Poststroke Lung Infection by Opportunistic Commensal Bacteria Is Not Mediated by Their Expansion in the Gut Microbiota.

BACKGROUND: Respiratory and urinary tract infections are frequent complications in patients with severe stroke. Stroke-associated infection is mainly due to opportunistic commensal bacteria of the microbiota that may translocate from the gut. We investigated the mechanisms underlying gut dysbiosis and poststroke infection. METHODS: Using a model of transient cerebral ischemia in mice, we explored the relationship between immunometabolic dysregulation, gut barrier dysfunction, gut microbial alterations, and bacterial colonization of organs, and we explored the effect of several drug treatments. RESULTS: Stroke-induced lymphocytopenia and widespread colonization of lung and other organs by opportunistic commensal bacteria. This effect correlated with reduced gut epithelial barrier resistance, and a proinflammatory sway in the gut illustrated by complement and nuclear factor-κB activation, reduced number of gut regulatory T cells, and a shift of gut lymphocytes to γδT cells and T helper 1/T helper 17 phenotypes. Stroke increased conjugated bile acids in the liver but decreased bile acids and short-chain fatty acids in the gut. Gut fermenting anaerobic bacteria decreased while opportunistic facultative anaerobes, notably Enterobacteriaceae, suffered an expansion. Anti-inflammatory treatment with a nuclear factor-κB inhibitor fully abrogated the Enterobacteriaceae overgrowth in the gut microbiota induced by stroke, whereas inhibitors of the neural or humoral arms of the stress response were ineffective at the doses used in this study. Conversely, the anti-inflammatory treatment did not prevent poststroke lung colonization by Enterobacteriaceae. CONCLUSIONS: Stroke perturbs homeostatic neuro-immuno-metabolic networks facilitating a bloom of opportunistic commensals in the gut microbiota. However, this bacterial expansion in the gut does not mediate poststroke infection.

Oxidative phosphorylation selectively orchestrates tissue macrophage homeostasis.

In vitro studies have associated oxidative phosphorylation (OXPHOS) with anti-inflammatory macrophages, whereas pro-inflammatory macrophages rely on glycolysis. However, the metabolic needs of macrophages in tissues (TMFs) to fulfill their homeostatic activities are incompletely understood. Here, we identified OXPHOS as the highest discriminating process among TMFs from different organs in homeostasis by analysis of RNA-seq data in both humans and mice. Impairing OXPHOS in TMFs via Tfam deletion differentially affected TMF populations. Tfam deletion resulted in reduction of alveolar macrophages (AMs) due to impaired lipid-handling capacity, leading to increased cholesterol content and cellular stress, causing cell-cycle arrest in vivo. In obesity, Tfam depletion selectively ablated pro-inflammatory lipid-handling white adipose tissue macrophages (WAT-MFs), thus preventing insulin resistance and hepatosteatosis. Hence, OXPHOS, rather than glycolysis, distinguishes TMF populations and is critical for the maintenance of TMFs with a high lipid-handling activity, including pro-inflammatory WAT-MFs. This could provide a selective therapeutic targeting tool.

TurboPutative: A web server for data handling and metabolite classification in untargeted metabolomics.

Untargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e., amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases, thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger, and TPMetrics) that streamlines data handling, classification, and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves putative annotation handling and visualization, allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size, facilitating the manual comparison among annotations. Finally, TPMetrics combines different datasets with feature intensity and relevant information for the researcher and calculates a score based on adduct probability and feature correlations, facilitating further identification, assessment, and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80%-90%, thus facilitating the extrapolation of biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated, and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful API services. Users can explore the performance of each module through demo datasets supplied on the website. The platform will help the metabolomics community to speed up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.

Bone marrow activation in response to metabolic syndrome and early atherosclerosis.

AIMS: Experimental studies suggest that increased bone marrow (BM) activity is involved in the association between cardiovascular risk factors and inflammation in atherosclerosis. However, human data to support this association are sparse. The purpose was to study the association between cardiovascular risk factors, BM activation, and subclinical atherosclerosis. METHODS AND RESULTS: Whole body vascular 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) was performed in 745 apparently healthy individuals [median age 50.5 (46.8-53.6) years, 83.8% men] from the Progression of Early Subclinical Atherosclerosis (PESA) study. Bone marrow activation (defined as BM 18F-FDG uptake above the median maximal standardized uptake value) was assessed in the lumbar vertebrae (L3-L4). Systemic inflammation was indexed from circulating biomarkers. Early atherosclerosis was evaluated by arterial metabolic activity by 18F-FDG uptake in five vascular territories. Late atherosclerosis was evaluated by fully formed plaques on MRI. Subjects with BM activation were more frequently men (87.6 vs. 80.0%, P = 0.005) and more frequently had metabolic syndrome (MetS) (22.2 vs. 6.7%, P < 0.001). Bone marrow activation was significantly associated with all MetS components. Bone marrow activation was also associated with increased haematopoiesis-characterized by significantly elevated leucocyte (mainly neutrophil and monocytes) and erythrocyte counts-and with markers of systemic inflammation including high-sensitivity C-reactive protein, ferritin, fibrinogen, P-selectin, and vascular cell adhesion molecule-1. The associations between BM activation and MetS (and its components) and increased erythropoiesis were maintained in the subgroup of participants with no systemic inflammation. Bone marrow activation was significantly associated with high arterial metabolic activity (18F-FDG uptake). The co-occurrence of BM activation and arterial 18F-FDG uptake was associated with more advanced atherosclerosis (i.e. plaque presence and burden). CONCLUSION: In apparently healthy individuals, BM 18F-FDG uptake is associated with MetS and its components, even in the absence of systemic inflammation, and with elevated counts of circulating leucocytes. Bone marrow activation is associated with early atherosclerosis, characterized by high arterial metabolic activity. Bone marrow activation appears to be an early phenomenon in atherosclerosis development.[Progression of Early Subclinical Atherosclerosis (PESA); NCT01410318].

Metabolism of tissue macrophages in homeostasis and pathology.

Cellular metabolism orchestrates the intricate use of tissue fuels for catabolism and anabolism to generate cellular energy and structural components. The emerging field of immunometabolism highlights the importance of cellular metabolism for the maintenance and activities of immune cells. Macrophages are embryo- or adult bone marrow-derived leukocytes that are key for healthy tissue homeostasis but can also contribute to pathologies such as metabolic syndrome, atherosclerosis, fibrosis or cancer. Macrophage metabolism has largely been studied in vitro. However, different organs contain diverse macrophage populations that specialize in distinct and often tissue-specific functions. This context specificity creates diverging metabolic challenges for tissue macrophage populations to fulfill their homeostatic roles in their particular microenvironment and conditions their response in pathological conditions. Here, we outline current knowledge on the metabolic requirements and adaptations of macrophages located in tissues during homeostasis and selected diseases.

Microbiota Sensing by Mincle-Syk Axis in Dendritic Cells Regulates Interleukin-17 and -22 Production and Promotes Intestinal Barrier Integrity.

Production of interleukin-17 (IL-17) and IL-22 by T helper 17 (Th17) cells and group 3 innate lymphoid cells (ILC3s) in response to the gut microbiota ensures maintenance of intestinal barrier function. Here, we examined the mechanisms whereby the immune system detects microbiota in the steady state. A Syk-kinase-coupled signaling pathway in dendritic cells (DCs) was critical for commensal-dependent production of IL-17 and IL-22 by CD4+ T cells. The Syk-coupled C-type lectin receptor Mincle detected mucosal-resident commensals in the Peyer's patches (PPs), triggered IL-6 and IL-23p19 expression, and thereby regulated function of intestinal Th17- and IL-17-secreting ILCs. Mice deficient in Mincle or with selective depletion of Syk in CD11c+ cells had impaired production of intestinal RegIIIγ and IgA and increased systemic translocation of gut microbiota. Consequently, Mincle deficiency led to liver inflammation and deregulated lipid metabolism. Thus, sensing of commensals by Mincle and Syk signaling in CD11c+ cells reinforces intestinal immune barrier and promotes host-microbiota mutualism, preventing systemic inflammation.

Metabolomics changes in patients with PAPP-A2 deficiency in response to rhIGF1 treatment.

OBJECTIVE: Mutations in the pregnancy-associated plasma protein A2 (PAPP-A2) gene have recently been shown to cause postnatal growth failure in two prepubertal patients from a non-consanguineous Spanish family due to the resulting decrease in IGF1 bioavailability. Although a specific pharmacological treatment of this entity is yet to be established, both children received progressive subcutaneous doses (40 to 120 µg/kg) of rhIGF1 twice daily for 2 years. The improvements in growth, hyperinsulinemia and bone mineral density have been previously reported. The objective of this study was to analyze the changes in metabolism associated with these responses to rhIGF1 treatment. DESIGN: Herein we present a detailed characterization of the acute and long-term changes in the metabolic profiles of these two siblings with PAPP-A2 deficiency after the initial injections of rhIGF1 and after two years of treatment. RESULTS: By using a GC-MS-based untargeted metabolomics approach, metabolic fingerprinting yielded the identification of 70 serum metabolites including amino acids (46%), organic acids (21%) carbohydrates (16%), fatty acids (14%), and purine bases (3%). Free fatty acids (FFAs) and amino acids showed the largest changes in the compared metabolic profiles, suggesting that rhIGF1 treatment has the greatest effects on lipid and protein metabolic pathways in the PAPP-A2 deficient subjects. CONCLUSIONS: The administration of rhIGF1 resulted in changes related to crucial metabolic pathways, including lipid and protein metabolism, and this could be associated with the previously reported treatment-induced improvement in the mild basal hyperinsulinemia.

"Gear mechanism" of bariatric interventions revealed by untargeted metabolomics.

Mechanisms responsible for metabolic gains after bariatric surgery are not entirely clear. The purpose of this study was evaluation of metabolic changes after laparoscopic Roux-en-Y gastric bypass or laparoscopic sleeve gastrectomy in semi-annual follow up. The study participants were selected from obese patients with T2DM who underwent one of the mentioned bariatric procedures. Serum metabolic fingerprinting by use of liquid and gas chromatography with mass spectrometry detection was performed on samples obtained from studied patients before, one, and six months post-surgery. Performed analyses resulted in 49 significant and identified metabolites. Comparison of the two described procedures has allowed to detect metabolites linked with numerous pathways, processes and diseases. Based on the metabolites detected and pathways affected, we propose a "gear mechanism" showing molecular changes evoked by both bariatric procedures. Critical evaluation of clinical data and obtained metabolomics results enables us to conclude that both procedures are very similar in terms of general clinical outcome, but they strongly differ from each other in molecular mechanisms leading to the final effect. For the first time general metabolic effect of bariatric procedures is described. New hypotheses concerning molecular mechanisms induced by bariatric surgeries and new gut microbiota modulations are presented.

Discriminant biomarkers of acute respiratory distress syndrome associated to H1N1 influenza identified by metabolomics HPLC-QTOF-MS/MS platform.

Acute respiratory distress syndrome (ARDS) is a serious complication of influenza A (H1N1) virus infection. Its pathogenesis is unknown and biomarkers are lacking. Untargeted metabolomics allows the analysis of the whole metabolome in a biological compartment, identifying patterns associated with specific conditions. We hypothesized that LC-MS could help identify discriminant metabolites able to define the metabolic alterations occurring in patients with influenza A (H1N1) virus infection that developed ARDS. Serum samples from patients diagnosed with 2009 influenza A (H1N1) virus infection with (n = 25) or without (n = 32) ARDS were obtained on the day of hospital admission and analyzed by LC-MS/MS. Metabolite identification was determined by MS/MS analysis and analysis of standards. The specificity of the patterns identified was confirmed in patients without 2009 influenza A(H1N1) virus pneumonia (15 without and 17 with ARDS). Twenty-three candidate biomarkers were found to be significantly different between the two groups, including lysophospholipids and sphingolipids related to inflammation; bile acids, tryptophan metabolites, and thyroxine, related to the metabolism of the gut microflora. Confirmation results demonstrated the specificity of major alterations occurring in ARDS patients with influenza A (H1N1) virus infection.

Chronic Diseases and Lifestyle Biomarkers Identification by Metabolomics.

Chronic diseases, also known as noncommunicable diseases (NCDs), are complex disorders that last for long periods of time and progress slowly. They currently account for the major cause of death worldwide with an alarming increase in rate both in developed and developing countries. In this chapter, the principal metabolomic-based investigations on chronic diseases (cardiovascular diseases, diabetes, and respiratory chronic diseases) and their major risk factors (particularly overweight/obesity) are described by focusing both on metabolites and metabolic pathways. Additional information on the contribution of metabolomics strategies in the ambit of the biomarker discovery for NCDs is also provided by exploring the major prospective studies of the last years (i.e., Framingham Heart Study, EPIC, MONICA, KORA, FINRIK, ECLIPSE). The metabolic signature of diseases, which arises from the metabolomic-based investigation, is therefore depicted in the chapter by pointing out the potential of metabolomics to explain the pathophysiological mechanisms underlying a disease, as well as to propose new therapeutic targets for alternative treatments.

New insight on obesity and adipose-derived stem cells using comprehensive metabolomics.

Obesity affects the functional capability of adipose-derived stem cells (ASCs) and their effective use in regenerative medicine through mechanisms that are still poorly understood. In the present study we used a multiplatform [LC/MS, GC/MS and capillary electrophoresis/MS (CE/MS)], metabolomics, untargeted approach to investigate the metabolic alteration underlying the inequalities observed in obesity-derived ASCs. The metabolic fingerprint (metabolites within the cells) and footprint (metabolites secreted in the culture medium), from obesity- and non-obesity-derived ASCs of humans or mice, were characterized to provide valuable information. Metabolites associated with glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway and the polyol pathway were increased in the footprint of obesity-derived human ASCs, indicating alterations in carbohydrate metabolism, whereas, from the murine model, deep differences in lipid and amino acid catabolism were highlighted. Therefore, new insights on the ASCs' metabolome were provided that enhance our understanding of the processes underlying ASCs' stemness capacity and its relationship with obesity, in different cell models.

From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS.

This tutorial provides a comprehensive description of the GC-MS-based untargeted metabolomics workflow including: ethical approval requirement, sample collection and storage, equipment maintenance and setup, sample treatment, monitoring of analytical variability, data pre-processing including deconvolution by free software such as AMDIS, data processing, statistical analysis and validation, detection of outliers and biological interpretation of the results. For each stage tricks will be suggested, pitfalls will be highlighted and advice will be provided on how to get the best from this methodology and technique. In addition, a step-by-step procedure and an example of our in-house library have been included in the supplementary material to lead the user through the concepts described herein. As a case study, an interesting example from one of our experiments at CEMBIO Research Centre is described, presenting an example of the use of this ready-to use protocol for identification of a metabolite that was not previously included in Fiehn commercial target library.

Altered metabolic and stemness capacity of adipose tissue-derived stem cells from obese mouse and human.

Adipose stem cells (ASCs) are an appealing source of cells for therapeutic intervention; however, the environment from which ASCs are isolated may impact their usefulness. Using a range of functional assays, we have evaluated whether ASCs isolated from an obese environment are comparable to cells from non-obese adipose tissue. Results showed that ASCs isolated from obese tissue have a reduced proliferative ability and a loss of viability together with changes in telomerase activity and DNA telomere length, suggesting a decreased self-renewal capacity. Metabolic analysis demonstrated that mitochondrial content and function was impaired in obese-derived ASCs resulting in changes in favored oxidative substrates. These findings highlight the impact of obesity on adult stem properties. Hence, caution should be exercised when considering the source of ASCs for cellular therapies since their therapeutic potential may be impaired.

Metabolomic evaluation of Mitomycin C and rapamycin in a personalized treatment of pancreatic cancer.

In a personalized treatment designed for a patient with pancreatic cancer resistant to other treatments, the success of Mitomycin C (MMC) has been highlighted. This was revealed in a murine xenograft tumor model encompassing pancreatic adenocarcinoma cells extracted from the patient. The patient was found to exhibit a biallelic inactivation of the PALB2 gene, involved in DNA repair in addition to another mutation in the TSC2 gene that induces susceptibility of the tumor to therapeutic targets of the PI3K-mTOR pathway. The aim of the study was to apply metabolomics to elucidate the modes of action of each therapy, suggesting why MMC was so successful in this patient and why it could be a more popular choice in future pancreatic cancer treatment. The effectiveness of MMC compared to rapamycin (RM), another relevant therapeutic agent has been evaluated through liquid- and gas-chromatography mass spectrometry-based metabolomic analyses of the xenograft tumors. The relative concentrations of many metabolites in the xenograft tumors were found to be increased by MMC relative to other treatments (RM and a combination of both), including a number that are involved in central carbon metabolism (CCM). Metabolic fingerprinting revealed statistically significantly altered pathways including, but not restricted to, the pentose phosphate pathway, glycolysis, TCA cycle, purine metabolism, fatty acid biosynthesis, in addition to many significant lipid and amino acid alterations. Given the genetic background of the patient, it was expected that the combined therapy would be most effective; however, the most effective was MMC alone. It is proposed that the effectiveness of MMC is owed to its direct effect on CCM, a vital region of tumor metabolism.

Metabolomics as a tool for drug discovery and personalised medicine. A review.

Studying the effects of drugs on the metabolome constitutes a huge part of the metabolomics discipline. Whether the approach is associated with drug discovery (altered pathways due to the disease that provide future targets and information into the mechanism of action or resistance, etc.) or pharmacometabolomics (studying the outcome of treatment), there have been many aspiring published articles in this area. With specific experimental design, including fingerprinting analysis with different analytical platforms in a non-targeted way, the approach is advancing towards the discovery of markers for the implication of personalised medicine, while also providing information that could help to improve the efficacy and reduce the side effects associated with a treatment. In this review, the evolution of pharmacometabolomics from other areas of drug efficacy metabolomics studies is explored.