Hypoxia and <i>Giardia duodenalis</i>: The Role of Hypoxia-Inducible Factor in Altered Epithelial Glucose Metabolism
Bibliographic record
Abstract
The gastrointestinal system experiences frequent oxygenic fluctuations and must be able to maintain barrier integrity during periods of suboptimal oxygen concentration, or hypoxia. Epithelial cells rely on the Hypoxia-Inducible Factor (HIF) complex to activate genes that combat cellular stress and adapt cellular metabolism during hypoxia. Importantly, protozoan parasites can modulate host tissue oxygen tension and HIF activation, yet little is known regarding the relationship between enteric protozoa and hypoxia. This research aims to uncover the role of HIF upon Giardia duodenalis infection, a top cause of global diarrheal disease and an excellent infection model for the study of GI physiology. We hypothesize that HIF-target genes are activated upon Giardia infection, promoting alternative cellular glucose metabolism to sustain bioenergetic homeostasis. Caco-2 colonic epithelial cells were infected with Giardia isolate GS/M (MOI 10) for 1.5 or 4.5 hours under normoxic (21% O2) or hypoxic (~1%O2, STEMCELL hypoxia incubator) conditions to capture the early or peak activation of the oxygen-dependent HIF subunit (HIF-1α). RNA was extracted for assessment of transcriptional alterations of HIF-target genes via reverse transcriptase quantitative polymerase chain reaction. Investigation of HIF-mediated intracellular metabolic changes was carried out via liquid-chromatography mass-spectrometry analysis (hydrophilic-interaction chromatography method) on cocultures supplemented with a hypoxia mimetic (DMOG) or a HIF inhibitor (PX-478). Metabolomics experiments were repeated using Caco-2 cells transfected with sodium-dependent glucose cotransporter 1 (SGLT1), a key glucose transporter in the small intestine where Giardia localizes which is not reliably expressed in Caco-2 cells. Under normoxic conditions, genes that aid in cell stress responses ( VEGFA, ANKRD37, GADD45A) and glycolysis ( HK2, LDHA) are upregulated in Giardia-infected cells in a time-dependent manner (p<0.05). Fewer HIF-target genes are upregulated under hypoxic conditions (e.g., VEGFA, GADD45A, PGK1; p<0.05), indicating Giardia-infected cells exhibit a transcriptional profile similar to hypoxic uninfected cells. Interestingly, HIF1α was upregulated in Giardia-infected cells under hypoxic conditions at 1.5 hours (p<0.05). Analysis of the Caco-2 intracellular metabolome indicated HIF-dependent changes to nucleic acid (e.g., guanosine, inosine, uracil) and amino acid (e.g., glycine, threonine) metabolism. DMOG treatment increased the abundance of glycolytic intermediates (e.g., PEP, DHAP) in both cell lines and inhibited the depletion of the Kreb’s Cycle intermediate aconitate in the Caco-2-SGLT1 transfected cells, confirming the promotion of glycolytic flux by HIF. Taken together, our findings indicate Giardia-infected cells illustrate a hypoxic signature, a novel metabolic cell rescue mechanism in response to enteropathogens. Elucidating the role of HIF during enteric parasitic infections will aid in our understanding of the cellular adaptations underpinning GI pathophysiology, while also shedding light on the potential of HIF as a therapeutic target. Natural Science and Engineering Research Council of Canada. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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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) | 0.000 | 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) | 0.001 | 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".