A Comprehensive Multi-Omics Study of Serum Alterations in Red Deer Infected by the Liver Fluke Fascioloides magna

Abstract

Liver fluke infections are acknowledged as diseases with global prevalence and significant implications for both veterinary and public health. The large American liver fluke, Fascioloides magna, is a significant non-native parasite introduced to Europe, threatening the survival of local wildlife populations. The aim of this study was to analyze differences in the serum proteome and metabolome between F. magna-infected and control red deer. Serum samples from red deer were collected immediately following regular hunting operations, including 10 samples with confirmed F. magna infection and 10 samples from healthy red deer. A proteomics analysis of the serum samples was performed using a tandem mass tag (TMT)-based quantitative approach, and a metabolomics analysis of the serum was performed using an untargeted mass spectrometry-based metabolomics approach. A knowledge-driven approach was applied to integrate omics data. Our findings demonstrated that infection with liver fluke was associated with changes in amino acid metabolism, energy metabolism, lipid metabolism, inflammatory host response, and related biochemical pathways. This study offers a comprehensive overview of the serum proteome and metabolome in response to F. magna infection in red deer, unveiling new potential targets for future research. The identification of proteins, metabolites, and related biological pathways enhances our understanding of host-parasite interactions and may improve current tools for more effective liver fluke control.

Keywords: host–pathogen interaction; liver fluke; metabolomics; proteomics; wildlife.

Figure 1

Volcano plot showing proteins with differential abundance in the F. magna-infected group (N = 10) compared to the control group (N = 10). Red points indicate significantly increased abundances in the infected group and blue points indicate significantly decreased protein abundances. Features that do not meet the significance threshold are shown in gray.

Figure 2

Principal component analysis (PCA) score plot showing the distribution of samples from the control (red dots) and F. magna-infected (green dots) group.

Figure 3

Gene ontology analysis for proteins with different abundance in serum between the control and F. magna-infected red deer using the PANTHER GO-Slim analysis: (A) molecular function; (B) biological process; (C) cellular component; (D) protein class.

Figure 4

Reactome pathways enriched from serum proteins with differential abundance between the control and F. magna-infected red deer (FDR < 0.05).

Figure 5

Distribution of main chemical classes of identified differential metabolites between the control and F. magna-infected red deer.

Figure 6

Volcano plot showing features with differential intensities between the control (N = 10) and F. magna-infected group (N = 10). Red points indicate significantly increased levels in the infected group and blue points indicate significantly decreased levels. Features that do not meet the significance threshold are shown in gray.

Figure 7

Principal component analysis (PCA) score plot showing the distribution of samples from the control group (green dots), and the F. magna-infected group (red dots), with the green and red backgrounds representing 95% confidence intervals.

Figure 8

Hierarchical cluster analysis (HCA) based on the top 100 features with significantly differential intensities between the control (green panel) and F. magna-infected (red panel) group using Euclidean as a distance measure and Ward as a clustering algorithm. Each colored cell on the map corresponds to the intensity value, with the red color indicating increased, and blue a decreased level of a specific feature.

Figure 9

Pathway enrichment (top 25) of significant metabolites identified by the untargeted metabolomic approach between control and F. magna-infected group based on 3694 metabolites and lipid pathways from RaMP-DB.

Figure 10

The multi-omics network created with OmicsNet. Yellow nodes represent metabolites, while salmon colored nodes represent proteins. Significant modules of connected metabolites and proteins are highlighted in different colors and annotated with their most significant top integrative KEGG pathways.