Sleep Deprivation Induces Gut Damage via Ferroptosis.

Sleep deprivation (SD) has been associated with a plethora of severe pathophysiological syndromes, including gut damage, which recently has been elucidated as an outcome of the accumulation of reactive oxygen species (ROS). However, the spatiotemporal analysis conducted in this study has intriguingly shown that specific events cause harmful damage to the gut, particularly to goblet cells, before the accumulation of lethal ROS. Transcriptomic and metabolomic analyses have identified significant enrichment of metabolites related to ferroptosis in mice suffering from SD. Further analysis revealed that melatonin could rescue the ferroptotic damage in mice by suppressing lipid peroxidation associated with ALOX15 signaling. ALOX15 knockout protected the mice from the serious damage caused by SD-associated ferroptosis. These findings suggest that melatonin and ferroptosis could be targets to prevent devastating gut damage in animals exposed to SD. To sum up, this study is the first report that proposes a noncanonical modulation in SD-induced gut damage via ferroptosis with a clearly elucidated mechanism and highlights the active role of melatonin as a potential target to maximally sustain the state during SD.

Cinnamaldehyde targets SarA to enhance ß-lactam antibiotic activity against methicillin-resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is a current global public health problem due to its increasing resistance to the most recent antibiotic therapies. One critical approach is to develop ways to revitalize existing antibiotics. Here, we show that the phytogenic compound cinnamaldehyde (CIN) and ß-lactam antibiotic combinations can functionally synergize and resensitize clinical MRSA isolates to ß-lactam therapy and inhibit MRSA biofilm formation. Mechanistic studies indicated that the CIN potentiation effect on ß-lactams was primarily the result of inhibition of the mecA expression by targeting the staphylococcal accessory regulator sarA. CIN alone or in combination with ß-lactams decreased sarA gene expression and increased SarA protein phosphorylation that impaired SarA binding to the mecA promoter element and downregulated virulence genes such as those encoding biofilm, α-hemolysin, and adhesin. Perturbation of SarA-mecA binding thus interfered with PBP2a biosynthesis and this decreased MRSA resistance to ß-lactams. Furthermore, CIN fully restored the anti-MRSA activities of ß-lactam antibiotics in vivo in murine models of bacteremia and biofilm infections. Together, our results indicated that CIN acts as a ß-lactam adjuvant and can be applied as an alternative therapy to combat multidrug-resistant MRSA infections.

Prevalence and molecular characteristics of intestinal pathogenic Escherichia coli isolated from diarrheal pigs in Southern China.

Intestinal pathogenic Escherichia coli (InPEC) is one of the most common causes of bacterial diarrhea in farm animals, including profuse neonatal diarrhea and post weaning diarrhea (PWD) in piglets. In this study, we investigated the prevalence of InPEC and associated primary virulence factors among 543 non-duplicate E. coli isolates from diarrheal pigs from 15 swine farms in southern China. Six major virulence genes associated with InPEC were identified among 69 (12.71 %) E. coli isolates and included est (6.62 %), K88 (4.79 %), elt (3.68 %), eae (1.47 %), stx2 (0.92 %) and F18 (0.55 %). Three pathotypes of InPEC were identified including ETEC (8.10 %), EPEC (1.29 %) and STEC/ETEC (0.92 %). In particular, K88 was only found in ETEC from breeding farms, whereas F18 was only present in STEC/ETEC hybrid from finishing farms. Whole genome sequence analysis of 37 E. coli isolates revealed that InPEC strains frequently co-carried multiple antibiotic resistance gene (ARG). est, elt and F18 were also found to co-locate with ARGs on a single IncFIB/IncFII plasmid. InPEC isolates from different pathotypes also possessed different profiles of virulence genes and antimicrobial resistance genes. Population structure analysis demonstrated that InPEC isolates from different pathotypes were highly heterogeneous whereas those of the same pathotype were extremely similar. Plasmid analysis revealed that K88 and/or est/elt were found on pGX18-2-like/pGX203-2-like and pGX203-1-like IncFII plasmids, while F18 and elt/est, as well as diverse ARGs were found to co-locate on IncFII/IncFIB plasmids with a non-typical backbone. Moreover, these key virulence genes were flanked by or adjacent to IS elements. Our findings indicated that both clonal expansion and horizontal spread of epidemic IncFII plasmids contributed to the prevalence of InPEC and the specific virulence genes (F4, F18, elt and est) in the tested swine farms.

Daily occupational exposure in swine farm alters human skin microbiota and antibiotic resistome.

Antimicrobial resistance (AMR) is a major threat to global public health, and antibiotic resistance genes (ARGs) are widely distributed across humans, animals, and environment. Farming environments are emerging as a key research area for ARGs and antibiotic resistant bacteria (ARB). While the skin is an important reservoir of ARGs and ARB, transmission mechanisms between farming environments and human skin remain unclear. Previous studies confirmed that swine farm environmental exposures alter skin microbiome, but the timeline of these changes is ill defined. To improve understanding of these changes and to determine the specific time, we designed a cohort study of swine farm workers and students through collected skin and environmental samples to explore the impact of daily occupational exposure in swine farm on human skin microbiome. Results indicated that exposure to livestock-associated environments where microorganisms are richer than school environment can reshape the human skin microbiome and antibiotic resistome. Exposure of 5 h was sufficient to modify the microbiome and ARG structure in workers' skin by enriching microorganisms and ARGs. These changes were preserved once formed. Further analysis indicated that ARGs carried by host microorganisms may transfer between the environment with workers' skin and have the potential to expand to the general population using farm workers as an ARG vector. These results raised concerns about potential transmission of ARGs to the broader community. Therefore, it is necessary to take corresponding intervention measures in the production process to reduce the possibility of ARGs and ARB transmission.

Mechanisms of gastrointestinal toxicity in neuromyelitis optica spectrum disorder patients treated with mycophenolate mofetil: insights from a mouse model and human study.

Mycophenolate mofetil (MMF) is commonly utilized for the treatment of neuromyelitis optica spectrum disorders (NMOSD). However, a subset of patients experience significant gastrointestinal (GI) adverse effects following MMF administration. The present study aims to elucidate the underlying mechanisms of MMF-induced GI toxicity in NMOSD. Utilizing a vancomycin-treated mouse model, we compiled a comprehensive data set to investigate the microbiome and metabolome in the GI tract to elucidate the mechanisms of MMF GI toxicity. Furthermore, we enrolled 17 female NMOSD patients receiving MMF, who were stratified into non-diarrhea NMOSD and diarrhea NMOSD (DNM) groups, in addition to 12 healthy controls. The gut microbiota of stool samples was analyzed using 16S rRNA gene sequencing. Vancomycin administration prevented weight loss and tissue injury caused by MMF, affecting colon metabolomes and microbiomes. Bacterial ß-glucuronidase from Bacteroidetes and Firmicutes was linked to intestinal tissue damage. The DNM group showed higher alpha diversity and increased levels of Firmicutes and Proteobacteria. The ß-glucuronidase produced by Firmicutes may be important in causing gastrointestinal side effects from MMF in NMOSD treatment, providing useful information for future research on MMF. IMPORTANCE: Neuromyelitis optica spectrum disorder (NMOSD) patients frequently endure severe consequences like paralysis and blindness. Mycophenolate mofetil (MMF) effectively addresses these issues, but its usage is hindered by gastrointestinal (GI) complications. Through uncovering the intricate interplay among MMF, gut microbiota, and metabolic pathways, this study identifies specific gut bacteria responsible for metabolizing MMF into a potentially harmful form, thus contributing to GI side effects. These findings not only deepen our comprehension of MMF toxicity but also propose potential strategies, such as inhibiting these bacteria, to mitigate these adverse effects. This insight holds broader implications for minimizing complications in NMOSD patients undergoing MMF therapy.

AutoESDCas: A Web-Based Tool for the Whole-Workflow Editing Sequence Design for Microbial Genome Editing Based on the CRISPR/Cas System.

Genome editing is the basis for the modification of engineered microbes. In the process of genome editing, the design of editing sequences, such as primers and sgRNA, is very important for the accurate positioning of editing sites and efficient sequence editing. The whole process of genome editing involves multiple rounds and types of editing sequence design, while the development of related whole-workflow design tools for high-throughput experimental requirements lags. Here, we propose AutoESDCas, an online tool for the end-to-end editing sequence design for microbial genome editing based on the CRISPR/Cas system. This tool facilitates all types of genetic manipulation covering diverse experimental requirements and design scenarios, enables biologists to quickly and efficiently obtain all editing sequences needed for the entire genome editing process, and empowers high-throughput strain modification. Notably, with its off-target risk assessment function for editing sequences, the usability of the design results is significantly improved. AutoESDCas is freely available at https://autoesdcas.biodesign.ac.cn/with the source code at https://github.com/tibbdc/AutoESDCas/.

Innovative Delivery System Combining CRISPR-Cas12f for Combatting Antimicrobial Resistance in Gram-Negative Bacteria.

Antimicrobial resistance poses a significant global challenge, demanding innovative approaches, such as the CRISPR-Cas-mediated resistance plasmid or gene-curing system, to effectively combat this urgent crisis. To enable successful curing of antimicrobial genes or plasmids through CRISPR-Cas technology, the development of an efficient broad-host-range delivery system is paramount. In this study, we have successfully designed and constructed a novel functional gene delivery plasmid, pQ-mini, utilizing the backbone of a broad-host-range Inc.Q plasmid. Moreover, we have integrated the CRISPR-Cas12f system into the pQ-mini plasmid to enable gene-curing in broad-host of bacteria. Our findings demonstrate that pQ-mini facilitates the highly efficient transfer of genetic elements to diverse bacteria, particularly in various species in the order of Enterobacterales, exhibiting a broader host range and superior conjugation efficiency compared to the commonly used pMB1-like plasmid. Notably, pQ-mini effectively delivers the CRISPR-Cas12f system to antimicrobial-resistant strains, resulting in remarkable curing efficiencies for plasmid-borne mcr-1 or blaKPC genes that are comparable to those achieved by the previously reported pCasCure system. In conclusion, our study successfully establishes and optimizes pQ-mini as a broad-host-range functional gene delivery vector. Furthermore, in combination with the CRISPR-Cas system, pQ-mini demonstrates its potential for broad-host delivery, highlighting its promising role as a novel antimicrobial tool against the growing threat of antimicrobial resistance.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

Concentration-resistance relationship and PK/PD evaluation of danofloxacin against emergence of resistant Pasteurella multocida in an in vitro dynamic model.

AIMS: This study aimed to assess the pharmacokinetic/pharmacodynamic (PK/PD) targets of danofloxacin to minimize the risk of selecting resistant Pasteurella multocida mutants and to identify the mechanisms underlying their resistance in an in vitro dynamic model, attaining the optimum dosing regimen of danofloxacin to improve its clinical efficacy based on the mutant selection window (MSW) hypothesis. METHODS AND RESULTS: Danofloxacin at seven dosing regimens and 5 days of treatment were simulated to quantify the bactericidal kinetics and enrichment of resistant mutants upon continuous antibiotic exposure. The magnitudes of PK/PD targets associated with different efficacies were determined in the model. The 24 h area under the concentration-time curve (AUC) to minimum inhibitory concentration (MIC) ratios (AUC24h/MIC) of danofloxacin associated with bacteriostatic, bactericidal and eradication effects against P. multocida were 34, 52, and 64 h. This translates to average danofloxacin concentrations (Cav) over 24 h being 1.42, 2.17, and 2.67 times the MIC, respectively. An AUC/MIC-dependent antibacterial efficacy and AUC/mutant prevention concentration (MPC)-dependent enrichment of P. multocida mutants in which maximum losses in danofloxacin susceptibility occurred at a simulated AUC24h/MIC ratio of 72 h (i.e. Cav of three times the MIC). The overexpression of efflux pumps (acrAB-tolC) and their regulatory genes (marA, soxS, and ramA) was associated with reduced susceptibility in danofloxacin-exposed P. multocida. The AUC24h/MPC ratio of 19 h (i.e. Cav of 0.8 times the MPC) was determined to be the minimum mutant prevention target value for the selection of resistant P. multocida mutants. CONCLUSIONS: The emergence of P. multocida resistance to danofloxacin exhibited a concentration-dependent pattern and was consistent with the MSW hypothesis. The current clinical dosing regimen of danofloxacin (2.5 mg kg-1) may have a risk of treatment failure due to inducible fluoroquinolone resistance.

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli.

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.

REME: an integrated platform for reaction enzyme mining and evaluation.

A key challenge in pathway design is finding proper enzymes that can be engineered to catalyze a non-natural reaction. Although existing tools can identify potential enzymes based on similar reactions, these tools encounter several issues. Firstly, the calculated similar reactions may not even have the same reaction type. Secondly, the associated enzymes are often numerous and identifying the most promising candidate enzymes is difficult due to the lack of data for evaluation. Thirdly, existing web tools do not provide interactive functions that enable users to fine-tune results based on their expertise. Here, we present REME (https://reme.biodesign.ac.cn/), the first integrated web platform for reaction enzyme mining and evaluation. Combining atom-to-atom mapping, atom type change identification, and reaction similarity calculation enables quick ranking and visualization of reactions similar to an objective non-natural reaction. Additional functionality enables users to filter similar reactions by their specified functional groups and candidate enzymes can be further filtered (e.g. by organisms) or expanded by Enzyme Commission number (EC) or sequence homology. Afterward, enzyme attributes (such as kcat, Km, optimal temperature and pH) can be assessed with deep learning-based methods, facilitating the swift identification of potential enzymes that can catalyze the non-natural reaction.

Antimicrobial resistance and population genomics of emerging multidrug-resistant Salmonella 4,[5],12:i:- in Guangdong, China.

Salmonella 4,[5],12:i:-, a monophasic variant of Salmonella Typhimurium, has emerged as a global cause of multidrug-resistant salmonellosis and has become endemic in many developing and developed countries, especially in China. Here, we have sequenced 352 clinical isolates in Guangdong, China, during 2009-2019 and performed a large-scale collection of Salmonella 4,[5],12:i:- with whole genome sequencing (WGS) data across the globe, to better understand the population structure, antimicrobial resistance (AMR) genomic characterization, and transmission routes of Salmonella 4,[5],12:i:- across Guangdong. Salmonella 4,[5],12:i:- strains showed broad genetic diversity; Guangdong isolates were found to be widely distributed among the global lineages. Of note, we identified the formation of a novel Guangdong clade (Bayesian analysis of population structure lineage 1 [BAPS1]) genetically diversified from the global isolates and likely emerged around 1990s. BAPS1 exhibits unique genomic features, including large pan-genome, decreased ciprofloxacin susceptibility due to mutation in gyrA and carriage of plasmid-mediated quinolone resistance (PMQR) genes, and the multidrug-resistant IncHI2 plasmid. Furthermore, high genetic similarity was found between strains collected from Guangdong, Europe, and North America, indicating the association with multiple introductions from overseas. These results suggested that global dissemination and local clonal expansion simultaneously occurred in Guangdong, China, and horizontally acquired resistance to first-line and last-line antimicrobials at local level, underlying emergences of extensive drug and pan-drug resistance. Our findings have increased the knowledge of global and local epidemics of Salmonella 4,[5],12:i:- in Guangdong, China, and provided a comprehensive baseline data set essential for future molecular surveillance.IMPORTANCESalmonella 4,[5],12:i:- has been regarded as the predominant pandemic serotype causing diarrheal diseases globally, while multidrug resistance (MDR) constitutes great public health concerns. This study provided a detailed and comprehensive genome-scale analysis of this important Salmonella serovar in the past decade in Guangdong, China. Our results revealed the complexity of two distinct transmission modes, namely global transmission and local expansion, circulating in Guangdong over a decade. Using phylogeography models, the origin of Salmonella 4,[5],12:i:- was predicted from two aspects, year and country, that is, Salmonella 4,[5],12:i:- emerged in 1983, and was introduced from the UK, and subsequently differentiated into the local endemic lineage circa 1991. Additionally, based on the pan-genome analysis, it was found that the gene accumulation rate in local endemic BAPS 1 lineage was higher than in other lineages, and the horizontal transmission of MDR IncHI2 plasmid associated with high resistance played a major role, which showed the potential threat to public health.

Tobramycin-resistant small colony variant mutant of Salmonella enterica serovar Typhimurium shows collateral sensitivity to nitrofurantoin.

The increasing antibiotic resistance poses a significant global health challenge, threatening our ability to combat infectious diseases. The phenomenon of collateral sensitivity, whereby resistance to one antibiotic is accompanied by increased sensitivity to another, offers potential avenues for novel therapeutic interventions against infections unresponsive to classical treatments. In this study, we elucidate the emergence of tobramycin (TOB)-resistant small colony variants (SCVs) due to mutations in the hemL gene, which render S. Typhimurium more susceptible to nitrofurantoin (NIT). Mechanistic studies demonstrate that the collateral sensitivity in TOB-resistant S. Typhimurium SCVs primarily stems from disruptions in haem biosynthesis. This leads to dysfunction in the electron transport chain (ETC) and redox imbalance, ultimately inducing lethal accumulation of reactive oxygen species (ROS). Additionally, the upregulation of nfsA/B expressions facilitates the conversion of NIT prodrug into its active form, promoting ROS-mediated bacterial killing and contributing to this collateral sensitivity pattern. Importantly, alternative NIT therapy demonstrates a significant reduction of bacterial load by more than 2.24-log10 cfu/g in the murine thigh infection and colitis models. Our findings corroborate the collateral sensitivity of S. Typhimurium to nitrofurans as a consequence of evolving resistance to aminoglycosides. This provides a promising approach for treating infections due to aminoglycoside-resistant strains.

DeepSub: Utilizing Deep Learning for Predicting the Number of Subunits in Homo-Oligomeric Protein Complexes.

The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from Corynebacterium glutamicum, Matrilin-4 from Mus musculus and Homo sapiens, and the Multimerins protein family from M. musculus and H. sapiens. The predicted results align closely with the reported findings in the literature, underscoring the reliability and utility of DeepSub.

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models.

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we've enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package (https://pypi.org/project/ECMpy/).

Pharmacokinetic/pharmacodynamic evaluation of gamithromycin against rabbit pasteurellosis.

BACKGROUND: Gamithromycin is an effective therapy for bovine and swine respiratory diseases but not utilized for rabbits. Given its potent activity against respiratory pathogens, we sought to determine the pharmacokinetic profiles, antimicrobial activity and target pharmacokinetic/pharmacodynamic (PK/PD) exposures associated with therapeutic effect of gamithromycin against Pasteurella multocida in rabbits. RESULTS: Gamithromycin showed favorable PK properties in rabbits, including high subcutaneous bioavailability (86.7 ± 10.7%) and low plasma protein binding (18.5-31.9%). PK analysis identified a mean plasma peak concentration (Cmax) of 1.64 ± 0.86 mg/L and terminal half-life (T1/2) of 31.5 ± 5.74 h after subcutaneous injection. For P. multocida, short post-antibiotic effects (PAE) (1.1-5.3 h) and post-antibiotic sub-inhibitory concentration effects (PA-SME) (6.6-9.1 h) were observed after exposure to gamithromycin at 1 to 4× minimal inhibitory concentration (MIC). Gamithromycin demonstrated concentration-dependent bactericidal activity and the PK/PD index area under the concentration-time curve over 24 h (AUC24h)/MIC correlated well with efficacy (R2 > 0.99). The plasma AUC24h/MIC ratios of gamithromycin associated with the bacteriostatic, bactericidal and bacterial eradication against P. multocida were 15.4, 24.9 and 27.8 h in rabbits, respectively. CONCLUSIONS: Subcutaneous administration of 6 mg/kg gamithromycin reached therapeutic concentrations in rabbit plasma against P. multocida. The PK/PD ratios determined herein in combination with ex vivo activity and favorable rabbit PK indicate that gamithromycin may be used for the treatment of rabbit pasteurellosis.

Dual-ROS Sensitive Moieties Conjugate Inhibits Curcumin Oxidative Degradation for Colitis Precise Therapy.

Curcumin, a natural bioactive polyphenol with diverse molecular targets, is well known for its anti-oxidation and anti-inflammatory potential. However, curcumin exhibits low solubility (<1 µg mL-1), poor tissue-targeting ability, and rapid oxidative degradation, resulting in poor bioavailability and stability for inflammatory therapy. Here, poly(diselenide-oxalate-curcumin) nanoparticle (SeOC-NP) with dual-reactive oxygen species (ROS) sensitive chemical moieties (diselenide and peroxalate ester bonds) is fabricated by a one-step synthetic strategy. The results confirmed that dual-ROS sensitive chemical moieties endowed SeOC-NP with the ability of targeted delivery of curcumin and significantly suppress oxidative degradation of curcumin for high-efficiency inflammatory therapy. In detail, the degradation amount of curcumin for SeOC is about 4-fold lower than that of free curcumin in an oxidative microenvironment. As a result, SeOC-NP significantly enhanced the antioxidant activity and anti-inflammatory efficacy of curcumin in vitro analysis by scavenging intracellular ROS and suppressing the secretion of nitric oxide and pro-inflammatory cytokines. In mouse colitis models, orally administered SeOC-NP can remarkably alleviate the symptoms of IBD and maintain the homeostasis of gut microbiota. This work provided a simple and effective strategy to fabricate ROS-responsive micellar and enhance the oxidation stability of medicine for precise therapeutic inflammation.

Omics data analysis reveals the system-level constraint on cellular amino acid composition.

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.

Chitosan oligosaccharide accelerates the dissemination of antibiotic resistance genes through promoting conjugative plasmid transfer.

The dissemination of antibiotic resistance genes (ARGs), especially via plasmid-mediated horizontal gene transfer, poses a pervasive threat to global health. Chitosan-oligosaccharide (COS) is extensively utilized in medicine, plant and animal husbandry. However, their impact on microflora implies the potential to exert selective pressure on plasmid transfer. To explore the role of COS in facilitating the dissemination of ARGs via plasmid conjugation, we established in vitro mating models. The addition of COS to conjugation mixtures significantly enhanced the transfer of RP4 plasmid and mcr-1 positive IncX4 plasmid in both intra- and inter-specific. Phenotypic and transcriptome analysis revealed that COS enhanced intercellular contact by neutralizing cell surface charge and increasing cell surface hydrophobicity. Additionally, COS increased membrane permeability by inhibiting the Tol-Pal system, thereby facilitating plasmid conjugative transfer. Furthermore, COS served as the carbon source and was metabolized by E. coli, providing energy for plasmid conjugation through regulating the expression of ATPase and global repressor factor-related genes in RP4 plasmid. Overall, these findings improve our awareness of the potential risks associated with the presence of COS and the spread of bacterial antibiotic resistance, emphasizing the need to establish guidelines for the prudent use of COS and its discharge into the environment.