Determining virus-host interactions and glycerol metabolism profiles in geographically diverse solar salterns with metagenomics

Abstract

Solar salterns are excellent model ecosystems for studying virus-microbial interactions because of their low microbial diversity, environmental stability, and high viral density. By using the power of CRISPR spacers to link viruses to their prokaryotic hosts, we explored virus-host interactions in geographically diverse salterns. Using taxonomic profiling, we identified hosts such as archaeal Haloquadratum, Halorubrum, and Haloarcula and bacterial Salinibacter, and we found that community composition related to not only salinity but also local environmental dynamics. Characterizing glycerol metabolism genes in these metagenomes suggested Halorubrum and Haloquadratum possess most dihydroxyacetone kinase genes while Salinibacter possesses most glycerol-3-phosphate dehydrogenase genes. Using two different methods, we detected fewer CRISPR spacers in Haloquadratum-dominated compared with Halobacteriaceae-dominated saltern metagenomes. After CRISPR detection, spacers were aligned against haloviral genomes to map virus to host. While most alignments for each saltern metagenome linked viruses to Haloquadratum walsbyi, there were also alignments indicating interactions with the low abundance taxa Haloarcula and Haloferax. Further examination of the dinucleotide and trinucleotide usage differences between paired viruses and their hosts confirmed viruses and hosts had similar nucleotide usage signatures. Detection of cas genes in the salterns supported the possibility of CRISPR activity. Taken together, our studies suggest similar virus-host interactions exist in different solar salterns and that the glycerol metabolism gene dihydroxyacetone kinase is associated with Haloquadratum and Halorubrum.

Distinctive Archaeal Composition of an Artisanal Crystallizer Pond and Functional Insights Into Salt-Saturated Hypersaline Environment Adaptation

“…The only exceptions were the GSL-Ant-Is and IC21 metagenomes, which clustered with the salt-saturated metagenomes despite having 15 and 21% salinity, respectively ( Figure 3 ). In agreement with their highly similar microbial community compositions ( Moller and Liang, 2017 ), the IC21 metagenome displayed the highest similarity of functional genes abundance to CL34 ( Figure 3 ). The analysis presented here showed that the functional category “Replication, recombination, and repair” presented the most COGs (19) among the 100 most abundant functions in all the hypersaline metagenomes assessed here ( Supplementary Data Sheet 2 ).…”

“…Their higher representation among salt-saturated hypersaline metagenomes can be attributed to the high abundance of viruses in these environments. Virus counts appear to be higher in crystallizer ponds than in sub-saturated hypersaline waters (10 9 vs. 10 7 viruses per mL respectively) ( Santos et al, 2012 ; Moller and Liang, 2017 ). Also, three main transcriptional regulator families were overrepresented in salt-saturated environments: the ArsR (COG0640); Lrp (COG1522); and TrmB (COG1378).…”

MetaCRAST: reference-guided extraction of CRISPR spacers from unassembled metagenomes

“…More broadly, increasing spacer matching would provide a fuller appreciation of a microbial ecosystem’s phage-host interaction space. We have recently used MetaCRAST to improve our determination of virus-host interactions in solar salterns ( Moller & Liang, 2017 ), complementing Crass with our spacer detection method. MetaCRAST complements de novo methods like Crass because it avoids the heuristics they use to reduce false positive spacers.…”

“…MetaCRAST was run with a taxonomy-guided query ( Tables S3 and S4 ), a maximum spacer length of 60, a maximum allowed edit distance (insertions, deletions, or substitutions) between query and target direct repeats of three, a CD-HIT clustering similarity threshold of 0.9, and a total of 16 parallel threads ( MetaCRAST -p query.fa -i grinder-reads.fa -o MetaCRAST -d 3 -l 60 -c 0.90 -a 0.90 -n 16 -t tmp ). We selected a maximum allowed edit distance of three based on results of our prior metagenomic CRISPR detection studies, which showed MetaCRAST searches with a taxonomy-guided query that found similar numbers of spacers to Crass when we set this edit distance ( Moller & Liang, 2017 ). For all analyses, detected spacers were clustered with CD-HIT with a similarity threshold of 0.9 ( cdhit -i spacers.fa -o spacersCD90.fa -c 0.9 ) to reduce spacer redundancy.…”

Untitled

“…MetaCRAST was run with a taxonomy-guided query (Tables S3 and S4), a maximum spacer length of 60, a maximum allowed edit distance (insertions, deletions, or substitutions) between query and target direct repeats of 3, a CD-HIT clustering similarity threshold of 0.9, and a total of 16 parallel threads (MetaCRAST -p query.fa -i grinder-reads.fa -o . We selected a maximum allowed edit distance of 3 based on results of our prior metagenomic CRISPR detection studies, which showed MetaCRAST searches with a taxonomy-guided query found similar numbers of spacers to Crass when we set this edit distance (Moller & Liang, 2017). For all analyses, detected spacers were clustered with CD-HIT with a similarity threshold of 0.9 (cdhit -i spacers.fa -o spacersCD90.fa -c 0.9) to reduce spacer redundancy.…”