Rejuvenating macrophages by FcγR-targeted tuftsin clusters for sepsis-associated secondary infection
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PROVIDER: PRJNA1300452 | ENA |
REPOSITORIES: ENA
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Project description:Sepsis-associated secondary infection often leads to high mortality rate. Dysfunctional macrophages are the primary contributors to inadequate antimicrobial defense in patients with sepsis-associated immunosuppression, elevating the risk of opportunistic infections. Here, a bacteria-targeted transformable macrophage nanorejuvenator, designated as BATMAN, is developed to effectively tackle sepsis-associated secondary infection by coordinating the arrest of invasive bacteria and rejuvenation of dysfunctional macrophages based on the in situ responsive transformation. The results affirm the BATMAN’s effective binding and transformation on bacterial surface. The enzymatic removal of Chems by bacterial lipase disrupts the hydrophilic-hydrophobic balance of BATMAN, triggering the inside-out rearrangement that exposes the concealed tuftsin and facilitates its assembly into polyvalent peptide clusters on trapped bacteria driven by the assembly of FFVLK. This transformation not only reduces the invasiveness of bacteria, but enhances the interaction between tuftsin clusters and macrophage Fc gamma receptor, promoting bacterial phagocytosis and driving macrophage repolarization. As a result, BATMAN rejuvenates the compromised immune response to eliminate the polymicrobial- and even multidrug-resistant pathogen-induced pulmonary infection in septic mice. Consequently, we anticipate that our in situ transformable BATMAN holds promise as a therapeutic alternative for sepsis-associated secondary infection.
2025-08-08 | GSE304357 | GEO
Project description:Nucleation and spreading rejuvenate polycomb domains every cell cycle
2023-12-01 | GSE209818 | GEO
Project description:Senolytics rejuvenate aging cardiomyopathy in human cardiac organoids
| PRJNA1176030 | ENA
Project description:Introduction: Various immune cell types play critical roles in sepsis with numerous distinct subsets exhibiting unique phenotypes even within the same cell population. In this study, we have unveiled the transcriptomic landscape of immune cells in sepsis through single-cell RNA sequencing (scRNA-seq) analysis. We induced sepsis in mice by cecal ligation and puncture. 20 h after the surgery, the spleen and peritoneal lavage were collected. Single-cell suspensions were processed using a 10× Genomics pipeline and sequenced on an Illumina platform. Count matrices were generated using the CellRanger pipeline, which maps reads to the mouse reference transcriptome, GRCm38/mm10. Subsequent scRNA-seq analysis was performed using the R package Seurat. Results: After quality control, we subjected the entire data set to unsupervised classification.Four major clusters were identified as neutrophils, macrophages, B cells, and T cells according to their putative markers. Based on the differentially-expressed genes, we identified activated pathways in sepsis for each cell type. In neutrophils, pathways related to inflammatory signaling, such as NF‐κB and responses to pathogen-associated molecular patterns (PAMPs), cytokines and hypoxia were activated. In macrophages, activated pathways were the ones related to cell aging, inflammatory signaling and responses to PAMPs. In B cells, pathways related to endoplasmic reticulum stress were activated. In T cells, activated pathways were the ones related to inflammatory signaling, responses to PAMPs, and acute lung injury. Next, we further classified each cell type into subsets. Neutrophils consisted of four clusters. Some subsets were activated in inflammatory signaling or cell metabolism, while others possessed immunoregulatory or aging properties. Macrophages consisted of four clusters, including the ones with enhanced aging, lymphocyte activation, extracellular matrix organization, or cytokine activity. B cells consisted of four clusters, including the ones possessing the phenotype of cell maturation or aging. T cells consisted of six clusters, whose phenotypes include molecular translocation or cell activation. Conclusions: Transcriptomic analysis by scRNA-seq has unveiled a comprehensive spectrum of immune cell responses and distinct subsets in the context of sepsis. These findings are poised to enhance our understanding of sepsis pathophysiology, offering avenues for targeting novel molecules, cells, and pathways to combat infectious disease.
2024-01-09 | GSE249975 | GEO
Project description:Background The heterogeneity of sepsis represents a major problem for the development of personalized sepsis therapies. Thus, sepsis subtyping emerged as an important tool to approach this problem, but little progress was made due to insufficient molecular insights. Modern proteomics techniques allow the identification of subtypes and enable molecular and mechanistical insights. Here, we analyzed a prospective multi-center sepsis cohort using plasma proteomics to describe and characterize sepsis plasma proteome subtypes. Methods Plasma samples from 333 patients collected at days 1 and 4 of sepsis were analyzed using liquid-chromatography coupled to tandem mass spectrometry. Plasma proteome subtypes were identified using K-means clustering and were characterized based on clinical routine data, cytokine measurements and proteomics data. A random forest machine learning (ML) classifier was generated to enable the assignment of patients to the subtypes in future. Results Four subtypes with different sepsis severity were identified. Cluster 0 represented the most severe sepsis with 100 % mortality. Cluster 1, 2 and 3 showed a gradual decrease of the median SOFA score, which was reflected by clinical data and cytokine measurements. On the proteome level, the subtypes were characterized by distinct molecular features. We found an alternating immune response with cluster 1 showing prominent activation of the adaptive immune system as indicated by elevated levels of immunoglobulins (Ig) that were verified using orthogonal measurements. Cluster 2 was characterized by acute inflammation and the lowest Ig levels. Cluster 3 represented the sepsis proteome baseline of the investigated cohort. We generated a ML classifier and optimized it for a minimum number of proteins that could realistically be implemented into routine diagnostics. The final model was based on 10 proteins and Ig quantities and allowed the assignment of patients to clusters 1, 2 and 3 with high confidence. Conclusion The identified plasma proteome subtypes provide insights into immune response and disease mechanisms and allow conclusions on appropriate therapeutic measures. Thus, they represent a step forward in the development of targeted therapies and personalized medicine in sepsis.
2025-09-04 | PXD064125 | Pride
Project description:Diverse expression patterns for secondary metabolism gene clusters from Aspergillus flavus under different environmental conditions and in genetic mutants: Insights into regulation of cyclopiazonic acid along with aflatoxin Species of Aspergillus produce a diverse array of secondary metabolites, and recent genomic analysis predicts that these species have the capacity to synthesize many more compounds. It has been possible to infer the presence of 55 gene clusters associated with secondary metabolism in A. flavus. Presumably, secondary metabolites play important roles in the ecology of the producing species, but functions for most secondary metabolites remain unknown. Only three metabolic pathways have been associated with the predicted clusters in A. flavus. These include aflatoxin, cyclopiazonic acid (CPA), and aflatrem. To gain insight into the regulation of, and infer ecological significance for the 55 secondary metabolite gene clusters predicted in A. flavus, we examined their expression over 28 diverse conditions. Variables included culture media and temperature, fungal development, colonization of developing maize seeds, and misexpression of laeA, the global regulator of secondary metabolism. Hierarchical clustering analysis of expression profiles allowed us to categorize the gene clusters into four distinct clades. Gene clusters for the production of aflatoxins, CPA, and seven other unknown compound(s) were identified as belonging to one clade. To further explore the relationships found by gene expression analysis, aflatoxin and CPA production were quantified under five different cell culture environments known to be conducive or non-conducive for aflatoxin biosynthesis and during colonization of developing maize seeds. Results from these studies showed that secondary metabolism gene clusters have distinctive gene expression profiles. Aflatoxin and CPA were found to have unique regulation but similar enough that they would be expected to co-occur in commodities colonized with A. flavus.
2010-03-11 | GSE15435 | GEO
Project description:The temporal evolution of sepsis was monitored by transcriptional profiling of five critically ill children with meningococcal sepsis and sepsis-induced multiple organ failure. Blood was sampled at 6 time points during the first 48 hours of their admission to pediatric intensive care, where the children received standard clinical treatment including organ support and antimicrobial therapy. Striking transcript instability was observed over the 48 hours, with increasing numbers of regulated genes over time. Most notably, proposed biomarkers for sepsis risk stratification also showed expression instability, with varied expression levels over 48 hours. This study demonstrates the extent of the complexity of temporal changes in gene expression that occur during the evolution of sepsis-induced multiple organ failure. Importantly, stratification tools that propose expression of biomarkers must take into account the temporal changes, over the use of single snapshots that may be less informative.
2013-04-03 | E-MEXP-3850 | biostudies-arrayexpress
Project description:Nucleation and spreading rejuvenate polycomb domains every cell cycle
| PRJNA862526 | ENA
Project description:The peripheral whole-blood transcriptome analysis has been used to the identification of biomarkers in different clinical syndromes. The ability to simultaneously measure the gene expression levels of the whole transcriptome is providing a broader view of clinical syndromes such as sepsis. The aim of this study was to assess the gene expression profiles of post-surgical patients with septic shock compared with patients without sepsis.
2014-03-15 | E-MTAB-1548 | biostudies-arrayexpress
Project description:Background: Sepsis therapy is still limited to the treatment of the underlying infection and supportive measures. Therapeutic options that address the molecular changes of sepsis have not yet been identified. With the aim of a future individualized therapy, we used unsupervised machine learning (ML) to identify clinical phenotypes in a prospective multicenter cohort of patients with sepsis and characterized them using plasma proteomics. Methods: Routine clinical data and blood samples were collected from 384 sepsis patients. Clinical phenotypes were identified based on clinical routine measurements using the k-means algorithm. In addition, plasma samples from 276 patients were analyzed using liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). The obtained data were analyzed and interpreted in the context of the phenotypes and supervised ML classifiers were developed to prospectively allocate patients to the clusters and to identify the most important features for discrimination of the phenotypes. Results: Three clinical phenotypes were identified. Cluster C was characterized by the highest disease severity and multi-organ failure with leading liver failure and a mortality rate of 92%. Cluster B, with a mortality rate of 45 %, also showed relevant organ failure, with renal damage being particularly prominent in comparison to cluster A. The plasma proteome reflected the clinical features of the phenotypes and revealed the excessive consumption of complement and coagulation factors in severe sepsis. Supervised ML and feature importance analysis underlined these findings and highlighted specific clinical measures and proteins. Conclusions: ML identified clinical phenotypes showed different degrees of sepsis severity and could be translated to the plasma proteome. Plasma proteomics provided novel insights into the molecular processes of sepsis and allowed the characterization of the phenotypes. This approach represents a blueprint to identify molecular features of sepsis subgroups and may pave the way for future targeted sepsis therapy.
2025-12-08 | PXD058562 | Pride