Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital aspect of metabolomics is sample preparation whereby inconsistent techniques generate unreliable results. This technique encompasses protein precipitation, liquid-liquid extraction, and solid-phase extraction as a means of fractionating metabolites into four distinct classes. Improved enrichment of low abundance molecules with a resulting increase in sensitivity is obtained, and ultimately results in more confident identification of molecules. This technique has been applied to plasma, bronchoalveolar lavage fluid, and cerebrospinal fluid samples with volumes as low as 50 µl. Samples can be used for multiple downstream applications; for example, the pellet resulting from protein precipitation can be stored for later analysis. The supernatant from that step undergoes liquid-liquid extraction using water and strong organic solvent to separate the hydrophilic and hydrophobic compounds. Once fractionated, the hydrophilic layer can be processed for later analysis or discarded if not needed. The hydrophobic fraction is further treated with a series of solvents during three solid-phase extraction steps to separate it into fatty acids, neutral lipids, and phospholipids. This allows the technician the flexibility to choose which class of compounds is preferred for analysis. It also aids in more reliable metabolite identification since some knowledge of chemical class exists.
Cigarette smoke exposure is linked to the development of a variety of chronic lung and systemic diseases in susceptible individuals. Metabolomics approaches may aid in defining disease phenotypes, may help predict responses to treatment, and could identify biomarkers of risk for developing disease. Using a mouse model of chronic cigarette smoke exposure sufficient to cause mild emphysema, we investigated whether cigarette smoke induces distinct metabolic profiles and determined their persistence following smoking cessation. Metabolites were extracted from plasma and fractionated based on chemical class using liquid-liquid and solid-phase extraction prior to performing liquid chromatography mass spectrometry-based metabolomics. Metabolites were evaluated for statistically significant differences among group means (p-value?0.05) and fold change ?1.5). Cigarette smoke exposure was associated with significant differences in amino acid, purine, lipid, fatty acid, and steroid metabolite levels compared to air exposed animals. Whereas 60% of the metabolite changes were reversible, 40% of metabolites remained persistently altered even following 2 months of smoking cessation, including nicotine metabolites. Validation of metabolite species and translation of these findings to human plasma metabolite signatures induced by cigarette smoking may lead to the discovery of biomarkers or pathogenic pathways of smoking-induced disease.
Although R packages exist for the pre-processing of metabolomic data, they currently do not incorporate additional analysis steps of summarization, filtering and normalization of aligned data. We developed the MSPrep R package to complement other packages by providing these additional steps, implementing a selection of popular normalization algorithms and generating diagnostics to help guide investigators in their analyses.
Genomics and proteomics have emerged as key technologies in biomedical research, resulting in a surge of interest in training by investigators keen to incorporate these technologies into their research. At least two types of training can be envisioned in order to produce meaningful results, quality publications and successful grant applications: (1) immediate short-term training workshops and (2) long-term graduate education or visiting scientist programs. We aimed to fill the former need by providing a comprehensive hands-on training course in genomics, proteomics and informatics in a coherent, experimentally-based framework. This was accomplished through a National Heart, Lung, and Blood Institute (NHLBI)-sponsored 10-day Genomics and Proteomics Hands-on Workshop held at National Jewish Health (NJH) and the University of Colorado School of Medicine (UCD). The course content included comprehensive lectures and laboratories in mass spectrometry and genomics technologies, extensive hands-on experience with instrumentation and software, video demonstrations, optional workshops, online sessions, invited keynote speakers, and local and national guest faculty. Here we describe the detailed curriculum and present the results of short- and long-term evaluations from course attendees. Our educational program consistently received positive reviews from participants and had a substantial impact on grant writing and review, manuscript submissions and publications.
Galectin-9 is a pleiotropic immune modulator affecting numerous cell types of innate and adaptive immunity. Patients with chronic infection with either hepatitis C virus (HCV) or HIV have elevated circulating levels. Limited data exist on the regulation of natural killer (NK) cell function through interaction with galectin-9. We found that galectin-9 ligation downregulates multiple immune-activating genes, including eight involved in the NK cell-mediated cytotoxicity pathway, impairs lymphokine-activated killing, and decreases the proportion of gamma interferon (IFN-?)-producing NK cells that had been stimulated with interleukin-12 (IL-12)/IL-15. We demonstrate that the transcriptional and functional changes induced by galectin-9 are independent of Tim-3. Consistent with these results for humans, we find that the genetic absence of galectin-9 in mice is associated with greater IFN-? production by NK cells and enhanced degranulation. We also show that in the setting of a short-term (4-day) murine cytomegalovirus infection, terminally differentiated NKs accumulate in the livers of galectin-9 knockout mice, and that hepatic NKs spontaneously produce significantly more IFN-? in this setting. Taken together, our results indicate that galectin-9 engagement impairs the function of NK cells, including cytotoxicity and cytokine production.
To investigate autoantigens in ?-cells, we have used a panel of pathogenic T-cell clones that were derived from the NOD mouse. Our particular focus in this study was on the identification of the target antigen for the highly diabetogenic T-cell clone BDC-5.2.9.
Technological innovations have increased our potential to evaluate global changes in protein and small molecule levels in a rapid and comprehensive manner. This is especially true in mass spectrometry-based research where improvements, including ease-of-use, in high performance liquid chromatography (HPLC), column chemistries, instruments, software, and molecular databases have advanced the fields of proteomics and metabolomics considerably. Applications of these technologies in clinical research include biomarker discovery, drug targeting, and elucidating molecular networks, and a systems-based approach, utilizing multiple "omics," can also be taken. While the exact choice of workflow can dramatically impact the results of a study, the basic steps are similar, both within and between metabolomics and proteomics experiments. Although gel-based methods of quantitation are still widely used, our laboratory focuses on mass spectrometry-based methods, specifically protein and small molecule profiling.
Autoreactive CD4(+) T cells are involved in the pathogenesis of many autoimmune diseases, but the antigens that stimulate their responses have been difficult to identify and in most cases are not well defined. In the nonobese diabetic (NOD) mouse model of type 1 diabetes, we have identified the peptide WE14 from chromogranin A (ChgA) as the antigen for highly diabetogenic CD4(+) T cell clones. Peptide truncation and extension analysis shows that WE14 bound to the NOD mouse major histocompatibility complex class II molecule I-A(g7) in an atypical manner, occupying only the carboxy-terminal half of the I-A(g7) peptide-binding groove. This finding extends the list of T cell antigens in type 1 diabetes and supports the idea that autoreactive T cells respond to unusually presented self peptides.
To describe clinical proteomics from discovery techniques and their limitations, to applications in allergy, asthma, and immunology, and finally to how proteomics can be integrated into clinical practice.
Recombinant human activated protein C (rhAPC) is associated with improved survival in high-risk patients with severe sepsis; however, the effects of both lipopolysaccharide (LPS) and rhAPC on the bronchoalveolar lavage fluid (BALF) proteome are unknown.
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