Project description:Ultra-high performance liquid chromatography-high resolution mass spectrometry (UPHLC-HRMS) is used to discover and monitor single or sets of biomarkers informing about metabolic processes of interest. The technique can detect thousands of molecules (i.e., metabolites) in a single instrument run and provide a measurement of the global metabolome, which could be a fingerprint of activity. Despite the power of this approach, technical challenges have hindered the effective use of metabolomics to interrogate microbial communities implicated in the removal of priority contaminants. Herein, our efforts to circumvent these challenges and apply this emerging systems biology technique to synthetic microbiomes relevant for contaminant biodegradation will be discussed. Chlorinated ethenes impact many contaminated sites, and detoxification can be achieved by organohalide-respiring bacteria, a process currently assessed by quantitative gene-centric tools (e.g., quantitative PCR). This laboratory study monitored the metabolome of the SDC-9 bioaugmentation consortium during cis-1,2-dichloroethene conversion to vinyl chloride and nontoxic ethene. Untargeted metabolomics using an UPLC-Orbitrap mass spectrometer and performed on SDC-9 cultures at different stages of the reductive dechlorination process detected ~10,000 spectral features per sample arising from water-soluble molecules with both known and unknown structures. Multivariate statistical techniques including partial least squares-discriminate analysis (PLS-DA) identified patterns of measurable spectral features (peak patterns) that correlated with dechlorination (in)activity. ANOVA analyses identified 18 potential biomarkers of reductive dechlorination activity. Statistical clustering of samples with these 18 features predicted dechlorination activity more reliably than clustering of samples based only on chlorinated ethene concentration and Dhc 16S rRNA gene abundance data, highlighting the value of metabolomic workflows as an innovative site assessment and bioremediation monitoring tool.

Project description:We used the mummichog (Fundulus heteroclitus) array we developed to test whether our arrays could be used to monitor the efficacy of remediation at an estuarine Superfund site. Shipyard Creek is a chromium-contaminated Superfund site in Charleston, SC undergoing remediation, therefore it provides a unique opportunity to study the efficacy of arrays as a molecular biomarker in of toxicant effects in mummichogs. Mummichogs were captured in Shipyard Creek in Charleston, SC prior to remediation (2000), after remediation began (2003), and as remediation further progressed (2005). Simultaneously, mummichogs were collected from a reference site at the Winyah-Bay National Estuarine Research Reserve (NERR). The hepatic gene expression pattern of fish captured at Shipyard Creek showed wide differences from the fish captured at NERR in 2000. As remediation progressed the gene expression pattern of fish captured at Shipyard Creek became increasingly similar to fish captured at NERR, and the number of genes differently expressed dropped from 22 to 4. The magnitude of differential gene expression of the individual genes also decreased during remediation. The recovering gene expression profile is associated with lower chromium bioavailability, demonstrated through significantly decreased body burden and sediment concentrations. For example, sediment concentrations at Shipyard Creek were 80-fold greater than NERR in 2000, 51-fold greater in 2003, and only 8-fold greater in 2005. However, hydraulic dredging in 2005 stirred up the sediments and increased body burden of chromium even though chromium sediment concentrations continued to drop. Therefore, the number of differentially expressed genes increased to 9. Overall, the data supports our hypothesis that arrays can be used to monitor site mitigation, as the number of genes differentially expressed mimics the body burden and also indicates when on-site remediation is increasing bioavailability. Keywords: Field site

			Action	DRS
	ERR11271600_1.fastq.gz	Fastqsanger.gz
	ERR11271600_2.fastq.gz	Fastqsanger.gz
	ERR11271601_1.fastq.gz	Fastqsanger.gz
	ERR11271601_2.fastq.gz	Fastqsanger.gz
	ERR11271602_1.fastq.gz	Fastqsanger.gz

Dataset Information

Undefined

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets