Metabolomics Coupled with Transcriptomics Approach Deciphering Age Relevance in Sepsis

Novel applications of metformin in the treatment of septic myocardial injury based on metabolomics and network pharmacology

BACKGROUND

While metformin has shown promise in treating septic myocardial injury (SMI), its underlying mechanisms and impact on metabolic disturbances remain poorly understood.

METHODS

This study employed an integrated approach of metabolomics and network pharmacology to identify key targets and pathways through which metformin may act against SMI. Findings were validated using a lipopolysaccharide (LPS)-induced mouse model.

RESULTS

Metformin was found to counter myocardial metabolic disruptions, indicated by the reversal of 49 metabolites primarily involved in purine metabolism, pantothenate and CoA biosynthesis, and histidine metabolism. In vivo, metformin significantly improved survival rates and cardiac function, reduced cardiomyocyte apoptosis, and inhibited inflammation and oxidative stress in LPS-induced mice. Integrated analyses identified 27 potential targets for metformin in SMI treatment. KEGG pathway analysis revealed significant enrichment in TNF, HIF-1, IL-17, and PI3K/AKT signaling pathways, while protein-protein interaction analysis pinpointed ten core targets, including IL6, IL1B, CCL2, CASP3, MMP9, HIF1A, IGF1, NOS3, MMP2, and LEP. Molecular docking and dynamics simulations demonstrated metformin's high affinity for these core targets. Further, RT-qPCR and Western blot analyses confirmed that metformin modulates core target expression to mitigate SMI. Notably, our data underscore the importance of PI3K/AKT and MMP2/MMP9 signaling pathways in SMI therapy.

CONCLUSION

This study elucidates the metabolic and molecular mechanisms of metformin in SMI treatment, supporting its potential repurposing for SMI.

Effect of neutrophil to lymphocyte ratio on prognosis of elderly patients with severe sepsis combined with diabetes mellitus

BACKGROUND

To investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) in the short-term prognosis of elderly patients with severe sepsis combined with diabetes mellitus (DM).

METHODS

The clinical data of 162 elderly patients with severe sepsis combined with DM from January 2018 to December 2022 were retrospectively collected. These patients were divided into a survival group (n = 104) and a death group (n = 58) according to 90-day prognosis. The number of neutrophils, lymphocytes, and NLR were compared. The optimal cut-off value for NLR to predict 90-day prognosis in elderly patients with severe sepsis combined with DM was determined using Receiver Operator Characteristic (ROC) curves, and the patients were divided into high and low NLR groups depending on the optimal cut-off value. The Kaplan-Meier method was used to plot the survival curves of the high and low NLR groups. Risk factors for the 90-day death in elderly patients with severe sepsis combined with DM were analyzed by a multivariate cox regression model.

RESULTS

There were no significant differences in gender, age, history of hypertension and hyperlipidemia, intensive care unit (ICU) stay, duration of mechanical ventilation, and oxygenation index between the survival group and death group (p > 0.05). However, acute physiological and chronic health evaluation II (APACHE II) scores, and sepsis-related organ failure assessment (SOFA) scores were significantly lower in the survival group compared with the death group (p < 0.05). In the survival group, neutrophils counts and NLR were much lower than those in the death group, while lymphocytes counts were much higher (p < 0.05). ROC curves showed that the optimal cut-off value for NLR to predict 90-day mortality in elderly patients with severe sepsis combined with DM was 3.482. Patients were divided into high NLR and low NLR groups based on whether NLR was ≥ 3.482. In terms of the log-rank test results, patients in the low NLR group had a significantly higher 90-day survival rate than those in the high NLR group (Logrank χ2 = 8.635, p = 0.003). The multivariate cox regression model showed that the length of ICU stay longer than 15 days and NLR ≥ 3.482 were independent risk factors for 90-day prognosis in elderly patients with severe sepsis combined with DM.

CONCLUSION

NLR ≥ 3.482 can be used to predict whether poor prognosis occurs in the short term after illness in elderly patients with severe sepsis combined with DM, and has good assessment value.

Cellular specificity of lactate metabolism and a novel lactate-related gene pair index for frontline treatment in clear cell renal cell carcinoma

BACKGROUND

Although lactate metabolism-related genes (LMRGs) have attracted attention for their effects on cancer immunity, little is known about their function in clear cell renal cell carcinoma (ccRCC). The aim of this study was to examine the cellular specificity of lactate metabolism and how it affected the first-line treatment outcomes in ccRCC.

METHODS

GSE159115 was used to examine the features of lactate metabolism at the single-cell level. Utilizing the transcriptome, methylation profile, and genomic data from TCGA-KIRC, a multi-omics study of LMRG expression characteristics was performed. A prognostic index based on a gene-pair algorithm was created to assess how LMRGs affected patients' clinical outcomes. To simulate the relationship between the prognostic index and the frontline treatment, pRRophetic and Subclass Mapping were used. E-MTAB-1980, E-MTAB-3267, Checkmate, and Javelin-101 were used for external validation.

RESULTS

The variable expression of some LMRGs in ccRCC can be linked to variations in DNA copy number or promoter methylation levels. Lactate metabolism was active in tumor cells and vSMCs, and LDHA, MCT1, and MCT4 were substantially expressed in tumor cells, according to single-cell analysis. The high-risk patients would benefit from immune checkpoint blockade monotherapy (ICB) and ICB plus tyrosine kinase inhibitors (TKI) therapy, whereas the low-risk individuals responded to mTOR-targeted therapy.

CONCLUSIONS

At the single-cell level, our investigation demonstrated the cellular specificity of lactate metabolism in ccRCC. We proposed that the lactate-related gene pair index might be utilized to identify frontline therapy responders in ccRCC patients as well as predict prognosis.

Alterations in the Gut Microbiome and Metabolisms in Pregnancies with Fetal Growth Restriction

Fetuses diagnosed with fetal growth restriction (FGR) are at an elevated risk of stillbirth and adulthood morbidity. Gut dysbiosis has emerged as one of the impacts of placental insufficiency, which is the main cause of FGR. This study aimed to characterize the relationships among the intestinal microbiome, metabolites, and FGR. Characterization was conducted on the gut microbiome, fecal metabolome, and human phenotypes in a cohort of 35 patients with FGR and 35 normal pregnancies (NP). The serum metabolome was analyzed in 19 patients with FGR and 31 normal pregnant women. Multidimensional data was integrated to reveal the links between data sets. A fecal microbiota transplantation mouse model was used to determine the effects of the intestinal microbiome on fetal growth and placental phenotypes. The diversity and composition of the gut microbiota were altered in patients with FGR. A group of microbial species altered in FGR closely correlated with fetal measurements and maternal clinical variables. Fecal and serum metabolism profiles were distinct in FGR patients compared to those in the NP group. Altered metabolites were identified and associated with clinical phenotypes. Integrated multi-omics analysis revealed the interactions among gut microbiota, metabolites, and clinical measurements. Microbiota from FGR gravida transplanted to mice progestationally induced FGR and placental dysfunction, including impaired spiral artery remodeling and insufficient trophoblast cell invasion. Taken together, the integration of microbiome and metabolite profiles from the human cohort indicates that patients with FGR endure gut dysbiosis and metabolic disorders, which contribute to disease pathogenesis. IMPORTANCE Downstream of the primary cause of fetal growth restriction are placental insufficiency and fetal malnutrition. Gut microbiota and metabolites appear to play an important role in the progression of gestation, while dysbiosis induces maternal and fetal complications. Our study elaborates the significant differences in microbiota profiles and metabolome characteristics between women with FGR and normal pregnancies. This is the first attempt so far that reveals the mechanistic links in multi-omics in FGR, providing a novel insight into host-microbe interaction in placenta-derived diseases.

Exploration of the Shared Gene Signatures between Myocardium and Blood in Sepsis: Evidence from Bioinformatics Analysis

Background

Septic cardiomyopathy is widespread during sepsis and has adverse effects on mortality. Diagnosis of septic cardiomyopathy now mainly depends on transthoracic echocardiogram. Although some laboratory tests such as troponin T and atrial brain natriuretic peptide play a role in the diagnosis, specific blood biochemistry biomarkers are still lacking. Objective and Methods. In our study, we sought to find potential biological markers from genes and pathways that are covariant in the blood and myocardium of septic patients. Bioinformatics and machine learning methods were applied to achieve our goal. Datasets of myocardium and peripheral blood of patients with sepsis were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected and received functional enrichment analysis. Unsupervised hierarchical clustering analysis was performed to identify the subtypes of sepsis. Random forest, lasso regression, and logistic regression were used for variable screening and model construction. Internal and external validation sets were applied to verify the efficiency of the model in classifying disease and predicting mortality.

Results

By defining significance for genes using Student's t-test, we obtained 1,049 genes commonly changed in both myocardium and blood of patients with sepsis. The upregulated genes (LogFC >0) were related to inflammation pathways, and downregulated (LogFC <0) genes were related to mitochondrial and aerobic metabolism. We divided 468 sepsis patients into two groups with different clinical result based on the mortality-related commonly changed genes (104 genes), using unsupervised hierarchical clustering analysis. In our validation datasets, a six-gene model (SMU1, CLIC3, SP100, ARHGAP25, DECR1, and TNS3) was obtained and proven to perform well in classifying groups and predicting mortality.

Conclusion

We have identified genes that have the potential to become biomarkers for septic cardiomyopathy. Additionally, the pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis.

Targeting HMGB1 for the treatment of sepsis and sepsis-induced organ injury

High mobility group box 1 (HMGB1) is a ubiquitous nuclear protein that is present in almost all cells and regulates the activity of innate immune responses in both intracellular and extracellular settings. Current evidence suggests that HMGB1 plays a pivotal role in human pathological and pathophysiological processes such as the inflammatory response, immune reactions, cell migration, aging, and cell death. Sepsis is a systemic inflammatory response syndrome (SIRS) that occurs in hosts in response to microbial infections with a proven or suspected infectious etiology and is the leading cause of death in intensive care units worldwide, particularly in the aging population. Dysregulated systemic inflammation is a classic characteristic of sepsis, and suppression of HMGB1 may ameliorate inflammation and improve patient outcomes. Here, we focus on the latest breakthroughs regarding the roles of HMGB1 in sepsis and sepsis-related organ injury, the ways by which HMGB1 are released, and the signaling pathways and therapeutics associated with HMGB1. This review highlights recent advances related to HMGB1: the regulation of HMBG1 might be helpful for both basic research and drug development for the treatment of sepsis and sepsis-related organ injury.

Gender-specific association of oxidative stress and immune response in septic shock mortality using NMR-based metabolomics

Background: Sepsis and septic shock are still associated with a high mortality rate. The early-stage prediction of septic shock outcomes would be helpful to clinicians for designing their treatment protocol. In addition, it would aid clinicians in patient management by understanding gender disparity in terms of clinical outcomes of septic shock by identifying whether there are sex-based differences in sepsis-associated mortality. Objective: This study aimed to test the hypothesis that gender-based metabolic heterogeneity is associated with sepsis survival and identify the biomarkers of mortality for septic shock in an Indian cohort. Method: The study was performed in an Indian population cohort diagnosed with sepsis/septic shock within 24 hours of admission. The study group was 50 patients admitted to intensive care, comprising 23 females and 27 males. Univariate and multivariate analysis were performed to identify the biomarkers for septic shock mortality and the gender-specific metabolic fingerprint in septic shock-associated mortality. Results: The energy-related metabolites, ketone bodies, choline, and NAG were found to be primarily responsible for differentiating survivors and non-survivors. The gender-based mortality stratification identified a female-specific association of the anti-inflammatory response, innate immune response, and β oxidation, and a male-specific association of the pro-inflammatory response to septic shock. Conclusion: The identified mortality biomarkers may help clinicians estimate the severity of a case, as well as predict the outcome and treatment efficacy. The study underlines that gender is one of the most significant biological factors influencing septic shock metabolomic profiles. This understanding can be utilized to identify novel gender-specific biomarkers and innovative targets relevant for gender medicine.

Circulating Metabolomic Analysis following Cecal Ligation and Puncture in Young and Aged Mice Reveals Age-Associated Temporal Shifts in Nicotinamide and Histidine/Histamine Metabolic Pathways

INTRODUCTION

Aged individuals are at higher risk for morbidity and mortality following acute stressors than similarly stressed young people. Evaluation of age-associated metabolic changes could lead to the identification of specific therapeutic targets to improve outcomes from acute stressors, such as infections, in the elderly. We thus compared the plasma metabolomes of both young and old mice following cecal ligation and puncture (CLP), an accepted model of acute infection and stress.

METHODS

Young (9-17 wks) and aged (78-96 wks) male C57bl/6 mice were subjected to a retro-orbital bleed and two-week recovery prior to sham surgery (laparotomy alone) or CLP. Animals were sacrificed at 4 h, 8 h, or 12 h following intervention, and plasma was isolated from blood for subsequent analysis. Metabolomic analysis of samples were performed (Metabolon; Durham, NC).

RESULTS

Aged animals demonstrated greater intraprocedural mortality than young (30.2% vs. 17.4%, χ ² p = 0.0004), confirming enhanced frailty. Principal component analysis and partial-least squares discriminant analysis of 566 metabolites demonstrated distinct metabolomic shifts following sham surgery or CLP in both young and aged animals. Identification of metabolites of interest using a consensus statistical approach revealed that both the histidine/histamine pathway and the nicotinamide pathway have significant age-associated alterations following CLP.

CONCLUSIONS

The application of untargeted plasma metabolomics identified key pathways underpinning metabolomic responses to CLP in both young and aged animals. Ultimately, these data provide a robust foundation for future mechanistic studies that may assist in improving outcomes in frail patients in response to acute stressors such as infection, trauma, or surgery.

Exosomal hsa_circRNA_104484 and hsa_circRNA_104670 may serve as potential novel biomarkers and therapeutic targets for sepsis

In order to explore the role of exosomal circRNAs in the occurrence and development of sepsis, we looked for potential diagnostic markers to accurately identify sepsis and to lay a molecular basis for precise treatment. Ultracentrifugation was used to extract exosomes from the serum of patients with sepsis and healthy individuals. Then, changes in circRNA expression in exosomes were studied by circRNA microarray analysis. Gene ontology (GO) analysis and Kyoto City Encyclopaedia of Genes and Genomes (KEGG) pathway analysis were used to annotate the biological functions and pathways of genes, and a circRNA-miRNA-mRNA regulatory network was constructed. In the microarray analysis, 132 circRNAs were significantly differentially expressed, including 80 and 52 that were upregulated and downregulated, respectively. RT-qPCR verified the results of microarray analysis: hsa_circRNA_104484 and hsa_circRNA_104670 were upregulated in sepsis serum exosomes. ROC analysis showed that hsa_circRNA_104484 and hsa_circRNA_104670 in serum exosomes have the potential to be used as diagnostic markers for sepsis. The circRNA-miRNA-mRNA network predicted the potential regulatory pathways of differentially expressed circRNAs. There are differences in the expression of circRNA in serum exosomes between patients with sepsis and healthy individuals, which may be involved in the occurrence and development of the disease. Among them, elevations in hsa_circRNA_104484 and hsa_circRNA_104670 could be used as novel diagnostic biomarkers and molecular therapeutic targets.

Serum metabolic profiles of septic shock patients based upon co-morbidities and other underlying conditions

Diagnosis and management of patients with septic shock is still a significant challenge for clinicians with its high mortality amongst hospitalized patients. Septic shock is a heterogeneous condition and is usually accompanied by various underlying disease conditions. Dissecting the specific metabolic changes induced by these underlying disease conditions through metabolomics has shown the potential to improve our understanding of the disease's relevant pathophysiological mechanisms, leading to improved treatment. This study has shown the metabolic alterations caused due to co-morbid conditions like diabetes, hypertension, CAD, and CKD in septic shock. It has also shown the distinct metabolic profiles of septic shock patients with underlying respiratory illnesses and encephalopathy. Metabolic profiling of sera obtained from 50 septic shock patients and 20 healthy controls was performed using high-resolution 1D ¹H CPMG and diffusion-edited NMR spectra. Univariate and multivariate statistical analyses were performed to identify the potential molecular biomarkers. Noted dysregulations in amino acids, carbohydrates, and lipid metabolism were observed in septic shock patients. Further stratification within the septic shock patients based on co-morbid conditions and primary diagnosis has shown their role in causing metabolic alterations. Evaluation of these compounds during treatment will help design a personalized treatment protocol for the patients, improving therapeutics.

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

BACKGROUND

Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets.

METHODS

The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance.

FINDINGS

A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 - 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 - 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 - 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211).

INTERPRETATION

Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership.

FUNDING

The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections

Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.

Integrated transcriptomic and metabolomic analyses to characterize the anti-cancer effects of (-)-epigallocatechin-3-gallate in human colon cancer cells

(-)-Epigallocatechin-3-gallate (EGCG) is the main bioactive component in tea (Camellia sinensis) catechins, and exhibits potential antitumor activity against colorectal cancer (CRC). However, the underlying mechanisms are largely unclear. We investigated the effects of EGCG on activities of CRC cells and the exact molecular mechanism. We used human colon cancer cells (HT-29) and exposed them to EGCG at various concentrations. The MTT assay, flow cytometry, and TUNEL staining were used to study the underlying mechanisms of EGCG (proliferation, apoptosis, autophagy). Western blotting was used to measure expression of marker proteins of the cell cycle, apoptosis, and autophagy. Using a combined microarray-based transcriptomic and ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight tandem mass spectrometry (UHPLC-QTOF/MS)-based metabolomic approach, we investigated the perturbed pathways induced by EGCG treatment at transcript and metabolite levels. Transcriptomic analyses showed that 486 genes were differentially expressed between untreated and EGCG-treated cells. Also, 88 differentially expressed metabolites were identified between untreated and EGCG-treated cells. The altered metabolites were involved in the metabolism of glutathione, glycerophospholipids, starch, sucrose, amino sugars, and nucleotide sugars. There was substantial agreement between the results of transcriptomics and metabolomics analyses. Our data indicate that the anticancer activity of EGCG against HT-29 cells is mediated by induction of cell-cycle arrest, apoptosis, and autophagy. EGCG modulates cancer-cell metabolic pathways. These results provide a platform for future molecular mechanistic studies of EGCG.

Inhibition of Respiration of Candida albicans by Small Molecules Increases Phagocytosis Efficacy by Macrophages

Candida albicans adapts to various conditions in different body niches by regulating gene expression, protein synthesis, and metabolic pathways. These adaptive reactions not only allow survival but also influence the interaction with host cells, which is governed by the composition and structure of the fungal cell wall. Numerous studies had shown linkages between mitochondrial functionality, cell wall integrity and structure, and pathogenicity. Thus, we decided to inhibit single complexes of the respiratory chain of C. albicans and to analyze the resultant interaction with macrophages via their phagocytic activity. Remarkably, inhibition of the fungal bc₁ complex by antimycin A increased phagocytosis, which correlated with an increased accessibility of β-glucans. To contribute to mechanistic insights, we performed metabolic studies, which highlighted significant changes in the abundance of constituents of the plasma membrane. Collectively, our results reinforce the strong linkage between fungal energy metabolism and other components of fungal physiology, which also determine the vulnerability to immune defense reactions.IMPORTANCE The yeast Candida albicans is one of the major fungal human pathogens, for which new therapeutic approaches are required. We aimed at enhancements of the phagocytosis efficacy of macrophages by targeting the cell wall structure of C. albicans, as the coverage of the β-glucan layer by mannans is one of the immune escape mechanisms of the fungus. We unambiguously show that inhibition of the fungal bc₁ complex correlates with increased accessibilities of β-glucans and improved phagocytosis efficiency. Metabolic studies proved not only the known direct effects on reactive oxygen species (ROS) production and fermentative pathways but also the clear downregulation of the ergosterol pathway and upregulation of unsaturated fatty acids. The changed composition of the plasma membrane could also influence the interaction with the overlying cell wall. Thus, our work highlights the far-reaching relevance of energy metabolism, indirectly also for host-pathogen interactions, without affecting viability.