Proteomics Profiling to Distinguish DOCK8 Deficiency From Atopic Dermatitis
Bibliographic record
Abstract
Dedicator of cytokinesis 8 deficiency is an autosomal recessive primary immune deficiency disease belonging to the group of hyperimmunoglobulinemia E syndrome (HIES). The clinical phenotype of dedicator of cytokinesis 8 (DOCK8) deficiency, characterized by allergic manifestations, increased infections, and increased IgE levels, overlaps with the clinical presentation of atopic dermatitis (AD). Despite the identification of metabolomics and cytokine biomarkers, distinguishing between the two conditions remains clinically challenging. The present study used a label-free untargeted proteomics approach using liquid-chromatography mass spectrometry with network pathway analysis to identify the differentially regulated serum proteins and the associated metabolic pathways altered between the groups. Serum samples from DOCK8 ( n = 10), AD ( n = 9) patients and healthy control (Ctrl) groups ( n = 5) were analyzed. Based on the proteomics profile, the PLS-DA score plot between the three groups showed a clear group separation and sample clustering ( R 2 = 0.957, Q 2 = 0.732). Significantly differentially abundant proteins ( p < 0.05, FC cut off 2) were identified between DOCK8-deficient and AD groups relative to Ctrl ( n = 105, and n = 109) and between DOCK8-deficient and AD groups ( n = 85). Venn diagram analysis revealed a differential regulation of 24 distinct proteins from among the 85 between DOCK8-deficient and AD groups, including claspin, haptoglobin-related protein, immunoglobulins, complement proteins, fibulin, and others. Receiver-operating characteristic curve (ROC) analysis identified claspin and haptoglobin-related protein, as potential biomarkers with the highest sensitivity and specificity (AUC = 1), capable of distinguishing between patients with DOCK8 deficiency and AD. Network pathway analysis between DOCK8-deficiency and AD groups revealed that the identified proteins centered around the dysregulation of ERK1/2 signaling pathway. Herein, proteomic profiling of DOCK8-deficiency and AD groups was carried out to determine alterations in the proteomic profiles and identify a panel of the potential proteomics biomarker with possible diagnostic applications. Distinguishing between DOCK8-deficiency and AD will help in the early initiation of treatment and preventing complications.
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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".