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Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 577 records · Page 32Linked to original sources

A neutral process of genome reduction in marine bacterioplankton

Marine bacterioplankton communities are dominated by cells equipped with small genomes. Streamlining selection has been accepted as the main force driving their genome reduction. Here, we report that a neutral evolutionary mechanism governs genome reduction in the Roseobacter group that represents 5-20% of the bacterioplankton cells in coastal waters. Using representative strains that fall into three genome size groups (2-3, 3-4, and 4-5 Mbp), we measured their genomic mutation rates () through long-term mutation accumulation experiments followed by genome sequencing the resulting 437 mutant lines. We further calculated their effective population sizes (Ne) based on and the neutral genetic diversity of the studied species, the latter estimated based on multiple genome sequences of natural isolates collected from global oceans with their population structure considered. A surprising finding is that Ne scales positively with genome size, which is the opposite of the expectation from the streamlining selection theory. As the strength of random genetic drift is the inverse of Ne, this result instead suggests drift as the primary driver of genome reduction. Additionally, we report a negative scaling between and genome size, which is the first experimental evidence for the long-lasting hypothesis that mutation rate increases play a part in marine bacterial genome reduction. As scales inversely with Ne, genetic drift appears to be the ultimate cause of genome reduction in these Roseobacters. Our finding discounts, but is insufficient to reject, the streamlining theory because streamlining process is expected to be more effective in oligotrophic open ocean waters.

microbiology↗

Replicating enzymatic activity by positioning active sites with synthetic protein scaffolds

Evolutionary constraints significantly limit the diversity of naturally occurring enzymes, thereby reducing the sequence repertoire available for enzyme discovery and engineering. Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable us to create enzymes with desired functions without relying on traditional genome mining. Here, we demonstrate a computational strategy for creating new-to-nature PET hydrolases by leveraging the known catalytic mechanisms and implementing multiple deep learning algorithms and molecular computations. This strategy includes the extraction of functional motifs from a template enzyme (here we use leaf-branch compost cutinase, LCC), regeneration of new protein scaffolds, computational screening, experimental validation, and sequence refinement. We successfully replicate PET hydrolytic activity with designer enzymes that are at least 30% shorter in sequence length than LCC. Among them, RsPETase 1 stands out due to its robust expressibility. It exhibits comparable activity to IsPETase and considerable thermostability with a melting temperature of 56 {degrees}C, despite sharing only 34% sequence similarity with LCC. This work suggests that enzyme diversity can be expanded by recapitulating functional motifs with computationally built protein scaffolds, thus generating opportunities to acquire highly active and robust enzymes that do not exist in nature.

biochemistry↗

SKSR1 identified as key virulence factor in Cryptosporidium by genetic crossing

Cryptosporidium parvum is a major cause of severe diarrhea. Although isolates of this zoonotic parasite exhibit significant differences in infectivity and virulence, the genetic determinants for these traits are not clear. In this study, we used classical genetics to cross two C. parvum isolates of different virulence and used bulked segregant analysis of whole-genome sequence data from the progeny to identify quantitative trait loci (QTL) associated with Cryptosporidium infectivity and virulence. Of the 26 genes in three QTL, two had loss-of-function mutations in the low-virulence isolates. Deletion of the SKSR1 gene or expression of the frame-shift mutant sequence reduced the pathogenicity of infection in vivo. SKSR1 is a polymorphic secretory protein expressed in small granules and secreted into the parasite-host interface. These results demonstrate that SKSR1 is an important virulence factor in Cryptosporidium, and suggest that this extended family may contribute to pathogenesis.

microbiology↗

LRG1 promotes atherosclerosis by activating macrophages

BackgroundAtherosclerosis is a chronic inflammatory disease of the arterial wall characterized by the accumulation of cholesterol-rich lipoproteins in macrophages. Leucine-rich alpha-2 glycoprotein 1 (LRG1) is a circulating protein associated with inflammation, however, its role in atherosclerosis remains unclear. This study identified its role in macrophage pro-inflammatory differentiation and revealed the relationship between LRG1 and atherosclerosis. MethodWe evaluated the impact of LRG1 on atherosclerosis progression by analyzing atherosclerotic tissue and serum samples from patients with coronary artery disease (CAD) and healthy individuals and analyzed its role in such a process using two types of mice models: Apoe knock-out mice (Apoe-/-) and Apoe and Lrg1 double knock-out mice (Apoe-/-/Lrg1-/-). These mice were fed with a high-fat diet for 16 to 32 weeks to simulate conditions exacerbating atherosclerosis. To examine the effects of inhibiting LRG1 on atherogenesis, we administered intraperitoneal injections of LRG1 neutralizing antibody (50g/kg) weekly to Apoe-/- mice for 8 weeks. We conducted in vitro assays using bone marrow-derived macrophages isolated from wild-type mice and analyzed transcriptional signatures using RNA sequencing. Additionally, we utilized small molecular inhibitors to validate the signaling pathway through which LRG1 promotes macrophage-driven inflammation. ResultsLRG1 levels were found to be elevated in patients with atherosclerosis and correlated with higher levels of a plasma pro-inflammatory biomarker high-sensitive C-reactive protein (hsCRP), and several macrophage-related pro-inflammatory markers including CD68, VE-Cadherin and VCAM-1. In a high fat diet induced Apoe-/- mouse atherosclerosis model, the deletion of LRG1 gene significantly delayed atherogenesis progression and reduced levels of macrophage-related pro-inflammatory cytokines. Addition of purified LRG1 to cultured macrophages stimulated those macrophages to pro-inflammatory M1-like polarization regulated by the activation of ERK and JNK pathways. An anti-LRG1 neutralizing antibody effectively blocked LRG1-induced macrophage M1-like polarization in vitro and conferred therapeutic benefits to animals with ApoE deficiency-induced atherosclerosis. ConclusionLRG1 plays an important pro-inflammatory role in atherosclerosis by influencing macrophage polarization towards a pro-inflammatory state. The inhibition of LRG1 with neutralizing antibodies may offer a potential therapeutic strategy for patients with atherosclerosis by mitigating the pro-inflammatory response and delaying disease progression, offering a novel therapy in atherosclerosis management. Translational PerspectiveAtherosclerosis, a persistent inflammatory condition affecting the arterial wall, serves as the underlying pathophysiological basis for acute ischemic cardiovascular events. The involvement of macrophages is crucial in the advancement of atherosclerosis. In this investigation, heightened levels of plasma LRG1 were observed in individuals with coronary artery disease. Moreover, this study presents initial evidence highlighting LRG1 as a pivotal activator of macrophages, instigating a pro-inflammatory M1 polarization during atherogenesis through the activation of ERK1/2 and JNK pathways. The use of an anti-LRG1 neutralizing antibody demonstrated a delay in atherosclerosis progression in an animal model, suggesting a potential therapeutic target for atherosclerosis treatment. Suppression of LRG1 production could impede atherosclerosis advancement and enhance plaque stability. Utilizing neutralizing antibodies against LRG1 emerges as a promising therapeutic approach for treating atherosclerosis.

pathology↗

Development of immobilized antibody-based affinity grid strategy and application in on-grid purification of target proteins for high-resolution cryo-EM

In cryo-electron microscopy (cryo-EM), sample preparation, especially for rare or fragile macromolecular assemblies and those suffering from air-water interface denaturation and particle orientation distribution problems, is a major bottleneck. Here, we developed and characterized an immobilized antibody-based affinity grid (IAAG) strategy based on the high-affinity PA tag/NZ-1 antibody epitope tag system. We used Pyr-NHS as a linker to immobilize NZ-1 Fab on the graphene oxide or carbon covered grid surface. We showed that the IAAG grid can enrich the PA-tagged target proteins, and overcome preferred orientation problems. Furthermore, we demonstrated that our IAAG strategy can be utilized for on-grid purification of low-abundance target complexes from cell lysates and enables atomic resolution cryo-EM. This approach greatly streamlines the purification process, reduces the need for large quantities of biological samples, and addresses common challenges encountered in cryo-EM sample preparation. Collectively, our IAAG strategy provides an efficient and robust means for combined sample purification and vitrification feasible for high-resolution cryo-EM. This approach also has the potential for broader applicability in cryo-EM and cryo-ET.

molecular biology↗

PbrSYP71 regulates ER accumulation by interacting with actin during pollen tube growth in Pyrus

The uneven distribution of endoplasmic reticulum (ER) underlies the rapid polar growth of pollen tubes. However, the mechanism governing ER distribution remains elusive. In this study, we have identified a pollen tube-specific syntaxin protein, PbrSYP71. Our findings reveal that both overexpression and knocking down of PbrSYP71 inhibited pollen tube growth. Subcellular localization analysis demonstrates that PbrSYP71 anchors to the ER via its transmembrane structure. Overexpression of PbrSYP71 leads to clustered ER distribution in the pollen tube, while knocking down of PbrSYP71 abolishes the uneven ER distribution. Remarkably, transient overexpression of PbrSYP71{Delta}ABD, lacking the actin binding domain (ABD) of PbrSYP71, has no impact on ER distribution or pollen tube growth. Further investigation indicates that ABD is positioned on F-actin in the pollen tube and has a direct interaction with F-actin. PbrSYP71 assists the ER in moving towards the apex of pollen tube, with ABD displaying autonomous mobility. Our study elucidates that PbrSYP71 maintains uneven distribution of the ER by tethering ER to F-actin, facilitating ER movement towards the pollen tube apex for pear pollen tube elongation. These insights shed light on the mechanisms governing ER distribution in polarized cell growth.

cell biology↗

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

neuroscience↗

Neutralizing gut-derived lipopolysaccharide as a novel therapeutic strategy for severe leptospirosis

Leptospirosis is an emerging infectious disease caused by pathogenic Leptospira spp. Humans and some mammals can develop severe forms of leptospirosis accompanied by a dysregulated inflammatory response, which often results in death. The gut microbiota has been increasingly recognized as a vital element in systemic health. However, the precise role of the gut microbiota in severe leptospirosis is still unknown. Here, we aimed to explore the function and potential mechanisms of the gut microbiota in a hamster model of severe leptospirosis. Our study showed that leptospires were able to multiply in the intestine, cause pathological injury, and induce intestinal and systemic inflammatory responses. 16S rRNA gene sequencing analysis revealed that Leptospira infection changed the composition of the gut microbiota of hamsters with an expansion of Proteobacteria. In addition, gut barrier permeability was increased after infection, as reflected by a decrease in the expression of tight junctions. Translocated Proteobacteria were found in the intestinal epithelium of moribund hamsters, as determined by fluorescence in situ hybridization, with elevated LPS levels in the serum. Moreover, gut microbiota depletion reduced the survival time, increased the leptospiral load, and promoted the expression of proinflammatory cytokines after Leptospira infection. Intriguingly, fecal filtration and serum from moribund hamsters both increased the transcription of TNF-, IL-1{beta}, IL-10, and TLR4 in macrophages compared with those from uninfected hamsters. These stimulating activities were inhibited by LPS neutralization using polymyxin B. Based on our findings, we identified an LPS neutralization therapy that significantly improved the survival rates in severe leptospirosis when used in combination with antibiotic therapy or polyclonal antibody therapy. In conclusion, our study not only uncovers the role of the gut microbiota in severe leptospirosis but also provides a therapeutic strategy for severe leptospirosis.

microbiology↗

Root-specific secondary metabolism at the single-cell level: a case study of theanine metabolism and regulation in the roots of tea plants (Camellia sinensis)

Root-synthesized secondary metabolites are critical quality-conferring compounds of foods, plant-derived medicines, and beverages. However, information at a single-cell level on root-specific secondary metabolism remains largely unexplored. L-theanine, an important quality component of tea, is primarily synthesized in roots, from which it is then transported to new shoots of tea plant. In this study, we present a single-cell RNA sequencing (scRNA-seq)-derived map for the tea plant root, which enabled cell-type-specific analysis of glutamate and ethylamine (two precursors of theanine biosynthesis) metabolism, and theanine biosynthesis, storage, and transport. Our findings support a model in which the theanine biosynthesis pathway occurs via multicellular compartmentation and does not require high co-expression levels of transcription factors and their target genes within the same cell cluster. This study provides novel insights into theanine metabolism and regulation, at the single-cell level, and offers an example for studying root-specific secondary metabolism in other plant systems.

plant biology↗

Cerebral Blood Volume Modulates Glymphatic Influx Through Extra-ventricular Cerebrospinal Fluid Volume

The glymphatic system facilitates waste removal via cerebrospinal fluid (CSF) influx alongside perivascular spaces throughout the brain. Vasomotion, the slow motion of blood vessel (0.1-0.3 Hz), has been found to be one of the driving forces for perivascular clearance, but it is not clear whether more chronical change of vessel diameter, as reflected by macroscopic cerebral blood volume (CBV), has any impact on glymphatic function. Combining multimodal mouse MRI techniques, we investigated the relationship among glymphatic influx, CBV, CSF volume and EEG power under six different conditions (awake, dexmedetomidine, isoflurane, isoflurane/dexmedetomidine, ketamine/xylazine and awake with caffeine). We found dexmedetomidine and caffeine enhanced glymphatic influx, while isoflurane reduced it compared with awake condition. Quantitative CBV imaging revealed that glymphatic influx was negatively correlated to CBV across the above conditions. Furthermore, such negative correlation was found to be mediated in part by changes of extra-ventricular CSF volume, which was quantified using T1 MRI. Taken together, our results suggest that CBV is a consciousness independent modulator of glymphatic function and modulates glymphatic influx through extra-ventricular CSF volume. This new finding opens potential avenues to enhance brain waste clearance by regulating CBV, which could be beneficial for protein deposition related neurological diseases. TeaserCBV is a consciousness independent modulator of glymphatic function and modulates glymphatic influx through extra-ventricular CSF volume.

neuroscience↗

Molecular and cellular dynamics of the developing human neocortex at single-cell resolution

The development of the human neocortex is a highly dynamic process and involves complex cellular trajectories controlled by cell-type-specific gene regulation1. Here, we collected paired single-nucleus chromatin accessibility and transcriptome data from 38 human neocortical samples encompassing both the prefrontal cortex and primary visual cortex. These samples span five main developmental stages, ranging from the first trimester to adolescence. In parallel, we performed spatial transcriptomic analysis on a subset of the samples to illustrate spatial organization and intercellular communication. This atlas enables us to catalog cell type-, age-, and area-specific gene regulatory networks underlying neural differentiation. Moreover, combining single-cell profiling, progenitor purification, and lineage-tracing experiments, we have untangled the complex lineage relationships among progenitor subtypes during the transition from neurogenesis to gliogenesis in the human neocortex. We identified a tripotential intermediate progenitor subtype, termed Tri-IPC, responsible for the local production of GABAergic neurons, oligodendrocyte precursor cells, and astrocytes. Remarkably, most glioblastoma cells resemble Tri-IPCs at the transcriptomic level, suggesting that cancer cells hijack developmental processes to enhance growth and heterogeneity. Furthermore, by integrating our atlas data with large-scale GWAS data, we created a disease-risk map highlighting enriched ASD risk in second-trimester intratelencephalic projection neurons. Our study sheds light on the gene regulatory landscape and cellular dynamics of the developing human neocortex.

neuroscience↗

BATF controls IFN I production via DC-SCRIPT in plasmacytoid dendritic cells

The basic leucine zipper ATF-like transcription factor (BATF) plays a pivotal role in coordinating various aspects of lymphoid cell biology, yet essential functions in dendritic cells (DCs) have not been reported. Here we demonstrate that BATF deficiency leads to increased interferon (IFN) I production in Toll-like receptor 9 (TLR9)-activated plasmacytoid dendritic cells (pDCs), while BATF overexpression has an inhibitory effect. BATF-deficient mice exhibit elevated IFN I serum levels early in lymphocytic choriomeningitis virus (LCMV) infection. Through ATAC-Seq analysis, BATF emerges as a pioneer transcription factor, regulating approximately one third of the known transcription factors in pDCs. Integrated transcriptomics and ChIP-Seq approaches identified the transcriptional regulator DC-SCRIPT as a direct target of BATF that suppresses IFN I promoter activity by interacting with the interferon regulatory factor 7 (IRF7). Genome-wide association study (GWAS) analyses further implicate BATF in pDC-mediated human diseases. Our findings establish a novel negative feedback axis in IFN I regulation in pDCs during anti-viral immune responses orchestrated by BATF and DC-SCRIPT, with broader implications for pDC and IFN I-mediated autoimmunity.

immunology↗

Emotional prosody modulates visual mental imagery

Perceptual stimulis emotional properties are vital for human evolution and adaptation. While visual imagery is predominantly regarded as a weak form of perception, the influence of cross-modal emotional features on imagery is still unknown. The present study aims to investigate how emotional prosody modulates imagery quality (i.e., accuracy and clarity) and neural mechanisms using a combination of behavioral tasks and functional magnetic resonance imaging (fMRI). At the behavioral level, our results showed that frustrated conditions induced significant prosody effects on visual mental imagery quality measures, and the effects were particularly pronounced in individuals with lower imagery use tendency. At the neural level, compared with the neutral condition, the emotional prosody conditions (both happy and frustrated) showed stronger activation in various regions including the middle occipital gyrus, supporting the critical role of primary visual system in imagery. Moreover, compared to the frustrated prosody condition, the happy prosody showed stronger activation in the precuneus and anterior cingulate cortex, which are core components of the default mode network. A machine learning prediction analysis with a random forest model identified a significant brain-behavioral correlation between prosody-linked neural activity and individual imagery use tendency. A subsequent Shapley Additive exPlanations (SHAP) analysis further highlighted the primary visual and default mode regions as top contributors to this prediction. Taken together, these results provide new insights for the understanding of how emotional prosody modulates visual mental imagery, considering individual differences, and provide compelling evidence for incorporating emotion as important shaping factor in more general model for imagery.

neuroscience↗

Growth phase-dependent reorganization of cryptophyte photosystem I antennae

Photosynthetic cryptophytes are eukaryotic algae that utilize membrane-embedded chlorophyll a/c binding proteins (CACs) and lumen-localized phycobiliproteins (PBPs) as their light-harvesting antennae. Cryptophytes go through logarithmic and stationary growth phases, and may adjust their light-harvesting capability according to their particular growth state. How cryptophytes change the type/arrangement of the photosynthetic antenna proteins to regulate their light-harvesting remains unknown. Here we solved four structures of cryptophyte photosystem I (PSI) bound with CACs that show the rearrangement of CACs at different growth phases. We identified a cryptophyte-unique protein, PsaQ, which harbors three chlorophyll molecules. PsaQ specifically binds to the lumenal region of PSI during logarithmic growth phase and may assist the association of PBPs with photosystems and energy transfer from PBPs to photosystems.

plant biology↗

Blocker-SELEX: A Structure-guided Strategy for Developing Inhibitory Aptamers Disrupting Undruggable Transcription Factor Interactions

Despite the well-established significance of transcription factors (TFs) in pathogenesis, their utilization as pharmacological targets has been limited by the inherent challenges associated with modulating their protein-protein and protein-DNA interactions. The lack of defined small-molecule binding pockets and the nuclear localization of TFs do not favor the use of small-molecule inhibitors, or neutral antibodies, in blocking TF interactions. Aptamers are short oligonucleotides exhibiting high affinity and specificity for a diverse range of targets. Large molecular weights, expansive blocking surfaces and efficient cellular internalization make aptamers a compelling molecular tool for use as traditional TF interaction modulators. Here, we report a structure-guided design strategy called Blocker-SELEX to develop inhibitory aptamers (iAptamer) that selectively block TF interactions. Our approach led to the discovery of iAptamers that cooperatively disrupts SCAF4/SCAF8-RNA Polymerase II (RNAP2) interactions, thereby dysregulating RNAP2-dependent gene expression and splicing and, in turn, leading to the impairment of cell proliferation. This approach was further applied to develop iAptamers to efficiently block WDR5-MYC interaction with a nexus in cancer. Taken together, our study highlights the potential of Blocker-SELEX in developing iAptamers that effectively disrupt potentially pathogenic TF interactions with attendant implications for iAptamers as chemical tools for use in the study of biological functions of TF interactions, but also for potential use in nucleic acids drug discovery.

biochemistry↗

Generalizable Quantum Computing Pipeline for Real World Drug Discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing an advanced quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the pragmatic application of quantum computation and propels it towards practical industrial adoption. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

bioinformatics↗

TrustAffinity: accurate, reliable and scalable out-of-distribution protein-ligand binding affinity prediction using trustworthy deep learning

Polypharmacology has emerged as a new paradigm to discover novel therapeutics for unmet medical needs. Accurate, reliable and scalable predictions of protein-ligand binding affinity across multiple proteins are essential for polypharmacology. Machine learning is a promising tool for multi-target binding affinity predictions, often formulated as a multi-modal regression problem. Despite considerable efforts, three challenges remain: out-of-distribution (OOD) generalizations for compounds with new chemical scaffolds, uncertainty quantification of OOD predictions, and scalability to billions of compounds, which structure-based methods fail to achieve. To address aforementioned challenges, we propose a new model-agnostic anomaly detection-based uncertainty quantification method, embedding Mahalanobis Outlier Scoring and Anomaly Identification via Clustering (eMOSAIC). eMOSAIC uniquely quantifies distribution similarities or differences between the multi-modal representation of known cases and that of a new unseen one. We apply eMOSAIC to a multi-modal deep neural network model for multi-target ligand binding affinity predictions, leveraging a pre-trained strucrture-informed large protein language model. We extensively validate eMOSAIC in OOD settings, showing that it significantly outperforms state-of-the-art sequence-based deep learning and structure-based protein-ligand docking (PLD) methods by a large margin as well as existing uncertainty quantification methods. This finding highlights eMOSAICs potential for real-world polypharmacology and other applications.

bioinformatics↗

A pathologically expanded, clonal lineage of IL-21 producing CD4+ T cells drives Inflammatory neuropathy

Inflammatory neuropathies, which include CIDP (chronic inflammatory demyelinating polyneuropathy) and GBS (Guillain Barre Syndrome), result from autoimmune destruction of the peripheral nervous system (PNS) and are characterized by progressive weakness and sensory loss. CD4+ T cells play a key role in the autoimmune destruction of the PNS. Yet, key properties of pathogenic CD4+ T cells remain incompletely understood. Here, we use paired scRNAseq and scTCRseq of peripheral nerves from an inflammatory neuropathy mouse model to identify IL-21 expressing CD4+ T cells that are clonally expanded and multifunctional. These IL-21-expressing CD4+ T cells are comprised of two transcriptionally distinct expanded populations, which express genes associated with Tfh and Tph subsets. Remarkably, TCR clonotypes are shared between these two IL-21-expressing populations, suggesting a common lineage differentiation pathway. Finally, we demonstrate that IL-21 signaling is required for neuropathy development and pathogenic T cell infiltration into peripheral nerves. IL-21 signaling upregulates CXCR6, a chemokine receptor that promotes CD4+ T cell localization in peripheral nerves. Together, these findings point to IL-21 signaling, Tfh/Tph differentiation, and CXCR6-mediated cellular localization as potential therapeutic targets in inflammatory neuropathies.

immunology↗