Bone marrow Ly6Chi Monocytes are recruited to injured kidney and differentiate into Ly6Clo profibrotic macrophages (92.7)
Bibliographic record
Abstract
Abstract Macrophages (M(s) play decisive roles in different models of organ fibrosis. Prevailing concepts support circulating monocyte heterogeneity, subpopulation discrete recruitment into injured tissues, differentiation into M(s, and possibly subpopulation discrete roles in injury and repair. Here we show discrete subpopulations of M(s in fibrotic kidney induced by unilateral ureteral obstruction (UUO) and their discrete functional roles in renal fibrosis. Fluorescence-activated cell sorting (FACS) of purified UUO kidney leukocytes reveals 3 populations of M(s separated by Ly6C: high, intermediate, and low. FACS of peripheral blood mononuclear cells (PBMC) showed a marked increase in Ly6chi-Mo in response to kidney injury. Depletion of M(s in CD11b-DTR mice during UUO fibrosis inhibits the progression of fibrosis by an indirect mechanism. However, Ly6clo kidney M(s were selectively depleted indicating their predominant role in fibrogenesis. To determine the origins of kidney M(s, we lineage-traced 3 different populations of monocytes (Mo) by adoptive transfer of CD45.1+ bone marrow (BM)-Ly6chi Mo or peripheral blood (PB) Ly6chi Mo or PB-Ly6clo-Mo into CD45.2 mice with UUO. Ly6Chi BM-Mo were avidly recruited to injured kidney, and differentiated into Ly6Chi, Ly6Cint, and Ly6Clo kidney M(s. PB-Mo also differentiated into 3 populations but 20-fold lower recruitment was observed. We characterized kidney-M(-subpopulation transcript levels by branched-chain-DNA-technology which demonstrated discrete differences in expression of genes including TNF(, IL1(, MIP2, MIP1(. We conclude that Ly6Chi BM-Mo are recruited to kidney by mobilization from BM, differentiate into 3 functionally discrete kidney M(s one of which is profibrotic, and resident M(s play a non-fibrogenic role in kidney injury.
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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) | 0.003 | 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".