Biology subjects

Zeng, X.

Publications and source records attributed to Zeng, X..

At least 127 records · Page 7Linked to original sources

bioRxiv2023

Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Lever-aging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 {micro}m, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

neuroscience↗

bioRxiv2023

Linking root-associated fungal and bacterial functions to root economics

Tree roots form symbioses with soil microbes to acquire nutrients, but the relationships between root nutrient acquisition strategies and microbial community composition remain poorly understood. Here, we measured root traits and root-associated fungal and bacterial guilds in 336 trees of 52 species from a subtropical forest. We found a fungal gradient from ectomycorrhizal to saprotrophic dominance, which corresponded with a shift from organic to mineral nutrient economics. This fungal gradient was aligned with the increase of root nitrogen concentration, suggesting a linkage from simple root trait to fungal-mediated carbon-nutrient cycling. We also found that the functional composition of fungal and bacterial communities was closely correlated with host root-zone pH, which often varied among coexisting trees. Root-zone pH was independent of the common root traits, underpinning a potential new gradient in the root trait space. Our findings integrate microbial functions into the root economics framework, thereby advancing the understanding of diversity of nutrient acquisition strategies across forest trees.

ecology↗

bioRxiv2023

Comparative single-cell transcriptomic analysis reveals key differentiation drivers and the potential origin of the vertebrate retina

Despite known single-cell expression profiles in vertebrate retinas, understanding of their developmental and evolutionary expression patterns among homologous cell classes remains limited. We examined and compared approximately 240, 000 retinal cells from four species and found significant similarities among homologous cell classes, indicating inherent regulatory patterns. To understand these shared patterns, we constructed gene regulatory networks for each developmental stage for three of these species. We identified 690 regulons governed by 530 regulators across three species, along with 10 common cell class-specific regulators and 16 preserved regulons. RNA velocity analysis pinpointed conserved putative driver genes and regulators to retinal cell differentiation in both mouse and zebrafish. Investigation of the origins of retinal cells by examining conserved expression patterns between vertebrate retinal cells and invertebrate Ciona intestinalis photoreceptor-related cells implied functional similarities in light transduction mechanisms. Our findings offer insights into the evolutionarily conserved regulatory frameworks and differentiation drivers of vertebrate retinal cells.

bioinformatics↗

bioRxiv2023

Viruses that 'infect' viruses: Cas12f1 and Cas9 gene drive in HSV1

Synthetic CRISPR-Cas9 gene drive has been developed as a potential tool to control harmful species. However, Cas9 gene drive faces high resistance rate and mitigation strategies developed so far are difficult to implement. Furthermore, studying the resistance to gene drive is time consuming and challenging in higher organisms. We here tackled these two challenges simultaneously by generating Cas9 and Un1Cas12f1 gene drive in a fast-replicating DNA virus, HSV1. We assessed the transmission dynamics and resistance formation through phenotypical staining and next-generation sequencing, and demonstrated that HSV1 supported fast and effective transmission of gene drives, and the Un1Cas12f1 gene drives yielded greater conversion and lower resistance than did the Cas9 gene drives. This positions the Un1Cas12f1 gene drive as a promising alternative, and HSV1 emerges as a dependable and swift platform for gene drive assessment. The gene drive viruses function like pathogens that specifically infect viruses, offering potential applications in attenuating viral infections.

microbiology↗

bioRxiv2023

Chromosome-level scaffolding of haplotype-resolved assemblies using Hi-C data without reference genomes

Scaffolding is crucial for constructing most chromosome-level genomes. The high-throughput chromatin conformation capture (Hi-C) technology has become the primary scaffolding strategy due to its convenience and cost-effectiveness. As sequencing technologies and assembly algorithms advance, constructing haplotype-resolved genomes is increasingly preferred because haplotypes can provide additional genetic information on allelic and non-allelic variations. ALLHiC is a widely used allele-aware scaffolding tool designed for this purpose. However, its dependence on chromosome-level reference genomes and a higher chromosome misassignment rate still impede the unraveling of haplotype-resolved genomes. In this paper, we present HapHiC, a reference-independent allele-aware scaffolding tool with superior performance on chromosome assignment as well as contig ordering and orientation. Additionally, we provide new insights into the challenges in allele-aware scaffolding by conducting comprehensive analyses on various adverse factors. Finally, with the help of HapHiC, we constructed the haplotype-resolved allotriploid genome for Miscanthus x giganteus, an important lignocellulosic bioenergy crop. HapHiC is available at https://github.com/zengxiaofei/HapHiC.

bioinformatics↗

bioRxiv2023

Nonribosomal peptide synthetases require dynamic interaction between modular domains

Nonribosomal peptide synthetases (NRPSs) are large multidomain enzymes for the synthesis of a variety of bioactive peptides in a modular and pipelined fashion. Here, we investigated how the condensation (C) domain and the adenylation (A) domain cooperate with each other for the efficient catalytic activity in microcystin NRPS modules. We solved two crystal structures of the microcystin NRPS modules, representing two newly identified conformations in the NRPS catalytic cycle. Our data reveals that the dynamic interaction between the C and the A domains in these modules are mediated by the conserved "RXGR" motif, and this interaction is important for the adenylation activity. Furthermore, the "RXGR" motif-mediated dynamic interaction and its functional regulation is prevalent in different NRPSs modules possessing both the A and the C domains. This study provides new insight into the catalytic mechanism of NRPSs and should inspire novel ideas in NRPS enzyme engineering in synthetic biology.

biochemistry↗

bioRxiv2023

Amalga: Designable Protein Backbone Generation with Folding and Inverse Folding Guidance

Recent advances in deep learning enable new approaches to protein design through inverse folding and backbone generation. However, backbone generators may produce structures that inverse folding struggles to identify sequences for, indicating designability issues. We propose Amalga, an inference-time technique that enhances designability of backbone generators. Amalga leverages folding and inverse folding models to guide backbone generation towards more designable conformations by incorporating "folded-from-inverse-folded" (FIF) structures. To generate FIF structures, possible sequences are predicted from step-wise predictions in the reverse diffusion and further folded into new backbones. Being intrinsically designable, the FIF structures guide the generated backbones to a more designable distribution. Experiments on both de novo design and motif-scaffolding demonstrate improved designability and diversity with Amalga on RFdiffusion.

bioinformatics↗

bioRxiv2023

Robust and Adaptive Deep Model Ensemble Framework Fine-tuned by Structural Information for Drug-Target Interactions

In the fields of new drug development and drug repositioning, drug-target interactions (DTI) play a pivotal role. Although deep learning models have already made significant contributions in this domain, the state-of-the-art models still exhibit shortcomings in predictive performance and issues of false-negative errors. Based on these observations, we constructed a streamlined yet effective base learner model. With our designed adaptive feature weight network, the model can capture key features within drugs (targets). Furthermore, by cross-partitioning the training data, multiple base learners are integrated into a powerful ensemble model named EADTN. The performance of the model is further enhanced as the number of base learners increases. Additionally, we employed a single-linkage clustering algorithm to cluster drugs and proteins and leveraged this clustering information to fine-tune the base learners, which elevates the value of EADTN in real-world applications like drug repositioning and targeted drug development. Our designed substructure importance ranking method also demonstrates the models exceptional capability to recognize key substructures. Benefiting from the models low generalization error capability, we successfully identified false-negative samples within the dataset, revealing new interaction relationships. Experimental results indicate that EADTN consistently outperforms existing state-of-the-art models across multiple datasets. More importantly, the ensemble learning and clustering fine-tuning approaches adopted by our model offer a fresh perspective for related fields.

bioinformatics↗

bioRxiv2023

An interface-based molecular generative framework for protein-protein interaction inhibitors

Protein-protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs, particularly by considering PPI complexes or interface hotspot residues, remains a significant challenge. In this work, we constructed a comprehensive dataset of PPI interfaces with active and inactive compound pairs. Based on this, we propose a novel molecular generative framework tailored to PPI interfaces, named GENiPPI. Our evaluation demonstrates that GENiPPI captures the implicit relationships between the PPI interfaces and the active molecules, and can generate novel compounds that target these interfaces. Moreover, GENiPPI can generate structurally diverse novel compounds with limited PPI interface modulators. To the best of our knowledge, this is the first exploration of a structure-based molecular generative model focused on PPI interfaces, which could facilitate the design of PPI modulators. The PPI interface-based molecular generative model enriches the existing landscape of structure-based (pocket/interface) molecular generative model.

bioinformatics↗

bioRxiv2023

Segment-specific axon guidance by Wnt/Fz signaling diversifies motor commands in Drosophila larvae

Functional diversification of homologous neuronal microcircuits is a widespread feature observed across brain regions as well as across species, while its molecular and developmental mechanisms remain largely unknown. We address this question in Drosophila larvae by focusing on segmentally homologous Wave command-like neurons, which diversify their wiring and function in a segment-specific manner. Anterior Wave (a-Wave) neurons extend axons anteriorly and connect to circuits inducing backward locomotion, whereas posterior Wave (p-Wave) neurons extend axons posteriorly and trigger forward locomotion. Here, we show that Frizzled receptors DFz2 and DFz4, together with the DWnt4 ligand, regulate the segment-specific Wave axon projection. DFz2 knock-down (KD) not only reroutes Wave axons to posterior neuromeres but also biases its motor command to induce forward instead of backward locomotion as tactile response. Thus, segment-specific axon guidance diversifies the function of homologous command neurons in behavioral regulation. Since control of anterior-posterior (A-P) axon guidance by Wnt/Fz-signaling is evolutionarily conserved, our results reveal a potentially universal molecular principle for formation and diversification of the command system in the nerve cord. Furthermore, this work indicates that sensorimotor transduction can be rerouted by manipulating a single gene in a single class of neurons, potentially facilitating the evolutionary flexibility in action selection. Impact statementRewiring the command neurons in the tactile circuit suffices to change the behavioral strategy of a whole animal, implying a "plug-and-play" flexibility in sensorimotor circuits.

neuroscience↗

bioRxiv2023

Herpes zoster mRNA vaccine induces superior vaccine immunity over licensed vaccine in mice and rhesus macaques

Herpes zoster remains an important global health issue and mainly occurs in aged and immunocompromised individuals with an early exposure history to Varicella Zoster Virus (VZV). Although the licensed vaccine Shingrix has a remarkably high efficacy, undesired reactogenicity and increasing global demand causing vaccine shortage urged the development of improved or novel VZV vaccines. In this study, we developed a novel VZV mRNA vaccine candidate (named as ZOSAL) containing sequence-optimized mRNAs encoding full-length glycoprotein E encapsulated in an ionizable lipid nanoparticle. In mice and rhesus macaques, ZOSAL demonstrated superior immunogenicity and safety in multiple aspects over Shingrix, especially in the induction of strong T cell immunity. Transcriptomic analysis revealed that both ZOSAL and Shingrix could robustly activate innate immune compartments, especially Type-I IFN signaling and antigen processing/presentation. Multivariate correlation analysis further identified several early factors of innate compartments that can predict the magnitude of T cell responses, which further increased our understanding of the mode of action of two different VZV vaccine modalities. Collectively, our data demonstrated the superiority of VZV mRNA vaccine over licensed subunit vaccine. The mRNA platform therefore holds prospects for further investigations in next-generation VZV vaccine development.

immunology↗

bioRxiv2023

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or autoregressive pre-training objectives, which makes them struggle to handle protein understanding and generation tasks concurrently. We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework. Our key technical contribution is an exploration of the compatibility and the potential for joint optimization of the two types of objectives, which has led to a strategy for training xTrimoPGLM at an unprecedented scale of 100 billion parameters and 1 trillion training tokens. Our extensive experiments reveal that 1) xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. The model also facilitates an atomic-resolution view of protein structures, leading to an advanced 3D structural prediction model that surpasses existing language model-based tools. 2) xTrimoPGLM not only can generate de novo protein sequences following the principles of natural ones, but also can perform programmable generation after supervised fine-tuning (SFT) on curated sequences. These results highlight the substantial capability and versatility of xTrimoPGLM in understanding and generating protein sequences, contributing to the evolving landscape of foundation models in protein science. Trained weight for the xTrimoPGLM model, and downstream datasets are available at https://huggingface.co/proteinglm.

synthetic biology↗

bioRxiv2023

Large Scale Foundation Model on Single-cell Transcriptomics

Large-scale pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models in life science for deciphering the "languages" of cells and facilitating biomedical research is promising yet challenging. We developed a large-scale pretrained model scFoundation with 100M parameters for this purpose. scFoundation was trained on over 50 million human single-cell transcriptomics data, which contain high-throughput observations on the complex molecular features in all known types of cells. scFoundation is currently the largest model in terms of the size of trainable parameters, dimensionality of genes and the number of cells used in the pre-training. Experiments showed that scFoundation can serve as a foundation model for single-cell transcriptomics and achieve state-of-the-art performances in a diverse array of downstream tasks, such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, and single-cell perturbation prediction.

bioinformatics↗

bioRxiv2023

Sex-biased regulatory changes in the placenta of native highlanders contribute to adaptive fetal development

Compared with lowlander migrants, native Tibetans have a higher reproductive success at high altitude though the underlying mechanism remains unclear. Here, we compared the transcriptome and histology of full-term placentas between native Tibetans and Han migrants. We found that the placental trophoblast shows the largest expression divergence between Tibetans and Han, and Tibetans show decreased immune response and endoplasmic reticulum stress. Remarkably, we detected a sex-biased expression divergence, where the male-infant placentas show a greater between-population difference than the female-infant placentas. The umbilical cord plays a key role in the sex-biased expression divergence, which is associated with the higher birth weight of the male newborns of Tibetans. We also identified adaptive histological changes in the male-infant placentas of Tibetans, including larger umbilical artery wall and umbilical artery intima and media, and fewer syncytial knots. These findings provide valuable insights into the sex-biased adaptation of human populations, with significant implications for medical and genetic studies of human reproduction.

evolutionary biology↗

bioRxiv2023

CCR9 overexpression promotes T-ALL progression by enhancing cholesterol biosynthesis

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy of the lymphoid progenitor cells contributing to [~] 20% of the total ALL cases with higher prevalence in adults than the children. Despite the important role of human T-ALL cell lines in understanding the biology and treatment options, a detailed comparison of the tumorigenic potential of two commonly used T-ALL cell lines, MOLT4 and JURKAT cells is still lacking. In the present study, we compared the leukemogenic potentials of the two T-ALL cell lines (MOLT4 and JURKAT) in NOD-PrkdcscidIL2rgdull (NTG) mice and found that MOLT4 cells possessed a relatively higher aggressive phenotype characterized by their enhanced tissue infiltration as compared to the JURKAT cells. Gene expression profiling of the two cell lines revealed numerous differentially expressed genes (DEGs) including CC chemokine receptor 9 (CCR9), which augmented the invasion and metastasis of MOLT4 and JURKAT cells in vitro. The upregulation of CCR9 also promoted the tissue infiltration of JURKAT cells in the NTG mice. CCR9 overexpression increased cholesterol production by upregulating the expression of the core regulatory genes of the cholesterol biosynthesis pathway including MSMO1, MVD, and HMGCS1. Moreover, the upregulated expression of EGR1 was also found with CCR9 overexpression that modulated the expression of MSMO1, MVD, and HMGCS1. Notably, the treatment of the cells with simvastatin and siRNA-EGR1 decreased the aggressiveness of the CCR9 overexpressing JURKAT cells in vitro, suggesting the CCR9-EGR1 axis in T-ALL progression. This study highlights the distinct tumorigenic potentials of two T-ALL cell lines and reveals CCR9-regulated enhanced cholesterol biosynthesis in T-ALL. Simple summaryT-ALL is an aggressive cancer of the blood and bone marrow. In order to understand the biological mechanism of T-ALL, in vitro T-ALL cell lines are commonly employed. However, a comprehensive comparison of two common T-ALL cell lines, MOLT4 and JURKAT cells for T-ALL development is not yet available. We compared MOLT4 and JURKAT cells for T-ALL inducing potential and found that MOLT4 cells exhibited a relatively increased aggressiveness in mice as compared to JURKAT cells. We examined the molecular characteristics of two cell lines that could lead to differences in cancer development. Transcriptional profiling of MOLT4 and JURKAT cells revealed significant changes in the expression of several genes including CCR9. This aberrant expression of CCR9 impacted the migration and invasion of the T-ALL cell lines in vitro. In addition, higher expression levels of CCR9 also promoted T-ALL progression in vivo. Transcriptome analysis and gene ontology analysis of the DEGs of CCR9 over-expressing JURKAT cells indicated the enrichment of the genes in the cholesterol biosynthesis pathway, suggesting this metabolic rewiring mechanism as a strategy to cope with the increased energy demand of the malignant T-ALL cells.

cancer biology↗

bioRxiv2023

Single-Cell Transcriptomes and Immune Repertoires Reveal the Cell State and Molecular Changes in Pemphigus Vulgaris

The etiology and pathogenesis of pemphigus vulgaris (PV) are closely related to both immune cells and epithelial cells, but the specific subtypes of immune cells involved in PV and their roles are not yet fully understood. Additionally, the specific functions and mechanisms of first-line treatment glucocorticoids on cell types of PV remain to be elucidated. We performing 5 single-cell RNA sequencing, combined with V(D)J enrichment on buccal mucosal lesions and peripheral blood samples from treatment-naive patients with PV, in conjunction with post-treatment peripheral blood samples obtained after oral prednisone treatment. Our findings suggest that IL-1 signaling pathway, myeloid antigen presenting cells, inflammatory CD8+ Trm, and dysfunctional CD4+ Treg are crucial in PV. Our results were also supported by immunohistochemical assays. Furthermore, our results show that prednisone has a significant impact on monocytes and MAIT, but a limited effect on CD4+ Treg. Finally, we provide CDR3 amino acid sequence data of BCR that may be used as therapeutic targets. In conclusion, this study provides a comprehensive understanding of PV, particularly in the mucosal-dominant type, and the effect of GCs on PV, which could effectively lead to the development of new therapeutic strategies.

bioinformatics↗

bioRxiv2023

Neural representations in visual and parietal cortex differentiate between imagined, perceived, and illusory experiences

Humans constantly receive massive amounts of information, both perceived from the external environment and imagined from the internal world. To function properly, the brain needs to correctly identify the origin of information being processed. Recent work has suggested common neural substrates for perception and imagery. However, it has remained unclear how the brain differentiates between external and internal experiences with shared neural codes. Here we tested this question by systematically investigating the neural processes underlying both the generation and maintenance of information from voluntary imagery, veridical perception, and illusion. The inclusion of illusion allowed us to differentiate between objective and subjective internality: while illusion has an objectively internal origin and can be viewed as involuntary imagery, it is also subjectively perceived as having an external origin like perception. Combining fMRI, eye-tracking, multivariate decoding and encoding approaches, we observed superior orientation representations in parietal cortex during imagery compared to perception, and conversely in early visual cortex. This imagery dominance gradually developed along a posterior-to-anterior cortical hierarchy from early visual to parietal cortex, emerged in the early epoch of imagery and sustained into the delay epoch, and persisted across varied imagined contents. Moreover, representational strength of illusion was more comparable to imagery in early visual cortex, but more comparable to perception in parietal cortex, suggesting content-specific representations in parietal cortex differentiate between subjectively internal and external experiences, as opposed to early visual cortex. These findings together support a domain-general engagement of parietal cortex in the generation and maintenance of internally-generated experience. Significance StatementHow does the brain differentiate between imagined and perceived experiences? Combining fMRI, eye-tracking, multivariate decoding and encoding approaches, the current study revealed enhanced stimulus-specific representations in visual imagery originating from IPS, supporting the subjective experience of imagery. This neural principle was further validated by evidence from visual illusion, wherein illusion resembled perception and imagery at different levels of cortical hierarchy. Our findings provide direct evidence for the critical role of parietal cortex as a domain-general source region for the generation and maintenance of content-specific imagery, and offer new insights into the neural mechanisms underlying the differentiation between subjectively internal and external experiences.

neuroscience↗

bioRxiv2023

xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

AO_SCPLOWBSTRACTC_SCPLOWThe advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions in single-cell level. The amount of publicly available single-cell RNA-seq (scRNA-seq) data is already surpassing 50M records for human with each record measuring 20,000 genes. This highlights the need for unsupervised representation learning to fully ingest these data, yet classical transformer architectures are prohibitive to train on such data in terms of both computation and memory. To address this challenge, we propose a novel asymmetric encoder-decoder transformer for scRNA-seq data, called xTrimoGene, which leverages the sparse characteristic of the data to scale up the pre-training. This scalable design of xTrimoGene reduces FLOPs by one to two orders of magnitude compared to classical transformers while maintaining high accuracy, enabling us to train the largest transformer models over the largest scRNA-seq dataset today. Our experiments also show that the performance of xTrimoGene improves as we increase the model sizes, and it also leads to SOTA performance over various downstream tasks, such as cell classification, perturb-seq effect prediction, and drug combination prediction.

bioinformatics↗