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    Data yielded from RIViT-seq increased the number of sigma factor-gene pairs confirmed in Streptomyces coelicolor from 209 to 399. Here, grey arrows denote previously known regulation and red arrows are regulation identified by RIViT-seq; orange nodes mark sigma factors while gray nodes mark other genes. (Otani, H., Mouncey, N.J. Nat Commun 13, 3502 (2022). https://doi.org/10.1038/s41467-022-31191-w)
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    The HPCwire Editors Choice Award for Best Use of HPC in Life Sciences went to the Berkeley Lab team comprised of JGI and ExaBiome Project team, supported by the DOE Exascale Computing Project for MetaHipMer, an end-to-end genome assembler that supports “an unprecedented assembly of environmental microbiomes.”

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    Yeast strains engineered for the biochemical conversion of glucose to value-added products are limited in chemical output due to growth and viability constraints. Cell extracts provide an alternative format for chemical synthesis in the absence of cell growth by isolating the soluble components of lysed cells. By separating the production of enzymes (during growth) and the biochemical production process (in cell-free reactions), this framework enables biosynthesis of diverse chemical products at volumetric productivities greater than the source strains. (Blake Rasor)
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Home › Webinars › Engagement Webinar: Harnessing JGI’s Metabolomics Capabilities

November 19, 2020

Engagement Webinar: Harnessing JGI’s Metabolomics Capabilities

The JGI Engagement Webinar held November 5, 2020 (watch the video above, captions are available) spotlighted the metabolomic analysis capability available through the Community Science Program (CSP). This includes mass spectrometry-based measurements of primary and secondary metabolites from plants, microorganisms, and environmental samples using targeted and untargeted approaches. The webinar featured  Trent Northen and Ben Bowen of the JGI Metabolomics Program, along with Jenny Mortimer, Director of Plant Systems Biology at the Joint BioEnergy Institute (JBEI). Questions and answers from the session appear below.

Q&A from the Webinar:

Q(uestion): Is it possible to directly measure environmental or biological samples (like cell media or lake water) without doing a metabolite extraction? Or are these samples too dirty to be measured?
A(nswer): Possibly, it would depend on the sample. However, we don’t do this because they typically contain salts and other contaminants that interfere with metabolomics analysis. 

Q: Can you give us an idea about the scale of an average study? How many samples are typically involved? How long does it take to do all the analyses?
A: For the JGI New Investigator and Functional Genomics proposal calls, projects can request up to 50 polar and 150 nonpolar metabolomics analyses. For the other calls, we allow up to 200 polar and up to 500 nonpolar metabolomic analyses.  

Q: Can a Scientist from India can apply for support?
A: Anyone from anywhere around the world can apply to access the JGI’s resources.

Q: Where can I get information for funding by JGI?
A: The JGI is not a funding organization. Rather, it makes its sequencing, synthesis, metabolomics and data analyses capabilities available on a peer-reviewed competitive basis via its User Programs.

Q: Are applicants outside of JGI eligible to apply for CSP?  I am at NIST.
A: The JGI User Programs are designed to be available to academic, government agency, non-profit, and industry researchers worldwide. 

Q: To what extent is natural soil a limit to identify molecules exchanged between plants and microbiomes during interactions? Is it reliable performing an untargeted metabolomic analysis directly from the rhizosphere in natural soil?
A: It depends on the soil. Typically soils that have a high clay content are not well suited for metabolomics because metabolites are strongly sorbed to the clays. Sandy soils are much more likely to work well for metabolomics. 

Q: What tool or method is being used to integrate the metabolomics and transcriptomic data?
A: We use MAGI.
Reference: Erbiligin et al. MAGI: A Method for Metabolite Annotation and Gene Integration. ACS Chem Biol. 2019 Apr 19;14(4):704-714. doi: 10.1021/acschembio.8b01107. Epub 2019 Apr 4.

Q: Any advice on normalizing metabolomic data from a whole microbial community, without having cell counts or biovolume?
A:We typically use a measure of biomass, e.g. wet weight, total carbon analysis of the biomass, etc.

Q: If we have only the metabolomics data (secondary metabolomics), how can we map it into these networks?
A:We use networks based on the fragmentation spectra of the metabolites themselves for example using GNPS.

Q: Since the enzymes are doing the heavy lifting to make the metabolites, are there any plans to add proteomic data to the metabolomic and transcriptomic analyses?
A: This can be requested through the JGI/EMSL FICUS user program. 

Q: Is it possible to use these same techniques in drug discovery and synthesis from medicinal plants? If yes, we are based in South Africa, How can this team be of help to us in research design and execution?
A: Human health is outside of JGI’s mission. However, analysis of novel secondary metabolites from plants could be a good. 

Q: Do you offer any training opportunities for those of us looking to get into metabolomic analyses?
A: Not currently, but this is something we are considering

Q (re Jenny Mortimer’s presentation): What stressors do you think are affecting your results – could it be wind/light/nutrients.. any ideas?
A: At this point, we’re really not sure, although I suspect it’s a combination of those factors. While the plots were irrigated and fertilized to some extent, the water availability, temperature variations etc are all more extreme in the field. By having transcriptomics data across 2 years, as well as a second field trial (which will include a drought treatment), we hope to understand this better.

Q (re Jenny Mortimer’s presentation): You used 4 replicates for transcriptome analysis and 8 replicates for metabolomics. Why is this?
A: We did a pilot experiment with the JGI to look at the variability in the samples and test how many they thought would be necessary. Switchgrass has an enormous metabolome, and we wanted to have enough replicates that would allow us to identify significant changes. 

Q: Any advice on metabolomics result interpretation for beginners?
A: Please see this Metabolomics Data Analysis – Tips from Users page. 

Q: Is the metabolic study able to build a profile for a not very well studied plant? Both qualify and quantify?
A: Yes, metabolomics itself does not require that a plant be well characterized. Our results provide relative quantification of metabolites across your sample groups. We don’t typically do absolute quantification (uM etc). 

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