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Record W3000625341 · doi:10.1101/2020.01.18.908624

A Refined View of Airway Microbiome in Chronic Obstructive Pulmonary Disease at Species and Strain-levels

2020· preprint· en· W3000625341 on OpenAlexaff
Zhang Wang, Haiyue Liu, Fengyan Wang, Yuqiong Yang, Xiaojuan Wang, Boxuan Chen, Martin R. Stämpfli, Hongwei Zhou, Wensheng Shu, Christopher E. Brightling, Zhenyu Liang, Rongchang Chen

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersNational Natural Science Foundation of China
KeywordsMicrobiomeCOPDBiologyImmunologySputumMicrobiologyMedicineGeneticsPathologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Little is known about the species and strain-level diversity of the airway microbiome, and its implication in chronic obstructive pulmonary disease (COPD). Here we report the first comprehensive analysis of the COPD airway microbiome at species and strain-levels. The full-length 16S rRNA gene was sequenced from sputum in 98 stable COPD patients and 27 age-matched healthy controls, using the ‘third-generation’ Pacific Biosciences sequencing platform. Individual species within the same genus exhibited reciprocal relationships with COPD and disease severity. Species dominant in health can be taken over by another species within the same genus in GOLD IV patients. Such turnover was also related to enhanced symptoms and exacerbation frequency. Ralstonia mannitolilytica , an opportunistic pathogen, was significantly increased in COPD frequent exacerbators. There were inflammatory phenotype-specific associations of microbiome at the species-level. One group of four pathogens including Haemophilus influenzae and Moraxella catarrhalis , were specifically associated with sputum mediators for neutrophilic inflammation. Another group of seven species, including Tropheryma whipplei , showed specific associations with mediators for eosinophilic inflammation. Strain-level detection uncovered three non-typeable H. influenzae strains PittEE, PittGG and 86-028NP in the airway microbiome, where PittGG and 86-028NP abundances may inversely predict eosinophilic inflammation. The full-length 16S data augmented the power of functional inference and led to the unique identification of butyrate-producing and nitrate reduction pathways as significantly depleted in COPD. Our analysis uncovered substantial intra-genus heterogeneity in the airway microbiome associated with inflammatory phenotypes and could be of clinical importance, thus enabled a refined view of the airway microbiome in COPD. “Take-home” message The species-level analysis using the ‘third-generation’ sequencing enabled a refined view of the airway microbiome and its relationship with clinical outcome and inflammatory phenotype in COPD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.232
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2020
Admission routes1
Has abstractyes

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