Biology subjects

Zou, B.

Publications and source records attributed to Zou, B..

30 records · Page 2Linked to original sources

bioRxiv2024

Development of a DNA-encoded library screening method DEL Zipper to empower the study of RNA-targeted chemical matter

To date, RNA-targeted chemical matter is under explored due to a lack of robust screening assays. In this study, we present a novel RNA-targeted small molecule screening approach using a specialized DNA-encoded library (DEL). Our findings reveal that the specialized DEL library, called "DEL Zipper", can significantly reduce single-stranded DNA-RNA region interaction signals during various kinds of RNA selection. By performing the selection against both G-quadruplex, we have identified novel hits that interact with RNA targets and the results are validated through binding. This study demonstrates that the "DEL Zipper" method is a robust screening assay that has potential for discovering small molecule ligands for diverse RNA targets.

molecular biology↗

bioRxiv2024

A host enzyme reduces non-alcoholic fatty liver disease by inactivating intestinal lipopolysaccharide

The incidence of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) has been increasing world-wide. Since gut-derived bacterial lipopolysaccharides (LPS) can travel via the portal vein to the liver and play an important role in producing hepatic pathology, it seemed possible that (1) LPS stimulates hepatic cells to accumulate lipid, and (2) inactivating LPS can be preventive. Acyloxyacyl hydrolase (AOAH), the eukaryotic lipase that inactivates LPS and oxidized phospholipids, is produced in the intestine, liver, and other organs. We fed mice either normal chow or a high-fat diet for 28 weeks and found that Aoah-/- mice accumulated more hepatic lipid than did Aoah+/+ mice. In young mice, before increased hepatic fat accumulation was observed, Aoah-/-mouse livers increased their abundance of Sterol Regulatory Element-Binding Protein 1 (SREBP1) and the expression of its target genes that promote fatty acid synthesis. Aoah-/- mice also increased hepatic expression of CD36 and Fabp3, which mediate fatty acid uptake, and decreased expression of fatty acid oxidation-related genes Acot2 and Ppar-. Our results provide evidence that increasing AOAH abundance in the gut, bloodstream and/or liver may be an effective strategy for preventing or treating MASLD.

immunology↗

bioRxiv2024

GhCPK28 negatively regulates the immune response by phosphorylating GhTIFY3b

The soil-borne fungus Verticillium dahliae (V. dahliae) seriously inhibits cotton (Gossypium hirsutum) growth and productivity. The immune system of cotton against this pathogen is largely unknown. Here we investigated the involvement of Ca2+-dependent protein kinases (CPKs) in this immunity. One CPK coding gene GhCPK28 had reduced expression after infection by V. dahliae in cotton. Knocking down of the GhCPK28 by virus induced gene silencing led to enhanced resistance to V. dahliae which is accompanied by a higher ROS accumulation and systemic acquired response. GhCPK28 was found to phosphorylate a TIFY family protein GhTIFY3b and reduce its stability. Silencing GhTIFY3b increased the susceptibility of cotton to V. dahliae. Together, this study indicate that GhCPK28 is a negative regulator of defense against V. dahliae infection and the GhTIFY3b might be a target protein of its activity. It sheds light on the immune system against V. dahliae and provides candidate genes for improving resistance against Verticillium wilt in cotton.

plant biology↗

bioRxiv2023

A Deep Neural Network Two-part Model and Feature Importance Test for Semi-continuous Data

Semi-continuous data frequently arise in clinical practice. For example, while many surgical patients suffer from varying degrees of acute postoperative pain (POP) post surgery (i.e., POP score > 0), others experience none (i.e., POP score = 0), indicating the existence of two distinct data processes at play. Existing parametric or semi-parametric two-part modeling methods for this type of semicontinuous data can fail to appropriately model these two underlying data processes as such methods rely heavily on (generalized) linear additive assumptions. However, many factors may interact to jointly influence the experience of POP non-additively and non-linearly. Motivated by this challenge and inspired by the flexibility of deep neural networks (DNN) to accurately approximate complex functions universally, we derive a DNN-based two-part model by adapting the conventional DNN methods by adding two additional components: a bootstrapping procedure along with a filtering algorithm to boost the stability of the conventional DNN, an approach we denote as sDNN. To improve the interpretability and transparency of sDNN, we further derive a feature importance testing procedure to identify important features contributing to the outcome measurements of the two data processes, denoting this approach fsDNN. We show that fsDNN not only offers a valid feature importance test but also that using the identified features can further improve the predictive performance of sDNN. The proposed sDNN- and fsDNN-based twopart models are applied to the analysis of real data from a POP study, in which application they clearly demonstrate advantages over the existing parametric and semi-parametric two-part models. Further, we conduct extensive numerical studies to demonstrate that sDNN and fsDNN consistently outperform the existing two-part models regardless of the data complexity. An R package implementing the proposed methods has been developed and deposited on GitHub (https://github.com/SkadiEye/fsDNN).

bioinformatics↗

bioRxiv2021

Structural basis for the assembly and electron transport mechanisms of the dimeric photosynthetic RC-LH1 supercomplex

The reaction center (RC) and light-harvesting complex 1 (LH1) form a RC-LH1 core supercomplex that is vital for the primary reactions of photosynthesis in purple photosynthetic bacteria. Some species possess the dimeric RC-LH1 complex with an additional polypeptide PufX, representing the largest photosynthetic complex in anoxygenic phototrophs. However, the details of the architecture and assembly mechanism of the RC-LH1 dimer are unclear. Here we report seven cryo-electron microscopy (cryo-EM) structures of RC-LH1 supercomplexes from Rhodobacter sphaeroides. Our structures reveal that two PufX polypeptides are positioned in the center of the S-shaped RC-LH1 dimer, interlocking association between the components and mediating RC-LH1 dimerization. Moreover, we identify a new transmembrane peptide, designated PufY, which is located between the RC and LH1 subunits near the LH1 opening. PufY binds a quinone molecule and prevents LH1 subunits from completely encircling the RC, creating a channel for quinone/quinol exchange. Genetic mutagenesis, cryo-EM structures, and computational simulations enable a mechanistic understanding of the assembly and electron transport pathways of the RC-LH1 dimer and elucidate the roles of individual components in ensuring the structural and functional integrity of the photosynthetic supercomplex.

biochemistry↗

bioRxiv2021

A highly conserved lipase deacylates oxidized phospholipids and ameliorates acute lung injury

Oxidized phospholipids have diverse biological activities, many of which can be pathological, yet how they are inactivated in vivo is not fully understood. Here we present evidence that a previously unsuspected host lipase, acyloxyacyl hydrolase (AOAH), can play a significant role in reducing the pro-inflammatory activities of two prominent products of phospholipid oxidation, 1-palmitoyl-2-glutaryl-sn-glycero-3- phosphocholine (PGPC) and 1-palmitoyl-2-(5-oxovaleroyl)-sn-glycero-3-phosphocholine (POVPC). AOAH removed the sn-2 and sn-1 acyl chains from both lipids and reduced their ability to induce macrophage inflammasome activation and cell death in vitro and acute lung injury in vivo. In addition to transforming Gram-negative bacterial lipopolysaccharide from stimulus to inhibitor, its most studied activity, AOAH can inactivate these important danger-associated molecular pattern (DAMP) molecules and reduce tissue inflammation and cell death.

immunology↗

bioRxiv2021

GABAergic neuronal IL-4R mediates T cell effect on memory

Mechanisms governing how immune cells and their derived molecules impact homeostatic brain function are still poorly understood. Here, we elucidate neuronal mechanisms underlying effects of T cells on synaptic function and episodic memory. Depletion of CD4 T cells led to memory deficits and impaired long term potentiation. Severe combined immune-deficient (SCID) mice exhibited amnesia, which was reversible by repopulation with T cells from wild-type but not from IL-4-knockout mice. This rescue was mediated via IL-4 receptors (IL-4R) expressed on neurons. Exploration of snRNAseq of neurons participating in memory processing provided insights into synaptic organization and plasticity-associated pathways and genes regulated by immune cells and molecules. IL-4R knockout in inhibitory neurons impaired contextual fear memory, suggesting participation of an IL-4-associated switch in regulating synaptic function and promoting contextual fear memory. These findings provide insights into neuroimmune interactions at the transcriptional and functional levels in neurons.

neuroscience↗

bioRxiv2020

A Deep Learning Semiparametric Regression for Adjusting Complex Confounding Structures

Deep Treatment Learning (deepTL), a robust yet efficient deep learning-based semiparametric regression approach, is proposed to adjust the complex confounding structures in comparative effectiveness analysis of observational data, e.g. electronic health record (EHR) data, in which complex confounding structures are often embedded. Specifically, we develop a deep learning neural network with a score-based ensembling scheme for flexible function approximation. An improved semiparametric procedure is further developed to enhance the performance of the proposed method under finite sample settings. Comprehensive numerical studies have demonstrated the superior performance of the proposed methods as compared with existing methods, with a remarkably reduced bias and mean squared error in parameter estimates. The proposed research is motivated by a post-surgery pain study, which is also used to illustrate the practical application of deepTL. Finally, an R package, "deepTL", is developed to implement the proposed method.

neuroscience↗

bioRxiv2020

Permutation-based Identification of Important Biomarkers for Complex Diseases via Black-box Models

Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods, including deep learning and random forest, have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting the role of each individual feature due to their intrinsic black-box natures. However, identifying important biomarkers associated with complex human diseases is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in various black-box frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available at https://github.com/SkadiEye/deepTL) is implemented in a computationally efficient manner, without model refitting for each permuted data. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also helps to improve the prediction accuracy of black-box models with top selected features. With the application to the Cancer Genome Atlas (TCGA) kidney tumor data and the HITChip atlas BMI data, PermFIT clearly demonstrates its practical usage in identifying important biomarkers and boosting performance of black-box predictive models.

bioinformatics↗

bioRxiv2019

DeepMicrobes: taxonomic classification for metagenomics with deep learning

Taxonomic classification is a crucial step for metagenomics applications including disease diagnostics, microbiome analyses, and outbreak tracing. Yet it is unknown what deep learning architecture can capture microbial genome-wide features relevant to this task. We report DeepMicrobes (https://github.com/MicrobeLab/DeepMicrobes), a computational framework that can perform large-scale training on > 10,000 RefSeq complete microbial genomes and accurately predict the species-of-origin of whole metagenome shotgun sequencing reads. We show the advantage of DeepMicrobes over state-of-the-art tools in precisely identifying species from microbial community sequencing data. Therefore, DeepMicrobes expands the toolbox of taxonomic classification for metagenomics and enables the development of further deep learning-based bioinformatics algorithms for microbial genomic sequence analysis.

bioinformatics↗

bioRxiv2019

Recognition Genes Which Are Relative With Cancer By Deep Learning Algorithm

Finding key genes which are relative with cancer is the first essential step to understand what has taken place in the tumor cell. At present, the most methods which can discover key genes make a contrast between normal samples and tumor samples and base on the statistical test. However, those methods face on some problems like the insufficient of statistical test in unbalanced samples, defect of only using single data that can not display the holistic situation in tumor cell. For solving those issues, i proposed a innovation method that uses semi-supervised and unsupervised algorithm to discover key genes which are linked with cancer. The genes in the final result list are not only in the double category but with distinct hierarchy and those genes are all detected from diversity data like methylation, gene expression RNA-Seq, exon expression RNA-Seq and so on. At last, for comparing the result of this method and traditional statistical method, i used the conception of information gain ratio to prove the advantage of this deep learning method in mathematical.

bioinformatics↗

bioRxiv2018

DrugCombDB: a comprehensive database of drug combinations toward network medicine and combination therapy

Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administrations in cancer therapies, and thus draw intensive attentions from researchers and pharmaceutical enterprises. Thanks to the fast development of high-throughput screening (HTS) methods, the amount of available drug combination datasets has tremendously increased. However, existing drug combination databases are lack of indications of the drug combinations and quantitative dose-responses. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of drug combinations. In this paper, we present DrugCombDB, a comprehensive database dedicated to integrating drug combinations from various data sources. Concretely, the data sources include 1) high-throughput screening assays of drug combinations, 2) external databases, and 3) manual curations from PubMed literature. In total, DrugCombDB includes 1,127,969 experimental data points with quantitative dose response and concentrations of drug combinations covering 561 unique drugs and 104 human cancer cell lines, and 1,875 FDA approved or literature-supported drug combinations. In particular, we adopted the zero interaction potency (ZIP) model [2] to compute the scores determining the synergy or antagonism of two drugs. To facilitate the downstream usage of our data resource, we prepared multiple datasets that are ready for building prediction models of classification and regression analysis. A website with user-friendly data visualization is provided to help users access the wealth of data. Users can input a drug of interest to retrieve associated drug combinations, together with the supporting evidence sources and drug targets. Our database is available at http://drugcombdb.denglab.org/.

bioinformatics↗