Biology subjects

Liu, W.

Publications and source records attributed to Liu, W..

At least 235 records · Page 13Linked to original sources

bioRxiv2024

Cyclocarya paliurus polysaccharides improve high fat diet-fed mice lipid dysmetabolism through gut microbiota

Obesity is associated with low-grade chronic inflammation and intestinal dysbiosis. Cyclocarya paliurus (C. paliurus) has been shown to improve lipid metabolism and gut microbiota communities in animals and humans; however, it remains to know whether Cyclocarya paliurus polysaccharides (CCPP) prevents obesity through gut microbiota. Here, we found that high fat diet promoted the lipid accumulation and intestinal microbiota dysbiosis in mice, while drink Cyclocarya paliurus polysaccharides reduces body weight, inflammation, alleviated the lipid accumulation, and reversed gut microbiota dysbiosis, including the diversity of intestinal microbiota, relative abundances of Lachnospiraceae. By transplanting faeces from CP-treated mice to HFD-fed mice, the effects of anti-obesity and microbiome regulation can be transferred through gut microbiota. Our results indicate that Cyclocarya paliurus polysaccharides can prevent gut dysbiosis and obesity-related metabolic disorders in obese individuals, the mechanism is related to the remodeling of intestinal flora.

microbiology↗

bioRxiv2024

Targeting Fatty Acid Desaturase I Inhibits Renal Cancer Growth Via ATF3-mediated ER Stress Response

Monounsaturated fatty acids (MUFAs) play a pivotal role in maintaining endoplasmic reticulum (ER) homeostasis, an emerging hallmark of cancer. However, the role of polyunsaturated fatty acid (PUFAs) desaturation in persistent ER stress driven by oncogenic abnormalities remains elusive. Fatty Acid Desaturase 1 (FADS1) is a rate-limiting enzyme controlling the bioproduction of long-chain PUFAs. Our previous research has demonstrated the significant role of FADS1 in cancer survival, especially in kidney cancers. We explored the underlying mechanism in this study. We found that pharmacological inhibition or knockdown of the expression of FADS1 effectively inhibits renal cancer cell proliferation and induces cell cycle arrest. The stable knockdown of FADS1 also significantly inhibits tumor formation in vivo. Mechanistically, we show that while FADS1 inhibition induces ER stress, its expression is also augmented by ER-stress inducers. Notably, FADS1-inhibition sensitized cellular response to ER stress inducers, providing evidence of FADS1s role in modulating the ER stress response in cancer cells. We show that, while FADS1 inhibition-induced ER stress leads to activation of ATF3, ATF3-knockdown rescues the FADS1 inhibition-induced ER stress and cell growth suppression. In addition, FADS1 inhibition results in the impaired biosynthesis of nucleotides and decreases the level of UPD-N-Acetylglucosamine, a critical mediator of the unfolded protein response. Our findings suggest that PUFA desaturation is crucial for rescuing cancer cells from persistent ER stress, supporting FADS1 as a new therapeutic target.

cancer biology↗

bioRxiv2024

Hypothalamic Sex-Specific Metabolic Shift by Canagliflozin during Aging

The hypothalamus undergoes significant changes with aging and plays crucial roles in age-related metabolic alterations. Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are anti-diabetic agents that promote glucose excretion, and promote metabolic homeostasis. Recent studies have shown that SGLT2i, Canagliflozin (Cana), can extend the median survival of genetically heterogeneous UM-HET3 male mice and improve central metabolic control via increases in hypothalamic insulin responsiveness in aged males, as well as reduced age-associated hypothalamic inflammation. We studied the long- and short-term effects of Cana on hypothalamic metabolic control in UM-HET3 mice. We show that Cana treatment reduced body weight and fat mass in male mice that was associated with enhanced glucose tolerance and insulin sensitivity observed by 12 months. Indirect calorimetry showed that short-term Cana treatment (5 months) increased energy expenditure in male, but not female mice, at 12 months of age. Long-term Cana treatment (18 months) increased metabolic rates in both sexes, and markedly increasing formation of both orexigenic and anorexigenic projections to the paraventricular nucleus of the hypothalamus (PVH) mostly in females by 25 months. Hypothalamic RNA-sequencing analysis revealed sex-specific genes and signaling pathways related to insulin signaling, glycogen catabolic pathway, neuropeptide signaling, and mitochondrial function upregulated by Cana, with males showing a more pronounced and sustained effect on metabolic pathways at both age groups. Overall, our data provide critical evidence for sex-specific mechanisms that are impacted by Cana during aging suggesting key targets of hypothalamic Cana-induced neuroprotection for metabolic control.

neuroscience↗

bioRxiv2024

Metric Ion Classification (MIC): A deep learning tool for assigning ions and waters in cryo-EM and x-ray crystallography structures

At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for understanding structure and function as well as guiding downstream design tasks, including structure-based drug discovery and de novo biomolecule design. However, direct identification of these sites from experimental data can prove extremely challenging, and existing empirical approaches leveraging the local environment can only characterize limited ion types. We present a novel representation of chemical environments using interaction fingerprints and develop a machine-learning model to predict the identity of input water and ion sites. We validate the method, named Metric Ion Classification (MIC), on a wide variety of biomolecular examples to demonstrate its utility, identifying many probable mismodeled ions deposited in the PDB. Finally, we collect all steps of this approach into an easy-to-use open-source package that can integrate with existing structure determination pipelines.

biophysics↗

bioRxiv2024

A Protein Language Model for Exploring Viral Fitness Landscapes

Successively emerging SARS-CoV-2 variants lead to repeated epidemic surges through escalated spreading potential (i.e., fitness). Modeling genotype-fitness relationship enables us to pinpoint the mutations boosting viral fitness and flag high-risk variants immediately after their detection. Here, we introduce CoVFit, a protein language model able to predict the fitness of variants based solely on their spike protein sequences. CoVFit was trained with genotype-fitness data derived from viral genome surveillance and functional mutation data related to immune evasion. When limited to only data available before the emergence of XBB, CoVFit successfully predicted the higher fitness of the XBB lineage. Fully-trained CoVFit identified 549 fitness elevation events throughout SARS-CoV-2 evolution until late 2023. Furthermore, a CoVFit-based simulation was able to predict the higher fitness of JN.1 subvariants before their detection. Our study provides both insight into the SARS-CoV-2 fitness landscape and a novel tool potentially transforming viral genome surveillance.

microbiology↗

bioRxiv2024

Deep learning based genomic breeding of pest-resistant grapevine

Crop pests have profoundly deleterious effects on crop yield and food security. However, conventional pest control depends heavily on the utilization of insecticides, which develops strong pesticide resistance and concerns of food safety. Crop and their wild relatives display diverse levels of pest resistance, indicating the feasibility for breeding of pest-resistant crop varieties. In this study, we integrate deep learning (DL)/machine learning (ML) algorithms, plant phenomics and whole genome sequencing (WGS) data to conduct genomic selection (GS) of pest-resistance in grapevine. We employ deep convolutional neural networks (DCNN) to accurately calculate the severity of damage by pests on grape leaves, which achieves a classification accuracy of 95.3% (Visual Geometry Group 16, VGG16, for binary trait) and a correlation coefficient of 0.94 in regression analysis (DCNN with Pest Damage Score, DCNN-PDS, for continuous trait). We apply DL models to predict and integrate phenotype (both binary and continuous) along with WGS data from 231 grape accessions, conducting Genome-Wide Association Studies (GWAS). This analysis detects a total of 69 QTLs, encompassing 139 candidate genes involved in pathways associated with pest resistance, including jasmonic acid (JA), salicylic acid (SA), ethylene, and other related pathways. Furthermore, through the combination with transcriptome data, we identify specific pest-resistant genes, such as ACA12 and CRK3, which play distinct roles in resisting herbivore attacks. Machine learning-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting the leaf area damaged by pests as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.

plant biology↗

bioRxiv2024

Insight from Wild Yeast Isolates into the Molecular Mechanisms of Lifespan Variation Mediated by Caloric Restriction

Caloric restriction (CR) is known to extend lifespan across different species and holds great promise for preventing human age-onset pathologies. However, two major challenges exist. First, despite extensive research, the mechanisms of lifespan extension in response to CR remain elusive. Second, genetic differences causing variations in response to CR and genetic factors contributing to variability of CR response on lifespan are largely unknown. Here, we took advantage of natural genetic variation across 46 diploid wild yeast isolates of Saccharomyces species and the lifespan variation under CR conditions to uncover the molecular factors associated with CR response types. We identified genes and metabolic pathways differentially regulated in CR-responsive versus non-responsive strains. Our analysis revealed that altered mitochondrial function and activation of GCN4-mediated environmental stress response are inevitably linked to lifespan variation in response to CR and a unique mitochondrial metabolite might be utilized as a predictive marker for CR response rate. In sum, our data suggests that the effects of CR on longevity may not be universal, even among the closely related species or strains of a single species. Since mitochondrial-mediated signaling pathways are evolutionarily conserved, the dissection of related genetic pathways will be relevant to understanding the mechanism by which CR elicits its longevity effect. Author summaryCaloric restriction (CR) is an energy-balanced nutrient intake without malnutrition to reduce food intake by 20-40%. CR leads to distinct metabolic reprogramming and adaptive changes in gene expression and, as a result, increases health and lifespan in various model organisms, from yeast to most likely primates. Besides extending lifespan, CR also holds great promise for treating many human age-onset pathologies, and the molecules underlying its effects are sought as targets of pharmaceutical aging therapeutics. However, despite extensive research, the mechanisms of lifespan extension in response to CR remain elusive. In addition, several studies in different aging models have now demonstrated that the longevity effect of CR can vary dramatically across different genotypes within a population. As such, CR might be beneficial for some yet detrimental for others, and the mechanisms underlying such genotype-dependent variation are not clear. In this study, we meet these challenges by dissecting molecular response to CR in diverse wild isolates of yeast strains, aiming to characterize pathways and molecules mediating CRs effects on replicative lifespan (RLS) diversity. We found that the RLS significantly differs across genetically diverse wild yeast isolates under CR conditions. Examining the relationships among the RLS phenotypes under CR and non-CR conditions, transcript, and metabolite provided insights into the role of mitochondrial functions in CR-mediated lifespan extension.

molecular biology↗

bioRxiv2024

Late-Life Alcohol Exposure Does Not Exacerbate Age-Dependent Reductions in Mouse Spatial Memory and Brain TFEB Activity

Alcohol consumption is believed to affect Alzheimers disease (AD) risk, but the contributing mechanisms are not well understood. A potential mediator of the proposed alcohol-AD connection is autophagy, a degradation pathway that maintains organelle and protein homeostasis. Autophagy is in turn regulated through the activity of Transcription factor EB (TFEB), which promotes lysosome and autophagy-related gene expression. To explore the effect of alcohol on brain TFEB and autophagy, we exposed young (3-month old) and aged (23-month old) mice to two alcohol-feeding paradigms and assessed biochemical, transcriptome, histology, and behavioral endpoints. In young mice, alcohol decreased hippocampal nuclear TFEB staining but increased SQSTM1/p62, LC3-II, ubiquitinated proteins, and phosphorylated Tau. Hippocampal TFEB activity was lower in aged mice than it was in young mice, and Gao-binge alcohol feeding did not worsen the age-related reduction in TFEB activity. To better assess the impact of chronic alcohol exposure, we fed young and aged mice alcohol for four weeks before completing Morris Water and Barnes Maze spatial memory testing. The aged mice showed worse spatial memory on both tests. While alcohol feeding slightly impaired spatial memory in the young mice, it had little effect or even slightly improved spatial memory in the aged mice. These findings suggest that aging is a far more important driver of spatial memory impairment and reduced autophagy flux than alcohol consumption.

animal behavior and cognition↗

bioRxiv2024

Pathway-enhanced Transformer-based model for robust enumeration of cell types from the cell-free transcriptome

Analyzing cell types of origin of cell-free RNA can enhance the resolution of liquid biopsies, thereby deepening the understanding of molecular and cellular changes in development and disease processes. Existing deconvolution methods typically rely on meticulously curated gene expression profiles or employ deep neural network with vast and complex solution spaces that are difficult to interpret. These approaches overlook the synergistic and co-expression effects among genes in biological signaling pathways, compromising their generalizability and robustness. we developed Deconformer, a Transformer-based deconvolution model that integrates biological signaling pathways at the embedding stage, to address these issues. Compared to popular methods on multiple datasets, Deconformer demonstrates superior performance and robustness, and is capable of tracking the developmental process of the fetal and placenta. Additionally, pathway-level interpretability of Deconformer offers new insights into crosstalk, dependencies, and other interactions within cell-free RNA pathways, supporting further biological discoveries. We posit that Deconformer represents a significant advancement in the precise analysis of the cell-free transcriptome. It holds the promise of describing disease progression and severity with a new level of accuracy, focusing on the contributions of originating cell types and their pathway dependencies. This model has the potential to catalyze the development of non-invasive diagnostic tools and enhance our understanding of the underlying biology of diseases.

bioinformatics↗

bioRxiv2024

Psychological stress disturbs bone metabolism via miR-335-3p/Fos signaling in osteoclast

It has been well-validated that chronic psychological stress leads to bone loss, but the underlying mechanism remains unclarified. In this study, we established and analyzed the chronic unpredictable mild stress (CUMS) mice to investigate the miRNA-related pathogenic mechanism involved in psychological stress induced osteoporosis. Our result found that these CUMS mice exhibited osteoporosis phenotype that mainly attributed to the abnormal activities of osteoclasts. Subsequently, miRNA sequencing and other analysis showed that miR-335-3p, which is normally highly expressed in the brain, was significantly down-regulated in the nucleus ambiguous (NAC), serum, and bone of the CUMS mice. Additionally, in vitro studies detected that miR-335-3p is important for osteoclast differentiation, with its direct targeting site in FOS. Further studies demonstrated FOS was upregulated in CUMS osteoclast, and the inhibition of FOS suppressed the accelerated osteoclastic differentiation, as well as the expression of osteoclastic genes, such as Nfatc1, Acp5, Mmp9, in miR-335-3p restrained osteoclasts. In conclusion, this work indicated that psychological stress may down-regulate the miR-335-3p expression, which resulted in the accumulation of FOS and the up-regulation of NFACT1 signaling pathway in osteoclasts, leading to its accelerated differentiation and abnormal activity. These results decipher a previously unrecognized paradigm that miRNA can act as a link between psychological stress and bone metabolism. Impact statementmiR-335-3p, which targets FOS and inhibits its activation of NFATC1 signaling, is an important regulator for osteoclast function and responsible for the psychological stress induced osteoporosis

neuroscience↗

bioRxiv2024

Arginine Di-methylation of RIPK3 Safeguards Necroptosis for Intestinal Homeostasis

The necroptosis mediated by RIPK3 is stringently regulated for intestinal homeostasis. Here we found that mice lacking Prmt5 (Protein arginase methyltransferase 5) in intestinal epithelial cells (IECs) caused premature death with IECs necroptosis, villus atrophy and loss of Paneth cells. This pathology can be partially rescued by antibiotic treatment, germ-free breeding condition and pharmaceutical inhibition of RIPK1 and RIPK3, but aggravated for embryonic lethality by Caspase-8 deficiency, which demonstrating the importance of commensal bacteria and necroptosis for the Prmt5-IEC deficiency. Intriguingly, tumor-necrosis factor (TNF) receptor 1(Tnfr1) deficiency could not completely rescue the pathology, and mice deficit in Z- DNA binding protein 1(ZBP1) exhibited shorter lifespan compared with Prmt5 null mice, suggesting Prmt5 loss might trigger TNFR-RIPK1-depenfent and ZBP1- dependent necroptosis. Mechanically, we identified the 479-arginine residue of RIPK3 di-methylated by PRMT5 was an endogenous checkpoint for necroptosis. Furthermore, RIPK3-R479K mutation had higher affinity with both RIPK1 and ZBP1 by immunoprecipitation and STORM (Stochastic Optical Reconstruction Microscopy) analysis, which might explain the endogenous necroptosis triggered by mutated RIPK3 even without upstream stimuli. Moreover, the peptide of RIPK3-SDMA (Symmetric dimethylarginine of 479) could rescue lethality of Prmt5 lacking mice through necrosome formation inhibition, which demonstrating the great potential for necroptosis-related disease treatment through RIPK3 dimethylation targeting.

cell biology↗

bioRxiv2024

Hosts Manipulate Lifestyle Switch and Pathogenicity Heterogeneity of Opportunistic Pathogens in the Single-cell Resolution

Host-microbe interactions are virtually bidirectional, but how the host affects their microbiome is poorly understood. Here, we report that the host is a critical modulator to regulate the lifestyle switch and pathogenicity heterogeneity of the opportunistic pathogens Serratia marcescens utilizing the Drosophila and bacterium model system. First, we find that Drosophila larvae efficiently outcompete S. marcescens and typically drive a bacterial switch from pathogenicity to commensalism toward the fly. Furthermore, Drosophila larvae reshape the transcriptomic and metabolic profiles of S. marcescens characterized by a lifestyle switch. More important, the host alters pathogenicity and heterogeneity of S. marcescens in the single-cell resolution. Finally, we find that larvae-derived AMPs are required to recapitulate the response of S. marcescens to larvae. Altogether, our findings provide an insight into the pivotal roles of the host in harnessing the life history and heterogeneity of symbiotic bacterial cells, advancing knowledge of the reciprocal relationships between the host and pathogen.

microbiology↗

bioRxiv2024

Fast and accurate modeling and design of antibody-antigen complex using tFold

Accurate prediction of antibody-antigen complex structures holds significant potential for advancing biomedical research and the design of therapeutic antibodies. Currently, structure prediction for protein monomers has achieved considerable success, and promising progress has been made in extending this achievement to the prediction of protein complexes. However, despite these advancements, fast and accurate prediction of antibody-antigen complex structures remains a challenging and unresolved issue. Existing end-to-end prediction methods, which rely on homology and templates, exhibit sub-optimal accuracy due to the absence of co-evolutionary constraints. Meanwhile, conventional docking-based methods face difficulties in identifying the contact interface between the antigen and antibody and require known structures of individual components as inputs. In this study, we present a fully end-to-end approach for three-dimensional (3D) atomic-level structure predictions of antibodies and antibody-antigen complexes, referred to as tFold-Ab and tFold-Ag, respectively. tFold leverages a large protein language model to extract both intra-chain and inter-chain residue-residue contact information, as well as evolutionary relationships, avoiding the time-consuming multiple sequence alignment (MSA) search. Combined with specially designed modules such as the AI-driven flexible docking module, it achieves superior performance and significantly enhanced speed in predicting both antibody (1.6% RMSD reduction in the CDR-H3 region, thousand times faster) and antibody-antigen complex structures (37% increase in DockQ score, over 10 times faster), compared to AlphaFold-Multimer. Given the performance and speed advantages, we further extend the capability of tFold for structure-based virtual screening of binding antibodies, as well as de novo co-design of both structure and sequence for therapeutic antibodies. The experiment results demonstrate the potential of tFold as a high-throughput tool to enhance processes involved in these tasks. To facilitate public access, we release code and offer a web service for antibody and antigen-antibody complex structure prediction, which is available at https://drug.ai.tencent.com/en.

bioinformatics↗

bioRxiv2024

Dissecting reprogramming heterogeneity at single-cell resolution using scTF-seq

Reprogramming approaches often produce heterogeneous cell fates and the mechanisms behind this heterogeneity are not well-understood. To address this gap, we developed scTF-seq, a technique inducing single-cell barcoded and doxycycline-inducible TF overexpression while quantifying TF dose-dependent transcriptomic changes. Applied to mouse embryonic multipotent stromal cells (MSCs), scTF-seq produced a gain-of-function atlas for 384 murine TFs. This atlas offers a valuable resource for gene regulation and reprogramming research, identifying key TFs governing MSC lineage differentiation, cell cycle control, and their interplay. Leveraging the single-cell resolution, we dissected reprogramming heterogeneity along dose and pseudotime. We thereby revealed TF dose-dependent and stochastic cell fate branching, unveiling gene expression signatures that enhance our understanding and prediction of reprogramming efficiency. scTF-seq also allowed us to classify TFs into four sensitivity classes based on dose response and determining features. Finally, in combinatorial scTF-seq, we observed that the same TF can exhibit both synergistic and antagonistic effects on another TF depending on its dose. In summary, scTF-seq provides a powerful tool for gaining mechanistic insights into how TFs determine cell states, while offering novel perspectives for cellular engineering strategies.

cell biology↗

bioRxiv2024

IL-1beta-induced epithelial cell and fibroblast transdifferentiation fuels neutrophil recruitment in chronic rhinosinusitis with nasal polyps

Neutrophilic inflammation contributes to multiple chronic inflammatory airway diseases, including asthma and chronic rhinosinusitis with nasal polyps (CRSwNP), and is associated with an unfavorable prognosis. Here, using single-cell RNA sequencing (scRNA-seq) to profile human nasal mucosa obtained from the inferior turbinates, middle turbinates, and nasal polyps of CRSwNP patients, we identified two IL-1 signaling-induced cell subsets--LY6D+ club cells and IDO1+ fibroblasts--that promote neutrophil recruitment by respectively releasing S100A8/A9 and CXCL1/2/3/5/6/8 into inflammatory regions. IL-1{beta}, a pro-inflammatory cytokine involved in IL-1 signaling, induces the transdifferentiation of LY6D+ club cells and IDO1+ fibroblasts from primary epithelial cells and fibroblasts, respectively. In an LPS-induced neutrophilic CRSwNP mouse model, blocking IL-1{beta} activity with a receptor antagonist significantly reduced the numbers of LY6D+ club cells and IDO1+ fibroblasts and mitigated nasal inflammation. This study reveals the roles of two cell subsets in neutrophil recruitment and demonstrates an IL-1-based intervention for mitigating neutrophilic inflammation in CRSwNP.

immunology↗

bioRxiv2024

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest. Author summaryMulti-omics network inference is crucial for identifying disease-specific molecular interactions across various molecular profiles, which helps understand the biological processes related to disease etiology. Traditional multi-omics integration methods focus mainly on pairwise interactions by only considering two molecular profiles at a time. This approach overlooks the complex, higher-order correlations often present in multi-omics data, especially when analyzing more than two types of -omics data and phenotypes. Higher-order correlation, by definition, refers to the simultaneous relationships among more than two types of -omics data and phenotype, providing a more complex and complete understanding of the interactions in biological systems. Our research introduces Sparse Generalized Tensor Canonical Correlation Network Analysis (SGTCCA-Net), a novel framework that effectively utilizes both higher-order and lower-order correlations for multi-omics network inference. SGTCCA-Net is adaptable for exploring diverse correlation structures within multi-omics data and is able to construct complex multi-omics networks in a two-dimensional space. This method offers a comprehensive view of molecular feature interactions with respect to complex diseases. Our simulation studies and real data experiments validate SGTCCA-Net as a potent tool for biomarker identification and uncovering biological mechanisms associated with targeted diseases.

bioinformatics↗

bioRxiv2024

Defining cell type-specific immune responses in a mouse model of allergic contact dermatitis by single-cell transcriptomics

Allergic contact dermatitis (ACD), a prevalent inflammatory skin disease, is elicited upon repeated skin contact with protein-reactive chemicals through a complex and poorly characterized cellular network between immune cells and skin resident cells. Here, single-cell transcriptomic analysis of the murine hapten-elicited model of ACD reveals that upon elicitation of ACD, infiltrated CD4+ or CD8+ lymphocytes were primarily the IFN{gamma}-producing type 1 central memory phenotype. In contrast, type 2 cytokines (IL4 and IL13) were dominantly expressed by basophils, IL17A was primarily expressed by {delta}{gamma} T cells, and IL1{beta} was identified as the primary cytokine expressed by activated neutrophils/monocytes and macrophages. Furthermore, analysis of skin resident cells identified a sub-cluster of dermal fibroblasts with preadipocyte signature as a prominent target for IFN{gamma}+ lymphocytes and dermal source for key T cell chemokines CXCL9/10. IFN{gamma} treatment shifted dermal fibroblasts from collagen-producing to CXCL9/10-producing, which promoted T cell polarization toward the type-1 phenotype through a CXCR3-dependent mechanism. Furthermore, targeted deletion of Ifngr1 in dermal fibroblasts in mice reduced Cxcl9/10 expression, dermal infiltration of CD8+ T cell, and alleviated ACD inflammation in mice. Finally, we showed that IFN{gamma}+ CD8+ T cells and CXCL10-producing dermal fibroblasts co-enriched in the dermis of human ACD skin. Together, our results define the cell type-specific immune responses in ACD, and recognize an indispensable role of dermal fibroblasts in shaping the development of type-1 skin inflammation through the IFNGR-CXCR3 signaling circuit during ACD pathogenesis.

immunology↗

bioRxiv2024

Integrated analysis and systematic characterization of the regulatory network for human germline development

Primordial germ cells (PGCs) are the precursors of germline that are specified at embryonic stage. Recent studies reveal that humans employ different mechanisms for PGC specification compared with model organisms such as mice. Moreover, the specific regulatory machinery is still largely unexplored, mainly due to the inaccessible nature of this complex biological process. Here, we collect and integrate multi-omics data, including 581 RNA-seq, 54 ATAC-seq, 45 ChIP-seq, and 69 single-cell RNA-seq samples from different stages of human PGC development to recapitulate the precisely controlled and stepwise process, presenting an atlas in the human PGC database (hPGCdb). With these uniformly processed data and integrated analyses, we characterize the potential key transcription factors and regulatory networks governing human germ cell fate. We validate the important roles of some of the key factors in germ cell development by CRISPRi knockdown. We also identify the soma-germline interaction network and discover the involvement of SDC2 and LAMA4 for PGC development, as well as soma-derived NOTCH2 signaling for germ cell differentiation. Taken together, we have built a database for human PGCs and demonstrate that hPGCdb enables the identification of the missing pieces of mechanisms governing germline development, including both intrinsic and extrinsic regulatory programs.

developmental biology↗