Early-Life Exposure to Lipopolysaccharide Induces Persistent Changes in Gene Expression Profiles in the Liver and Spleen of Female FVB/N Mice
Bibliographic record
Abstract
The objective of this study was to investigate how subcutaneous (sc) lipopolysaccharide (LPS) administration affects the gene expression profiles of insulin signaling as well as innate and adaptive immunity genes in mouse livers and spleens. FVB/N female mice were randomly assigned to one of two treatment groups at 5 weeks of age: (1) a six-week subcutaneous injection of saline at 11 μL/h (control—CON), or (2) a six-week subcutaneous injection of LPS from Escherichia coli 0111:B4 at 0.1 μg/g body weight at 11 μL/h. At 106 weeks (i.e., 742 days) after the last treatment, mice were euthanized. Following euthanasia, liver and spleen samples were collected, snap frozen, and stored at −80 °C until gene expression profiling. LPS upregulated nine genes in the liver, according to the findings (Pparg, Frs3, Kras, Raf1, Gsk3b, Rras2, Hk2, Pik3r2, and Myd88). With a 4.18-fold increase over the CON group, Pparg was the most up-regulated gene in the liver. Based on the annotation cluster analysis, LPS treatment upregulated liver genes which are involved in pathways associated with hepatic steatosis, B- and T-cell receptor signaling, chemokine signaling, as well as other types of cancers such as endometrial cancer, prostate cancer, and colorectal cancer. LPS increased the spleen expression of Ccl11, Ccl25, Il6, Cxcl5, Pparg, Tlr4, Nos2, Cxcl11, Il1a, Ccl17, and Fcgr3, all of which are involved in innate and adaptive immune responses and the regulation of cytokine production. Furthermore, functional analysis revealed that cytokine–cytokine receptor interaction and chemokine signaling pathways were the most enriched in LPS-treated mice spleen tissue. Our findings support the notion that early-life LPS exposure can result in long-term changes in gene expression profiling in the liver and spleen tissues of FVB/N female mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".