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Bai, Z.

Publications and source records attributed to Bai, Z..

At least 37 records · Page 2Linked to original sources

Generation of a novel constitutive smooth muscle cell-specific Myh11-driven Cre mouse model

Dysfunction in either embryonic or postnatal vascular smooth muscle cells (SMCs) significantly contributes to the progression of various cardiovascular diseases. Therefore, elucidating the molecular mechanisms governing VSMC development and homeostasis is crucial. MYH11 is the most reliable lineage gene for SMCs and has been utilized to develop tamoxifen-inducible Cre driver lines for achieving SMC-specific gene manipulation by crossing with mice carrying the loxP-flanked gene, particularly in adult mice. For studies involving SMCs during embryogenesis, the commonly used constitutive Cre driver is controlled by the Tagln (Sm22) promoter. However, this Cre driver exhibits activity in multiple non-SMC populations, including cardiomyocytes and skeletal muscle precursors, introducing confounding effects. Additionally, most existing SMC-specific Cre drivers are generated using a transgenic approach, raising concerns about random site integration and variable gene copy numbers. To address these limitations, we report a novel Cre mouse model generated by knock-in (KI) of a nuclear-localized Cre recombinase into the Myh11 gene locus using homologous recombination. We confirmed that the Cre activity precisely recapitulates endogenous Myh11 expression by crossing with Rosa26 mTmG or tdTomato reporter mice. Moreover, Myh11-driven Cre can efficiently delete the floxed allele of the transcription factor Tead1 specifically in SMCs. The Tead1 SMC-specific knockout mice did not exhibit an overt phenotype, thereby circumventing the embryonic lethal phenotype mediated by Tagln-driven Cre, as we previously reported. These findings establish this novel Cre driver line as a robust tool for tracing the Myh11-positive SMC lineage and manipulating gene function specifically in SMCs during embryonic development in mice.

genetics↗

PharaCon: A new framework for identifying bacteriophages via conditional representation learning

MotivationIdentifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. However, these models are typically pre-trained with self-supervised tasks that do not consider label variance in the pre-training data. This presents a challenge for phage identification as pre-training on mixed bacterial and phage data may lead to information bias due to the imbalance between bacterial and phage samples. ResultsTo overcome this limitation, this study proposed a novel conditional BERT framework that incorporates labels during pre-training. We developed an approach using a conditional BERT model for pre-training labeled data, incorporating label constraints with modified language modeling tasks. This approach allows the model to acquire label-conditional sequence representations. Additionally, we proposed a solution that utilizes conditional BERT in the fine-tuning phase as a classifier. We applied this conditional BERT framework to identify phages using a novel fine-tuning strategy, introducing PharaCon. We evaluated PharaCon against several existing methods on both simulated sequence datasets and real metagenomic contig datasets. The results demonstrate PharaCon's potential as an effective and efficient method for phage identification, highlighting the effectiveness of conditional B ERT as a solution for learning label-specific representations during pre-training on mixed sequence data. AvailabilityThe codes of PharaCon are now available in: https://github.com/Celestial-Bai/PharaCon. Contactyaozhong@ims.u-tokyo.ac.jp and imoto@hgc.jp

bioinformatics↗

Genome-wide association study elucidates the genetic architecture of manganese tolerance in Brassica napus

Brassica napus (canola) is a significant contributor to the worlds oil production and is cultivated across continents, yet acidic soils with Al3+ and Mn2+ toxicities limit its production. The genetic determinants underlying acidic soil tolerance in canola are unknown and require to be uncovered for canola breeding and production. Here, through comprehensive phenotyping, whole genome resequencing, and genome-wide association analysis, we identified three QTLs for tolerance to Mn2+ toxicity on chromosomes A09, C03, and C09. Allelism tests between four tolerance sources confirmed that at least one locus on A09 controls Mn2+ tolerance in B. napus. Integrated analysis of genomic and expression QTL and Mn2+ tolerance data reveals that BnMTP8.A09, in conjunction with BnMATE.C03, BnMTP8.C04 and BnMTP8.C08, play a central role in conferring Mn2+ tolerance in B. napus. Gene expression analysis revealed a high correlation (R2 = 0.74) between Mn2+ tolerance and the BnMTP8.A09 expression. Yeast complementation assays show that BnMTP8.A09 can complement manganese-hypersensitive yeast mutant strain PMR1{Delta} and restore Mn2+ tolerance to wild-type levels. Inductively coupled plasma mass spectrometry revealed that Mn2+ tolerant accessions accumulate less Mn in the shoots compared to Mn2+ sensitives, suggesting that the BnMTP8.A09 transporter likely sequesters Mn2+ into the tonoplast. Taken together, our research unveils the genetic architecture of Mn2+ tolerance and identifies BnMTP8.A09 as a major gene imparting tolerance to Mn2+ toxicity in B. napus.

plant biology↗

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex Diseases

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the models performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR models ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits, potentially leading to tailored therapeutic interventions.

genomics↗

Evaluation of Bayesian Linear Regression Derived Gene Set Test Methods

Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations. Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. This suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.

genomics↗

Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues

Spatial transcriptomics has emerged as a powerful tool for dissecting spatial cellular heterogeneity but as of today is largely limited to gene expression analysis. Yet, the life of RNA molecules is multifaceted and dynamic, requiring spatial profiling of different RNA species throughout the life cycle to delve into the intricate RNA biology in complex tissues. Human disease-relevant tissues are commonly preserved as formalin-fixed and paraffin-embedded (FFPE) blocks, representing an important resource for human tissue specimens. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology. Here, we present Patho-DBiT combining in situ polyadenylation and deterministic barcoding for spatial full coverage transcriptome sequencing, tailored for probing the diverse landscape of RNA species even in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory. High resolution Patho-DBiT at the cellular level reveals a spatial neighborhood and traces the spatiotemporal kinetics driving tumor progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to study human tissue biology and aid in clinical pathology evaluation.

systems biology↗

Large-scale multi-omics analyses identified root-microbiome associations underlying plant nitrogen nutrition

The microbiome determines the performance and fitness of the host plant. Nevertheless, the causal interaction between host genetic variation, gene regulation and the impact of the microbiome on the host phenotype remain unknown. Here we generated 1,341 paired root transcriptome, rhizosphere microbiome and root ionome samples and performed a multi-omics analyses of the host-microbe association at the root-soil interface using 175 rapeseeds (Brassica napus L.) resequenced ecotypes at two field environments. We observed the highest statistically explained variance for root nitrogen uptake among natural ionomic variation by overall transcriptome-wide gene expression and microbial abundance variation. Moreover, we identified significant genome-wide associations for 203 highly heritable amplicon sequence variants (ASVs) at multiple genetic loci regulated by eQTL hotspots associated with nitrogen metabolism components. These associations involved a central bacterial genus (Sphingopyxis), which plays a dominant role on gene regulatory effect on its variation regulated by eQTL hotspots. In addition, we performed high-throughput bacterial cultivation from rapeseed roots and subjected Sphingopyxis to whole genome sequencing. Finally, targeted metabolite profiling and confocal imaging assays demonstrated a host-microbiome regulatory effect on Sphingopyxis established by lateral root development and plant nitrogen nutrition. In summary, our integrative approach reveals the genetic basis of host-microbiome trait associations in the transcriptional, nutritional and environmental domains and suggests that the microbiome might have causal effects on root development with implications towards the breeding of nutrient-efficient crops.

plant biology↗

Spatial Transcriptomics Sequencing of Mouse Liver at 2 Micron Resolution Using a Novel Spatial DNA Chip

Spatial transcriptomics enables analysis of gene expression that is spatially resolved within a tissue section, making it possible to elucidate the relationship between individual cells within the context of the tissue. This transformative technology enables researchers to better understand gene function within the context of health tissue, developmental processes, and disease. In this study, we present an innovative spatial transcriptomics technology and data using a high-resolution DNA chip with a total capture region size of 6.5 x 6.5 mm containing 2 x 2 {micro}m features for spatial barcoding with no gaps between the features, thereby maximizing the capture area. These chips are manufactured at wafer scale using photolithography and are transferred to hydrogels, making them compatible with existing workflows for fresh frozen or paraffin-embedded samples. Herein, we examined a fresh frozen sample from an adult mouse liver. To analyze the data, we binned 10 x 10 features to represent a 20 {micro}m x 20 {micro}m capture area. We obtained 1.3 billion unique mapped reads, 68.78% sequencing saturation, with a median of 16,967 unique reads per region, indicating the potential for more unique reads with deeper sequencing. This high-resolution mapping of liver cell types and the visualization of gene expression patterns illustrate significant advancements in spatial sequencing technology.

genomics↗

An enclosed live-imaging platform to reveal ultrasound-inflicted mechanolysis and wound healing response

Focused ultrasound (FUS) has been widely adopted in medical and life science researches. Although various physical and biological effects of FUS have been well-documented, there is still a lack of understanding and direct evidence on the biological mechanism of therapeutic cell ablation caused by high-intensity ultrasound (HIFU) and the subsequent wound healing responses. This study develops an enclosed cell culture device that synergistically combines non-invasive FUS stimulation and real-time, on-the-fly live-cell imaging, providing an in vitro platform to explore short and long-term biological effects of ultrasound. The process, mechanism, and wound healing response of cell ablation induced by HIFU are elucidated, revealing a unique mechanism, termed ultrasound-inflicted cellular mechanolysis, that is mediated by growing subcellular cavitation air bubbles under confined contact with cells. This provides a previously unappreciated mechanism for understanding the biomechanical principles of ultrasound-based ablative therapy. We also reveal a post-ablation phantom layer that serves as a guiding cue for collective cell migration during wound healing, thereby providing a biomimetic model for studying wound healing after HIFU-inflicted damage. Together, this study provides theoretical and technological basis for advancing our understanding of the biological effects of ultrasound-based ablative therapy and inspiring clinically relevant applications in the future. Key Points1) Development of an integrated platform for real-time, on-the-fly imaging of FUS-induced cell ablation and response processes at cellular and subcellular levels 2) Focused ultrasound induces cellular mechanolysis through previously unappreciated subcellular cavitation bubbles that grow under confined contact with cells 3) Post-ablation phantom cell layer could serve as a guiding cue for collective wound healing after ultrasound-inflicted ablation

bioengineering↗

Dynamic Functional Connectivity Encodes Generalizable Representations of Emotional Arousal But Not Valence

Human affective experience varies along the dimensions of valence (positivity or negativity) and arousal (high or low activation). It remains unclear how these dimensions are encoded in the brain and if the representations are shared across different individuals and diverse situational contexts. Here we utilized two publicly available functional MRI datasets of participants watching movies to build predictive models of moment-to-moment valence and arousal from dynamic functional brain connectivity. We tested the models both within and across datasets and identified a generalizable arousal representation characterized by the interaction between multiple large-scale functional networks. The arousal representation generalized to two additional movie-watching datasets. Predictions based on multivariate patterns of activation underperformed connectome-based predictions and did not generalize. In contrast, we found no evidence of a generalizable valence representation. Taken together, our findings reveal a generalizable representation of arousal encoded in patterns of dynamic functional connectivity, revealing an underlying similarity in how arousal is encoded across individuals and situational contexts.

neuroscience↗

Impact of Memory T Cells on SARS-COV-2 Vaccine Response in Hematopoietic Stem Cell Transplant

During the COVID-19 pandemic, hematopoietic stem cell transplant (HSCT) recipients faced an elevated mortality rate from SARS-CoV-2 infection, ranging between 10-40%. The SARS-CoV-2 mRNA vaccines are important tools in preventing severe disease, yet their efficacy in the post-transplant setting remains unclear, especially in patients subjected to myeloablative chemotherapy and immunosuppression. We evaluated the humoral and adaptive immune responses to the SARS-CoV-2 mRNA vaccination series in 42 HSCT recipients and 5 healthy controls. Peripheral blood mononuclear nuclear cells and serum were prospectively collected before and after each dose of the SARS-CoV-2 vaccine. Post-vaccination responses were assessed by measuring anti-spike IgG and nucleocapsid titers, and antigen specific T cell activity, before and after vaccination. In order to examine mechanisms behind a lack of response, pre-and post-vaccine samples were selected based on humoral and cellular responses for single-cell RNA sequencing with TCR and BCR sequencing. Our observations revealed that while all participants eventually mounted a humoral response, transplant recipients had defects in memory T cell populations that were associated with an absence of T cell response, some of which could be detected pre-vaccination.

immunology↗

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of complex traits in pig.

Understanding the molecular and cellular mechanisms that underlie complex traits in pigs is crucial for enhancing their genetic improvement program and unleashing their substantial potentials in human biomedicine research. Here, we conducted a meta-GWAS analysis for 232 complex traits with 28.3 million imputed whole-genome sequence variants in 70,328 individuals from 14 pig breeds. We identified a total of 6,878 genomic regions associated with 139 complex traits. By integrating with the Pig Genotype-Tissue Expression (PigGTEx) resource, we systemically explored the biological context and regulatory circuits through which these trait-associated variants act and finally prioritized 16,664 variant-gene-tissue-trait circuits. For instance, rs344053754 regulates the expression of UGT2B31 in the liver by affecting the activity of regulatory elements and ultimately influences litter weight at weaning. Furthermore, we investigated the conservation of genetic and regulatory mechanisms underlying 136 human traits and 232 pig traits. Overall, our multi-breed meta-GWAS in pigs provides invaluable resources and novel insights for understanding the regulatory and evolutionary mechanisms of complex traits in both pigs and humans.

genetics↗

Predicting Small Molecule Ligand/RNA Binding Pocket Binding Modes Using Metadynamics

Understanding the structural dynamics of how small molecule ligand recognize its RNA binding pocket is always a crucial determinant in pharmaceutical research. Molecular dynamics (MD) simulation is often used to interpretate this process at atomic resolution. However, the insurmountable high energy barriers in the binding pathway results in the nonergodic dynamics for unbiased MD sampling. To address this limitation, we applied well-tempered metadynamics coupled with upper wall restrain in this work, therefore providing an novel modeling approach for sampling the multiple state transitions during this binding process and probing the most energy favorable binding modes through two-dimensional free energy landscape reconstructed by incorporating couple possible hydrogen binding interactions between small molecule ligand and its RNA binding pocket as collective variables (CVs). Our computational predictions of binding modes for all five cases studied are in quantitative agreement with structures solved by X-ray crystallography or NMR with RMSD less than 2.0 [A]. In addition, we presented the first molecular dynamics binding pathway and binding mechanism for the three cases of in vitro selected RNA aptamer. Our study demonstrated that metadynamics can be applied to effectively sampling state transitions of ligand binding events. By coupling with upper wall restrain, we have enabled fast free energy profile calculation and binding mode prediction for small molecule-RNA binding process, facilitating RNA-ligand binding investigation. This method therefore could be much-needed in computer-aided drug design pipelines of RNA-targeted small molecule compounds. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/560960v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@8049b0org.highwire.dtl.DTLVardef@65ed27org.highwire.dtl.DTLVardef@1b4b54aorg.highwire.dtl.DTLVardef@ea2ef9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Our aim was to evaluate Bayesian Linear Regression (BLR) models with BayesC and BayesR priors as a fine mapping tool and compare them to the state-of-the-art external models: FINEMAP, SuSIE-RSS, SuSIE-Inf and FINEMAP-Inf. Based on extensive simulations, we evaluated the different models based on F1 classification score. The different models were applied on quantitative and binary UK Biobank (UKB) phenotypes and evaluated based upon predictive accuracy and features of credible sets (CSs). We used over 533K genotyped and 6.6 million imputed single nucleotide polymorphisms (SNPs) for simulations and UKB phenotypes respectively, from over 335K UKB White British Unrelated samples. We simulated phenotypes from low (GA1) to moderate (GA2) polygenicity, heritability (h2) of 10% and 30%, causal SNPs ({pi}) of 0.1% and 1% sampled genome-wide, and disease prevalence (PV) of 5% and 15%. Single marker summary statistics and in-sample linkage disequilibrium were used to fit models in regions defined by lead SNPs. BayesR improved the F1 score, averaged across all simulations, between 27.26% and 13.32% relative to the external models. Predictive accuracy quantified as variance explained (R2), averaged across all the UKB quantitative phenotypes, with BayesR was decreased by 5.32% (SuSIE-Inf) and 3.71% (FINEMAP-Inf), and was increased by 7.93% (SuSIE-RSS) and 8.3% (BayesC). Area under the receiver operating characteristic curve averaged across all the UKB binary phenotypes, with BayesR was increased between 0.40% and 0.05% relative to the external models. SuSIE-RSS and BayesR, demonstrated the highest number of CSs, with BayesC and BayesR exhibiting the smallest average median size CSs in the UKB phenotypes. The BLR models performed similar to the external models. Specifically, BayesRs performance closely aligned with SuSIE-Inf and FINEMAP-Inf models. Collectively, our findings from both simulations and application of the models in the UKB phenotypes support that the BLR models are efficient fine mapping tools.

genomics↗

The ChickenGTEx pilot analysis: a reference of regulatory variants across 28 chicken tissues

Chicken is a valuable model for understanding fundamental biology, vertebrate evolution and diseases, as well as a major source of nutrient-dense and lean-protein-enriched food globally. Although it is the first non-mammalian amniote genome to be sequenced, the chicken genome still lacks a systematic characterization of functional impacts of genetic variants. Here, through integrating 7,015 RNA-Seq and 2,869 whole-genome sequence data, the Chicken Genotype- Tissue Expression (ChickenGTEx) project presents the pilot reference of regulatory variants in 28 chicken tissue transcriptomes, including millions of regulatory effects on primary expression (including protein-coding genes, lncRNA and exon) and post-transcriptional modifications (alternative splicing and 3 untranslated region alternative polyadenylation). We explored the tissue-sharing and context-specificity of these regulatory variants, their underlying molecular mechanisms of action, and their utility in interpreting adaptation and genome-wide associations of 108 chicken complex traits. Finally, we illustrated shared and lineage-specific features of gene regulation between chickens and mammals, and demonstrated how the ChickenGTEx resource can further assist with translating genetic findings across species. One-Sentence SummaryThe ChickenGTEx provides a multi-tissue reference of regulatory variants for chicken genetics and genomics, functional genomics, precision breeding, veterinary medicine, vertebrate evolution and even human biomedicine.

genetics↗

Construction of a multi-tissue cell atlas reveals cell-type-specific regulation of molecular and complex phenotypes in pigs

The systematic characterization of cellular heterogeneity among tissues and cell-type-specific regulation underlying complex phenotypes remains elusive in pigs. Within the Pig Genotype-Tissue Expression (PigGTEx) project, we present a single-cell transcriptome atlas of adult pigs encompassing 229,268 high-quality nuclei from 19 tissues, annotated to 67 major cell types. Besides cellular heterogeneity within and across tissues, we further characterize prominent tissue-specific features and functions of muscle, epithelial, and immune cells. Through deconvoluting 3,921 bulk RNA-seq samples from 17 matching tissues, we dissect thousands of genetic variants with cell-type interaction effects on gene expression (ieQTL). By colocalizing these ieQTL with variants associated with 268 complex traits, we provide new insights into the cellular mechanisms behind these traits. Moreover, we highlight that orthologous genes with cell-type-specific regulation in pigs exhibit significant heritability enrichment for some human complex phenotypes. Altogether, our work provides a valuable resource and highlights novel insights in cellular regulation of complex traits for accelerating pig precision breeding and human biomedical research.

genetics↗

Soil intake modifies the gut microbiota and alleviates ovalbumin-induced mice asthma inflammation

BackgroundLow cleanliness living environment (LCLE) can increase gut microbial diversity and prevent allergic diseases, whereas gut microbial dysbiosis is closely related to the pathogenesis of asthma. Our previous studies suggested that soil in the LCLE is a key factor in shaping intestinal microbiota. ObjectiveWe aimed to explore if sterilized soil intake as prebiotics while being incubated with microbes in the air can attenuate mice asthma symptoms by modifying gut microbiota. Methods16S rRNA gene sequencing was used to analyze the gut microbial composition, in combination with immune parameters measured in the lung and serum samples. Results16S rRNA gene sequencing results showed significant differences in the fecal microbiota composition between the test and control mice, with a higher abundance of Allobaculum, Alistipes, and Lachnospiraceae_UCG-001, which produce short-chain fatty acids and are beneficial for health in the test mice. Soil intake downregulated the concentrations of IL-6, IL-4, IL-17F, TNF-, and IL-22 in serum and increased the expression of IFN-{gamma}, which regulated the Th1/Th2 balance in lung by polarizing the immune system toward Th1, strongly alleviating ovalbumin-induced asthma inflammation. The effect of sensitization on gut microbiota was greater than that of air microbes and age together, but weaker than that of soil. ConclusionSoil intake had a significant therapeutic effect on mouse asthma, possibly by promoting the growth of multiple beneficial bacteria. The results indicated that the development of soil-based prebiotic products might be used for allergic asthma management and our study provides further evidence for the hygiene hypothesis. ImportanceExposure to a low cleanliness living environment (LCLE), of which soil is an important component, can shape the gut microbiota and support immune tolerance, preventing allergic diseases such as eczema and asthma. However, with the rapid progress of urbanization, it is impossible to return to farm-like living and we are becoming disconnected from the soil. Here, our study found that ingesting sterilized soil and living in an LCLE have the same protective effects on asthma inflammation. Ingestion of sterilized soil significantly altered the gut microbial composition and exerted significant therapeutic effects on asthmatic mice. However, edible sterilized soil possesses more advantages than LCLE exposure, such as the absence of pathogenic bacteria, safer, and convenience. The results indicate that the development of soil-based prebiotic products might be used for allergic asthma management and our study further supports the hygiene hypothesis. NotificationThe article is currently undergoing peer review in the World Allergy Organization Journal.

microbiology↗

Longitudinal analysis of memory T follicular helper cells and antibody response following CoronaVac vaccination

The inactivated vaccine CoronaVac is one of the most widely used COVID-19 vaccines globally. However, the longitudinal evolution of the immune response induced by CoronaVac remains elusive compared to other vaccine platforms. Here, we recruited 88 healthy individuals that received 3 doses of CoronaVac vaccine. We longitudinally evaluated their polyclonal and antigen-specific CD4+ T cells and neutralizing antibody response after receiving each dose of vaccine for over 300 days. Both the 2nd and 3rd dose of vaccination induced robust spike-specific neutralizing antibodies, with a 3rd vaccine further increased the overall magnitude of antibody response, and neutralization against Omicron sub-lineages B.1.1.529, BA.2, BA.4/BA.5 and BA.2.75.2. Spike-specific CD4+ T cell and circulating T follicular helper (cTFH) cells were markedly increased by the 2nd and 3rd dose of CoronaVac vaccine, accompanied with altered composition of functional cTFH cell subsets with distinct effector and memory potential. Additionally, cTFH cells are positively correlated with neutralizing antibody titers. Our results suggest that CoronaVac vaccine-induced spike-specific T cells are capable of supporting humoral immunity for long-term immune protection.

immunology↗