Biology subjects

Shen, J.

Publications and source records attributed to Shen, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

KIF18A in HCC Metastasis: Dual Role in Anoikis Resistance and Chromosome Instability

The study explores the role of KIF18A, a gene linked to whole-genome doubling (WGD) and chromosomal instability (CIN), in the metastatic progression of hepatocellular carcinoma (HCC). We found that KIF18A is a critical link between CIN and anoikis resistance, key factors in HCC metastasis. Through the bulk-seq analysis, we identified the pressures of anoikis and CIN faced by tumor cells during HCC metastasis and identified hub genes central to this process. Our results reveal that E2F activation during HCC progression leads to the transcription of KIF18A, promoting the survival and metastasis of CIN tumors. Overexpression of KIF18A leads to longer tumor life, more micronuclei, and increased survival and metastasis through non-canonical NF-kB activation. Deletion of KIF18A stabilizes the nuclear membrane of the micronucleus, silences cGAS-STING and PI3K-AKT pathways, and inhibits classical NF-kB. The studys limitations include the need for further animal studies to validate the findings and explore the transient activation of the cGAS-STING pathway. Additionally, future research could focus on the potential therapeutic implications of targeting KIF18A in cancer treatment.

cancer biology↗

bioRxiv2024

A Commander-independent function of COMMD3 in endosomal trafficking

Endosomal recycling is a branch of intracellular membrane trafficking that retrieves endocytosed cargo proteins from early and late endosomes to prevent their degradation in lysosomes. A key player in endosomal recycling is the Commander complex, a 16-subunit protein assembly that cooperates with other endosomal factors to recruit cargo proteins and facilitate the formation of tubulo-vesicular carriers. While the crucial role of Commander in endosomal recycling is well established, its molecular mechanism remains poorly understood. Here, we genetically dissected the Commander complex using unbiased genetic screens and comparative targeted mutations. Unexpectedly, our findings revealed a Commander-independent function for COMMD3, a subunit of the Commander complex, in endosomal recycling. COMMD3 regulates a subset of cargo proteins independently of the other Commander subunits. The Commander-independent function of COMMD3 is mediated by its N-terminal domain (NTD), which binds and stabilizes ADP-ribosylation factor 1 (ARF1), a small GTPase regulating endosomal recycling. Mutations disrupting the COMMD3-ARF1 interaction diminish ARF1 expression and impair COMMD3-dependent cargo recycling. These data provide direct evidence that Commander subunits can function outside the holo-complex and raise the intriguing possibility that components of other membrane trafficking complexes may also possess functions beyond their respective complexes.

cell biology↗

bioRxiv2024

MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery

A key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.g. taxon or pathway) relates to one or more covariates. Few of these, however, simultaneously control false discovery rates, achieve reasonable power, incorporate complex modeling terms such as random effects, and also permit assessment of prevalence (presence/absence) associations and absolute abundance associations (when appropriate measurements are available, e.g. qPCR or spike-ins). Here, we introduce MaAsLin 3 (Microbiome Multivariable Associations with Linear Models), a modeling framework that simultaneously identifies both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 also newly accounts for compositionality with experimental (spike-ins and total microbial load estimation) or computational techniques, and it expands the space of biological hypotheses that can be tested with inference for new covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed current state-of-the-art differential abundance methods in testing and inferring associations from compositional data. When applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated many previously reported microbial associations with the inflammatory bowel diseases, but notably 77% of associations were with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations with higher accuracy and more specific association types, especially in complex datasets with multiple covariates and repeated measures.

microbiology↗

bioRxiv2024

L2G: Repurposing Language Models for Genomics Tasks

Pre-trained language models have transformed the field of natural language processing (NLP), and their success has inspired efforts in genomics to develop domain-specific foundation models (FMs). However, creating high-quality genomic FMs from scratch is resource-intensive, requiring significant computational power and high-quality pre-training data. The success of large language models (LLMs) in NLP has largely been driven by industrial-scale efforts leveraging vast, diverse corpora and massive computing infrastructure. In this work, we aim to bypass the data and computational bottlenecks of creating genomic FMs from scratch and instead propose repurposing existing LLMs for genomics tasks. Inspired by the recently observed cross-modal transfer phenomenon - where transformers pre-trained on natural language can generalize to other modalities - we introduce L2G, which adapts a pre-trained LLM architecture for genomics using neural architecture search (NAS) and a novel three-stage training procedure. Remarkably, without requiring extensive pre-training on DNA sequence data, L2G achieves superior performance to fine-tuned genomic FMs and task-specific models on more than half of tasks across multiple genomics benchmarks. In an enhancer activity prediction task, L2G further demonstrates its capacity to identify significant transcription factor motifs. Our work not only highlights the generalizability and efficacy of language models in out-of-domain tasks such as genomics, but also opens new avenues for more efficient and less resource-intensive methodologies in genomic research.

bioinformatics↗

bioRxiv2024

Human mitochondrial RNA polymerase structures reveal transcription start-site and slippage mechanism

Human mitochondrial RNA polymerase (POLRMT) and protein factors TFAM and TFB2M assemble on mitochondrial DNA promoters to initiate promoter-specific transcription. We present cryo-EM structures of two initiation complexes, IC3 and slipped-IC3, with fully resolved transcription bubbles containing RNA transcripts starting from +1 and -1 positions, respectively. These structures reveal the mechanisms of promoter melting, start site selection, and slippage synthesis. Promoter melting begins at -4 with base-specific interactions of -4 and -3 template guanines with POLRMT and -1 non-template adenine with TFB2M, stabilizing the bubble and facilitating initiation from +1. Slippage occurs when a synthesized 2-mer RNA shifts to -1; the -1 position is not an alternative start-site. The conserved non-template sequence (-1)AAA(+2) is recognized by a non-template stabilizing loop (K153LDPRSGGVIKPP165) and Y209 from TFB2M and W1026 of POLRMT. The initiation complex on cryo-EM grids exist in equilibrium with apo and dimeric POLRMTs, whose relative concentrations may regulate transcription initiation. HIGHLIGHTSO_LICryo-EM structures of active human mitochondrial transcription initiation complexes C_LIO_LIDe novo RNA synthesis begins at +1, linking +1 and +2 NTPs C_LIO_LISynthesized 2-mer RNA anneals at -1, initiating RNA slippage synthesis C_LIO_LIPOLRMT fingers motion alters TFB2M interactions with non-template strand C_LI

biochemistry↗

bioRxiv2024

Single-cell spatial mapping reveals alteration of cell type composition and tissue microenvironment during early colorectal cancer formation

Familial adenomatous polyposis (FAP) is a rare, hereditary syndrome that raises the risk of developing colorectal cancer (CRC). This disease model is well suited for studying the early stages of malignant transformation. Our spatial CODEX experiments reveal that, in contrast to normal mucosa, FAP mucosa, pre-cancer polyps and colorectal cancers exhibit substantial alterations in the cell type composition and tissue microenvironment. These early alterations include: an increase in the population of cancer-associated fibroblasts (CAFs), and the inhibition of tumor infiltrated lymphocytes and cell-adhesion protein by CAFs, the transformation of memory T cells into regulatory T cells, nuclear translocation of beta-catenin from the cell membrane, a decrease in the M1:M2 macrophage ratio, a notable increase in angiogenesis events. Our studies define the early stem cell, stromal, and immune steps of colorectal cancer and may benefit early detection, and therapeutic intervention.

cancer biology↗

bioRxiv2024

KaMLs for Predicting Protein pKa Values andIonization States: Are Trees All You Need?

Despite its importance in understanding biology and computer-aided drug discovery, the accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by scarcity of experimental data. Here we report the development of pKa ML (KaML) models based on decision trees and graph attention networks (GAT), exploiting physicochemical understanding and a new experiment pKa database (PKAD-3) enriched with highly shifted pKas. KaML-CBtree significantly outperforms the current state of the art in predicting pKa values and ionization states across all six titratable amino acids, notably achieving accurate predictions for deprotonated cysteines and lysines - a blind spot in previous models. The superior performance of KaMLs is achieved in part through several innovations, including separate treatment of acid and base, data augmentation using AlphaFold structures, and model pretraining on a theoretical pKa database. We also introduce the classification of protonation states as a metric for evaluating pKa prediction models. A meta-feature analysis suggests a possible reason for the lightweight tree model to outperform the more complex deep learning GAT. We release an end-to-end pKa predictor based on KaML-CBtree and the new PKAD-3 database, which facilitates a variety of applications and provides the foundation for further advances in protein electrostatics research. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/622800v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@15d2256org.highwire.dtl.DTLVardef@1795151org.highwire.dtl.DTLVardef@1c9ae02org.highwire.dtl.DTLVardef@1bf17a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Structural basis of pseudoGTPase-mediated protein-protein interactions

GTPases regulate various cellular processes through conformational changes triggered by GTP or GDP binding. Recently, pseudoGTPases, the catalytically inactive counterparts of GTPases, have been identified across species from bacteria to human, although their functions and mechanisms remain unexplored. In this study, we demonstrate that the N-terminal region of the assembly chaperone AAGAB is a type i pseudoGTPase using biochemistry and X-ray crystallography. Furthermore, we discovered that the AAGAB pseudoGTPase domain (psGD) interacts with the {sigma} subunits of AP1 and AP2 adaptor complexes, heterotetrameric complexes involved in clathrin-mediated membrane trafficking. AAGAB psGD engages the {sigma} subunits via a unique interface distinct from the conventional GTPase interacting regions. Further biochemical and cell-based assays confirmed the crucial role of the newly identified interface in binding and membrane trafficking. Collectively, our results establish AAGAB pseudoGTPase domain as a critical protein-protein interaction module. These findings offer new insight into the structural basis and molecular mechanisms of pseudoGTPases. HighlightsO_LIAAGAB N-terminal region is a pseudoGTPase C_LIO_LIAAGAB pseudoGTPase domain interacts with and stabilizes AP1 and AP2 {sigma} subunits C_LIO_LIThe crystal structure of AAGAB psGD:AP1{sigma}3 is the first reported psGD complex structure, revealing a unique interface independent of guanine nucleotide regulation C_LIO_LIThe psGD:AP1{sigma}3 structure offers mechanistic insights into {sigma} subunits stabilization and protection through adaptor complex assembly C_LI

biochemistry↗

bioRxiv2024

Computational Analysis of the Binding Poses of Nitazene Derivatives at the mu-Opioid Receptor

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of {micro}OR-opioid receptor ({micro}OR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the {micro}OR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of {micro}OR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for {micro}OR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln1242.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a{pi} -hole interaction with the conserved Tyr751.39. Our computational analysis provides new insights into the mechanism of {micro}OR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/616560v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@f3a933org.highwire.dtl.DTLVardef@e98f41org.highwire.dtl.DTLVardef@1bc0962org.highwire.dtl.DTLVardef@c416b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

FragLlama: Next-fragment prediction for molecular design

The emergence of ChatGPT has drawn significant attention to Large Language Models (LLMs) due to their impressive performance. While LLMs primarily focus on next token/word prediction, we apply this principle to molecular design by reframing the task as predicting the next token/fragment. We present FragLlama, a large language model trained for molecular design, featuring custom tokens that represent molecular fragments and functional groups. The model is for generating molecules given one or two fragments, for application scenarios like general hit-to-lead and lead optimization stage drug design, PROTAC linker design; mapping to commonly used drug design strategies like fragment growing and scaffold hopping. In the pre-training stage, we adapted the Llama 3 architecture to create FragLlama, training it to learn conditional probabilities of these fragment-level tokens. The subsequent alignment stage employed fine-tuning to guide the model towards generating molecules with desired properties. The effectiveness of FragLlama is demonstrated through its applications in designing molecular glue libraries, PROTAC linkers and EGFR binders. FragLlama demonstrates proficiency in reproducing expert-level designs while also exploring novel and promising chemical spaces, highlighting its potential to augment the capabilities of medicinal chemists in drug design.

bioinformatics↗

bioRxiv2024

Genetic and microbial analysis of invasiveness for Escherichia coli strains associated with inflammatory bowel disease

Background & AimsThe adherent-invasive Escherichia coli (AIEC) pathotype is implicated in inflammatory bowel disease (IBD) pathogenesis. AIEC strains are currently defined by phenotypic measurement of their pathogenicity, including invasion of epithelial cells. This broad definition, combined with the genetic diversity of AIEC across IBD patients, has complicated the identification of virulence determinants. We sought to quantify the invasion phenotype of clinical isolates from IBD patients and identify the genetic basis for their invasion into epithelial cells. MethodsA pangenome with core and accessory genes (genotype) was assembled using whole genome sequencing of 168 E. coli samples isolated from 13 IBD patients. A modified assay for invasion of epithelial cells (phenotype) was established with consideration of antibiotic resistance phenotypes. Isolate genotype was correlated to invasiveness phenotype to identify genetic factors that co-segregate with invasion. ResultsPangenome-wide comparisons of E. coli clinical isolates identified accessory genes that can co-segregate with invasion phenotype. These correlations found the acquisition of antibiotic resistance genes in clinical isolates compromised the traditional gentamicin protection assays used to quantify invasion. Therefore, an alternate assay, based on amikacin resistance, identified genes co-segregating with invasion. These genes encode an arylsulfatase, a glycoside hydrolase, and genetic islands carrying propanediol utilization and sulfoquinovose metabolism pathways. ConclusionsThis study highlights the importance of incorporating antibiotic resistance screening for invasion assays used in AIEC identification. Accurately screened invasion phenotypes identified accessory genome elements among E. coli IBD isolates that correlate with their ability to invade epithelial cells. These results help explain why single genetic markers for the AIEC phylotype are challenging to identify. SynopsisWe established a pangenome of Escherichia coli isolates from inflammatory bowel disease patients using whole genome sequencing. The genotypes were correlated with a newly developed measurement of invasion phenotype to identify co-segregating genes.

microbiology↗

bioRxiv2024

Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics

All-atom constant pH molecular dynamics simulations offer a powerful tool for understanding pH-mediated and proton-coupled biological processes. As the protonation equilibria of protein sidechains are shifted by electrostatic interactions and desolvation energies, pKa values calculated from the constant pH simulations may be sensitive to the underlying protein force field and water model. Here we investigated the force field dependence of the all-atom particle mesh Ewald (PME) continuous constant pH (PME-CpHMD) simulations of a mini-protein BBL. The replica-exchange titration simulations based on the Amber ff19sb and ff14sb force fields with the respective water models showed significantly overestimated pKa downshifts for a buried histidine (His166) and for two glutamic acids (Glu141 and Glu161) that are involved in salt-bridge interactions. These errors (due to undersolvation of neutral histidines and over-stabilization of salt bridges) are consistent with the previously reported pKas based on the CHARMM c22/CMAP force field, albeit in larger magnitudes. The pKa calculations also demonstrated that ff19sb with OPC water is significantly more accurate than ff14sb with TIP3P water, and the salt-bridge related pKa downshifts can be partially alleviated by the atom-pair specific Lennard-Jones corrections (NBFIX). Together, these data suggest that the accuracies of the protonation equilibria of proteins from constant pH simulations can significantly benefit from improvements of force fields.

biophysics↗

bioRxiv2024

Exploring the effect of capsaicin on gene expression and chemotherapy sensitization in gastric cancer cells

PurposeCapsaicin has previously been demonstrated to exhibit anti-tumor effect in various cancer type. However, the effect of capsaicin on gene expression and its potential mechanism on chemotherapy sensitization were still uncertain. MethodHuman AGS gastric cancer cell line was treated with different concentrations of capsaicin, 5-fluorouracil and oxaliplatin. Cell viability was assessed using cell viability assay. High throughput RNA sequencing was used to screen differentially expressed genes triggered by capsaicin in AGS cells. qPCR and Western blotting were used to detect the expression of mRNAs and proteins induced by capsaicin. ResultCapsaicin could significantly inhibit cell viability at a dose-depend manner in AGS gastric cancer cell line. Through high-throughput RNA sequencing, genes regulating DNA repair, DNA replication and chromosome assemble pathways were analyzed to be down-regulated by capsaicin. qPCR and western blot assay demonstrated that capsaicin could inhibit expression of the key enzymes (FEN1, LIG1 and PARP1) which play critical roles in DNA damage response and chemotherapy resistance. In vitro assay demonstrated that capsaicin could significantly induce chemo-sensitivity of 5-FU and Oxaliplatin at low dose. ConclusionCapsaicin could inhibit DNA repair pathway, which might contribute to cell growth inhibition and improvement of chemotherapy sensitization. These results revealed a novel function of capsaicin in DNA damage repair and provided new potential targets in cancer therapy.

cancer biology↗

bioRxiv2024

SUV39H1 Preserves Cancer Stem Cell Chromatin State and Properties in Glioblastoma

Of the more than 100 types of brain cancer, glioblastoma (GBM) is the deadliest. As GBM stem cells (GSCs) are considered to be responsible for therapeutic resistance and tumor recurrence, effective targeting and elimination of GSCs could hold promise for preventing GBM recurrence and achieving potential cures. We show here that SUV39H1, which encodes a histone-3, lysine-9 methyltransferase, plays a critical role in GSC maintenance and GBM progression. Upregulation of SUV39H1 was observed in GBM samples compared to normal brain tissues, and knockdown of SUV39H1 in patient-derived GSCs impaired their proliferation and stemness. Single-cell RNA-seq analysis demonstrated restricted expression of SUV39H1 is in GSCs relative to non-stem GBM cells, likely due to super-enhancer-mediated transcriptional activation, while whole cell RNA-seq analysis revealed that SUV39H1 regulates G2/M cell cycle progression, stem cell maintenance, and cell death pathways in GSCs. By integrating the RNA-seq data with ATAC-seq (assay for transposase-accessible chromatin followed by sequencing), we further demonstrated altered chromatin accessibility in key genes associated with these pathways following SUV39H1 knockdown. Treatment with chaetocin, a SUV39H1 inhibitor, mimicked the functional effects of SUV39H1 knockdown in GSCs and sensitized GSCs to the GBM chemotherapy drug temozolomide. Furthermore, targeting SUV39H1 in vivo using a patient-derived xenograft model for GBM inhibited GSC-driven tumor formation. This is the first report demonstrating a critical role for SUV39H1 in GSC maintenance. SUV39H1-mediated targeting of GSCs could enhance the efficacy of existing chemotherapy, presenting a promising strategy for improving GBM treatment and patient outcomes. HighlightsO_LISUV39H1 is upregulated in GBM, especially GSCs C_LIO_LITargeting SUV39H1 disrupts GSC maintenance and sensitizes GSCs to TMZ C_LIO_LITargeting SUV39H1 alters chromatin accessibility at cell cycle and stemness genes C_LIO_LITargeting SUV39H1 suppresses GSC-driven tumors in a patient-derived xenograft model C_LI

cancer biology↗

bioRxiv2024

A Self-Assembling Immune-Featured Osteosarcoma Patient/PDX Derived Organoid Model and Biobank for Personalized Immune Therapy

Osteosarcoma (OS) exhibit intra- and inter-heterogeneity, complicating the exploration of effective therapeutic strategies. Traditional in vitro and in vivo models are limited in inheriting biological and genomic heterogeneities of OS patients, even in inheriting the features on tumor microenvironment. The prolonged generation time of current models makes the drug development of OS slow and is not suitable to clinically rapid timing. Here, we introduce methods for generating and biobanking patient/PDX-derived osteosarcoma organoids (OS PD(X)Os) that recapitulate the histological, biological and genomic features of their paired OS patients. OS PD(X)Os can be generated quickly with high reliability in vitro or transplanted to immunodeficient mice. We further demonstrate an immune-featured OS PD(X)O (named iOS) model and its method for testing personalized chemotherapy response, personalized immune therapeutic strategy and target drug development, such as a novel PRMT5MTA inhibitor ARPN2169 on MTAP-deleted OS. Our studies show that iOS models maintain many typical features of OS and could be rapidly employed to investigate patient-specific therapeutic strategies. Additionally, our biobank establishes a rich resource for basic, translational and even clinical OS researches.

cancer biology↗

bioRxiv2024

F-CPI: Prediction of activity changes induced by fluorine substitution using multimodal deep learning

There are a large number of fluorine (F)-containing compounds in approved drugs, and F substitution is a common method in drug discovery and development. However, F is difficult to form traditional hydrogen bonds and typical halogen bonds. As a result, accurate prediction of the activity after F substitution is still impossible using traditional drug design methods, whereas artificial intelligence driven activity prediction might offer a solution. Although more and more machine learning and deep learning models are being applied, there is currently no model specifically designed to study the effect of F on bioactivities. In this study, we developed a specialized deep learning model, F-CPI, to predict the effect of introducing F on drug activity, and tested its performance on a carefully constructed dataset. Comparison with traditional machine learning models and popular CPI task models demonstrated the superiority and necessity of F-CPI, achieving an accuracy of approximately 89% and a precision of approximately 67%. In the end, we utilized F-CPI for the structural optimization of hit compounds against SARS-CoV-2 3CLpro. Impressively, in one case, the introduction of only one F atom resulted in a more than 100-fold increase in activity (IC50: 22.99 nM vs. 28190 nM). Therefore, we believe that F-CPI is a helpful and effective tool in the context of drug discovery and design.

bioinformatics↗

bioRxiv2024

c-di-GMP modulates ribosome assembly by inhibiting rRNA methylation

Cyclic diguanosine monophosphate (c-di-GMP) is a ubiquitous bacterial secondary messenger, with diverse functions, many of which are yet to be uncovered. Stemming from an Escherichia coli proteome microarray, we found that c-di-GMP bound to 23S rRNA methyltransferases (RlmI and RlmE). rRNA methylation assays showed that c-di-GMP inhibits RlmI activity, thereby modulating ribosome assembly. Based on molecular dynamic simulation and mutagenesis studies, we found that c-di-GMP binds to RlmI at residues R64, R103, G114, and K201. Structural simulation revealed that c-di-GMP quenches RlmI activity by inducing the closure of the catalytic pocket. Furthermore, we revealed that c-di-GMP promotes antibiotic tolerance by regulating RlmI activity, which played a role in antibiotic-resistant strains. Finally, the binding and methylation assays showed that the effect of c-di-GMP on RlmI is conserved, at least in various pathogenic bacteria. This study discovered an unexpected functional role of c-di-GMP in regulating ribosome assembly by inhibiting rRNA methylases. This study identified an unexpected but crucial member among the c-di-GMP effectors. HighlightsO_LIc-di-GMP regulates ribosome assembly in Escherichia coli. C_LIO_LIc-di-GMP inhibits rRNA methylation activity of RlmI by inducing catalytic pocket closure. C_LIO_LIc-di-GMP promotes antibiotic resistance by regulating ribosome assembly. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/597503v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@52baforg.highwire.dtl.DTLVardef@176f86dorg.highwire.dtl.DTLVardef@1afed11org.highwire.dtl.DTLVardef@f53687_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

An anti-SARS-CoV-2 formula consisting volatile oil from TCM through inhibiting multiple targets in cultured cells

At present, coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variant virus are prevalent all over the world, taking a major toll on human lives worldwide. Oral mucosa and saliva are the high-risk routes of transmission. Inactivation of the virus in the mouth is one of the important strategies to reduce the source infectivity of the virus. However, the current preparations for inactivating oral virus are mainly aimed at ACE2 protein, and its effect needs to be improved. In this study, through literature mining, multi-target screening and other methods, we screened and optimized the volatile oil formula (Artemisiae argyi folium, Chrysanthemum morifolium, Trollii chinensis flos, Lonicerae japonicae flos) from the volatile oil of traditional Chinese medicine to inhibit the invasion and replication of SARS-CoV-2. A test using pseudotype virus with S glycoprotein confirmed that this formula could effectively bind to the S glycoprotein to prevent SARS-CoV-2 host cell entry. Molecular docking experiments showed that the m ultiple molecules in the formula might weakly bind to these targets including ACE2, cathepsin L, furin, mpro/3cl. In all, the volatile oil formula designed in this paper offers a general affordable strategy to protect patients from SARS-CoV-2 infections through debulking or minimizing transmission to others.

pharmacology and toxicology↗