Hair Metabolomic Profiling of Diseased Forest Musk Deer (Moschus berezovskii) Using Ultra-High-Performance Liquid Chromatography-Tandem Mass Spectrometry (UHPLC-MS/MS)

Abstract

Hair, as a non-invasive biospecimen, retains metabolic deposits from sebaceous glands and capillaries, reflecting substances from the peripheral circulation, and provides valuable biochemical information linked to phenotypes, yet its application in animal disease research remains limited. This work applied ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) to compare the hair metabolomic characteristics of healthy forest musk deer (FMD, Moschus berezovskii) and those diagnosed with hemorrhagic pneumonia (HP), phytobezoar disease (PD), and abscess disease (AD). A total of 2119 metabolites were identified in the FMD hair samples, comprising 1084 metabolites in positive ion mode and 1035 metabolites in negative ion mode. Differential compounds analysis was conducted utilizing the orthogonal partial least squares-discriminant analysis (OPLS-DA) model. In comparison to the healthy control group, the HP group displayed 85 upregulated and 92 downregulated metabolites, the PD group presented 124 upregulated and 106 downregulated metabolites, and the AD group exhibited 63 upregulated and 62 downregulated metabolites. Functional annotation using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the differential metabolites exhibited significant enrichment in pathways associated with cancer, parasitism, energy metabolism, and stress. Receiver operating characteristic (ROC) analysis revealed that both the individual and combined panels of differential metabolites exhibited area under the curve (AUC) values exceeding 0.7, demonstrating good sample discrimination capability. This research indicates that hair metabolomics can yield diverse biochemical insights and facilitate the development of non-invasive early diagnostic techniques for diseases in captive FMD.

Keywords: Moschus berezovskii; UHPLC-MS/MS; disease; forest musk deer; hair; metabolomics.

Figure 1

Experimental framework for the untargeted hair metabolomics of captive forest musk deer (FMD) in the present study. The hair samples (hemorrhagic pneumonia, (n = 6); phytobezoar disease, (n = 6); abscess disease, (n = 6); healthy controls, (n = 6)) were plucked, washed, dried, weighed, and milled, followed by being subjected to ultrasonic extraction at low temperature using methanol, and subsequently underwent UHPLC-MS/MS analysis. The raw mass spectrometry data were normalized via Progenesis QI software (v3.0). The markedly modified hair metabolites were screened out by a metabolomic workflow to identify potential biomarkers of FMD diseases.

Figure 2

Hair samples from forest musk deer featuring fine, short, spindle-shaped hair roots: (A) captured by camera, with red boxes indicating hair follicles; and (B) captured by scanning electron microscope.

Figure 3

Anatomical examination of lesions in the sampled diseased forest musk deer (FMD). (A,B) Red arrows indicate pulmonary hemorrhages and pleural effusion in FMD diagnosed with hemorrhagic pneumonia; (C,D) yellow arrows indicate phytobezoars removed by surgery from the digestive tracts of FMD with phytobezoar disease; and (E,F) blue arrows indicate oral and visceral abscesses found in FMD with abscess disease.

Figure 4

Representative total ion chromatograms (TICs) from collected samples. (A,B) TICs of experimental samples in positive (A) and negative (B) ion modes; (C,D) TICs of quality control samples in positive (C) and negative (D) ion modes. POS, positive ion mode; NEG, negative ion modes. HP, hemorrhagic pneumonia (n = 6); PD, phytobezoar disease (n = 6); AD, abscess disease (n = 6); C, healthy controls (n = 6); and QC, quality control samples (n = 5).

Figure 5

Classification of hair samples from captive forest musk deer based on the metabolomic compositions. (A) Correlation heatmap analysis. HP, hemorrhagic pneumonia; PD, phytobezoar disease; AD, abscess disease; C, healthy control; and QC, quality control. (B) Principal component plot. (C) Venn diagram. (D) Metabolite classification derived from the HMDB superclass level.

Figure 6

OPLS-DA score plot and permutation test for hair metabolome of healthy and diseased forest musk deer. (A,B) Hemorrhagic pneumonia vs. healthy control; (C,D) phytobezoar disease vs. healthy control; (E,F) abscess disease vs. healthy control. C, healthy controls; HP, hemorrhagic pneumonia; PD, phytobezoar disease; AD, abscess disease.

Figure 7

Volcano plots of markedly differential metabolites. (A) Hemorrhagic pneumonia vs. healthy control; (B) phytobezoar disease vs. healthy control; and (C) abscess disease vs. healthy control. Metabolites were selected based on VIP value > 1, log₂ fold change > 1, and p < 0.01. Red bubbles represent upregulation, blue bubbles represent downregulation, and gray bubbles represent no significant difference.

Figure 8

Screening differential hair metabolites between diseased and healthy forest musk deer. (A) Hemorrhagic pneumonia vs. healthy controls. (B) Phytobezoar disease vs. healthy controls. (C) Abscess disease vs. healthy controls. *** p < 0.001, ** p < 0.01, * p < 0.05.

Figure 9

KEGG pathway topological and enrichment analyses of altered metabolites. (A,B) Hemorrhagic pneumonia vs. healthy control. (C,D) Phytobezoar disease vs. healthy control. (E,F) Abscess disease vs. healthy control. KEGG pathway level I abbreviations: OS, Organismal Systems; EIP, Environmental Information Processing; HD, Human Diseases; CP, Cellular Processes; M, Metabolism. *** p < 0.001, ** p < 0.01, * p < 0.05.

Figure 10

Receiver operating characteristic (ROC) curves for differential hair metabolites between healthy and diseased forest musk deer. (A) Hemorrhagic pneumonia vs. healthy control. (B) Phytobezoar disease vs. healthy control. (C) Abscess disease vs. healthy control. The specificity and sensitivity corresponding to the optimal cutoff point of the ROC curve are labeled on the curves. The area under the curve value (AUC) and 95% confidence interval (CI) are shown beneath all the curves. “Combine” refers to the set of all the tested metabolites.

Figure 11

Regulatory networks of metabolic pathways enriched in hair samples from forest musk deer (FMD) with three diseases. For hemorrhagic pneumonia (HP), the (A) pathways “Amoebiasis” and (B) “Pathways in cancer” are displayed; for phytobezoar disease (PD), the (C) “Citrate cycle” (TCA cycle) and the (D) “Pentose phosphate pathway” are shown; for abscess disease (AD), the pathway (E) “Retrograde endocannabinoid signaling” is presented. Red dots represent considerably increased metabolites in the hair of sick FMD, green dots denote strongly downregulated metabolites, and blue dots show spots where both upregulated and downregulated metabolites coexist.

Figure 11

Regulatory networks of metabolic pathways enriched in hair samples from forest musk deer (FMD) with three diseases. For hemorrhagic pneumonia (HP), the (A) pathways “Amoebiasis” and (B) “Pathways in cancer” are displayed; for phytobezoar disease (PD), the (C) “Citrate cycle” (TCA cycle) and the (D) “Pentose phosphate pathway” are shown; for abscess disease (AD), the pathway (E) “Retrograde endocannabinoid signaling” is presented. Red dots represent considerably increased metabolites in the hair of sick FMD, green dots denote strongly downregulated metabolites, and blue dots show spots where both upregulated and downregulated metabolites coexist.

Figure 11

Regulatory networks of metabolic pathways enriched in hair samples from forest musk deer (FMD) with three diseases. For hemorrhagic pneumonia (HP), the (A) pathways “Amoebiasis” and (B) “Pathways in cancer” are displayed; for phytobezoar disease (PD), the (C) “Citrate cycle” (TCA cycle) and the (D) “Pentose phosphate pathway” are shown; for abscess disease (AD), the pathway (E) “Retrograde endocannabinoid signaling” is presented. Red dots represent considerably increased metabolites in the hair of sick FMD, green dots denote strongly downregulated metabolites, and blue dots show spots where both upregulated and downregulated metabolites coexist.