Differential expression of genes in fetal brain as a consequence of maternal protein deficiency and nematode infection
Bibliographic record
Abstract
Maternal dietary protein deficiency and gastrointestinal nematode infection during early pregnancy have negative impacts on both maternal placental gene expression and fetal growth in the mouse. Here we used next-generation RNA sequencing to test our hypothesis that maternal protein deficiency and/or nematode infection also alter the expression of genes in the developing fetal brain. Outbred pregnant CD1 mice were used in a 2×2 design with two levels of dietary protein (24% versus 6%) and two levels of infection (repeated sham versus Heligmosomoides bakeri beginning at gestation day 5). Pregnant dams were euthanized on gestation day 18 to harvest the whole fetal brain. Four fetal brains from each treatment group were analyzed using RNA Hi-Seq sequencing and the differential expression of genes was determined by the edgeR package using NetworkAnalyst. In response to maternal H. bakeri infection, 96 genes (88 up-regulated and eight down-regulated) were differentially expressed in the fetal brain. Differentially expressed genes were involved in metabolic processes, developmental processes and the immune system according to the PANTHER classification system. Among the important biological functions identified, several up-regulated genes have known neurological functions including neuro-development (Gdf15, Ing4), neural differentiation (miRNA let-7), synaptic plasticity (via suppression of NF-κβ), neuro-inflammation (S100A8, S100A9) and glucose metabolism (Tnnt1, Atf3). However, in response to maternal protein deficiency, brain-specific serine protease (Prss22) was the only up-regulated gene and only one gene (Dynlt1a) responded to the interaction of maternal nematode infection and protein deficiency. In conclusion, maternal exposure to GI nematode infection from day 5 to 18 of pregnancy may influence developmental programming of the fetal brain.
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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".