Impact of Iron Deficiency on the Growth and Bioelectrical Profile of Different Gut Bacteria
Bibliographic record
Abstract
SCOPE: Iron deficiency (ID) is the most common nutritional deficiency worldwide, impacting gut bacteria's metabolism and cellular biochemistry, but its effects on the microbiota-gut-brain axis (MGB) are poorly understood. Early-life ID-related dysbiosis is linked to neurodevelopmental impairments like autism and attention deficit hyperactivity disorder. Studying ID's impact on bacterial signaling can guide interventions to target MGB in iron-deficient populations. This study examined the responses of Escherichia coli (E. coli) and Limosilactobacillus reuteri (L. reuteri) to in-vitro ID conditions using the iron chelator 2,2'-Bipyridyl (BP). METHODS AND RESULTS: We assessed and modeled their growth and cultivability and explored their bioelectric profiles using the voltage-sensitive dye DiBAC4(3). Results showed differential responses: L. reuteri's growth and cultivability were unaffected by BP, while E. coli's growth rate and cultivability decreased under ID. Additionally, we created a deterministic mathematical model that demonstrated a decrease in the population's average reproduction rate in E. coli under ID. Only E. coli exhibited an altered bioelectric profile, marked by increased cell depolarization in ID conditions, which was largely rescued upon the addition of a saturating concentration of iron. CONCLUSION: These findings highlight specific bioelectrical responses in gut bacteria to ID. Understanding this variability is crucial for deciphering the microbiota's role in health and disease, particularly concerning nutritional iron imbalance and bacterial signaling in the MGB.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".