Extracellular vesicles are the main contributor to the non-viral protected extracellular sequence space
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Abstract
Environmental virus metagenomes, commonly referred to as “viromes”, are typically generated by physically separating virus-like particles (VLPs) from the microbial fraction based on their size and mass. However, most methods used to purify VLPs, enrich extracellular vesicles (EVs) and gene transfer agents (GTAs) simultaneously. Consequently, the sequence space traditionally referred to as a “virome” contains host-associated sequences, transported via EVs or GTAs. We therefore propose to call the genetic material isolated from size-fractionated (0.22 µm) and DNase-treated samples protected environmental DNA (peDNA). This sequence space contains viral genomes, DNA transduced by viruses and DNA transported in EVs and GTAs. Since there is no genetic signature for peDNA transported in EVs, GTAs and virus particles, we rely on the successful removal of contaminating remaining cellular and free DNA when analyzing peDNA. Using marine samples collected from the North Sea, we generated a thoroughly purified peDNA dataset and developed a bioinformatic pipeline to determine the potential origin of the purified DNA. This pipeline was applied to our dataset as well as existing global marine “viromes”. Through this pipeline, we identified known GTA and EV producers, as well as organisms with actively transducing proviruses as the source of the peDNA, thus confirming the reliability of our approach. Additionally, we identified novel and widespread EV producers, and found quantitative evidence suggesting that EV-mediated gene transfer plays a significant role in driving horizontal gene transfer (HGT) in the world’s oceans.
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Thank you so much for your comments! I found all your comments, suggestions and criticism fair and reasonable. We hope to have clarified some crucial points and I adjusted the manuscript according to your comments and the comments of the official reviewers.
All the best
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How would the read classifier resolve that bias? I'm not sure I follow.
However, I agree, that we introduce a bias in this step.
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Thanks for the comment and good thinking: we did indeed analyze the genes next to potential transposons in our EV producers, however the results are not ready yet and therefore are not part of this publication.
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Thanks for your comment. Absolutely, thats why I would hesitate to state that all DNA mapping to this EV producer are necessarily transported by EVs, but 'predominantly'. Mind, that in Figure 4B, the y axis is labelled "Number of MAGs" not "number of peDNA reads transported". Having said all that, yes I agree, we will miss non-integrated, generalized transduction. However general transduction events occur less frequent than specific transduction events, for which an integration is necessary (Griffiths AJ, Miller JH, Suzuki DT, Lewontin RC, Gelbart WM (2000). "Transduction". An Introduction to Genetic Analysis)
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The main take away point here is, that traditional measures of contamination (e.g. SSU alignment rates) are not great predictors for actual contamination, since peDNA samples which are in enriched in EV-mediated peDNA easily have high SSU alignment rates.
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We speculated in internal discussions that this should be possible and potentially is an interesting experiment to perform.
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please refer to the publication where this data was generated: Bartlau N, Wichels A, Krohne G, Adriaenssens EM, Heins A, Fuchs BM, et al. Highly diverse flavobacterial phages isolated from North Sea spring blooms. ISME J. 2022 Feb;16(2):555–68.
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it has been shown, that DNA at least partially can be protected from DNA degradation when attached to surface of membrane vesicles. I dont have the citation at hand right now, might add it later!
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Conclusion
This is a cool concept! You are addressing an important issue of non-viral reads in 'virome' data. Really interesting to think about the roles that non-viral extracellular DNA might be playing in the ecosystem. I wonder how your findings transfer to non-marine environments. Your proposal of the involvement of EVs is a valuable perspective. Although unclear if the everything classified here as EV is actually an extracellular vesicle, it is useful to think about what unknown agents might be involved here.
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Transposons have been shown to mobilize not only themselves but also adjacent ’passenger genes’, genes that are located in proximity to transposons and are therefore co-mobilized by transposons
to this point, if you have the MAG regions that these reads mapped to, did you do any classification of the functions associated with these regions? it seems like this may be more interesting and possibly better than classifying partial orfs from the reads?
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Thus, only MAGs which showed the expected coverage profiles were labeled as transducers
Lytic phages can also participate in generalized transduction, this approach doesn't account for that . By requiring that a prophage be present for peDNA to "count" as being transduced, I feel like you might be missing a lot of instance of legitimate viral transduction coming from non-integrating phages
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MAG was labeled as ‘EV produce
Does this account for transduction-like mechanisms? It seems like you could also get this signal from viruses packaging up host DNA, I dont see why this would necessarily require an extracellular vesicle
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Calculation of the percentage of non-viral associated reads
This metric is calculated only using reads that map to assembled contigs (either viral or non-viral) , which is going to be a very biased subset of reads. The contigs that are assembling well from a "virome" prep but that are not predicted to be viruses by VirSorter and DeepVirFinder may be other types of MGEs. It seems difficult to get assembly of reads that are derived from host DNA that is being sporadically packaged by GTAs or EVs, with the exception of cases in which there is strong enrichment for packaging of a particular host sequence. I think that a more fair calculation of viral vs. non-viral reads would be to use a read classifier (potentially using a custom database that includes your verified viral contigs) , and to report the number of viral reads, bacterial …
Calculation of the percentage of non-viral associated reads
This metric is calculated only using reads that map to assembled contigs (either viral or non-viral) , which is going to be a very biased subset of reads. The contigs that are assembling well from a "virome" prep but that are not predicted to be viruses by VirSorter and DeepVirFinder may be other types of MGEs. It seems difficult to get assembly of reads that are derived from host DNA that is being sporadically packaged by GTAs or EVs, with the exception of cases in which there is strong enrichment for packaging of a particular host sequence. I think that a more fair calculation of viral vs. non-viral reads would be to use a read classifier (potentially using a custom database that includes your verified viral contigs) , and to report the number of viral reads, bacterial reads, and unclassifiable reads .
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Figure S4.
Should this be figure S1?
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we compared the SSU rRNA alignment rates
Is there a reason that alignments are done to SSU rRNA only? Could you use a read based classifier like sourmash or kraken to fully identify bacterial reads present?
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SSU rRNA hits in these datasets are enclosed in VLPs, GTA’s or EVs
Is it possible to distinguish between SSU rRNA coming from EVs vs. VLPs/GTAs by using chloroform treatment to disrupt the lipid vesicles?
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1.81
Can you clarify if all the reads from Prochlorococcus EV prep map back to the Prochlorococcus genome? What do the rest of the reads pertain to?
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DNA extracted from virus isolates, purified by sequential plaque assays
Can you clarify 1) what host(s) are these viruses are infecting and 2) how the viral particles were purified? In my mind, plaque purification relates to genetically purifying a phage isolate to make sure that the phage population is isogenic, but the purification in terms of separating host from phage would be physical in nature . How did you recover the phage only fraction in this experiment?
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he remaining DNA makes up the sequence space of protected extracellular DNA, peDNA (bottom panel).
Why is the DNA on the outside of EVs protected from DNase treatment (bottom left example in the peDNA box)?
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Figure 1:
I think that the little gray face under 'DNase treatment' is a DNase enzyme? Although is it cute, I'm not sure that this is the best representation. You might consider only keeping the text for this step, or showing dna being cleaved.
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<0.02
should this be less than 0.22?
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viruses-like particles
virus-like particles
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Marine
You might consider removing 'marine' here so that it doesn't sound like you are defining viromes specifically as being marine, when this statement would be true for all environments
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