Microbial occurrence and symbiont detection in a global sample of lichen metagenomes
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Abstract
In lichen research, metagenomes are increasingly being used for evaluating symbiont composition and metabolic potential, but the overall content and limitations of these metagenomes have not been assessed. We reassembled over 400 publicly available metagenomes, generated metagenome-assembled genomes (MAGs), constructed phylogenomic trees, and mapped MAG occurrence and frequency across the data set. Ninety-seven percent of the 1,000 recovered MAGs were bacterial or the fungal symbiont that provides most cellular mass. Our mapping of recovered MAGs provides the most detailed survey to date of bacteria in lichens and shows that 4 family-level lineages from 2 phyla accounted for as many bacterial occurrences in lichens as all other 71 families from 16 phyla combined. Annotation of highly complete bacterial, fungal, and algal MAGs reveals functional profiles that suggest interdigitated vitamin prototrophies and auxotrophies, with most lichen fungi auxotrophic for biotin, most bacteria auxotrophic for thiamine and the few annotated algae with partial or complete pathways for both, suggesting a novel dimension of microbial cross-feeding in lichen symbioses. Contrary to longstanding hypotheses, we found no annotations consistent with nitrogen fixation in bacteria other than known cyanobacterial symbionts. Core lichen symbionts such as algae were recovered as MAGs in only a fraction of the lichen symbioses in which they are known to occur. However, the presence of these and other microbes could be detected at high frequency using small subunit rRNA analysis, including in many lichens in which they are not otherwise recognized to occur. The rate of MAG recovery correlates with sequencing depth, but is almost certainly influenced by biological attributes of organisms that affect the likelihood of DNA extraction, sequencing and successful assembly, including cellular abundance, ploidy and strain co-occurrence. Our results suggest that, though metagenomes are a powerful tool for surveying microbial occurrence, they are of limited use in assessing absence, and their interpretation should be guided by an awareness of the interacting effects of microbial community complexity and sequencing depth.
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Fig. 2.
It would help provide additional context for these genomes if additional layers to the tree were added showing completeness, redundancy, genome size, etc. so it's easy to compare across the tree the quality of these genomes. This can be done with iTOL or EMPRESS as added metadata layers
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Four bacterial families dominate lichen metagenomes
It would be interesting to follow up for groups that are core such as Lichenihabitans if they are the same/different strains in these samples and if there are differences where those hotspots of diversity are relative to the lichen type
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A striking potential metabolic complementarity to emerge from our annotations is the capacity of many frequent lichen bacteria to code for cofactors needed by one of the dominant eukaryotic symbionts
I'm interpreting up to this point that functional annotation and pathway exploration was only performed for the bacterial genomes and not fungal/algal MAGs? Was this because of the difficult in performing ORF prediction/functional annotations without corresponding RNAseq data or something planned for the future? Because it would be interesting to see if the corresponding fungi have transporters for those cofactors
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Supplementary Table 2).
Something useful to add to this table and suggestion to Figure 2 for added metadata would be the # of contigs for each genome. I'm assuming most or all of these metagenomes were obtained from Illumina sequencing data and I would presume that a lot of the eukaryotic MAGs are going to be pretty fragmented and that's important information to include
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Supplementary Table 1).
I think it's important to include in this table the sequencing technology for each metagenome (example Illumina HiSeq PE 2x150bp sequencing) even though I could find that clicking through the SRA accessions, it helps to have it directly here especially because it seems a lot of the data comes from the 2019 UC Boulder study
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Supplementary Table 1).
I think it's important to include in this table the sequencing technology for each metagenome (example Illumina HiSeq PE 2x150bp sequencing) even though I could find that clicking through the SRA accessions, it helps to have it directly here especially because it seems a lot of the data comes from the 2019 UC Boulder study
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Supplementary Table 2).
Something useful to add to this table and suggestion to Figure 2 for added metadata would be the # of contigs for each genome. I'm assuming most or all of these metagenomes were obtained from Illumina sequencing data and I would presume that a lot of the eukaryotic MAGs are going to be pretty fragmented and that's important information to include
-
A striking potential metabolic complementarity to emerge from our annotations is the capacity of many frequent lichen bacteria to code for cofactors needed by one of the dominant eukaryotic symbionts
I'm interpreting up to this point that functional annotation and pathway exploration was only performed for the bacterial genomes and not fungal/algal MAGs? Was this because of the difficult in performing ORF prediction/functional annotations without corresponding RNAseq data or something planned for the future? Because it would be interesting to see if the corresponding fungi have transporters for those cofactors
-
Four bacterial families dominate lichen metagenomes
It would be interesting to follow up for groups that are core such as Lichenihabitans if they are the same/different strains in these samples and if there are differences where those hotspots of diversity are relative to the lichen type
-
Fig. 2.
It would help provide additional context for these genomes if additional layers to the tree were added showing completeness, redundancy, genome size, etc. so it's easy to compare across the tree the quality of these genomes. This can be done with iTOL or EMPRESS as added metadata layers
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