2.1 Differentially expressed proteins (DEPs)

Differential protein screening was performed using the trusted protein dataset. Fold change (log₂ [Fold change]) was calculated as the mean expression value of the experimental group (A. cantonensis-infected) minus that of the control group (uninfected). A Student’s t-test was used to calculate p-values for statistical significance. The overall distribution of the DEPs was visualized using volcano plots (Fig 2A). In the Lissachatina group, 36 proteins were up-regulated (≥1.2 FC, P-value <0.05) and 104 proteins were down-regulated (≤0.83 FC, P-value <0.05) in the A. cantonensis-infected group when compared to the uninfected group (Fig 2A). In the Pomacea group, there was a higher amount of both up-regulated (94) and down-regulated (364) proteins in the A. cantonensis-infected group compared to the uninfected group (Fig 2A). Next, to further evaluate the expression profiles of the DEPs, an unsupervised hierarchical cluster heatmap analysis was conducted (Fig 2B). In both the Lissachatina and Pomacea datasets, samples grouped according to their infection status, indicating high internal consistency within each group. Proteins exhibiting similar expression patterns were also grouped together, highlighting patterns of co-expression. The clear separation of infected and uninfected groups demonstrates the robustness of the DEP analysis. Lastly, a correlation analysis was conducted on the top 50 DEPs (ranked by p-value; S2 Fig). Pearson correlation coefficients were calculated to assess the similarity of expression patterns between proteins, with coefficients closer to 1 indicating stronger positive correlations. Both groups exhibited a predominance of positively correlated protein pairs, with the Pomacea group showing more defined clusters of highly co-expressed proteins (S2 Fig).

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Fig 2. Overview of differentially expressed proteins (DEPs) in A. cantonensis-infected samples when compared to uninfected control samples for both the Lissachatina (Lis) and Pomacea (Pom) groups.

(A) Volcano plots illustrating differential protein expression using Fold Change (log₂) values, and p-values (-log₁₀). Red dots represent up-regulated proteins, blue dots represent down-regulated proteins, and gray dots represent non-significant proteins. Vertical dashed lines indicate the fold change thresholds (Fold Change = 1.2 and Fold Change = 1/1.2, or 0.833), and the horizontal dashed line indicates the p-value cutoff (p = 0.05). (B) Hierarchical cluster heat maps of DEPs. Heat maps were generated based on protein expression levels using unsupervised hierarchical clustering. Red indicates proteins with high expression levels, and blue indicates proteins with low expression levels. Each row represents a DEP, and each column represents a sample.

https://doi.org/10.1371/journal.pntd.0013812.g002

2.2 Functional classification and enrichment of DEPs

After identifying the DEPs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to reveal significant functional associations of the DEPs, and to better understand the biological roles and pathways involved. These analyses aimed to identify which biological processes, cellular components, and molecular functions were significantly over-represented among the DEPs. To highlight the most relevant functions, the top 30 GO terms were selected based on the number of associated differential proteins (List Hits >1) across the three GO categories: biological process (BP), cellular component (CC), and molecular function (MF). When looking at the total GO terms, we found the Lissachatina group had 111 GO terms (BP: 31, CC: 32, MF: 48) made up of 71 DEPs (Fig 3A). Of the 71 DEPs, 20 were upregulated and 51 were downregulated. Conversely, the Pomacea group exhibited a substantially higher number of enriched genes and GO terms, with a total of 484 terms (BP: 158, CC: 83, MF: 243) made up of 389 DEPs (Fig 3A). Of those, 74 DEPS were upregulated and 315 were downregulated. When we compared the top GO terms for each group, for the Lissachatina group we found translation (13 proteins) was the most enriched term, followed by cytoplasm (12), structural constituent of ribosome (11), and ATP binding (10). In contrast, the highest terms for the Pomacea group were ATP binding (60), followed by cytoplasm (44), translation (39), and structural constituent of ribosome (39; Fig 3A). This disparity suggests that the Pomacea group displays greater molecular and cellular activity, potentially reflecting a broader metabolic and physiological response to infection compared to the Lissachatina group. Moreover, the pronounced difference in the number of downregulated DEPs for both groups, particularly within molecular function, suggests the widespread modulation of enzymatic activity, binding, and other molecular mechanisms.

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Fig 3. Gene Ontology (GO) functional enrichment analysis of differentially expressed proteins (DEPs) in infected Lissachatina (Lis) and Pomacea (Pom) groups.

(A) Bar plots showing the total number of Level 2 Gene Ontology (GO) annotated DEPs, categorized into Biological Process, Cellular Component, and Molecular Function. (B) Bubble chart of the top five significantly enriched GO terms per ontology category. Bubble size indicates the number of associated proteins, while color intensity represents statistical significance (red = lower adjusted p-value). Only GO terms with more than one protein were included. Enrichment was assessed via hypergeometric testing with Benjamini–Hochberg correction, and results are shown as adjusted p-values and enrichment scores. Note: Full details of GO terms can be found in S1 Dataset.

https://doi.org/10.1371/journal.pntd.0013812.g003

Next, a top five GO enrichment terms per ontology analysis revealed distinct transcriptional responses to infection between the Lissachatina and Pomacea groups. In the Lissachatina group, most of the enriched terms were downregulated, with the biological process category showing the highest enrichment. Key downregulated terms included sulfur compound metabolic process, ribosomal small subunit assembly, and GMP biosynthetic process, while arginine biosynthetic process via ornithine was the only upregulated term. The cellular component and molecular function categories had the next highest terms with membrane coat (downregulated) and oxygen carrier activity (upregulated), respectively (Fig 3b). In contrast, all of the top-enriched GO terms in the Pomacea group were down-regulated across all three ontologies. The top terms were equally enriched, with regulation of protein catabolic process, cytosolic large ribosomal subunit, and proteasome-activating activity representing the most significant terms within the biological process, cellular component, and molecular function categories, respectively (Fig 3b). This uniform down-regulation in the Pomacea group suggests a broad repression of cellular functions, whereas the Lissachatina group retains some selective activation of stress and metabolic pathways. Furthermore, a chord analysis of the two groups showed a total of 31 DEPs were identified in the Lissachatina group, including 9 upregulated and 22 downregulated proteins (S3 Fig). Conversely, the Pomacea group had 65 DEPs, all of which were downregulated (S3 Fig).

Next, a Level 3 KEGG pathway enrichment analysis was performed to cluster DEPs into orthologous groups (KOs) and map them to known metabolic and cellular pathways (Fig 4). In the Lissachatina group, the majority of the top 20 enriched pathways (12 out of 20) were significantly downregulated. Notably, pathways associated with detoxification and redox balance, including monobactam biosynthesis, sulfur metabolism, and selenocompound metabolism, were suppressed, suggesting reduced antioxidant activity and impaired oxidative stress defences (Fig 4A). Additionally, PI3K-Akt signaling and ribosome-related pathways were downregulated, indicating decreased cell growth, weakened immune signaling, and repression of protein synthesis (Fig 4A). Interestingly, several pathways annotated in KEGG under “human disease” (e.g., coronavirus infection, legionellosis) were also significantly downregulated. Although these labels originate from KEGG’s human-based classification framework, they represent conserved cellular functions such as cytoskeletal organization, vesicular trafficking, and protein degradation, which are broadly shared across metazoans [33]. The proteins contributing to these terms (e.g., tubulin β-chain, proteasome α-subunit, V-type proton ATPase, GAPDH, α-actinin, Rab GTPases, and serine/threonine phosphatases) are core regulators of stress response and intracellular transport in invertebrates. In this context, the downregulation of these human disease pathways in Lissachatina snails likely represents suppression of analogous stress and immune-related responses rather than direct involvement in human disease. Conversely, upregulated pathways included biofilm formation, collecting duct acid secretion, and arginine biosynthesis (a precursor to nitric oxide). These may reflect compensatory stress and immune responses, as proteins in these pathways are linked to cytoskeletal organization, pH regulation, and nitric oxide-mediated antimicrobial activity, respectively. When placed into Level 2 KEGG categories, we found there was a predominance of pathways associated with human disease (e.g., viral, bacterial, and parasitic infections), comprising 59 proteins across nine KO terms (Fig 4B). Additionally, pathways related to genetic information processing (e.g., ribosome, spliceosome, and mRNA surveillance) were enriched, comprising 34 proteins in four KO terms. Furthermore, when classified, we found these categories were largely driven by downregulated proteins, with 34 downregulated and 27 upregulated genes in human disease pathways, and 36 downregulated and 2 upregulated in genetic information processing (Fig 4C). The predominance of “human disease” and “genetic information” categories reflect annotation overlap with conserved eukaryotic defence and signalling mechanisms, rather than true pathogenic pathways.

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Fig 4. KEGG pathway enrichment analysis of differentially expressed proteins (DEPs) in infected Lissachatina (Lis) and Pomacea (Pom) groups.

(A) Bubble plot of the top 20 most significantly enriched KEGG pathways (Level 3 classification) based on p-values (-log₁₀). Bubble size represents gene count, and color scale denotes significance (p-value). (B) Bar graph summarizing the top 20 KEGG categories (Level 2) derived from the annotated pathways. (C) Bar chart illustrating the number of upregulated and downregulated DEPs within each Level 2 KEGG category. Note: Full details of GO terms can be found in S1 Dataset.

https://doi.org/10.1371/journal.pntd.0013812.g004

When looking at the Level 3 KEGG pathway enrichment of the Pomacea group, we found the majority of enriched pathways (18 of 20) were significantly downregulated (Fig 4A). The top ranked downregulated pathways were proteasome, ribosome, and aminoacyl-tRNA biosynthesis, indicating a potential disruption of protein synthesis and degradation. This widespread repression suggests that A. cantonensis infection interferes with the host’s proteostatic machinery, potentially impairing hemocyte activity and tissue repair. Other top pathways were metabolic in nature, with fructose and mannose metabolism, pyruvate metabolism, glycolysis/gluconeogenesis, and citrate cycle (TCA) being downregulated, suggesting infection may be reducing host metabolic efficiency. Similarly, the downregulation of mRNA surveillance, mTOR signaling pathways, phagosome formation and endocytosis were observed, implying a decrease in cellular stress response, growth regulation and compromised immune function. Together, these patterns reflect parasite-driven suppression of metabolic and immune functions, consistent with the downregulated pathways observed in the Lissachatina group. Only two pathways, selenocompound metabolism and purine metabolism, were upregulated, possible as a result of oxidative stress mitigation, and DNA/RNA repair, respectively. When grouped into Level 2 categories, we found the Pomacea group showed strong enrichment in genetic information processing (e.g., ribosome, proteasome, and aminoacyl-tRNA biosynthesis) pathways, with 77 proteins across four KO terms (Fig. 4B). Additionally, metabolic (e.g., purine metabolism, pyruvate metabolism, and glycolysis) pathways were also enriched, comprising 68 proteins in 13 terms. The majority of these enriched pathways were driven by downregulated proteins for both genetic information processing (92 downregulated and 6 upregulated) and metabolism (53 downregulated and 19 upregulated; Fig 4C), further supporting extensive suppression of core biosynthetic and energy-producing functions in Pomacea during infection.

2.3 Protein–protein association of DEPs

Following GO and KEGG enrichment analyses, a protein–protein association (PPA) network was constructed using the same set of DEPs to explore potential functional relationships among pathways and to identify hub proteins with central regulatory roles. While GO and KEGG enrichment highlight the biological pathways most affected by infection, the PPA network reveals how key proteins are functionally associated within those pathways [32]. In the PPA analysis, each node represents a protein and each edge represents an inferred functional association between two proteins. The degree value indicates how many connections (edges) a protein has within the network, serving as a measure of its centrality. Proteins with higher degree values are considered hub proteins that occupy key regulatory positions, linking multiple biological processes. The PPA network of the Lissachatina group revealed nearly all of the top proteins (ranked by degree) were downregulated, except for two, A0A0B7B7P1 (proteasome subunit alpha) and A0A8S3YI15 (tropomyosin), which were upregulated (Fig 5 and Table 1). The most highly connected node was a Tr-type G domain-containing protein, followed by T-complex protein 1 subunit theta, proteasome subunit alpha type, and RNA processing proteins, such as RNA helicase and multiple RRM domain-containing proteins (Fig 5 and Table 1). In the Pomacea group, the PPA analysis revealed that all of the top proteins were all downregulated. Several highly connected proteins were identified, with two Tr-type G domain-containing proteins being the most connected nodes (Fig 5 and Table 1). Additional proteins, such as phosphoglycerate kinase, serine/threonine-protein phosphatase, and several T-complex protein 1 subunits were also highly connected.

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Table 1. Top ten hub proteins in PPA networks of infected Lissachatina and Pomacea groups, ranked by degree value (number of associations per protein). Higher degree values indicate proteins with greater network connectivity and potential central regulatory roles. Proteins found in both snail species are shown in bold.

https://doi.org/10.1371/journal.pntd.0013812.t001

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Fig 5. Protein–protein association (PPA) networks of the top 25 differentially expressed proteins (DEPs) in Lissachatina (LIS) and Pomacea (POM) groups following A. cantonensis infection.

The PPA networks were generated using STRING and visualized with Cytoscape, highlighting significant DEPs. Each node represents a protein, and each connecting line (edge) represents a predicted functional association between proteins. Node size reflects degree centrality (the number of connections a protein has within the network), indicating its relative importance, while node color denotes log₂ fold change (red = upregulated, green = downregulated). Note: Full details of GO terms can be found in S1 Dataset.

https://doi.org/10.1371/journal.pntd.0013812.g005

When comparing the PPA network analysis of both Lissachatina and Pomacea snails, we found several highly represented proteins present in both groups. This included Tr-type G domain-containing protein, RNA helicase, T-complex protein 1 subunits (α, β, γ, δ, ε, and θ), proteasome subunits, ribosomal proteins (60S and 40S), elongation factors (EF1-gamma), lysine–tRNA ligase, IMP dehydrogenase, AAA + ATPase domain-containing proteins, translation elongation factors, and eukaryotic peptide chain release factors. However, only Tr-type G domain-containing proteins were present in the top ten for both groups, ranked as number 1 (Table 1). This suggests that both groups employ conserved stress-response mechanisms alongside translation regulation and nucleic acid-based defences during A. cantonensis infection. This shared network topology suggests that both snail species employ conserved cellular defence mechanisms against A. cantonensis infection, dominated by stress-response, proteostasis, and translational regulation processes, possibly reflecting convergent responses to parasitic stress. Identifying these hub proteins can reveal central components of the host’s response to A. cantonensis infection and potential molecular targets for further study. However, the Lissachatina network exhibited substantially fewer connections (lower node degree values) than the Pomacea network. This difference likely reflects the limited number of identified DEPs and the incomplete proteomic annotation available for Lissachatina fulica, which constrains STRING-based homology mapping and reduces detectable associations. In contrast, Pomacea canaliculata benefits from a well-annotated reference genome, resulting in a denser and more connected network.

4.1 Snail collection

Wild Lissachatina fulica snails were morphologically identified and collected from Danzhou City, Hainan, China (19°24’29.7“N, 109°35’01.4”E) in November 2022. A total of 65 Lissachatina snails, with an average shell length of 4-5cm, were collected and transported to the laboratory for subsequent experiments. Similarly, wild Pomacea canaliculata snails were morphologically identified and collected from Kangqiao Ecological Park, Shanghai, China (31°09’14.9”N, 121°37’43.0”E) in August 2022. A total of 120 Pomacea snails, averaging 3–4 cm in shell length, were collected and transported to the laboratory for subsequent experiments. Snails were maintained at room temperature under conditions simulating their natural habitats. To ensure that any Angiostrongylus cantonensis infections had reached full maturity, the snails were maintained under laboratory conditions for three weeks prior to dissection. This holding period allowed for larval development to the third-stage (L3) phase, minimizing variation due to mixed larval stages.

4.2 Angiostrongylus infection and hemolymph collection

After the three week holding period, the Lissachatina snails had their hemolymph collected and were screened for A. cantonensis infection (as described in Jiang et al. 2025 [50]). Of the 65 Lissachatina snails tested, 21 were found naturally infected with A. cantonensis. For the Pomacea snails, 40 of the 120 collected snails were randomly selected and tested for natural A. cantonensis infection using the lung microscopy method (as described in Zhao et al. 2024 [51]). However, no A. cantonensis infected snails were found. Previous years of routine infection surveillance have shown that A. cantonensis is not present in Shanghai. As a result, the remaining collected Pomacea snails were instead artificially infected with A. cantonensis in a laboratory setting. The procedures for snail maintenance and infection followed the protocol described by Zhao et al. 2024 [51]. In brief, 40 Pomacea snails were exposed to 200 A. cantonensis L1 larvae for 24hrs and then monitored for 28 days to allow development to the infective L3 stage. Additionally, 40 uninfected snails served as negative control and were not exposed to A. cantonensis. After the 28 day period, hemolymph was collected from the Pomacea snails, which were then dissected to confirm successful A. cantonensis infection.

Prior to hemolymph collection, all snails were thoroughly washed to remove surface debris and contaminants. Residual moisture on the shell surface, particularly around the shell aperture, was carefully dried. In the case of Lissachatina snails, hemolymph was collected by inserting a sterile lancet into the heart of the snail. For Pomacea snails, hemolymph was collected by inserting a sterile lancet at the edge of the operculum and puncturing into the foot muscle. Hemolymph was slowly released, with 1ml being collected per snail. The hemolymph samples were then centrifuged at 800 x g for 15 minutes at 4°C. Next, 300μL of the resulting supernatant (plasma) was carefully transferred into pre-chilled 2ml cryovial tubes and kept on ice. The hemolymph plasma samples were then flash-frozen in liquid nitrogen and stored at −80°C until use. Following hemolymph extraction, all individuals were tested for A. cantonensis infection using the lung microscopy method, and individual hemolymph samples were retroactively labelled as being either infected or uninfected. In total the hemolymph was collected from 16 A. cantonensis negative Lissachatina snails (Lis-Con), 16 A. cantonensis positive Lissachatina snails (Lis-Ac), 16 A. cantonensis negative Pomacea snails (Pom-Con) and 16 A. cantonensis positive Pomacea snails (Pom-Ac).

4.3 Hemolymph protein extraction and preparation

Of the Lissachatina and Pomacea snails collected, eight replicates (four uninfected and four infected) were prepared for each species, with each replicate consisting of pooled hemolymph from four individuals (S1 Table). Of the four replicates per group, only the best three were chosen for proteomic analysis. Protein extraction, enrichment, and purification and analysis were performed by Shanghai Luming Biotechnology Company using the magnetic nanoparticle suspension method. Briefly, 1mg (40µL) of magnetic nanomaterial was suspended in 100µL of wash buffer, followed by removal of the supernatant. Next, 100µL of hemolymph, pre-mixed with an equal volume of wash buffer, was added and incubated at 37°C for 1 hour on a 360° rotary mixer to facilitate protein binding. Unbound components were removed through multiple wash steps, leaving the enriched proteins retained on the magnetic nanoparticles. For proteolysis, 40µL of Reagent A (reductive alkylation and enzymatic hydrolysis solution) was added to the washed magnetic nanoparticles and thoroughly mixed by pipetting. Next, 1.2µL of Reagent B (reducing agent) was added, and the mixture was heated to 95°C for 5 minutes at 1000 rpm. Once cooled to room temperature, 2µL of Reagent C (equilibration buffer) and 5µL of Reagent D (proteolytic enzyme) were added, thoroughly mixed, and incubated at 37°C for 2 hours. Proteolysis was terminated by adding 3µL of Reagent E (stopping reagent), resulting in the formation of a precipitate. The mixture was centrifuged at 20,000g for 1 minute, and the supernatant was collected for subsequent desalting.

Peptide desalting was performed using a desalting column. The column was first activated with 100µL of methanol and centrifuged at 700g for 1 minute. To remove impurities, 100µL of Conditioning Buffer was added and centrifuged under the same conditions. The column was then equilibrated by adding 100µL of Wash Buffer and centrifuging at 700g for 1 minute. Next, 1–30µg of the sample was then loaded into the column and centrifuged at 700g for 1 minute. This process was repeated to ensure complete loading. The column was then subsequently washed with 100µL of Wash Buffer and centrifuged at 700g for 1 minute, repeating the step for thorough purification. For elution, 30µL of Elution Buffer was added and centrifuged at 700g for 1 minute, with the process repeated to obtain a final sample volume of 60µL. Finally, the eluted peptides were concentrated using a vacuum-frozen centrifugal concentrator in preparation for further analysis.

4.4 Proteomic analysis (LC-MS/MS)

The proteomic analysis was performed by Shanghai Luming Biotechnology Company using a 4D label-free quantitative proteomic approach. In brief, liquid chromatography-mass spectrometry (LC-MS) was employed to separate concentrated peptides using a 25 cm C18 analytical column (25 cm × 75μm ID, 1.6μm C18, IonOpticks) at a flow rate of 300nL/min. Peptides were eluted using a binary solvent system consisting of mobile phase A (99.9% H₂O, 0.1% formic acid) and mobile phase B (99.9% acetonitrile, 0.1% formic acid). The gradient elution conditions were as follows: 0 ~ 75 min: 2–22% mobile phase B; 75 ~ 80 min: 22–37% mobile phase B; 80 ~ 85 min: 37–80% mobile phase B; and 85 ~ 90min: 80% mobile phase B. Mass spectrometry (MS) analysis was conducted using an electrospray ionization (ESI) source with a capillary voltage of 1.4kV. The drying gas temperature was set to 180°C, with a flow rate of 3.0L/min. Data acquisition was performed within a mass range of 100-1700m/z, an ion mobility range of 0.6-1.6Vs/cm², and a collision energy range of 20-59eV.

4.5 Bioinformatics and data analysis

The bioinformatic analysis was performed by Shanghai Luming Biotechnology Company. In brief, the raw LC-MS/MS data were analyzed using MaxQuant (version 1.6.17.0) with the Andromeda search engine for label-free quantification (LFQ). Protein identification was performed using the UniProt database, with reference proteomes being used for our Lissachatina (uniprot-Helicina-216366-2022.12.19.fasta) and Pomacea (uniprot-Pomacea-canaliculata-400727-2022.12.19.fasta) datasets. To improve data reliability, a reverse database and a common contaminant database were included for filtering. The search parameters were optimized to minimize false discoveries, setting the false discovery rate (FDR) at 0.01. Trypsin was used as the digestion enzyme, allowing up to two missed cleavages. Carbamidomethylation of cysteine was set as a fixed modification, while methionine oxidation and N-terminal acetylation were considered variable modifications, with label-free quantification was applied to assess protein abundance.

Next, biological process, cellular component, molecular function and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to classify the Differentially Expressed Proteins (DEPs) by Gene Ontology (GO) enrichment analysis. The GO analysis was done using the Gene Ontology Consortium database, which provides a controlled vocabulary to describe gene and protein functions across different organisms. The KEGG analysis (KEGG database release 91.0) was used to analyze the biological pathways associated the DEPs, the KEGG Orthology (KO) system groups genes with similar functions, and these KO entries map to pathways in the KEGG Pathway database. In the KEGG database, pathways are organized hierarchically across three levels: level 1 represents broad functional classes (e.g., metabolism), level 2 corresponds to specific subcategories within these classes (e.g., carbohydrate metabolism), and level 3 includes the most detailed individual pathways (e.g., glycolysis/gluconeogenesis). The KEGG level 1 pathways were classified into seven major categories: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, organism systems, and drug development. The p-value representing whether the functional set was significantly enriched in the list of differential proteins was calculated using the hypergeometric distribution test, and the p-value was corrected by the Benjamini & Hochberg multiple test to obtain the FDR [52] (S1 Doc). Protein–protein association (PPA) networks were constructed using the STRING database v12.0 (https://string-db.org/) with a confidence score threshold of 0.4. Only annotated DEPs identified from the proteomic analysis were included. STRING predicts functional associations based on evidence from co-expression, homology, curated databases, and text mining. Networks were visualized in Cytoscape v3.10, with node degree (connectivity) calculated using the Network Analyzer plugin. The top 25 most connected proteins (hub nodes) were displayed. Nodes represent proteins, edges represent predicted functional associations, node color reflects log₂ fold change, and node size corresponds to degree centrality.