Searching for blood biomarkers and treatment targets in Women with fibromyalgia – Protein interaction patterns and anti-satellite glia cell IgG antibodies as promising candidates
Bibliographic record
Abstract
BACKGROUND: Recent studies suggest that autoreactive immunoglobulin G (IgG) antibodies binding to satellite glia cells (anti-SGC IgG) in the dorsal root ganglia influence pain intensity in a subgroup of fibromyalgia subjects (FMS), thus indicating altered immune activation. The main aim of this study was to identify proteins distinguishing female FMS from female healthy controls (HC) and within the FM group, proteins distinguishing FMS with high vs low levels of anti-SGC IgG. The secondary aim was to assess the associations between serum proteins and anti-SGC IgG, respectively, and FM symptoms. METHODS: Anti-SGC IgG was quantified using an immunofluorescence assay. Proteins in serum were assessed using Olink® Explore 384 Inflammation panel, regarding differences between FMS (n = 93) and HC (n = 40) and regarding differences between FMS with high (≥50 %) and low (<50 %) anti-SGC IgG, respectively. Proteins found to differ between groups (VIP ≥ 1.3) were further analyzed regarding protein-interactions using the software tool STRING (FM vs HC n = 56, high vs low anti-SGC IgG n = 55). Results from the FM group were also compared with two nociceptive pain conditions. RESULTS: In FMS, a cluster of immune system-related proteins was found among upregulated proteins, including CD40 and CD40L, with central roles in humoral immune response. CD40 levels were associated with more severe FM symptoms. In contrast, a cluster of tissue development-/regeneration-related proteins was found among downregulated proteins, this was not seen in nociceptive pain conditions. In FMS with high anti-SGC IgG, clusters dominated by immune system-related proteins were found among both upregulated and downregulated proteins. The cluster of upregulated proteins included CD79b, a protein necessary for B-cell receptor function, and CD4, a co receptor needed for T cell activation, thus with central role in activating various immune responses, including B-cell activation. Positive correlations were seen between some of these proteins and symptoms. On the contrary, several of the downregulated proteins correlated negatively to symptoms. CONCLUSION: Our data support the involvement of the immune system in FM and indicate that further studies on autoimmune mechanisms, proteomics, and protein interaction analysis could lead to new objective diagnostic criteria identifying FMS likely to benefit from immunomodulatory treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".