Flaxseed Alters Gut Microbiota-Mammary Gland MicroRNA Relationships Differently Than its Oil and Lignan Components
Bibliographic record
Abstract
Consumption of flaxseed (FS) and its oil (FSO) and lignan (secoisolariciresinol diglucoside, SDG) components have been associated with changes during mammary gland (MG) development which then lead to reduced breast cancer risk in adulthood. FS components are metabolized by the gut microbiota and MG microRNA (miRNA) response to FS may be an underlying mechanism. We aimed to determine if there is an association between the cecal microbiota and MG miRNome, if it is affected by FS or its components, and to identify altered pathways. We used MG miRNome (NanoString nCounterâ system) and cecal microbiota (16S rRNA gene sequencing) data from our previous study where 4–5 week old female C57BL/6 mice were randomized to receive one of four isocaloric diets (n = 5–6/group): basal AIN-93G diet (BD), 10% FS, 3.67% FSO, and 0.15% SDG for 3 weeks. Within-group Spearman correlations between paired (by mouse) miRNA and bacterial genera were compared across the 4 conditions by Differential Correlation Analysis (DGCA v1.0.2 R Package). Genes targeted by miRNA in significantly (q < 0.15) altered correlations were identified with miRDB v6.0 and used for pathway analysis (pathDIP 4, Bonferroni adjusted p-value < 0.05). 619,541, 811, and 768 significant miRNA-genera correlations were found in the BD, FS, FSO, and SDG groups, respectively. FS reversed 24 of the BD within-group correlations, but FSO or SDG did not. SDG reversed 24 of the FS within-group correlations, but FSO did not. Of correlations reversed by FS when compared to BD, enriched pathways of miRNA gene targets included cadherin and Wnt signalling, processes vital to MG development, tumour formation and metastasis. When comparing SDG correlations to FS, involved pathways were related to MG development, tumour initiation and progression. There is a relationship between gut microbial taxa and MG miRNAs, which is modified by FS. This suggests that the microbiota is a mediator of FS miRNA-dependent effects in the MG. SDG, but not FSO, may participate in FS effects but it does not influence microbial genera-miRNA relationships alone. This suggests that whole FS consumption is necessary for benefits in MG development and breast cancer risk via cecal microbiota-MG miRNA mechanisms. Natural Sciences and Engineering Research Council of Canada.
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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 | 0.001 |
| 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".