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Investigation of Effects of Hyperglycaemia on the Lung Microbiome in Diabetic Mice

2022· article· en· W4225420276 on OpenAlexaff
Stanislavs Vasiljevs, Deborah L. Baines

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMicrobiomeDiabetes mellitusLungBronchoalveolar lavagePopulationBiologyMedicineEndocrinologyInternal medicinePhysiologyImmunologyAndrologyBioinformatics

Abstract

fetched live from OpenAlex

The lungs are constantly exposed to a diversity of microbes. On average, the human inhales between 0.7 and 7000 bacterial colony forming units (CFU) every minute. The airway epithelium and the airway surface liquid (ASL) which lines the luminal surface, play a vital role in the defence against these inhaled organisms. Glucose concentration in the ASL is much lower than that of blood (approximately 12.5 times lower). It was proposed that low glucose concentration in the ASL contributes to innate protection against the growth of pathogenic organisms which can utilise glucose for growth. Previous research demonstrated that a sustained increase in blood glucose concentration (such as diabetes) led to increased glucose concentration found in the ASL in both human and animals. We therefore hypothesised that the microbial population of the lung would change in the diabetic lung. Seven‐week‐old female db/db (BKS.Cg‐+Leprdb/+Leprdb/OlaHsd) and non‐diabetic littermates (BKS.Cg‐(Lean)/OlaHsd) db/db mice and non‐diabetic littermate controls were purchased from Envigo (UK). Mice were maintained in standard animal housing in a 12h light/dark cycle; water and standard rodent chow available ad libitum and allowed to acclimatise for three weeks before lung microbiome collection. Mice were terminated with an overdose of pentobarbital (0.2ml of 100mg/ml i.p.). Blood was collected for glucose measurement. Bronchoalveolar lavage was performed and 1 mL of solution was used to extract bacterial DNA using QIAamp DNA Microbiome Kit (Qiagen). The V3‐V4‐region of the 16S rRNA gene was amplified and sequenced using 300 bp paired‐end reads on the Illumina MiSeq platform. Bioinformatic analysis was performed using Mothur v1.39.5 as per the MiSeq SOP pipeline. After removing of contaminant sequence reads, downstream statistical analyses were performed using R statistical software. The bacterial diversity in BAL samples was highly variable within and between diabetic and non‐diabetic mice. Hyperglycaemia did not affect the a‐diversity of the lung microbiome (Inverse Simpson rating). However, hyperglycaemia had a significant effect on the b‐diversity of lung microbiome (analysed with AMOVA, p=0.011, n=9) with the microbiome from diabetic mice clustering together. At the genus level, bacteria of genus Staphylococcus were more abundant in the normoglycaemic mice (n=9, p=0.019). The genus Pseudomonas were more abundant in diabetic mice (n=9, p=0.028) and Corynebacterium (n=9, p=0.0018), which are frequently found in the lung microbiome as commensal organisms, were decreased. Taken together, these data indicate that sustained hyperglycaemia modifies the lung microbiome, decreasing the abundance of commensal bacteria and promoting the growth of glucose‐utilising bacteria such as Pseudomonas which may include potential pathogenic species such as P. aeruginosa.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 designBench or experimental
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

Citations1
Published2022
Admission routes1
Has abstractyes

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