Comparative omics analysis of the impact of Mycoplasma synoviae infection on the immune mechanisms of poultry spleen

ABSTRACT

Mycoplasma synoviae (MS) infection can cause severe inflammatory responses in avian species, evading immune recognition, leading to chronic infection and immune dysfunction. Although the pathogenic mechanisms and immune evasion strategies of MS have not been fully elucidated, the crucial role of the spleen in immune responses cannot be overlooked. Building on preliminary findings related to the tissue damage and inflammatory responses in chicken spleens caused by MS infection, this study further utilizes transcriptomic and proteomic technologies to thoroughly investigate the comprehensive effects of MS infection on the chicken spleen. We detected 946 differentially expressed genes (DEGs) and 305 differentially expressed proteins (DEPs) during MS infection, including 771 up-regulated and 175 down-regulated DEGs, along with 145 up-regulated and 160 down-regulated DEPs. Gene Ontology (GO) analysis indicated that the DEGs/DEPs are enriched in processes related to immune activation, antioxidation/cell apoptosis, inflammation, and endoplasmic reticulum protein processing. KEGG pathway enrichment analysis revealed immune-related pathways closely associated with MS infection, particularly in endoplasmic reticulum protein processing, cytokine-cytokine receptor interaction, and the myosin light chain kinase (MAPK) signalling pathway. Suggesting that the activation and interaction of these pathways may play a critical role in the immune response during MS infection. Overall, this study, for the first time, employs RNA-Seq and TMT quantitative proteomics to comprehensively analyse the interaction mechanisms between MS and chicken spleen, enhancing our understanding of the complexity of the immune mechanisms induced by MS. This provides valuable insights for future research and intervention strategies targeting MS infection.

Introduction

Mycoplasma synoviae (MS), a significant avian pathogen, can cause acute or chronic diseases in poultry, including respiratory diseases, infectious synovitis, and reproductive system disorders [1]. Research indicates, that MS is an active participant in host-pathogen interactions, engaging with the host’s immune system on multiple levels, including modulating the immune response and stimulating inflammatory reactions [2,3]. However, the mechanisms of its infection and the corresponding immune responses remain complex and not fully elucidated. Given the pathogenic characteristics of MS, existing research has primarily focused on the effects of MS infection on the respiratory and joint systems of chickens. Dusanic et al. pointed out through in vitro experiments that MS infection regulates multiple immune genes in chicken macrophages and apoptosis genes in chicken chondrocytes [4]. Research by Lavrič M, et al., also shows that MS infection can activate and regulate the immune system [5]. These studies mainly focus on the effects on the spinal cord and joints. However, the spleen, a crucial role in the immune system [6,7], in MS infection has not yet received sufficient attention.

Our previous study demonstrated that MS infection can activate a series of key genes and signalling pathways in the chicken spleen, particularly the NF-κB/MAPK signalling axis. Additionally, necropsy revealed splenomegaly, while histopathological analysis showed significant inflammatory cell infiltration, lymphocyte reduction and disorganization, as well as cell membrane rupture. These findings further confirm that MS infection can induce splenic necrosis and inflammation [8]. This reveals the secondary effects of MS infection on immune organs, particularly the spleen, which plays a crucial role in the context of MS infection. This finding underscores the importance of further exploring the molecular impacts of MS infection on the chicken spleen. To further describe its complex pathogenic mechanisms, we still need to explore the host-pathogen condition at the molecular level.

RNA sequencing (RNA-seq) provides comprehensive transcriptome information for solving complex biological problems [9]. However, to deeply understand gene regulatory mechanisms, tandem mass tag (TMT)-based proteomics analysis is also crucial. By integrating comprehensive proteomic and transcriptomic analyses, we are able to gain a deeper understanding of the complex pathogenic mechanisms induced by MS infection [10,11].

This study aims to enrich the existing data on MS and reveal the molecular mechanism changes caused by MS infection in the chicken spleen. In particular, we focus on key genes related to immune function and the changes in their protein expression. The findings of this study will provide new insights into the molecular-level changes induced by MS infection and reveal the critical signalling and immune response pathways involved during the infection process.

Materials and methods

Mycoplasma synoviae strains and culture

The MS-Y strain, previously isolated from a local poultry farm in Fujian Province by our laboratory and characterized by our laboratory [12]. Then cultured in Modified Frey’s medium [13]. This cultivation occurred at 37°C under an atmosphere containing 5% CO₂, until the medium changed colour from red to orange, following the methods we’ve previously described [14]. The number of colony-forming units (CFUs) was calculated during its mid-exponential growth phase. For experimental challenges with chickens, MS with a density of 1 × 10⁹ CCU/ml was used [15].

Experimental chickens, infection and sample collection

Thirty healthy one-day-old SPF chickens were purchased from Jinan Spafas Co., Ltd. Prior to the experiment, the chicks were acclimatized for seven days in a specific environment and then randomly divided into two groups: a normal control group (CG) and an MS-infected group (MS), (n = 15/group). The control group chickens were housed in thoroughly disinfected isolation conditions. Each chicken in the infected group was treated with 1 mL of MS-NP strain (1 × 10⁹ CCU/ml) through the nose and eye. At post-infection days (dpi) 3, 6 and 12, five chickens from each cohort were euthanized, the spleen tissues were rapidly excised, processed on ice, washed with phosphate-buffered saline (PBS), and stored at −80°C. The specific labels for the 18 samples used in the experiment are listed in Table 1.

Table 1.

Sample labels in this study.

Group	CG	MS
3 dpi	A-1, A-2, A-3	D-1, D-2, D-3
6 dpi	B-1,B-2, B-3	E-1, E-2, E-3
12dpi	C-1, C-2, C-3	F-1, F-2, F-3

Transcriptome analysis

Eighteen cDNA libraries, representing the 30 samples, were generated from a pool of total RNA derived from three individual animals per library. Following the manufacturer’s instructions, total RNA was extracted from chicken spleen tissues using Trizol (Invitrogen, California, USA). The quality and integrity of the total RNA were assessed using a Nanodrop 2000 spectrophotometer and an Agilent 2100 Bioanalyzer (Thermo Fisher Scientific, Massachusetts, USA). Subsequently, mRNA molecules were purified from the total RNA using Oligo(dT) attached magnetic beads. For quality control, the cDNA library was validated using an Agilent Technologies 2100 Bioanalyzer. Finally, the library was constructed using the BGISEQ-500 platform from BGI (Shenzhen, China).

The library was sequenced on a dual-end 2 × 150 nt Illumina Hiseq platform. Raw images and data obtained from Illumina HiSeq sequencing were converted into sequence data in FASTQ format, yielding raw data. Quality control of the raw data was performed using Fast-QC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and the raw data were preprocessed using Trimmomatic (Illumina) software to remove adapter sequences, ribosomal RNA, and other contaminants that might interfere with clustering and assembly. This preprocessing also involved calculating Q20, Q30, and GC content to ensure the reliability of all subsequent analyses using Clean data. The clean reads were stored in FASTQ format and used for quantitative analysis. The clean data were then compared with the corresponding reference genome of Gallus gallus (GRCg6a, https://www.ncbi.nlm.nih.gov/gdv/browser/genome/?id=GCF_000002315.6) in the NCBI database to obtain comprehensive transcriptomic information for analysing DEGs. All DEGs were mapped to gene ontology terms in the database (GO, http://www.geneontology.org/) for functional annotation and to the Kyoto Encyclopedia of Genes and Genomes database (KEGG, www.genome.jp/kegg/pathway.html) for pathway enrichment analysis. The corrected p-value (q-value) ≤ 0.05 was defined as significant [16].

Proteomic analysis

Three biological replicates per group were used for proteomic analysis. The samples were ultrasonicated in lysis buffer (8 M urea, 1% protease inhibitor) on ice. After centrifugation at 12,000 g for 10 minutes at 4°C, the supernatant was transferred to a new centrifuge tube. The protein concentration and quality were determined using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific, MA, USA) and confirmed by SDS-PAGE. For digestion, the protein solution was treated with 5 mm dithiothreitol at 56°C for 30 minutes and alkylated with 11 mm iodoacetamide in the dark at room temperature for 15 minutes. The sample was then diluted with 100 mm triethylammonium bicarbonate (TEAB) (Sigma, St. Louis, MO, USA) to reduce the urea concentration to <2 M. For trypsin digestion, trypsin was added at a protein-to-trypsin ratio of 50:1 for overnight digestion, followed by a 100:1 ratio for 4 hours. After trypsin digestion, the peptides were desalted using Strata X C18 SPE columns (Phenomenex, Torrance, USA) and vacuum dried. The dried peptide samples were reconstituted in 0.5 M TEAB solution, and 100 μg of peptides from each sample were labelled.

According to the TMT (Thermo Fisher Scientific, Waltham, MA, USA) labelling kit instructions. The labelled peptides were mixed in equal amounts and incubated at room temperature for 1 hour, then quenched with 8 µl of 5% hydroxylamine, and the mixed samples were vacuum dried. The TMT-labelled samples were fractionated using an L-3000 hPLC system with a Waters BEH C18 (4.6 × 250 mm, 5 μm) column. Mass spectrometric analysis was performed on a Q-Exactive (Thermo Fisher Scientific, USA) mass spectrometer, collecting the mass-to-charge ratios of peptides and peptide fragments, generating raw mass spectrometry data.

The proteomic raw data were searched using Proteome Discoverer (Version 2.4) software and the Uniprot-taxonomy_721805 fasta database. Statistical analysis of the proteomics data was carried out using a criterion of log2(FC) >1 or log2(FC) < −1, and p < 0.05 to screen for differentially expressed proteins. GO and KEGG pathway analyses of all DEPs were performed in the same manner as for DEGs.

Integrated analysis of proteomics and transcriptomics

Gene expression can be characterized at both transcriptional (mRNA) and translational (protein) levels. To investigate the potential correlation between mRNA and protein quantitative data, subsets of mRNA and proteins were selected using the following cut-off values: fold change (FC) ≥ 2.0 and q-value ≤0.001 for differentially expressed genes (DEGs); FC > 1.3 and p-value <0.05 for differentially expressed proteins (DEPs). Comparative analysis of the transcriptome and proteome was conducted to identify commonly enriched Gene Ontology (GO) terms and KEGG pathways. Subsequently, the number of co-expressed differentially expressed genes and proteins was scanned and compared, to select important pathways with the highest number of DEGs/DEPs.

Real-time polymerase chain reaction (RT-PCR)

Total RNA from the collected tissues was extracted using TRIzol reagent (Dalian, China), following the manufacturer’s instructions. Primers were designed using Primer 6.0 software based on the reference sequence of chicken (Gallus) available in NCBI, with primer details provided in Table 2. The expression levels of genes related to signalling pathways were quantified using the relative quantification 2^−ΔΔCt method in qRT-PCR [6]. Expression analysis was conducted using Graph Pad Prism 8 software (β-actin was used as an internal control in the qRT-PCR reactions). The PCR cycling conditions were as follows: one cycle at 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 5 seconds and extension at 60°C for 35 seconds, and then a melting curve analysis step. All experiments were performed in triplicate, providing three technical replicates.

Table 2.

List of primers used in qRT-PCR.

Target	Sequence (5’~3’)	Product length/bp	Gene Bank Accession No.
β-actin	F:AGCCAACAGAGAGAAGATGACAC R:CATCACCAGAGTCCATCACAATA	134	NM_205518.1
IL-1B	F:AGCAGCCTCAGCGAAGAGACC R:GTCCACTGTGGTGTGCTCAGAATC	91	NM_204524.1
PIK3R2	F:GCTCTAAGAACCGATTGGA R:CAGGATGCGAAGGATGTC	141	XM_001233340.7
STK4	F:GTGTCTGATATTATTCGGTTAC R:ATGTCCCTCCGTGTTTAG	159	NM_001030853.1
CCL17	F:TCTCGAAGCGCTGAAGAGTT R:TTTCACCCAAGGTGCGTTTG	118	NM_204596.1
CXCL13	F:GCCTGTGCCTGGTGCTC R:TGCCCCCTTCCCCTAAC	116	XM_420474
IL2RB	F:TTCCTCCACTCATCTCTGT R:GATTCCTCGTCGTTCTTCT	139	XM_046906241.1
BLB2	F:GCACAACTACGGGATTCTGG R:TCAGGAACCACTTCACCTCG	161	NM_001318995
MX1	F:TGTGGGGTTGATCAAAGCGT R:ATGTCCGAAACTCTCTGCGG	116	NM_204609.1
ERO1A	F:ACCTGATGCCTGGAAGAT R:AACCTCTGCTGGAACTCT	276	NM_001277569.2
MAPK10	F:CAGCAGACCATTCCAGAA R:CAGGTAGGACATTCGTTCA	223	NM_001318224.4

Results

Overall statistics of transcriptome and proteome sequencing

We carried out time-course experiments to identify dynamic changes in transcripts in response to MS based on the infected and mock-infected groups across three time points (3, 6, 12 dpi), in which three biologically independent replicates for each treatment group were used for constructing cDNA libraries. After RNA isolation and sequencing, we obtained 2,285,509,310 raw reads. After removing adapter sequences and low-quality reads, high-throughput sequencing generated an average of 124.21 million paired-end reads per sample, indicating high sequencing quality. The unique mapping rate (unique mapped reads/all reads) was > 80% (Table 3), demonstrating the reliability of our transcriptomic data. Furthermore, the distribution and correlation coefficients of gene expression levels within and between groups were calculated based on the FPKM values of all genes in each sample, as shown in the figure below (Figure 1). All sequencing data have been uploaded to the Sequence Read Archive (SRA) at the National Center for Biotechnology Information (NCBI: accession number PRJNA1058982).

Table 3.

Filtering of the transcriptome data of the 18 samples.

sample	Raw reads	Clean reads	total_map	unique_map	Q20	Q30
A_1	129,229,848	125,100,534	112,256,093 (89.73%)	109,442,825 (87.48%)	97.1	92.58
A_2	1,290,15,444	126,247,470	112,304,873 (88.96%)	108,837,467 (86.21%)	97.16	92.7
A_3	142,948,326	139,900,890	126,289,411 (90.27%)	122,830,042 (87.8%)	97.35	93.37
B_1	124,223,312	121,908,378	111,599,512 (91.54%)	109,267,676 (89.63%)	97.2	92.6
B_2	127,167,644	124,716,848	114,651,380 (91.93%)	112,263,200 (90.01%)	97.36	92.92
B_3	122,383,954	120,537,572	111,554,637 (92.55%)	108,019,043 (89.61%)	97.38	92.95
C_1	123,571,922	121,453,520	111,842,037 (92.09%)	109,586,922 (90.23%)	97.25	92.68
C_2	132,102,628	129,637,328	120,593,674 (93.02%)	118,425,403 (91.35%)	97.31	92.76
C_3	126,870,560	124,407,656	115,342,074 (92.71%)	113,268,403 (91.05%)	97.26	92.69
D_1	136,943,040	134,485,288	121,812,407 (90.58%)	119,642,687 (88.96%)	97.11	92.77
D_2	1,264,45,584	120,788,294	110,251,065 (91.28%)	108,237,571 (89.61%)	97.24	92.7
D_3	125,872,538	123,218,960	111,505,971 (90.49%)	109,000,629 (88.46%)	96.98	92.19
E_1	123,518,640	120,714,000	109,741,831 (90.91%)	107,394,089 (88.97%)	97.2	92.72
E_2	118,922,212	116,510,772	107,554,351 (92.31%)	105,396,755 (90.46%)	97.14	92.35
E_3	122,766,880	120,201,966	109,691,296 (91.26%)	107,451,736 (89.39%)	97	92.17
F_1	126,934,796	124,607,620	114,130,815 (91.59%)	111,019,523 (89.1%)	97.25	92.68
F_2	126,868,354	124,426,448	114,912,507 (92.35%)	112,753,026 (90.62%)	97.23	92.62
F_3	119,723,628	116,917,730	108,086,333 (92.45%)	105,982,121 (90.65%)	97.33	92.83

Figure 1.

Distribution of gene expression. (a) Box plot of gene expression levels in samples. (b) Heatmap of sample-to-sample correlations.

To reveal the impact of MS infection on host gene expression at the translational level, TMT proteomics was employed for identification and quantification studies. A total of 456,579 spectra (126,078 matched) were obtained from 18 samples. Among these spectra 58,507 peptides and 7,037 identified proteins (7,001 quantifiable proteins) were detected. The relative molecular weights of the identified proteins were predominantly distributed in the range of 10–100 kDa, with about 15% exceeding 100 kDa (Figure 2a). All sample expression sets were combined for hierarchical clustering analysis and C-means clustering analysis, with results shown in Figure 2b,c. All proteomics sequencing data have been uploaded to the iProX (Project ID: IPX0007718000).

Figure 2.

Protein expression distribution. (a) Protein molecular weight distribution chart. The horizontal axis represents the molecular weight of identified proteins (in kilodaltons, kDa), and the vertical axis represents the number of identified proteins. (b) Clustering diagram of differentially expressed protein levels. Vertical clustering represents samples, and horizontal clustering represents proteins; shorter branches indicate higher similarity. (c) C-means clustering diagram of differentially expressed proteins. The horizontal axis represents the grouping, and the vertical axis represents the Z-score normalized expression levels. Each line represents one protein, with the colour indicating the magnitude of the value; a larger value indicates that the protein is closer to the average level in that category.

Identification of differentially expressed genes/proteins (DEG/DEP) in response to MS infection

To further elucidate the overall changes in host gene expression following MS infection, we identified DEGs that were up-regulated and down-regulated at three assessed time points during MS infection. The number of differentially expressed genes (including both up-regulated and down-regulated) for each comparison set, as well as the criteria for differential selection, are shown in Table 4. Compared to the control group, 946 DEGs were found in the spleens of infected chickens, with up-regulated genes (771/946, 81.50%) significantly outnumbering down-regulated genes (175/946, 18.50%). Compared to the control, these genes were expressed at significantly different levels at 3 dpi, 6 dpi, and 12 dpi, with p values < 0.05 (Figure 3). The top 30 significantly up-regulated genes and 15 significantly down-regulated genes at three assessment time points (3 dpi, 6 dpi, 12 dpi) are listed in Tables S1 and S2, respectively. Among them, DEGs related to immune and defence responses may play an important role in the host’s defensive reaction to MS infection.

Table 4.

Differential gene statistics.

compare	all	up	down	threshold
AvsD	572	475	97	DESeq2 padj ≤ 0.05 |log2FoldChange|≥1.0
BvsE	247	196	51	DESeq2 padj ≤ 0.05 |log2FoldChange|≥1.0
CvsF	127	100	27	DESeq2 padj ≤ 0.05 |log2FoldChange|≥1.0

Figure 3.

Distribution of differentially expressed genes (DEGs) at three time points. (a) Overall DEGs identified between the MS infection group and the control group. Categorized by 3 dpi (b), 6 dpi (c), and 12 dpi (d), the volcano plots of differentially expressed genes in chicken spleens. Red dots represent upregulated genes, green dots represent downregulated genes, and blue dots represent genes with no significant difference.

Based on a threshold of P-value <0.05 and fold change > 1, differentially abundant proteins (DEPs) were screened between the experimental group and the control group at three time points. For instance, in the comparison group at 3 dpi (A.vs.D), 180 differentially abundant proteins were identified, of which 86 were up-regulated and 94 were down-regulated. At 6 dpi in the comparison group (B.vs.E), 45 differentially abundant proteins were identified, with 31 up-regulated and 14 down-regulated. Similarly, at 12 dpi in the comparison group (C.vs.F), 80 differentially abundant proteins were identified, including 28 up-regulated and 52 down-regulated proteins (Table 5). The P-values were converted into absolute values of their logarithms to the base 10 to create volcano plots, as shown in Figure 4. The top 20 significantly up-regulated proteins and 15 significantly down-regulated proteins at the three assessment time points (3 dpi, 6 dpi, 12 dpi) are listed in Tables S3-S4.

Table 5.

Protein differential analysis results.

Compared Samples	Num. Of Total Quant.	regulated Type	fold-change >1.2	fold-change >1.3
A.vs.D	7001	up-regulated	86	40
		down-regulated	94	36
B.vs.E	7001	up-regulated	31	19
		down-regulated	14	9
C.vs.F	7001	up-regulated	28	13
		down-regulated	52	26

Figure 4.

Distribution of differentially expressed proteins (DEPs) at three time points. (a) Overall DEPs identified between the MS infection group and the control group. Volcano plots of differentially expressed proteins in chicken spleens in the (b) 3 dpi group (A.Vs.d group), (c) 6 dpi (B.Vs.e group), and (d) 12 dpi (C.Vs.f group). Red dots represent upregulated proteins, green dots represent downregulated proteins, and black dots represent proteins with no significant difference.

GO functional analysis of DEGs and DEPs

GO enrichment analysis was performed on DEGs and DEPs during the three infection periods to identify significantly enriched GO terms. GO terms with P-values <0.05 were considered significantly enriched. Overall, the GO enrichment analysis (Figure 5a) showed significantly enriched pathways detected during MS infection. Among them, in the Cellular Component, “cell part” (GO:0044464) enriched 1694 genes; in the Biological Process, “biological regulation” (GO:0065007) enriched 1161 genes; and in the Molecular Function, “binding” (GO:0005488) enriched 508 genes. These findings indicate that these GO terms play a significant role in the process of MS infection in chicken spleen.

Figure 5.

GO analysis of differentially expressed genes (DEGs). (a) GO enrichment analysis of DEGs between the MS infection group and the control group. (b) Displays the top 30 significantly enriched GO terms for DEGs in the A.Vs.D group at 3 dpi, (c) displays the top 30 significantly enriched GO terms for DEGs in the B.Vs.E group at 6 dpi, and (d) displays the top 30 significantly enriched GO terms for DEGs in the C.Vs.F group at 12 dpi.

At 3 dpi, a total of 572 DEGs were assigned to 125 GO terms, including 42 biological process (BP) terms, 45 cellular component (CC) terms, and 38 molecular function (MF) terms. The up-regulated DEGs were primarily enriched in the biological process category. The top 30 significantly enriched terms, as shown in Figure 5b, include “regulation of metabolic process,” “biosynthetic process,” “integral component of membrane,” “nucleotide binding,” and “transferase activity.” At 6 dpi, the up-regulated DEGs were mainly enriched in the cellular component category. A total of 247 DEGs were assigned to 71 GO terms, including 24 BP terms, 23 CC terms, and 24 MF terms. The top 30 significantly enriched entries, as shown in Figure 5c, include “cell differentiation/development process,” “cell periphery/plasma membrane,” “external stimulus/stimuli,” and “cellular processes (such as: cellular response to chemical stimulus, regulation of biosynthetic process).” At 12 dpi, the up-regulated DEGs were also mainly enriched in the biological process category. A total of 127 DEGs were assigned to 57 GO terms, including 20 BP terms, 19 CC terms, and 18 MF terms. The top 30 significantly enriched entries, as shown in Figure 5d, include “metabolic process,” “response to stimulus,” “immune system process/development,” and “immune and inflammatory responses.” Additionally, most down-regulated genes at the three time points were significantly enriched in GO terms related to adhesion (such as: cell/biological adhesion), biological regulation (regulation of signalling, biological regulation, or regulation of cell migration), and cellular processes (such as: cell periphery, plasma membrane, integral component of membrane), RNA processing, and transcription. Detailed data are presented in Supplementary Table S5.

To gain a deeper understanding of the biological functions of DEPs, we performed GO functional enrichment analysis. The majority were assigned to subcategories within Molecular Function, including “protein binding” (GO:0005515), enriching 973 proteins, “ATP binding” (GO:0005524), enriching 436 proteins, and “nucleic acid binding” (GO:0003676), enriching 233 proteins. In Cellular Component, the majority of DEPs were assigned to membrane-related subcategories (such as:periphery, extracellular vesicle, cortex, cortical and endothelial layers), including GO:0016021 (208 proteins), GO:0016020 (184 proteins), and GO:0005634 (165 proteins). In Biological Process, the majority of DEPs were assigned to “oxidation-reduction process” (GO:0006468), enriching 202 proteins, and “protein phosphorylation” (GO:0006468), enriching 200 proteins. The top 30 most enriched GO terms related to DEPs are shown in Figure 6a.

Figure 6.

GO analysis of differentially expressed proteins (DEPs). (a) GO enrichment analysis of DEPs between the MS infection group and the control group. (b) Displays the significantly enriched GO terms of DEPs in the A.Vs.D group at 3 dpi, (c) displays the significantly enriched GO terms of DEPs in the B.Vs.E group at 6 dpi, and (d) displays the significantly enriched GO terms of DEPs in the C.Vs.F group at 12 dpi.

The significantly enriched entries at the three time points are shown in Figure 6b-d. In the MS-infected group at all three time points, the majority of up-regulated DEPs were predominantly enriched in the MF category. These include “binding” (GO:0005488), “hydrolase activity” (GO:0016787), and “metal ion binding” (GO:0046872). Additionally, down-regulated proteins were mainly enriched in the BP category, including “metabolic process” (GO:0008152), “single-organism process” (GO:0044699), and “catalytic activity” (GO:0003824), among others. Such analysis indicates that a significant portion of the total proteins is dedicated to specific molecular functions, including catalytic activity (43.81%) and binding (40.21%). Detailed data are presented in Supplementary Table S6.

KEGG pathway enrichment analysis of DEGs and DEPs

To identify the signalling pathways primarily involved by DEGs and DEPs, this study performed KEGG enrichment analysis, defining enriched pathways with p < 0.05 as significantly enriched. All DEGs were enriched in 169 KEGG pathways, with the top 20 pathways by enrichment level shown in Figure 7a. Among these, the “Metabolic pathways” had the highest level of enrichment with 1490 genes, followed by “Neuroactive ligand-receptor interaction” enriching 363 genes, and “MAPK signaling pathway” enriching 273 genes.

Figure 7.

KEGG pathway analysis of differentially expressed genes (DEGs). (a) Top 20 KEGG pathways of DEGs between the MS infection group and the control group. (b) Top 20 KEGG pathways of DEGs in the A.Vs.D group at 3 dpi; (c) top 20 KEGG pathways of DEGs in the B.Vs.E group at 6 dpi; (d) top 20 KEGG pathways of DEGs in the C.Vs.F group at 12 dpi. The X-axis shows enrichment as -log10(p); the Y-axis displays the KEGG pathways. The colour of the points represents the q-value, and the size of the points represents the number of DEGs enriched in the referenced pathway.

During different time points of MS infection, the majority of up-regulated genes in the infected group were significantly enriched in environmental information processing (such as: cytokine-cytokine receptor interaction, TNF/NF-kappa B/MAPK signalling pathways, and neuroactive ligand-receptor interaction), metabolism-related, and biological system pathways (such as: Toll-like/IgA/NOD-like receptor signalling pathways). At 3 dpi, 5,215 transcripts mapped to 87 KEGG pathways. The top three enriched terms were “Protein processing in endoplasmic reticulum (gga04141),” “Regulation of actin cytoskeleton (gga04810),” and “Focal adhesion (gga04510)” (Figure 7b); at 6 dpi, 5,297 transcripts mapped to 66 KEGG pathways, with the top three enriched terms being “Neuroactive ligand-receptor interaction (gga04080),” “MAPK signaling pathway (gga04010),” and “Apoptosis (gga04210)” (Figure 7c); at 12 dpi, 5,269 transcripts mapped to 54 KEGG pathways. The top three enriched terms were “NOD-like receptor signaling pathway (gga04621),” “Neuroactive ligand-receptor interaction (gga04080),” and “Cytokine-cytokine receptor interaction (gga04060)” (Figure 7d). Detailed data are presented in Supplementary Table S7.

The functional categorization of DEPs was conducted by mapping them to KEGG pathways using KAAS. The results showed that 2,846 identifiable proteins were significantly enriched in 27 KEGG pathways. The top 20 most enriched KEGG pathways are shown in Figure 8a. The most abundant pathways included “Global and overview maps” enriching 717 proteins, “Transport and catabolism” enriching 499 proteins, and “Signal transduction” enriching 400 proteins. This indicates that these KEGG pathways play a significant role in the process of MS infection in chicken spleen.

Figure 8.

KEGG pathway analysis of differentially expressed proteins (DEPs). (a) The top 20 KEGG pathways of DEPs between the MS infection group and the control group. (b-d) the top 15 KEGG pathways of DEPs expressed at 3 dpi, 6 dpi, and 12 dpi, respectively.

Pathway enrichment analysis using the KEGG database was performed for the data at 3 dpi, 6 dpi, and 12 dpi. The results indicated that at 3 dpi, DEPs were involved in 38 signalling pathways, with 6 immune-related pathways among the top 15 enriched pathways. These included NOD-like receptor signalling pathway, Cytosolic DNA-sensing pathway, Toll-like receptor signalling pathway, AGE-RAGE signalling pathway in diabetic complications, PPAR signalling pathway, and ECM-receptor interaction (Figure 8b). At 6 dpi, DEPs were involved in 15 signalling pathways, with 5 immune-related pathways: NOD-like receptor signalling pathway, Influenza A, Lysosome, PPAR signalling pathway, and MAPK signalling pathway (Figure 8c). At 12 dpi, DEPs were involved in 37 signalling pathways, with 6 immune-related pathways among the top 15 enriched pathways, including Endocytosis, Cytokine-cytokine receptor interaction, Cell adhesion molecules (CAMs), Lysosome, PPAR signalling pathway, and Purine metabolism (Figure 8d). Other listed pathways, although not exclusively immune pathways, have indirect associations with immune regulation, such as “Arachidonic acid metabolism” and “Purine metabolism,” which indirectly participate in or affect the activities of the immune system through different mechanisms. The analysis of these pathways indicates that biosynthesis/metabolic reactions, response to stimuli, and immune system processes play a dominant role in chickens’ response to MS infection. Detailed data are presented in Supplementary Table S8.

Correlation analysis of RNA and protein expressions

Next, we investigated whether changes at the protein level corresponded with changes in the corresponding transcripts. To do this, we first identified a match rate between DEGs and DEPs by comparing the data at 3 dpi, 6 dpi, and 12 dpi, which revealed match rates of 73.33% (132/180), 80% (36/45), and 68.75% (55/80) respectively. Furthermore, we found that 132 DEPs (59.19%, 132/223) exhibited the same direction (either upregulation or downregulation) in both mRNA and protein data (Table S9).

We further analysed the overlapping expression of DEGs and DEPs across different groups, and the results showed (Figure 9) that only 6 overlapping genes meeting the criteria were identified at 3 dpi (Table 6). These genes were mainly involved in endoplasmic reticulum protein processing pathways, antioxidant responses, and signal transduction within the immune system. No significant overlapping genes/proteins were found at 6 dpi and 12 dpi.

Figure 9.

Transcriptomic and proteomic expression regulation analysis. (a-c) Venn diagrams of transcriptomic and proteomic expression regulation at 3 dpi, 6 dpi, and 12 dpi. “all_tran” represents all genes identified by transcriptomics, “diff_tran” represents DEGs identified by transcriptomics, “all_prot” represents all proteins identified by proteomics, and “diff_prot” represents DEPs identified by proteomics. The different areas of the diagram show the common and unique genes (proteins) in each set. (d-f) correlation analysis of transcriptomic and proteomic expression at 3 dpi, 6 dpi, and 12 dpi. Each point in the diagram represents a protein, with green points indicating proteins with significant expression differences, and blue points indicating proteins with no significant expression differences.

Table 6.

Statistical results of DEGs and DEPs.

Gene_id	Tran_id	Prot_id	Protein(pval)	Tran(pval)
P3H2	414143	Q6JHU7	0.0042	0.00002485
TXNDC5	420867	F1P4H4	0.0027	0.0000199
PRDX4	418601	F1NNS8	0.0034	0.001472362
EDEM3	424450	A0A1D5PEI6	0.0207	0.000298479
C14orf2	772187	A0A3Q2UCU2	0.00297	0.000170721
IRF1	396384	A0A3Q2UA99	0.0007	0.003157574

To elucidate the macro-level distribution of DEGs/DEPs, a Sankey bubble chart (sankey + dot plot) was drawn based on GO enrichment results, considering GO terms with p < 0.05 as significantly enriched. The results, shown in Figure 10, highlight highly enriched GO terms related to Molecular Function (MF), with core structures involving molecular activity, metal ion binding, catalytic activity, and enzyme activities related to peroxidases and inhibitors. These enriched terms reveal a range of key immune-related functions, including but not limited to the maintenance of molecular structure and activity, catalysis of biochemical reactions, and recognition and decomposition of reagent components. These molecular functions are not only foundational to the protective measures of the immune system but also play vital roles in cellular biological actions during infection. These function categories’ response to MS infection (such as pattern recognition receptors) suggests that in-depth studies of the biological processes represented by these enriched GO terms will help uncover the molecular changes and deeper biological responses of cells during infection. More details and related data are available in Supplementary Table S10.

Figure 10.

GO enrichment analysis Sankey bubble chart of DEGs/DEPs. (a-c) GO functional enrichment analysis results of transcriptomics and proteomics at 3 dpi, 6 dpi, and 12 dpi. The left side of the chart is a Sankey diagram, representing the genes contained in each pathway. The right side is a conventional bubble chart, where the size of the bubble indicates the number of genes in the pathway, and the colour of the bubble represents the p-value.

We examined the significant signalling pathways between transcripts and proteins in KEGG analysis. At 3 dpi, 131 DEGs/DEPs were involved in 27 signalling pathways, including 11 immune-related pathways such as MAPK signalling pathway, NOD-like receptor signalling pathway, Toll-like receptor signalling pathway, Cytokine-cytokine receptor interaction, and Apoptosis. Most of these pathways are involved in the regulation of immune responses or immune cell functions (Figure 11a). At 6 dpi, 64 DEGs/DEPs participated in 6 signalling pathways, with 4 being immune-related: Cell adhesion molecules (CAMs), NOD-like receptor signalling pathway, Influenza A, and MAPK signalling pathway. The other two pathways, GnRH signalling pathway and Protein processing in endoplasmic reticulum, while not typically classified as immune pathways, may play an indirect role in immunity or participate in broader processes including components of the immune system. For instance, the endoplasmic reticulum is involved in the folding and modification of proteins, including those that are part of the immune system such as antibodies and cell surface receptors (Figure 11b). At 12 dpi, 49 DEGs/DEPs were involved in 20 signalling pathways, including 8 immune-related pathways: Cytokine-cytokine receptor interaction, TGF-beta signalling pathway, Toll-like receptor signalling pathway, Cell adhesion molecules (CAMs), and FoxO signalling pathway. Other pathways are involved in various cellular functions, including metabolism and cell growth (Figure 11c), which is highly relevant to the context of MS infection. Detailed information can be found in Supplementary Table S11.

Figure 11.

KEGG pathway enrichment results for DEGs/DEPs. (a-c) Bar charts of KEGG pathway enrichment analysis for transcriptomics and proteomics at 3 dpi, 6 dpi, and 12 dpi.

Through a comprehensive analysis of KEGG pathways at the three time points, we identified that “Cell adhesion molecules (CAMs)”, “GnRH signalling pathway,” “Cytokine-cytokine receptor interaction,” “MAPK signalling pathway,” “Toll-like receptor signalling pathway,” “Protein processing in endoplasmic reticulum,” “NOD-like receptor signalling pathway,” “Influenza A,” “Apoptosis,” and “AGE-RAGE signalling pathway in diabetic complications” were significantly enriched in correlation analyses at at least two time points, indicating that these 10 pathways are predominantly activated. Detailed information can be found in Supplementary Table S12.

Among the overlapping pathways, 75 overlapping molecules were identified between DEGs and DEPs (Table S12). Of these, the endoplasmic reticulum protein processing pathway enriched 17 genes, while Cytokine-cytokine receptor interaction and MAPK signalling pathway each enriched 14 genes. This indicates that these three pathways are the main overlapping pathways involved (45/75). Consequently, we further investigated the association of differentially expressed genes and proteins in these three pathways. We found that 36 genes in these pathways were significantly up-regulated at both transcript and protein levels. These include STK4, MAPK10, CCR10, IL1B, CXCL13, IL2RB, CCL17, and CD88. These genes can be categorized into multiple classes, including signal transduction, growth factors and receptors, cytokines and their receptors, genes regulating cell cycle and death, as well as developmental-related genes. They all play roles in different biological processes of the cell, especially in immune responses and stress responses.

Validation of gene expression levels

Given that the sequencing data was most abundant at 3 dpi, we selected spleen tissue from the 3 dpi group as our sample. Ten candidate DGEs were chosen for RT-qPCR validation, including 6 up-regulated DEGs (IL-1B, PIK3R2, STK4, CCL17, CXCL13, IL2RB) and 4 significantly down-regulated DEGs (BLB2, MX1, CTGF, CST3). As shown in Figure 12, the qRT-PCR results were consistent with the trends observed in the RNA-seq data, thereby confirming the reliability of the transcriptomic sequencing results.

Figure 12.

Gene expression levels (Log2FC) of 10 candidate genes assessed by RNA-Seq and RT-qPCR. The transcript expression levels of the randomly selected genes were each normalized to that of the β-actin gene. Bars represent mean ± SE (n = 3).

Discussion

The spleen plays a critical role in regulating immune responses [6,10]. Previous research has indicated that in pathological conditions, the changes in gene expression in the spleen are relatively greater than in other tissues [6,17]. To our knowledge, this is the first study to combine proteomics and transcriptomics analysis techniques to uncover the molecular events occurring in spleen tissue during MS infection. We have previously published on the pathological damage induced by MS infection in chicken spleen tissue, including oxidative stress, apoptosis, and inflammation [8]. Therefore, this study utilized the same experimental samples, aiming to uncover the underlying molecular mechanisms of MS infection by integrating proteomics and transcriptomics approaches, and comparing samples at different time points (3 dpi, 6 dpi, and 12 dpi). The results indicate that at the early stage of infection (3 dpi), the most abundant signalling and gene expression events were triggered, with 572 DEGs and 180 DEPs, and most of the DEGs were related to metabolic pathways and amino acid metabolism. At 6 dpi (247 DEGs, 45 DEPs), the immune genes were mainly associated with metabolic pathways, apoptosis, and signalling pathways. By 12 dpi (127 DEGs, 80 DEPs), the regulation of the actin cytoskeleton and FoxO signalling pathway was affected, indicating involvement in more complex immune responses and gene expression. This reflects the rapid response of the host immune system during the early stage of infection. These dynamic changes suggest that the spleen plays multiple roles during MS infection, serving not only as a site for the generation and maturation of immune cells but also as a key regulator of immune responses.

Based on transcriptomic and proteomic analyses, a comparison of correlation was conducted between the 223 significantly enriched differentially expressed proteins and the corresponding mRNAs from the transcriptome analysis. Among them, 132 genes showed the same trend in expression levels at both the transcript and protein levels. In contrast, the remaining 91 proteins exhibited opposite trends in expression levels at the transcript and protein levels. This discrepancy may be due to a range of post-transcriptional modifications, such as phosphorylation [18], post-transcriptional regulation [19], translation efficiency/post-translational modifications, and protein stability [20]. Furthermore, under the influence of multi-level gene expression regulation, the expression and activity of related proteins are also affected [21,22]. These differences support the existence of post-transcriptional regulatory mechanisms in the spleen in response to MS infection. Therefore, these findings enhance our understanding of the molecular basis of the chick spleen’s response to MS infection. Total of 223 DEGs/DEPs were categorized into four broad categories based on GO annotation, manual literature and pathway analyses. These categories include immune activation, antioxidant/cell apoptosis, inflammation, and the relationship with endoplasmic reticulum protein processing (Table S9). Below, we highlight several key components within these categories and their potential functions in the host’s defence response against Mycoplasma synoviae.

Immune activation

This study conducted GO analysis on DEGs/DEPs by integrating proteomics and transcriptomics (Table S10), and found that GO terms related to immune response were significantly enriched. Specifically, these terms include biological process (BP) subcategories such as redox processes, metabolic processes, regulation by RNA polymerase II, and protein transport, as well as terms related to cellular components (CC) such as the cytoplasm, mitochondria, and endoplasmic reticulum. In molecular function (MF), categories covered range from cofactor activity, enzymatic catalysis, molecular responses to substance transport. These enriched GO items of DEGs/DEPs play a crucial role in the immune response of the chicken spleen to MS. By weaving these GO terms together, we can see a detailed picture of how the immune system operates: structural molecules that provide the framework for immune cells to function, enzymes that catalyse defensive responses, metal ions that enhance various molecular functions, and transport mechanisms/responsiveness that facilitate communication and immunity. These terms represent cogs in the larger mechanism of immune response, from pathogen recognition to establishing a tailored and effective defence [23,24]. The complex interactions of these functions and activities enable the immune system to operate precisely and efficiently, protecting the body from a variety of threats [6].

Through KEGG annotation, we identified 16 KEGG pathways related to immune response (Table S11). These pathways are primarily involved in various biological systems (such as the PPAR signalling pathway, the intestinal immune network for IgA production, the NOD-like receptor signalling pathway, the MAPK signalling pathway, and the “AGE-RAGE signaling pathway in diabetic complications”) and cytokine-cytokine receptor interactions (including the Jak-STAT signalling pathway, the Toll-like receptor signalling pathway, and the RIG-I-like receptor signalling pathway). The activation of these pathways can regulate cell proliferation, survival, and activation signals, thereby aiding the cellular response to mycoplasma [25,26]. Furthermore, the significant enrichment of the cytokine-cytokine receptor pathway suggests that during the process of pathogen infection, cells release and uptake cytokines to trigger immune and cytokine responses [27]. Thereby potentially reflecting the activation of cellular immune responses and defence mechanisms following infection [28–30].

Antioxidant/Apoptosis

In our previous research, we observed significant oxidative stress and apoptosis phenomena in cells infected with MS, such as excessive production of reactive oxygen species (ROS), impaired mitochondrial metabolic function, mitochondrial swelling, chromatin condensation, and nuclear damage [8]. Continuing with this line of investigation, the data from this study further supports these initial findings, showing significant enrichment of differentially expressed genes and proteins in several signalling pathways closely related to apoptosis, especially in the MAPK signalling pathway, apoptosis, and p53 signalling pathway. This enrichment not only validates our previous observations but also emphasizes the close connection between mitochondrial dynamics imbalance and the cellular responses to oxidative stress and the triggering of apoptosis. Research has found that mitochondria are key organelles regulating oxidative stress and apoptosis, and their imbalance is closely related to oxidative stress and apoptosis [31,32]. Therefore, this study particularly focuses on the changes in mitochondrial function-related genes after MS infection. Through bioinformatics analysis, we identified five key genes closely related to mitochondrial function: STK4, MAPK10, DDIT4, PIK3R2, and CAPN1, which play crucial roles in pathways such as mitochondrial dynamics, cellular stress response, energy metabolism, and mitochondria-induced apoptosis [33–36].

Inflammatory response

Innate immunity serves as the first line of defence against pathogens, and inflammatory response is a core component of the host’s defence mechanisms [37]. The activation of the innate immune system relies on the recognition of pathogen-associated molecular patterns (PAMPs) by specific pattern recognition receptors (PRRs) [38]. TLRs are highly active in the inflammatory response to pathogens [39]. In this study, it was observed that the pathways regulating Toll-like receptors via endogenous ligands were upregulated. Additionally, pathways related to leukocyte interleukin signalling transduction, chemokine detection, and interferon regulation were also upregulated. Specifically, inflammatory factors such as IL-2, IL-6, IL-10, CXCL11, CCR9, CARD9, IL-12, IL-1β, IFN, and TNF were significantly increased after MS infection, indicating an active response of the host immune system to MS infection through inflammatory reactions. The result is consistent with previous studies. For example, Xu et al (2020), also reported in their study that IL-6 and TNF levels significantly increased after MS infection, and these factors are crucial for triggering acute responses and subsequent immune regulation [40]. Additionally, the study by Abdulkhaleq et al (2018), emphasized the role of CXCL11 and CCR9 in regulating cell migration and localization, which is essential for cell-mediated immune responses in the process of localization and clearance. These studies suggest that the upregulation of signalling plays an important role in promoting defence mechanisms and helps the immune system respond to MS quickly and effectively [41].

The relationship between DEGs/DEPs and endoplasmic reticulum protein processing

The endoplasmic reticulum is a critical membranous organelle that carries out a variety of important cellular functions. Research has found that significant enrichment associated with “Endoplasmic Reticulum Protein Processing” reflects the importance of protein synthesis and modification in the immune response [30,42]. Furthermore, the interaction and regulation of endoplasmic reticulum proteins are crucial for maintaining cellular homoeostasis and responding to cellular stress [43,44].

In this investigation, a significant enrichment of 17 differentially expressed genes and proteins (DEGs/DEPs) was observed in the “Endoplasmic Reticulum Protein Processing” pathway according to KEGG pathway analysis (refer to Table S12). The majority of these DEGs/DEPs are involved with the endoplasmic reticulum stress pathway, including genes such as ERN2, EIF2AK3, ERO1A, PDIA6. ERN2 is a key regulator of the unfolded protein response (UPR), activated under ER stress conditions to restore protein homoeostasis [45]. EIF2AK3, a key protein kinase in the endoplasmic reticulum stress response. It plays an important role in cellular response to endoplasmic reticulum stress and maintains cellular homoeostasis by regulating protein synthesis and gene expression [45,46]. ERO1A, is an oxidoreductase located in the endoplasmic reticulum, which is mainly responsible for catalysing the formation of protein disulphide bonds in the endoplasmic reticulum, thus promoting the correct folding and maturation of proteins. It plays a key role in maintaining endoplasmic reticulum redox homoeostasis and protein quality control [47]. PDIA6, is a member of the protein disulphide bond isomerase (PDI) family. It functions mainly in the endoplasmic reticulum and is involved in the formation, rearrangement and repair of protein disulphide bonds, thereby promoting proper protein folding and function [48]. These proteins adapt to variations in endoplasmic reticulum load through various mechanisms, managing protein synthesis, boosting the capacity for protein folding, or mitigating the accumulation of improperly folded proteins by enhancing their degradation. These insights underline the pivotal role of the endoplasmic reticulum in responding to MS infection and demonstrate the critical importance of the “Endoplasmic Reticulum Protein Processing” pathway in the modulation of immune responses.

Conclusion

In conclusion, this study combined transcriptomic and proteomic analyses to identify 946 DEGs and 305 DEPs, revealing that the complex immune responses and defence mechanisms triggered by Mycoplasma synoviae (MS) infection involve multiple coordinated immunobiological processes, including immune activation, antioxidant reactions, and the formation of immune memory. We specifically identified a series of key DEGs and DEPs that play a central role in splenic damage caused by MS infection, covering aspects such as regulation of cell proliferation and apoptosis, mitochondrial dysfunction, activation of inflammatory mediators, and endoplasmic reticulum stress responses. The analysis of these resources deepens our understanding of the impact of MS infection on the spleen of young chickens and provides valuable biological resources for further exploration of the interactions between MS and its host.

Funding Statement

This work was supported by the University-Industry Cooperation Project [2022N5001] and the Fujian Provincial Science and Technology Major Special Topic: Research on the Industrialisation and Application of New Veterinary Traditional Chinese Medicine Products in Livestock and Poultry Antibiotic-Free Breeding [2021NZ029008].

Author contributions

LZ designed and conducted the study and wrote the manuscript. CW contributed significantly to the analysis and manuscript preparation; LL and JL performed the data analyses and wrote the manuscript. YW helped perform the analysis with. All the authors have read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files. These data encompass, but are not limited to, raw experimental data, statistical analyses, and additional charts or datasets that support the findings of this study. These materials provide support for the transparency and reproducibility of the research and are available for peer review and public inquiry. All supplementary tables (S1-S12) associated with this article have been deposited in the Zenodo repository, The data are publicly accessible via the following DOI link: https://doi.org/10.5281/zenodo.15104466. All transcriptome sequencing data have been uploaded to the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI; accession number PRJNA1058982. All proteome sequencing data have been uploaded to iProX (Project ID: IPX0007718000). The datasets supporting the conclusions of this article are included within the article and its additional files. All raw data are available in the Science Data Bank. (DOI: 10.57760/sciencedb.16191, https://cstr.cn/31253.11.sciencedb.16191; DOI: 10.57760/sciencedb.16199, https://cstr.cn/31253.11.sciencedb.16199.

Ethics declarations

All animal treatments utilized in this research and all experimental steps were completed in accordance with the laws and guidelines of the Fujian Agricultural and Forestry University Animal Care and Use Committee (Approval No: PZCASFAFU22015).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21505594.2025.2507197

Abbreviations

MS:

Mycoplasma synoviae

DEG:

Differentially expressed genes

DEP:

Differentially expressed proteins

MLCK:

Myosin light chain kinase

Dpi:

Days post-infection

PBS:

Phosphate-buffered saline

RNA-seq:

RNA sequencing

TMT:

tandem mass tag

CFU:

Colony-forming unit

GO:

Gene ontology terms

KEGG:

Kyoto Encyclopedia of Genes and Genomes database

SRA:

Sequence Read Archive

NCBI:

National Center for Biotechnology Information

BP:

Biological process

CC:

Cellular component

MF:

Molecular function

CAMs:

Cell adhesion molecules