Alterations in Innate Immune Populations During Systemic Autoimmune Rheumatic Disease Development
Bibliographic record
Abstract
Objectives Systemic autoimmune rheumatic diseases (SARD) are a group of chronic diseases characterized by the presence of antinuclear antibodies (ANAs).[1] However, ANAs cannot reliably be used as a diagnostic tool because a subset of healthy women are ANA+ (~20%) and the majority of these individuals will not progress to SARD.[2] Why some individuals progress while others remain asymptomatic is unknown. Our objective is to evaluate functional alterations in innate immune populations during SARD development. Methods Experiments have been completed to examine the composition of innate immune cells in PBMCs using CITE-Seq. Samples were used from 5 ANA- healthy controls, 11 ANA+ asymptomatic (5 progressors prior to progression, 6 non-progressors) and 8 early SARD patients. Five million freshly thawed PBMCs were depleted of T and B cells using α-CD3, -CD19 biotinylated antibodies with streptavidin coated magnetic beads. Purified innate immune cells were stained with a panel of oligo-conjugated antibodies for the identification of DC/monocyte populations. 9000 cells were sequenced at a depth of 50000 reads for gene expression and 5000 reads for CITE-Seq. Results Using both gene and surface protein expression, we identified 11 DC and monocyte populations (Figure 1A). Proportional analysis revealed an expansion of non-classical and activated non-classical monocytes in progressor and SARD patients (Figure 1B). SARD patients also exhibited higher proportions of cDC3s, VCAN+ monocytes and MME+ DCs than ANA+ individuals regardless of progression status suggesting a role for these cells in active disease (Figure 1B). Comparing ANA+ patients, classical, intermediate, and non-classical monocytes were expanded in progressors while cDC1s, cDC2s and pDCs were expanded in non-progressors (Figure 1B). Differential expression analysis indicated high expression of interferon stimulated genes in progressors and SARD patients, while non-progressors showed high expression of heat-shock proteins which have been shown to promote tolerogenic conditions. These differences in gene expression were observed across cell types, but intermediate monocytes are shown as representative cells due to their role in antigen presentation (Figure 1C). Several genes were differentially expressed between progressors and SARD, potentially highlighting distinct roles for genes in initiating and driving disease (Figure 1C). Further analysis will be done to examine pathways associated with these genes. Figure 1. A) UMAP of 11 annotated monocyte and DC clusters. B) Proportional analysis showing each cluster for control, non-progressor, progressor and SARD groups. Data was normalized to account for differences in cell counts per cluster and per patient sample. C) Heatmap showing differentially expressed genes in intermediate monocytes as a representative cell cluster. Similar trends were seen in the remaining cell types. Red arrows indicate genes of interest between progressor and SARD groups. Blue bars indicate differentially expressed genes between progressors and non-progressors. Conclusion Our data reveals differences in proportions and gene expression between progressors, non-progressors and SARD patients. Importantly, ANA+ progressors show expanded monocyte populations and functional differences compared to non-progressors even prior to progression. These results will allow us to investigate the immunological differences that may drive progression in SARD. [1.] Goldblatt F. Lancet 2013;382:797-808. [2.] Wither J. Arthritis Res Ther 2017;19:41.
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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 | 0.001 | 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".