Dataset Information

RNAi-based biosynthetic pathway screens to identify in vivo functions of non-nucleic acid-based metabolites such as lipids.

ABSTRACT: The field of metabolomics continues to catalog new compounds, but their functional analysis remains technically challenging, and roles beyond metabolism are largely unknown. Unbiased genetic/RNAi screens are powerful tools to identify the in vivo functions of protein-encoding genes, but not of nonproteinaceous compounds such as lipids. They can, however, identify the biosynthetic enzymes of these compounds-findings that are usually dismissed, as these typically synthesize multiple products. Here, we provide a method using follow-on biosynthetic pathway screens to identify the endpoint biosynthetic enzyme and thus the compound through which they act. The approach is based on the principle that all subsequently identified downstream biosynthetic enzymes contribute to the synthesis of at least this one end product. We describe how to systematically target lipid biosynthetic pathways; optimize targeting conditions; take advantage of pathway branchpoints; and validate results by genetic assays and biochemical analyses. This approach extends the power of unbiased genetic/RNAi screens to identify in vivo functions of non-nucleic acid-based metabolites beyond their metabolic roles. It will typically require several months to identify a metabolic end product by biosynthetic pathway screens, but this time will vary widely depending, among other factors, on the end product's location in the pathway, which determines the number of screens required for its identification.

SUBMITTER: Zhang H

PROVIDER: S-EPMC5597045 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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RNAi-based biosynthetic pathway screens to identify in vivo functions of non-nucleic acid-based metabolites such as lipids.

Zhang Hongjie H Abraham Nessy N Khan Liakot A LA Gobel Verena V

Nature protocols 20150402 5

The field of metabolomics continues to catalog new compounds, but their functional analysis remains technically challenging, and roles beyond metabolism are largely unknown. Unbiased genetic/RNAi screens are powerful tools to identify the in vivo functions of protein-encoding genes, but not of nonproteinaceous compounds such as lipids. They can, however, identify the biosynthetic enzymes of these compounds-findings that are usually dismissed, as these typically synthesize multiple products. Here ...[more]

PMID: 25837419

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Project description:Unbiased global metabolomic profiling has not been used to identify distinct subclasses in patients with early sepsis and sepsis-associated acute respiratory distress syndrome. In this study, we examined whether the plasma metabolome reflects systemic illness in early sepsis and in acute respiratory distress syndrome.DesignPlasma metabolites were measured in subjects with early sepsis.SettingPatients were admitted from the emergency department to the ICU in a plasma sample collected within 24 hours of ICU admission. Metabolic profiling of 970 metabolites was performed by Metabolon (Durham, NC). Hierarchical clustering and partial least squares discriminant clustering were used to identify distinct clusters among patients with early sepsis and sepsis-associated acute respiratory distress syndrome.InterventionsNone.Measurements and main resultsAmong critically ill patients with early sepsis (n = 197), three metabolically distinct subgroups were identified, with metabolic subtype driven by plasma lipids. Group 1, with 45 subjects (23% of cohort), had increased 60-day mortality (odds ratio, 2; 95% CI, 0.99-4.0; p = 0.04 for group 1 vs all others). This group also had higher rates of vasopressor-dependent shock, acute kidney injury, and met Berlin acute respiratory distress syndrome criteria more often (all p < 0.05). Conversely, metabolic group 3, with 76 subjects (39% of cohort), had the lowest risk of 60-day mortality (odds ratio, 0.44; 95% CI, 0.22-0.86; p = 0.01) and lower rates of organ dysfunction as reflected in a lower Simplified Acute Physiology Score II (p < 0.001). In contrast, global metabolomic profiling did not separate patient with early sepsis with moderate-to-severe acute respiratory distress syndrome (n = 78) from those with sepsis without acute respiratory distress syndrome (n = 75).ConclusionsPlasma metabolomic profiling in patients with early sepsis identified three metabolically distinct groups that were characterized by different plasma lipid profiles, distinct clinical phenotypes, and 60-day mortality. Plasma metabolites did not distinguish patients with early sepsis who developed acute respiratory distress syndrome from those who did not.

Dataset Information

RNAi-based biosynthetic pathway screens to identify in vivo functions of non-nucleic acid-based metabolites such as lipids.

Publications

RNAi-based biosynthetic pathway screens to identify in vivo functions of non-nucleic acid-based metabolites such as lipids.

Similar Datasets

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