Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 415 records · Page 23Linked to original sources

bioRxiv2024

Deep learning facilitates precise identification of disease-resistance genes in plants

The identification of plant disease-resistance genes is essential for understanding the plant immune system and accelerating crop breeding with disease resistance. Therefore, there is a pressing need for a method capable of accurately and comprehensively annotating resistance genes at a genome-wide scale. In this study, we propose Evolutionary Scale Modeling for LRR (ESM-LRR), a novel approach based on the deep protein language model to accurately identify LRR domains which are substantially variable structures in disease-resistance proteins. ESM-LRR achieved its highest F1 score of 0.80 on a test set using the 90% identity as the matching threshold. Building upon ESM-LRR, we developed the Plant Disease-Resistance Gene Predictor (R-Predictor), a framework designed to simultaneously annotate 15 diverse domain topologies, extensively covering recently characterised resistance genes across the entire genome. R-Predictor integrates four modules, each employing superior methods that outperform existing methods (F1 score of 0.89 for RLKs and 0.88 for NLRs), demonstrating its high accuracy and practicality in annotating disease-resistance genes (R genes). R-Predictor were also applied to identify R genes in rice, tomato, and grapevine. Furthermore, AlphaFold3 was employed to screen interactions between 1,116 protein pairs involving three NLRs identified exclusively by R-Predictor and 372 literature-reported plant-pathogen effectors, resulting in the identification of 15 putative NLR-effector complexes. Overall, this study presents a novel approach for advancing our understanding of plant immune mechanisms.

bioinformatics↗

bioRxiv2024

Structural Characterization of mRNA Lipid Nanoparticles in the Presence of Intrinsic Drug-free Lipid Nanoparticles

Lipid nanoparticles (LNPs) have emerged as a versatile platform for mRNA delivery across a range of applications, including disease prevention, cancer immunotherapy, and gene editing. Structural models of mRNA-containing lipid nanoparticles (mRNA-LNPs) have also been proposed based on characterization of samples by using various advanced techniques. Among these, small angle neutron scattering (SANS) has proven essential for elucidating the lipid distribution within mRNA-LNPs, a factor crucial to both their preparation and efficacy. However, recent findings suggest that the mRNA-LNP samples prepared via commercial microfluidic techniques may contain a substantial fraction of drug-free LNPs, casting doubt on the validity of earlier structural models. In this study, we employed contrast variation SANS to characterize both drug-free LNPs and our mRNA-LNP sample, and quantified the proportion of drug-free LNPs present to be [~]30% in our mRNA-LNP sample using nano flow cytometry. By removing the contributions of drug-free LNPs from the SANS data of our mRNA-LNP sample, we were able to precisely characterize the structure of mRNA-LNPs. Consequently, we proposed structural models for both drug-free LNPs and mRNA-LNPs. Notably, our analysis revealed similar lipid distributions and shell thicknesses between the two particle types, while the solvent content in mRNA-LNPs was significantly higher, leading to a larger core size. This work not only offers a method for accurately characterizing the structure of mRNA-LNPs, but also establishes criteria for selecting appropriate analytical techniques based on the structural parameters of interest. Therefore, our findings hold significant implications for the mechanistic understanding and quality control of mRNA-based vaccines. SignificancePrecise structural determination of mRNA-containing lipid nanoparticles (mRNA-LNPs) is vital for mechanistic insights into their preparation, delivery, immunogenicity, and storage, which are critical to the development of mRNA-based vaccines. However, most previous studies overlooked the substantial presence of drug-free LNPs within these samples. Here, we identified that approximately 30% of the nanoparticles in our mRNA-LNP sample were drug-free. By integrating contrast variation small angle neutron scattering (SANS) data from both drug-free LNPs and mRNA-LNPs, we developed structural models for both particle types, and provided a guidance for characterization technique selection based on concerned structural features. Beyond mechanistic insight on structure, our approach offers a robust method for quality assessment and process monitoring in mRNA-based vaccine production.

biophysics↗

bioRxiv2024

The exit of naïve pluripotency contains a lipid metabolism-induced checkpoint for genome integrity.

Pluripotent progenitors undergo dramatic cellular and biochemical transformations during peri-implantation development. These large-scale reprogramming events are fundamental for subsequent differentiation, but how they are integrated and co-ordinated with the preservation of genome integrity remain unknown. Here, we uncover a metabolism-induced telomere checkpoint that takes place in pluripotent progenitors as they form rosette-like epithelial structures. We show that the glycolytic switch at the exit of naive pluripotency is preceded by an acceleration of mitochondrial respiration and de novo lipogenesis, fuelling the accumulation of lipid droplets required for morphogenesis. We find that downstream of these CIDEA-promoted metabolic events is the induction of ZSCAN4, a key pluripotency-associated regulator of telomere stability. Surprisingly, the build-up of lipid droplets corresponds to a transient shortening of telomeres, which triggers the activation of an elongation mechanism via ZSCAN4. Thus, telomere homeostasis can be safeguarded as essential lipid metabolic reprogramming unfolds to drive developmental progression.

developmental biology↗

bioRxiv2024

An Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control

Nucleotide modifications deviate nanopore sequencing readouts, therefore generating artifacts during the basecalling of sequence backbones. Here, we present an iterative approach to polish modification-disturbed basecalling results. We show such an approach is able to promote the basecalling accuracy of both artificially-synthesized and real-world molecules. With demonstrated efficacy and reliability, we exploit the approach to precisely basecall therapeutic RNAs consisting of artificial or natural modifications, as the basis for quantifying the purity and integrity of vaccine mRNAs which are transcribed in vitro, and for determining modification hotspots of novel therapeutic RNA interference (RNAi) molecules which are bioengineered (BioRNA) in vivo.

bioinformatics↗

bioRxiv2024

The Precise Basecalling of Short-Read Nanopore Sequencing

The nanopore sequencing of short sequences, whose lengths are typically less than 0.3kb therefore comparable with Illumina sequencing techniques, has recently gained wide attention. Here, we design a scheme for training nanopore basecallers that are specialized for short biomolecules. With bioengineered RNA (BioRNA) molecules as examples, we demonstrate the superior accuracy of basecallers trained by our scheme.

bioinformatics↗

bioRxiv2024

The transcription factor ZmbZIP75 promotes both grain filling and kernel dehydration in maize

Selecting both high-yield and low-kernel-moisture varieties is essential for modern maize production, but relevant breeding efforts are hindered by a lack of valuable regulatory genes. Here, we demonstrate that the transcription factor (TF) basic leucine zipper 75 (ZmbZIP75) promotes grain yield and reduces kernel moisture in maize. Knockout of ZmbZIP75 results in defective grain filling and kernel dehydration, whereas ZmbZIP75 overexpression confers increased grain yield per plant and decreased kernel moisture without altering plant architecture. Mechanistically, during the grain filling stage, ZmbZIP75 is transcriptionally induced by maternal-derived basal abscisic acid (ABA) and directly activates multiple core starch synthesis-related genes and key TFs, thereby promoting grain filling and final yield. In the late stage of kernel development, high concentrations of zygotic ABA enhance ZmbZIP75 phosphorylation through SnRK2.10. The phosphorylated ZmbZIP75 subsequently transactivates and interacts with TF VP1 to synergistically promote kernel dehydration. This study thus highlights the potential of ZmbZIP75 for engineering both high-yield and low-kernel-moisture varieties to meet the demands of high-efficient maize production. IN A NUTSHELLO_ST_ABSBackgroundC_ST_ABSHigh grain yield in maize is generally associated with elevated kernel moisture at harvest, which is the main limiting factor for modern maize production. Therefore, it is of importance to select varieties with both high-yield and low-kernel-moisture. These traits are largely controlled by grain filling and kernel dehydration, two tightly connected processes during maize kernel development. Abscisic acid (ABA) is well-documented for its vital role in grain filling and dehydration maturation. However, the molecular mechanisms by which ABA coordinates these two processes remain unclear. QuestionWhat are the regulatory factors involved, and how do they mediate ABA signaling to coordinate grain filling and dehydration maturation in maize? FindingsZmbZIP75 is transcriptionally induced by ABA and directly activates multiple core starch synthesis-related genes and key TFs in developing maize endosperm, thereby promoting grain filling. In developing embryo, ABA enhances ZmbZIP75 phosphorylation via SnRK2.10. The phosphorylated ZmbZIP75 then transactivates and interacts with VP1 to synergistically promote kernel dehydration. Moreover, ZmbZIP75 overexpression confers increased grain yield and reduced kernel moisture in maize. Next stepsWhile ZmbZIP75 is directly phosphorylated to mediate ABA signaling during dehydration maturation, ZmbZIP75 is transcriptionally induced by ABA during grain filling. We plan to identify upstream factors that mediate ABA signaling to regulate ZmbZIP75 expression, thereby enhancing our understanding of ABA-promoted grain filling in maize.

plant biology↗

bioRxiv2024

Transdiagnostic Symptom Domains are Associated with Head Motion During Multimodal Imaging in Children

BackgroundHead motion is a challenge for neuroimaging research in developmental populations. However, it is unclear how transdiagnostic symptom domains including attention, disruptive behavior (e.g., externalizing behavior), and internalizing problems are linked to scanner motion in children, particularly across structural and functional MRI. The current study examined whether transdiagnostic domains of attention, disruptive behavior, and internalizing symptoms are associated with scanner motion in children during multimodal imaging. MethodsIn a sample of 9,045 children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) Study, logistic regression and linear mixed-effects models were used to examine associations between motion and behavior. Motion was indexed using ABCD Study quality control metrics and mean framewise displacement for the following: T1-weighted structural, resting-state fMRI, diffusion MRI, Stop-Signal Task, Monetary Incentive Delay task, and Emotional n-Back task. The Child Behavior Checklist was used as a continuous measure of symptom severity. ResultsGreater attention and disruptive behavior problem severity was associated with a lower likelihood of passing motion quality control across several imaging modalities. In contrast, increased internalizing severity was associated with a higher likelihood of passing motion quality control. Increased attention and disruptive behavior problem severity was also associated with increased mean motion, whereas increased internalizing problem severity was associated with decreased mean motion. ConclusionTransdiagnostic domains emerged as predictors of motion in youths. These findings have implications for advancing development of generalizable and robust brain-based biomarkers, computational approaches for mitigating motion effects, and enhancing accessibility of imaging protocols for children with varying symptom severities.

neuroscience↗

bioRxiv2024

Rapid production of modified cattle and sheep from spermatid-like stem cells

Genome-modified ruminants, such as cattle and sheep, are important germplasm for agricultural applications. Haploid androgenetic stem cells (haSCs) can support the full-term development of embryos upon intracytoplasmic haSCs injection (iCHI) into intact oocytes. Thus, haSCs are invaluable resources for studying economic traits and significantly impact livestock breeding; however, ruminant haSCs have yet to be obtained. Here, we report the derivation of cattle and sheep haSCs using a recombinant FACE medium. These cells display characteristics of formative-state pluripotency and can differentiate into three germ layers, both in vitro and in vivo. Interestingly, the ectopic expression of protamine in haSCs converts their nuclei into a spermatid-like structure and synergistic enhancement of full-term development of iCHI embryos, named as Pro-iCHI approach. Furthermore, gene-modified cattle and sheep can be efficiently produced through the combinatorial use of the CRISPR and Pro-iCHI approach. Overall, Pro-iCHI provides an easy-to-manipulate agrobiotechnology tool for rapidly producing genetically modified livestock.

bioengineering↗

bioRxiv2024

Haplotype-resolved assemblies provide insights into genomic makeup of the oldest grapevine cultivar (Munage) in Xingjiang

AbstractsMunage, an ancient grape variety that has been cultivated for thousands of years in Xinjiang, China, is recognized for its exceptional fruit traits. There are two main types of Munage: white fruit (WM) and red fruit (RM). However, the lack of a high-quality genomic resources has impeded effective breeding and restricted the potential for expanding these varieties to other growing regions. In this study, we assembled haplotype-resolved genome assemblies for WM and RM, alongside integrated whole genome resequencing (WGS) data and transcriptome data to illuminate specific mutations and associated genes in Munake and the genes associated with fruit color traits. Selective analysis between Munage clones and Eurasian grapes suggested that adaptive selection exists in Munage grapes, with genes enriched in processes including cell maturation, plant epidermal cell differentiation, and root epidermal cell differentiation. The study examined the mutations within Munage grapes and found that the genes PMAT2 on chromosome 12 and MYB123 on chromosome 13 are likely responsible for color variation in RM. These findings provide crucial genetic resources for investigating the genetics of the ancient Chinese grape variety, Munage, and will facilitate the genetic improvement in grapevine.

genomics↗

bioRxiv2024

High-efficiency discovery and structure-activity-relationship analysis of non-substrate-based covalent inhibitors of S-adenosylmethionine decarboxylase

Targeted covalent inhibitors (TCIs) form covalent bonds with targets following initial non-covalent binding. The advantages of TCIs have driven a resurgence in rational TCI design over the past decade, resulting in the approval of several blockbuster covalent drugs. To support TCI discovery, various computational methods have been developed. However, accurately predicting TCI reactivity remains challenging due to interference between non-covalent scaffolds and reactive warheads, leading to inefficiencies in computational screening and high experimental costs. In this study, we enhanced the SCARdock protocol, a validated computational screening tool developed by our lab, by incorporating quantum chemistry-based warhead reactivity calculations. By integrating these calculations with non-covalent docking scores, docking ranks, and bonding-atom distances, non-covalent and covalent inhibitors of S-adenosylmethionine decarboxylase (AdoMetDC) were correctly classified. Using the optimized SCARdock, we successfully identified twelve new AdoMetDC covalent inhibitors from 17 compounds, achieving a 70.6% hit rate. From these novel inhibitors, we analyzed the contributions of non-covalent interactions and covalent bonding, enabling a structure-activity relationship (SAR) analysis for AdoMetDC covalent inhibitors, which was previously unexplored with substrate-based inhibitors. Overall, this work presents an efficient computational protocol for TCI discovery and offers new insights into AdoMetDC inhibitor design. We anticipate that this approach will stimulate TCI development by improving computational screening efficiency and reducing experimental costs.

pharmacology and toxicology↗

bioRxiv2024

Leveraging large language models for metabolic engineering design

Establishing efficient cell factories involves a continuous process of trial and error due to the intricate nature of metabolism. This complexity makes predicting effective engineering targets a challenging task. Therefore, it is vital to learn from the accumulated successes of previous designs for advancing future cell factory development. In this study, we developed a method based on large language models (LLMs) to extract metabolic engineering strategies from research articles on a large scale. We created a database containing over 29006 metabolic engineering entries, 1210 products and 751 organisms. Using this extracted data, we trained a hybrid model combining deep learning and mechanistic approaches to predict engineering targets. Our model outperformed traditional metabolic engineering target prediction algorithms, excelled in predicting the effects of gene modifications, and generalized well to out-of-distribution products and multiple gene combinations. Our study provides a valuable dataset, a chatbot, and an engineering target prediction model for the metabolic engineering field and exemplifies an efficient method for leveraging existing knowledge for future predictions.

systems biology↗

bioRxiv2024

Magnetic Resonance Imaging of Gastric Motility in Conscious Rats

IntroductionGastrointestinal (GI) magnetic resonance imaging (MRI) can simultaneously capture gastric peristalsis, emptying, and intestinal filling and transit. Performing GI MRI with animals requires anesthesia, which complicates physiology and confounds interpretation and translation from animals to humans. This study aims to enable MRI in conscious rats, and for the first time, characterize GI motor functions in awake versus anesthetized conditions. MethodsWe acclimated rats to remain awake, still, and minimally stressed during MRI. We scanned 14 Sprague-Dawley rats in both awake and anesthetized conditions after voluntarily consuming a contrast-enhanced test meal. ResultsAwake rats remained physiologically stable during MRI, showed gastric emptying of 23.7{+/-}1.4% after 48 minutes, and exhibited strong peristaltic contractions propagating through the antrum with a velocity of 0.72{+/-}0.04 mm/s, a relative amplitude of 40.7{+/-}2.3%, and a frequency of 5.1{+/-}0.1 cycles per minute. In the anesthetized condition, gastric emptying was about half of that in the awake condition, likely due to the effect of anesthesia in halving the amplitudes of peristaltic contractions rather than their frequency (not significantly changed) or velocity. In awake rats, the intestine filled more quickly and propulsive contractions were more occlusive. ConclusionWe demonstrated the effective acquisition and analysis of GI MRI in awake rats. Awake rats show faster gastric emptying, stronger gastric contraction with a faster propagation speed, and more effective intestinal filling and transit, compared to anesthetized rats. Our protocol is expected to benefit future preclinical studies of GI physiology and pathophysiology.

bioengineering↗

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

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

bioinformatics↗

bioRxiv2024

Molecular and spatiotemporal characterization of cells in murine atherosclerotic plaques

ObjectiveSingle-cell technologies have revolutionized our understanding of the phenotypic and transcriptional diversity of aortic leukocytes in atherosclerotic humans and mice. However, enzymatically dissociated tissues lose the spatial context of plaque cells in situ. Here we utilized imaging mass cytometry (IMC) combining with single-cell RNA sequencing (scRNA-seq) to characterize the spatial distribution dynamics, phenotypic transitions, metabolic and functional phenotypes, and the intercellular interaction networks of plaque cells during atherosclerotic progression. Additionally, the dynamic immune landscape of circulating leukocytes associated with atherosclerosis was characterized using cytometry of time of flight (CyTOF). Approach and ResultsA highly multiplexed IMC panel with 33 metal-conjugated antibodies was designed to generate 11 highly multiplexed histology images of aortic root tissues from ApoE-/- mice on high-fat diet at different stage of atherosclerosis. Using histoCAT, we identified 8 principal cell subtypes with distinct phenotypic and geographic dynamics. Furthermore, IMC-defined cell subsets partially corresponded to scRNA-seq-annotated aortic cell subtypes, including 4 macrophage subsets, neutrophils, smooth muscle cells (SMCs) and SMC-derived SEMs (Stem cell, endothelial cell and macrophage-like cell). Activation of inflammatory pathways, increased oxidative phosphorylation and augmented osteoclast differentiation were observed in macrophage populations, SMCs and SEMs from an early stage to advanced stage of atherosclerosis. Notably, cell neighborhood analysis by IMC uncovered multifaceted cell-cell interactions within the plaque, in particular in neutrophil-mediated interactions with smooth muscle cells and macrophages, which were confirmed by ligand-receptor interactions based on scRNA-seq data. Additionally, characterization of the peripheral immune cells by CyTOF revealed an increased ratio of myeloid cells to lymphocytes, and certain neutrophil and monocyte subpopulations also exhibited enhanced lipid metabolism and glycolysis as well as activated inflammatory signaling. ConclusionThis study provides a dynamic spatiotemporal landscape of atherosclerotic lesions and peripheral leukocytes. The new information based on IMC may help understand atherosclerotic pathology and develop novel therapeutic strategies.

pathology↗

bioRxiv2024

Predicting the effect of non-coding mutations on single-cell DNA methylation using deep learning

Predicting the effects of non-coding mutations on DNA methylation is crucial for advancing our understanding of gene expression, epigenetic inheritance, and its role in disease mechanisms. Current methods lack the capability to predict the impact of non-coding mutations on DNA methylation at single-cell resolution and long range, while remain challenges in tracking SNP influences throughout disease progression. Here, we introduce Methven, a deep learning-based framework designed to predict the effects of non-coding mutations on DNA methylation at single-cell resolution, to overcome the challenges. Methven integrates DNA sequences and ATAC-seq data, employing a divide-and-conquer approach to handle varying scales of SNP-CpG interactions. By leveraging a pretrained DNA language model, Methven accurately predicts both the direction and magnitude of methylation changes across a 100kbp range with a lightweight architecture. The evaluation results demonstrate the superior performance of Methven in prioritizing functional non-coding mutation, model interpretability, and its potential for revealing personalized mutation-disease associations.

bioinformatics↗

bioRxiv2024

Protein degradation by small tag artificial bacterial E3 ligase

Targeting of proteins for degradation in a reversible manner is a powerful approach to decipher gene function and mimic drug effects, with great potential for drug target discovery and validation. A generalized approach is to tag a protein of interest and then use this tag to recruit an endogenously or exogenously expressed E3 ligase for its polyubiquitination and subsequent degradation via 26S proteasome. However, the often bulky size of the tag and the great variability of substrate-dependent degradation efficiency of mammalian E3 ligases pose great challenges in practice. Here we show that small tags (10-15 amino acids) can be used to efficiently tag endogenous proteins for degradation when coupled with an exogenously expressed artificial bacterial E3 ligase (ABEL) consisting of a tag-interacting moiety and the catalytic domain of the bacterial E3 ligase IpaH9.8. We name this versatile and efficient platform degradation by small tag ABEL (DESTABEL). Furthermore, we show that an ABEL containing a nanobody against human -synuclein mediates efficient degradation in primary neurons as well as in the adult mouse brain. Taken together, our data show that tag-dependent and independent ABELs are powerful yet flexible tools for studies of protein function and drug target validation.

molecular biology↗

bioRxiv2024

Organoid Modeling and Single-Cell Profiling Uncover the Migration Mechanism of Smooth Muscle Cells in Moyamoya Disease

Moyamoya disease (MMD) is a chronic cerebrovascular disorder characterized by progressive occlusion of the intracranial arteries, resulting in severe ischemic or hemorrhagic stroke. The main characteristic of the affected vessels in MMD is arterial intimal thickening. However, there are no in vitro or in vivo models that can mimic its vascular characteristics. Moreover, the mechanisms underlying the intimal thickening remain unexplained. Here, we generated vascular organoids by differentiating human induced pluripotent stem cell derived from the peripheral blood of MMD patients, thereby creating an organoid model reflecting both the genetic background and characteristics of the affected vessels. Through single-cell sequencing, we found the increased vascular smooth muscle cell (VSMC) proportion and its functional abnormalities in MMD organoids. Proteomics and RNA sequencing identified abnormal TUBA4A and TUBB4B overexpression in both the organoids and patient serum. The following in vitro experiments demonstrated that TUBA4A and TUBB4B promote the contractile-to-synthetic phenotypic switching, migration and proliferation in VSMC. Further experiments identified the GJA1-mediated PI3K/AKT/KLF4 pathway as a key regulating pathway of these phenotypic changes in VSMCs. Our findings demonstrate that the abnormal expression of TUBA4A and TUBB4B in VSMC might be a significant contributor to the intimal thickening in the affected vessels of MMD.

cell biology↗