Serotyping, molecular typing, and vaccine protein screening for Riemerella anatipestifer: overcoming challenges in prevention and treatment

doi:10.21203/rs.3.rs-3395668/v1

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Serotyping, molecular typing, and vaccine protein screening for Riemerella anatipestifer: overcoming challenges in prevention and treatment

https://doi.org/10.21203/rs.3.rs-3395668/v1

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Background

Riemerella anatipestifer affects the duck farming industry worldwide, causing substantial economic losses. Disease prevention and treatment mainly rely on vaccines and antibiotics; however, the large number of serotypes and increasing resistance of R. anatipestifer have caused great difficulties in preventing and treating the infection.

Results

Serotyping and molecular typing of 51 strains of R. anatipestifer was performed. We predicted vaccine proteins based on pan-genome analysis and via cross-immune protection potential. Rabbits were immunized with antigens, and nine serotyped sera were prepared, which were submitted to peer experts for serotype identification; six serotypes with two unformed strains were identified. The results for the self-made serotypes were consistent with those obtained from the externally submitted strains. Pan-genome analysis was carried out on 51 strains of R. anatipestifer, and an open pan-genome set of 5,094 genes was constructed. COG annotation classification revealed that the core and non-core genomes had apparent differences in gene function. Of the 51 strains of R. anatipestifer isolates, 18 belonged to the 3 previously known serotypes, and the 33 represented newly identified serotypes, totaling 13 species. The clustering analysis demonstrated a significant genetic variation at the gene level among the housekeeping genes of R. anatipestifer. The pan-genome phylogenetic tree demonstrated that the 51 strains of R. anatipestifer could be roughly divided into 8 branches, each with a similar unique gene distribution and biological characteristics. The core gene tree showed that the 51 strains of R. anatipestifer could be divided into 6 major branches and that the strains from the same region had a clustered distribution in the evolutionary tree, forming multiple geographic subpopulations. This information served as a guide for dividing the strains into smaller geographic subgroups. Five genes of interest were screened from 1,116 core genomes that have the potential to serve as better cross-protective vaccine proteins.

Conclusions

Fills the gaps in the existing typing systems for Riemerella anatipestifer by utilizing a combination of serotyping, MLST typing, and pan-genome analysis, it provided valuable insights into the epidemiology, evolution, and pathogenesis of this bacterium. Our research not only enhances our understanding of Riemerella anatipestifer but also paves the way for the development of effective cross-protective vaccines.

Riemerella anatipestifer

serotyping

molecular typing

vaccine protein

Riemerella anatipestifer is an important pathogenic bacterium that harms waterfowl, including ducks and geese [1]. R. anatipestifer infection has a high incidence rate, infectivity, and mortality [2]. Ducks that suffer from R. anatipestifer infection grow poorly and lose economic value, causing huge economic losses to the duck industry. In poultry breeding, the prevention and treatment of R. anatipestifer infection pose considerable challenges despite the common utilization of vaccines and antibiotics. The formidable obstacle lies in the existence of numerous serotypes and alarming prevalence of drug resistance [3]. Consequently, combating R. anatipestifer becomes an intricate endeavor, demanding innovative approaches for prevention and treatment. Currently, 21 R. anatipestifer serotypes have been identified [4–9]. The distribution of serotypes exhibits geographical variation, where in different regions display varying prevalence rates. Furthermore, even within the same region, the prevalence of serotypes undergoes continuous fluctuations over time. There is a lack of effective immune cross-protection between the different serotypes of R. anatipestifer, substantially reducing the vaccine prevention effects [10]. At present, there is no national standard serum or strain for R. anatipestifer, which may lead to deviations in the traditional serum typing system between different research institutions. Research on the molecular typing system of R. anatipestifer remains lacking, and common molecular typing methods have not been widely studied and applied to this bacterium. There are fewer than 100 ST types of R. anatipestifer in the multilocus sequence typing (MLST) database, fewer than 50 whole genomes in the National Center for Biotechnology Information (NCBI) database, and fewer studies based on pan-genome or multi-genomics, thereby extensively limiting the epidemiology and pathogenesis of R. anatipestifer infection. In this study, we carried out serotyping, MLST typing, and pan-genome typing of 51 strains of R. anatipestife and screened five protein genes from the core genome of the 51 strains of R. anatipestife using pan-genome analysis and reverse vaccine technology. These five proteins have potential for use as cross-protective vaccines.

Source of R. anatipestifer

In this study, 51 strains of R. anatipestifer were isolated and preserved in the laboratory. Our laboratory conducted the isolation, identification, and genome sequencing of isolated strains of R. anatipestifer in its early stages. The genome data were uploaded to the NCBI database, and a login number was obtained (Supplementary Table 1). We conducted a detailed study on the drug resistance phenotype, resistance genes, and consistency between the drug resistance phenotype and resistance genes of the strains (Xihui et al., 2023).

Typing Methods

Serotyping. Since the serotypes of strains from different regions are different, we first selected three R. anatipestifer strains from different regions to prepare antigens for immunization. Then, we used the serum after immunization to conduct plate agglutination tests on all strains. Three strains were carefully chosen from various regions based on their non-agglutination characteristics. These selected strains were utilized to generate antigens for immunization purposes. Subsequently, post-immunization, a plate agglutination assay was conducted to assess the agglutination response. This step was repeated until all strains were agglutinated.

The bacterial solution was cultured in a large amount in a conical flask. The obtained bacterial solution supplemented with formaldehyde solution to 0.2% of the final concentration of formaldehyde and inactivated in a constant temperature shaker for 24 h to obtain the inactivated bacterial solution. After inactivation, the bacterial solution was centrifuged at 8,000 r/min for 10 min, and the supernatant was removed to achieve toxin removal and concentration. The bacterial precipitate was washed with sterile physiological saline and prepared as a 10¹¹ colony-forming unit (CFU)/mL (100 billion) antigen solution. Inoculate the prepared antigen into the ear vein of approximately 2–3 kg of New Zealand white rabbits (female) for initial immunization, 0.5 mL on the first day, 1 mL on the third day, and 1.5 mL on the fifth day; after one week of initial immunization, booster immunization was carried out every other week at a dose of 1 mL each time. One week after the booster immunization, blood was collected from the ear vein to detect serum antibody specificity and antibody titers.

Using the plate agglutination method to detect antibody titers, the prepared agglutination antigens and serum were subjected to plate agglutination experiments at ratios of 1:1, 1:2, 1:4, 1:8, and 1:16. The maximum dilution ratio at which extensive agglutination can occur is the serum titer. Sera were collected from different species and regions, and the plates with Escherichia coli, Salmonella, Proteus mirabilis, and immune R. anatipestifer were stored in the laboratory for slide agglutination (Table 1). If Escherichia coli, Salmonella, and Proteus mirabilis are negative and the immune R. anatipestifer is positive, good serum specificity is indicated.

Table 1

Background information of Bacteria
Bacteria	Strains	Species	Region	Disengaging time
Escherichia coli	EC23001	pig	Shandong	2023.5
Escherichia coli	EC23026	pig	Hebei	2023.6
Escherichia coli	EC23033	pig	Jiangsu	2023.6
Escherichia coli	E1	chicken	Shandong	2019.9
Escherichia coli	E62	chicken	Shandong	2019.10
Escherichia coli	E92	duck	Shandong	2020.6
Salmonella	S1	chicken	Shandong	2019.6
Salmonella	S19	chicken	Shandong	2020.2
Salmonella	S26	chicken	Sichuan	2020.9
Salmonella	S70	chicken	Anhui	2020.4
Salmonella	S74	chicken	Anhui	2020.6
Salmonella	S80	goose	Heilongjiang	2020.5
Salmonella	S85	chicken	Shandong	2021.3
Salmonella	S87	chicken	Guangdong	2021.3
Salmonella	S95	chicken	Guangdong	2021.6
Salmonella	S100	chicken	Shandong	2021.8
Proteus mirabilis	B1	duck	Shandong	2021.3
Proteus mirabilis	B8	chicken	Shandong	2021.5
Proteus mirabilis	B24	chicken	Shandong	2020.9
Proteus mirabilis	B28	goose	Neimenggu	2020.6
Proteus mirabilis	B31	duck	Shandong	2020.9
Proteus mirabilis	B41	chicken	Liaoning	2020.5
Proteus mirabilis	B58	duck	Hebei	2020.5
Proteus mirabilis	B78	duck	Henan	2020.5
Proteus mirabilis	B80	chicken	Hebei	2020.9
Proteus mirabilis	B84	chicken	Jiangsu	2020.3

We began by taking 1 mL of the bacterial solution under examination and subjecting it to centrifugation at 10,000 r/min for 1 min. After discarding the supernatant, we resuspended the bacterial body in 50 µL of sterile physiological saline to create the agglutination antigen. Next, a clean slide was prepared, onto which we added 10 µL of the agglutination antigen, followed by 10 µL of the serotype serum being tested. We carefully blew and mixed the prepared serotype serum with the antigen. Within 2 min, we considered the serotype to be positive if noticeable agglutination occurred. The proportions of serotypes and cross-agglutinating strains were determined.

Multilocus Sequence Typing. The genome sequences of 51 strains of R. anatipestifer were uploaded to the PubMLST website(https://pubmLst.org/). The system automatically identifies and extracts the sequences of seven housekeeping genes in the genome and matches the extracted housekeeping gene sequence with the existing housekeeping gene in the database to obtain the serial number of the housekeeping genes. A combination of serial numbers composed of seven housekeeping genes can form one ST type. The combination of serial numbers of housekeeping genes not included in the database will become a new ST type that will be obtained after a systematic review. Cluster analysis was performed for all ST genotypes in the database. According to the number of housekeeping gene variations among different ST genotypes, the variation was divided into three categories: single site variation, that is, only one housekeeping gene was mutated between two ST genotypes; double site variation, that is, two housekeeping genes were mutated between the two ST types; and triple site variation (TLV), that is, three housekeeping genes were mutated between the two ST types. According to the clustering results, the degree of variation of the new ST type found in this study and the existing ST type in the database were analyzed, and the degree of variation of the current R. anatipestifer was analyzed.

Pan-Genome Analysis and Typing. In a previous study, 51 strains of R. anatipestifer were analyzed by Roary [11] to form a pan-genome set. According to the frequency of genes appearing in different strains, the pan-genome set is divided into four types: core genes, which appear in all strains; soft-core genes, which appear in 95–99% of strains; shell genes, which appear in 95–15% of strains; and cloud genes, which appear in less than 15% of strains. The number and proportion of various genes were counted, and the composition of the pan-genome and the number of genes in each part were analyzed using the EggNOG online website [12] (http://eggnog-mapper.embl.de/). By annotating the COG database for core and non-core genes [13], we obtained the COG functional classification of each gene and compared the functional differences between the core and non-core genes. Roary was used to map the distribution of each gene in the 51 strains of R. anatipestifer pan-genome, a pan-genome cluster tree was built, and Evolview [14] was used to draw a cluster tree for the pan-genome core genome.

Cross Protective Vaccine Protein Screening

Target Gene Screening. First, duck homeotic genes were removed from the core genome, and homeotic genes of immune animals were removed to maintain the heterogeneity of proteins and improve the immune effects (https://www.ncbi.nlm.nih.gov/taxonomy). The duck genome was acquired and subsequently utilized for building a database by executing the appropriate command using BLAST software. The constructed duck genome database was then employed for comparing and analyzing the core genome using BLAST software. To ensure accuracy, the comparison threshold was set at 1e-5. Notably, highly similar duck homeotic genes were excluded from the core genome during the analysis. Next, to enhance the accuracy of the protein's cell localization prediction, PSORTb [15] (https://www.psort.org/psortb/) was utilized. This approach was chosen in comparison to Cell-PLoc [16] (http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2/) to improve the reliability of the prediction. Two websites predicted the cell sublocations of non-homeotic genes. When both websites predict the gene to be located in the extracellular region or membranes, the prediction is considered effective. We organized the effectively predicted genes, calculated the protein molecular weight using EditSeq software, and screened for proteins with molecular weights above 10 kDa using VaxiJen [17] (http://www.ddg-pharmfac.net/vaxijen/VaxiJen). We predicted the possibility of this protein as a protective antigen, set the threshold to 0.4, and used the DeepTMHMM website [18] (https://dtu.biolib.com/DeepTMHMM). Proteins with fewer than four transmembrane structures were more likely to be expressed and purified.

Protein Structure and Epitope Prediction. To predict the three-dimensional structure of the target genes, homologous modeling was performed using SWISS-MODEL (https://swissmodel.expasy.org/assess/3hDj2n/01) and I-TASSER (https://zhanggroup.org/I-TASSER/). The Global Model Quality Estimate index was utilized to evaluate the overall quality and integrity of the constructed models. The gene with the highest score was selected as a template to build the protein structure, enhancing the model’s reliability. Subsequently, the three-dimensional structure of the protein was examined, and the protein structure file was obtained.

To predict B-cell epitope information of the protein, the predicted protein structure file and sequence were employed. The Immune Epitope Database ༆ Tools (IEDB) website (https://www.iedb.org/) was utilized for B-cell epitope prediction.

Positive Serum Preparation and Serum Typing Results

The prepared serum did not agglutinate multiple strains of E. coli, Salmonella, or Proteus, with good serum specificity. The serum titer was determined by plate agglutination and ranged from 1:4 to 1:16. Subsequently, 41 strains of R. anatipestifer starting with the number R were grouped multiple times, and 9 serotypes were produced by 9 strains of bacteria. Additionally, one R-19 strain exhibited strong agglutination with the RS-5 and RS-2 serotypes, belonging to a cross-reactive strain. Forty-one strains of each serotype were sent to Professor Zhu Dekang of Sichuan Agricultural University for identification. The identification results showed 6 serotypes of the 41 strains of R. anatipestifer, including types 1, 2, 5, 6, 11, and 18. Among these, type 11 R. anatipestifer was the main serotype, accounting for 39.02%, followed by type 1, accounting for 26.83% (Table 2). When considering the serogroups, two out of the nine serogroups were categorized as unformed serotypes. Among the remaining serogroups, RS-23 and RS-38 belong to the serotype 5 strain of R. anatipestifer. Additionally, each of the other five serogroups is associated with a distinct serotype, demonstrating a strong correlation between the serogroup and traditional serotyping. Moreover, the serogroup classification exhibits higher resolution and consistency, making it a valuable tool for bacterial serotyping purposes.

Table 2

Serum typing results
Serotype group	Serotype	Number of strains	Proportion
RS-2	1	11	26.83%
RS-4	unformed	2	4.88%
RS-5	11	16	39.02%
RS-9	2	2	4.88%
RS-21	18	1	2.44%
RS-23	5	5	12.20%
RS-30	6	1	2.44%
RS-38	5	1	2.44%
RS-41	unformed	1	2.44%
cross connection	1	1	2.44%

MLST Typing and Cluster Analysis Results

The 51 isolated R. anatipestifer sequences were submitted to the MLST database, and 18 were identified as 3 existing ST types: ST4, ST17, and ST58. The remaining 33 were newly discovered (Table 3). Currently, 13 ST types have been identified: ST91, ST93, ST94, ST96, ST97, ST98, ST99, ST100, ST102, ST103, ST106, ST107, and ST109. ST4 had the highest proportion (31.37%), followed by ST96 (23.53%; Table 4). Using goeBURST to perform TLV clustering analysis on the ST types in the database (Fig. 1), the clustering results showed that 95 ST types in the database were divided into 6 clone complexes (CCs). Furthermore, three ST types (ST37, ST94, and ST107) were not successfully clustered and belonged to the monomers. The 13 newly discovered ST types belonged to 6 CCS, and 2 ST types were not clustered. ST98, ST99, ST100, and ST102 belong to CCS38; ST91 belongs to CCS51; ST93 belongs to CCS62; ST103 belongs to CCS20; ST96 and ST97 belong to CCS96; and ST106 and ST109 belong to CCS106. From the clustering analysis results, the overall clustering structure showed dispersion, indicating a remarkable degree of variation between the different ST types of R. anatipestifer. Some newly discovered ST types did not cluster with existing ST types, indicating a marked degree of variation between newly discovered and existing ST types. Genetic variation in R. anatipestifer may be gradually accelerating in recent years.

Table 3

MLST typing results
ID	dnaB	groEL	gyra	mdh	gluD	gpi	rpoB	ST type
R-1	2	1	1	1	1	2	7	4
R-2	3	29	6	4	29	6	17	91
R-3	6	6	15	26	8	15	18	96
R-4	24	1	18	54	2	24	6	93
R-5	2	1	1	1	1	2	7	4
R-6	6	6	15	26	8	15	18	96
R-7	6	6	15	26	8	16	18	97
R-8	2	1	1	1	1	2	7	4
R-9	2	1	1	1	1	24	7	98
R-10	2	1	1	1	1	2	7	4
R-11	3	29	6	4	29	6	17	91
R-12	2	1	1	1	1	2	7	4
R-13	3	29	6	4	29	6	17	91
R-14	2	1	1	1	1	2	7	4
R-15	2	1	1	1	1	2	7	4
R-16	6	6	15	26	8	15	18	96
R-17	24	1	18	54	2	24	6	93
R-18	2	1	1	54	1	2	7	99
R-19	2	1	1	1	1	2	7	4
R-20	2	1	1	1	1	2	7	4
R-21	1	2	4	3	29	1	11	94
R-22	2	1	1	1	1	2	7	4
R-23	6	6	15	26	8	15	18	96
R-25	6	6	15	26	8	15	18	96
R-26	2	1	1	1	1	2	7	4
R-27	6	6	15	26	8	15	18	96
R-28	6	6	15	26	8	15	18	96
R-29	2	1	1	1	1	2	7	4
R-30	2	1	1	1	1	2	23	100
R-31	6	6	15	26	8	15	18	96
R-32	6	6	15	26	8	15	18	96
R-33	6	6	15	26	8	15	18	96
R-34	2	1	1	54	1	2	7	99
R-35	2	1	1	1	1	2	7	4
R-36	6	6	15	26	8	15	18	96
R-37	6	6	15	26	8	15	18	96
R-38	2	1	1	1	1	2	23	100
R-39	2	1	1	54	1	2	7	99
R-40	6	6	15	26	8	16	18	97
R-41	24	1	2	54	2	4	6	17
R-42	2	1	1	26	9	2	7	102
NN71	2	1	5	1	1	2	8	58
NN74	2	1	1	1	1	2	7	4
NN118	3	2	4	3	54	8	25	103
NN133	2	1	1	1	1	2	7	4
NN137	3	2	4	3	54	8	25	103
NN151	2	1	1	1	1	2	7	4
NN159	7	8	18	25	54	17	18	106
NN162	8	29	17	26	29	18	25	107
NN187	3	2	4	3	54	8	25	103
NN210	1	8	20	25	54	17	18	109

Table 4

ST type classification statistics
ST type	Number of strains	Proportion
4	16	31.37%
17	1	1.96%
58	1	1.96%
91	3	5.88%
93	2	3.92%
94	1	1.96%
96	12	23.53%
97	2	3.92%
98	1	1.96%
99	3	5.88%
100	2	3.92%
102	1	1.96%
103	3	5.88%
106	1	1.96%
107	1	1.96%
109	1	1.96%

Pan-Genome Analysis and Typing Results

Fifty-one strains of R. anatipestifer genomes were used for pan-genome analysis, and a pan-genome set containing 5,094 genes was successfully constructed, including 1,116 core genes, 49 soft-core genes, 1,606 shell genes, and 2,323 cloud genes (Fig. 2). Based on the presence or absence of genes across all strains, the 5,094 genes within the pan-genome set were categorized into two distinct groups. The first group is the core genome set, consisting of 1,116 genes shared among all 51 strains. The second group is the non-core genome set, which encompasses 3,978 genes present in the pan-genome set but are not part of the common gene pool. The COG annotation results (Fig. 3) showed that of the 1,116 genes in the core genome, 1,113 were successfully annotated, 45 were not assigned to the COG category, and the number of effective genes accounted for 95.70% of the total genome set. Of the 3,978 genes in the non-core genome, 2,895 were successfully annotated, and 648 genes were not assigned to the COG category. The number of effective genes accounted for 56.49% of the total genome set. This indicates that there are still a large number of genes whose functions have not been studied or included in the non-core genes of R. anatipestifer. Statistical comparison of the top 10 functional gene classifications that account for a high proportion of each genome set shows that the core genome set has extensively different functions from the non-core genome set. Compared with the non-core genome set, the core genome set had a more uniform distribution of functions. This unique classification includes translation, ribosome structure-related classification, and energy production and conversion-related classification, which are closely related to the maintenance of normal life activities in bacteria. In contrast, the functional classification of the non-core genome set is relatively concentrated. This unique classification includes defense mechanism-related and transcription-related genes, which are strongly related to the unique characteristics of bacteria, indicating differences in the functions of core and non-core genes of bacteria. Most of the core genes are related to basic life activities, such as energy and gene replication; non-core genes also include genes related to defense and bacteria-specific traits.

The phylogenetic tree established based on the pan-genome set (Fig. 4) shows that the 51 strains of R. anatipestifer can be roughly divided into 8 branches; each branch has a similar functional gene distribution, and the same branch strains may have similar biological characteristics, such as virulence and drug resistance. According to the evolutionary tree of the core genome set (Fig. 5), the 51 strains of R. anatipestifer can be divided into 6 major branches. Upon conjoint analysis of the distribution of strains in the isolation area and the evolutionary tree, we found that strains in the same area had a cluster distribution in the evolutionary tree. Multiple strains in the same area can be located near the evolutionary tree to form a geographical subgroup, and strains in a region can be composed of multiple geographical subgroups. The results showed that the core gene evolution tree based on pan-genome analysis could classify the strains from the same source in a more detailed way, providing a reference for the strains to be divided into smaller geographical subgroups. In addition, strains from different regions can be distributed on the same branch of the evolutionary tree and have close phylogenetic relationships with bacterial evolution, indicating that R. anatipestifer may be prevalent across different regions.

Cross Protective Vaccine Protein Prediction

Target Gene Screening Results. Five target genes (Table 5) were screened from 1,116 core genomes using homeotic gene elimination, cell sublocation prediction and screening, protein molecular weight calculation and screening, protective antigen prediction and screening, and transmembrane structure prediction and screening. These genes are non-homeotic genes located outside the cell or on the outer membrane, with a molecular weight of more than 50 kDa, a protective antigen prediction score of more than 0.4, and four or fewer transmembrane structures; they have the potential to serve as good cross-protective vaccine proteins.

Table 5

Screening results of target genes
Gene	PSORTB prediction	Cell-PLoc prediction	Vaxijen prediction	molecular weight/KDa	Number of predicted transmembrane structures
oprM_1	outer membrane	outer membrane	0.4622	53.41	0
oprM_2	outer membrane	outer membrane	0.4900	50.76	4
group_248	outer membrane	extra cell	0.5361	145.84	0
group_980	outer membrane	outer membrane	0.5023	47.61	4
group_1716	outer membrane	extra cell	0.6560	55.20	0

Protein Structure Prediction Results. Among the five proteins examined, four demonstrated compatibility and high ratings as templates in the SWISS-MODEL for the modeling process. However, group_1716 did not find a suitable protein structure template in the SWISS-MODEL and was successfully modeled using I-TASSER instead. The modeling results, as depicted in Fig. 6, revealed that three out of the five proteins exist as trimers, whereas the remaining two proteins exist as monomers. In terms of morphology, three proteins exhibited a cylindrical shape, while the other two displayed a chain-like or irregularly curled structure.

Protein Epitope Prediction Results

Prediction Results of Linear Epitopes in B Cells. The statistical analysis revealed the prediction of 6 − 21 linear B cell epitopes for the five proteins, as outlined in Table 6. Among them, oprM_2 had the lowest number of predicted epitopes, with only 6 identified, whereas group_248 had the highest number, with 21 predicted epitopes. The predicted epitope peptide segments displayed a distribution of amino acid numbers ranging from 4 to 91, with an average size of 27 amino acids. Most segments (36 out of 51) fell within the range of 15–50 amino acids. Peptide segments with fewer than 10 amino acids, deemed too small, were discarded, resulting in 43 effective peptide segments, accounting for 84.31% of the predictions. Several examples of epitope prediction models can be observed in Fig. 7.

Table 6

Prediction results of linear epitopes of B cells
Gene		Starting point of peptide segment		Peptide endpoint		Number of amino acids		Predicted score		Total
oprM_1		102		147		46		0.795		8
		205		236		32		0.795
		330		365		36		0.747
		418		441		24		0.72
		265		298		34		0.695
		49		70		22		0.691
		153		156		4		0.564
		322		326		5		0.524
oprM_2		167		198		32		0.804		6
		67		111		45		0.778
		290		331		42		0.756
		227		258		32		0.704
		24		35		12		0.698
		377		407		31		0.685
group_248		822		841		20		0.864		21
		1143		1189		47		0.85
		228		247		20		0.82
		1233		1248		16		0.815
		974		988		15		0.797
		1044		1122		79		0.775
		481		505		25		0.773
		736		774		39		0.739
		336		426		91		0.737
		290		316		27		0.729
		1206		1214		9		0.728
		1273		1284		12		0.721
		180		204		25		0.715
		707		727		21		0.696
		432		476		45		0.682
		260		271		12		0.641
		858		866		9		0.618
		926		947		22		0.61
		507		521		15		0.597
		1004		1025		22		0.549
		952		959		8		0.529
group_980	254		298		45		0.816		5
	141		172		32		0.767
	61		91		31		0.733
	201		230		30		0.692
	339		374		36		0.686
group_1716	190		226		37		0.821		11
	431		480		50		0.778
	405		428		24		0.738
	366		390		25		0.701
	1		47		47		0.655
	50		61		12		0.622
	94		108		15		0.616
	113		125		13		0.604
	179		184		6		0.55
	166		171		6		0.531
	157		160		4		0.505

Prediction Results of B Cell Conformational Epitopes. The predicted conformational epitopes of B cells are shown in Table 7. Five proteins had predicted conformational epitopes ranging from 3 to 10, among which group_1716 had the least three conformational epitopes, whereas oprM_2 had 10 conformational epitopes. Compared with linear epitopes, there was a marked difference in the number of amino acids among the different conformational epitopes. The predicted 35 conformational epitopes had amino acid numbers ranging from 3 to 288, of which 13 were below 10 amino acids (37.14%), nine were above 100 amino acids (25.71%), and 16 were between 10 and 100 amino acids (37.14%). Several examples of epitope prediction models can be observed in Fig. 8.

Table 7

Prediction results of B cell conformational epitopes
Gene	Number of amino acids	Predicted score	Total
oprM_1	250	0.78	9
	178	0.754
	56	0.704
	58	0.695
	58	0.691
	3	0.639
	3	0.628
	3	0.51
	3	0.502
oprM_2	6	0.963	10
	3	.0.908
	242	0.78
	201	0.74
	43	0.721
	41	0.714
	41	0.707
	3	0.585
	3	0.575
	11	0.513
group_248	15	0.797	8
	200	0.756
	81	0.754
	261	0.72
	7	0.66
	3	0.638
	13	0.537
	4	0.518
group_980	234	0.772	5
	288	0.727
	17	0.664
	5	0.626
	5	0.621
group_1716	43	0.783	3
	121	0.749
	97	0.637

R. anatipestifer is a major bacterial pathogen that causes remarkable economic losses to the global duck farming industry. At present, serum and molecular typing systems for R. anatipestifer are incomplete, and limited research has been conducted on this particular topic. Currently, no national standard serum or strain of R. anatipestifer exists. Serotype identification mainly depends on the serum circulated among researchers or the serum produced by animals immunized with strains identified by others. It is difficult to avoid the cross-contamination of strains during practical operation; therefore, cross-reactions often occur. In this study, various agglutinated sera were generated through the immunization of rabbits. Subsequently, a process of serum grouping for R. anatipestifer was conducted, enabling rapid self-typing of strains. Additionally, a local serotype bank exclusive to the laboratory was established. Comparisons were made between the typing results obtained using the traditional serotyping method and the newly developed self-built serotype. Remarkably, the self-built serotype demonstrated consistency with the identification outcomes of expert peers and scholars. This finding not only highlights the convenience and expedience of the self-built serotyping approach but also emphasizes the strong consensus and unity among researchers. Overall, the self-made serotyping method presented in this study proves to be a rapid and accurate means of serotype determination.

Molecular typing methods such as MLST and CGMLST have emerged [19]. Liu et al. inferred, through MLST analysis, that Guangdong Province could be the geographical origin of R. anatipestifer, indicating that MLST analysis can also be used for strain tracing, providing a reference for epidemiological investigations [20]. MLST represents a powerful tool to investigate the epidemiology of most microorganisms [21]. In addition, it allows determination of transmission routes and identifying sources of infection [22]. This was clearly displayed by this newly developed MLST. Core genes, as commonly shared genes across species, play a vital role in regulating the normal metabolism and heredity of organisms [23]. In contrast, non-core genes, including pan-genome genes, refer to those present in certain strains or a subset of strains. These non-core genes typically exhibit a close association with the biological characteristics of the respective strains, complementing the functions governed by core genes. The core genes of strains serve as a valuable reference for studying the metabolism and various life processes within a particular genus of strains [24]. They provide insights into the fundamental biological mechanisms shared by these strains. In contrast, the non-core genes of strains offer a reference point for investigating drug resistance, virulence, serotype, and other specific characteristics. These non-core genes contribute to understanding the diverse traits and adaptations exhibited by different strains within the genus. In this study, using pan-genome analysis, an evolutionary tree was established based on the core genome, and 51 strains of R. anatipestifer were preliminarily classified. It was found that strains from the same region can form different geographical subpopulations, and strains from different regions also exist in the same branch of the evolutionary tree, indicating that the established R. anatipestifer evolutionary tree can provide a reference for the reclassification of strains from the same region, Moreover, this indicates that R. anatipestifer from different regions may spread across regions. Zhu et al. compared the topological structure and classification of the commonly used 16S evolutionary tree and the core genome evolution tree and found that the core gene tree was considerably stronger than the 16S tree in terms of phylogenetic resolution [25].

By comparing the relationships between different typing methods and serotypes, we found that the corresponding relationship between the molecular typing results of the strains and the serotype results was poor. Multiple serotypes of R. anatipestife could be mixed in the same branch of the molecular typing tree, which may be because serotype classification was based on the serotype antigen-determining protein on the surface of R. anatipestife, whereas the core gene and non-core gene evolution trees are constructed based on the core genome and non-core genomes, respectively. In contrast, the MLST evolution tree relies on seven housekeeping genes. It is important to note that the genes determining the classification results in different trees are located in different genome regions and belong to distinct functional gene categories. As a result, there may be a lack of correspondence between the classification outcomes derived from these different trees.

However, it is worth highlighting that different classification trees serve different purposes. For instance, serotyping plays a crucial role in guiding vaccine production [26]. Despite the lack of direct correspondence between the classification results, each tree has its own specific utility and contributes valuable insights within its respective field of application. The core genes and MLST typing are used for strain evolution analysis, and different classification methods can be selected based on different classification purposes. The field of bacterial typing and evolutionary analysis has undergone significant advancements with the continuous progress of molecular biology. From relying on biochemical and agglutination characteristics to the utilization of 16S-based bacterial evolutionary analysis, the current approaches have further evolved to encompass average nucleotide similarity [27], Average Nucleotide Identity core gene evolution analysis based on pan-genome, MLST housekeeping genes, and various PCR serotyping techniques. These methods have significantly enhanced our understanding and recognition of bacterial typing. As a result of these advancements, bacteria can now be categorized into more refined and smaller groups, leading to a continuous improvement in our understanding of bacterial classification. It is expected that with ongoing research and development, our knowledge of bacterial typing and classification will continue to improve and expand in the future.

Reverse vaccinology technology based on genome analysis has gradually become an important tool for vaccine protein prediction and analysis. Through genomic or pan-genomic analyses, the overall characteristics and individual differences in a bacterial population can be determined and applied to actual production [28]. This study screened 5 target genes from 1,116 core genomes that could potentially become good cross-protective vaccine proteins.

In terms of composition, it was observed that out of the five proteins, three exist as trimers, whereas the remaining two exist as monomers. From a morphological standpoint, three of these proteins exhibit a cylindrical structure, whereas the other two are either in a chain-like arrangement or have an irregularly curled shape. Furthermore, the analysis predicts that each of the five proteins contains a varying number of epitopes. Specifically, they are anticipated to have 6–21 linear epitopes and 3–10 conformational epitopes, which are recognized by B cells. The protein screened using the pan-genome core gene in this experiment can be used to study vaccines against multiple serotypes. At present, this method has been gradually widely studied and applied in animal immunity. Fatima Shahid et al. designed the chimeric vaccine of Acinetobacter baumannii using pan-genome and reverse vaccinology methods [29]. Furthermore, Mahreen Nawaz et al. designed the staphylococcal vaccine using pan-genome, proteomics, and immunoinformatics [30]. Yamini Chand et al. designed a subunit vaccine [31] for multidrug-resistant Salmonella typhimurium through proteomics and immunoinformatics. These pan-genome and bioinformatics analyses of proteins can be optimized through molecular dynamics simulation, codon optimization, computer immune simulation evaluation, and other methods to obtain a final reliable candidate vaccine.

Availability of data and materials

The data supporting the findings are included in the manuscript.

Acknowledgments

Not applicable.

Funding

This work was supported by Key Research and Development Program of Shandong Province [grant numbers 2022CXGC010606]; the Natural Science Foundation of Shandong Province [grant numbers ZR2023QC082]; Open Project of Liaocheng University Animal Husbandry Discipline [grant numbers 319312101-01]; The Natural Science Foundation of Shandong Province [grant numbers ZR2020MC175]; and the Natural Science Foundation of Shandong Province [grant numbers ZR2022MC107].

Author information

Yanlan Li and Xihui Zhu contributed equally to this work and share the first authorship

Authors and Affiliations

Phage Research Center, Liaocheng University, No. 1 Hunan Road, 252000, Liaocheng, Shandong, China

Yanlan Li,Xihui Zhu, Zhiwei Wang, Zheyu Pang, Zhenshu Si, Cheng Liu, Jianbiao Lu , Shengliang Cao, Qinghai Ren, Xusheng Du, Duanduan Chen, Xiaoting Li, Lanying Pei, and Yubao Li

School of Agricultural Science and Engineering, Liaocheng University, No. 1 Hunan Road, 252000, Liaocheng, Shandong, China

Yanlan Li,Xihui Zhu, Zhiwei Wang, Zheyu Pang, Zhenshu Si, Cheng Liu, Jianbiao Lu , Shengliang Cao, Qinghai Ren, Xusheng Du, Duanduan Chen, Xiaoting Li, Lanying Pei, and Yubao Li

Contributions

Y.L., X.Z., and Y.L. conducted the research and interpreted the results. Z.W., Z.P., Z.S., C.L., J.L., and S.C. participated in data collection. Q.R., X.D., D.C., X.L., and L.P., contributed to data analysis and helped draft the manu script. All authors read and approved the fnal manuscript.

Corresponding author

Correspondence to Yubao Li.

Ethics Approval and Consent to Participate

The sample was collected and handled in accordance with the good animal practices required by the Animal Ethics Procedures and Guidelines of the People’s Republic of China. All the animal protocols and procedures were performed according to the Chinese Regulations of Laboratory Animals and were approved by the Animal Ethics Committee of Liaocheng University.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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Serotyping, molecular typing, and vaccine protein screening for Riemerella anatipestifer: overcoming challenges in prevention and treatment

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Abstract

Background

Results

Conclusions

Figures

INTRODUCTION

MATERIALS AND METHODS

Source of R. anatipestifer

Typing Methods

Cross Protective Vaccine Protein Screening

RESULTS

Positive Serum Preparation and Serum Typing Results

MLST Typing and Cluster Analysis Results

Pan-Genome Analysis and Typing Results

Cross Protective Vaccine Protein Prediction

Protein Epitope Prediction Results

DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

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