High-efficiency preparation technology of sulforaphane: an overview

Sulforaphane is an isothiocyanate metabolite of cruciferous plants, which obtain antioxidant, anticancer and anti-COVID-19 functions. However, due to its unstable structure, it is easy to de-composite, thus the utilization of sulforaphane is difficult. With the advancement of the preparation of sulforaphane, the purpose of inhibiting sulforaphane inactivation and improving its utilization is expected to be realized. The existing preparation technologies are mainly myrosinase enzymatic hydrolysis, microbial transformation and chemical synthesis. Myrosinase enzymatic hydrolysis mainly utilizes endogenous myrosinase, exogenous myrosinase and heterologously expressed myrosinase. Myrosinase enzymatic hydrolysis technology not only obtain the advantage of high preparation efficiency, but also obtain the disadvantage that the activity of myrosinase cannot be stabilized. Microbial transformation mainly utilizes the function of microorganisms to convert glucosinolates to sulforaphane, and obtain the advantages of easy control of reaction conditions and low cost. Chemical synthesis mainly includes de novo synthesis and semi-synthesis, and semi-synthesis is the most widely used method at present. Chemical synthesis obtains the advantages of easy control of reaction conditions, but chemical synthesis techniques have the problems of high risk and low yield. This research reviews the preparation technology of sulforaphane, aiming to provide a reference for the efficient utilization of sulforaphane and its product development.

Dried blood spot (DBS) sample analysis for drug and metabolomic profiling in oncology clinical trials: Cost-effective decentralized sampling modality for precision Oncology

The COVID19 pandemic accelerated opportunities for innovation within the decentralization process of clinical trials with opportunities for implementation of patient-centric workflows for efficiency and cost-reduction. Decentralized sample collection, particularly whole blood using dried blood spots (DBS) provides the ideal mechanism for patient driven sample collection with ease of access to sample generation, drug level assessments and metabolomic prMegofiling, providing longitudinal real-time measure of drug specific pharmacodynamic readout for safety and efficacy. In this study, we report the development of a protocol for the capture and comprehensive profiling of metabolomics using dried blood spots from a cohort of 49 healthy volunteer donors. Using liquid chromatography combined with mass spectrometric (UPLC-MS/MS) methods an untargeted metabolomic approach resulted in the identification of >800 biochemicals of which a significant subset was found to be presented in corresponding matched plasma (from whole blood) samples. The biochemicals identified from the DBS samples included metabolites that were part of the lipid, amino acid, nucleotide, peptide, cofactors, carbohydrate and energy super pathways. A significant number of metabolites identified in the DBS samples were xenobiotics including those representing the biotransformation products of drugs. The overall metabolite profiles were analyzed for precision and accuracy of measure, variability in performance and dynamic range to establish benchmarks for evaluation. An additional cohort with a longitudinal sampling as part of the protocol provided the reproducibility of the analytic method for inter-day variability of metabolite performance over time. Although metabolomic profiles varied between individuals from a population perspective, there was minimal variation observed within individuals when samples were profiled longitudinally over several weeks. Thus, the protocols for DBS collection and the corresponding capture of a large set of metabolites with reproducible performance provides an opportunity for its implementation in oncological clinical trials as part of a de-centralized clinical trial solution.

COVID-19 Metabolomics and Diagnosis: Chemical Science for Prevention and Understanding Outbreaks of Infectious Diseases

This book focus on COVID-19 topics, with emphasis on metabolomics and diagnosis. The chapters cover the chemical science for prevention and understanding outbreaks of infectious diseases. This book compiles the most widespread methodologies of application of quality statistical tools added to the evaluation of diagnostic tests for detection of SARS-CoV-2, metabolic behavior of COVID infection severity, and trends in rapid test for COVID-19. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.

The use of NMR based metabolomics to discriminate patients with viral diseases

Infectious diseases are one of the most common conditions impacting global health, being a matter of concern for health agencies due to their contagious capacity and periodic outbreaks of new diseases, such as the global pandemic COVID-19. Viruses are among the main causes of this illness and it is defined as obligate intracellular parasites for their need to have a host cell to live and reproduce, since they won't produce proteins and compete for nutrients and metabolites leading to the alteration of the host metabolome. The diagnosis of these viral infections can be done by detecting viral particles or components, isolating the virus in cell culture, or even by evaluating immune responses. In this context, metabolomics comes as a very useful tool that reflects all "omics" techniques and best represents the phenotype. Since water-soluble metabolites and lipids are the major molecular constituents of human plasma, their abnormalities are commonly observed during disease, which contributes to the understanding of physiology and pathology. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the most widely used techniques in metabolomics. NMR spectroscopy has emerged as a valuable application due to its ability to identify compounds with simple sample preparation, in addition, to being a non-destructive, highly reproducible, and quantitative technique (primary ratio method). These features make NMR a valuable tool that is frequently used in metabolomics analysis, and nowadays used in the diagnosis of viral diseases. Therefore, in this chapter, we will address a short integrative description of viral diseases and diagnostics, metabolomics, and NMR concepts. Furthermore, we will explore the advances in NMR-based metabolomics applied in medicine, and finally, the viral diseases discriminated by NMR-based metabolomics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients.

Background: Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course. Methods: This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). 1H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes. Results: Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients. Conclusion: The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

Authentication of Tinospora cordifolia derived herbal supplements using high resolution mass spectrometry-based metabolomics approach – A pilot study

Tinospora cordifolia herbal supplements have recently gained prominence due to their promising immunomodulatory and anti-viral effects against SARS-CoV-2. Mislabelling or diluting Tinospora supplements for profit may harm public health. Thus, validating the label claim of these supplements in markets is critical. This study investigated how high resolution mass spectrometry-based metabolomics and chemometrics can be used to distinguish Tinospora cordifolia from two other closely related species (T. crispa and T. sinensis). The Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) and PLS-DA based chemometric models predicted the species identity of Tinospora with 94.44% accuracy. These classification models were trained using 54 T. cordifolia, 21 T. crispa, and 21 T. sinensis samples. We identified 7 biomarkers, including corydine, malabarolide, ecdysterone, and reticuline, which discriminated Tinospora cordifolia from the two other species. The label claim of 25 commercial Tinospora samples collected from different parts of India was verified based on the relative abundance of the biomarker compounds, of which 20 were found authentic. The relative abundance of biomarkers significantly varied in the 5 suspicious market samples. This pilot study demonstrates a robust metabolomic approach for authenticating Tinospora species, which can further be used in other herbal matrices for product authentication and securing quality. © 2023 Elsevier B.V.

Metabolomics: From Scientific Research to the Clinical Diagnosis

Metabolomics supports uncovering relevant pathophysiological mechanisms and identifying biomarkers of risk and progression in diseases. Furthermore, metabolomics has allowed the characterization of the proteins and metabolites of COVID-19, neurodegenerative processes, gestational diabetes mellitus, cancer breast, process of kidney transplantation, and Parkinson diagnosis, among other diseases (Table 7.1). Metabolomics employs noninvasive human biological samples such as serum, breath, and urine to screen and identify novel biomarkers. The combination of NMR, LC/MS, and CG/MS is desirable to detect, identify, and quantify hundreds of thousands of metabolites, useful in biomarker discovery toward clinical applications. The generation of biological information has led to the creation of databases such as BioBankWarden, which can be used to store and retrieve specific information from different clinical fields linked to biomaterials collected from patients. The use of metabolomics allows greater precision in the diagnosis and follow-up of the treatment of any disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Metabolic Adaptations Correlated with Antibody Response after Immunization with Inactivated SARS-CoV-2 in Brazilian Subjects.

The adsorbed vaccine SARS-CoV-2 (inactivated) produced by Sinovac (SV) was the first vaccine against COVID-19 to be used in Brazil. To understand the metabolic effects of SV in Brazilian subjects, NMR-based metabolomics was used, and the immune response was studied in Brazilian subjects. Forty adults without (group-, n = 23) and with previous COVID-19 infection (group+, n = 17) were followed-up for 90 days postcompletion of the vaccine regimen. After 90 days, our results showed that subjects had increased levels of lipoproteins, lipids, and N-acetylation of glycoproteins (NAG) as well as decreased levels of amino acids, lactate, citrate, and 3-hydroxypropionate. NAG and threonine were the highest correlated metabolites with N and S proteins, and neutralizing Ab levels. This study sheds light on the immunometabolism associated with the use of SV in Brazilian subjects from Rio de Janeiro and identifies potential metabolic markers associated with the immune status.

Acute Respiratory Distress Syndrome; A Review of Recent Updates and a Glance into the Future.

Acute respiratory distress syndrome (ARDS) is a rapidly progressive form of respiratory failure that accounts for 10% of admissions to the ICU and is associated with approximately 40% mortality in severe cases. Despite significant mortality and healthcare burden, the mainstay of management remains supportive care. The recent pandemic of SARS-CoV-2 has re-ignited a worldwide interest in exploring the pathophysiology of ARDS, looking for innovative ideas to treat this disease. Recently, many trials have been published utilizing different pharmacotherapy targets; however, the long-term benefits of these agents remain unknown. Metabolomics profiling and stem cell transplantation offer strong enthusiasm and may completely change the outlook of ARDS management in the near future.

Immunology of SARS-CoV-2 infection and vaccination.

Comprehensive elucidation of humoral immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination is critical for understanding coronavirus disease 2019 (COVID-19) pathogenesis in general and developing antibody-based diagnostic and therapeutic strategies specifically. Following the emergence of SARS-CoV-2, significant scientific research has been conducted worldwide using omics, sequencing and immunologic approaches. These studies have been critical to the successful development of vaccines. Here, the current understanding of SARS-CoV-2 immunogenic epitopes, humoral immunity to SARS-CoV-2 structural proteins and non-structural proteins, SARS-CoV-2-specific antibodies, and T-cell responses in convalescents and vaccinated individuals are reviewed. Additionally, we explore the integrated analysis of proteomic and metabolomic data to examine mechanisms of organ injury and identify potential biomarkers. Insight into the immunologic diagnosis of COVID-19 and improvements of laboratory methods are highlighted.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.

INTRODUCTION: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. OBJECTIVES: Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). METHODS: High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. RESULTS: The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86). CONCLUSION: Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2.

Introduction: The routine clinical diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely restricted to real-time reverse transcription quantitative PCR (RT-qPCR), and tests that detect SARS-CoV-2 nucleocapsid antigen. Given the diagnostic delay and suboptimal sensitivity associated with these respective methods, alternative diagnostic strategies are needed for acute infection. Methods: We studied the use of a clinically validated liquid chromatography triple quadrupole method (LC/MS-MS) for detection of amino acids from plasma specimens. We applied machine learning models to distinguish between SARS-CoV-2-positive and negative samples and analyzed amino acid feature importance. Results: A total of 200 samples were tested, including 70 from individuals with COVID-19, and 130 from negative controls. The top performing model overall allowed discrimination between SARS-CoV-2-positive and negative control samples with an area under the receiver operating characteristic curve (AUC) of 0.96 (95%CI 0.91, 1.00), overall sensitivity of 0.99 (95%CI 0.92, 1.00), and specificity of 0.92 (95%CI 0.85, 0.95). Discussion: This approach holds potential as an alternative to existing methods for the rapid and accurate diagnosis of acute SARS-CoV-2 infection.

The mechanism of Qingwen Gupi decoction on pulmonary fibrosis based on metabolomics and intestinal flora.

AIMS: To evaluate the effects of the Qingwen Gupi decoction (QGT) in a rat model of bleomycin-induced pulmonary fibrosis (PF), and explore the underlying mechanisms by integrating UPLC-Q-TOF/MS metabolomics and 16S rDNA sequencing of gut microbiota. METHODS AND RESULTS: The animals were randomly divided into the control, PF model, pirfenidone-treated, and low-, medium-, and high-dose QGT groups. The lung tissues were examined and the expression of TGF-ß, SMAD-3, and SMAD-7 mRNAs in the lung tissues were analyzed. Metabolomic profiles were analyzed by UPLC-QTOF/MS, and the intestinal flora were examined by prokaryotic 16 rDNA sequencing. Pathological examination and biochemical indices revealed that QGT treatment improved the symptoms of PF by varying degrees. Furthermore, QGT significantly downregulated TGF-ß1 and Smad-3 mRNAs and increased the expression levels of Smad-7. QGT-L in particular increased the levels of 18 key metabolic biomarkers that were associated with nine gut microbial species and may exert antifibrosis effects through arachidonic acid metabolism, glycerophospholipid metabolism, and phenylalanine metabolism. CONCLUSIONS: QGT alleviated PF in a rat model through its anti-inflammatory, antioxidant, and anti-fibrotic effects, and by reversing bleomycin-induced gut dysbiosis.This study lays the foundation for further research on the pathological mechanisms of PF and the development of new drug candidates.

Metabolomics meets systems immunology.

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Multi-Omics Profiling for Health.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Morphological, Cellular, and Molecular Basis of Brain Infection in COVID-19 Patients and Its Association to Psychiatric Symptoms

Background: Although increasing evidence confirms neuropsychiatric manifestations associated mainly with severe COVID-19 infection, long-term neuropsychiatric dysfunction (recently characterized as part of "long COVID-19" syndrome) has been frequently observed after mild infection. Method(s): We performed a broad translational investigation, employing brain imaging and cognitive tests in 81 living COVID-19 patients (mildly infected individuals) as well as flow cytometry, respirometry, microscopy, proteomics, and metabolomics in postmortem brain samples, and in preclinical in vitro and ex vivo models. Result(s): We observed orbitofrontal cortical atrophy, neurocognitive impairment, excessive fatigue and anxiety symptoms in living individuals. Postmortem brain tissue from 26 individuals who died of COVID-19 revealed histopathological signs of brain damage. Five individuals out of the 26 exhibited foci of SARS- CoV-2 infection and replication, particularly in astrocytes. Supporting the hypothesis of astrocyte infection, neural stem cell-derived human astrocytes in vitro are susceptible to SARS-CoV-2 infection through a non-canonical mechanism that involves spike-NRP1 interaction. SARS-CoV-2-infected astrocytes manifested changes in energy metabolism and in key proteins and metabolites used to fuel neurons, as well as in the biogenesis of neurotransmitters. Moreover, human astrocyte infection elicits a secretory phenotype that significantly reduces neuronal viability. Conclusion(s): Our data support the model in which COVID-19 alter cortical thickness, promoting psychiatric symptoms. In addition, SARS-CoV-2 is able to reach the brain, infects astrocytes, and consequently, leads to neuronal death or dysfunction. These deregulated processes could contribute to the structural and functional alterations seen in the brains of COVID-19 patients. Funding Source: Sao Paulo Research Foundation (FAPESP) Keywords: COVID-19, Anxiety, Astrocytes, Multi-omics, Brain Magnetic Resonance Imaging (MRI)Copyright © 2023

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research.

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Metabolites and metabolomics in COVID-19

Metabolomics is a comprehensive approach for identifying, quantifying, and characterizing entire metabolites in a biological system using a high-throughput technique. Metabolomics has great potential in discovering biochemical pathways and pathway interactions, disease diagnosis, and biomarker discovery. Viral infections induce changes in the host cell metabolism resulting in cellular reprogramming. In COVID-19, the dysregulation of host metabolites has been implemented in various aspects including diagnosis and management. This chapter summarizes various applications of metabolites and metabolomics approaches in COVID-19. © 2023 Elsevier Inc. All rights reserved.

Fetal effects of mild maternal COVID-19 infection: metabolomic profiling of cord blood.

INTRODUCTION: The impact of maternal coronavirus disease 2019 (COVID-19) infection on fetal health remains to be precisely characterized. OBJECTIVES: Using metabolomic profiling of newborn umbilical cord blood, we aimed to investigate the potential fetal biological consequences of maternal COVID-19 infection. METHODS: Cord blood plasma samples from 23 mild COVID-19 cases (mother infected/newborn negative) and 23 gestational age-matched controls were analyzed using nuclear magnetic spectroscopy and liquid chromatography coupled with mass spectrometry. Metabolite set enrichment analysis (MSEA) was used to evaluate altered biochemical pathways due to COVID-19 intrauterine exposure. Logistic regression models were developed using metabolites to predict intrauterine exposure. RESULTS: Significant concentration differences between groups (p-value < 0.05) were observed in 19 metabolites. Elevated levels of glucocorticoids, pyruvate, lactate, purine metabolites, phenylalanine, and branched-chain amino acids of valine and isoleucine were discovered in cases while ceramide subclasses were decreased. The top metabolite model including cortisol and ceramide (d18:1/23:0) achieved an Area under the Receiver Operating Characteristics curve (95% CI) = 0.841 (0.725-0.957) for detecting fetal exposure to maternal COVID-19 infection. MSEA highlighted steroidogenesis, pyruvate metabolism, gluconeogenesis, and the Warburg effect as the major perturbed metabolic pathways (p-value < 0.05). These changes indicate fetal increased oxidative metabolism, hyperinsulinemia, and inflammatory response. CONCLUSION: We present fetal biochemical changes related to intrauterine inflammation and altered energy metabolism in cases of mild maternal COVID-19 infection despite the absence of viral infection. Elucidation of the long-term consequences of these findings is imperative considering the large number of exposures in the population.

Metabolomic Profiling of Lungs from Mice Reveals the Variability of Metabolites in Pneumocystis Infection and the Metabolic Abnormalities in BAFF-R-Deficient Mice.

Purpose: The incidence of Pneumocystis pneumonia (PCP) in patients without human immunodeficiency virus (HIV) has been increasing. In this study, we aimed to investigate the metabolic changes in Pneumocystis infection and the metabolic abnormalities in B-cell-activating factor receptor (BAFF-R)-deficient mice with Pneumocystis infection. Methods: The important function of B cells during Pneumocystis infection is increasingly recognized. In this study, a Pneumocystis-infected mouse model was constructed in BAFF-R-/- mice and wild-type (WT) mice. Lungs of uninfected WT C57BL/6, WT Pneumocystis-infected, and BAFF-R-/- Pneumocystis-infected mice were used for metabolomic analyses to compare the metabolomic profiles among the groups, with the aim of exploring the metabolic influence of Pneumocystis infection and the influence of mature B-cell deficiency during infection. Results: The results indicated that many metabolites, mainly lipids and lipid-like molecules, were dysregulated in Pneumocystis-infected WT mice compared with uninfected WT C57BL/6 mice. The data also demonstrated significant changes in tryptophan metabolism, and the expression levels of key enzymes of tryptophan metabolism, such as indoleamine 2,3-dioxygenase 1 (IDO1), were significantly upregulated. In addition, B-cell development and function might be associated with lipid metabolism. We found a lower level of alitretinoin and the abnormalities of fatty acid metabolism in BAFF-R-/- Pneumocystis-infected mice. The mRNA levels of enzymes associated with fatty acid metabolism in the lung were upregulated in BAFF-R-/- Pneumocystis-infected mice and positively correlated with the level of IL17A, thus suggesting that the abnormalities of fatty acid metabolism may be associated with greater inflammatory cell infiltration in the lung tissue of BAFF-R-/- Pneumocystis-infected mice compared with the WT Pneumocystis-infected mice. Conclusion: Our data revealed the variability of metabolites in Pneumocystis-infected mice, suggesting that the metabolism plays a vital role in the immune response to Pneumocystis infection.