Biology subjects

Tang, Z.

Publications and source records attributed to Tang, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Pan-Prediction of MHC-II Restricted Epitopes Across Species via an Alphafold-based Quantification Scheme

Predicting MHC-II restricted epitopes across species used to be challenging, but Alphafold (AF) may provide a structure-based pan-prediction solution. In this study, we established the new tool AF-pred with a clear standard for quantitative prediction results. Compared to the sequence-based tools heavily trained with human ligandome, AF-pred does not show advantage in predicting the binding patterns of human HLA-II but has far better performance in predicting binding patterns of other animals MHC-II. Using recently resolved bat MHC-II structures, we analyzed AF-preds prediction capability, logic and limitation. In addition, we also explored the impact of AF algorithm iterations on the prediction of MHC-II restricted epitopes. The results demonstrated that AF-pred is capable of cross-species prediction of MHC-II restricted epitopes and is conducive to the development of novel veterinary vaccines.

immunology↗

bioRxiv2024

RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-cell Acute Lymphoblastic Leukemia

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identifying B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic design. Existing methods for B-ALL subtyping primarily depend on immunophenotyping, cytogenetic tests and genomic profiling, which would be costly, complicated, and laborious. To overcome these challenges, we present RanBALL (an ensemble Random projection-based model for identifying B-ALL subtypes), an accurate and cost-effective model for B-ALL subtype identification. By leveraging random projection (RP) and ensemble learning, RanBALL can preserve patient-to-patient distances after dimension reduction and yield robustly accurate classification performance for B-ALL subtyping. Benchmarking results based on >1700 B-ALL patients demonstrated that RanBALL achieved remarkable performance (accuracy: 0.93, F1-score: 0.93, and Matthews correlation coefficient: 0.93), significantly outperforming state-of-the-art methods like ALLSorts in terms of all performance metrics. In addition, RanBALL performs better than tSNE in terms of visualizing B-ALL subtype information. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets to have consequential positive impacts on downstream risk stratification and tailored treatment design. To extend its applicability and impacts, a Python-based RanBALL package is available at https://github.com/wan-mlab/RanBALL.

bioinformatics↗

bioRxiv2024

Deep Generative Optimization of mRNA Codon Sequences for Enhanced Protein Production and Therapeutic Efficacy

Messenger RNA (mRNA) therapeutics show immense promise, but their efficacy is limited by suboptimal protein expression. Here, we present RiboDecode, a deep learning framework that generates mRNA codon sequences for enhanced mRNA translation. RiboDecode introduces several advances, including direct learning from large-scale ribosome profiling data and generative exploration of a large sequence space. In silico analysis demonstrates RiboDecode s robust predictive accuracy for unseen genes and cellular environments. In vitro experiments showed substantial improvements in protein expression, significantly outperforming past methods. In addition, RiboDecode enables mRNA design with consideration of cellular context and demonstrates robust performance across different mRNA formats, including m1{Psi}-modified and circular mRNAs, an important feature for mRNA therapeutics. In vivo mouse studies showed that optimized influenza hemagglutinin mRNAs induce ten times stronger neutralizing antibody responses against influenza virus compared to the unoptimized sequence. In an optic nerve crush model, optimized nerve growth factor mRNAs achieve equivalent neuroprotection of retinal ganglion cells at one-fifth the dose of the unoptimized sequence. Collectively, RiboDecode represents a paradigm shift from rule-based to a data-driven, context-aware approach for mRNA therapeutic applications, enabling the development of more potent and dose-efficient treatments.

bioinformatics↗

bioRxiv2024

Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks

Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5-20 {micro}m thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 200-{micro}m thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring, and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor-immune interactions in human skin cancer.

genomics↗

bioRxiv2024

Metabolomics profiling reveals association of fermented herbal feed additive, growth performance and gut microbiota in piglets

Fermented Chinese medicine (FCM), as a functional feed additive, has been widely recognized to play a significant role in protecting the intestinal health of piglets and enhancing productive performance. However, the relationship between the active components of FCM, gut microbiota, and their beneficial effects on animal performance remains unclear. In this study, metabolomics analysis revealed a significant increase in the main contents of lactic acid and propionic acid in FCM, while most glycosides and their derivatives decreased after three days of microbial fermentation. Subsequently, piglets were fed a basic diet supplemented with 1% FCM, while the control group received only the basic diet. The results indicated a significant increase in feed intake and average daily gain within 14 days (P<0.05) due to FCM supplementation. Additionally, FCM significantly improved feed efficiency from 1.76 to 1.50 (P<0.05). Meanwhile, piglets in the FCM group exhibited significantly lower frequencies of diarrhea and coughing, indicating better health. Moreover, high-throughput sequencing analysis revealed higher abundances of Lactobacillus agilis, Megasphaera elsdenii, Ligilactobacillus, and Veillonellaceae in piglets fed with FCM. In summary, FCM increased the content of active ingredients through microbial fermentation and regulated the intestinal microbiota to improve the health of piglets. FCM offers a promising potential for enhancing production performance and economic efficiency in the livestock industry.

microbiology↗

bioRxiv2024

The WAVE complex forms linear arrays at negative membrane curvature to instruct lamellipodia formation.

Cells generate a wide range of actin-based membrane protrusions for various cell behaviors. These protrusions are organized by different actin nucleation promoting factors. For example, N-WASP controls finger-like filopodia, whereas the WAVE complex controls sheet-like lamellipodia. These different membrane morphologies likely reflect different patterns of nucleator self-organization. N-WASP phase separation has been successfully studied through biochemical reconstitutions, but how the WAVE complex self-organizes to instruct lamellipodia is unknown. Because WAVE complex self-organization has proven refractory to cell-free studies, we leverage in vivo biochemical approaches to investigate WAVE complex organization within its native cellular context. With single molecule tracking and molecular counting, we show that the WAVE complex forms highly regular multilayered linear arrays at the plasma membrane that are reminiscent of a microtubule-like organization. Similar to the organization of microtubule protofilaments in a curved array, membrane curvature is both necessary and sufficient for formation of these WAVE complex linear arrays, though actin polymerization is not. This dependency on negative membrane curvature could explain both the templating of lamellipodia and their emergent behaviors, including barrier avoidance. Our data uncover the key biophysical properties of mesoscale WAVE complex patterning and highlight an integral relationship between NPF self-organization and cell morphogenesis.

cell biology↗

bioRxiv2024

AB-free kava enhances resilience against the adverse health effects of tobacco smoke in mice

Tobacco smoke remains a serious global issue, resulting in serious health complications, contributing to the onsets of numerous preventive diseases, and imposing significant financial burdens. Despite regulatory policies and cessation measures aimed at curbing its usage, novel interventions are urgently needed for effective damage reduction. Our preclinical and pilot clinical studies showed that AB-free kava has the potential to reduce tobacco smoke-induced lung cancer risk, mitigate tobacco dependence, and reduce tobacco use. To understand the scope of its benefits in damage reduction and potential limitations, this study evaluated the effects of AB-free kava on a panel of health indicators in mice exposed to 2 - 4 weeks of daily tobacco smoke exposure. Our comprehensive assessments included global transcriptional profiling of the lung and liver tissues, analysis of lung inflammation, evaluation of lung function, exploration of tobacco nicotine withdrawal, and characterization of the causal PKA signaling pathway. As expected, Tobacco smoke exposure perturbed a wide range of biological processes and compromised multiple functions in mice. Remarkably, AB-free kava demonstrated the ability to globally mitigate tobacco smoke-induced deficits at the molecular and functional levels with promising safety profiles, offering a unique promise to mitigate tobacco smoke-related health damages. Further pre-clinical evaluation and clinical translation are warranted to fully harness the potential of AB-free kava in combating tobacco smoke-related harms. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/599576v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f6ee4corg.highwire.dtl.DTLVardef@96e062org.highwire.dtl.DTLVardef@16579a0org.highwire.dtl.DTLVardef@1140a8e_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

Towards a universal spatial molecular atlas of the mouse brain

Spatially mapping a comprehensive brain at the single-cell level with molecular resolution is crucial for understanding its biological structure, mechanisms, and functions. Recent scalable spatial transcriptomics technologies1-7 have revolutionized our ability to examine millions of cells within the complex brain network, allowing for the generation of multiple mouse brain atlases that illuminate diverse transcriptomic and spatial cell types. However, despite enormous efforts, these spatial transcriptomics atlases still face challenges in providing a comprehensive brain representation: each atlas only captures a fraction of the vast cellular population of the brain and is limited in one or more aspects of spatial resolution, gene detection sensitivity, and gene throughput8. Here, we introduce FuseMap, a deep-learning-based framework for spatial transcriptomics that bridges single-cell or single-spot gene expression with spatial contexts and consolidates various gene panels across spatial transcriptomics atlases. By training on an extensive spatial transcriptomics atlas corpus of the mouse brain1-7, comprising over 18.6 million cells or spots and 26,665 genes from 434 tissue sections across seven datasets in a self-supervised way, FuseMap gains a fundamental understanding of cell identities and gene characteristics. We thus create a comprehensive molecular spatial reference atlas of the whole mouse brain with transcriptome-wide information, achieving multiple tasks including nomenclature harmonization of molecular cell types and tissue regions, identification of novel molecular brain regions, spatial gene imputation, targeted gene-panel selection, and region-specific cell-type interactions inference. Furthermore, the pretrained brain FuseMap model enables mapping new query data to the molecular spatial reference brain through transfer learning, allowing for automated cell and tissue annotations and replacing laborious and suboptimal manual annotations. Overall, we offer FuseMap as a novel computational framework for integration across various spatial transcriptomics platforms and provide a molecular common coordinate framework (molCCF) with pretrained FuseMap brain model for exploration of the brain molecular and cellular landscape with harmonized annotations and coordinates.

genomics↗

bioRxiv2024

Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion

Designing regulatory DNA with tunable and context-specific activity is a major goal in biotechnology and medicine. Deep generative models offer a promising route for sequence design, yet it remains unclear whether synthetic sequences faithfully recapitulate the motif organization and functional specificity of natural regulatory DNA. Here we present DNA Discrete Diffusion (D3), a generative model that designs regulatory DNA through an iterative nucleotide-substitution process. Across computational benchmarks, D3 improves regulatory sequence generation relative to matched diffusion baselines, producing sequences that more closely match target activity, activity distributions, and sequence composition. In K562 lentiMPRA experiments, D3-designed sequences retained measurable regulatory activity and more closely recapitulated the activity distribution of genomic regulatory sequences than matched diffusion baselines. D3 performs robustly with limited training data and generates sequences informative enough to improve predictive models when labeled data are scarce. When trained without activity labels on task-specific regulatory sequence sets, D3 learns frozen sequence representations that are predictive of enhancer activity and compare favorably to several off-the-shelf genomic language model embeddings. Analysis of the sampling process identifies reproducible phases of sequence exploration, compositional refinement, and motif-associated convergence, providing an interpretable view of how D3 constructs regulatory sequences. These results establish D3 as a practical framework for designing synthetic regulatory DNA and studying sequence features associated with context-specific regulatory activity.

genomics↗

bioRxiv2024

Clonal expansion dictates the efficacy of mitochondrial lineage tracing in single cells

Although mitochondrial DNA (mtDNA) variants hold promise as endogenous barcodes for tracking human cell lineages, their efficacy as reliable lineage markers is hindered by the complex dynamics of mtDNA in somatic tissues. Here, we utilized computational modeling and single-cell genomics to thoroughly interrogate the origin and clonal dynamics of mtDNA lineage markers across various biological settings. Our findings revealed the majority of purported "clone informative variants (CIVs)" were pre-existing heteroplamies in the first cell instead of de novo somatic mutations during divisions. Moreover, CIVs demonstrated limited discriminatory power among different lineages during normal development; however, certain CIVs with consistently high allele frequencies proved capable of faithfully labeling cell lineages in scenarios of stringent clonal expansion. Inspired by our simulations, we introduced a lineage informative (LI) score, facilitating the identification of reliable mitochondrial lineage markers across different modalities of single-cell genomic data.

bioinformatics↗

bioRxiv2024

SLC13A2-transported citrate is a metabolic signal for PKM2 acetylation and degradation to suppress tumor growth

Background and aimsMetabolic reprogramming represents as a pivotal hallmark for cancer, but TCA cycle in tumorigenesis and progression has long been neglected. Solute carrier (SLC) transporters mediate the transport of TCA cycle intermediates across membrane, but their functions in cancer pathogenesis remains unclear. Approaches & ResultsUsing integrated analysis of solute carrier (SLC) transporters for TCA cycle intermediates, we found that SLC13A2 was consistently downregulated in hepatocellular carcinoma (HCC) cells and liver tissues from human patients and heterogeneous mouse models. Adeno-associated virus (AAV)-transduced liver-specific knockout or overexpression of SLC13A2 promoted or ameliorated HCC progression in the primary mouse model, demonstrating that SLC13A2 served as a protective factor during HCC pathogenesis. SLC13A2 inhibited HCC cell proliferation by decreasing mitochondrial function via suppressed oxygen consumption and ATP production. Combined with metabolic flux analysis, we found that SLC13A2 imported citrate, which secreted acetyl-CoA as a precursor for the acetylation of pyruvate kinase muscle isozyme M2 (PKM2), which led to its protein degradation. Decreased activity of pyruvate kinase depleted pyruvate for the TCA cycle, thus inhibiting amino acid synthesis and nucleotide metabolism. Additionally, a decrease in nuclear PKM2 protein transduced to reprogrammed gene transcription for cell proliferation and metabolism which is required for tumor growth. ConclusionsThis study revealed that citrate transported by SLC13A2 acts as a signal to disrupt metabolic homeostasis for tumor growth and suggests potential drug targets for HCC therapy. graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=165 SRC="FIGDIR/small/591017v2_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@80c95eorg.highwire.dtl.DTLVardef@124750corg.highwire.dtl.DTLVardef@809ab4org.highwire.dtl.DTLVardef@12cbe74_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

xSiGra: Explainable model for single-cell spatial data elucidation

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multi-channel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multi-modal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of Grad-CAM component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within the complex cellular communications.

bioinformatics↗

bioRxiv2024

PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies

In addition to being a major source of animal protein, pigs are important model for the study of development and diseases in humans. During the past two decades, thousands of high-throughput sequencing studies in pigs have been performed using a variety of tissues from different breeds and developmental stages. However, the multi-omics database specifically used for pig functional genomic research is still limited. Here, we present a user-friendly database of pig multi-omics named PIGOME. PIGOME contains seven types of pig omics datasets, including whole-genome sequencing, RNA-seq, miRNA-seq, ChIP-seq, ATAC-seq, bisulfite-seq, and MeRIP-seq, from 6,901 samples and 392 projects with manually curated metadata, integrated gene annotation, and quantitative trait locus information. Furthermore, various explore and browse functions have been established for user-friendly access to omics information. PIGOME implemented several tools to visualize genomic variants, gene expression, and epigenetic signals of a given gene in the pig genome, enabling efficient exploration of spatial-temporal expression/epigenetic pattern, function, regulatory mechanism, and associated economic traits. Collectively, PIGOME provides valuable resources for pig breeding and is helpful for human biomedical research. PIGOME is available at https://pigome.com.

bioinformatics↗

bioRxiv2024

Evaluating the representational power of pre-trained DNA language models for regulatory genomics

The emergence of genomic language models (gLMs) offers an unsupervised approach to learning a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of functional activity generated by wet-lab experiments. Previous evaluations have shown that pre-trained gLMs can be leveraged to improve predictive performance across a broad range of regulatory genomics tasks, albeit using relatively simple benchmark datasets and baseline models. Since the gLMs in these studies were tested upon fine-tuning their weights for each downstream task, determining whether gLM representations embody a foundational understanding of cis-regulatory biology remains an open question. Here we evaluate the representational power of pre-trained gLMs to predict and interpret cell-type-specific functional genomics data that span DNA and RNA regulation. Our findings suggest that probing the representations of pre-trained gLMs do not offer substantial advantages over conventional machine learning approaches that use one-hot encoded sequences. This work highlights a major gap with current gLMs, raising potential issues in conventional pre-training strategies for the non-coding genome.

bioinformatics↗

bioRxiv2024

Predicting protein functions using positive-unlabeled ranking with ontology-based priors

Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive samples are trained as negatives. We introduce a novel approach named PU-GO, wherein we address function prediction as a positive-unlabeled ranking problem. We apply empirical risk minimization, i.e., we minimize the classification risk of a classifier where class priors are obtained from the Gene Ontology hierarchical structure. We show that our approach is more robust than other state-of-the-art methods on similarity-based and time-based benchmark datasets. Data and code are available at https://github.com/bio-ontology-research-group/PU-GO.

bioinformatics↗

bioRxiv2024

Single-cell RNA sequencing reveals recruitment of the M2-like CCL8high macrophages in Lewis lung carcinoma-bearing mice following hypofractionated radiotherapy

Tumor-associated macrophages (TAMs) are a crucial factor in reprogramming the tumor microenvironment following radiotherapy. The mechanisms underlying this process remain to be elucidated. Here, we seek to investigate the effects of hypofractionated radiotherapy on macrophages dynamics in a subcutaneous Lewis lung carcinoma murine model. Utilizing single-cell RNA sequencing, we identified a distinct M2-like population of macrophages with high Ccl8 expression level post-hypofractionated radiotherapy. Remarkbly, hypofractionated radiotherapy promoted CCL8high macrophages infiltration and reprogrammed them by upregulating immunosuppressive genes and downregulating antigen-presenting genes, leading to an immunosuppressive tumor microenvironment. Further, we demonstrated that hypofractionated radiotherapy amplified the CCL signaling pathway, enhancing the pro-tumorigenic functions of CCL8high macrophages and promoting macrophages recruitment. The combination therapy of hypofractionated radiotherapy with the CCL signal inhibitor Bindarit was effective in reducing M2 macrophages infiltration and extending the duration of local tumor control. This research highlights the potential of targeting TAMs and introduces a novel combination to improve the efficacy of hypofractionated radiotherapy.

immunology↗

bioRxiv2023

Comprehensive Assessment of Ischemic Stroke in Nonhuman Primates: Neuroimaging, Behavioral, and Serum Proteomic Analysis

Ischemic strokes, prevalence and impactful, underscore the necessity of advanced research models closely resembling human physiology. O integrating n ur study in nonhuman primates (NHPs) offers a comprehensive exploration of ischemic stroke, integrating neuroimaging data, behavioral outcomes, and serum proteomics to elucidate the complex interplay of factors involved in stroke pathophysiology. We observed a consistent pattern in infarct volume, peaking at 1-month post-middle cerebral artery occlusion (MCAO) and stabilizing thereafter. This trend was closely correlated with notable changes in motor function and working memory performance. Using diffusion tensor imaging (DTI), we detected significant alterations in fractional anisotropy (FA) and mean diffusivity (MD) values, indicative of microstructural changes in the brain. These findings were strongly correlated with the observed neurological and cognitive deficits, highlighting the sensitivity of DTI metrics in stroke assessment. Behaviorally, the monkeys exhibited a reliance on their unaffected limb for compensatory movements, a response commonly observed in stroke impairment. This adaptation, alongside the consistent findings in DTI metrics, suggests a substantial impact of stroke on motor function and spatial perception. Proteomic analysis through MS/MS functional enrichment revealed two distinct groups of proteins with significant changes post-MCAO. Notably, MMP9, THBS1, MB, PFN1, and YWHAZ emerged as potential biomarkers and therapeutic targets in ischemic stroke. Our findings underscore the complex nature of stroke and the potential of an integrated approach, combining neuroimaging, behavioral studies, and proteomics, for advancing our understanding and treatment of this condition.

neuroscience↗

bioRxiv2023

Unveiling interactions of spatial-temporal information in tactile motion perception

The intricate interplay of spatial and temporal information in tactile-motion perception remains elusive. Despite strides in decoding neural signals for direction, speed and texture in tactile perception, nuanced interactions persist as challenges. Addressing this, we investigated direction and speed tactile perception, exploring the intricate spatial-temporal dynamics. Psychophysical experiments manipulated direction and speed parameters using a laboratory-designed fingerpad ball-stimulator. A pivotal discovery includes quadrant-dependent anisotropic distortion in perceived motion direction, expanding the well-known notion of a specific preferred orientation. Spatial features primarily influence inherent responses, while temporal features impact stimulus-specific responses, shedding light on dynamic directional perception. The introduction of a psychometric function improved the modeling of tactile-motion speed perception, incorporating both linear and nonlinear components for a more accurate representation. This study provides intriguing insights into the neural mechanisms in tactile-motion perception, with potential applications for somatosensation in brain-machine interfaces. TeaserThis study unveils the intricate interplay of perceiving tactile motion, shedding light on the mysteries of tactile sensations.

neuroscience↗