We thank Angelika Berg for laboratory assistance. This work was supported and financed by the Max Planck Society and the Jena School for Microbial Communication (JSMC).

1. Insect Rearing
<ol>
	<li>Purchase or obtain eggs clutches of Spodoptera littoralis from your own rearing. Maintain them in sterile Petri dishes at room temperature (RT) until hatching.</li>
	<li>Prepare the artificial diet for the insects rearing as follows:
	<ol>
		<li>Soak 500 g ground white beans overnight in 100 ml of water.</li>
		<li>Add 9.0 g ascorbic acid and 75 g agar to 1,000 ml distilled H2O and afterwards boil it.</li>
		<li>Let the mixture to cool down and when it solidifies (to a white waxy solid mixture) store it at 4 &#176;C until use.</li>
	</ol>
	</li>
	<li>Transfer the newly hatched larvae to a clean plastic box and rear them on the artificial diet at 23-25 &#176;C under a long-day regime with 16 hr of illumination and 8 hr of darkness. Change the food every day until the larvae go through all six larval instars.</li>
</ol>
2. 13C-labeling of DNA in Metabolically Active Gut Bacteria
<ol>
	<li>Dissolve 186.1 mg of fully 13C-labeled glucose with distilled and deionized water (ddH2O) and mix it well with 100 g of artificial diet for the SIP experiment. Ensure a final 13C-glucose concentration of 10 mM in the artificial diet. This mimics the in situ glucose concentration in the gut of S. littoralis larvae feeding on cotton plants according to Shao et al. (submitted).</li>
	<li>Transfer 9-15 healthy second instar larvae from the rearing box to sterile plastic Petri dishes (one larva per Petri dish) containing fresh 13C-glucose spiked artificial diet. Use artificial diet containing the same amount of native glucose (12C-glucose), as described, for feeding the control group.</li>
	<li>Renew the artificial diet frequently during the incubation period (1 day). Use rearing conditions as in step 1.3.</li>
	<li>Collect nine larvae from each treatment for later processing (DNA extraction). Each replicate is constituted by three individuals; make at least three replicates per treatment.</li>
</ol>
3. Insect Dissection
<ol>
	<li>Clean and disinfect the dissecting tools at the beginning of the work with ethanol 70% (Tools= Vannas scissors, forceps and scalpel with fine disposable blades).</li>
	<li>Take the insects with a soft forceps and wash their skin superficially with distilled water. Place them on ice for at least 30 min to anesthetize them.</li>
	<li>While still anesthetized place them at 0 &#176;C for 1 hr to kill them. Disinfect the dead insects superficially by immersing them in ethanol (70%) for a couple of minutes.</li>
	<li>Perform the insect dissection into a volume of approximately 15 ml of 1x PBS deposited on a sterile Petri dish (1XPBS= 137 mM NaCl, 2.7 mM KCl, 4.3 mM Na2HPO4, 1.47 mM KH2PO4, adjust the pH to 7.4 , autoclave). Be sure of having the whole insect body fully immersed in the solution during the whole dissecting period.</li>
	<li>Begin the dissection by making an incision in the skin along the right and left sides of the insect, commence on the head and finish on the anus. Remove the whole skin, Malpighian tubules, and fat body. Change to fresh buffer before cutting open the gut. Section the gut in fore-, mid-, and hindgut when required. Store the tissue in the freezer (-80 to -20 &#176;C) until DNA extraction</li>
</ol>
4. DNA Extraction and Amplification
<ol>
	<li>Place the insect tissue in a 1.5 ml sterile centrifuge tube and dry all samples at 45 &#176;C in a vacuum concentrator device. Crush dried tissue with a sterile plastic pestle.</li>
	<li>Extract the genomic DNA using a suitable kit for soil DNA extraction. Transfer the dry tissue to the reaction tube of the kit and follow the instructions as indicated by the manufacturer.</li>
	<li>Determine the concentration of the final eluted DNA using a spectrophotometer.</li>
	<li>Verify a successful extraction of the microbial metagenomic DNA from the insect gut by preparing a diagnostic PCR assay using Eubacterial general primers 27f (5- AGAGTTTGATCCTGGCTCAG -3) and 1492r (5- GGTTACCTTGTTACGACTT -3). Set the PCR reaction as follows:
	<ol>
		<li>Set a 50 &#181;l reaction mixture containing 1x Buffer, 1.5 mM MgCl2, 10 mM four deoxynucleoside triphosphates (dNTPs), 2.5 U of Taq DNA Polymerase, 0.5 mM of each primer and 60 ng of the extracted DNA as template.</li>
		<li>Run the PCR reactions using a thermocycler as follows: initial denaturation at 94 &#176;C for 3 min; followed by 35 cycles: 94 &#176;C for 45 sec, annealing at 55 &#176;C for 30 sec, and extension at 72 &#176;C for 1 min. Use a final extension step at 72 &#176;C for 10 min.</li>
		<li>Verify the successful PCR amplification in a 1% agarose electrophoresis ethidium bromide (EB) stained gel (dissolve 1 g of agarose in 100 ml of 1x TAE buffer and stain with 1&#181;l EB ). Deposit 5 &#181;l of the PCR product + 1&#181;l of 6x loading dye into the gel pockets. (1x TAE=40 mM Tris-Base, 20 mM glacial acetic acid, 1 mM EDTA, adjust to pH 8).</li>
		<li>Run the gel using 1xTAE buffer at 300 V, 115 mA for ca. 20 min in an electrophoresis chamber. By using a gel documentation chamber (UV transilluminator connected to a digital camera and computer) observe the gel and compare the size of your final product with the also loaded gene marker; the correct size of the amplicons must be of approximately 1.5 kb.</li>
	</ol>
	</li>
	<li>Store the PCR products under deep freezing conditions, preferentially -80 &#176;C, until use.</li>
</ol>
5. DNA Separation-CsCl Gradient Ultracentrifugation
<ol>
	<li>Prepare the reagents for the gradients for ultracentrifugation as follows:
	<ol>
		<li>Prepare a 7.163 M cesium chloride (CsCl) solution by gradually dissolving 603.0 g of CsCl in ddH2O and fill up to a final volume of 500 ml.</li>
		<li>Accurately determine the density of the prepared CsCl solution by weighing a 1.0 ml aliquot on a three digit balance by triplicate. This usually ranges from 1.88 and 1.89 g/ml at RT.</li>
		<li>Prepare the gradient buffer (100 mM Tris, 100 mM KCl and 1 mM EDTA) by combining 50 ml of 1 M Tris-HCl (pH 8.0), 1 ml of 0.5 M EDTA (pH 8) and 3.75 g of KCl in a final volume of 500 ml using ddH2O. Filter (0.22 &#181;m) sterilize and autoclave this solution.</li>
	</ol>
	</li>
	<li>DNA Separation
	<ol>
		<li>Having determined the individual DNA concentration of the treated insects, measured as in point 4.3, pool those insects corresponding to a replicate together and normalize their DNA concentration to ensure that each one, equally contributes to the pooled sample. A final concentration of 500-5,000 ng of DNA (measure again the final concentration) is suitable for the ultracentrifugation process21.</li>
		<li>To set up the gradient medium, combine the quantified DNA, gradient buffer and 4.8 ml of 7.163 M CsCl solution together to a mixed volume of 6.0 ml in a sterile 15 ml screw-cap tube (generation of a gradient medium-DNA mixture).</li>
		<li>Carefully place the prepared gradient medium-DNA mixture into a 5.1 ml ultracentrifuge tube (fill in until the base of the tube neck) using a syringe and needle. Avoid pumping or forming any air bubbles. Include at least one blank gradient (using ddH2O instead of DNA) in each centrifuge run as a reference gradient.</li>
		<li>Balance tube pairs to within 10 mg of weight after each required gradient medium is filled into the respective ultracentrifuge tube. Seal the tube with a &#34;Tube Topper&#34; and ensure that the seal is leak-free by inverting the tube and applying moderate pressure by hand.</li>
		<li>Use a near vertical rotor with eight wells for holding 5.1 ml ultracentrifugation tubes for the ultracentrifugation. Carefully seal the rotor wells and load the rotor into the ultracentrifuge according to the manufacturer&#39;s instructions. Set the ultracentrifugation conditions: spin at 50,000 rpm (around 177,000 x g), temperature at 20 &#176;C, and ultracentrifugation running time for 40 hr with vacuum. Select maximum acceleration and the deceleration without brake.</li>
		<li>Once finished, carefully remove the tubes from the centrifuge rotor using forceps. Place them in a rack without disturbing the formed gradients within the tubes. Process the gradient fractionation samples as soon as possible to minimize any diffusion.</li>
	</ol>
	</li>
	<li>Retrieval of DNA from Isopycnic Separated Gradients by Fractionation
	<ol>
		<li>Use an accurate HPLC pump system to carry out the gradient fractionation, which equally separates the formed gradients from the ultracentrifuge tube top displacement method. Connect the HPLC pump (100% flow gradient) to the bottle filled with 1 L displacement ddH2O and tightly attach to the open pump tubing with a 23 G 1 in syringe needle. Add sufficient bromophenol blue dye to the ddH2O to give it a dark blue color. This facilitates the visualization of the interface between the gradient medium and the displacement of ddH2O.</li>
		<li>Set the pump to a flow rate of 850 &#181;l/min and equilibrate the system until a single drop of the blue-colored ddH2O emerges out of the syringe needle.</li>
		<li>Carefully fix the ultracentrifuge tube to a clamp stand and pierce the bottom of the tube with a syringe 23 G 1 in long needle. Connect the pump to the ultracentrifuge tube by inserting the needle attached to the pump tubing into the top of the tube, and collect drops from the bottom directly into sterile 1.5 ml microcentrifuge tubes. Collect a fraction every 30 sec, amounting to approximately 425 &#181;l per fraction and a total of 12 fractions per gradient. Note that the last fraction (fraction 12) usually contains the displacement ddH2O and should be discarded.</li>
		<li>After fractionation, measure the density of all separated fractions using an analytical balance as mentioned in step 5.1.2.</li>
		<li>Precipitate the DNA from each fraction (around 425 &#181;l) by first adding 1 &#181;l glycogen (20 &#181;g/&#181;l in ddH2O, autoclaved) as a carrier for the precipitation of low amount of DNA. Mix it well by inversion.</li>
		<li>Add two volumes (850 &#181;l) of polyethylene glycol (PEG) solution (dissolve 150 g of polyethylene glycol 6000 and 46.8 g of NaCl in ddH2O to a total volume of 500 ml. Filter, sterilize and autoclave. The PEG solution is 30% PEG 6000 and 1.6 M NaCl), and mix again by inverting ten times.</li>
		<li>Leave the tubes at RT for at least two hours (if necessary, overnight) to precipitate the DNA.</li>
		<li>Centrifuge the precipitates at 13,000 x g for 30 min at RT. A visible pellet should form.</li>
		<li>Carefully remove the supernatant with a pipette and wash the pellet with 500 &#181;l of 70% ethanol. Centrifuge under the same conditions for 10 min again.</li>
		<li>Carefully discard the supernatant and air-dry the pellet at RT for 15 min.</li>
		<li>Redissolve the dried DNA pellet in 30 &#181;l of ddH2O and mix well by gently tapping the tube. Quantify the DNA in each gradient fraction as in step 4.3 Store the samples under -20 or -80 &#176;C if long term storage is required.</li>
	</ol>
	</li>
</ol>
6. DNA Characterization by Pyrosequencing
<ol>
	<li>Ensure that the unlabeled native (12C) DNA occupies the density range from 1.705 g/ml to 1.720 g/ml, whereas 13C-labeled DNA has a density of around 1.720-1.735 g/ml. Note that both the native and 13C-labeled DNA extracted from environmental samples can span several gradient fractions. Expect the light DNA to be associated with fractions 9-11 and the heavy DNA to be associated with fractions 4-6.</li>
	<li>Combine key fractions to create a single &#34;compiled&#34; representative fraction according to the specific peak allocation of the density-resolved DNA. Alternatively, other means to examine the separated fractions by using the isotopic ratio mass spectrometry (IRMS)22 or a fingerprinting method, such as denaturing gradient gel electrophoresis (DGGE)23, can help to choose appropriate fractions before the sequencing.</li>
	<li>Perform pyrosequencing of the PCR-amplified bacterial 16S rRNA genes from the compiled heavy, middle and light fractions of each individual gradient. Using this method, identification of the lineage and relative abundance of metabolically active bacteria species in the SIP incubated samples is possible. Perform pyrosequencing as follows:
	<ol>
		<li>Perform amplicon pyrosequencing using a Roche 454 FLX instrument with Titanium reagents.</li>
		<li>Prepare barcoded amplicons for multiplexing using the fusion primer set Gray28F (5- GAGTTTGATCNTGGCTCAG -3) and Gray519r (5- GTNTTACNGCGGCKGCTG -3)24,25 extended with the respective primer Adaptor A/B and sample-specific multiplex identifiers (MID).</li>
		<li>Apply a one-step PCR with 30 cycles, using a Hot Start Taq polymerase, to generate the sequencing library.</li>
		<li>Sequence the amplicons based upon the supplier protocol, and extend the reads from the forward direction (Gray28F) as described in <a href="http://www.researchandtesting.com/">http://www.researchandtesting.com/</a>.</li>
	</ol>
	</li>
</ol>
7. Pyrosequencing Data Analysis
<ol>
	<li>Analyze the raw sequence reads generated by the 454 pyrosequencing with the &#34;quantitative insights into microbial ecology&#34;, QIIME software pipeline26. Perform the processing as follows:
	<ol>
		<li>Initially, convert the 454-machine-generated sff file to FASTA, QUAL and Flowgram files with the process_sff.py script.</li>
		<li>Validate the mapping file (based on check_id_map.py) and assign multiplexed reads to biological samples with the split_libraries.py script. Trim the low-quality ends with a sliding window size of 50 and an average quality cutoff parameter of 25, eliminate also short ambiguous sequences (the length &#60;200 bp) from the dataset.</li>
		<li>Denoise the 454 data using the denoise_wrapper.py.</li>
		<li>Next, cluster the high-quality reads into operational taxonomic units (OTUs) using the multiple OTU picking method (pick_otus.py with 97% sequence similarity cut-offs).</li>
		<li>Pick a representative sequence from each OTU using pick_rep_set.py.</li>
		<li>Assign taxonomy to the representative sequence set based on assign_taxonomy.py. Use the Ribosomal Database Project (RDP) classifier with a minimum confidence to record assignment set to 0.80.</li>
		<li>Align the representative sequence set against the prealigned Greengenes core 16S sequences using the algorithm PyNast (align_seqs.py with the minimum sequence identity percent set to 75).</li>
		<li>Additionally, deplete chimeras from further analysis by running identify_chimeric_seqs.py.</li>
		<li>Remove all gap positions (not useful for phylogenetic inference) with the lanemask template (filter_alignment.py) prior to building a phylogenetic tree (make_phylogeny.py).</li>
		<li>Finally, generate OTU tables with make_otu_table.py, describing the occurrence of bacterial phylotypes within each sample. Use the OTU table to construct the heatmap for comparing the bacterial abundance and activity.</li>
		<li>If required, compute the alpha and beta diversity within and between samples, respectively. In the same manner, rarefaction curves (graphs of diversity versus sequencing depth) and Principal Coordinates Analysis (PCoA) plots representing the relationships among microbial communities can be also prepared.</li>
	</ol>
	</li>
</ol>

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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+gut+bacteria+of+insects:+nonpathogenic+interactions.">The gut bacteria of insects: nonpathogenic interactions.</a> Annu. Rev. Entomol. 49, 71-92 (2004).</li><li>Neufeld, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+stable-isotope+probing.">DNA stable-isotope probing.</a> Nat. Protoc. 2, 860-866 (2007).</li><li>Dumont, M. G., Murrell, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stable+isotope+probing+-+linking+microbial+identity+to+function.">Stable isotope probing - linking microbial identity to function.</a> Nat. Rev. Microbiol. 3, 499-504 (2005).</li></ol>

We have nothing to disclose.

The gut of most insects harbors a rich and complex microbial community; typically 107-109 prokaryotic cells lodge there, outnumbering the host&#39;s own cells in most cases. Thus, the insect gut is a &#34;hot spot&#34; for diverse microbial activities, representing multiple aspects of microbial relationships, from pathogenesis to obligate mutualism27. Although many studies have described an amazing variety of insect gut microbial communities, characterization of the metabolic activity of ...

Insect-bacterial symbiotic associations are known for a great number of insect species7. In these symbiotic associations, microorganisms play important roles in the growth and development of insects. Microbes have been shown to contribute to insect reproduction1, pheromone biosynthesis3, nutrition, including the synthesis of essential amino acids4, and digestion of inaccessible food to the host. Despite the vast variety of gut-bacterial associations, much less is known about the functional role they play in favor of the insect. Only in case of the termites, the symbiotic digestion of lignocellulose carried out by prokaryotes, protozoa, and fungi, has been widely studied8,9. In contrast to this, little is known about the symbiotic association present in the gut of generalist insects i.e. the cotton leafworm, Spodoptera littoralis. Moreover, due to their frequent shift of plant hosts, generalist insects and their gut associated bacterial communities are permanently exposed to new challenges linked to their feeding habits consuming plants with a plethora of phytochemicals. Beside this, the gut environment in lepidopterans, represents per se a harsh environment for the growth of bacteria because of the high gut pH10. Particularly in the case of S. littoralis, it ranges from 10.5 in the foregut, ca. 9 in the midgut to pH almost 7 in the hindgut11. On the other hand, the bacterial community associated with the gut of S. littoralis is simple. Tang, Freitak, et al.12 reported a maximum of 36 phylotypes belonging to a total of 7 different bacterial species as the only members of the bacterial community associated with this insect. Besides this, no complicated rearing procedure is required for the insect growth in the laboratory. Furthermore, this and the short life cycle of the insect facilitate multi-generational studies, turning this species into an ideal model system for studying gut-microbe interactions.
With the advent of PCR-based sequencing technologies, the number of studies dealing with gut biota of several organisms (i.e. humans, insects, or marine organisms) has increased. Moreover, the results are independent from isolation and cultivation of the gut harbored bacteria as in the past. Almost 99% of bacteria are not cultivable and the simulation of the environmental conditions prevailing in the gut is difficult12. By using PCR, 16S rRNA gene fragments (a widely used phylogenetic gene marker among bacteria) could be selectively amplified from a mixed DNA template of gut bacterial communities, sequenced, and cloned. With this information, the user is able to identify the bacterial species after retrieving the sequence information from public databases13,14. Nevertheless, the sequencing approaches to describe bacterial communities remain insufficient due to lack of information on the intrinsic metabolic contribution of the individual species within the community.
Stable-isotope probing (SIP) is a promising culture-free technique. It is often used in environmental microbiology to analyze microbial phylogenies linked to particular metabolic activities. This is achieved by tracking stable, isotope labeled atoms from substrates into microbial biomarkers, such as phospholipid-derived fatty acids, DNA, and RNA5. When considering nucleic acids, the methodology is based upon the separation of 13C-labeled DNA or RNA from the unlabeled DNA by density-gradient ultracentrifugation6. Due to this direct connection between the DNA label and metabolic activity, a down&#173;stream molecular analysis of the nucleic acids identifies the species and provides information on metabolic activities. Moreover, combination of DNA-SIP and pyrosequencing as applied by Pilloni, von Netzer, et al.15, permits a particular simple and sensitive identification of the bacterial species present in the heavy 13C-labeled DNA fraction. Up to now, this technique has been applied to describe the bacterial communities involved in biogeochemical processes in the soil under aerobic16,17 and anaerobic conditions18,19. Besides of the use in environmental science, the technique has been applied in medical sciences as reported by Reichardt, et al.5, who described the metabolic activities of different phylogenetic groups of the human intestinal microbiota in response to a nondigestible carbohydrate.
Here we use 13C-glucose to &#39;label&#39; the DNA of the metabolically active bacterial species in the gut. Glucose is a sugar utilized by most bacterial species along the widespread Entner-Doudoroff (ED) pathway, although exceptions are known20. This justifies the use of 13C-glucose as a reliable metabolic probe that provides a link between metabolites of interest and the carbon source along established pathways. Depending on the scientific question, other substrates, i.e. 13C-methane, 13CO2, or plants raised under a 13CO2 atmosphere, can be used to address metabolic activities.
At this point, we present the protocol applied in the metabolic characterization of the gut bacterial community of a generalist insect, namely S. littoralis (Lepidoptera, Noctuidae). Moreover, the technique was coupled to pyrosequencing, which in turn allows the identification of insect gut bacterial community with high resolution and precision. As the main substrate, 13C-labeled glucose was utilized during the experiments.

<table><tbody><tr><td>Dumont #5 Mirror Finish Forceps</td><td>Fine Science Tools</td><td>11251-23</td><td>&amp;nbsp;</td></tr><tr><td>Vannas Spring Scissors</td><td>Fine Science Tools</td><td>15000-00</td><td>&amp;nbsp;</td></tr><tr><td>Speed Vacuum Concentrator 5301</td><td>Eppendorf Germany</td><td>5305 000.304</td><td>&amp;nbsp;</td></tr><tr><td>Plastic pestle</td><td>Carl Roth GmbH Co. Germany</td><td>P986.1</td><td>&amp;nbsp;</td></tr><tr><td>NanoVue spectrophotometer</td><td>GE HealthCare, UK</td><td>28-9569-58</td><td>&amp;nbsp;</td></tr><tr><td>Mastercycler pro/thermocycler</td><td>Eppendorf Germany</td><td>6321 000.515</td><td>&amp;nbsp;</td></tr><tr><td>Agagel Standard Horizontal Gel Electrophoresis chamber</td><td>Biometra</td><td>Discontinued</td><td>&amp;nbsp;</td></tr><tr><td>Ultracentrifuge (Optima L-90K)</td><td>Beckman</td><td>A20684</td><td>&amp;nbsp;</td></tr><tr><td>Ultracentrifuge rotor (NVT 90)</td><td>Beckman</td><td>362752</td><td>&amp;nbsp;</td></tr><tr><td>HPLC pump</td><td>Agilent</td><td>1100</td><td>&amp;nbsp;</td></tr><tr><td>Quick-Seal, Polyallomer tube</td><td>Beckman</td><td>342412</td><td>&amp;nbsp;</td></tr><tr><td>Transilluminator UVstar 15&amp;nbsp;</td><td>Biometra</td><td></td><td>&amp;nbsp;</td></tr></tbody></table>

identification of metabolically active bacteria in the gut of the generalist spodoptera littoralis via dna stable isotope probing using 13c-glucose

Guts of most insects are inhabited by complex communities of symbiotic nonpathogenic bacteria. Within such microbial communities it is possible to identify commensal or mutualistic bacteria species. The latter ones, have been observed to serve multiple functions to the insect, i.e. helping in insect reproduction1, boosting the immune response2, pheromone production3, as well as nutrition, including the synthesis of essential amino acids4, among others. &#160; &#160;
Due to the importance of these associations, many efforts have been made to characterize the communities down to the individual members. However, most of these efforts were either based on cultivation methods or relied on the generation of 16S rRNA gene fragments which were sequenced for final identification. Unfortunately, these approaches only identified the bacterial species present in the gut and provided no information on the metabolic activity of the microorganisms.
To characterize the metabolically active bacterial species in the gut of an insect, we used stable isotope probing (SIP) in vivo employing 13C-glucose as a universal substrate. This is a promising culture-free technique that allows the linkage of microbial phylogenies to their particular metabolic activity. This is possible by tracking stable, isotope labeled atoms from substrates into microbial biomarkers, such as DNA and RNA5. The incorporation of 13C isotopes into DNA increases the density of the labeled DNA compared to the unlabeled (12C) one. In the end, the 13C-labeled DNA or RNA is separated by density-gradient ultracentrifugation from the 12C-unlabeled similar one6. Subsequent molecular analysis of the separated nucleic acid isotopomers provides the connection between metabolic activity and identity of the species.
Here, we present the protocol used to characterize the metabolically active bacteria in the gut of a generalist insect (our model system), Spodoptera littoralis (Lepidoptera, Noctuidae). The phylogenetic analysis of the DNA was done using pyrosequencing, which allowed high resolution and precision in the identification of insect gut bacterial community. As main substrate, 13C-labeled glucose was used in the experiments. The substrate was fed to the insects using an artificial diet.

To achieve sufficient labeling of the metabolically active bacteria present in the insect gut, the insect must be exposed to the 13C-rich substrate for a previously optimized period sufficient to allow the separation of the labeled heavier fraction easily from the unlabeled lighter one. In our case, 13C-glucose was supplemented in the artificial diet at a final concentration of 10 mM for 1 day (Figure 1A). The same amount of normal glucose (Figure 1B) was supplied i...

Watch this Scientific Journal Video about Identification of Metabolically Active Bacteria in the Gut of the Generalist Spodoptera littoralis via DNA Stable Isotope Probing Using 13C-Glucose at JoVE.co

Identification of Metabolically Active Bacteria in the Gut of the Generalist Spodoptera littoralis via DNA Stable Isotope Probing Using 13C-Glucose

The active bacterial community associated with the gut of Spodoptera littoralis, was determined by stable-isotope-probing (SIP) coupled to pyrosequencing. Using this methodology, identification of the metabolically active bacteria species within the community was done with high resolution and precision.

Identification of Metabolically Active Bacteria in the Gut of the Generalist Spodoptera littoralis via DNA Stable Isotope Probing Using 13C-Glucose

Guts of most insects are inhabited by complex communities of symbiotic nonpathogenic bacteria. Within such microbial communities it is possible to ...

identification-metabolically-active-bacteria-gut-generalist

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

15.3K Views.  Max Planck Institute for Chemical Ecology. The active bacterial community associated with the gut of Spodoptera littoralis, was determined by stable-isotope-probing (SIP) coupled to pyrosequencing. Using this methodology, identification of the metabolically active bacteria species within the community was done with high resolution and precision.

Video: Identification of Metabolically Active Bacteria in the Gut of the Generalist Spodoptera littoralis via DNA Stable Isotope Probing Using 13C-Glucose

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. Moreover, even cultures of a single cell type may contain cells in distinct metabolic states, such as resting or cycling cells. Methods for measuring metabolic activities of such subpopulations are valuable tools for understanding cellular metabolism. Complex cell populations are most commonly separated using a cell sorter, and subpopulations isolated by this method can be analyzed by metabolomics methods. However, a problem with this approach is that the cell sorting procedure subjects cells to stresses that may distort their metabolism.
To overcome these issues, we reasoned that the mass isotopomer distributions (MIDs) of metabolites from cells cultured with stable isotope-labeled nutrients are likely to be more stable than absolute metabolite concentrations, because MIDs are formed over longer time scales and should be less affected by short-term exposure to cell sorting conditions. Here, we describe a method based on this principle, combining cell sorting with liquid chromatography-high resolution mass spectrometry (LC-HRMS). The procedure involves analyzing three types of samples: (1) metabolite extracts obtained directly from the complex population; (2) extracts of "mock sorted" cells passed through the cell sorter instrument without gating any specific population; and (3) extracts of the actual sorted populations. The mock sorted cells are compared against direct extraction to verify that MIDs are indeed not altered by the cell sorting procedure itself, prior to analyzing the actual sorted populations. We show example results from HeLa cells sorted according to cell cycle phase, revealing changes in nucleotide metabolism.

This article describes a method for studying cellular metabolism in complex communities of multiple cell types, using a combination of stable isotope tracing, cell sorting to isolate specific cell types, and mass spectrometry.

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. ...

Biochemistry

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about the overall structure of terrestrial microbial communities. PLFA profiling has been extensively used in a range of ecosystems as a biological index of overall soil quality, and as a quantitative indicator of soil response to land management and other environmental stressors.
The standard method presented here outlines four key steps: 1. lipid extraction from soil samples with a single-phase chloroform mixture, 2. fractionation using solid phase extraction columns to isolate phospholipids from other extracted lipids, 3. methanolysis of phospholipids to produce fatty acid methyl esters (FAMEs), and 4. FAME analysis by capillary gas chromatography using a flame ionization detector (GC-FID). Two standards are used, including 1,2-dinonadecanoyl-sn-glycero-3-phosphocholine (PC(19:0/19:0)) to assess the overall recovery of the extraction method, and methyl decanoate (MeC10:0) as an internal standard (ISTD) for the GC analysis.

Phospholipid fatty acids provide information about the structure of soil microbial communities. We present methods for extraction from soil samples with a single-phase chloroform mixture, fractionation of extracted lipids using solid phase extraction columns, and methanolysis to produce fatty acid methyl esters, which are analyzed by capillary gas chromatography.

Phospholipid fatty acids (PLFAs) are key components of microbial cell membranes. The analysis of PLFAs extracted from soils can provide information about ...

Environment

Fatty Acid 13C Isotopologue Profiling Provides Insight into Trophic Carbon Transfer and Lipid Metabolism of Invertebrate Consumers

Fatty acids (FAs) are useful biomarkers in food web ecology because they are typically assimilated as a complete molecule and transferred into consumer tissue with minor or no modification, allowing the dietary routing between different trophic levels. However, the FA trophic marker approach is still hampered by the limited knowledge in lipid metabolism of the soil fauna. This study used entirely labelled palmitic acid (13C16:0, 99 atom%) as a tracer in fatty acid metabolism pathways of two widespread soil Collembola, Protaphorura fimata and Heteromurus nitidus. In order to investigate the fate and metabolic modifications of this precursor, a method of isotopologue profiling is presented, performed by mass spectrometry using single ion monitoring. Moreover, the upstream laboratory feeding experiment is described, as well as the extraction and methylation of dominant lipid fractions (neutral lipids, phospholipids) and the related formula and calculations. Isotopologue profiling does not only yield the overall 13C enrichment in fatty acids derived from the 13C labeled precursor but also produces the pattern of isotopologues exceeding the mass of the parent ion (i.e., the FA molecular ion M+) of each labeled FA by one or more mass units (M+1, M+2, M+3, etc.). This knowledge allows conclusions on the ratio of dietary routing of an entirely consumed FA in comparison to de novo biosynthesis. The isotopologue profiling is suggested as a useful tool for evaluation of fatty acid metabolism in soil animals to disentangle trophic interactions.

Fatty Acid 13C Isotopologue Profiling Provides Insight into Trophic Carbon Transfer and Lipid Metabolism of Invertebrate Consumers

The fatty acid trophic marker approach, i.e., the assimilation of fatty acids as entire molecule and transfer into consumer tissue with no or minor modification, is hampered by knowledge gaps in fatty acid metabolism of small soil invertebrates. Isotopologue profiling is provided as a valuable tool to disentangle trophic interactions.

Fatty acids (FAs) are useful biomarkers in food web ecology because they are typically assimilated as a complete molecule and transferred into consumer ...

DNA Stable-Isotope Probing (DNA-SIP)

DNA stable-isotope probing (DNA-SIP) is a powerful technique for identifying active microorganisms that assimilate particular carbon substrates and nutrients into cellular biomass. As such, this cultivation-independent technique has been an important methodology for assigning metabolic function to the diverse communities inhabiting a wide range of terrestrial and aquatic environments. Following the incubation of an environmental sample with stable-isotope labelled compounds, extracted nucleic acid is subjected to density gradient ultracentrifugation and subsequent gradient fractionation to separate nucleic acids of differing densities. Purification of DNA from cesium chloride retrieves labelled and unlabelled DNA for subsequent molecular characterization (e.g. fingerprinting, microarrays, clone libraries, metagenomics). This JoVE video protocol provides visual step-by-step explanations of the protocol for density gradient ultracentrifugation, gradient fractionation and recovery of labelled DNA. The protocol also includes sample SIP data and highlights important tips and cautions that must be considered to ensure a successful DNA-SIP analysis.

DNA Stable-Isotope Probing DNA-SIP

DNA stable-isotope probing is a cultivation-independent method to identify and characterize active communities of microorganisms that are capable of utilizing specific substrates. Assimilation of substrate enriched in heavy isotope leads to incorporation of labelled atoms into microbial biomass. Density gradient ultracentrifugation retrieves labelled DNA for downstream molecular analyses.

DNA stable-isotope probing (DNA-SIP) is a powerful technique for identifying active microorganisms that assimilate particular carbon substrates and ...

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

Bioengineering

Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

Stable isotopic profiling has long permitted sensitive investigations of the metabolic consequences of genetic mutations and/or pharmacologic therapies in cellular and mammalian models. Here, we describe detailed methods to perform stable isotopic profiling of intermediary metabolism and metabolic flux in the nematode, Caenorhabditis elegans. Methods are described for profiling whole worm free amino acids, labeled carbon dioxide, labeled organic acids, and labeled amino acids in animals exposed to stable isotopes either from early development on nematode growth media agar plates or beginning as young adults while exposed to various pharmacologic treatments in liquid culture. Free amino acids are quantified by high performance liquid chromatography (HPLC) in whole worm aliquots extracted in 4% perchloric acid. Universally labeled 13C-glucose or 1,6-13C2-glucose is utilized as the stable isotopic precursor whose labeled carbon is traced by mass spectrometry in carbon dioxide (both atmospheric and dissolved) as well as in metabolites indicative of flux through glycolysis, pyruvate metabolism, and the tricarboxylic acid cycle. Representative results are included to demonstrate effects of isotope exposure time, various bacterial clearing protocols, and alternative worm disruption methods in wild-type nematodes, as well as the relative extent of isotopic incorporation in mitochondrial complex III mutant worms (isp-1(qm150)) relative to wild-type worms. Application of stable isotopic profiling in living nematodes provides a novel capacity to investigate at the whole animal level real-time metabolic alterations that are caused by individual genetic disorders and/or pharmacologic therapies.

Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

Stable isotopic profiling by gas chromatography mass spectrometric analysis of intermediary metabolic flux is described in the nematode, Caenorhabditis elegans. Methods are detailed for assessing isotopic enrichment in carbon dioxide, organic acids, and amino acids following isotope exposure either during development on agar plates or during adulthood in liquid culture.

Stable isotopic profiling has long permitted sensitive investigations of the metabolic consequences of genetic mutations and/or pharmacologic therapies in ...

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine cell central metabolism and discover new enzymes is 13C-assisted metabolism analysis 1. This technique is based on isotopic labeling, whereby microbes are fed with a 13C labeled substrates. By tracing the atom transition paths between metabolites in the biochemical network, we can determine functional pathways and discover new enzymes. 
As a complementary method to transcriptomics and proteomics, approaches for isotopomer-assisted analysis of metabolic pathways contain three major steps 2. First, we grow cells with 13C labeled substrates. In this step, the composition of the medium and the selection of labeled substrates are two key factors. To avoid measurement noises from non-labeled carbon in nutrient supplements, a minimal medium with a sole carbon source is required. Further, the choice of a labeled substrate is based on how effectively it will elucidate the pathway being analyzed. Because novel enzymes often involve different reaction stereochemistry or intermediate products, in general, singly labeled carbon substrates are more informative for detection of novel pathways than uniformly labeled ones for detection of novel pathways3, 4. Second, we analyze amino acid labeling patterns using GC-MS. Amino acids are abundant in protein and thus can be obtained from biomass hydrolysis. Amino acids can be derivatized by N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (TBDMS) before GC separation. TBDMS derivatized amino acids can be fragmented by MS and result in different arrays of fragments. Based on the mass to charge (m/z) ratio of fragmented and unfragmented amino acids, we can deduce the possible labeled patterns of the central metabolites that are precursors of the amino acids. Third, we trace 13C carbon transitions in the proposed pathways and, based on the isotopomer data, confirm whether these pathways are active 2. Measurement of amino acids provides isotopic labeling information about eight crucial precursor metabolites in the central metabolism. These metabolic key nodes can reflect the functions of associated central pathways.
13C-assisted metabolism analysis via proteinogenic amino acids can be widely used for functional characterization of poorly-characterized microbial metabolism1. In this protocol, we will use Cyanothece 51142 as the model strain to demonstrate the use of labeled carbon substrates for discovering new enzymatic functions.

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

13C-isotope labeling is a useful technique for determining the cell central metabolism for various types of microorganisms. After cells have been cultured with a specific labeled substrate, GC-MS measurement can reveal functional metabolic pathways based on unique labeling patterns in proteinogenic amino acids.

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine ...

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell&#39;s survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.

The protocols describe high-performance liquid chromatography methods coupled to refractive index or mass spectrometric detection for studying metabolic reactions in complex lysate-based cell-free systems.

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the ...

Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter

The quantity and quality of detritus entering the soil determines the rate of decomposition by microbial communities as well as recycle rates of nitrogen (N) and carbon (C) sequestration1,2. Plant litter comprises the majority of detritus3, and so it is assumed that decomposition is only marginally influenced by biomass inputs from animals such as herbivores and carnivores4,5. However, carnivores may influence microbial decomposition of plant litter via a chain of interactions in which predation risk alters the physiology of their herbivore prey that in turn alters soil microbial functioning when the herbivore carcasses are decomposed6. A physiological stress response by herbivores to the risk of predation can change the C:N elemental composition of herbivore biomass7,8,9 because stress from predation risk increases herbivore basal energy demands that in nutrient-limited systems forces herbivores to shift their consumption from N-rich resources to support growth and reproduction to C-rich carbohydrate resources to support heightened metabolism6. Herbivores have limited ability to store excess nutrients, so stressed herbivores excrete N as they increase carbohydrate-C consumption7. Ultimately, prey stressed by predation risk increase their body C:N ratio7,10, making them poorer quality resources for the soil microbial pool likely due to lower availability of labile N for microbial enzyme production6. Thus, decomposition of carcasses of stressed herbivores has a priming effect on the functioning of microbial communities that decreases subsequent ability to of microbes to decompose plant litter6,10,11.
We present the methodology to evaluate linkages between predation risk and litter decomposition by soil microbes. We describe how to: induce stress in herbivores from predation risk; measure those stress responses, and measure the consequences on microbial decomposition. We use insights from a model grassland ecosystem comprising the hunting spider predator (Pisuarina mira), a dominant grasshopper herbivore (Melanoplus femurrubrum),and a variety of grass and forb plants9.

We present methods to evaluate how predation risk can alter the chemical quality of herbivore prey by inducing dietary changes to meet demands of heightened stress, and how the decomposition of carcasses from these stressed herbivores slows subsequent plant litter decomposition by soil microbes.

The quantity and quality of detritus entering the soil  determines the rate of decomposition by microbial communities as well as  recycle rates of ...

Comprehensive Protocol for Cell Identification and Metabolic Analysis Using OPTIR-FISH

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)

Understanding the metabolic activities of individual cells within complex communities is critical for unraveling their role in human disease. Here, we present a comprehensive protocol for simultaneous cell identification and metabolic analysis with the OPTIR-FISH platform by combining rRNA-tagged FISH probes and isotope-labeled substrates. Fluorescence imaging provides cell identification by the specific binding of rRNA-tagged FISH probes, while OPTIR imaging provides metabolic activities within single cells by isotope-induced red shift on OPTIR spectra. Using bacteria cultured with 13C-glucose as a test bed, the protocol outlines microbial culture with isotopic labeling, fluorescence in situ hybridization (FISH), sample preparation, optimization of the OPTIR-FISH imaging setup, and data acquisition. We also demonstrate how to perform image analysis and interpret spectral data at the single-cell level with high throughput. This protocol&#39;s standardized and detailed nature will greatly facilitate its adoption by researchers from diverse backgrounds and disciplines within the broad single-cell metabolism research community.

Author Spotlight: Integrated OPTIR-FISH for Single-Cell Metabolic and Identity Analysis in Complex Environments

Here, we present a protocol using optical photothermal infrared-fluorescence in situ hybridization (OPTIR-FISH), also known as mid-infrared photothermal-FISH (MIP-FISH), to identify individual cells and understand their metabolism. This methodology can be applied broadly for diverse applications, including mapping cellular metabolism with single-cell resolution.

Understanding the metabolic activities of individual cells within complex communities is critical for unraveling their role in human disease. Here, we ...

Identification of Metabolically Active Bacteria in the Gut of the Generalist Spodoptera littoralis via DNA Stable Isotope Probing Using ¹³C-Glucose

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A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

Fatty Acid ¹³C Isotopologue Profiling Provides Insight into Trophic Carbon Transfer and Lipid Metabolism of Invertebrate Consumers

DNA Stable-Isotope Probing (DNA-SIP)

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

Metabolic Pathway Confirmation and Discovery Through ¹³C-labeling of Proteinogenic Amino Acids

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)