Shotgun metagenomic analysis of the skin mucus bacteriome of the common carp ( Cyprinus carpio )

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The skin mucus bacteriome of fish plays an important role in the health of their hosts. Despite the economic importance of the common carp ( Cyprinus carpio ), research on its skin bacteriome composition is still missing. To date, most studies on the composition of fish skin bacteriome have used amplicon sequencing, despite the limitations associated with this method. In our study, a shotgun metagenomic approach was applied to characterize the external mucus bacteriome of 8 carp specimens from two different ponds on a fish farm in Hungary. Besides the carp samples, water was also sequenced from the two corresponding ponds. Each carp skin sample was dominated by the phylum Proteobacteria , followed by Actinobacteria, Bacteroidota, Firmicutes, Cyanobacteria and Planctomycetota . Additionally, we have found strong concordance between the water and carp skin mucus samples, despite most studies describing an opposite relationship. Furthermore, shotgun metagenomics allowed us to apply functional annotation to the metagenomes, which revealed several metabolic functions. We present, to our knowledge, the first description of the common carp ( Cyprinus carpio ) skin mucus bacteriome. Even though our results showed a high level of host genome contamination, we could still provide valuable insight into the external bacterial community of this species. The presented data can provide a basis for future metagenome studies of carp or other fish species.

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  1. This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at

    Papp et al. apply shotgun metagenomic sequencing to the skin mucus of the common carp. Their key findings include:

    • The bacteriome of carp skin mucus resembles the surrounding pond water.

    • The predominant composition of the community, especially at a phylum level.

    • Insight into some of the functional potential of the microbiome.

    Given the challenges of applying shotgun metagenomic sequencing to fish for microbial detection, the authors' effort was difficult. However, given the limitations of their data, it's commendable that they were able to assemble a coherent narrative and relate their biological findings to those in the field.

    Major issues

    • Improve the introduction: The introduction doesn't currently give the reader enough contextual information about the project and its challenges or about the potential usefulness of the results. I've included some suggestions below that might improve the intro.

      • Include a methodological discussion of why using shotgun metagenomic sequencing on fish samples is difficult for microbial cataloging.

      • A discussion of differences between wild and farm fish and how that might impact findings or methodology used in the study. In particular, how does the microbial load differ? How is the microbiome of the water different?

      • Can the microbiome be pathogenic, or is it only ever beneficial?

      • Specific examples of the economic importance of carp. It would also be helpful to know why this study would benefit the farmed carp industry.

    • Introduce the study's limitations earlier: Currently, the reader doesn't find out that less than 1% of the sample is bacterial until the results section. It would be very helpful to read this result in the abstract, as I think it would contextualize the paper better. It would also be helpful to know both the percent of reads and the number of reads that were carp.

    • Potential room for improvement in methodological approach: As I understand it, the authors quality controlled their reads, used Kraken to identify bacterial reads, and then analyze those reads. I think that this might dramatically undersample the bacterial reads present. I would take a different approach. Below are some ideas:

      • De novo assembly approach 1: Assemble all reads. Use a tool like diamond (see code suggestion below for making a diamond database) to assign the taxonomy of each contig. Filter out carp contigs. Analyze the non-carp contigs (which might also include archaea and fungi).

      • De novo assembly approach 2: map the reads back to the common carp genome. Assemble anything that doesn't map, and then work with those contigs.

      • Mapping approach: Use a tool like genome-grist to identify which microbes are in the sample. Genome-grist first uses sourmash to determine which microbes are present. It will then download those microbial genomes and map the reads back to those genomes. This might provide a fuller picture of what's in the sample, given that I expect you might have a highly fragmented assembly. Note, I am an author on the sourmash tool so this suggestion has a conflict of interest :)

        * It would be helpful to see the analysis code for this project. Would it be possible for it to be uploaded to a GitHub repository?

    Minor issues

    • Ideas for how this work could be used by others

      • Marker gene panel: Could the data in this study be used to generate a marker gene panel for taxonomic profiling? This would include more genes than 16s rRNA but help limit host contamination. How could this type of panel be used by the field? Would many researchers need to adopt it for it to be useful? A tool like singleM might help identify the marker genes (and taxonomic composition) in the sample.

      • Relate the microbiome to the host: Is there enough data in this study to relate the microbiome to the host genome (SNPs, copy number variation, etc)? If not, how much more data would need to be collected to enable this? What lessons could we learn to help the field with such a study?

    • It would be helpful if you could report how many reads were lost when filtering with TrimGalore. Also, note that fastp is generally now standard in the field for quality control (it would replace FastQC and TrimGalore). I'm not suggesting that the authors use a different tool, though.

    Competing interests

    The author declares that they have no competing interests.

  2. Discussion

    Given that you have a very low fraction of bacterial reads, which is a common problem in the field, I think a useful contribution from your data would be to create a panel of primers to amplify community members that you see are present. This would give you more resolution than 16s but allow you to avoid more of the host sequencing data. However, the usefulness of such a panel would be bounded by how it would be adopted by others in the field. It would probably be most useful if you applied it to this fish farm repeatedly, but I'm not sure if doing so is biologically interesting.

  3. However, by examining the bacteriome in detail, we can obtain much more information about its composition and function than diversity alone can tell us. Based on the taxonomic constitution of our samples, Proteobacteria and Actinobacteria phyla were clearly dominant both in fish skin mucus and water samples. The dominance of the Proteobacteria phylum is not an uncommon observation in fish external mucus samples1,3,5,6,8,11,21,62,63, however, differences between fish species have been observed for the other phyla1,11,62,63. Moreover, significant within-species variability in dominant phyla has been described64, and variability within individuals related to body sites should be noted12.The microbiome can be an important indicator of various pathological conditions, which has already been described in fish, for example, in the case of the gastrointestinal tract65. In this regard, the Bacteroidota phylum may be interesting, which has been highlighted as a marker for eutrophication9,66. Understanding the changes in the composition of the bacteriome or even the microbiome during different pathological conditions can be an important step in understanding and potentially diagnosing disease processes.Our results are therefore in line with the dominance of the Proteobacteria phylum observed in other fish species, but direct comparison with C. carpio is not possible due to the lack of available data. Of course, our observations on the bacteriome composition of our samples are also limited by their paramount host genome contamination, which reduced the coverage of bacterial genomes of interest in the sequencing reaction.

    Since you have the resolution to go below phylum, I think it would be interesting to focus on that more in the discussion.

  4. Even though this might limit our conclusions on the bacteriome composition of the common carp skin mucus, our samples still provide valuable insight into the main constitution of fish skin mucus bacteriome.

    I agree, but I think this would be worth mentioning in the abstract, and perhaps in the last paragraph of the introduction, to better prepare your readers about the types of results you are going to present

  5. Bacteria (mean ± SD) was 0.12 ± 0.12

    The percentage or fraction? If percentage, less than 1% is incredibly small and I would question any results in this report. How many reads total was this? If you used a very high depth, you might capture a substantial portion of the community.

  6. For functional prediction of the bacteriome, reads classified as originating from bacteria were assembled to contigs by MEGAHIT v1.2.940

    I imagine you might have a huge amount of drop out here by applying kraken first and then assembling with megahit. I would either:

    1. Map reads to carp first, and then assemble anything that doesn't map
    2. Assemble everything and then filter out carp contigs.
  7. Taxonomic classification of the reads was performed with Kraken v2.1.234 to the NCBI nt database (built on: 26.12.2022).

    It might be worth mapping back to the host genome if you have one prior to performing taxonomic classification.

    I would also be interested to see nonpareil curves of your sequencing data before and after host mapping. I would be curious if you reached saturation of the community -- this can usually be better assessed with raw sequencing data than with taxonomically classified reads.

  8. TrimGalore v0.6.732 was used for quality trimming of the merged and forward unmerged (see above) reads.

    What filters did you use here? I'm curious how many reads were lost to filtering.

  9. At the farm where samples were collected, both scaly and mirror carp phenotypes are kept. During the sample collection, we could sample two of each at one pond, however, only one scaly and three mirror carp at the other. Furthermore, it is worth mentioning that two specimens from pond 1 had ulcers on their skin, otherwise, all sampled fish appeared to be healthy. Details on the metadata on each sample, along with the number of reads used for classification, can be found in Supplementary File 1.In addition to the skin mucus samples, water was collected from each pond. Water and mucus samples were frozen immediately after collection on dry ice and were subjected to shotgun metagenomic sequencing.

    Do you have any idea if the bacterial load of the water, and therefore the skin, of the carp was much higher than for fish observed in the wild, or typically sequenced with 16s? I'm wondering if there was more bacteria than usual, and that was why you were able to get enough bacterial reads to perform an analysis

  10. Due to the economic importance of the common carp among freshwater fish species16–18

    Would you be able to provide some specific examples of the economic importance (even half a sentence)? I'm not a carp expert so I have no idea what these might be!

  11. The microorganisms that inhabit the skin are important for the well-being of their hosts3–5. They might even play a practical role in the maintenance of the health of these animals, for example, as an indicator of various pathological conditions13,14, or as a source for potential future probiotics15. Due to the economic importance of the common carp among freshwater fish species16–18, efforts to protect their health are particularly important.

    Can the microbiome also be pathogenic for carp?

  12. However, it should be noted that studies on the bacteriome and microbiome of this species are underrepresented compared to other species, especially considering the skin mucus bacteriome. For this reason, it would be beneficial to increase our knowledge on the bacteriome of the common carp as well.Despite the long history of the study of the microbial and bacterial community of the outer surface of fishes19,20, it has recently received much more attention due to the advent of next-generation sequencing (NGS) technologies4,14. However, it is important to note that 16S rRNA gene-based methods have been used in the majority of such studies on the bacteriome of fish skin mucus4,14,21. A review article from 2021 listed only one paper using shotgun metagenomics for the analysis of the external surface of eels21,22. Beyond which, to the best of our knowledge, we are aware of only one further shotgun metagenomics study from 202023 investigating the fish skin metagenome of cartilaginous and bony fishes from an evolutionary perspective. Despite the conflicting results on the effectiveness of the two methods in revealing microbial community structure24–27, it is certain that shotgun sequencing-based methods have the major advantage of providing much greater insight into the functional organization of microbial communities14,24,25.

    I think this section doesn't highlight the massive challenge in trying to get shotgun metagenomic sequencing data from fish. In the experiments where we have tried (killifish, different tissues), we end up with 98 or 99% killifish (host) reads. 16s allows us to amplify and get just the microbial signal.

    We have talked about trying to do a more balanced marker gene panel, but that has methodological problems like not having as many tools and determining the best marker genes to use.

    It would be nice if these challenges were better represented. I think the reason this gap exists is methodological (hard to get shotgun sequencing from fish), not for lack of interest

  13. The colonization of the skin mucus of fishes is assumed to originate from the surrounding water, which process may even start at the larval stage3. However, the fish skin bacteriome composition is influenced by several factors such as stress1, water pH level6 or other environmental influences7–9. Furthermore, even the genetics and diet of the host species can have an effect on its structure1,8,10,11. Moreover, even within a single individual, different body parts may show differences in microbiome composition12.

    I'm curious the extent to which these studies investigated farm vs. wild fish, and if you think that would make a difference on microbiome. It might be helpful to include that distinction in when covering literature in the introduction, given that you see some results that you don't expect relative to other observations in the field.