Protein biomarker discovery for canine cognitive dysfunction syndrome based on molecular alterations observed in nasal fluids

Protein biomarker discovery for canine cognitive dysfunction syndrome based on molecular alterations observed in nasal fluids | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Protein biomarker discovery for canine cognitive dysfunction syndrome based on molecular alterations observed in nasal fluids Jiwon Chae, Mina Choi, Juyoung Choi, Seung-Jun Yoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7142040/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cognitive dysfunction syndrome (CDS) is characterized by mental – behavioral deterioration in elderly dogs and often acknowledged as a canine analog of neurodegenerative diseases (NDDs). A commonly shared feature among NDDs is the accumulation of toxic proteins within the brain and consequential degenerations. Several studies have suggested that such events in the brain can be reflected in the nasal area due to its anatomical and systemic adjacency. Furthermore, disease-specific profiles were identified in nasal-derived samples of patients of certain human NDDs, with credible diagnostic potential. Therefore, we hypothesized that alterations in CDS would be reflected in the nose and aimed to identify potential protein biomarkers based on nasal discharge from 65 individuals. Among the differentially expressed proteins within CDS, six marker candidates were selected and evaluated through quantitative proteomics. Two potential markers - CTSG and TRIM14 - showed high specificity with strong diagnostic capability, and both presented particularly high associations with the mild stage of CDS, posing potential links to its progression. Thus, this study presents CTSG and TRIM14 proteins as nasal-based potential biomarkers of CDS, suggesting a diagnostic alternative and a possible new approach to further define the disease based on its underlying pathology. Health sciences/Biomarkers Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Canine cognitive dysfunction syndrome (CDS) refers to cognitive impairment mostly in elderly dogs, more than 10 years old, and is often characterized by memory impairment, motor function deterioration and altered sleep-wake cycles 1 , 2 . A number of survey-based assessment tools like DISHAA, CADES and CCDR have been established for CDS diagnosis, and are frequently utilized as they are considered the most efficient 3 – 5 . Yet, as the methods depend on behavioral observation, such methods hold a limitation in objectivity and struggle to distinguish normal aging from early-stage CDS. Alternative approaches have been proposed as well, such as PET imaging or blood biomarkers 4 , 6 , 7 . However, such methods have limitations in broader application, as they either lack well-established diagnostic criteria, require anesthesia that poses significant risks for elderly dogs, or are too invasive. Regarding well-defined neurodegenerative diseases (NDDs) with cognitive impairment, such as Alzheimer’s disease (AD) or Parkinson’s disease (PD), distinctive protein biomarkers have been identified for each 8 – 10 . As well-established diagnostic criteria exist for such diseases, novel diagnostic methods can be validated against them, enabling a variety of emerging approaches. One such newly suggested approach is utilizing nasal-derived biomarkers. Nasal discharge generally consists of water, salts, and proteins, among which glycoproteins such as mucins constitute the primary mucus component. In addition, it contains enzyme inhibitors and other regulatory proteins, and a variety of proteins involved in protective functions against external pathogens, including antimicrobial peptides, antiviral agents, and immune mediators 11 , 12 . In conditions of inflammation or irritation, cellular damage or death in the nasal mucosa can induce alterations in the protein composition of nasal discharge 11 . While a certain proportion of proteins detected in nasal discharge are derived from the nasal mucus itself, contributions from other sources have also been reported. Previous studies have demonstrated the presence of AD-associated proteins in nasal mucus and discussed the possibility that some of these proteins originate from cerebrospinal fluid (CSF) 13 . This phenomenon is plausibly explained by the established CSF drainage mechanism through the olfactory neuronal pathway into the nasal cavity 14 . The nose, as an anatomically connected region to the brain via the cribriform plate, has a direct systemic connection through axon extension of olfactory sensory neurons (OSNs) from the nasal region to the brain 15 . Moreover, the nasal cavity also serves as a major CSF drainage route, where CSF is flushed through after clearing soluble proteins in brain parenchyma by the glymphatic system to reach the lymphatic system 16 , 17 . Various tracer studies have confirmed that the route along the OSNs into the nasal cavity is the most prevalent pathway for CSF drainage, and this route is well established in mammals 18 – 23 . Based on this pathway, approaches to accessing or monitoring the brain via the nasal area have been suggested 24 , 25 . Considering that accumulation of toxic proteins and related proteinopathy are general characteristics of neurodegenerative diseases (NDDs), this route holds high potential for monitoring the status of diseases 26 . Moreover, various NDDs, including Alzheimer's disease (AD), are often associated with impairments in olfactory function 27 – 30 , further supporting the ‘brain-nose connection’. Based on such anatomic and systemic adjacency with the brain, there have been several attempts to identify biomarkers for NDDs in nasal-derived samples, such as nasal discharge and nasal epithelial tissues, with promising results 13 , 31 – 33 . Those findings align with the concept that the onset of NDDs is also reflected in the nasal region as detectable molecular alterations. Therefore, based on the well-established observation that CDS occur in aging dogs and is clinically akin to human dementia 34 , 35 , the study hypothesizes that CDS is a naturally occurring NDD in dogs. Furthermore, as seen in other NDDs, we assume that CDS progression might lead to molecular alterations in canine body fluids as well. Thus, we aim to identify a novel, non-invasive diagnostic criterion for CDS — protein biomarkers based on nasal discharge. By applying a commonly used biomarker discovery process to nasal discharge samples collected from a canine cohort, this study seeks to explore the feasibility of biomarker discovery and further definitions for CDS, a condition that appears similar to human NDDs, but lacks clear pathological evidence of resemblance. Results 1. Molecular alteration within CDS and its interpretations The whole protein profile of canine nasal discharge was obtained through proteomic analysis with liquid chromatography-tandem mass spectrometry (LC-MS/MS), on selected samples from CDS group and HC group. A total of 6,143 protein IDs were identified from nasal discharge, with each subject listing an average of 5,712 proteins (Supplementary Table 1). Eighty-nine differentially expressed proteins (DEPs) were identified from nasal discharge within the CDS state — 43 of which were downregulated and 46 upregulated (Fig. 1). Known human NDD-related markers such as β-Amyloid precursor protein (APP) and Apolipoprotein E (ApoE) were also detected in nasal discharge samples, yet quantitative comparison showed no statistically significant difference between CDS and control groups. Functional analysis on expanded DEPs revealed notable associations with immune-related pathways, protein synthesis and degradation processes, and cellular transport mechanisms (Fig. 2 ). Immune-related terms, including complement activation and humoral immune responses, were predominantly enriched among upregulated DEPs, suggesting a potential inflammatory component specific in CDS group (Fig. 2 A). This finding may reflect complement-mediated responses involved in CDS progression, possibly paralleling complement-related mechanisms reported in NDDs 36 – 38 . Alterations in protein translation, metabolism, and ribosome-associated functions were also observed (Fig. 2 B), which could indicate perturbations in protein metabolism under cellular stress conditions. Such disruptions are often implicated in protein misfolding and aggregation phenomena characteristic of neurodegeneration, though further validation is required. Notably, downregulated DEPs were enriched in pathways related to the regulation of protein synthesis, a process frequently reported as altered in NDDs 39 . Additionally, reduced expression of multiple proteins linked to synaptic vesicle formation and transport suggests possible synaptic dysfunction. This aligns with synaptic changes documented during neurodegenerative processes, such as decreased synaptic protein levels observed in the cerebrospinal fluid of AD patients, which may also be relevant to CDS-associated neurodegeneration 40 , 41 . 2. Selection and validation of potential marker candidates for CDS Candidate marker proteins were selected by considering fold change and p-value alongside functional analysis results, with emphasis placed on proteins demonstrating meaningful functional relevance to CDS pathology and potential diagnostic utility. From the 89 DEPs identified in nasal discharge, we selected three candidates from each upregulated and downregulated group – BUD31, CTSG, TRIM14, and ALDH1A1, BCO2, PSMB8. Marker candidates were mostly involved in biological functions related to strongly enriched terms in the CDS state, and a potential link to neurodegenerative diseases has been implied from previous research. A detailed description of each selected marker-candidate is provided in Table 1 . Table 1 The list of potential CDS marker candidate proteins Relative Abundance in CDS Group Name UniProt Accession Description Function Relation with neurodegenerative diseases References Upregulated BUD31 A0A8I3MQ46 Protein BUD31 homolog A spliceosomal component protein involved in pre-RNA splicing process 63 . Has a significantly decreased expression in plasma of AD-protective variant carrier. Possible positive relation in pathology of neurodegenerative disease 64 . Bertram et al. 2017 Wittrahm et al. 2023 Upregulated CTSG A0A8I3NK04 Cathepsin G A member of the cathepsin family, lysosomal proteases that play a vital role in charge of proteolytic pathway including cell death and phagocytosis. Frequently observed in sites of inflammation 65 , 66 . Engages in regulating disease-related proteins in the pathology of various neurodegenerative diseases as a lysosomal protein 67 – 73 . Uchiyama 2001; Yu et al. 2016 Kegel et al. 2000; Steinfeld et al. 2006; Khurana et al. 2010; Vidoni et al. 2016; McGlinchey et al. 2017; Klein and Mazzulli 2018; Drobny et al. 2022 Upregulated TRIM14 A0A8I3MZV3 Tripartite motif containing 14 A member of the tripartite motif family (TRIM), an essential regulator of antiviral innate immunity 74 – 76 . Related to a number of pathological conditions, including a variety of neurodegenerative diseases 77 – 79 . Polymorphisms in Tripartite Motif Family-Like 2 (TRIML2) that has a similar structural, functional feature with TRIM14, was defined to have significant association with AD 80 , 81 . Versteeg et al. 2013; Van Gent et al. 2018; Wu et al. 2019 Ozato et al. 2008; McNab et al. 2011; Hatakeyama 2017; Kang et al. 2016; Florentinus-Mefailoski et al. 2021 Downregulated ALDH1A1 A0A8I3M9Q4 Aldehyde dehydrogenase 1 family member A1 A member of the aldehyde dehydrogenases family that engages in various biological pathways 82 , 83 . Upregulated in early stages of AD, in an attempt to protect against oxidative stress-induced damage and its inactivation could induce neurotoxicity 84 . Downregulated in PD, possibly linked to oxidative stress caused by dysfunction of dopamine metabolism 85 , 86 . Manzer et al. 2003; Choudhary et al. 2005 Nikhil et al. 2019; Galter et al. 2003; Durrenberger et al. 2012 Downregulated BCO2 A0A8I3RRF3 6-pyruvoyl tetrahydrobiopterin synthase A mitochondrial enzyme that mediates metabolism of carotenoids 87 . Reported to be significantly decreased in human precuneus with mild AD, and its possible relation with early AD pathology was discussed in terms of microglial dysfunction 88 . Thomas et al. 2020 Sobue et al. 2023 Downregulated PSMB8 Q5W416 Proteasome subunit beta type-8 A subunit of proteasome which engages in protein degradation and inflammatory pathways 89 , 90 . Reported to be upregulated in human brains that have Lewy body dementia along with cathepsins 91 . Kitamura et al. 2011; Huber et al. 2012 Ding and Zhu 2018 The diagnostic potential of each marker candidate protein was evaluated (Fig. 3 ). The result of the quantitative analysis – based validation targeted on each marker candidate is presented in Fig. 3 A. of six tested marker candidates, two immune-related proteins, CTSG and TRIM14 showed relatively high expression level in the CDS group, with statistical significance. Based on the CDS status of each individual, ROC curve was generated to examine the diagnostic performance of each marker candidate in Fig. 3 B. TRIM14 yielded the highest AUC of 0.7747, and CTSG also achieved AUC above 0.7, thus suggesting its potential as a diagnostic marker. Yet, the other four markers, BUD31, ALDH1A1, BCO2, and PSMB8 showed relatively poor discriminatory potential, with AUC below 0.65. To further validate two most promising markers, the whole replication cohort was double validated with the enzyme-linked immunosorbent assay (ELISA) targeted on CTSG and TRIM14. The double validated results coincided with the prior result, as CTSG and TRIM14 both showed significantly higher expression levels in CDS group (Fig. 4 A). Based on the results of additional validation by ELISA, ROC curve analysis was also conducted in order to assess the diagnostic potential of two markers (Fig. 4 B). CTSG and TRIM14 each yielded AUC value of 0.7085 and 0.8047, providing that both markers hold acceptable potential as diagnostic biomarkers of CDS 42 . 3. Further analysis on two most promising markers – CTSG and TRIM14 CDS is usually classified into three stages – mild, moderate, and severe – based on its severity, and it progressively develops into the next stage as the disease worsens. While CTSG and TRIM14 were already confirmed to have significantly higher expression levels in CDS group, expression levels on each state of the disease were further examined to identify whether potential markers hold higher specificity with certain stages of the disease (Fig. 5 ). Compared to HC group, the expression level peaked at mild CDS group in both CTSG and TRIM14, followed by moderate CDS group, severe CDS group, and HC group. Compared to HC group, results in the mild and moderate CDS groups also showed significantly high specificity. Especially, CTSG showed a strong association with the early stage of the disease, while TRIM14 had relatively high specificity in the mild to moderate states of the disease. To assert the diagnostic value for CDS state, it was also confirmed that marker expressions are not correlated with variables other than the CDS state, and therefore their specificity solely depends on the presence of CDS. The correlation between age and marker expression was examined, and it was indicated that both markers had no significant relation with age in its expression (Fig. 6 A). Other variables in the sample cohort, such as gender and BSC score, showed no significant trends in relation to overall marker expression (Fig. 6 B, 6 C). Also, there was no significant differences in marker expression between individuals with and without other neurological comorbidities within the CDS group (Supplementary Fig. 1). Discussion CDS predominantly affects elderly dogs and is commonly regarded as a form of NDD in canines. Given that the alterations derived by CDS are irreversible, early disease detection would be crucial for therapeutic intervention 43 , 44 . However, current diagnostic approaches remain insufficient to clearly distinguish early-stage CDS from normal aging processes. Our proteomic analysis of nasal discharge identified DEPs associated with CDS, and functional enrichment of these DEPs revealed similarities to pathways implicated in human neurodegenerative diseases such as AD and PD, providing additional support for the classification of CDS as an NDD 45 , 46 . Well-established AD-related markers, such as amyloid beta precursor protein (APP) and apolipoprotein E (ApoE) 47 – 49 , were detected in the nasal discharge proteome. Although their differential expression did not reach statistical significance, some CDS cases exhibited elevated levels relative to controls, suggesting potential involvement of established NDD markers in CDS pathology; however, larger cohorts and targeted analyses are required to confirm these findings. The LC-MS/MS proteomic discovery phase focused primarily on individuals with moderate to severe CDS to maximize detection of protein alterations presumed to be more evident at advanced stages. Interestingly, subsequent validation indicated that two candidate biomarkers—cathepsin G (CTSG) and TRIM14—showed more pronounced elevation in mild-stage CDS cases, potentially reflecting early molecular changes. Given the clinical importance of early detection in CDS, the potential of these markers to signal early-stage alterations warrants further investigation. Moreover, while such observation is currently limited to these two proteins, these findings underscore the importance of exploring molecular alterations across all disease stages. CTSG, a lysosomal serine protease involved in apoptosis and inflammatory immune regulation predominantly in neutrophils 50 , and TRIM14, a regulator of innate immune signaling pathways including interferon responses 51 , are primarily expressed in immune cells. Although it remains unresolved whether the increased levels of these proteins in nasal secretions reflect local airway inflammation, systemic immune activation, or CNS-related processes, their elevation in CDS-affected dogs suggests that nasal biomarkers may capture biologically relevant signals along the brain–nose axis. Future studies incorporating larger cohorts and comparative analyses of nasal secretions and CNS-derived samples (e.g., CSF) will be necessary to clarify the source and clinical significance of these protein changes. At this point, rather than implying direct causative roles in CDS pathogenesis, these proteins are better considered peripheral indicators that may aid early detection or disease stratification in CDS. In selecting candidate biomarkers, traditional statistical parameters such as fold change and p-value were evaluated but, due to limited sample size, were insufficient as sole criteria. Consequently, functional relevance was integrated into the selection process to prioritize proteins with meaningful associations to CDS pathology. Accordingly, we identified two protein biomarkers with diagnostic potential—CTSG and TRIM14—from a list of differentially expressed proteins. Expression levels of two potential markers were weakly correlated, supporting a multi-marker approach for improved diagnostic accuracy as detailed in Table 2 . The potential markers showed no significant correlation with age or metabolic variables (Fig. 7 ). Table 2 Evaluation of diagnosis approaches based on single biomarkers and multiple biomarkers of CDS. Each performance metric is provided with a 95% CI Protein Sensitivity (95% CI) Specificity (95% CI) CTSG 69.23% (53.58% − 81.43%) 64.29% (38.76% − 83.66%) TRIM14 66.67% (50.98% − 79.37%) 92.86% (68.53% − 98.73%) Parallel Approach (OR) 84.62% (70.27% − 92.75%) 64.29% (38.76% − 83.66%) Serial Approach (AND) 51.28% (36.20% − 66.13%) 92.86% (68.53% − 98.73%) The discovery cohort’s design accounted for known age-related risk factors in CDS, resulting in age differences between CDS and control groups consistent with disease biology. Sex-related variability was minimized by including only neutered dogs in the discovery phase, reflecting the broader cohort composition (~ 89% neutered). Subsequent analyses confirmed no significant relevance with age or sex on candidate biomarker expression. Also, given the substantial breed diversity among dogs, concerns regarding breed-associated bias were addressed by principal component analysis (PCA) of DEP. In the discovery cohort, clustering analysis based on DEP expression data indicated that samples tended to group more by disease status than by breed. (Supplementary Fig. 2). The lack of tight clustering by breed suggests that individual variability and disease status contribute more substantially to proteomic differences than breed-related factors. The expression levels of CTSG and TRIM14 also did not exhibit breed-specific patterns across a diverse canine cohort comprising 13 pure breeds and mixed-breed individuals (Supplementary Fig. 3), further supporting the interpretation that the observed proteomic alterations are more closely associated with disease status than with breed. To assess their specificity to CDS status, marker expressions within the CDS group were compared between individuals with and without other neurological comorbidities (Supplementary Fig. 4), revealing no significant differences. However, given the small number of CDS dogs with comorbidities (n = 7), these findings require validation in larger, more comprehensive cohorts incorporating other neurological conditions. A primary limitation of this study lies in the lack of previous research on CDS, limiting contextual interpretation. Unlike human NDDs, for which behavioral assessments, fluid biomarkers, and imaging have been rigorously validated 44 , 55 – 58 CDS remains less well characterized and may represent a heterogeneous classification of cognitive impairments rather than a singular disease entity. Expanded cohorts and multi-fluid proteomic analyses will be essential to elucidate CDS pathophysiology. Although the present study employed a cross-sectional design, future longitudinal studies are planned to assess biomarker dynamics and clinical progression. Thus, this study provides initial insights into the molecular pathology of CDS as a canine NDD and proposes novel biomarker candidates for early-stage diagnosis based on nasal secretion proteomics, offering a foundation for further biomarker development and translational research. Methods 1. Canine Cohort A total of 65 domestic dogs were chosen for the study (Table 3). Canine cohort was divided into groups based on their CDS status - healthy controls (HCs) and CDS group. The status of CDS was examined by utilizing Dr. Gary Landsberg’s DISHAA questionnaire assessment tool provided by Purina Institute 1,56,57 . Its scale ranges from 0 to 45. A score of 4-15 is consistent with mild, 16-33 is moderate, and higher than 33 is regarded as severe CDS. For each individual, their corresponding questionnaire results were recorded. Also, demographic characteristics including age (in years), gender and neutered status, body condition score (BSC) a numerical scale used to assess obesity status in dogs — breed, and pre-existing medical conditions were collected and documented. In particular, the presence of any prior diagnoses of neurological disorders, such as intervertebral disc disease (IVDD), epilepsy, brain tumors, atlantoaxial instability (AAI), or cerebral infarction, was specifically identified. The cohort had a mean age of 11.76 years, while 48 of them were diagnosed as CDS positive – 12 of which mild, 28 moderate, and 8 severe. All dogs included in the study were client-owned, and informed consent was obtained from all owners prior to sample collection. 2. Sample Collection Nasal discharge was collected with aid from two veterinary facilities in South Korea. All samples were collected observing the following procedure. Sterilized nasal swabs were soaked in PBS and then inserted into nostril to rub the nasal cavity more than 10 times, gently scraping the surface if possible. Then fluids were immediately dissolved in PBS and stored at -80 °C before following procedures. No anesthesia or euthanasia was required or performed in this study, as the sample collection involved only non-invasive nasal swabbing in awake, client-owned dogs. 3. Quality Control and Cohort Stratification for Proteomic Analysis Among the initially collected samples, those exhibiting visible hemolysis were primarily excluded through visual inspection. For samples with a total volume of ≥30 μL, protein concentration (μg/μL) was quantified using the BCA Protein Assay Kit (Pierce, Cat#: 23227), and the total protein amount (μg) was subsequently calculated. Only samples containing ≥60 μg of total protein were selected for downstream LC-MS/MS analysis, thereby constituting the discovery cohort listed in Table 4. From the quality-controlled specimens, representative individuals were selected from both the combined CDS group and the control group (n=3 per group), yielding a total of six samples for discovery proteomics. To ensure minimal internal heterogeneity and to reflect the overall characteristics of the entire cohort, the selected discovery cohort was evaluated for demographic and clinical comparability—including age, BSC score, and DISHAA score —relative to the full dataset (Supplementary Fig. 4). Residual protein samples from the six discovery specimens were also utilized in subsequent quantitative analyses. In addition, protein concentrations in the remaining cohort samples were reassessed using the Qubit Protein Assay Kit (Thermo Fisher Scientific, Cat#: Q33211) in conjunction with the Qubit 4 Fluorometer (Thermo Fisher Scientific, Cat#: Q33238) to determine their suitability for quantitative analysis. Only samples meeting both criteria - protein concentration above 500μg/mL and total protein amount above 5μg - were included in the replication cohort. Based on these criteria, a total of 65 samples were subjected to the replication analysis. 4. Protein Sample Preparation - Protein Extraction, Reduction, Alkylation and Digestion The following procedure was carried out prior to TMT labeling for LC-MS/MS. The samples were centrifuged at 13,000 rpm, 4 °C for 10 min. The supernatants were separated, and acetone was added at a volume four times that of the sample. The samples were stored at -80 °C for 90 min, then centrifuged at 16,000 rpm, 4 °C, for 20 min. The supernatant was separated, and the protein concentration of each sample was measured. Each sample was then adjusted to 60 µg. Dithiothreitol was added to achieve a final concentration of 10 mM, and the reduction reaction was conducted at 37 °C and 450 rpm for 30 min in a Thermomixer. After the reduction was complete, iodoacetamide was added to reach a final concentration of 25 mM and the alkylation was carried out at RT for 30 min in the dark. 1 M, 100 mM ABC solution was added to the alkylated sample. Promega trypsin was added at an enzyme-to-protein ratio of 1:25 with a concentration of 0.4 µg/µL, and then the sample was incubated at 37 °C for 16 h for digestion. To terminate the trypsin reaction, TFA was added to reach a final concentration of 1%. The peptide samples were desalted using a SOLA HRP 96 well plate C18 cartridge (30 mg/2 mL). Desalted samples were dried in a SpeedVac, and reconstituted in 100 mM TEAB solution, and the peptide concentration was measured. - TMT Labeling and Peptide Fractionation Each of the six samples were labelled with 10-plex TMT reagent at 50mg. Labeling was designed for each sample as follows: TMT 126 (Lot# XJ344142), TMT 127N (Lot# XH343208), TMT 127C (Lot# XG344143), TMT 128N (Lot# XG341590), TMT 128C (Lot# XG344144), and TMT 129N (Lot# XG341592) in order. After 5 min in RT, TMT 10-plex reagent was dissolved in 41 µl of acetonitrile o, to be treated on each sample. After 1 h at RT, 8 µl hydroxylamine was added to stop the reaction. TMT-labeled samples were then pooled by group into new tubes and dried in a SpeedVac. The dried samples were reconstituted in 0.1% TFA, and any unbound TMT reagent was removed by a SOLA HRP 96 well plate C18 cartridge . Then samples were dried again with SpeedVac and reconstituted in 10 mM ABC solution for fractionation. Each fractionated sample was dried in SpeedVac, reconstituted in 25 µL of 0.1% TFA, and prepared for MS analysis. - Cell Lysis and Protein Extraction Each sample was centrifuged at 13,000 RPM for 10 min at 4 °C, and the supernatant was removed. The pellet was treated with lysis buffer, by treating RIPA buffer and protease in a 100:1 ratio. The sample was incubated on ice for 30 min with vortexing every 10 min, then centrifuged at 13,000 RPM for 10 min at 4 °C. The supernatant was separated into a new tube, and protein concentration was measured using the Qubit Protein Assay (Thermo Scientific; Cat#: A60668). 5. Proteome Analysis via LC-MS/MS LC-MS/MS analysis was performed using the Thermo Vanquish Neo HPLC system coupled with a Thermo Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific), equipped with a trap column (Acclaim PepMap™ 100, 100 µm × 2 cm) and an analytical column (PepMap™ RSLC C18, 75 µm × 50 cm) maintained at 50 °C. The injection volume was 5 µL and the flow rate was set to 0.25 µL/min. The mobile phases consisted of solvent A (0.1% formic acid in water containing 5% DMSO) and solvent B (0.1% formic acid in 80% acetonitrile with 5% DMSO). The gradient elution was programmed as follows: 0–5 min, 2% B; 5–125 min, 2–24% B; 125–155 min, 24–40% B; 155–157 min, 40–95% B; 157–180 min, 95% B. MS analysis was conducted in positive ion mode with an electrospray voltage of 2.5 kV and an ion transfer tube temperature of 275 °C. Data-dependent acquisition (DDA) was performed using Proteome Discoverer software (version 2.4.1.15). The database search was conducted against the UniProtKB Canis lupus familiaris reference proteome (UP000805418), which includes both reviewed and unreviewed entries. The Sequest HT algorithm was used for database searching, followed by validation using Percolator. Peptide-spectrum matches were filtered at a 1% false discovery rate (FDR) at the spectrum, peptide, and protein levels. Search parameters allowed for semi-tryptic peptides with lengths ranging from 6 to 144 amino acids, permitting up to two missed cleavages. The upper peptide length limit was set by default. The precursor mass tolerance was 10 ppm, and fragment mass tolerance was 0.02 Da. Cysteine carbamidomethylation (+57.0214 Da) was set as a fixed modification, and methionine oxidation (+15.9949 Da) and TMT 10-plex labeling (+229.1629 Da) at peptide N-termini and lysine residues were set as variable modifications. The majority of identified peptides were within the typical size range (7–20 amino acids), consistent with common LC-MS/MS performance. The charge state filtering (2–6) was applied during MS data acquisition and thus not explicitly set during the database search. 6. DEP analysis and Functional Analysis Based on the acquired mass spectrometry data, proteins were quantified and statistically analyzed to identify DEPs within the CDS group. DEPs were defined as those exhibiting a fold change greater than 1.5 or less than 0.67, with an associated unadjusted p-value < 0.05. For reliable identification, a false discovery rate (FDR) threshold of 1% was stringently applied at the spectrum, peptide, and protein levels during database searching and result filtering. Functional enrichment analysis was performed using the g:Profiler web server (accessed March 2025), with the organism set to Canis lupus familiaris (Dog). Annotation resources included Gene Ontology (GO) terms—Biological Process (BP) and Molecular Function (MF)—sourced from BioMart (release 2025-03-16), as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway data retrieved from the KEGG FTP release 2024-01-22. The input identifiers used for the enrichment analysis were canine UniProt accession numbers corresponding to proteins identified in the dataset. The analysis was conducted based exclusively on the Canis lupus familiaris annotation framework, without ortholog mapping to other species, including human. DEPs were selected based on a nominal p-value 1.25 or < 0.8. Significantly enriched terms were identified using the Benjamini–Hochberg procedure for multiple testing correction, and only terms with an adjusted p-value < 0.1 were retained for interpretation. All enrichment computations were carried out using g:GOSt, the core enrichment tool within the g:Profiler suite, which is optimized for species-specific annotation frameworks, and the term size was set to 5 to 500. The enriched results were plotted by https://www.bioinformatics.com.cn, an online platform for data analysis and visualization 58 . 7. Dot Blot Analysis Dot blot (DB) was used for quantitative validation of marker candidate proteins. Before the experiment, the membrane was activated in methanol for 10 min. Each sample was prepared and diluted to 500 µg/ml based on the measured protein concentration for each. A dot blot apparatus (Cleaver Scientific; Cat# CSL-D96) was used, following the manufacturer's instructions for slotter use. 1μl of each sample was loaded onto the membrane then were allowed to absorb onto the membrane for approximately 10 min to ensure complete transfer. Membranes were then dried for an additional 30 min and blocked in TBST with 1% skim milk for 30 min at RT. Target-specific primary antibody solution was treated after blocking, followed by overnight incubation at 4 °C. Membranes were washed with TBST for 5 min, three times. An HRP-conjugated secondary antibody targeting the appropriate species was applied, allowing for a 1-to 2-h reaction. Membranes were subsequently washed with TBST once for 5 min and with TBS twice for 5 min each. ECL solution (SuperSignal West Atto Ultimate Sensitivity Chemiluminescent Substrate; Thermo Fisher; Cat# A38554) was added for detection. Chemiluminescence was detected by C-DiGit Blot Scanner (LiCORbio; Model 3600; Lincoln, U.S.A.) and digitized via Image Studio™ Software. The experiments were performed in duplicate, and the average of the results was used. In the experiment, primary antibodies of BUD31(1:5000; LifeSpan Biosciences; Cat# LS-C30571), CTSG (1:1000; abcam; Cat# ab197354), TRIM14 (1:2500; Proteintech; Cat# 15742-1-AP), ALDH1A1 (1:10000; Origene; Cat# TA302641), BCO2 (1:2500; Proteintech; Cat# 14324-1-AP), PSMB8 (1:10000; Origene; Cat# TA305716) and HRP-conjugated secondary antibody (1:5000; abcam; Cat# ab97110, 1:10000; Thermo Fisher Scientific; Cat# G21234) were used. Antibody specificity was verified based on the manufacturer’s validation data, and cross-reactivity with canine proteins was confirmed or inferred from sequence homology (see supplementary material 1-8). Each dilution ratio followed manufacturer’s recommendations. (Detected membrane images are presented in supplementary figure 5.) 8. ELISA The potential of most promising candidates – CTSG and TRIM14 - were further determined using an ELISA kit (FineTest; Cat#: EH1903, EH13190; Wuhan, China) following the instructions given by manufacturer. Protein samples were diluted with sample dilution buffer to meet the optimized ratio, at 1:5 and 1:15 for CTSG and TRIM14. Each protein concentration was calculated based on measured optical density (OD) values via CurveExport software, following the best fitting regression curve with standard curve. Stand values were multiplied by each dilution coefficient. 9. Statistical Analysis To address the potential statistical bias arising from the unequal sample sizes between the CDS and HC groups, we assessed the normality of each marker’s expression distribution within each group prior to hypothesis testing. Normality was evaluated using both visual inspection via Q–Q plots and the D’Agostino & Pearson omnibus test (Supplementary Fig 6-7). To reduce skewness and approximate normality, log transformation was applied to the expression values of each marker. An unpaired two-tailed t-test was used to evaluate statistical significance, with Welch’s correction applied to accommodate unequal variances and imbalanced group sizes. Significance was set at p-value < 0.05, while all values were presented as a mean ± SEM. Extreme 10% were excluded from each group after experiment. The Pearson Product-Moment Correlation and the Spearman correlation test were used to evaluate correlation between factors and marker expression. When determining the correlation between two variables, existing interpretation of correlation coefficient was applied 59 . ROC curves were in evaluation of diagnostic performance of each marker. The optimal threshold value of ROC curve was determined based on Youden's index. AUC value, true positive rate (sensitivity), and false positive rate (specificity) were calculated 60 . Principal component analysis (PCA) was conducted using variance-scaled protein expression values of differentially expressed proteins (DEPs) to evaluate inter-individual variation and breed-associated clustering patterns. The analysis was performed in R (version 2025.05.1+513) using the prcomp function with standardization (scale. = TRUE), and visualized using the factoextra package. Declarations Ethic Declarations All procedures involving animals were performed in accordance with relevant guidelines and regulations. All experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of Petobio Inc (approval number: PTB-2023-IACUC-002-A). This study is reported in accordance with the ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments). Competing interests The authors declare no competing interests. Funding: This research was supported by the Technology Development Program RS-2023-00285280 and RS-2023-00285280. Author Contribution J.C. designed and performed experiments, analyzed data, and wrote the manuscript. M.C. and J.C. (Juyoung Choi) contributed to sample recruitment and management. S.-J.Y. supervised the project, contributed to manuscript writing and editing, and approved the final manuscript. All authors reviewed and approved the submitted manuscript. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Landsberg, G. M., Nichol, J. & Araujo, J. A. Cognitive Dysfunction Syndrome. Veterinary Clin. North. America: Small Anim. Pract. 42 , 749–768 (2012). Kim, S. S. et al. Prevalence and risk factors of canine cognitive dysfunction syndrome in South Korea. Appl. Anim. Behav. Sci. 268 , 106066 (2023). Salvin, H. E., McGreevy, P. D., Sachdev, P. S. & Valenzuela, M. J. The canine cognitive dysfunction rating scale (CCDR): A data-driven and ecologically relevant assessment tool. Vet. 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Supplementary Files SupplementaryMaterial18.AntibodyValidationProvidedbySuppliers.pdf SupplementaryTable1..xlsx SupplementaryFigureS1.pdf SupplementaryFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7142040","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496191927,"identity":"e1e9b88d-054c-491d-b860-242508f121d9","order_by":0,"name":"Jiwon Chae","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"prefix":"","firstName":"Jiwon","middleName":"","lastName":"Chae","suffix":""},{"id":496191928,"identity":"30aaac51-e77a-4193-b41e-ef4c5639f86c","order_by":1,"name":"Mina Choi","email":"","orcid":"","institution":"Keybasic Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Choi","suffix":""},{"id":496191929,"identity":"780f5844-95ca-4000-8e5d-2d13c3923c01","order_by":2,"name":"Juyoung Choi","email":"","orcid":"","institution":"Keybasic Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Juyoung","middleName":"","lastName":"Choi","suffix":""},{"id":496191930,"identity":"1b9b4adf-26f7-4816-be46-a45d6eb58fe5","order_by":3,"name":"Seung-Jun Yoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYNACHgYGfgkkPmMDMVokZ5CmBQgMbhCrRb69x+zhF5nDeca3m5895qm5Y9fAfvgB48w9eAw/c8bcWIbncLHZnWPmxjzHniU38KQZMG54hkeLRI6ZtATP4cRtNxLMpHnYDiczMOQwMD44gMdh899AtGyekf5NmucfUAv/G/xaGG7wmEl+AGrZALKOt+2wHYME0JYNeLQYnEkrk2bgSU+ccSOnTHJu3+EENolnBgdn4HNY++Ftkj97rBP7Z6Rvk3jz7bA9P3/yw4c9+BwGBMy8PRAGEzBKE9uADAIagBH34weMwcBgT0j1KBgFo2AUjDwAAMNxU5uCJDBSAAAAAElFTkSuQmCC","orcid":"","institution":"Keybasic Co., Ltd","correspondingAuthor":true,"prefix":"","firstName":"Seung-Jun","middleName":"","lastName":"Yoo","suffix":""}],"badges":[],"createdAt":"2025-07-16 16:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7142040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7142040/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88504028,"identity":"2ac5e093-8792-4426-abb4-1caa279a2863","added_by":"auto","created_at":"2025-08-07 07:06:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86971,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of DEPs within CDS compared to HC group. Each dot denotes one protein, while blue dots represent downregulated DEPs, and red dots indicate upregulated DEPs. Insignificant DEPs are presented by grey dots. The minimum p-value threshold is 1e-100.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/210e049600f881f65b094bb4.jpg"},{"id":88505013,"identity":"1f520308-4d27-479b-b87b-2090d5d95e3f","added_by":"auto","created_at":"2025-08-07 07:14:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84407,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional analysis of expanded DEPs. MF, BP and KEGG pathway terms (p-value \u0026lt; 0.1) are presented while each bubble represents a term related with A. upregulated DEPs and B. downregulated DEPs. This pathway diagram is adapted from the KEGG database and used with permission from Kanehisa Laboratories\u003csup\u003e61,62\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/a324143fca1c1b34ad2d1dc3.jpg"},{"id":88504000,"identity":"bd6ed0e5-3564-4f8e-8df1-21c928e118ea","added_by":"auto","created_at":"2025-08-07 07:05:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71035,"visible":true,"origin":"","legend":"\u003cp\u003eA. Validation of marker candidates in n=62 (n=47 in CDS, n=15 in HC, extreme 10% excluded from each group). The floating bar chart presents protein expression level in each group with median and individual data points. BUD31 (p=0.2721), CTSG (p=0.0117), TRIM14 (p=0.0039), ALDH1A1 (p=0.1368), BCO2 (p=0.3328), and PSMB8 (p=0.4588). ns=not significant, *p\u0026lt;0.05, **p\u0026lt;0.01 (two-tailed) The Y-axis of each graph represents the log10-transformed value of the measured protein signal intensity. B. Receiver operating characteristic (ROC) curve for CDS prediction based on level of marker candidate proteins. Area under the curve (AUC); BUD31:0.5971, CTSG: 0.7363, TRIM14: 0.7747, ALDH1A1: 0.6282, BCO2: 0.5887, PSMB8: 0.5256.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/3da287f6e46a548fb6aca796.jpg"},{"id":88504991,"identity":"1a501c1d-37c7-4c2e-9644-fe41ff277a8b","added_by":"auto","created_at":"2025-08-07 07:13:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54247,"visible":true,"origin":"","legend":"\u003cp\u003eA. Double validated result of highly potential markers in n=65 (n=48 in CDS, n=17 in HC group, extreme 10% excluded from each group). The floating bar chart presents protein expression level in each group with median and individual data points. CTSG (p=0.0064), TRIM14 (p=0.0004). **p\u0026lt;0.01, ***p\u0026lt;0.001 (two-tailed) The Y-axis of each graph represents the log10-transformed value of the measured level of the target protein. B. ROC curve for CDS prediction based on level of marker candidate proteins. AUC; CTSG: 0.7085, TRIM14: 0.8047.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/8e0f6db0b59463dcfdb1f286.jpg"},{"id":88504007,"identity":"a812d6b7-18f7-4ac5-8ef7-53a984da93e8","added_by":"auto","created_at":"2025-08-07 07:05:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32736,"visible":true,"origin":"","legend":"\u003cp\u003eMarker levels by CDS severity, mean with SD. A. CTSG marker level at each CDS state. Compared with HC group, mild CDS group (p = 0.0009), moderate CDS group (p = 0.0140), and severe CDS group (p = 0.3183) B. TRIM14 marker level at each CDS state. Compared with HC group, mild CDS group (p = 0.0058), moderate CDS group (p=0.0004), and severe CDS group (p=0.0641). ns=not significant, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001 (two-tailed).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/2bba02788acf3d1b8aca804c.jpg"},{"id":88505249,"identity":"9fc773c1-97f0-4698-ae90-527fd2337fef","added_by":"auto","created_at":"2025-08-07 07:22:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":65892,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis on marker levels with other demographic factors. A. Correlation analysis on age and marker expression levels. Each point denotes individual subject and a linear regression line fits to the data to visualize any potential trend between age and marker expression, with a 95% CI around it. B. Violin plot indicating the distribution of each marker levels in female and male. Median and quartiles are marked with dotted lines. C. Correlation analysis on BSC score and marker expression levels. A linear regression line indicates the trend line with a 95% CI around it. Each point represents an individual subject.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/d875a6d3dd264cf404372d4d.jpg"},{"id":88504002,"identity":"577f0a26-784b-4a0f-a980-39440e1c6167","added_by":"auto","created_at":"2025-08-07 07:05:58","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":38254,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between CTSG and TRIM14 levels in A. CDS group (n=48); R squared = 0.2945, B. HC group (n=17); R squared = 0.3731, and C. Whole replication cohort (n=65); R squared = 0.3299.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/c711abf852de871b3062ed0c.jpg"},{"id":89600818,"identity":"aa70492c-f218-483b-be33-b869876bca7e","added_by":"auto","created_at":"2025-08-21 18:08:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/e5318a86-c1fb-41bb-9ab3-45456ecf833a.pdf"},{"id":88505252,"identity":"99a98fa6-cc04-40af-8167-f529f3e2bf43","added_by":"auto","created_at":"2025-08-07 07:22:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":600121,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial18.AntibodyValidationProvidedbySuppliers.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/eae7fe9d4df7cb2e424417d5.pdf"},{"id":88504993,"identity":"e1b1379f-7a32-4ff1-85c8-f4c6c297174b","added_by":"auto","created_at":"2025-08-07 07:13:59","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1513665,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/8fd6df9a914f9039c4f8586b.xlsx"},{"id":88504003,"identity":"8548e3f8-39e9-44fa-a0f3-0c6261992ed5","added_by":"auto","created_at":"2025-08-07 07:05:58","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":329185,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/93e1fee758e3823a1f54ef82.pdf"},{"id":88504994,"identity":"b7f730c0-577d-420c-9069-73b2c15205ad","added_by":"auto","created_at":"2025-08-07 07:13:59","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1181639,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7142040/v1/f18148ef3921522e3d53790b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protein biomarker discovery for canine cognitive dysfunction syndrome based on molecular alterations observed in nasal fluids","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCanine cognitive dysfunction syndrome (CDS) refers to cognitive impairment mostly in elderly dogs, more than 10 years old, and is often characterized by memory impairment, motor function deterioration and altered sleep-wake cycles\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. A number of survey-based assessment tools like DISHAA, CADES and CCDR have been established for CDS diagnosis, and are frequently utilized as they are considered the most efficient\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Yet, as the methods depend on behavioral observation, such methods hold a limitation in objectivity and struggle to distinguish normal aging from early-stage CDS. Alternative approaches have been proposed as well, such as PET imaging or blood biomarkers\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, such methods have limitations in broader application, as they either lack well-established diagnostic criteria, require anesthesia that poses significant risks for elderly dogs, or are too invasive.\u003c/p\u003e\u003cp\u003eRegarding well-defined neurodegenerative diseases (NDDs) with cognitive impairment, such as Alzheimer\u0026rsquo;s disease (AD) or Parkinson\u0026rsquo;s disease (PD), distinctive protein biomarkers have been identified for each\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. As well-established diagnostic criteria exist for such diseases, novel diagnostic methods can be validated against them, enabling a variety of emerging approaches. One such newly suggested approach is utilizing nasal-derived biomarkers.\u003c/p\u003e\u003cp\u003eNasal discharge generally consists of water, salts, and proteins, among which glycoproteins such as mucins constitute the primary mucus component. In addition, it contains enzyme inhibitors and other regulatory proteins, and a variety of proteins involved in protective functions against external pathogens, including antimicrobial peptides, antiviral agents, and immune mediators\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In conditions of inflammation or irritation, cellular damage or death in the nasal mucosa can induce alterations in the protein composition of nasal discharge\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. While a certain proportion of proteins detected in nasal discharge are derived from the nasal mucus itself, contributions from other sources have also been reported. Previous studies have demonstrated the presence of AD-associated proteins in nasal mucus and discussed the possibility that some of these proteins originate from cerebrospinal fluid (CSF)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This phenomenon is plausibly explained by the established CSF drainage mechanism through the olfactory neuronal pathway into the nasal cavity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe nose, as an anatomically connected region to the brain via the cribriform plate, has a direct systemic connection through axon extension of olfactory sensory neurons (OSNs) from the nasal region to the brain\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Moreover, the nasal cavity also serves as a major CSF drainage route, where CSF is flushed through after clearing soluble proteins in brain parenchyma by the glymphatic system to reach the lymphatic system\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Various tracer studies have confirmed that the route along the OSNs into the nasal cavity is the most prevalent pathway for CSF drainage, and this route is well established in mammals\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Based on this pathway, approaches to accessing or monitoring the brain via the nasal area have been suggested\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Considering that accumulation of toxic proteins and related proteinopathy are general characteristics of neurodegenerative diseases (NDDs), this route holds high potential for monitoring the status of diseases\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Moreover, various NDDs, including Alzheimer's disease (AD), are often associated with impairments in olfactory function\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, further supporting the \u0026lsquo;brain-nose connection\u0026rsquo;. Based on such anatomic and systemic adjacency with the brain, there have been several attempts to identify biomarkers for NDDs in nasal-derived samples, such as nasal discharge and nasal epithelial tissues, with promising results\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Those findings align with the concept that the onset of NDDs is also reflected in the nasal region as detectable molecular alterations.\u003c/p\u003e\u003cp\u003eTherefore, based on the well-established observation that CDS occur in aging dogs and is clinically akin to human dementia\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, the study hypothesizes that CDS is a naturally occurring NDD in dogs. Furthermore, as seen in other NDDs, we assume that CDS progression might lead to molecular alterations in canine body fluids as well. Thus, we aim to identify a novel, non-invasive diagnostic criterion for CDS \u0026mdash; protein biomarkers based on nasal discharge. By applying a commonly used biomarker discovery process to nasal discharge samples collected from a canine cohort, this study seeks to explore the feasibility of biomarker discovery and further definitions for CDS, a condition that appears similar to human NDDs, but lacks clear pathological evidence of resemblance.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Molecular alteration within CDS and its interpretations\u003c/h3\u003e\n\u003cp\u003e The whole protein profile of canine nasal discharge was obtained through proteomic analysis with liquid chromatography-tandem mass spectrometry (LC-MS/MS), on selected samples from CDS group and HC group. A total of 6,143 protein IDs were identified from nasal discharge, with each subject listing an average of 5,712 proteins (Supplementary Table\u0026nbsp;1). Eighty-nine differentially expressed proteins (DEPs) were identified from nasal discharge within the CDS state \u0026mdash; 43 of which were downregulated and 46 upregulated (Fig.\u0026nbsp;1). Known human NDD-related markers such as β-Amyloid precursor protein (APP) and Apolipoprotein E (ApoE) were also detected in nasal discharge samples, yet quantitative comparison showed no statistically significant difference between CDS and control groups.\u003c/p\u003e\u003cp\u003eFunctional analysis on expanded DEPs revealed notable associations with immune-related pathways, protein synthesis and degradation processes, and cellular transport mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Immune-related terms, including complement activation and humoral immune responses, were predominantly enriched among upregulated DEPs, suggesting a potential inflammatory component specific in CDS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This finding may reflect complement-mediated responses involved in CDS progression, possibly paralleling complement-related mechanisms reported in NDDs\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Alterations in protein translation, metabolism, and ribosome-associated functions were also observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), which could indicate perturbations in protein metabolism under cellular stress conditions. Such disruptions are often implicated in protein misfolding and aggregation phenomena characteristic of neurodegeneration, though further validation is required. Notably, downregulated DEPs were enriched in pathways related to the regulation of protein synthesis, a process frequently reported as altered in NDDs\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Additionally, reduced expression of multiple proteins linked to synaptic vesicle formation and transport suggests possible synaptic dysfunction. This aligns with synaptic changes documented during neurodegenerative processes, such as decreased synaptic protein levels observed in the cerebrospinal fluid of AD patients, which may also be relevant to CDS-associated neurodegeneration\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e2. Selection and validation of potential marker candidates for CDS\u003c/h3\u003e\n\u003cp\u003eCandidate marker proteins were selected by considering fold change and p-value alongside functional analysis results, with emphasis placed on proteins demonstrating meaningful functional relevance to CDS pathology and potential diagnostic utility. From the 89 DEPs identified in nasal discharge, we selected three candidates from each upregulated and downregulated group \u0026ndash; BUD31, CTSG, TRIM14, and ALDH1A1, BCO2, PSMB8. Marker candidates were mostly involved in biological functions related to strongly enriched terms in the CDS state, and a potential link to neurodegenerative diseases has been implied from previous research. A detailed description of each selected marker-candidate is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe list of potential CDS marker candidate proteins\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative Abundance in CDS Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniProt Accession\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFunction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRelation with neurodegenerative diseases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReferences\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBUD31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA0A8I3MQ46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProtein BUD31 homolog\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA spliceosomal component protein involved in pre-RNA splicing process\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHas a significantly decreased expression in plasma of AD-protective variant carrier. Possible positive relation in pathology of neurodegenerative disease\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBertram et al. 2017\u003c/p\u003e\u003cp\u003eWittrahm et al. 2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTSG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA0A8I3NK04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCathepsin G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA member of the cathepsin family, lysosomal proteases that play a vital role in charge of proteolytic pathway including cell death and phagocytosis. Frequently observed in sites of inflammation\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEngages in regulating disease-related proteins in the pathology of various neurodegenerative diseases as a lysosomal protein\u003csup\u003e\u003cspan additionalcitationids=\"CR68 CR69 CR70 CR71 CR72\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUchiyama 2001; Yu et al. 2016\u003c/p\u003e\u003cp\u003eKegel et al. 2000; Steinfeld et al. 2006; Khurana et al. 2010; Vidoni et al. 2016; McGlinchey et al. 2017; Klein and Mazzulli 2018; Drobny et al. 2022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRIM14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA0A8I3MZV3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTripartite motif containing 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA member of the tripartite motif family (TRIM), an essential regulator of antiviral innate immunity\u003csup\u003e\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRelated to a number of pathological conditions, including a variety of neurodegenerative diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePolymorphisms in Tripartite Motif Family-Like 2 (TRIML2) that has a similar structural, functional feature with TRIM14, was defined to have significant association with AD\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVersteeg et al. 2013; Van Gent et al. 2018; Wu et al. 2019\u003c/p\u003e\u003cp\u003eOzato et al. 2008; McNab et al. 2011; Hatakeyama 2017; Kang et al. 2016; Florentinus-Mefailoski et al. 2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDownregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALDH1A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA0A8I3M9Q4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAldehyde dehydrogenase 1 family member A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA member of the aldehyde dehydrogenases family that engages in various biological pathways\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpregulated in early stages of AD, in an attempt to protect against oxidative stress-induced damage and its inactivation could induce neurotoxicity\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDownregulated in PD, possibly linked to oxidative stress caused by dysfunction of dopamine metabolism\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eManzer et al. 2003; Choudhary et al. 2005\u003c/p\u003e\u003cp\u003eNikhil et al. 2019; Galter et al. 2003; Durrenberger et al. 2012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDownregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA0A8I3RRF3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6-pyruvoyl tetrahydrobiopterin synthase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA mitochondrial enzyme that mediates metabolism of carotenoids\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReported to be significantly decreased in human precuneus with mild AD, and its possible relation with early AD pathology was discussed in terms of microglial dysfunction\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eThomas et al. 2020\u003c/p\u003e\u003cp\u003eSobue et al. 2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDownregulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSMB8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ5W416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProteasome subunit beta type-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA subunit of proteasome which engages in protein degradation and inflammatory pathways\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReported to be upregulated in human brains that have Lewy body dementia along with cathepsins\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKitamura et al. 2011; Huber et al. 2012\u003c/p\u003e\u003cp\u003eDing and Zhu 2018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic potential of each marker candidate protein was evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The result of the quantitative analysis \u0026ndash; based validation targeted on each marker candidate is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. of six tested marker candidates, two immune-related proteins, CTSG and TRIM14 showed relatively high expression level in the CDS group, with statistical significance. Based on the CDS status of each individual, ROC curve was generated to examine the diagnostic performance of each marker candidate in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. TRIM14 yielded the highest AUC of 0.7747, and CTSG also achieved AUC above 0.7, thus suggesting its potential as a diagnostic marker. Yet, the other four markers, BUD31, ALDH1A1, BCO2, and PSMB8 showed relatively poor discriminatory potential, with AUC below 0.65.\u003c/p\u003e\u003cp\u003eTo further validate two most promising markers, the whole replication cohort was double validated with the enzyme-linked immunosorbent assay (ELISA) targeted on CTSG and TRIM14. The double validated results coincided with the prior result, as CTSG and TRIM14 both showed significantly higher expression levels in CDS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Based on the results of additional validation by ELISA, ROC curve analysis was also conducted in order to assess the diagnostic potential of two markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). CTSG and TRIM14 each yielded AUC value of 0.7085 and 0.8047, providing that both markers hold acceptable potential as diagnostic biomarkers of CDS \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e3. Further analysis on two most promising markers – CTSG and TRIM14\u003c/h3\u003e\n\u003cp\u003eCDS is usually classified into three stages \u0026ndash; mild, moderate, and severe \u0026ndash; based on its severity, and it progressively develops into the next stage as the disease worsens. While CTSG and TRIM14 were already confirmed to have significantly higher expression levels in CDS group, expression levels on each state of the disease were further examined to identify whether potential markers hold higher specificity with certain stages of the disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Compared to HC group, the expression level peaked at mild CDS group in both CTSG and TRIM14, followed by moderate CDS group, severe CDS group, and HC group. Compared to HC group, results in the mild and moderate CDS groups also showed significantly high specificity. Especially, CTSG showed a strong association with the early stage of the disease, while TRIM14 had relatively high specificity in the mild to moderate states of the disease.\u003c/p\u003e\u003cp\u003eTo assert the diagnostic value for CDS state, it was also confirmed that marker expressions are not correlated with variables other than the CDS state, and therefore their specificity solely depends on the presence of CDS. The correlation between age and marker expression was examined, and it was indicated that both markers had no significant relation with age in its expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Other variables in the sample cohort, such as gender and BSC score, showed no significant trends in relation to overall marker expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Also, there was no significant differences in marker expression between individuals with and without other neurological comorbidities within the CDS group (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCDS predominantly affects elderly dogs and is commonly regarded as a form of NDD in canines. Given that the alterations derived by CDS are irreversible, early disease detection would be crucial for therapeutic intervention\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. However, current diagnostic approaches remain insufficient to clearly distinguish early-stage CDS from normal aging processes.\u003c/p\u003e\u003cp\u003eOur proteomic analysis of nasal discharge identified DEPs associated with CDS, and functional enrichment of these DEPs revealed similarities to pathways implicated in human neurodegenerative diseases such as AD and PD, providing additional support for the classification of CDS as an NDD\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Well-established AD-related markers, such as amyloid beta precursor protein (APP) and apolipoprotein E (ApoE)\u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, were detected in the nasal discharge proteome. Although their differential expression did not reach statistical significance, some CDS cases exhibited elevated levels relative to controls, suggesting potential involvement of established NDD markers in CDS pathology; however, larger cohorts and targeted analyses are required to confirm these findings.\u003c/p\u003e\u003cp\u003eThe LC-MS/MS proteomic discovery phase focused primarily on individuals with moderate to severe CDS to maximize detection of protein alterations presumed to be more evident at advanced stages. Interestingly, subsequent validation indicated that two candidate biomarkers\u0026mdash;cathepsin G (CTSG) and TRIM14\u0026mdash;showed more pronounced elevation in mild-stage CDS cases, potentially reflecting early molecular changes. Given the clinical importance of early detection in CDS, the potential of these markers to signal early-stage alterations warrants further investigation. Moreover, while such observation is currently limited to these two proteins, these findings underscore the importance of exploring molecular alterations across all disease stages.\u003c/p\u003e\u003cp\u003eCTSG, a lysosomal serine protease involved in apoptosis and inflammatory immune regulation predominantly in neutrophils\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and TRIM14, a regulator of innate immune signaling pathways including interferon responses\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, are primarily expressed in immune cells. Although it remains unresolved whether the increased levels of these proteins in nasal secretions reflect local airway inflammation, systemic immune activation, or CNS-related processes, their elevation in CDS-affected dogs suggests that nasal biomarkers may capture biologically relevant signals along the brain\u0026ndash;nose axis. Future studies incorporating larger cohorts and comparative analyses of nasal secretions and CNS-derived samples (e.g., CSF) will be necessary to clarify the source and clinical significance of these protein changes. At this point, rather than implying direct causative roles in CDS pathogenesis, these proteins are better considered peripheral indicators that may aid early detection or disease stratification in CDS.\u003c/p\u003e\u003cp\u003eIn selecting candidate biomarkers, traditional statistical parameters such as fold change and p-value were evaluated but, due to limited sample size, were insufficient as sole criteria. Consequently, functional relevance was integrated into the selection process to prioritize proteins with meaningful associations to CDS pathology. Accordingly, we identified two protein biomarkers with diagnostic potential\u0026mdash;CTSG and TRIM14\u0026mdash;from a list of differentially expressed proteins. Expression levels of two potential markers were weakly correlated, supporting a multi-marker approach for improved diagnostic accuracy as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The potential markers showed no significant correlation with age or metabolic variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation of diagnosis approaches based on single biomarkers and multiple biomarkers of CDS. Each performance metric is provided with a 95% CI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTSG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e69.23% (53.58% \u0026minus;\u0026thinsp;81.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e64.29% (38.76% \u0026minus;\u0026thinsp;83.66%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTRIM14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e66.67% (50.98% \u0026minus;\u0026thinsp;79.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e92.86% (68.53% \u0026minus;\u0026thinsp;98.73%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParallel Approach (OR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e84.62% (70.27% \u0026minus;\u0026thinsp;92.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e64.29% (38.76% \u0026minus;\u0026thinsp;83.66%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerial Approach (AND)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e51.28% (36.20% \u0026minus;\u0026thinsp;66.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e92.86% (68.53% \u0026minus;\u0026thinsp;98.73%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe discovery cohort\u0026rsquo;s design accounted for known age-related risk factors in CDS, resulting in age differences between CDS and control groups consistent with disease biology. Sex-related variability was minimized by including only neutered dogs in the discovery phase, reflecting the broader cohort composition (~\u0026thinsp;89% neutered). Subsequent analyses confirmed no significant relevance with age or sex on candidate biomarker expression. Also, given the substantial breed diversity among dogs, concerns regarding breed-associated bias were addressed by principal component analysis (PCA) of DEP. In the discovery cohort, clustering analysis based on DEP expression data indicated that samples tended to group more by disease status than by breed. (Supplementary Fig.\u0026nbsp;2). The lack of tight clustering by breed suggests that individual variability and disease status contribute more substantially to proteomic differences than breed-related factors. The expression levels of CTSG and TRIM14 also did not exhibit breed-specific patterns across a diverse canine cohort comprising 13 pure breeds and mixed-breed individuals (Supplementary Fig.\u0026nbsp;3), further supporting the interpretation that the observed proteomic alterations are more closely associated with disease status than with breed.\u003c/p\u003e\u003cp\u003eTo assess their specificity to CDS status, marker expressions within the CDS group were compared between individuals with and without other neurological comorbidities (Supplementary Fig.\u0026nbsp;4), revealing no significant differences. However, given the small number of CDS dogs with comorbidities (n\u0026thinsp;=\u0026thinsp;7), these findings require validation in larger, more comprehensive cohorts incorporating other neurological conditions.\u003c/p\u003e\u003cp\u003eA primary limitation of this study lies in the lack of previous research on CDS, limiting contextual interpretation. Unlike human NDDs, for which behavioral assessments, fluid biomarkers, and imaging have been rigorously validated\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan additionalcitationids=\"CR56 CR57\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e CDS remains less well characterized and may represent a heterogeneous classification of cognitive impairments rather than a singular disease entity. Expanded cohorts and multi-fluid proteomic analyses will be essential to elucidate CDS pathophysiology. Although the present study employed a cross-sectional design, future longitudinal studies are planned to assess biomarker dynamics and clinical progression.\u003c/p\u003e\u003cp\u003eThus, this study provides initial insights into the molecular pathology of CDS as a canine NDD and proposes novel biomarker candidates for early-stage diagnosis based on nasal secretion proteomics, offering a foundation for further biomarker development and translational research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e1. Canine Cohort\u003c/p\u003e\n\u003cp\u003eA total of 65 domestic dogs were chosen for the study (Table 3). Canine cohort was divided into groups based on their CDS status - healthy controls (HCs) and CDS group. The status of CDS was examined by utilizing Dr. Gary Landsberg\u0026rsquo;s DISHAA questionnaire assessment tool\u0026nbsp;provided by Purina Institute\u003csup\u003e1,56,57\u003c/sup\u003e. Its scale ranges from 0 to 45. A score of 4-15 is consistent with mild, 16-33 is moderate, and higher than 33 is regarded as severe CDS. For each individual, their corresponding questionnaire results were recorded. Also, demographic characteristics including age (in years), gender and neutered status, body condition score (BSC) a numerical scale used to assess obesity status in dogs \u0026mdash; breed, and pre-existing medical conditions were collected and documented. In particular, the presence of any prior diagnoses of neurological disorders, such as intervertebral disc disease (IVDD), epilepsy, brain tumors, atlantoaxial instability (AAI), or cerebral infarction, was specifically identified. The cohort had a mean age of 11.76 years, while 48 of them were diagnosed as CDS positive \u0026ndash; 12 of which mild, 28 moderate, and 8 severe.\u0026nbsp;All dogs included in the study were client-owned, and informed consent was obtained from all owners prior to sample collection.\u003c/p\u003e\n\u003cp\u003e2. Sample Collection\u003c/p\u003e\n\u003cp\u003eNasal discharge was collected with aid from two veterinary facilities in South Korea. All samples were collected observing the following procedure. Sterilized nasal swabs were soaked in PBS and then inserted into nostril to rub the nasal cavity more than 10 times, gently scraping the surface if possible. Then fluids were immediately dissolved in PBS and stored at -80 \u0026deg;C before following procedures.\u0026nbsp;No anesthesia or euthanasia was required or performed in this study, as the sample collection involved only non-invasive nasal swabbing in awake, client-owned dogs.\u003c/p\u003e\n\u003cp\u003e3. Quality Control and Cohort Stratification for Proteomic Analysis\u003c/p\u003e\n\u003cp\u003eAmong the initially collected samples, those exhibiting visible hemolysis were primarily excluded through visual inspection. For samples with a total volume of \u0026ge;30 \u0026mu;L, protein concentration (\u0026mu;g/\u0026mu;L) was quantified using the BCA Protein Assay Kit (Pierce, Cat#: 23227), and the total protein amount (\u0026mu;g) was subsequently calculated. Only samples containing \u0026ge;60 \u0026mu;g of total protein were selected for downstream LC-MS/MS analysis, thereby constituting the discovery cohort listed in Table 4. From the quality-controlled specimens, representative individuals were selected from both the combined CDS group and the control group (n=3 per group), yielding a total of six samples for discovery proteomics. To ensure minimal internal heterogeneity and to reflect the overall characteristics of the entire cohort, the selected discovery cohort was evaluated for demographic and clinical comparability\u0026mdash;including age, BSC score, and DISHAA score \u0026mdash;relative to the full dataset (Supplementary Fig. 4). Residual protein samples from the six discovery specimens were also utilized in subsequent quantitative analyses.\u003c/p\u003e\n\u003cp\u003eIn addition, protein concentrations in the remaining cohort samples were reassessed using the Qubit Protein Assay Kit (Thermo Fisher Scientific, Cat#: Q33211) in conjunction with the Qubit 4 Fluorometer (Thermo Fisher Scientific, Cat#: Q33238) to determine their suitability for quantitative analysis. Only samples meeting both criteria - protein concentration above 500\u0026mu;g/mL and total protein amount above 5\u0026mu;g - were included in the replication cohort. Based on these criteria, a total of 65 samples were subjected to the replication analysis.\u003c/p\u003e\n\u003cp\u003e4. Protein Sample Preparation\u003c/p\u003e\n\u003cp\u003e- Protein Extraction, Reduction, Alkylation and Digestion\u003c/p\u003e\n\u003cp\u003eThe following procedure was carried out prior to TMT labeling for LC-MS/MS. The samples were centrifuged at 13,000 rpm, 4 \u0026deg;C for 10 min. The supernatants were separated, and acetone was added at a volume four times that of the sample. The samples were stored at -80 \u0026deg;C for 90 min, then centrifuged at 16,000 rpm, 4 \u0026deg;C, for 20 min. The supernatant was separated, and the protein concentration of each sample was measured. Each sample was then adjusted to 60 \u0026micro;g. Dithiothreitol was added to achieve a final concentration of 10 mM, and the reduction reaction was conducted at 37 \u0026deg;C and 450 rpm for 30 min in a Thermomixer. After the reduction was complete, iodoacetamide was added to reach a final concentration of 25 mM and the alkylation was carried out at RT for 30 min in the dark. 1 M, 100 mM ABC solution was added to the alkylated sample. Promega trypsin was added at an enzyme-to-protein ratio of 1:25 with a concentration of 0.4 \u0026micro;g/\u0026micro;L, and then the sample was incubated at 37 \u0026deg;C for 16 h for digestion. To terminate the trypsin reaction, TFA was added to reach a final concentration of 1%. The peptide samples were desalted using a SOLA HRP 96 well plate C18 cartridge (30 mg/2 mL). Desalted samples were dried in a SpeedVac, and reconstituted in 100 mM TEAB solution, and the peptide concentration was measured.\u003c/p\u003e\n\u003cp\u003e- TMT Labeling and Peptide Fractionation\u003c/p\u003e\n\u003cp\u003eEach of the six samples were labelled with 10-plex TMT reagent at 50mg. Labeling was designed for each sample as follows: TMT 126 (Lot# XJ344142), TMT 127N (Lot# XH343208), TMT 127C (Lot# XG344143), TMT 128N (Lot# XG341590), TMT 128C (Lot# XG344144), and TMT 129N (Lot# XG341592) in order. After 5 min in RT, TMT 10-plex reagent was dissolved in 41 \u0026micro;l of acetonitrile o, to be treated on each sample. After 1 h at RT, 8 \u0026micro;l hydroxylamine was added to stop the reaction. TMT-labeled samples were then pooled by group into new tubes and dried in a SpeedVac. The dried samples were reconstituted in 0.1% TFA, and any unbound TMT reagent was removed by a SOLA HRP 96 well plate C18 cartridge\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThen samples were dried again with SpeedVac and reconstituted in 10 mM ABC solution for fractionation. Each fractionated sample was dried in SpeedVac, reconstituted in 25 \u0026micro;L of 0.1% TFA, and prepared for MS analysis.\u003c/p\u003e\n\u003cp\u003e- Cell Lysis and Protein Extraction\u003c/p\u003e\n\u003cp\u003eEach sample was centrifuged at 13,000 RPM for 10 min at 4 \u0026deg;C, and the supernatant was removed. The pellet was treated with lysis buffer, by treating RIPA buffer and protease in a 100:1 ratio. The sample was incubated on ice for 30 min with vortexing every 10 min, then centrifuged at 13,000 RPM for 10 min at 4 \u0026deg;C. The supernatant was separated into a new tube, and protein concentration was measured using the Qubit Protein Assay (Thermo Scientific; Cat#: A60668).\u003c/p\u003e\n\u003cp\u003e5. Proteome Analysis via LC-MS/MS\u003c/p\u003e\n\u003cp\u003eLC-MS/MS analysis was performed using the Thermo Vanquish Neo HPLC system coupled with a Thermo Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific), equipped with a trap column (Acclaim PepMap\u0026trade; 100, 100 \u0026micro;m \u0026times; 2 cm) and an analytical column (PepMap\u0026trade; RSLC C18, 75 \u0026micro;m \u0026times; 50 cm) maintained at 50 \u0026deg;C. The injection volume was 5 \u0026micro;L and the flow rate was set to 0.25 \u0026micro;L/min. The mobile phases consisted of solvent A (0.1% formic acid in water containing 5% DMSO) and solvent B (0.1% formic acid in 80% acetonitrile with 5% DMSO). The gradient elution was programmed as follows: 0\u0026ndash;5 min, 2% B; 5\u0026ndash;125 min, 2\u0026ndash;24% B; 125\u0026ndash;155 min, 24\u0026ndash;40% B; 155\u0026ndash;157 min, 40\u0026ndash;95% B; 157\u0026ndash;180 min, 95% B. MS analysis was conducted in positive ion mode with an electrospray voltage of 2.5 kV and an ion transfer tube temperature of 275 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003eData-dependent acquisition (DDA) was performed using Proteome Discoverer software (version 2.4.1.15). The database search was conducted against the UniProtKB Canis lupus familiaris reference proteome (UP000805418), which includes both reviewed and unreviewed entries. The Sequest HT algorithm was used for database searching, followed by validation using Percolator. Peptide-spectrum matches were filtered at a 1% false discovery rate (FDR) at the spectrum, peptide, and protein levels. Search parameters allowed for semi-tryptic peptides with lengths ranging from 6 to 144 amino acids, permitting up to two missed cleavages. The upper peptide length limit was set by default. The precursor mass tolerance was 10 ppm, and fragment mass tolerance was 0.02 Da. Cysteine carbamidomethylation (+57.0214 Da) was set as a fixed modification, and methionine oxidation (+15.9949 Da) and TMT 10-plex labeling (+229.1629 Da) at peptide N-termini and lysine residues were set as variable modifications. The majority of identified peptides were within the typical size range (7\u0026ndash;20 amino acids), consistent with common LC-MS/MS performance. The charge state filtering (2\u0026ndash;6) was applied during MS data acquisition and thus not explicitly set during the database search.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6. DEP analysis and Functional Analysis\u003c/p\u003e\n\u003cp\u003eBased on the acquired mass spectrometry data, proteins were quantified and statistically analyzed to identify DEPs within the CDS group. DEPs were defined as those exhibiting a fold change greater than 1.5 or less than 0.67, with an associated unadjusted p-value \u0026lt; 0.05. For reliable identification, a false discovery rate (FDR) threshold of 1% was stringently applied at the spectrum, peptide, and protein levels during database searching and result filtering.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis was performed using the g:Profiler web server (accessed March 2025), with the organism set to Canis lupus familiaris (Dog). Annotation resources included Gene Ontology (GO) terms\u0026mdash;Biological Process (BP) and Molecular Function (MF)\u0026mdash;sourced from BioMart (release 2025-03-16), as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway data retrieved from the KEGG FTP release 2024-01-22. The input identifiers used for the enrichment analysis were canine UniProt accession numbers corresponding to proteins identified in the dataset. The analysis was conducted based exclusively on the Canis lupus familiaris annotation framework, without ortholog mapping to other species, including human. DEPs were selected based on a nominal p-value \u0026lt; 0.1 and a fold change threshold of \u0026gt; 1.25 or \u0026lt; 0.8. Significantly enriched terms were identified using the Benjamini\u0026ndash;Hochberg procedure for multiple testing correction, and only terms with an adjusted p-value \u0026lt; 0.1 were retained for interpretation. All enrichment computations were carried out using g:GOSt, the core enrichment tool within the g:Profiler suite, which is optimized for species-specific annotation frameworks, and the term size was set to 5 to 500. The enriched results were plotted by https://www.bioinformatics.com.cn, an online platform for data analysis and visualization\u0026nbsp;\u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e7. Dot Blot Analysis\u003c/p\u003e\n\u003cp\u003eDot blot (DB) was used for quantitative validation of marker candidate proteins. Before the experiment, the membrane was activated in methanol for 10 min. Each sample was prepared and diluted to 500 \u0026micro;g/ml based on the measured protein concentration for each. A dot blot apparatus (Cleaver Scientific; Cat# CSL-D96) was used, following the manufacturer\u0026apos;s instructions for slotter use. 1\u0026mu;l of each sample was loaded onto the membrane then were allowed to absorb onto the membrane for approximately 10 min to ensure complete transfer. Membranes were then dried for an additional 30 min and blocked in TBST with 1% skim milk for 30 min at RT. Target-specific primary antibody solution was treated after blocking, followed by overnight incubation at 4 \u0026deg;C. Membranes were washed with TBST for 5 min, three times. An HRP-conjugated secondary antibody targeting the appropriate species was applied, allowing for a 1-to 2-h reaction. Membranes were subsequently washed with TBST once for 5 min and with TBS twice for 5 min each. ECL solution (SuperSignal West Atto Ultimate Sensitivity Chemiluminescent Substrate; Thermo Fisher; Cat# A38554) was added for detection. Chemiluminescence was detected by C-DiGit Blot Scanner (LiCORbio; Model 3600; Lincoln, U.S.A.) and digitized via Image Studio\u0026trade; Software. The experiments were performed in duplicate, and the average of the results was used. In the experiment, primary antibodies of BUD31(1:5000; LifeSpan Biosciences; Cat# LS-C30571), CTSG (1:1000; abcam; Cat# ab197354), TRIM14 (1:2500; Proteintech; Cat# 15742-1-AP), ALDH1A1 (1:10000; Origene; Cat# TA302641), BCO2 (1:2500; Proteintech; Cat# 14324-1-AP), PSMB8 (1:10000; Origene; Cat# TA305716) and HRP-conjugated secondary antibody (1:5000; abcam; Cat# ab97110, 1:10000; Thermo Fisher Scientific; Cat# G21234) were used. Antibody specificity was verified based on the manufacturer\u0026rsquo;s validation data, and cross-reactivity with canine proteins was confirmed or inferred from sequence homology (see supplementary material 1-8). Each dilution ratio followed manufacturer\u0026rsquo;s recommendations. (Detected membrane images are presented in supplementary figure 5.)\u003c/p\u003e\n\u003cp\u003e8. ELISA\u003c/p\u003e\n\u003cp\u003eThe potential of most promising candidates \u0026ndash; CTSG and TRIM14 - were further determined using an ELISA kit (FineTest; Cat#: EH1903, EH13190; Wuhan, China) following the instructions given by manufacturer. Protein samples were diluted with sample dilution buffer to meet the optimized ratio, at 1:5 and 1:15 for CTSG and TRIM14. Each protein concentration was calculated based on measured optical density (OD) values via CurveExport software, following the best fitting regression curve with standard curve. Stand values were multiplied by each dilution coefficient.\u003c/p\u003e\n\u003cp\u003e9. Statistical Analysis\u003c/p\u003e\n\u003cp\u003eTo address the potential statistical bias arising from the unequal sample sizes between the CDS and HC groups, we assessed the normality of each marker\u0026rsquo;s expression distribution within each group prior to hypothesis testing. Normality was evaluated using both visual inspection via Q\u0026ndash;Q plots and the D\u0026rsquo;Agostino \u0026amp; Pearson omnibus test (Supplementary Fig 6-7). To reduce skewness and approximate normality, log transformation was applied to the expression values of each marker. An unpaired two-tailed t-test was used to evaluate statistical significance, with Welch\u0026rsquo;s correction applied to accommodate unequal variances and imbalanced group sizes. Significance was set at p-value \u0026lt; 0.05, while all values were presented as a mean \u0026plusmn; SEM. Extreme 10% were excluded from each group after experiment. The Pearson Product-Moment Correlation and the Spearman correlation test were used to evaluate correlation between factors and marker expression. When determining the correlation between two variables, existing interpretation of correlation coefficient was applied\u003csup\u003e59\u003c/sup\u003e. ROC curves were in evaluation of diagnostic performance of each marker. The optimal threshold value of ROC curve was determined based on Youden\u0026apos;s index. AUC value, true positive rate (sensitivity), and false positive rate (specificity) were calculated\u0026nbsp;\u003csup\u003e60\u003c/sup\u003e. Principal component analysis (PCA) was conducted using variance-scaled protein expression values of differentially expressed proteins (DEPs) to evaluate inter-individual variation and breed-associated clustering patterns. The analysis was performed in R (version 2025.05.1+513) using the prcomp function with standardization (scale. = TRUE), and visualized using the factoextra package.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthic Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving animals were performed in accordance with relevant guidelines and regulations.\u0026nbsp;All experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of Petobio Inc (approval number: PTB-2023-IACUC-002-A).\u0026nbsp;This study is reported in accordance with the ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments).\u003c/p\u003e\n\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was supported by the Technology Development Program RS-2023-00285280 and RS-2023-00285280.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.C. designed and performed experiments, analyzed data, and wrote the manuscript. M.C. and J.C. (Juyoung Choi) contributed to sample recruitment and management. S.-J.Y. supervised the project, contributed to manuscript writing and editing, and approved the final manuscript. All authors reviewed and approved the submitted manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLandsberg, G. M., Nichol, J. \u0026amp; Araujo, J. A. Cognitive Dysfunction Syndrome. \u003cem\u003eVeterinary Clin. North. America: Small Anim. Pract.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 749\u0026ndash;768 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, S. S. et al. Prevalence and risk factors of canine cognitive dysfunction syndrome in South Korea. \u003cem\u003eAppl. Anim. Behav. 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Lett.\u003c/em\u003e \u003cb\u003e678\u003c/b\u003e, 131\u0026ndash;137 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7142040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7142040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCognitive dysfunction syndrome (CDS) is characterized by mental \u0026ndash; behavioral deterioration in elderly dogs and often acknowledged as a canine analog of neurodegenerative diseases (NDDs). A commonly shared feature among NDDs is the accumulation of toxic proteins within the brain and consequential degenerations. Several studies have suggested that such events in the brain can be reflected in the nasal area due to its anatomical and systemic adjacency. Furthermore, disease-specific profiles were identified in nasal-derived samples of patients of certain human NDDs, with credible diagnostic potential. Therefore, we hypothesized that alterations in CDS would be reflected in the nose and aimed to identify potential protein biomarkers based on nasal discharge from 65 individuals. Among the differentially expressed proteins within CDS, six marker candidates were selected and evaluated through quantitative proteomics. Two potential markers - CTSG and TRIM14 - showed high specificity with strong diagnostic capability, and both presented particularly high associations with the mild stage of CDS, posing potential links to its progression. Thus, this study presents CTSG and TRIM14 proteins as nasal-based potential biomarkers of CDS, suggesting a diagnostic alternative and a possible new approach to further define the disease based on its underlying pathology.\u003c/p\u003e","manuscriptTitle":"Protein biomarker discovery for canine cognitive dysfunction syndrome based on molecular alterations observed in nasal fluids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 07:05:53","doi":"10.21203/rs.3.rs-7142040/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88dab1f2-4fa8-4af9-a342-eae38a163588","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52700898,"name":"Health sciences/Biomarkers"},{"id":52700899,"name":"Health sciences/Neurology"},{"id":52700900,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-08-21T18:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 07:05:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7142040","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7142040","identity":"rs-7142040","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}