Biology subjects

Fu, Q.

Publications and source records attributed to Fu, Q..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Quantifying the ~75-95% of Peptides in DIA-MS Datasets that were not Previously Quantified

We demonstrate an algorithm termed GoldenHaystack (GH) that, compared to the leading DIA-MS algorithm, (a) quantifies and identifies with better FDR accuracy the peptides found in FASTA search spaces ([~]5-25% of analytes in DIA-MS datasets), (b) quantifies the remaining [~]75-95% of analytes that were previously unquantified, and (c) runs [~]40-200x faster (or [~]1-10x faster than the LC-MS). Specifically, without a FASTA or spectral library, GH can deconvolute and accurately quantify chimeric LC-MS spectra. The central idea that enables this claim is: for sufficiently sized projects (e.g., [≥] [~]50 LC-MS files), pairs of peptides that co-elute in one subset of LC-MS files do not exactly co-elute in a different subset of files. GH thus analyzes a project holistically: it uses multi-partite matching to match fragment ions across all samples, separates and regroups the fragment ions into unique analyte signatures, reduces stochastic noise, and then quantifies those unique analyte signatures.

bioinformatics↗

bioRxiv2024

Cortical neural activity during responses to mechanical perturbation: Effects of hand preference and hand used

Handedness, as measured by self-reported hand preference, is an important feature of human behavioral lateralization that has often been associated with hemispheric specialization. We examined the extent to which hand preference and whether the dominant hand is used or not influence the motor and neural response during voluntary unimanual corrective actions. The experimental task involved controlling a robotic manipulandum to move a cursor from a center start point to a target presented above or below the start. In some trials, a mechanical perturbation of the hand was randomly applied by the robot either consistent or against the target direction, while electroencephalography (EEG) was recorded. Twelve left-handers and ten right-handers completed the experiment. Left-handed individuals had a greater negative peak in the frontal event-related potential (ERP) than right-handed participants during the initial response phase (N150) than right-handed individuals. Furthermore, left-handed individuals showed more symmetrical ERP distributions between two hemispheres than right-handed individuals in the frontal and parietal regions during the late voluntary response phase (P390). To the best of our knowledge, this is the first evidence that demonstrates the differences in the cortical control of voluntary corrective actions between left-handers and right-handers.

neuroscience↗

bioRxiv2024

Identification of the Regulatory Elements and Protein Substrates of Lysine Acetoacetylation

Short chain fatty acylations establish connections between cell metabolism and regulatory pathways. Lysine acetoacetylation (Kacac) was recently identified as a new histone mark. However, regulatory elements, substrate proteins, and epigenetic functions of Kacac are not yet fully understood, hindering further in-depth understanding of acetoacetate modulated (patho)physiological processes. Here, we created a chemo-immunological approach for reliable detection of Kacac, and demonstrated that acetoacetate serves as the primary precursor for histone Kacac. We report the enzymatic addition of the Kacac mark by the acyltransferases GCN5, p300, and PCAF, and its removal by deacetylase HDAC3. Furthermore, we establish acetoacetyl-CoA synthetase (AACS) as a key regulator of cellular Kacac levels. A comprehensive proteomic analysis has identified 139 Kacac sites on 85 human proteins. Bioinformatics analysis of Kacac substrates and RNA-seq data reveal the broad impacts of Kacac on multifaceted cellular processes. These findings unveil pivotal regulatory mechanisms for the acetoacetate-mediated Kacac pathway, opening a new avenue for further investigation into ketone body functions in various pathophysiological states.

biochemistry↗

bioRxiv2024

A Refined Analysis of Neanderthal-Introgressed Sequences in Modern Humans with a Complete Reference Genome

BackgroundLeveraging long-read sequencing technologies, the first complete human reference genome, T2T-CHM13, corrects the assembly errors in prior references and addresses the remaining 8% of the genome. While the studies on archaic admixture in modern humans so far have been relying on the GRCh37 reference due to the archaic genome data, the impact of T2T-CHM13 in this field remains unknown. ResultsWe remapped the sequencing reads of the high-quality Altai Neanderthal and Denisovan genomes onto GRCh38 and T2T-CHM13 respectively. Compared with GRCh37, we found T2T-CHM13 has a significant improvement of read mapping quality in archaic samples. We then applied IBDmix to identify Neanderthal introgressed sequences in 2,504 individuals from 26 geographically diverse populations in different references. We observed different pre-phasing filtering strategies prevalently used in public data can largely impact determination of archaic ancestry, calling for consideration on the choice of filters. We discovered ~51Mb T2T-CHM13 unique Neanderthal sequences, which are predominantly located in regions where the variants distinct between the GRCh38 and T2T-CHM13 assemblies emerge. Besides, we unfolded new instances of population-specific archaic introgression in diverse populations, covering genes involved in metabolism, olfactory-related, and icon-channel. Finally, we integrated the introgressed sequences and adaptive signals with all references into a visualization database website, called ASH (www.arcseqhub.com), to facilitate the utilization of archaic alleles and adaptive signals in human genomics and evolutionary research. ConclusionsOur study refines the detection of archaic variations in modern humans, highlights the importance of T2T-CHM13 reference utility, and provides novel insights into functional consequences of archaic hominin admixture.

evolutionary biology↗

bioRxiv2024

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap Astral MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.

biochemistry↗

bioRxiv2024

Protein disulfide isomerase disassembles stress granules and blocks cytoplasmic aggregation of TDP-43 in ALS

Cytoplasmic aggregation of the transactive response DNA-binding protein-43 (TDP-43) in neurons, a pathological feature common to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration, has been found in some Alzheimers patients. Protein disulfide isomerase (PDI) functions as both an enzyme and a molecular chaperone to correct or eliminate misfolded proteins under pathological conditions. Here, we report that TDP-43 is mislocalized to the cytoplasm and colocalizes with PDI in the brain and spinal cord of two ALS patients and the brain of six Alzheimers patients compared to controls. TDP-43 selectively recruits wild-type PDI into its phase-separated condensate, which in turn slows down in vitro liquid-liquid phase separation of TDP-43, shifting the equilibrium phase boundary to higher protein concentrations. Importantly, wild-type PDI decreases oxidative stress-induced interaction between TDP-43 and G3BP1 to disassemble stress granules containing TDP-43 in neuronal cells. Wild-type PDI blocks the oxidative stress-induced mislocalization of TDP-43 to the cytoplasm, and blocks subsequent pathological phosphorylation and aggregation of TDP-43. We demonstrate that under pathological stress conditions, wild-type PDI disassembles stress granules, blocks cytoplasmic mislocalization and aggregation of TDP-43, and suppresses mitochondrial damage and TDP-43 toxicity. In the presence of abnormal forms of PDI, however, PDI loses its activity, and stress granules containing TDP-43 are assembled into amyloid fibrils, resulting in mitochondrial impairment and neuronal cell death in ALS patients and some Alzheimers patients. TeaserPDI disassembles SGs, blocks cytoplasmic mislocalization and aggregation of TDP-43, and suppress TDP-43 toxicity in ALS.

biochemistry↗

bioRxiv2024

A high resolution model of the grapevine leaf morphospace predicts synthetic leaves

O_LIGrapevine leaves are a model morphometric system. Sampling over ten thousand leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. C_LIO_LIWe measure the shapes of 139 grapevine leaves using 1672 pseudo-landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo-landmarks that results in a high-resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. C_LIO_LIWe visualize a high-resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, V. vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. C_LIO_LIBy predicting a high-resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high-resolution space; or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers. C_LI Societal Impact StatementGrapevine leaves are emblematic of the strong visual associations people make with plants. At a glance, leaf shape is immediately recognizable, and it is because of this reason it is used to distinguish grape varieties. In an era of computationally-enabled, machine learning-derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time-honored methods, we can predict theoretical leaves that are possible to understand the genetics, development, and environmental responses of plants in new ways.

plant biology↗

bioRxiv2023

Integrated data models on Receptor-Like Kinases for novel domain discovery and functional inference in the plant kingdom

Receptor-like kinases (RLKs) are the largest signal transduction component in plants, determining how different plants adapt to their ecological environment, resulting in plant-specific ecological niches. Current research on RLKs has focused mainly on a small number of typical RLK members of a few model plants. There is an urgent need to study the composition, distribution, and evolution of RLKs at the holistic level to accelerate the understanding of how RLK assists in the ecological adaptation of different plants. In this study, we have collected 528 plant genomes and established an RLK data model, resulting in the discovery and characterization of 524,948 RLK members. Each member is subject to systematic topology classification and coherent gene ID assignment. Using this data model, we discovered two novel families (Xiao and Xiang) of RLKs. Evolutionary analysis of the RLK families indicates that RLCK-XVII and RLCK-XII-2 exist exclusively in dicots, suggesting that the diversification in RLKs between monocots and dicots could cause differences in downstream cytoplasmic responses. We also use interaction proteome to help empower the data mining of inferring new functions of RLK from a global perspective, with the ultimate goal of understanding how RLKs shape the adaptation of different plants to the environment/ecology. The RLK data model compiled herein, together with the annotations and analytic tools, form an integrated data foundation involving multi-omics data and is publicly accessible via the web portal (http://metaRLK.biocloud.top).

bioinformatics↗

bioRxiv2023

High-Throughput Screening of the Effects of 90 Xenobiotics on the Simplified Human Gut Microbiota Model (SIHUMIx): A Metaproteomic and Metabolomic Study

The human gut microbiota is a complex microbial community with critical functions for the host, including the transformation of various chemicals. While effects on microorganisms has been evaluated using single-species models, their functional effects within more complex microbial communities remain unclear. In this study, we investigated the response of a simplified human gut microbiota model (SIHUMIx) cultivated in an in vitro bioreactor system in combination with 96 deep-well plates after exposure to 90 different xenobiotics, comprising 54 plant protection products and 36 food additives and dyes, at environmentally relevant concentrations. We employed metaproteomics and metabolomics to evaluate changes in bacterial abundances, the production of Short Chain Fatty Acids (SCFAs), and the regulation of metabolic pathways. Our findings unveiled significant changes induced by 23 out of 54 plant protection products and 28 out of 36 food additives across all three categories assessed. Notable highlights include azoxystrobin, fluroxypyr, and ethoxyquin causing a substantial reduction (log2FC <-0.5) in the concentrations of the primary SCFAs: acetate, butyrate, and propionate. Several food additives had significant effects on the relative abundances of bacterial species; for example, acid orange 7 and saccharin led to a 75% decrease in Clostridium butyricum, with saccharin causing an additional 2.5-fold increase in E. coli compared to the control. Furthermore, both groups exhibited up- and down-regulation of various pathways, including those related to the metabolism of amino acids such as histidine, valine, leucine, and isoleucine, as well as bacterial secretion systems and energy pathways like starch, sucrose, butanoate, and pyruvate metabolism. This research introduces an efficient in vitro technique that enables high-throughput screening of the structure and function of a simplified and well-defined human gut microbiota model against 90 chemicals using metaproteomics and metabolomics. We believe this approach will be instrumental in characterizing chemical-microbiota interactions especially important for regulatory chemical risk assessments.

molecular biology↗

bioRxiv2023

A framework for defining mesenchymal cell types associated with murine periosteal and endosteal bone

Single-cell RNA sequencing has led to numerous novel designations for mesenchymal cell types associated with bone. Consequently, there are now multiple designations for what appear to be the same cell type. In addition, existing datasets contain relatively small numbers of mature osteoblasts and osteocytes and there has been no comparison of periosteal bone cells to those at the endosteum and trabecular bone. The main goals of this study were to increase the amount of single cell RNA sequence data for osteoblasts and osteocytes, to compare cells from the periosteum to those inside bone, and to clarify the major categories of cell types associated with murine bone. To do this, we created an atlas of murine bone-associated cells by harmonizing published datasets with in-house data from cells targeted by Osx1-Cre and Dmp1-Cre driver strains. Cells from periosteal bone were analyzed separately from those isolated from the endosteum and trabecular bone. Over 100,000 mesenchymal cells were mapped to reveal 11 major clusters designated fibro-1, fibro-2, chondrocytes, articular chondrocytes, tenocytes, adipo-CAR, osteo-CAR, pre-osteoblasts, osteoblasts, osteocytes, and osteo-X, the latter defined in part by Postn expression. Osteo-X, osteo-CAR, and pre-osteoblasts were closely associated with osteoblasts at the trabecular bone surface. Wnt16 was expressed in multiple cell types from the periosteum but not in any cells from endocortical or cancellous bone. Fibro-2 cells, which express markers of skeletal stem cells, localized to the periosteum but not trabecular bone in adult mice. Suppressing bone remodeling eliminated osteoblasts and altered gene expression in pre-osteoblasts but did not change the abundance or location of osteo-X or osteo-CAR cells. These results provide a framework for identifying bone cell types in murine single cell RNA sequencing datasets and suggest that osteoblast progenitors reside near the surface of remodeling bone. Author SummaryThe skeleton of vertebrate animals is produced by bone forming cells known as osteoblasts. New osteoblasts are continually produced throughout life to maintain the skeleton. However, the identity of the cells from which new osteoblasts are derived is not clear. Single cell RNA sequencing provides a gene expression profile of individual cells and allows them to be grouped into clusters of cells with similar profiles. In many cases, distinct clusters represent distinct cell types. Recent single cell RNA sequencing studies of mouse bones by several different laboratories have identified many novel cell types, some of which may be osteoblast progenitors. In many cases, different laboratories have created different names for what appear to be the same cell type. To help clarify this situation, we performed single cell RNA sequencing studies and combined our results with those from several published studies to create a harmonized map of the cell types associated with mouse bones. We also identified gene signatures for each cell type that can be used to improve the consistency of cell type designation in future single cell RNA sequencing studies of mouse bone.

genomics↗

bioRxiv2023

Characterization of Tumor-Associated Endothelial Cells and the Development of a Prognostic Model in Pancreatic Ductal Adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a complex tumor microenvironment. Angiogenesis is of paramount importance in the proliferation and metastasis of PDAC. However, currently, there are no well-defined biomarkers available to guide the prognosis and treatment of PDAC. ResultsIn this study, we investigated the interactions between tumor-associated endothelial cells (TAECs) and tumor cells in PDAC, and identified a specific subset of TAECs characterized by high expression of COL4A1. COL4A1+ endothelial cells interact with tumor cells through the COLLAGEN signaling pathway to promote tumor cell proliferation, migration, and invasion. We also observed activation of HOXD9 in COL4A1+ endothelial cells. Based on these findings, we developed a prognostic model called TaEMS, which accurately predicts patient prognosis. TaEMS identified high-risk patients enriched in cell cycle-related pathways and low-risk patients enriched in focal adhesions, smooth muscle regulation, and immune pathways. Moreover, high-risk patients displayed a reduced level of immune cell infiltration, indicating the presence of a "cold tumor" phenotype. ConclusionsOverall, our study uncovered an intricate crosstalk between TAECs and tumor cells in PDAC, emphasizing the role of HOXD9 and highlighting the potential of TaEMS as a prognostic biomarker for precise therapies.

cancer biology↗

bioRxiv2023

AraENCODE: a comprehensive epigenomic database of Arabidopsis Thaliana

Arabidopsis (Arabidopsis thaliana) is a vital model organism in plant biology and genetics. In the last two decades, researchers have made significant progresses in characterizing the chromatin conformation and epigenomic information within the Arabidopsis genome. This information includes but is not limited to the higher structure of chromosomes, histone modification, DNA methylation, and chromatin accessibility. The results of these studies have provided an additional layer of information that complements the DNA sequence data. However, utilizing such knowledge poses a challenge for certain groups that lack bioinformatics analysts or adequate computing resources. A user-friendly and reproducible platform for accessing this information is urgently needed. In this study, we have developed a comprehensive epigenomic database for Arabidopsis (AraENCODE http://glab.hzau.edu.cn/AraENCODE), which comprises a total of 4,511 data libraries, including published chromatin conformation capture datasets (Hi-C, HiChIP), epigenomic datasets (ChIP-Seq, ATAC-Seq, FAIRE-Seq, MNase-Seq, DNase-Seq, BS-seq), and transcriptome data (RNA-Seq, miRNA-Seq). Furthermore, we have incorporated various existing resources, such as single nucleotide polymorphisms (SNPs), cis-regulatory modules, and multi-omics associations. We aim to provide a novel platform for investigating the regulation of epigenetic and chromatin interactions in Arabidopsis in relation to biological processes.

bioinformatics↗

bioRxiv2023

Signal Transduction and Kinase Control in Nanodisc Reconstituted E. coli Aerotaxis Receptor

WITHDRAWAL STATEMENTThe authors have withdrawn this manuscript because its being revised. The authors wish to withdraw this manuscript for two reasons. Firstly, the relevance and validity of some of the mass spectrometry data in the manuscript are being questioned by the authors. Secondly, some of the authors had not adequately reviewed the manuscript before it was uploaded to bioRxiv. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

biophysics↗

bioRxiv2022

Large-scale analysis of the N-terminal regulatory elements of the kinase domain in plant receptor-like kinase family

The N-terminal regulatory element of the kinase domain (NRE), which includes juxtamembrane segment (JM) of Receptor-like kinases (RLKs) and the N-terminal extension segment of the kinase domain in RLCKs, is the key component that regulates the activities of RLKs and RLCKs. However, their feature and functions remain largely unexplored. Herein, we perform a systematic analysis of 510,233 NRE sequences in RLKs and RLCKs from 528 plants by integrating information theory and genome-wide analysis to unravel their common characteristics and diversity. Recombinant RLKs are used to explore the structural-functional relationship of the newly-discovered motifs in vitro. Most NRE segments are around 40-80 amino acids, featuring a serine-rich region and a 14-amino-acid motif FSYEELEKATBNFS which harbors a characteristic -helix connecting to the core kinase domain. This -helix suppresses FERONIAs kinase activity. Motif discovery algorithm has identified 29 motifs with known phosphorylation sites highly conserved in RLK and RLCK classes, especially the motif VGPWKpTGLpSGQLQKAFVTGVP in LRR-VI-2 class. The NRE phosphorylation of an LRR-VI-2 member MDIS1 modulates the auto-phosphorylation of its co-receptor MIK1, indicating NREs potential role as a "kinase switch" in RLK activation. Consequently, the characterization of phosphorylatable NRE motifs improves the accuracy in predicting phosphorylatable serines. Altogether, our study provides an enriched and comprehensive dataset to investigate NRE segments from individual RLKs and helps understand the underlying mechanism of action of the RLK signal transduction and kinase activation processes in plant adaptation.

plant biology↗

bioRxiv2022

Optimization and redevelopment of single-cell data analysis workflow based on deep generative models

The present single-cell RNA sequencing(scRNA-seq) analysis pipelines require a combination of appropriate normalization, dimension reduction, clustering, and specific-gene analysis algorithms, but the rationale for the choice of these algorithms is relatively subjective because of the lack of ground truth assessment conclusions. As the number of captured single-cells increases, the number of different types of noise cells also increases, which can strongly affect the analysis efficiency. For scRNA-seq, a technology that generates data through multi-process operations, the deep generative model should be a good choice for this type of data analysis, allowing simultaneous estimation of multiple unobservable parameters assumed in the data generation process. Hence, in our study, we sequenced a pool of pre-labeled single cells to obtain a batch of scRNA-seq data with main and fine labels, which was then used to evaluate the clustering and specific-gene analysis methods. Afterward, we applied two deep generative models to infer the probabilities of pseudo and impurity cells. And by stepwise removing the inferred noise cells, the clustering performance and the consistency of different specific-gene analysis methods are both greatly improved. After that, we applied Deep-LDA (a latent Dirichlet allocation-based deep generative model) to scRNA-seq data analysis. And this model takes the count matrix as input, and makes the classification and specific gene optimization process mutually dependent, which has more practical sense and simplifies the analysis workflow. At last, we successfully implemented the model with transferred knowledge to make single-cell annotation and verified its superior performance.

bioinformatics↗

bioRxiv2022

Senescent cells develop PDK4-dependent hypercatabolism and form an acidic microenvironment to drive cancer resistance

Cellular senescence is a state of stable growth arrest, usually accompanied by development of the senescence-associated secretory phenotype (SASP). Although senescent cells remain metabolically active, little is known about their metabolic landscape and in vivo pathophysiological implications. Here we show that expression of the pyruvate dehydrogenase (PDH) inhibitory enzyme, pyruvate dehydrogenase kinase 4 (PDK4), is significantly upregulated in human senescent stromal cells. Preferentially expressed upon genotoxicity-induced senescence (GIS), PDK4 is negatively correlated with posttreatment survival of cancer patients. Upon cellular senescence, PDK4 shifts glucose metabolic flux from oxidative phosphorylation to aerobic glycolysis, causing enhanced lactate production and forming an acidic microenvironment. However, distinct from the cancer cell-featured Warburg effect, senescent cells maintain an intensive use of pyruvate through the tricarboxylic acid cycle (TCA), displaying increased respiration and redox activity, indicative of a special form of metabolic reprogramming. Conditioned media from PDK4+ stromal cells change global expression and promote malignancy of recipient cancer cells in vitro and accelerate tumor progression in vivo. Pharmacologically targeting PDK4 restrains the adverse effects of PDK4 in cell-based assays, while promoting tumor regression and extending posttreatment survival in preclinical trials. Together, our study substantiates the hypercatabolic nature of senescent cells, and reveals a metabolic link between senescence-associated acidic microenvironment and age-related pathologies including but not limited to cancer.

cell biology↗

bioRxiv2022

Plant Virus Database: a resource for exploring the diversity of plant viruses and their interactions with hosts

Plant viruses cause huge damage to commercial crops, yet the studies towards plant viruses are limited and the diversity of plant viruses are under-estimated yet. This study built an up-to-date atlas of plant viruses by computationally identifying viruses from the RNA-seq data in the One Thousand Plant Transcriptomes Initiative (1KP) and by integrating plant viruses from public databases, and further built the Plant Virus Database (PVD, freely available at http://47.90.94.155/PlantVirusBase/#/home) to store and organize these viruses. The PVD contained 3,206 virus species and 9,604 virus-plant host interactions which were more than twice that reported in previous plant virus databases. The plant viruses were observed to infect only a few plant hosts and vice versa. Analysis and comparison of the viromes in the Monocots and Eudicots, and those in the plants in tropical and temperate regions showed significant differences in the virome composition. Finally, several factors including the viral group (DNA and RNA viruses), enveloped or not, and the transmission mode of viruses, were found to have no or weak associations with the host range of plant viruses. Overall, the study not only provides a valuable resource for further studies of plant viruses, but also deepens our understanding towards the genetic diversity of plant viruses and the virus-host interactions.

microbiology↗

bioRxiv2022

Effects of Sanhuang plaster on expression of MyD88, TRAF6, MIP-1β and IL-23 in rats infected by MRSA

Objective: To investigate the effects of Sanhuang plaster on expression of myeloid differentiation factor 88 (MyD88), tumor necrosis factor receptor-related factor 6 (TRAF6), macrophage inflammatory protein-l{beta} (MIP-1{beta}) and its mediated cytokine interleukin-23 (IL-23) in soft tissues of rats infected by methicillin-resistant Staphylococcus aureus (MRSA). Methods: Ninety-six healthy rats were randomly divided into normal control group, model control group, Mupirocin group, high, medium and low dose groups of Sanhuang plaster, with 16 rats in each group. MRSA bacterial liquid was used to make skin and soft tissue infection models. The rats in the normal control group and the model control group were not given any treatment measures. The high, medium, and low dose groups of Sanhuang plaster were given to the affected area with Sanhuang plaster, and the Mupirocin group was given to the affected area for treatment for 10 days. The wound pathological changes were observed. The levels of MIP-1{beta} and IL-23 in serum and infected tissues of rats in each group were measured by ELISA. The mRNA expressions of MyD88, TRAF6, MIP-l{beta} and IL-23 were measured by Quantitative Real-time PCR, and Western blot was used to measure MyD88 and TRAF6 protein expression. Results: Compared with the model control group, the general condition of the Sanhuang plaster groups was significantly improved, and the pathological damage was reduced. The MIP-1{beta} and IL-23 levels in the serum and infected tissues of the high dose group of Sanhuang plaster, the mRNA expressions of MyD88, TRAF6, MIP-1{beta} and IL-23and in large and medium dose groups of Sanhuang plaster, and the protein expressions of Myd88 and TRAF6 in high dose of Sanhuang plaster were significantly decreased (p<0.05 or p<0.01). Conclusion: Sanhuang plaster may play a role in promoting healing of infected wounds by down-regulating the expression levels of MyD88, TRAF6, and MIP-1{beta}, and inhibiting the abnormal secretion of cytokine IL-23.

molecular biology↗