A transcriptomic analysis of the effects of macrophage polarization and endotoxin tolerance on the response to Salmonella
Bibliographic record
Abstract
Salmonella is an intracellular pathogen causing significant morbidity and mortality. Its ability to grow inside macrophages is important to virulence, and is dependent on the activation state of the macrophages. Classically activated M1 macrophages are non-permissive for Salmonella growth, while alternatively activated M2 macrophages are permissive for Salmonella growth. Here we showed that endotoxin-primed macrophages (MEP), such as those associated with sepsis, showed similar levels of Salmonella resistance to M1 macrophages after 2 hr of intracellular infection, but at the 4 hr and 24 hr time points were susceptible like M2 macrophages. To understand this mechanistically, transcriptomic sequencing, RNA-Seq, was performed. This showed that M1 and MEP macrophages that had not been exposed to Salmonella, demonstrated a process termed here as primed activation, in expressing relatively higher levels of particular anti-infective genes and pathways, including the JAK-STAT (Janus kinase-signal transducer and activator of transcription) pathway. In contrast, in M2 macrophages these genes and pathways were largely expressed only in response to infection. Conversely, in response to infection, M1 macrophages, but not MEP macrophages, modulated additional genes known to be associated with susceptibility to Salmonella infection, possibly contributing to the differences in resistance at later time points. Application of the JAK inhibitor Ruxolitinib before infection reduced resistance in M1 macrophages, supporting the importance of early JAK-STAT signalling in M1 resistance to Salmonella.
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) | 0.001 | 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".