GPP) into linalool and a small proportion of other monoterpenes in vitro. Spatial and temporal correlation analysis further confirmed the transcript level of OfTPS7 was closely associated with linalool content in diverse cultivars and different tissues, suggesting OfTPS7 was the essential linalool synthase gene. Combined with yeast one-hybrid screen and weighted correlation network analysis (WGCNA), a nucleus-localized transcriptional factor OfWRKY33 was considered as a potential modulator. Y1H, dual-luciferase assay and electrophoretic mobility shift assay (EMSA) demonstrated that OfWRKY33 directly bound to the W-box of OfTPS7 promoter to stimulate its transcription. OfWRKY33 could coordinately induce the expressions of OfTPS7, 1-deoxy-d-xylulose 1 (OfDXS1), thereby promoting the linalool formation. The results first revealed the hub linalool synthase gene OfTPS7 and a novel TF participating in the complex transcriptional regulation of linalool biosynthesis in O. fragrans flowers.

biochemistry↗

bioRxiv2024

Rfx3 controls outer hair cell differentiation, maintenance, and hair bundle formation by regulating the expression of Insm1, Ikzf2, and Triobp genes

The RFX family of transcription factors plays crucial roles in the regulation of ciliogenesis and organ development. Mutations of RFX transcription factors lead to various genetic diseases, including ciliopathies and hearing loss, although the underlying mechanisms remain unclear. This study comprehensively analyzed the expression patterns of RFX family members during different inner ear developmental stages. Rfx3 and Rfx7 were identified as the dominantly expressed members in cochlear hair cells, exhibiting alternative splicing variants and dynamic subcellular localization at different stages. Integration of single-cell transcriptomics, ChIP-seq, and ATAC-seq data indicates that Rfx3 functions as either a transcriptional activator or repressor, binding to numerous genes related to ciliogenesis, hair bundle structures, and planar cell polarity. Rfx3 regulates the spatiotemporal expression of hair bundle gene Triobp in hair cells by binding to and modulating its intronic enhancer. Additionally, Rfx3 binds to hair cell differentiation and fate determination genes Tbx2, Insm1, and Ikzf2. The results demonstrate that Rfx3 and Rfx7 are dominantly expressed in outer hair cells, with their subcellular localization shifting to cytoplasm at later developmental stages. This suggests a previously unknown function of Rfx3 and Rfx7 beyond transcriptional regulation, highlighting their complex roles in hair cell differentiation and maintenance.

developmental biology↗

bioRxiv2024

Sidechain chemistry-encoded solid/liquid phase transitions of condensates

Nature effectively leverages multivalent interactions among fundamental building blocks in solvents to create remarkable materials for various purposes. One prominent example is the formation of biomolecular condensates through the phase separation of proteins and nucleic acids. In particular, these condensates play crucial roles in regulating cellular functions and constructing natural materials. During the phase separation, solvents not only provide liquid environments for solvating molecules but play crucial roles in affecting the material properties of condensates. However, it remains controversial in the literature that alcohol molecules, as one type of solvents, can solidify some condensates while also melting others, leading to liquid-to-solid phase transition (LSPT) or solid-to-liquid phase transitions (SLPT), respectively. The mechanism underlying the alcohol-induced solid/liquid phase transitions of condensates remains poorly understood. Here, we combine systematic experimental characterizations with molecular dynamics simulations to demonstrate that the phase transitions of condensates depend on their sidechain chemistry and dominant molecular interactions. Specifically, "hydrophilic" condensates, which consist of many charged sidechains, undergo LSPT by adding alcohols due to strengthened electrostatic interactions. In contrast, "hydrophobic" condensates comprised of abundant aromatic sidechains undergo SLPT with the addition of alcohols because of weakened cation-{pi} and {pi}-{pi} interactions. Importantly, these findings are generally applicable for predicting phase transitions of a wide range of condensates formed by synthetic polyelectrolytes and intrinsically disordered proteins based on their sidechain hydrophobicity or amino acid compositions. Our work not only reconciles a conundrum in the literature but provides a fundamental framework for understanding the responsiveness of condensates to environmental stimuli. These insights are instrumental for developing therapeutic drugs to treat pathological aggregates and engineering stimuli-responsive biomaterials from the perspective of sidechain chemistry and molecular interactions.

biophysics↗

bioRxiv2024

MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs with Pixel Masking

Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates that as molecular similarity increases, graph-based methods struggle to capture these nuances, whereas imagebased approaches effectively retain the distinctions. Thus, we developed MaskMol, a knowledge-guided molecular image selfsupervised learning framework. MaskMol accurately learns the representation of molecular images by considering multiple levels of molecular knowledge, such as atoms, bonds, and substructures. By utilizing pixel masking tasks, MaskMol extracts fine-grained information from molecular images, overcoming the limitations of existing deep learning models in identifying subtle structural changes. Experimental results demonstrate MaskMols high accuracy and transferability in activity cliff estimation and compound potency prediction across 20 different macromolecular targets, outperforming 25 state-of-the-art deep learning and machine learning approaches. Visualization analyses reveal MaskMols high biological interpretability in identifying activity cliff-relevant molecular substructures. Notably, through MaskMol, we identified candidate EP4 inhibitors that could be used to treat tumors. This study not only raises awareness about activity cliffs but also introduces a novel method for molecular image representation learning and virtual screening, advancing drug discovery and providing new insights into structure-activity relationships (SAR). Code is available at https://github.com/ZhixiangCheng/MaskMol.

bioinformatics↗

bioRxiv2024

GenoM7GNet: An Efficient N7-methylguanosine Site Prediction Approach Based on a Nucleotide Language Model

N7-methylguanosine (m7G), one of the mainstream post-transcriptional RNA modifications, occupies an exceedingly significant place in medical treatments. However, classic approaches for identifying m7G sites are costly both in time and equipment. Meanwhile, the existing machine learning methods extract limited hidden information from RNA sequences, thus making it difficult to improve the accuracy. Therefore, we put forward to a deep learning network, called "GenoM7GNet," for m7G site identification. This model utilizes a Bidirectional Encoder Representation from Transformers (BERT) and is pretrained on nucleotide sequences data to capture hidden patterns from RNA sequences for m7G site prediction. Moreover, through detailed comparative experiments with various deep learning models, we discovered that the one-dimensional convolutional neural network (CNN) exhibits outstanding performance in sequence feature learning and classification. The proposed GenoM7GNet model achieved 0.953 in accuracy, 0.932 in sensitivity, 0.976 in specificity, 0.907 in Matthews Correlation Coefficient and 0.984 in Area Under the receiver operating characteristic Curve on performance evaluation. Extensive experimental results further prove that our GenoM7GNet model markedly surpasses other state-of-the-art models in predicting m7G sites, exhibiting high computing performance.

bioinformatics↗

bioRxiv2024

NRH, a potent NAD+ booster, improves glucose homeostasis and lipid metabolism in diet-induced obese mice though an active adenosine kinase pathway.

NAD+ deficiency underlies obesity-induced metabolic disturbances. Here we evaluated the treatment effect of a new and potent NAD+ enhancer, dihydronicotinamide riboside (NRH), in diet-induced obese mice with hyperglycemia and hyperlipidemia. Administering NRH for 7 weeks improved glucose homeostasis by enhancing pancreatic beta-cell functional mass, increasing muscle insulin sensitivity, and reducing hepatic gluconeogenesis. NRH treatment also mobilized fat deposition, reduced circulating lipid, and improved white adipose function. Significant elevation in multi-tissue NAD+ levels and sirtuin (SIRT) activities, especially SIRT3, mediated these metabolic improvements. Inhibiting adenosine kinase (ADK), a newly recognized enzyme in the NRH-induced NAD+ synthesis pathway, blocked NRHs effect in improving glucose and lipid metabolism. ADK inhibition also reduced tissue NAD+ elevation and the subsequent activation of SIRT3, suggesting an active ADK pathway is necessary for NRH-induced metabolic benefits. These observations, for the first time, establish NRH as a promising intervention for correcting obesity-induced metabolic syndrome.

pharmacology and toxicology↗

bioRxiv2024

PA2gamma promotes the malignant progression of tumor by elevating mitochondrial function via C1QBP

Proteasome activator 28{gamma} (PA28{gamma}) plays a critical role in malignant progression of various tumors, however, its role and regulation are not well understood. Here, using oral squamous cell carcinoma (OSCC) as main research model, we discovered that PA28{gamma} interacted with complement 1q binding protein (C1QBP), which is dependent on the N-terminus of C1QBP rather than the known functional domain (amino acids 168-213). Notably, we found that PA28{gamma} enhances C1QBP protein stability in OSCC. Functionally, PA28{gamma} contributes to the malignant progression of OSCC by affecting mitochondrial morphology and oxidative phosphorylation (OXPHOS) through C1QBP in vitro and vivo. Mechanically, PA28{gamma} upregulates the expression of optic atrophy 1 (OPA1), mitofusin 1 (MFN1), mitofusin 2 (MFN2), and the mitochondrial respiratory complex via C1QBP. Moreover, in a clinical cohort of OSCC patients, PA28{gamma} was positively correlated with C1QBP expression and negatively correlated with prognosis. Therefore, C1QBP represents a potential therapeutic target for cancer treatment and prognosis.

cancer biology↗

bioRxiv2024

Backbone-mediated weakening of pairwise interactions enables percolation in mimics of protein condensates

Biomolecular condensates formed by intrinsically disordered proteins (IDPs) condensates are semidilute solutions. These can be approximated as solutions of blob-sized segments, which can be as small as peptide-sized motifs. We leveraged the blob picture to quantify differences between inter-residue interactions in model compound and peptide-based mimics of dense versus dilute phases. The all-atom molecular dynamics simulations use the polarizable AMOEBA forcefield. In model compound solutions, the interactions between aromatic residues are stronger than interactions between cationic and aromatic residues. This holds in dilute and dense phases. Cooperativity within dense phases enhances pairwise interactions leading to finite-sized nanoscale clusters. The results for peptide-based condensates paint a different picture. Backbone amides add valence to the associating molecules. While this maintains or enhances pairwise inter-residue interactions in dilute phases, it weakens pair interactions in dense phases. Weakening of pair interactions enables fluidization characterized by short-range order and long-range disorder. The higher valence afforded by the peptide backbone becomes a generator of system-spanning networks. As a result, dense phases of peptides are best described as percolated network fluids. Overall, our results show how peptide backbones enhance pairwise interactions in dilute phases whole weakening these interactions in dense phases to enable percolation within dense phases.

biophysics↗

bioRxiv2024

LooMS: a novel peptide identification tools for data independent acquisition

Advancements in mass spectrometry (MS)-based proteomics have produced large-scale datasets, necessitating the development of effective tools for peptide identification. Here, we present LooMS, a novel tool specifically designed for identifying peptides in data-independent acquisition (DIA) datasets. LooMS employs an innovative approach, using an unbiased generation strategy for positive and negative samples, which reduces the risk of overfitting in peptide identification with deep learning models. Additionally, LooMS addresses various critical aspects of DIA mass spectra data analysis, constructing a comprehensive set of 43 features for training deep learning models, which cover different stages of DIA data analysis. Notably, we propose a false discovery rate (FDR) control strategy that integrates results from both LooMS and DiaNN, another leading peptide identification tool. Our results demonstrate significant improvements in peptide identification performance, with enhancements of 40.61% and 26.60% at the unique peptide level for human and mouse datasets, respectively. HighlightsO_LILooMS is a novel tool for identifying peptides in DIA datasets that adopts an innovative unbiased positive and negative sample generation strategy, which aim to avoid the overfilling in peptide identification with deep learning model. C_LIO_LILooMS comprehensively considers various aspects of data analysis for DIA mass spectra and builds 43 useful features for training deep learning models, which involve different stages of DIA data analysis. C_LIO_LIA FDR control strategy for integration of results from both LooMS and DiaNN is proposed, which can significantly improve the identification of peptides due to the differences in the features involved in peptide detection during their respective design. C_LI

bioinformatics↗

bioRxiv2024

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses significant challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov decision process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the lim-itation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies ten novel ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

bioinformatics↗

bioRxiv2024

DiffuST: a latent diffusion model for spatial transcriptomics denoising

Spatial transcriptomics technologies have enabled comprehensive measurements of gene expression profiles while retaining spatial information and matched pathology images. However, noise resulting from low RNA capture efficiency and experimental steps needed to keep spatial information may corrupt the biological signals and obstruct analyses. Here, we develop a latent diffusion model DiffuST to denoise spatial transcriptomics. DiffuST employs a graph autoencoder and a pre-trained model to extract different scale features from spatial information and pathology images. Then, a latent diffusion model is leveraged to map different scales of features to the same space for denoising. The evaluation based on various spatial transcriptomics datasets showed the superiority of DiffuST over existing denoising methods. Furthermore, the results demonstrated that DiffuST can enhance downstream analysis of spatial transcriptomics and yield significant biological insights.

genomics↗

bioRxiv2024

Direcred Micro-Ecology Application System: On-site One-step Fermentation Facilitating Microbial Application through Theoretical and Equipment Innovation

Functional microbial agents play a crucial role in various fields such as agriculture, feed fermentation, aquaculture, and environmental protection. However, traditional microbial agents were confronted with critical challenges such as limited shelf-life, reduced activity, and inconsistent efficacy. In this case, we innovatively proposed the concept of Directed Micro-Ecology (DME) and developed its application system, including a core module named DME intelligent fermentor (DME25). Over 40 functional strains, including bacterial strains and fungus strains, were successfully cultured to 10[~]50 x108 CFU/mL within 20[~]48 h and maintained a relatively low contamination rate (<2.5%). Finally, the stability and effectiveness of these DME-fermented strains were validated in different application areas, all of which exhibited perfect functional characteristics. Firstly, the bacillus strains inhibited the progression of wilt disease and significantly improved the growth of tomatoes. Secondly, all tested lactobacillus strains improved the nutrition and quality of fermented feed, complying with feed industry standards. Lastly, the ammonia nitrogen concentration, nitrite concentration of aquaculture water and phosphate concentration, COD of aquaculture tail water were significantly reduced within 1[~]4 d. The successful application of the DME intelligent fermentor in different fields marks a pivotal breakthrough in technological innovation of microbial agents on-site one-step fermentation. This technological advancement opens new avenues for enhancing the stability and effectiveness of microbial agents, infusing powerful impetus to the development of microbial application.

microbiology↗

bioRxiv2024

Horizontally acquired CSP genes contribute to wheat adaptation and improvement

Although horizontal gene transfer (HGT) often facilitates environmental adaptation of recipient organisms, whether and how they might affect crop evolution and domestication is unclear. Here we show that three genes encoding cold shock proteins (CSPs) were transferred from bacteria to the last common ancestor of Triticeae, a tribe of the grass family that includes several major staple crops such as wheat, barley, and rye. The acquired CSP genes in wheat (TaCSPs) are functionally conserved with their bacterial homologs by encoding a nucleic acid binding protein. Experimental evidence indicates that TaCSP genes positively regulate drought response and improve photosynthetic efficiency under water deficit conditions, by directly targeting a type 1 metallothionein gene to increase ROS scavenging, which in turn contributed to the geographic expansion of wheat. We identified an elite CSP haplotype in Aegilops-tauschii, introduction of which to wheat significantly increased drought tolerance, photosynthetic efficiency and grain yields. These findings not only provide major insights into the role of HGT in crop adaptation and domestication, but also demonstrate that novel microbial genes introduced through HGT offer a stable and naturally optimized resource for transgenic crop breeding and improvement.

plant biology↗