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A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes.

ABSTRACT: BACKGROUND:Transcriptome sequencing has become a method of choice for evolutionary studies in microbial eukaryotes due to low cost and minimal sample requirements. Transcriptome data has been extensively used in phylogenomic studies to infer ancient evolutionary histories. However, its utility in studying cryptic species diversity is not well explored. An empirical investigation was conducted to test the applicability of transcriptome data in resolving two major types of discordances at lower taxonomic levels. These include cases where species have the same morphology but different genetics (cryptic species) and species of different morphologies but have the same genetics. We built a species comparison bioinformatic pipeline that takes into account the nature of transcriptome data in amoeboid microbes exemplifying such discordances. RESULT:Our analyses of known or suspected cryptic species yielded consistent results regardless of the methods of culturing, RNA collection or sequencing. Over 95% of the single copy genes analyzed in samples of the same species sequenced using different methods and cryptic species had intra- and interspecific divergences below 2%. Only a minority of groups (2.91-4.87%) had high distances exceeding 2% in these taxa, which was likely caused by low data quality. This pattern was also observed in suspected genetically similar species with different morphologies. Transcriptome data consistently delineated all taxa above species level, including cryptically diverse species. Using our approach we were able to resolve cryptic species problems, uncover misidentification and discover new species. We also identified several potential barcode markers with varying evolutionary rates that can be used in lineages with different evolutionary histories. CONCLUSION:Our findings demonstrate that transcriptome data is appropriate for understanding cryptic species diversity in microbial eukaryotes.

SUBMITTER: Tekle YI

PROVIDER: S-EPMC6240226 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes.

Tekle Yonas I YI Wood Fiona C FC

BMC evolutionary biology 20181116 1

<h4>Background</h4>Transcriptome sequencing has become a method of choice for evolutionary studies in microbial eukaryotes due to low cost and minimal sample requirements. Transcriptome data has been extensively used in phylogenomic studies to infer ancient evolutionary histories. However, its utility in studying cryptic species diversity is not well explored. An empirical investigation was conducted to test the applicability of transcriptome data in resolving two major types of discordances at ...[more]

PMID: 30445905

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Project description:BACKGROUND: Because many picoplanktonic eukaryotic species cannot currently be maintained in culture, direct sequencing of PCR-amplified 18S ribosomal gene DNA fragments from filtered sea-water has been successfully used to investigate the astounding diversity of these organisms. The recognition of many novel planktonic organisms is thus based solely on their 18S rDNA sequence. However, a species delimited by its 18S rDNA sequence might contain many cryptic species, which are highly differentiated in their protein coding sequences. PRINCIPAL FINDINGS: Here, we investigate the issue of species identification from one gene to the whole genome sequence. Using 52 whole genome DNA sequences, we estimated the global genetic divergence in protein coding genes between organisms from different lineages and compared this to their ribosomal gene sequence divergences. We show that this relationship between proteome divergence and 18S divergence is lineage dependent. Unicellular lineages have especially low 18S divergences relative to their protein sequence divergences, suggesting that 18S ribosomal genes are too conservative to assess planktonic eukaryotic diversity. We provide an explanation for this lineage dependency, which suggests that most species with large effective population sizes will show far less divergence in 18S than protein coding sequences. CONCLUSIONS: There is therefore a trade-off between using genes that are easy to amplify in all species, but which by their nature are highly conserved and underestimate the true number of species, and using genes that give a better description of the number of species, but which are more difficult to amplify. We have shown that this trade-off differs between unicellular and multicellular organisms as a likely consequence of differences in effective population sizes. We anticipate that biodiversity of microbial eukaryotic species is underestimated and that numerous "cryptic species" will become discernable with the future acquisition of genomic and metagenomic sequences.

Project description:BACKGROUND:The use of implementation strategies is an active and purposive approach to translate research findings into routine clinical care. The Expert Recommendations for Implementing Change (ERIC) identified and defined discrete implementation strategies, and Proctor and colleagues have made recommendations for specifying operationalization of each strategy. We use empirical data to test how the ERIC taxonomy applies to a large dissemination and implementation initiative aimed at taking cardiac prevention to scale in primary care practice. METHODS:EvidenceNOW is an Agency for Healthcare Research and Quality initiative that funded seven cooperatives across seven regions in the USA. Cooperatives implemented multi-component interventions to improve heart health and build quality improvement capacity, and used a range of implementation strategies to foster practice change. We used ERIC to identify cooperatives' implementation strategies and specified the actor, action, target, dose, temporality, justification, and expected outcome for each. We mapped and compiled a matrix of the specified ERIC strategies across the cooperatives, and used consensus to resolve mapping differences. We then grouped implementation strategies by outcomes and justifications, which led to insights regarding the use of and linkages between ERIC strategies in real-world scale-up efforts. RESULTS:Thirty-three ERIC strategies were used by cooperatives. We identified a range of revisions to the ERIC taxonomy to improve the practical application of these strategies. These proposed changes include revisions to four strategy names and 12 definitions. We suggest adding three new strategies because they encapsulate distinct actions that were not described in the existing ERIC taxonomy. In addition, we organized ERIC implementation strategies into four functional groupings based on the way we observed them being applied in practice. These groupings show how ERIC strategies are, out of necessity, interconnected, to achieve the work involved in rapidly taking evidence to scale. CONCLUSIONS:Findings of our work suggest revisions to the ERIC implementation strategies to reflect their utilization in real-work dissemination and implementation efforts. The functional groupings of the ERIC implementation strategies that emerged from on-the-ground implementers will help guide others in choosing among and linking multiple implementation strategies when planning small- and large-scale implementation efforts. TRIAL REGISTRATION:Registered as Observational Study at www.clinicaltrials.gov ( NCT02560428 ).

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A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes.

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A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes.

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