Biology subjects

Chen, T.

Publications and source records attributed to Chen, T..

At least 217 records · Page 12Linked to original sources

bioRxiv2023

HIV-1 Vpr combats the PU.1-driven antiviral response in primary human macrophages.

HIV-1 Vpr promotes efficient spread of HIV-1 from macrophages to T cells by transcriptionally downmodulating restriction factors that target HIV-1 Envelope protein (Env). Here we find that Vpr induces broad transcriptomic changes by targeting PU.1, a transcription factor necessary for expression of host innate immune response genes, including those that target Env. Consistent with this, we find silencing PU.1 in infected macrophages lacking Vpr rescues Env. Vpr downmodulates PU.1 through a proteasomal degradation pathway that depends on physical interactions with PU.1 and DCAF1, a component of the Cul4A E3 ubiquitin ligase. The capacity for Vpr to target PU.1 is highly conserved across primate lentiviruses. In addition to impacting infected cells, we find that Vpr suppresses expression of innate immune response genes in uninfected bystander cells, and that virion-associated Vpr can degrade PU.1. Together, we demonstrate Vpr counteracts PU.1 in macrophages to blunt antiviral immune responses and promote viral spread.

microbiology↗

bioRxiv2023

Sequence and structure based deep learning models represent different aspects of protein biochemistry

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy.

biochemistry↗

bioRxiv2023

The Complexity of Functional Connectivity Profiles of the Subgenual Anterior Cingulate Cortex and Dorsal Lateral Prefrontal Cortex in Major Depressive Disorder: a DIRECT Consortium Study

BackgroundThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD), and its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. MethodsLeveraging a large multi-site cross-sectional sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC-FC. Then, we explored the potential impact of such group-level abnormality profiles on the TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to localize TMS targets individually. ResultsWe found an enhanced sgACC-DLPFC FC in MDD patients compared to healthy controls (HC). Such group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. In two independent clinical samples, we showed that the magnitude of TMS targets case-control differences in sgACC FC was related to clinical improvement. The MDD big data-guided individualized TMS targeting algorithm may generate individualized TMS targets that are clinically superior to group-level targets. InterpretationWe reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. FundingMinistry of Science and Technology of the Peoples Republic of China, National Natural Science Foundation of China, and Chinese Academy of Sciences

neuroscience↗

bioRxiv2023

Characterization of a mouse model of ICF syndrome reveals enhanced CD19 activation in inducing hypogammaglobulinemia

Immunodeficiency, centromeric instability and facial anomalies (ICF) syndrome is a rare autosomal recessive disorder characterized by DNA hypomethylation and antibody deficiency. It is caused by mutations in DNMT3B, ZBTB24, CDCA7 or HELLS. While progress has been made in elucidating the roles of these genes in regulating DNA methylation, little is known about the pathogenesis of the life-threatening hypogammaglobulinemia phenotype. Here we show that mice deficient for Zbtb24 in the hematopoietic lineage recapitulate major clinical features of patients with ICF syndrome. Specifically, Vav-Cre-mediated ablation of Zbtb24 does not affect lymphocyte development but results in reduced plasma cells and low levels of IgM, IgG1 and IgA. Zbtb24-deficient mice are hyper- and hypo-responsive to T-dependent and Tindependent type 2 antigens, respectively, and marginal zone B cell activation is impaired. B cells from Zbtb24-deficient mice display elevated CD19 phosphorylation. Heterozygous disruption of Cd19 can revert the hypogammaglobulinemia phenotype in these mice. Mechanistically, Il5ra (interleukin-5 receptor subunit alpha) is derepressed in Zbtb24-deficient B cells, and elevated IL-5 signaling enhances CD19 phosphorylation. Our results reveal a novel link between IL-5 signaling and CD19 activation and suggest that abnormal CD19 activity contributes to immunodeficiency in ICF syndrome. SIGNIFICANCE STATEMENTICF syndrome is a rare immunodeficiency disorder first reported in the 1970s. The lack of appropriate animal models has hindered the investigation of the pathogenesis of antibody deficiency, the major cause of death in ICF syndrome. Here we show that, in mice, disruption of Zbtb24, one of the ICF-related genes, in the hematopoietic lineage results in low levels of immunoglobulins. Characterization of these mice reveals abnormal B cell activation due to elevated CD19 phosphorylation. Mechanistically, Il5ra (interleukin-5 receptor subunit alpha) is derepressed in Zbtb24-deficient B cells, and increased IL-5 signaling enhances CD19 phosphorylation.

immunology↗

bioRxiv2023

Species-specific metabolic reprogramming in human and mouse microglia during inflammatory pathway induction

Metabolic reprogramming is a hallmark of the immune cells in response to inflammatory stimuli. This metabolic process involves a switch from oxidative phosphorylation (OXPHOS) to glycolysis, or alterations in other metabolic pathways. However, most of the experimental findings have been acquired in murine immune cells and little is known about the metabolic reprogramming of human microglia. In this study, we investigated the transcriptomic and metabolic profiles of mouse and iPSC-derived human microglia challenged with the TLR4 agonist LPS. We found that both species displayed a metabolic shift and an overall increased glycolytic gene signature in response to LPS treatment. The metabolic reprogramming was characterized by the upregulation of hexokinases in mouse microglia and phosphofructokinases in human microglia. This study provides the first direct comparison of energy metabolism between mouse and human microglia, highlighting the species-specific pathways involved in immunometabolism and the importance of considering these differences in translational research.

cell biology↗

bioRxiv2023

Dynamic network-guided CRISPRi screen reveals CTCF loop-constrained nonlinear enhancer-gene regulatory activity in cell state transitions

Comprehensive enhancer discovery is challenging because most enhancers, especially those affected in complex diseases, have weak effects on gene expression. Our network modeling revealed that nonlinear enhancer-gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Utilizing hESC definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. The screen discovered a comprehensive set of enhancers (4 to 9 per locus) for each of the core endoderm lineage-specifying transcription factors, and many enhancers had strong effects mid-transition but weak effects post-transition. Through integrating enhancer activity measurements and three-dimensional enhancer-promoter interaction information, we were able to develop a CTCF loop-constrained Interaction Activity (CIA) model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and more comprehensive enhancer discovery in both normal and pathological cell state transitions.

genetics↗

bioRxiv2023

Extraction of biological signals by factorization enables the reliable analysis of single-cell transcriptomics

Accurately and reliably capturing actual biological signals from single-cell transcriptomics is vital for achieving legitimate scientific results, which is unfortunately hindered by the presence of various kinds of unwanted variations. Here we described a deep auto-regressive factor model known as scPhenoXMBD, demonstrated that each genes expression can be split into discrete components that represent biological signals and unwanted variations, which effectively mitigated the effects of unwanted variations in the data of single-cell sequencing. Using scPhenoXMBD, we evaluated various factors affecting IFN {beta} -stimulated immune cells and demonstrated that biological signal extraction facilitates the identification of IFN{beta}-responsive pathways and genes. Numerous experiments were conducted to show that scPhenoXMBD could be utilized successfully in enhancing cell clustering stability, obtaining identical cell populations from diverse data sources, advancing the single-cell CRISPR screening of functional elements, and minimizing the influence of inter-subject discrepancies in the cell-disease relationships. scPhenoXMBD is anticipated to be a dependable and repeatable method for the precise analysis of single-cell data.

bioinformatics↗

bioRxiv2023

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

Major depressive disorder (MDD) is the most burdensome psychiatric disorder characterized by remarkably heterogeneous clinical phenotypes. It remains challenging to delineate the heterogeneity of neurobiological abnormalities underlying the clinical variance and, on this basis, to identify neurophysiological subtypes of MDD patients. Here, using a large multisite resting-state functional MRI data from 1,148 MDD patients and 1,079 healthy controls, we generated lifespan normative models of functional connectivity strengths, mapped the heterogeneity of patients individual deviations, and identified neurobiological MDD subtypes. MDD patients showed positive deviations mainly in the default mode and subcortical areas, and negative deviations widely distributed over the cortex. However, there was a great inter-subject heterogeneity as indicated by that no more than 3.14% of patients deviated from the normative range for any brain region. Two neurophysiological MDD subtypes were identified. Subtype 1 showed severe deviations with positive deviations in the default mode, limbic, and subcortical areas, and negative deviations in the sensorimotor, dorsal and ventral attention areas, while subtype 2 showed a moderate but conversed deviation pattern. The severe-deviation subtype had older age, higher medicated proportion, and higher Suicide item score, while the moderate-deviation subtype showed higher Work and Activities and Depressed Mood item scores. Moreover, the baseline deviations in the severe-deviation subtype were predictive of 6-month antidepressant treatment effects in a subsample. To our knowledge, the current study is the largest multisite analysis of neurophysiological MDD subtyping to date and the findings shed light on our understanding of the biological mechanisms underlying the intersubject heterogeneity of clinical phenotypes, which are informative for the development of personalized treatments for this disorder.

neuroscience↗

bioRxiv2023

RAMEN identifies effective indicators for severe COVID and Long COVID patients

The COVID-19 pandemic has significantly altered global socioeconomic structures and individual lives. Understanding the disease mechanisms and facilitating diagnosis requires comprehending the complex interplay among clinical factors like demographics, symptoms, comorbidities, treatments, lab results, complications, and other metrics, and their relation to outcomes such as disease severity and long term outcomes (e.g., post-COVID-19 condition/long COVID). Conventional correlational methods struggle with indirect and directional connections among these factors, while standard graphical methods like Bayesian networks are computationally demanding for extensive clinical variables. In response, we introduced RAMEN, a methodology that integrates Genetic Algorithms with random walks for efficient Bayesian network inference, designed to map the intricate relationships among clinical variables. Applying RAMEN to the Biobanque quebecoise de la COVID-19 (BQC19) dataset, we identified critical markers for long COVID and varying disease severity. The Bayesian Network, corroborated by existing literature and supported through multi-omics analyses, highlights significant clinical variables linked to COVID-19 outcomes. RAMENs ability to accurately map these connections contributes substantially to developing early and effective diagnostics for severe COVID-19 and long COVID.

bioinformatics↗

bioRxiv2023

CTCF is essential for proper mitotic spindle structure and anaphase segregation

Mitosis is an essential process in which the duplicated genome is segregated equally into two daughter cells. CTCF has been reported to be present in mitosis but its importance for mitotic fidelity remains to be determined. To evaluate the importance of CTCF in mitosis, we tracked mitotic behaviors in wild type and two different CTCF CRISPR-based genetic knockdowns. We find that knockdown of CTCF results in prolonged mitoses and failed anaphase segregation via time lapse imaging of SiR-DNA. CTCF knockdown did not alter cell cycling or the mitotic checkpoint, which was activated upon nocodazole treatment. Immunofluorescence imaging of the mitotic spindle in CTCF knockdowns revealed disorganization via tri/tetrapolar spindles and chromosomes behind the spindle pole. Imaging of interphase nuclei showed that nuclear size increased drastically, consistent with failure to divide the duplicated genome in anaphase. Population measurements of nuclear shape in CTCF knockdowns do not display decreased circularity or increased nuclear blebbing relative to wild type. However, failed mitoses do display abnormal nuclear morphologies relative to successful mitoses, suggesting population images do not capture individual behaviors. Thus, CTCF is important for both proper metaphase organization and anaphase segregation which impacts the size and shape of the interphase nucleus.

cell biology↗

bioRxiv2022

Deep Learning for Predicting 16S rRNA Copy Number

BackgroundCulture-independent 16S rRNA gene metabarcoding is a commonly used method in microbiome profiling. However, this approach can only reflect the proportion of sequencing reads, rather than the actual cell fraction. To achieve more quantitative cell fraction estimates, we need to resolve the 16S gene copy numbers (GCN) for different community members. Currently, there are several bioinformatic tools available to estimate 16S GCN, either based on taxonomy assignment or phylogeny. MethodHere we develop a novel algorithm, Stacked Ensemble Model (SEM), that estimates 16S GCN directly from the 16S rRNA gene sequence strings, without resolving taxonomy or phylogeny. For accessibility, we developed a public, end-to-end, web-based tool based on the SEM model, named Artificial Neural Network Approximator for 16S rRNA Gene Copy Number (ANNA16). ResultsBased on 27,579 16S rRNA gene sequence data (rrnDB database), we show that ANNA16 outperforms the most commonly used 16S GCN prediction algorithms. The prediction error range in the 5-fold cross validation of SEM is completely lower than all other algorithms for the 16S full-length sequence and partially lower at 16S subregions. The final test and a mock community test indicate ANNA16 is more accurate than all currently available tools (i.e., rrnDB, CopyRighter, PICRUSt2, & PAPRICA). SHAP value analysis indicates ANNA16 mainly learns information from rare insertions. ConclusionANNA16 represents a deep learning based 16S GCN prediction tool. Compared to the traditional GCN prediction tools, ANNA16 has a simple structure, faster inference speed without precomputing, and higher accuracy. With increased 16S GCN data in the database, future studies could improve the prediction errors for rare, high-GCN taxa due to current under sampling.

bioinformatics↗

bioRxiv2022

The Ruminococcus bromii amylosome protein Sas6 binds single and double helical α-glucan structures in starch.

Resistant starch is a prebiotic with breakdown by gut bacteria requiring the action of specialized amylases and starch-binding proteins. The human gut symbiont Ruminococcus bromii expresses granular starch-binding protein Sas6 (Starch Adherence System member 6) that consists of two starch-specific carbohydrate binding modules from family 26 (RbCBM26) and family 74 (RbCBM74). Here we present the crystal structures of Sas6 and RbCBM74 with a double helical dimer of maltodecaose bound along an extended surface groove. Binding data combined with native mass spectrometry suggest that RbCBM26 binds short maltooligosaccharides while RbCBM74 can bind single and double helical -glucans. Our results support a model by which RbCBM74 and RbCBM26 bind neighboring -glucan chains at the granule surface. CBM74s are conserved among starch granule-degrading bacteria and our work provides molecular insight into how this structure is accommodated by select gut species.

molecular biology↗

bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗

bioRxiv2022

Microglial process dynamics enhance neuronal activity by shielding GABAergic synaptic inputs

Microglia are resident immune cells of the central nervous system (CNS) and play key roles in brain homeostasis. During anesthesia, microglia increase their dynamic process surveillance and interact more closely with neurons. However, the functional significance of microglial process dynamics and neuronal interaction has remained unclear. Using in vivo two-photon imaging in awake mice, we discover that microglia enhance neuronal activity after the cessation of general anesthesia. Hyperactive neuron somata are directly contacted by microglial processes, which specifically co-localize with GABAergic boutons. Electron microscopy-based synaptic reconstruction after two-photon imaging reveals that microglial processes enter into the synaptic cleft to shield GABAergic inputs. Microglial ablation or loss of microglial {beta}2-adrenergic receptors prevent post-anesthesia neuronal hyperactivity. Together, our study demonstrates a previously unappreciated function of microglial process dynamics, which allow microglia to transiently boost neuronal activity by physically shielding inhibitory inputs.

neuroscience↗

bioRxiv2022

GSK-3484862 targets DNMT1 for degradation in cells

Maintenance of genomic methylation patterns at DNA replication forks by DNMT1 is the key to faithful mitotic inheritance. DNMT1 is often overexpressed in cancer cells and the DNA hypomethylating agents azacytidine and decitabine are currently used in the treatment of hematologic malignancies. However, the toxicity of these cytidine analogs and their ineffectiveness in treating solid tumors have limited wider clinical use. GSK-3484862 is a newly-developed, dicyanopyridine containing, non-nucleoside DNMT1-selective inhibitor with low cellular toxicity. Here, we show that GSK-3484862 targets DNMT1 for protein degradation in both cancer cell lines and murine embryonic stem cells (mESCs). DNMT1 depletion was rapid, taking effect within hours following GSK-3484862 treatment, leading to global hypomethylation. Inhibitor-induced DNMT1 degradation was proteasome-dependent, with no discernible loss of DNMT1 mRNA. In mESCs, GSK-3484862-induced Dnmt1 degradation requires Uhrf1, an accessory factor of Dnmt1 with E3 ubiquitin ligase activity. We also show that Dnmt1 depletion and DNA hypomethylation induced by the compound are reversible after its removal. Together, these results indicate that this DNMT1-selective degrader/inhibitor will be a valuable tool for dissecting both coordinated events linking DNA methylation to gene expression and identifying downstream effectors that ultimately regulate cellular response to altered DNA methylation patterns in a tissue/cell-specific manner. HighlightsO_LIGSK-3484862 targets DNMT1 for protein degradation in a wide-range of cancer cell lines, without a decrease in DNMT1 mRNA levels C_LIO_LIDNMT1 depletion leads to a >50% loss of global DNA methylation in cells within 2-days of treatment with GSK-3484862 C_LIO_LIGSK-3484862-induced DNMT1 degradation is proteasome-dependent C_LIO_LIIn mESCs, Uhrf1 is required for GSK-3484862 to induce Dnmt1 degradation C_LI

cancer biology↗

bioRxiv2022

Integrated transcriptome and lineage analyses reveal novel catecholaminergic cardiomyocytes contributing to the cardiac conduction system in murine heart

Cardiac conduction system (CCS) morphogenesis is essential for correct heart function yet is incompletely understood. Here we established the transcriptional landscape of cell types populating the developing heart by integrating single-cell RNA sequencing and spatial enhanced resolution omics-sequencing (Stereo-seq). Stereo-seq provided a spatiotemporal transcriptomic cell fate map of the murine heart with a panoramic field of view and in situ cellular resolution of the CCS. This led to the identification of a previously unrecognized cardiomyocyte population expressing dopamine beta-hydroxylase (Dbh+-CMs), which is closely associated with the CCS in transcriptomic analyses. To confirm this finding, genetic fate mapping by using DbhCre/Rosa26-tdTomato reporter mouse line was performed with Stereo-seq, RNAscope, and immunohistology. We revealed that Dbh+-derived CMs first emerged in the sinus venosus at E12.5, then populated the atrial and ventricular CCS components at E14.5, with increasing abundance towards perinatal stages. Further tracing by using DbhCFP reporter and DbhCreERT/Rosa26-tdTomato inducible reporter, we confirmed that Dbh+-CMs are mostly abundant in the AVN and ventricular CCS and this persists in the adult heart. By using DbhCre/Rosa26-tdTomato/Cx40-eGFP compound reporter line, we validated a clear co-localization of tdTomato and eGFP signals in both left and right ventricular Purkinje fibre networks. Finally, electrophysiological optogenetic study using cell-type specific Channelrhodopsin2 (ChR2) expression further elucidated that Dbh+-derived CMs form a functional part of the ventricular CCS and display similar photostimulation-induced electrophysiological characteristics to Cx40CreERT/ChR2-tdTomato CCS components. Thus, by utilizing advanced transcriptomic, mouse genetic, and optogenetic functional analyses, our study provides new insights into mammalian CCS development and heterogeneity by revealing novel Dbh+-CMs. HighlightsO_LIStereo-seq provided a spatiotemporal transcriptomic cell fate map of the murine heart with a panoramic field of view and in situ cellular resolution of the CCS. C_LIO_LIEstablished the transcriptional landscape of cell types populating the developing murine heart. C_LIO_LIRevealed previously unreported catecholaminergic cardiomyocyte populations contributing to the developing and mature murine cardiac conduction system. C_LI

cell biology↗

bioRxiv2022

One-Step PCR Amplicon Library Construction (OSPALC, version 1)

High-throughput sequencing of amplicons has been widely used to precisely and efficiently identify species compositions and analyze community structures, greatly promoting biological studies involving large amounts of complex samples, especially those involving environmental and pathogen-monitoring ones. However, commercial library preparation kits for amplicon sequencing, which generally require multiple steps, including adapter ligation and indexing, are expensive and time-consuming, especially for applications at a large scale. Here, to overcome this technical hurdle, we present a one-step PCR amplicon library construction (OSPALC) protocol for amplicon library preparations in the lab along with a QIIME2-based amplicon analysis pipeline. High-quality reads have been generated by this approach to reliably identify species compositions of mock bacterial communities and environmental samples. With this protocol, the amplicon library construction is completed through one regular PCR with long primers, and the total cost per DNA/cDNA sample decreases to just 1/15 of the typical cost via service providers. Empirically tested primers to construct OSPALC libraries for 16S rDNA V4 regions are demonstrated as a case study. Criteria to design primers targeting at any regions of prokaryotic and eukaryotic genomes are also suggested. In principle, OSPALC can be conveniently used in amplicon library constructions of any target gene of both prokaryotes and eukaryotes at the DNA or RNA levels, and will facilitate research in numerous fields.

genomics↗

bioRxiv2022

The protist ubiquitin ligase effector PbE3-2 targets RD21A to impede plant immunity

Clubroot caused by the soil-borne protist pathogen Plasmodiophora brassicae is one of the most devastating diseases of Brassica oil and vegetable crops worldwide. Understanding the pathogen infection strategy is crucial for the development of disease control. However, the molecular mechanism by which this pathogen promotes infection remains largely unknown. Here, we identified a P. brassicae-secreted effector PbE3-2 that impedes plant immunity by ubiquitinating the immune regulator RD21A for degradation. Overexpression of PbE3-2 in Arabidopsis thaliana resulted in higher susceptibility to P. brassicae and decreases in chitin-triggered reactive oxygen species burst and expression of marker genes in salicylic acid signaling. PbE3-2 interacted with and ubiquitinated RD21A in vitro and in vivo. Mutant plants deficient in RD21A exhibited similar susceptibility and compromsied immune responses as in PbE3-2 overexpression plants. These results suggest that P. brassicae promotes clubroot disease through RD21A degradation mediated by the effector PbE3-2. As PbE3-2 is widely conserved across different P. brassicae pathotypes, the degradation of RD21A by PbE3-2 might be a prevalent infection strategy in this pathogen.

microbiology↗