Plasma Proteomic Profiling of a Group of Anxious Dogs by LC‐MS/MS: A Case–Control Study
Bibliographic record
Abstract
PURPOSE: Anxiety is the most common underlying cause of behavioral problems in dogs, which remain a top reason for relinquishment and euthanasia. Despite its high prevalence, anxiety is often underdiagnosed, partly due to a limited understanding of biological processes and absence of diagnostic biomarkers. Our study aims to address this knowledge gap. EXPERIMENTAL DESIGN: Plasma from 10 anxious and 10 matched control dogs were analyzed following a label-free quantitation proteomics workflow based on data-dependent acquisition using a Thermo Q Exactive Plus coupled to an EASY-nLC 1200, Vanquish UHPLC, or Evosep One. Data were processed with Proteome Discoverer 2.4 (Thermo), Perseus (Max Planck Institute), Cytoscape and other bioinformatic tools. RESULTS: Between 279 and 350 proteins were identified, and proteins such as fibrinogen, apolipoproteins, and complement system and coagulation cascade proteins were significantly different between groups. Additionally, we identified two putative subgroups of anxious dogs, suggesting potentially different underlying pathophysiological mechanisms for a single anxiety phenotype. CONCLUSIONS AND CLINICAL RELEVANCE: To our knowledge, this is the first comprehensive clinical in-depth proteomic profiling of plasma from anxious dogs. Our findings lay the foundation for elucidating the pathophysiology of canine anxiety and for the future validation and establishment of novel candidate biomarkers for disease diagnosis. Novel biomarkers would allow for a more effective and objective diagnosis of anxiety, even when not phenotypically apparent. SUMMARY: Previous mass spectrometry (MS) studies have found proteomic profile differences in other diseases and other animal species. This is to our knowledge, the first unbiased and comprehensive clinical in-depth proteomic profiling of plasma from dogs suffering from anxiety disorders. These findings have an impact on animal health as they set the foundation to elucidate the pathophysiology of canine anxiety so that in the future novel candidate biomarkers can be established and validated, furthering the potential development of new drugs and guiding patient-specific therapeutic interventions based on biomarker profiles. In the clinic, novel biomarkers could allow for a more effective and objective diagnosis of anxiety disorders, even when not phenotypically apparent. Detection and measurement of early stages of anxiety disorders as well as treatment monitoring in pet dogs would allow patients to be treated quicker, before the potential onset of aggression, and a faster recovery, thus improving the welfare of companion animals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".