Investigation of Effects of Hyperglycaemia on the Lung Microbiome in Diabetic Mice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".