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Record W2463702166 · doi:10.1186/s12864-016-2831-y

A next generation sequencing-based method to study the intra-host genetic diversity of norovirus in patients with acute and chronic infection

2016· article· en· W2463702166 on OpenAlexaff
Maria E. Hasing, Bart Hazes, Bonita E. Lee, Jutta K. Preiksaitis, Xiaoli Pang

Bibliographic record

VenueBMC Genomics · 2016
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsProvincial Laboratory of Public HealthUniversity of Alberta
Fundersnot available
KeywordsBiologyNorovirusPopulationDeep sequencingDNA sequencingGenetic diversityRibosomal RNAViral evolutionGeneticsVirologyGenomeGeneVirus

Abstract

fetched live from OpenAlex

BACKGROUND: Immunocompromised individuals with chronic norovirus (NoV) infection and elderly patients are hypothesized to be reservoirs where NoV might accumulate mutations and evolve into pandemic strains. Next generation sequencing (NGS) methods can monitor the intra-host diversity of NoV and its evolution but low abundance of viral RNA results in sub-optimal efficiency. In this study, we: 1) established a next generation sequencing-based method for NoV using bacterial rRNA depletion as a viral RNA enrichment strategy, and 2) measured the intra-host genetic diversity of NoV in specimens of patients with acute NoV infection (n = 4) and in longitudinal specimens of an immunocompromised patient with chronic NoV infection (n = 2). RESULTS: A single Illumina MiSeq dataset resulted in near full-length genome sequences for 5 out of 6 multiplexed samples. Experimental depletion of bacterial rRNA in stool RNA provided up to 1.9 % of NoV reads. The intra-host viral population in patients with acute NoV infection was homogenous and no single nucleotide variants (SNVs) were detected. In contrast, the NoV population from the immunocompromised patient was highly diverse and accumulated SNVs over time (51 SNVs in the first sample and 122 SNVs in the second sample collected 4 months later). The percentages of SNVs causing non-synonymous mutations were 27.5 % and 20.5 % for the first and second samples, respectively. The majority of non-synonymous mutations occurred, in increasing order of frequency, in p22, the major capsid (VP1) and minor capsid (VP2) genes. CONCLUSIONS: The results provide data useful for the selection and improvement of NoV RNA enrichment strategies for NGS. Whole genome analysis using next generation sequencing confirmed that the within-host population of NoV in an immunocompromised individual with chronic NoV infection was more diverse compared to that in individuals with acute infection. We also observed an accumulation of non-synonymous mutations at the minor capsid gene that has not been reported in previous studies and might have a role in NoV adaptation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.308
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2016
Admission routes1
Has abstractyes

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