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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)
    Streamlining Regulon Identification in Bacteria
    Regulons are a group of genes that can be turned on or off by the same regulatory protein. RIViT-seq technology could speed up associating transcription factors with their target genes.

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    (PXFuel)
    Designer DNA: JGI Helps Users Blaze New Biosynthetic Pathways
    In a special issue of the journal Synthetic Biology, JGI scientific users share how they’ve worked with the JGI DNA Synthesis Science Program and what they’ve discovered through their collaborations.

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    A genetic element that generates targeted mutations, called diversity-generating retroelements (DGRs), are found in viruses, as well as bacteria and archaea. Most DGRs found in viruses appear to be in their tail fibers. These tail fibers – signified in the cartoon by the blue virus’ downward pointing ‘arms’— allow the virus to attach to one cell type (red), but not the other (purple). DGRs mutate these ‘arms,’ giving the virus opportunities to switch to different prey, like the purple cell. (Courtesy of Blair Paul)
    A Natural Mechanism Can Turbocharge Viral Evolution
    A team has discovered that diversity generating retroelements (DGRs) are not only widespread, but also surprisingly active. In viruses, DGRs appear to generate diversity quickly, allowing these viruses to target new microbial prey.

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    Photograph of a stream of diatoms beneath Arctic sea ice.
    Polar Phytoplankton Need Zinc to Cope with the Cold
    As part of a long-term collaboration with the JGI Algal Program, researchers studying function and activity of phytoplankton genes in polar waters have found that these algae rely on dissolved zinc to photosynthesize.

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    This data image shows the monthly average sea surface temperature for May 2015. Between 2013 and 2016, a large mass of unusually warm ocean water--nicknamed the blob--dominated the North Pacific, indicated here by red, pink, and yellow colors signifying temperatures as much as three degrees Celsius (five degrees Fahrenheit) higher than average. Data are from the NASA Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR SST) Analysis product. (Courtesy NASA Physical Oceanography Distributed Active Archive Center)
    When “The Blob” Made It Hotter Under the Water
    Researchers tracked the impact of a large-scale heatwave event in the ocean known as “The Blob” as part of an approved proposal through the Community Science Program.

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    A plantation of poplar trees. (David Gilbert)
    Genome Insider podcast: THE Bioenergy Tree
    The US Department of Energy’s favorite tree is poplar. In this episode, hear from ORNL scientists who have uncovered remarkable genetic secrets that bring us closer to making poplar an economical and sustainable source of energy and materials.

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    HPCwire Editor's Choice Award (logo crop) for Best Use of HPC in the Life Sciences
    JGI Part of Berkeley Lab Team Awarded Best Use of HPC in Life Sciences
    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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    With a common set of "baseline metadata," JGI users can more easily access public data sets. (Steve Wilson)
    A User-Centered Approach to Accessing JGI Data
    Reflecting a structural shift in data access, the JGI Data Portal offers a way for users to more easily access public data sets through a common set of metadata.

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    Phytozome portal collage
    A More Intuitive Phytozome Interface
    Phytozome v13 now hosts upwards of 250 plant genomes and provides users with the genome browsers, gene pages, search, BLAST and BioMart data warehouse interfaces they have come to rely on, with a more intuitive interface.

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    screencap from Amundson and Wilkins subsurface microbiome video
    Digging into Microbial Ecosystems Deep Underground
    JGI users and microbiome researchers at Colorado State University have many questions about the microbial communities deep underground, including the role viral infection may play in other natural ecosystems.

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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)
    Boosting Small Molecule Production in Super “Soup”
    Researchers supported through the Emerging Technologies Opportunity Program describe a two-pronged approach that starts with engineered yeast cells but then moves out of the cell structure into a cell-free system.

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    These bright green spots are fluorescently labelled bacteria from soil collected from the surface of plant roots. For reference, the scale bar at bottom right is 10 micrometers long. (Rhona Stuart)
    A Powerful Technique to Study Microbes, Now Easier
    In JGI's Genome Insider podcast: LLNL biologist Jennifer Pett-Ridge collaborated with JGI scientists through the Emerging Technologies Opportunity Program to semi-automate experiments that measure microbial activity in soil.

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    A view of the mangroves from which the giant bacteria were sampled in Guadeloupe. (Hugo Bret)
    Giant Bacteria Found in Guadeloupe Mangroves Challenge Traditional Concepts
    Harnessing JGI and Berkeley Lab resources, researchers characterized a giant - 5,000 times bigger than most bacteria - filamentous bacterium discovered in the Caribbean mangroves.

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    In their approved proposal, Frederick Colwell of Oregon State University and colleagues are interested in the microbial communities that live on Alaska’s glacially dominated Copper River Delta. They’re looking at how the microbes in these high latitude wetlands, such as the Copper River Delta wetland pond shown here, cycle carbon. (Courtesy of Rick Colwell)
    Monitoring Inter-Organism Interactions Within Ecosystems
    Many of the proposals approved through JGI's annual Community Science Program call focus on harnessing genomics to developing sustainable resources for biofuels and bioproducts.

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    Coloring the water, the algae Phaeocystis blooms off the side of the sampling vessel, Polarstern, in the temperate region of the North Atlantic. (Katrin Schmidt)
    Climate Change Threatens Base of Polar Oceans’ Bountiful Food Webs
    As warm-adapted microbes edge polewards, they’d oust resident tiny algae. It's a trend that threatens to destabilize the delicate marine food web and change the oceans as we know them.

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Our Science
Home › Our Science › Science Programs › Metabolomics Program › Metabolite Analyses

Metabolite Analyses

At the JGI we analyze metabolite profiles from a wide range of samples including microbes, plants and fungi, as well as the media in which they grow and environment in which they are found, including soil, bodies of water (e.g. ocean, lake), etc.  Metabolites are extracted from each type of sample using a variety of methods and solvent systems optimized for specific metabolites of interest. Metabolites from extracts are then detected using our liquid-chromatography tandem mass spectrometry (LC-MS/MS) system, and analyzed using a “targeted” approach, in which standards run in-house are used for identification, and/or an “untargeted” approach, in which software and algorithms are used to detect features and putatively identify compounds, or both depending on the experiment hypotheses.

Currently, the following types of metabolite analyses can be requested in a CSP proposal, to which a “targeted” and/or “untargeted” analysis method is applied:

  1. Polar Metabolite Analysis – small, hydrophilic polar metabolites such as amino acids, nucleic acids, sugars and small organic acids, often involved in primary metabolism.  Uses normal phase HILICZ chromatography. Limit 200 samples.
  2. Non-polar Metabolite Analysis – generally non-polar metabolites not directly involved in primary metabolism, such as antibiotics, polyketides, phenolics.  Uses reverse phase C18 chromatography. Limit 500 samples.

The JGI also has capabilities to analyze isotopically-labeled compounds, so each polar and non-polar analysis can also include stable isotope probing (SIP) analysis. Here, the relative amount of incorporated heavy isotope into a compound is measured in experimental samples. These analyses are limited to metabolites detected using a “targeted” approach.

Additional Capabilities – The JGI also has limited capacity for lipidomics samples. Please contact Metabolomics group lead Trent Northen or Katherine Louie to discuss additional metabolomics analyses complementary to your research.

Polar Metabolite Analysis

Polar metabolite analysis consists of small, polar metabolites such as amino acids, nucleic acids, sugars and small organic acids that are typically part of primary metabolism, often playing a role in normal growth and development and part of fundamental metabolic pathways essential for survival.  Characterizing primary metabolites is important for examining interspecies interactions and cross-feeding, and can be used to determine what substrates are synthesized, taken up or released by different organisms under various environmental conditions.

Extraction is performed on a sample (e.g. media, microbial pellet) that has been lyophilized dry, then 100% methanol added to extract the primary metabolites while precipitating most proteins and salts.  Each sample extract is run on the JGI LC-MS/MS system with HILIC chromatography, with acquisition of UV as well as MS and MS/MS fragmentation spectra. (~1 hr/sample)

Non-polar Metabolite Analysis

Non-polar metabolite analysis consists of metabolites that have non-polar or hydrophobic chemical properties, and often encompasses secondary metabolites such as polyketides and phenolics.  Although not usually essential, secondary metabolites can provide evolutionary advantages important for survival. As more and more organisms are sequenced, more and more new, unique biosynthetic clusters are revealed each day, with the associated secondary metabolites yet to be discovered and functionally characterized.

Analyzing the non-polar or secondary metabolite profile of organisms under various conditions provides the opportunity to identify new compounds, and to gain insight and create linkages between sequence and function, especially in combination with transcriptomics performed on replicate samples.  In synthetic biology, this information can be used to determine relative synthesis levels between constructs, discover new compounds as they relate to organism genomics, as well as identify pathway intermediate, shunt products and precursors to better understand pathway dynamics.

Since secondary metabolites are typically non-polar or hydrophobic in nature, either chloroform, methanol or ethyl acetate is used for extraction of a sample.  Each sample extract is run on the JGI LC-MS/MS system with C18 chromatography, with acquisition of UV as well as MS and MS/MS fragmentation spectra. (~35 min/sample)

Stable isotope labeling

Stable isotope labeling analysis consists of profiling the relative amount of incorporation of a heavy isotope (e.g. 13C, 2H, 15N) into synthesized metabolites. Mass spectrometry is an ideal analysis tool for stable isotope labeling, since the isotope of each compound is detected in a typical mass spectrum.  Here, by comparing ion intensities of isotopes for a metabolite in unlabeled vs. labeled (treated with 13C or deuterium source, for instance) samples, active metabolic pathways can be traced through an organism, fate of a carbon source can be identified, and newly synthesized compounds can be identified.

The heavy isotope can be introduced, for example, as a labeled metabolite serving as a carbon source (e.g. 13C-acetate) to a microbial culture, and then using mass spectrometry to track to what degree (and potentially where) 13C is incorporated into metabolites in various metabolic pathways.  For these types of experiments, unlabeled control samples must also be prepared for analysis.

This type of analysis can be performed on any ion feature (unique m/z, RT pair) detected by mass spectrometry; however, currently JGI Metabolomics is restricting analysis to “targeted” metabolites only.  Here, the metabolites of interest need to be specified and have a standard available.

“Targeted” Approach

To identify metabolites using a “targeted” approach, we have already run a large library of metabolite standards (currently >500 and growing) to which we are able to compare retention time, m/z (detected mass/charge ratio) and fragmentation spectra to definitively identify metabolites in a sample.  Here is a representative sampling of metabolite standards in our library.  This approach can be used for any metabolite in which a standard is available, including secondary metabolites (e.g. violacein, prodigiosin).

“Untargeted” Approach

Often, a metabolite standard is not available and no database contains MS/MS fragmentation spectra, especially in the case of many secondary metabolites, or when little is known about the sample submitted for analysis and many unknown or novel compounds are expected.  Here, metabolites are putatively identified using an “untargeted approach” with a variety of developed software and algorithms.

Pactolus, a new version of MIDAS (Metabolite Identification via Database Searching), is one solution for “untargeted” analysis developed by LBL researchers.  This is essentially an open-source software tool that is able to predict MS/MS fragments from any chemical structure, real or theoretical, available in a database.  A metabolite in a sample can then be putatively identified, with a percent probability, by comparing predicted fragments to actual measured fragments from a sample. In the absence of MS/MS fragmentation data from a standard, this is a powerful tool for metabolite identification and dereplication for discovery.  Our current database has over 180,000 compounds for untargeted analysis. Additional software tools are in development for identification of unknowns and linkage to genomics.

Lipidomics

A diversity of lipids are found across organisms and environments, each playing critical roles in membrane organization, structure, and signaling, responding to environmental conditions such as desiccation or light, sporulation, as well as being important precursors for production of biofuels or involved in secondary metabolite biosynthesis.  Often, species of lipids (e.g. phosphatidylcholine, or PC lipid) are distinguished by a common headgroup attached to fatty acid chains of various carbon length and degree of saturation. Although several hundred forms of each species may exist, fragmentation during mass spectrometry analysis often leads to a characteristic fragmentation ion (e.g. 184 for PC) or neutral loss (e.g. -179 for MGDG) allowing for identification.

Here, we are able to identify common lipid species that have characteristic fragmentation spectra, and are often able to purchase a standard of each species.  Some lipid species we have recently annotated include TG, DG, PC, PE, SQDG, SM, MGDG, DGDG, PG and respective lyso-lipids. Please contact Trent Northen or Katherine Louie to discuss available lipidomics analyses.

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    • Metabolomics Results - Basic
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    • Metabolomics Data Analysis - Tips From Users
  • Secondary Metabolites

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