Biology subjects

Jia, Y.

Publications and source records attributed to Jia, Y..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Protective Effect of 2-Hydroxyestrone and2-Hydroxyestradiol Against Chemically-Induced Hepatotoxicity In Vitro and In Vivo

Ferroptosis is a form of regulated cell death closely associated with glutathione depletion and accumulation of reactive lipid peroxides. In the present study, we seek to determine whether 2-hydroxyestrone (2-OH-E1) and 2-hydroxyestradiol (2-OH-E2), two major metabolites of endogenous estrone and 17{beta}-estradiol formed in liver by cytochrome P450 enzymes, can strongly protect against erastin- and RSL3-induced ferroptosis in hepatoma cells (H-4-II-E and HuH-7) in vitro and acetaminophen-induced mouse liver injury in vivo. We find that 2-OH-E1 and 2-OH-E2 can protect, in a dose-dependent manner, H-4-II-E hepatoma cells against erastin/RSL3-induced ferroptosis. Similar protective effect of 2-OH-E1 and 2-OH-E2 against RSL3-induced ferroptosis is also observed in HuH-7 hepatoma cells. These two estrogen metabolites strongly reduce the levels of erastin- and/or RSL3-induced accumulation of cellular NO, ROS and lipid-ROS. Mechanistically, 2-OH-E1 and 2-OH-E2 protect cells against chemically-induced ferroptosis mainly by binding to cellular protein disulfide isomerase (PDI), and then inhibit its catalytic activity and reduce PDI-catalyzed formation of iNOS dimer, thereby abrogating cellular NO, ROS and lipid-ROS accumulation. Animal studies show that 2-OH-E1 and 2-OH-E2 can also exert a strong protection against acetaminophen-induced liver injury in mice. Interestingly, while estrone and 17{beta}-estradiol display a very weak protective effect in cultured hepatoma cells, they exert a similarly-strong protective effect as 2-OH-E1 and 2-OH-E2 in vivo, suggesting that the metabolic conversion of estrone and 17{beta}-estradiol to 2-OH-E1 and 2-OH-E2 contributes importantly to their hepatoprotective effect. The results of this study reveal that 2-OH-E1 and 2-OH-E2 are important endogenous factors for protection against chemically-induced liver injury in vivo.

pharmacology and toxicology↗

bioRxiv2024

Synthetic bacterial consortium for degradation of plastic pyrolysis oil waste

The plasic crisis is ominipresent, from littering macroplastic to reports that document plastic in every niche of this planet, including the human body. In order to achieve higher recycling quotas, especially of mixed plastic waste, pyrolysis seems to be a viable option. However, depending on the process parameters, plastic pyrolysis oil waste is encountered, which is difficult to valorize, due to the enormous spread of the molecules included. To reduce the molecular heterogeneity, we here artificially compounded, monitored, and optimized the performance of a bacterial consortium, which has the ability to tolerate organic pollutants and use them as energy and carbon sources for their own metabolic activity. The primary constituents of the here used plastic pyrolysis oil waste (PPOW) were alkanes and {varepsilon}-caprolactam. The bacterial community exhibited noteworthy efficacy in eliminating alkanes of diverse chain lengths ranging from 71% to 100%. Additionally, within 7-days, the microbial community demonstrated a removal efficiency surpassing 50% for various aromatic hydrocarbons, along with complete eradication of {varepsilon}-caprolactam and naphthalene. Besides, a back-propagation (BP) neural network method is applied to evaluate O2 consumption as a measure of microbial activity. The insights gained were used to build a model, which is able to predict O2 depletion in long-time experiments and other experimental conditions. The results are discussed in the context of a developing (open) circular plastic economy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/590079v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@628f68org.highwire.dtl.DTLVardef@b5274eorg.highwire.dtl.DTLVardef@1278ceaorg.highwire.dtl.DTLVardef@194887a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightO_LISynthetic bacterial communities are used to remove plastic hydrolysis oil waste C_LIO_LIThe optimized biphase reaction system can remove the majority of pollutants C_LIO_LIThe biodegradation process can be monitored in a real-time bioprocess software C_LIO_LINeural network techniques are used to model and predict the removal process C_LI

bioengineering↗

bioRxiv2024

Positive Selection on Rare Variants Underlying the Cold Adaptation of Wild Boar

The wide geographical distribution of Eurasian wild boar (Sus scrofa) offers a natural experiment to study the thermoregulation. Here, we conducted whole-genome resequencing and chromatin profiling experiments on the local populations from cold regions (northern and northeastern Asia) and warm regions (southeastern Asia and southern China). Using genome-wide scans of four methods, we detected candidate genes underlying cold-adaptation with significant enrichment of pathways related to thermogenesis, fat cell development, and adipose tissue regulation. We also found two enhancer variants under positive selection, an intronic variant of IGF1R (rs341219502) and an exonic variant of BRD4 (rs327139795), which showed the highest differentiation between cold and warm region populations of wild boar and domestic pigs. Moreover, these rare variants were absent in outgroup species and warm-region wild boar but nearly fixed in cold-region populations, suggesting their de novo origins in cold-region populations. The experiments of CUT&Tag chromatin profiling showed that rs341219502 of IGF1R is associated with the gain of three novel transcription factors involving regulatory changes in enhancer function, while rs327139795 of BRD4 could result in the loss of a phosphorylation site due to amino acid alteration. We also found three genes (SLCO1C1, PDE3A, and TTC28) with selection signals in both wild boar and native human populations from Siberia, which suggests convergent molecular adaptation in mammals. Our study shows the adaptive evolution of genomic molecules underlying the remarkable environmental flexibility of wild boar.

evolutionary biology↗

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

ProAffinity-GNN: A Novel Approach to Structure-based Protein-Protein Binding Affinity Prediction via a Curated Dataset and Graph Neural Networks

Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essential for decoding PPIs, faces challenges due to the substantial time and financial costs involved in experimental and theoretical methods. This situation underscores the urgent need for more effective and precise methodologies for predicting binding affinity. Despite the abundance of research on PPI modeling, the field of quantitative binding affinity prediction remains underexplored, mainly due to a lack of comprehensive data. This study seeks to address these needs by manually curating pairwise interaction labels on all available 3D structures of proteins complexes, with experimentally determined binding affinities, creating the largest dataset for structure-based pairwise protein interaction with binding affinity to date. Subsequently, we introduce "ProAffinity-GNN", a novel deep learning framework using protein language model and graph neural network (GNN) to improve the accuracy of prediction of structure-based protein-protein binding affinities. The evaluation results across several benchmark test sets demonstrate that ProAffinity-GNN not only outperforms existing models in terms of accuracy but also shows strong generalization capabilities.

bioinformatics↗

bioRxiv2024

Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy

Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline AbGen that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. Our AbGen centers around a novel antibody language model (AbLM) that is pretrained on 12 million generic protein domain sequences and fine-tuned on 4,000+ paired VH-VL sequences, with IgG-specific CDR-masking and VH-VL cross-attention. AbLM provides a latent space of IgG sequence embeddings for AbGen, including (a) landscapes of IgGs activities in neutralizing the wild-type virus are analyzed through structure prediction for IgG and IgG-antigen (viral protein spikes receptor binding domain, RBD) interactions; and (b) landscapes of IgGs susceptibility in neutralizing variant viruses are predicted through Gaussian process regression, despite that as few as 14 clinical antibodies responses to variants of concern are available. The AbGen pipeline was applied to over 1300 IgG sequences we collected from RBD-binding B cells of convalescent patients. With experimental validations, AbGen efficiently prioritized IgG candidates against a broad spectrum of viral variants (wildtype, Delta, and Omicron), preventing the infection of host cells in vitro and hACE2 transgenic mice in vivo. Compared to other existing protein language models that require 10-100 times more model parameters, AbLM improved the precision from around 50% to 75% to predict IgGs with low variant susceptibility. Furthermore, AbGen enables structure-based computational protein redesign for selected IgG clones with single amino acid substitutions at the RBD-binding interface that doubled the IgG blockade efficacy for one of the severe, therapy-resistant strains - Delta (B.1.617). Our work expedites applications of artificial intelligence in antibody screen and re- design combining data-driven protein language models and Kriging for antibody sequence analysis and activity prediction, in synergy with physics-driven protein docking and design for antibody-antigen interface analyses and functional optimization.

immunology↗

bioRxiv2024

Chlomito: a novel tool for precise elimination of organelle genome contamination in nuclear genome assemblies

Accurate genome assemblies are crucial for understanding biological evolution, mechanisms of disease, and biodiversity. However, contamination from organelle genomes in nuclear genome analyses often leads to inaccuracies and unreliability in results. To address this issue, we developed a tool named Chlomito, which employs innovative algorithms to precisely identify and eliminate organelle genome contamination sequences from nuclear genome assemblies. Compared to conventional approaches, Chlomito can not only detect and eliminate organelle sequences but also effectively distinguish true organelle sequences from those transferred into the nucleus via horizontal gene transfer. To evaluate the accuracy of Chlomito, we conducted tests using sequencing data from Plum and Mango. The results confirmed that Chlomito can accurately detect contigs originating from the organelle genome, and the identified contigs covered most regions of the organelle reference genomes, demonstrating its efficiency and precision in comprehensively recognizing organelle genome sequences. Additionally, for user convenience, we packaged this method into a Docker image, simplifying the data processing workflow. Overall, Chlomito provides a highly efficient and accurate method for identifying and removing contigs derived from organelle genomes in genomic assembly data, thereby contributing to the improvement of genome assembly quality and advancing research in genomics and evolutionary biology.

bioinformatics↗

bioRxiv2024

Near telomere-to-telomere genome assemblies of two Chlorella species unveil the composition and evolution of centromeres in green algae

BackgroundCentromeres play a crucial and conserved role in cell division, although their composition and evolutionary history in green algae, the evolutionary ancestors of land plants, remains largely unknown. ResultsWe constructed near telomere-to-telomere (T2T) assemblies for two Trebouxiophyceae species, Chlorella sorokiniana NS4-2 and Chlorella pyrenoidosa DBH, with chromosome numbers of 12 and 13, and genome sizes of 58.11 Mb and 53.41 Mb, respectively. We identified and validated their centromere sequences using CENH3 ChIP-seq and found that, similar to humans and higher plants, the centromeric CENH3 signals of green algae display a pattern of hypomethylation. Interestingly, the centromeres of both species largely comprised transposable elements, although they differed significantly in their composition. Species within the Chlorella genus display a more diverse centromere composition, with major constituents including members of the LTR/Copia, LINE/L1, and LINE/RTEX families. This is in contrast to green algae including Chlamydomonas reinhardtii, Coccomyxa subellipsoidea, and Chromochloris zofingiensis, in which centromere composition instead has a pronounced single-element composition. Moreover, we observed significant differences in the composition and structure of centromeres among chromosomes with strong collinearity within the Chlorella genus, suggesting that centromeric sequence evolves more rapidly than sequence in non-centromeric regions. ConclusionsThis study not only provides high-quality genome data for comparative genomics of green algae but gives insight into the composition and evolutionary history of centromeres in early plants, laying an important foundation for further research on their evolution.

genomics↗

bioRxiv2024

High-accuracy crRNA array assembly strategy for multiplex CRISPR

Simultaneous targeting of multiple loci with CRISPR system, a tool known as multiplex CRISPR, offers us more feasibility to manipulate and elucidate the intricate and redundant endogenous networks underlying complex cellular functions. Owing to the versatility of the continuously emerging Cas nucleases and the utilization of CRISPR arrays, multiplex CRISPR has been implemented by numerous in vitro and in vivo studies. However, a streamlined practical strategy for CRISPR array assembly with both convenience and accuracy is still lacking. Here, we present a novel high-accuracy, cost- and time-saving strategy for CRISPR array assembly. Using this strategy, we efficiently accomplished the assembly of up to twelve crRNAs (for AsCas12a) or fifteen crRNAs (for RfxCas13d) in a single reaction. Moreover, we reveal that CRISPR arrays driven by Pol II promoters exhibit a distinct targeting pattern compared to Pol III promoters, which could be exploited for specific distributions of CRISPR intensity. For long CRISPR arrays, we designed a better approach than simply selecting one from U6 or EF1a. Collectively, our study provides a flexible and powerful tool for the convenient implementation of multiplex CRISPR across DNA to RNA, facilitating the dissection of sophisticated cellular networks and the future realization of multi-target gene therapy.

synthetic biology↗

bioRxiv2024

DOPAMINE D4 RECEPTOR DOWN-REGULATES RENAL SODIUM CHLORIDE COTRANSPORTER VIA UBIQUITINATION-ASSOCIATED LYSOSOME DEGRADATION

BackgroundThe thiazide-sensitive sodium chloride cotransporter (NCC) is the major apical sodium transporter located in the mammalian renal distal convoluted tubule (DCT). The amount of sodium reabsorbed in the DCT through NCC plays an important role in the regulation of extracellular fluid volume and blood pressure. Dopamine and its receptors constitute a renal antihypertensive system in mammals. The disruption of Drd4 in mice causes kidney-related hypertension. However, the pathogenesis of D4R-deficiency associated hypertension is not well documented. MethodWe assessed the effects of D4R on NCC protein abundances and activities of DCT in mice with renal or global Drd4-deficiencies and expressing human D4.7 variant and in cultured mouse DCT cells, and explored the molecular mechanism. ResultsNCC inhibitor hydrochlorothiazide enhanced the natriuresis in Drd4-/- mice. Renal NCC protein was greater while ubiquitination of NCC was less in Drd4-/- than Drd4+/+ mice. Silencing of D4R in cultured mouse DCT cells increased NCC protein but decreased NCC ubiquitination. D4R agonist had opposite effects that were blocked by the antagonist. In mouse kidneys and DCT cells D4R and NCC colocalized and co-immunoprecipitated. Moreover, D4R-agonist promoted the binding between the two proteins demonstrated by fluorescence resonance energy transfer. D4R agonism internalized NCC, decreased NCC in the plasma membrane, increased NCC in lysosomes and reduced NCC-dependent-intracellular-sodium transport. The lysosomal inhibitor chloroquine prevented the D4R-induced NCC-reduction. A shortened NCC half-life was suggested by its decay under cycloheximide-chase. Ubiquitin-specific-protease 48 (USP48, a deubiquitinating enzyme) was increased in the kidneys and cells with Drd4-deficiency while D4R stimulation decreased it in vitro and reduction of USP48 with siRNA decreased NCC expression. The mice carrying human D4.7 variant or with renal reduction of D4R developed hypertension with increased NCC. ConclusionOur data demonstrates that D4R downregulates NCC by promoting USP48-associated deubiquitination and subsequent internalization, lysosome relocation and degradation.

physiology↗

bioRxiv2024

Metabotropic NMDA Receptor Signaling Contributes to Sex Differences in Synaptic Plasticity and Episodic Memory

Men generally outperform women on encoding spatial components of episodic memory whereas the reverse holds for semantic elements. Here we show that female mice outperform males on tests for non-spatial aspects of episodic memory ("what", "when"), suggesting that the human findings are influenced by neurobiological factors common to mammals. Analysis of hippocampal synaptic plasticity mechanisms and encoding revealed unprecedented, sex-specific contributions of non-classical metabotropic NMDA receptor (NMDAR) functions. While both sexes used non-ionic NMDAR signaling to trigger actin polymerization needed to consolidate long-term potentiation (LTP), NMDAR GluN2B subunit antagonism blocked these effects in males only and had the corresponding sex-specific effect on episodic memory. Conversely, blocking estrogen receptor alpha eliminated metabotropic stabilization of LTP and episodic memory in females only. The results show that sex differences in metabotropic signaling critical for enduring synaptic plasticity in hippocampus have significant consequences for encoding episodic memories.

neuroscience↗

bioRxiv2024

Liver cancer initiation is dependent on metabolic zonation but decoupled from premalignant clonal expansion

The origin of cancer is poorly understood because cells that obtain truncal mutations are rarely fate mapped in their native environments. A defining feature of the liver is zonation, or the compartmentalization of metabolic functions in hepatocytes located in distinct regions of the lobule 1. However, it is unknown if cancers develop in some zones but not others, and if there are metabolic determinants of cancer risk that track with cellular position. To study cancer initiation, we examined the effect of activating mutations in Ctnnb1 and loss of function mutations in Arid2, two of the most commonly co-mutated genes in hepatocellular carcinoma (HCC) 2. We exploited glutamine synthetase (GS) as a faithful fate mapping marker of Ctnnb1 mutant hepatocytes. By introducing mutations in distinct zones in a mosaic fashion, we showed that position and metabolic context regulate clone expansion. Mutant clones were maintained in zone 1 but largely outcompeted in zone 3. Paradoxically, clonal maintenance was anti-correlated with cancer initiation, as zone 3 mutant livers showed increased tumorigenesis. To define mechanisms, we individually deleted eleven zone specific genes in HCC mouse models, revealing that Gstm2 and Gstm3 were required for efficient HCC initiation in zone 3. These data indicate that liver cancer initiation is dependent on zonation but independent of clonal expansion.

cancer biology↗

bioRxiv2024

Metabolomics unveiled the impact of alterations in nucleotide glucose metabolism on palmitic acid-induced TLR4 activation in THP-1 macrophages

OBJECTIVEThe association of palmitic acid with macrophage inflammation and its promotion of the expression of inflammatory factors through TLR4 have been demonstrated. It has been observed that immature TLR4 localizes to the endoplasmic reticulum and Golgi apparatus, necessitating glycosylation for migration to the cell membrane. The objective of this study was to identify potential biomarkers associated with N-glycosylation subsequent to the activation of TLR4 inflammatory signaling in human macrophages by palmitic acid. APPROACH AND RESULTSThe co-cultivation of palmitic acid with THP-1 macrophages was conducted for a duration of 24 hours, followed by the collection of cell extracts for subsequent metabolomic and lipidomic analyses using high performance liquid chromatography-tandem mass spectrometry. Multivariate and univariate statistical analyses were conducted to identify potential biomarkers, in accordance with established scientific protocols. The impact of palmitic acid on the TLR4 signaling pathway and macrophage N-glycosylation was assessed at various time points using Western blot analysis, immunofluorescence staining, Elisa assays, and chemical labeling techniques. The TLR4 inflammatory signaling pathway was examined in macrophages at different time points, revealing that PA induced the upregulation of MyD88 and TRAF6 expression as well as NF-{kappa}B phosphorylation, indicating the activation of classical NF-{kappa}B signaling. After 24 hours, TLR4 translocated from the cell membrane to the cytoplasm and initiated internalization, accompanied by significant colocalization with GalAz in the cytosol. In addition, the metabolites in the cell extract were found to be altered in both the control and model groups. Significantly alterations in two N-glycosylation related metabolites were observed in the model group, including guanosine diphosphate-L-fucose and uridine diphosphate-N-acetylglucosamine/uridine diphosphate-N-acetylgalactosamine. CONCLUSIONSTHP-1 macrophages incubated with palmitic acid exhibited a distinct metabolomic profile compared to the control group. Our findings suggest that metabolomics analysis holds promise in identifying disease-specific biomarkers for diagnosing fatty acid-induced inflammatory responses in macrophages.

immunology↗

bioRxiv2023

scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data

Clustering genes in single-cell RNA sequencing plays a pivotal role in unraveling a plethora of biological processes, from cell differentiation to disease progression and metabolic pathways. Traditional time-domain methods are instrumental in certain analyses, yet they may overlook intricate relationships. For instance, genes that appear distinct in the time domain might exhibit striking similarities in the frequency domain. Recognizing this, we present scGeneRhythm, an innovative deep learning technique that employs Fourier transformation. This approach captures the rich tapestry of gene expression from both the time and frequency domains. When evaluated across a spectrum of single-cell datasets, scGeneRhythm consistently outperforms conventional approaches. The gene clusters it identifies not only demonstrate heightened statistical significance in enriched pathways but also bring to light underlying gene relationships previously obscured. Through integrating frequency-domain data, scGeneRhythm not only refines gene grouping but also uncovers pivotal biological insights, such as nuanced gene rhythmicity. By deploying scGeneRhythm, we foster a richer, multi-dimensional understanding of gene expression dynamics, enriching the potential avenues of cellular and molecular biology research.

bioinformatics↗

bioRxiv2023

CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing Data via Deep Contrastive Learning

A vast amount of single-cell RNA-seq (SC) data has been accumulated via various studies and consortiums, but the lack of spatial information limits its analysis of complex biological activities. To bridge this gap, we introduce cellContrast, a computational method for reconstructing spatial relationships among SC cells from spatial transcriptomics (ST) reference. By adopting a contrastive learning framework and training with ST data, cellContrast projects gene expressions into a hidden space where proximate cells share similar representation values. We performed extensive benchmarking on diverse platforms, including SeqFISH, Stereo-Seq, 10X Visium, and MERSCOPE, on mouse embryo and human breast cells. The results reveal that cellContrast substantially outperforms other related methods, facilitating accurate spatial reconstruction of SC. We further demonstrate cellContrasts utility by applying it to cell-type co-localization and cell-cell communication analysis with real-world SC samples, proving the recovered cell locations empower novel discoveries and mitigate potential false positives.

bioinformatics↗

bioRxiv2023

Optimization for High-Throughput BiFC screening

The Cell-PCA screen, since its inception, has provided an efficient method for analyzing cellular interactomes and has been used in various biological studies involving proteins like MYC, PER2, and ERK. With rapid advancements in biotechnology, including tools for protein function investigation, the Cell-PCA screen remains relevant. However, despite its successful application in recent studies, there are areas for optimization to ensure its continued relevance in the face of evolving technological advancements.

bioengineering↗

bioRxiv2023

Trifunctional linkers enable improved visualization of actin by expansion microscopy

Expansion Microscopy (ExM) revolutionized the field of super-resolution microscopy by allowing for subdiffraction resolution fluorescence imaging on standard fluorescence microscopes. However, it has been found that it is hard to visualize actin filaments efficiently using ExM. To improve actin imaging, multifunctional molecules have been designed, however, with moderate success. Here, we present optimized methods for phalloidin conjugate grafting that have high efficiency for both cellular and tissue samples. Our optimized strategy improves anchoring and signal retention by [~]10 times. We demonstrate the potential of trifunctional linkers (TRITON) for actin imaging in combination with immunolabeling using different ExM protocols. 10x ExM of actin labeled with TRITON enabled us to visualize the periodicity of actin rings in cultured hippocampal neurons and brain slices by Airyscan confocal microscopy. Thus, TRITON linkers provide an efficient grafting method, especially in cases where the concentration of target-bound monomers is insufficient for high-quality ExM.

biochemistry↗

bioRxiv2023

Distinct mechanisms for sebaceous gland self-renewal and regeneration provide durability in response to injury

Sebaceous glands (SGs) release oils that protect our skin, but how these glands respond to injury has not been previously examined. Here, we report that SGs are largely self-renewed by dedicated stem cell pools during homeostasis. Using targeted single cell RNA-sequencing, we uncovered both direct and indirect paths by which these resident SG progenitors ordinarily differentiate into sebocytes, including transit through a PPAR{gamma}+Krt5+ transitional cell state. Upon skin injury, however, SG progenitors depart their niche, reepithelialize the wound, and are replaced by hair follicle-derived stem cells. Furthermore, following targeted genetic ablation of >99% of SGs from dorsal skin, these glands unexpectedly regenerate within weeks. This regenerative process is mediated by alternative stem cells originating from the hair follicle bulge, is dependent upon FGFR signaling, and can be accelerated by inducing hair growth. Altogether, our studies demonstrate that stem cell plasticity promotes SG durability following injury.

developmental biology↗