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Introduction: Longitudinal biomarkers in patients with community-acquired pneumonia (CAP) may help in monitoring of disease progression and treatment response. The metabolic host response could be a potential source of such biomarkers since it closely associates with the current health status of the patient., Objectives: In this study we performed longitudinal metabolite profiling in patients with CAP for a comprehensive range of metabolites to identify potential host response biomarkers., Methods: Previously collected serum samples from CAP patients with confirmed Streptococcus pneumoniae infection (n = 25) were used. Samples were collected at multiple time points, up to 30 days after admission. A wide range of metabolites was measured, including amines, acylcarnitines, organic acids, and lipids. The associations between metabolites and C-reactive protein (CRP), procalcitonin, CURB disease severity score at admission, and total length of stay were evaluated., Results: Distinct longitudinal profiles of metabolite profiles were identified, including cholesteryl esters, diacyl-phosphatidylethanolamine, diacylglycerols, lysophosphatidylcholines, sphingomyelin, and triglycerides. Positive correlations were found between CRP and phosphatidylcholine (34:1) (cor = 0.63) and negative correlations were found for CRP and nine lysophosphocholines (cor = - 0.57 to - 0.74). The CURB disease severity score was negatively associated with six metabolites, including acylcarnitines (tau = - 0.64 to - 0.58). Negative correlations were found between the length of stay and six triglycerides (TGs), especially TGs (60:3) and (58:2) (cor = - 0.63 and - 0.61)., Conclusion: The identified metabolites may provide insight into biological mechanisms underlying disease severity and may be of interest for exploration as potential treatment response monitoring biomarker., (© 2024. The Author(s).)

Background: Metabolic changes induced by the host immune response to pathogens found in patients with community-acquired pneumonia (CAP) may provide insight into its pathogenesis. In this study, we characterized differences in the host metabolic response to common CAP-associated pathogens., Method: Targeted metabolomic profiling was performed on serum samples obtained from hospitalized CAP patients (n = 119) at admission. We quantified 347 unique metabolites across multiple biochemical classes, including amines, acylcarnitines, and signaling lipids. We evaluated if unique associations between metabolite levels and specific CAP-associated pathogens could be identified., Results: Several acylcarnitines were found to be elevated in C. burnetii and herpes simplex virus and lowered in M. pneumoniae as compared to other pathogens. Phenylalanine and kynurenine were found elevated in L. pneumophila as compared to other pathogens. S-methylcysteine was elevated in patients with M. pneumoniae , and these patients also showed lowered cortisol levels in comparison to almost all other pathogens. For the herpes simplex virus, we observed a unique elevation of eicosanoids and several amines. Many lysophosphatidylcholines showed an altered profile in C. burnetii versus S . pneumoniae, L. pneumophila, and respiratory syncytial virus. Finally, phosphatidylcholines were negatively affected by the influenza virus in comparison to S. pneumoniae ., Conclusions: In this exploratory analysis, metabolites from different biochemical classes were found to be altered in serum samples from patients with different CAP-associated pathogens, which may be used for hypothesis generation in studies on differences in pathogen host response and pathogenesis of CAP., Competing Interests: None to declare., (© 2023 The Authors.)

Immunometabolism, which concerns the interplay between metabolism and the immune system, is increasingly recognized as a potential source of novel drug targets and biomarkers. In this context, the use of metabolomics to identify metabolic characteristics associated with specific functional immune response processes is of value. Currently, there is a lack of tools to determine known associations between metabolites and immune processes. Consequently, interpretation of metabolites in metabolomics studies in terms of their role in the immune system, or selection of the most relevant metabolite classes to include in metabolomics studies, is challenging. Here, we describe the Immunometabolic Atlas (IMA), a public web application and library of R functions to infer immune processes associated with specific metabolites and vice versa. The IMA derives metabolite-immune process associations utilizing a protein-metabolite network analysis algorithm that associates immune system-associated annotated proteins in Gene Ontology to metabolites. We evaluated IMA inferred metabolite-immune system associations using a text mining strategy, identifying substantial overlap, but also demonstrating a significant chemical space of immune system-associated metabolites that should be confirmed experimentally. Overall, the IMA facilitates the interpretation and design of immunometabolomics studies by the association of metabolites to specific immune processes., Competing Interests: The authors have declared that no competing interests exist.

Diagnosis of microbial disease etiology in community-acquired pneumonia (CAP) remains challenging. We undertook a large-scale metabolomics study of serum samples in hospitalized CAP patients to determine if host-response associated metabolites can enable diagnosis of microbial etiology, with a specific focus on discrimination between the major CAP pathogen groups S. pneumoniae, atypical bacteria, and respiratory viruses. Targeted metabolomic profiling of serum samples was performed for three groups of hospitalized CAP patients with confirmed microbial etiologies: S. pneumoniae (n = 48), atypical bacteria (n = 47), or viral infections (n = 30). A wide range of 347 metabolites was targeted, including amines, acylcarnitines, organic acids, and lipids. Single discriminating metabolites were selected using Student's T-test and their predictive performance was analyzed using logistic regression. Elastic net regression models were employed to discover metabolite signatures with predictive value for discrimination between pathogen groups. Metabolites to discriminate S. pneumoniae or viral pathogens from the other groups showed poor predictive capability, whereas discrimination of atypical pathogens from the other groups was found to be possible. Classification of atypical pathogens using elastic net regression models was associated with a predictive performance of 61% sensitivity, 86% specificity, and an AUC of 0.81. Targeted profiling of the host metabolic response revealed metabolites that can support diagnosis of microbial etiology in CAP patients with atypical bacterial pathogens compared to patients with S. pneumoniae or viral infections., Competing Interests: The authors have declared that no competing interests exist.

The rapidly growing field of microbiome research presents a need for better methods of monitoring gut microbes in vivo with high spatial and temporal resolution. We report a method of tracking microbes in vivo within the gastrointestinal tract by programming them to incorporate nonstandard amino acids (NSAA) and labeling them via click chemistry. Using established machinery constituting an orthogonal translation system (OTS), we engineered Escherichia coli to incorporate p-azido-l-phenylalanine (pAzF) in place of the UAG (amber) stop codon. We also introduced a mutant gene encoding for a cell surface protein (CsgA) that was altered to contain an in-frame UAG codon. After pAzF incorporation and extracellular display, the engineered strains could be covalently labeled via copper-free click reaction with a Cy5 dye conjugated to the dibenzocyclooctyl (DBCO) group. We confirmed the functionality of the labeling strategy in vivo using a murine model. Labeling of the engineered strain could be observed using oral administration of the dye to mice several days after colonization of the gastrointestinal tract. This work sets the foundation for the development of in vivo tracking microbial strategies that may be compatible with noninvasive imaging modalities and are capable of longitudinal spatiotemporal monitoring of specific microbial populations.

Antibody detection is of fundamental importance in many diagnostic and bioanalytical assays, yet current detection techniques tend to be laborious and/or expensive. We present a new sensor platform (LUMABS) based on bioluminescence resonance energy transfer (BRET) that allows detection of antibodies directly in solution using a smartphone as the sole piece of equipment. LUMABS are single-protein sensors that consist of the blue-light emitting luciferase NanoLuc connected via a semiflexible linker to the green fluorescent acceptor protein mNeonGreen, which are kept close together using helper domains. Binding of an antibody to epitope sequences flanking the linker disrupts the interaction between the helper domains, resulting in a large decrease in BRET efficiency. The resulting change in color of the emitted light from green-blue to blue can be detected directly in blood plasma, even at picomolar concentrations of antibody. Moreover, the modular architecture of LUMABS allows changing of target specificity by simple exchange of epitope sequences, as demonstrated here for antibodies against HIV1-p17, hemagglutinin (HA), and dengue virus type I. The combination of sensitive ratiometric bioluminescent detection and the intrinsic modularity of the LUMABS design provides an attractive generic platform for point-of-care antibody detection that avoids the complex liquid handling steps associated with conventional immunoassays.