Project description:This project is about an untargeted metabolomic analysis in samples that come from chronic lung allograft dysfunction disease patients.
Project description:Background and aims: Gene mutations or variants leading to insufficient reactive oxygen species (ROS) production have been associated with inflammatory bowel disease (IBD). In particular, 40-50% of patients with chronic granulomatous disease have IBD (CGD-IBD). CGD is caused by inherited defects in any one of the 5 subunits forming the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex 2 (NOX2), leading to severely reduced or absent phagocyte-derived ROS production. While conventional IBD therapies can treat CGD-IBD, their benefits must be weighed against the risk of infection in this immune compromised population. Understanding the impact of NOX2 defects on the composition and function of the intestinal microbiota may lead to the identification of treatments for CGD-IBD. Methods: We evaluated GI symptom and quality of life scores, and clinical biomarkers of local (i.e. fecal occult blood and calprotectin) and systemic (i.e. CBC, CRP, ESR, and albumin) inflammation in a cohort of 79 patients with CGD, 8 mutation carriers and 17 healthy controls followed at the National Institutes of Health (NIH). We profiled the intestinal microbiome by 16S rRNA (V4 region) sequencing and the stool metabolome by mass spectrometry in all fecal samples, and further validated our findings by profiling the stool microbiome in a second cohort of 36 patients with CGD recruited from 11 centers across North-America through the Primary Immune Deficiency Treatment Consortium (PIDTC). Predictive functional profiling of the microbial communities based on 16S rRNA sequencing was also performed. Results: After controlling for significant variables, we show decreased alpha diversity and identified distinct intestinal microbiome and metabolomic profiles in patients with CGD compared to healthy individuals. In particular, we observed enrichment for Erysipelatoclostridium spp., Sellimonas spp. and Lachnoclostridium spp. in stool samples from patients with CGD. Despite differences in alpha and beta diversity in samples from the NIH compared to the PIDTC cohort, there were several bacterial taxa that correlated significantly between both cohorts. We further demonstrate that patients with active IBD and/or a history of IBD have a distinct microbiome and metabolomic profile compared to patients without CGD-IBD and identified bacterial taxa to be evaluated as potential markers of disease severity, as well as targets for future therapeutic interventions. Conclusions: Intestinal microbiome and metabolomic signatures distinguished patients with CGD and CGD-IBD and identified microbial and metabolomic candidates to be further evaluated as potential targets to improve the management of patients with CGD-IBD.
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing interstitial lung disease that is unresponsive to current therapy. While it carries a median survival of less than 3 years its rate of progression varies widely between patients. We hypothesized that studying the gene expression profiles of physiologically stable patients and those in which the disease progressed rapidly after the initial diagnosis would aid in the search for biomarkers and contribute to the understanding of disease pathogenesis. We generated 12 Idiopathic Pulmonary Fibrosis (IPF) lung parenchyma SAGE profiles. Initial cluster analysis including 8 other public available lung SAGE libraries verified that the IPF transcriptome is distinct from normal lung tissue and other lung diseases like COPD. In order to identify candidate markers of disease progression we segregated the IPF SAGE profiles in two groups based on clinical parameters regarding lung volume and lung function.
Project description:Identifying protein biomarkers for chronic obstructive pulmonary disease (COPD) has been challenging. Most previous studies have utilized individual proteins or pre-selected protein panels measured in blood samples. To identify COPD protein biomarkers by applying comprehensive mass spectrometry proteomics in lung tissue samples. We utilized mass spectrometry proteomic approaches to identify protein biomarkers from 152 lung tissue samples representing COPD cases and controls.
Project description:Rationale: Sepsis is a leading cause of morbidity and mortality; early diagnosis and prediction of progression is difficult to determine. The integration of metabolomic and transcriptomic data in an experimental model of sepsis may be a novel method to identify molecular signatures of clinical sepsis. Objectives: Develop a biomarker panel for earlier diagnosis and prognostic characterization of sepsis patients to inform personalized clinical management and improve understanding of the pathophysiology of sepsis progression. Methods: Mild to severe sepsis, lung injury and death was recapitulated in Macaca fascicularis by intravenous inoculation of Escherichia coli. Plasma samples were obtained at time of challenge and at one, three, and five days later or time of euthanasia. Necropsy was performed and blood, lung, kidney and spleen samples were obtained. An integrative analysis of comprehensive metabolomic and transcriptomic datasets was performed to identify and parameterize a biomarker panel. Measurements and Main Results: Pathogen invasion, respiratory distress, lethargy and mortality was dose dependent. Severe infection and death were associated with metabolomic and transcriptomic changes indicative of mitochondrial, peroxisomal and liver dysfunction. Analysis of reciprocal pulmonary transcriptome and plasma metabolome data revealed an integrated host response that suggested dysregulated fatty acid catabolism resulting from peroxisome-proliferator activated receptor signaling. A representative 4-metabolite model effectively diagnosed sepsis in primates (AUC 0.966) and in two human sepsis cohorts (AUC=0.78 and 0.82). Conclusion: A model to guide early management of patients with sepsis was developed by analysis of reciprocal metabolomic and transcriptomic data in primates that diagnosed sepsis in humans. Transcriptomic analysis of lungs from Cynomolgus macaques challenged with E. coli