Hypothetical Proteins of Mycoplasma synoviae Reannotation and Expression Changes Identified via RNA-Sequencing
College of Animal Science and Technology, Clinical Veterinary Laboratory, Ningxia University, Yinchuan 750021, China
*
Authors to whom correspondence should be addressed.
†
Current address: Ningxia Xiaoming Agriculture and Animal Husbandry Co., Ltd., Yinchuan 750011, China.
Microorganisms 2023, 11(11), 2716; https://doi.org/10.3390/microorganisms11112716
Submission received: 20 September 2023
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Revised: 25 October 2023
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Accepted: 1 November 2023
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Published: 6 November 2023
(This article belongs to the Special Issue Avian Pathogens 2.0)
Abstract
:Mycoplasma synoviae infection rates in chickens are increasing worldwide. Genomic studies have considerably improved our understanding of M. synoviae biology and virulence. However, approximately 20% of the predicted proteins have unknown functions. In particular, the M. synoviae ATCC 25204 genome has 663 encoding DNA sequences, among which 155 are considered encoding hypothetical proteins (HPs). Several of these genes may encode unknown virulence factors. This study aims to reannotate all 155 proteins in M. synoviae ATCC 25204 to predict new potential virulence factors using currently available databases and bioinformatics tools. Finally, 125 proteins were reannotated, including enzymes (39%), lipoproteins (10%), DNA-binding proteins (6%), phase-variable hemagglutinin (19%), and other protein types (26%). Among 155 proteins, 28 proteins associated with virulence were detected, five of which were reannotated. Furthermore, HP expression was compared before and after the M. synoviae infection of cells to identify potential virulence-related proteins. The expression of 14 HP genes was upregulated, including that of five virulence-related genes. Our study improved the functional annotation of M. synoviae ATCC 25204 from 76% to 95% and enabled the discovery of potential virulence factors in the genome. Moreover, 14 proteins that may be involved in M. synoviae infection were identified, providing candidate proteins and facilitating the exploration of the infection mechanism of M. synoviae.
1. Introduction
Mycoplasma synoviae is a bacterium within the Mollicutes class that lacks a cell wall and causes respiratory damage, infectious synovitis, and arthritis in chickens. M. synoviae is a devastating pathogen of chickens that costs the poultry industry billions of dollars each year [1].
Mycoplasma synoviae infection in chickens is susceptible to secondary infection by other pathogenic microorganisms, leading to the manifestation of atypical clinical symptoms during the initial stages. At present, the understanding of the pathogenesis of M. synoviae in chickens remains incomplete, encompassing both host and bacterial factors as key contributors to its pathogenicity. The primary determinants of its host are the innate and adaptive immune responses, such as the interaction between M. synoviae and chicken synovial sheath cells (SSCs), which contribute to the inflammatory response through the upregulation of cytokines and the attraction of macrophages [2]. Secondary host factors are somatic changes that affect the execution of normal cell functions, such as CCH deformation, which increased cytokine gene expression, and extensive metabolic and sensitivity changes in cells when exposed to M. synoviae [3]. Bacterial virulence factors play a crucial role in various processes, such as the adhesion to host cells, maintenance of homeostasis, invasion of host cells, and regulation of the immune response. These factors significantly impact the colonization, immunogenicity, and transmissibility of pathogens within the host [4]. The known virulence factors of M. synoviae in chickens are adhesins, proteases, and membrane transporters, which regulate several biological processes, such as cell adhesion, overall metabolism, and host–pathogen interactions [4,5,6]. Highly variable virulence factors of M. synoviae contribute to immune escape [7]. In addition to these virulence factors, uncharacterized proteins have the potential to perform virulence-related functions.
The M. synoviae genome is 846,495 bp in length, with a G + C content of 28.3%, on a mol basis, and 34.2% estimated by the buoyant density [8]. The genome encodes 673 proteins, 10 of which are repeated and 155 annotated as hypothetical proteins (HPs). Many proteins are involved in M. synoviae infection. Dihydrolipoamide dehydrogenase, NADH oxidase, and the pyruvate dehydrogenase complex (PDC) E1 alpha and beta subunits of M. synoviae are all immunogenic, may bind the fibronectin/plasminogen protein, and are involved in the host adhesin process [9,10]. Oligopeptide (Opp) permease, recognized for its participation in humoral immune responses, can be researched as a potential candidate antigen [11]. NADH oxidase participates in M. synoviae adhesion to host cells can be studied as a diagnostic antigen and a potential protective vaccine candidate [6]. However, the functions of the HPs are unknown. We hypothesize that the reannotation of unannotated coding sequences (CDSs) in the M. synoviae genome may lead to the discovery of novel structures and functions and provide new ideas and insights into protein-interaction network pathways. Furthermore, such novel structures and functions may become new targets for drug design.
Therefore, this study analyzes the 155 unknown HPs for function predictions using bioinformatics web tools, including BLAST, INTERPROSCAN, PFAM, and COGs. The accuracy of these tools is verified by the receiver operating characteristic (ROC) curve analysis. The expression of HPs is identified by RNA-Seq. Among those HPS, 28 are predicted as virulence proteins, including 19 virulence proteins, 125 are reannotated, and 9 virulence proteins are not reannotated. Fourteen HP genes are significantly upregulated, nine of which are reannotated, and five are determined as putative virulence genes. Overall, an improved understanding of the biology and pathogenicity of M. synoviae is achieved as a result of these newly discovered virulence factors. The upregulated genes may help in developing strategies to control M. synoviae infection.
2. Materials and Methods
2.1. HPs’ Amino Acid Sequence Retrieval
The sequences of M. synoviae HPs were downloaded from the NCBI database (https://www.ncbi.nlm.nih.gov/nuccore/NZ_CP011096.1, accessed on 13 July 2021) and 155 identified HPs were analyzed.
2.2. HPs’ Physicochemical Properties and Subcellular Localization Predictions
Physicochemical properties and subcellular localization predictions were achieved using a variety of tools summarized in Table 1.
2.3. Function Prediction of HPs
The tools used for predicting the HPs’ functions are listed in Table 2.
2.4. Accuracy Assessment of Tools
Fifty known proteins randomly selected from the NCBI protein library were annotated using each tool (Supplementary Table S1). The results of applying each tool to each protein were assigned, as previously described [33], and the accuracy of each tool was evaluated using the ROC (http://www.rad.jhmi.edu/jeng/javarad/roc/JROCFITi.html, accessed on 2 March 2023).
2.5. Prediction of HPs’ Association to Virulence
The virulence association of HPs was characterized using the Virulent Pred server. The virulence factor database (VFDB) [34] was analyzed to determine the presence of any potential orthologs for the predicted virulence factors. BTX Pred [35] and DBETH [36] were used to analyze the cytotoxic potential of HPs, and CARD software (Version 3.2.5) [37] was used to analyze their association to antibiotic resistance.
2.6. Prediction of HPs’ Antigenicity
An antigenicity analysis was performed based on the location of HPs predicted to reside in cytoplasmic membranes and/or extracellular milieu. An antigenicity prediction tool was used to assess the epitopes of the HPs (http://imed.med.ucm.es/Tools/antigenic.pl, Accessed on 2 November 2022). The epitopes were retrieved and their lengths were measured to determine the epitope coverage. Antigenic HPs were subsequently identified based on the corresponding epitope predictions.
2.7. Changes in HP Expression Values upon Exposure to Chicken Cells
2.7.1. The Source of the RNA-Seq Data
The data used for the HP expression analysis were derived from the prokaryotic transcription data obtained from our laboratory. MS-Host1, MS-Host2, and MS-Host3 samples were M. synoviae exposed to a mixture of chicken macrophages (HD-11) and chicken embryo fibroblasts (DF-1); MS-1, MS-2, and MS-3 were M. synoviae cultured in vitro (Supplementary Table S2).
2.7.2. RNA-Seq Analyses of HPs
An RNA-Seq approach was used to explore the changes in M. synoviae HP expression after the exposure of DF-1 and HD-11 cells to M. synoviae. Differentially expressed HP genes (DEHPGs) were analyzed using Omicshare (https://www.omicshare.com/tools/Home/Soft/diffanalysis, accessed on 9 March 2023). Gene Ontology (GO) enrichment analyses were also conducted using Omicshare (https://www.omicshare.com/tools/Home/Soft/gogseasenior, accessed on 10 March 2023), with p < 0.05 corresponding to a significant GO term enrichment. The KEGG database (http://www.kegg.jp/show_organism?menu_type=pathway_map&org=mbi, accessed on 10 March 2023) was used to analyze the pathways for which DEHPGs were enriched.
3. Results
3.1. Hypothetical Proteins Encoded by the M. synoviae ATCC-25204 Genome
The M. synoviae ATCC 25204 genome consists of 720 CDSs and 673 genes, among which 155 encode HPs. However, only 8.39% of all HPs (13 out of 155) have been detected in membrane-associated lipoprotein proteomic studies [38].
3.2. Physicochemical Properties and Subcellular Localization of M. synoviae ATCC-25204 HPs
The physicochemical properties of all the HPs were predicted (Supplementary Table S3). These HPs ranged from 45–1575 amino acid residues in size and had molecular weights ranging from 5.2–184.2 kDa. The isoelectric points of the HPs ranged from 4.28–11.62 and 66% of these proteins had an isoelectric point above pH 7.0. The grand average hydropathicity index of HPs ranged from −1.077 to 1.067, with 85% of them having a negative index. The extinction coefficients of the HPs ranged from 1490–426,540. Of the HPs, 86% were predicted to be stable according to the instability index. Furthermore, the predicted subcellular localization of 155 HPs (Supplementary Table S4) resulted in 67 assigned to the cytoplasm (~43.23%) and 37 assigned to the cytoplasmic membrane (~23.87%), and 52 were putative extracellular proteins (~33.5%) (Figure 1). Nineteen and 33 extracellular HPs were considered to be secreted by classical and non-classical pathways, respectively.
3.3. Putative Function of M. synoviae ATCC 25204 HPs
Hypothetical proteins were functionally annotated using the GO categorization, identifying homologs, and predicting functional domains and partners. A total of nine and five proteins were assigned to the subcategories ‘activity’ and ‘binding’, respectively, among the HPs categorized under molecular function (MF). DNA binding was the most represented subcategory in the MF category (3 out of 13 proteins), followed by ‘N-methyltransferase activity’ (2 out of 13 proteins). The biological process (BP) subcategories were assigned to three HPs, and two HPs were related to DNA methylation (Supplementary Table S5a). Eleven HPs had hits for functional homologs predicted by the three used tools (Supplementary Table S5b). After the functional homologs were identified, functional domains and partners were predicted for HPs (Supplementary Table S5c). Finally, putative functional partners were predicted for the 155 HPs (Supplementary Table S5d). Altogether, a function was assigned to 125 out of 155 HPs (80.65%, Supplementary Table S6). The most represented putative functional groups were enzymes, lipoproteins, DNA-binding proteins, and phase-variable hemagglutinins (Figure 2). Forty-nine proteins were predicted to have an enzymatic function; most of them were thought to be functional enzymes, accounting for 20% of the PPFs. These enzymes belonged to the five major enzyme groups of transferases, synthetase, hydrolases, isomerases, and oxidoreductases. Among them, transferase accounted for the highest proportion, followed by synthetase. We also found that two enzymes, recombinase and permease, were not listed among the seven enzymes (Table 3). Moreover, 24 proteins were presumed to be hemagglutinin (Table 4). Thirteen proteins were presumed to be lipoproteins (Table 5). Seven proteins were presumed to have DNA-binding proteins (Table 6). Thirty-two proteins were listed in other functional categories, three proteins were predicted to be aromatic cluster surface proteins, two were thought to be lipocalins, and an elongation factor EF-Ts was annotated this time (Table 7). Whether the elongation factor EF-Ts was virulent was also revealed by a subsequent analysis. These HPs were considered proteins with putative functions (PPFs). Our results improve the annotation status of the M. synoviae ATCC 25204 genome from 76% to 95%.
3.4. Accuracy Assessment of Functional Prediction Tools
The mean accuracy value of the tools used for this study was 0.95 (Supplementary Table S7), indicating that the prediction results obtained for M. synoviae ATCC-25204 HPs were reliable.
3.5. Prediction of HP Genes’ Association to the Virulence of M. synoviae
Based on VirulentPred, a total of 149 proteins among the 155 (96.13%) comprising the entire set of M. synoviae ATCC 25204 HPs (Supplementary Table S8) were considered as putative virulence factors. In silico predictions of putative associations with virulence identified at least 28 novel potential virulence-related proteins, combined with the VFDB. These 28 putative virulence factors included 11 enzymes (VY93_RS00915, VY93_RS00970, VY93_RS02560, VY93_RS01570, VY93_RS00275, VY93_RS03120, VY93_RS01815, VY93_RS00470, VY93_RS00400, VY93_RS00850, and VY93_RS02300), three lipoproteins (VY93_RS01720, VY93_RS00960, and VY93_RS01825), one phase-variable hemagglutinin (VY93_RS01280), one replication initiation and membrane attachment protein (VY93_RS02865), one aromatic cluster surface protein (VY93_RS00705), one SMC-like protein (VY93_RS02250), one PotD/PotF family extracellular solute-binding protein (VY93_RS02790), and ten proteins with unknown functions (VY93_RS01950, VY93_RS02530, VY93_RS03220, VY93_RS01855, VY93_RS02530, VY93_RS02755, VY93_RS00345, VY93_RS01755, VY93_RS00115, and VY93_RS02740). Additionally, no potential virulence factor was assessed as a cytotoxic protein. Overall, the high number of predicted putative virulence factors may have reflected their relevance to M. synoviae homeostasis.
3.6. Prediction of Antigenicity
Following the antigenicity analyses of the HPs, a set of 89 HPs predicted to be localized in the cytoplasmic membrane and/or produced as extracellular proteins was used to perform an epitope prediction. All the analyzed HPs presented at least one predicted epitope, with the epitope numbers ranging from 1–53 (Supplementary Table S9). Moreover, these predicted epitopes covered 20.33–84.05% of the protein length. The potential virulence factors presented at least three predicted epitopes, with the epitope numbers ranging from 39–552 and epitope cover from 25.19–56.10%.
3.7. Identification of Differentially Expressed HP Genes
Changes in the HP transcriptome were evident following the infection of chicken cells (Supplementary Table S10). Furthermore, the transcriptome analysis revealed that 45 out of 83 genes were upregulated (Figure 3).
3.8. GO and Pathway Enrichment Analyses
Initially, 51 of the 155 HP genes (32.90%) were categorized according to GO terms into ‘cellular component’. Furthermore, among all the HP genes, the expressions of 10 were upregulated, and 139 were categorized into the ‘biological process’ group (Supplementary Table S11).
Most DEGs associated with these GO categories were downregulated, and a significant enrichment of the nucleotide-binding pathway was observed. Moreover, the top 25 GO categories that were enriched with upregulated DEGs (upregulated DEGs/total DEGs enriched in a GO category) included developmental processes, sulfur amino acid transmembrane transporter activity, and tRNA binding. Most latent virulence factors and pathogenic effector genes were enriched in the cellular component category (Figure 4). Proteins VY93_RS02865, VY93_RS01280, VY93_RS00400, VY93_RS01815, and VY93_RS00275 among the 28 putative virulence-factor genes were enriched in 19, 18, 12, 7, and 3 GO categories, respectively.
The KEGG pathway analysis of the identified the HPs revealed their enrichment in 99 pathways (Supplementary Table S12). The ribosome pathway was the most enriched (51 genes), followed by the ABC transporters (32 genes) and aminoacyl-tRNA biosynthesis (24 genes) pathways. Other highly enriched pathways included glycolysis/gluconeogenesis, quorum-sensing, oxidative phosphorylation, homologous recombination, photosynthesis, pyrimidine metabolism, DNA replication, mismatch repair, pentose phosphate pathway, urine metabolism, and protein export pathways, which contained 18, 17, 14, 14, 13, 12, 12, 11, 10, 10, and 10 genes, respectively. A total of 28 putative virulence-factor genes were subjected to the pathway enrichment analysis, as a result of which VY93_RS00275 and VY93_RS00400 were associated with mismatch repair, DNA replication, and homologous recombination pathways (Figure 5).
4. Discussion
In this study, we used previously predicted in silico approaches to reannotate M. synoviae ATCC-25204 HPs and to identify potential virulence-related proteins [39].
The physicochemical properties of these HPs indicated that they had high heterogenicity results, as they varied over wide ranges concerning the factors of length, molecular weight, theoretical isoelectric point, grand average of hydropathicity, and extinction coefficient. These HPs seemingly encoded different functional products, given their divergent intrinsic sequence properties. These characteristics are likely to have contributed to their distinct functions [40]. Most of these proteins were predicted to be stable in vitro, a finding that indicated their potential for heterologous protein expression, which enabled functional and/or immunological studies in vitro [41].
The subcellular localization predictions indicated that M. synoviae HPs would be found in the cytoplasm, cytoplasmic membrane, or extracellular fractions. Some HPs predicted to be both cytoplasmic and extracellular proteins were found in the surface fraction in our previous proteomic study [38]. Therefore, some HPs were also located in multiple compartments and/or served moonlight functions [42]. Furthermore, approximately 63.46% of the extracellular HPs were secreted by non-classical pathways, suggesting that they may have been secreted via membrane vesicles [43]. Overall, >57% of the HPs were predicted as surface or extracellular fractions, indicating that they may have been relevant to M. synoviae pathogenesis and were potentially involved in host–pathogen processes.
The GO classification, functional homolog search, and the predictions of functional domains and partners were combined based on the selected functional domains to assign M. synoviae HPs’ putative functions. Thus, different prediction strategies with overlapping assigned functions to HPs supported their functional annotations. Moreover, each strategy identified the functions unassigned by other prediction methods. Therefore, the M. synoviae ATCC 25204 genomic annotation was improved by these analyses, and many diverse functional groups were found to contain enzymes (38.58% of PPFs), lipoproteins (10.24% of PPFs), DNA-binding proteins (5.51% of PPFs), and phase-variable hemagglutinin (19.69% of PPFs). The assignment of the putative functional roles for HPs provides new insights into M. synoviae biology, including the homeostasis and pathogenesis of bacteria.
We speculated that these proteins had an association with several relevant functions in M. synoviae, varying from various growth stress responses and virulence mechanisms to bacterial survival when exposed to the host [44]. Additionally, several proteins were identified as putative proteases. Thus, for example, these enzymes played important roles in the adhesion of mycoplasmas to the host and its immunomodulatory effects [45]. Phenylalanyl-tRNA synthetase and NADH-ubiquinone oxidoreductase were related to bacterial growth or responses to various growth conditions [46,47]. Cell division and virulence were facilitated by intracellular septation proteins [48]. All these proteins played an important role in cell envelope biogenesis, maintaining the integrity of the cell envelope and ensuring membrane homeostasis [49,50,51,52]. In addition to the putative functions associated with bacterial growth and homeostasis, the putative functions related to M. synoviae virulence were also predicted. A case in point, lipoproteins are known to play an important role in infection and do not cause clinical symptoms. Additionally, the immune system uses them to promote inflammation and leukocyte recruitment to infected tissues [53]. Hemagglutinins, which mediate the adhesion of mycoplasma to cells, are encoded by the M. synoviae vlhA gene, which belongs to a reservoir of pseudogene alleles. Variants of vlhA are expressed from the same unique vlhA promoter by recruiting pseudogene sequences via site-specific recombination events to generate antigenic variability [54]. Each expresses a different allele of vlhA as an antigen-diversifying mechanism to evade the adaptive immune system of the host [55]. Among them, 25 hemagglutinins were identified in this study, 1 of which was detected in an upregulated state and was believed to participate in the adhesion to and invasion of host cells by M. synoviae.
Moreover, these putative proteins had potential antigenic properties mainly associated with the regulation of host immune responses as potential mechanisms of antigenic variation [56]. This study found that there were many PPFs generally associated with M. synoviae virulence, although M. synoviae survival depended on certain putative functions.
In the entire HP transcriptome, VY93_RS02530, VY93_RS03220, VY93_RS00345, and VY93_RS02935 were differentially expressed when infecting host cells and were predicted as bacterial virulence factors, although they were not annotated. Putative aromatic cluster surface proteins (VY93_RS00715 and VY93_RS00710), which played a role in protein–protein interactions [57], were also significantly upregulated. Furthermore, RNA polymerase, an essential enzyme for bacterial viability, was also found to be upregulated in this study. Finally, intracellular septation proteins were also upregulated, indicating that these proteins may have participated in M. synoviae proliferation [58]. Thus, all upregulated proteins may be involved in the invasion and pathogenicity of M. synoviae. The specific functions of these proteins during M. synoviae infection and their effect on the host require verification through further experimentation.
5. Conclusions
In this study, the M. synoviae ATCC-25204 genome was reannotated and an increase in protein-coding CDSs was observed. A total of 125 proteins were functionally annotated and were predicted to be virulence factors. Among the 155 HP genes, 14 were upregulated upon exposure to host cells, including several genes that promoted M. synoviae division and growth. Furthermore, five of the 28 potential virulence factors were also upregulated, and these proteins were predicted to play a crucial role in M. synoviae infection. However, the elucidation of the actual roles of the novel M. synoviae potential virulence factors requires further experimental work. Overall, our findings contribute to developing new strategies for treating, preventing, and controlling infections caused by M. synoviae.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms11112716/s1, Supplementary Table S1: Function annotation of 50 M. synoviae characterized proteins using BLAST2GO, HMMER, FASTA, Pfam, SUPERFAMILY, CATH, CDASRT, SMART, SBASE, INTERPRO and GPCRpred.; Supplementary Table S2: Prokaryotic transcriptome annotation results; Supplementary Table S3: Physicochemical properties of M. synoviae ATCC 25204 HPs; Supplementary Table S4: Subcellular localization and secretion pathways predicted to HPs of M. synoviae ATCC 25204; Supplementary Table S5: Fuction predicted result via different tools; Supplementary Table S6: Putative functions assigned to HPs; Supplementary Table S7: Functional prediction methods and tools accuracy determination.; Supplementary Table S8: Prediction of putative virulence factors among M. synoviae ATCC 25204 HPs; Supplementary Table S9: Prediction of antigens of M. synoviae ATCC 25204 HPs; Supplementary Table S10: Datas for DEGs analysis; Supplementary Table S11: Datas for GO analysis; Supplementary Table S12: Datas for KEGG analysis.
Author Contributions
Conceptualization, S.H. and J.L.; methodology, D.S. and J.S.; software, D.S. and F.Y.; formal analysis, D.S. and X.T.; resources, S.H., J.L. and D.S.; writing—original draft preparation, D.S.; writing—review and editing, S.H. and J.L.; visualization, D.S.; supervision, L.G., S.H. and J.L.; project administration, L.G. and J.L. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Ningxia University’s production–education integration postgraduate joint training demonstration base construction project and the Ningxia Hui Autonomous Region Science and Technology Innovation Team Building Project (funding number: 2022BSB03107) and ‘Integrated application and demonstration of layer biosecurity and green and healthy breeding technology’, funding number [2022XQ009].
Data Availability Statement
The data are available in the article or its Supplementary Materials.
Acknowledgments
We would like to thank OmicShare for the prokaryotic transcriptome sequencing. This work was supported by the Department of Science and Technology of Ningxia Hui Autonomous Region and Ningxia Xiaoming Agriculture and Animal Husbandry Co., Ltd.
Conflicts of Interest
Author Lei Guo has received research grants from Ningxia Xiaoming Agriculture and Animal Husbandry Co., Ltd.
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Figure 1.
Prediction of subcellular localization of M. synoviae ATCC 25204 hypothetical proteins. Percentages of proteins assigned to a given subcellular localization (cytoplasm, cytoplasmic membrane, or extracellular) are expressed relative to the total number of analyzed proteins.
Figure 1.
Prediction of subcellular localization of M. synoviae ATCC 25204 hypothetical proteins. Percentages of proteins assigned to a given subcellular localization (cytoplasm, cytoplasmic membrane, or extracellular) are expressed relative to the total number of analyzed proteins.
Figure 2.
Distribution of the main functional groups assigned to M. synoviae ATCC-25204 hypothetical proteins. Percentages of PPFs assigned to ‘enzymes’, ‘phase-variable hemagglutinins’, ‘lipoproteins’, and ‘DNA-binding proteins’; functional groups are indicated. Some proteins were assigned to more than one functional group.
Figure 2.
Distribution of the main functional groups assigned to M. synoviae ATCC-25204 hypothetical proteins. Percentages of PPFs assigned to ‘enzymes’, ‘phase-variable hemagglutinins’, ‘lipoproteins’, and ‘DNA-binding proteins’; functional groups are indicated. Some proteins were assigned to more than one functional group.
Figure 3.
Volcano plot of differentially expressed hypothetical protein genes. Cutoff for log2(fc) is 1; the numbers of upregulated and downregulated genes were 45 and 38, respectively; The red dots represent significantly upregulated genes, blue dots represent significantly downregulated genes, and grey dots represent non-significant genes.
Figure 3.
Volcano plot of differentially expressed hypothetical protein genes. Cutoff for log2(fc) is 1; the numbers of upregulated and downregulated genes were 45 and 38, respectively; The red dots represent significantly upregulated genes, blue dots represent significantly downregulated genes, and grey dots represent non-significant genes.
Figure 4.
GO analysis of differentially expressed hypothetical protein genes.
Figure 5.
KEGG pathway analysis of differentially expressed hypothetical protein genes.
Table 1.
Bioinformatics tools used for the determination of physicochemical properties and subcellular localization predictions of M. synoviae hypothetical proteins.
Table 1.
Bioinformatics tools used for the determination of physicochemical properties and subcellular localization predictions of M. synoviae hypothetical proteins.
| Name | URL | Reference |
|---|---|---|
| ExPASy-ProtParam | https://web.expasy.org/protparam/, accessed on 3 September 2022 | [12] |
| PSORTb | https://www.psort.org/psortb/, accessed on 5 September 2022 | [13] |
| PSLPred | http://crdd.osdd.net/raghava/pslpred/, accessed on 6 September 2022 | [14] |
| LOCTree3 | https://rostlab.org/services/loctree3/, accessed on 7 September 2022 | [15] |
| HMMTOP | http://www.enzim.hu/hmmtop/, accessed on 8 September 2022 | [16] |
| TMHMM | http://www.cbs.dtu.dk/services/TMHMM/, accessed on 9 September 2022 | [17] |
| Phobius | https://phobius.sbc.su.se/, accessed on 13 September 2022 | [18] |
| SignalP | http://www.cbs.dtu.dk/services/SignalP-3.0/, accessed on 15 September 2022 | [19] |
| SecretomeP | http://www.cbs.dtu.dk/services/SecretomeP/, accessed on 15 September 2022 | [20] |
Table 2.
Bioinformatics tools used for the function prediction of Mycoplasma synoviae hypothetical proteins.
Table 2.
Bioinformatics tools used for the function prediction of Mycoplasma synoviae hypothetical proteins.
| Name | URL | Reference |
|---|---|---|
| BLAST2GO | https://www.blast2go.com/, accessed on 25 September 2022 | [21] |
| HMMER | https://www.ebi.ac.uk/Tools/hmmer/, accessed on 29 September 2022 | [22] |
| FASTA | https://fasta.bioch.virginia.edu/fasta_www2/fasta_www.cgi, accessed on 29 September 2022 | [23] |
| Pfam | https://pfam.xfam.org/, accessed on 5 October 2022 | [24] |
| SUPERFAMILY | http://www.supfam.org/SUPERFAMILY/, accessed on 6 October 2022 | [25] |
| CATH | https://www.cathdb.info/, accessed on 11 October 2022 | [26] |
| CDART | https://www.ncbi.nlm.nih.gov/Structure/lexington/lexington.cgi, accessed on 15 October 2022 | [27] |
| SMART | https://smart.embl-heidelberg.de/, accessed on 17 October 2022 | [28] |
| SBASE | http://pongor.itk.ppke.hu/protein/sbase. html#/sbase_blast, accessed on 19 October 2022 | [29] |
| InterPro | https://www.ebi.ac.uk/interpro/, accessed on 11 October 2022 | [30] |
| STRING | https://string-db.org/, accessed on 23 October 2022 | [31] |
| STITCH | http://stitch.embl.de/, accessed on 29 October 2022 | [32] |
Table 3.
M. synoviae ATCC 25204 PPFs predicted as putative enzymes.
| NCBI Gene ID | Putative Function |
|---|---|
| Transferases | |
| VY93_RS00970 | DNA polymerase; luciferase; phosphatidate Cytidylyltransferase |
| VY93_RS02560 | Protein kinase |
| VY93_RS01570 | Protein kinase |
| VY93_RS03110 | Riboflavin kinase |
| VY93_RS00830 | Sensor kinase |
| VY93_RS00850 | Phosphotransferase; hemagglutinin |
| VY93_RS01710 | Glutathione S-transferase |
| VY93_RS02970 | Mycoplasma MFS transporter; histidine kinase A |
| VY93_RS00115 | Transferase (glycosyl, DHHC palmitoyl); histidine kinase |
| VY93_RS01810 | Membrane-bound O-acyl transferase |
| VY93_RS02170 | tRNA/rRNA methyltransferase |
| VY93_RS02255 | Protein kinase |
| VY93_RS03860 | RNA-binding S4 domain-containing protein; protein kinase |
| VY93_RS00470 | Protein kinase |
| VY93_RS01555 | ATP-dependent protease; BTB protein DNA polymerase |
| VY93_RS00275 | DNA polymerase III |
| VY93_RS01930 | ParB-like nuclease; DNA-directed RNA polymerase |
| VY93_RS00350 | RNA polymerase |
| VY93_RS00400 | DNA polymerase III |
| VY93_RS02300 | Methyltransferase type 12 |
| VY93_RS03705 | DNA methylase; S-adenosyl-L-methionine-dependent methyltransferase; typeIII restriction-modification system StyLTI enzyme |
| Synthetase | |
| VY93_RS03395 | Phenylalanyl-tRNA synthetase |
| VY93_RS03120 | tRNA pseudouridine synthase B |
| VY93_RS01715 | Copper-transporting ATPase |
| VY93_RS00840 | ATP synthase |
| VY93_RS01815 | Phox homology (PX) domain protein; cysteinyl-tRNA synthetase |
| VY93_RS04225 | DNA ligase |
| VY93_RS02000 | Mur ligase |
| VY93_RS03720 | ATPase |
| VY93_RS04185 | ATPase |
| VY93_RS00770 | IVS-encoded protein-like superfamily; AAA ATPase |
| Hydrolases | |
| VY93_RS01095 | Primase-polymerase; phosphohydrolase |
| VY93_RS02860 | P-loop containing nucleoside triphosphate hydrolases |
| VY93_RS02175 | Phosphomevalonate kinase; P-loop containing nucleoside triphosphate hydrolase |
| VY93_RS03870 | Clostridium epsilon toxin ETX; bacillus mosquitocidal toxin MTX2; deoxyribonuclease I |
| VY93_RS03200 | Alkaline phosphatase |
| VY93_RS02315 | Peptidase M32 |
| VY93_RS02730 | Peptidase M13 |
| VY93_RS00915 | Peptidase C39 |
| VY93_RS04205 | Ribonuclease H |
| VY93_RS02305 | Bifunctional 2′,3′-cyclic-nucleotide 2′-phosphodiesterase/3′-nucleotidase; bacterial extracellular solute-binding protein; fibronectin-binding protein |
| VY93_RS03875 | DNA methylase |
| Isomerases | |
| VY93_RS02425 | Isomerase (sugar; phosphoglucose) |
| VY93_RS01705 | Topoisomerase-primase |
| VY93_RS00205 | Galactose mutarotase |
| Oxidoreductases | |
| VY93_RS00910 | NADH-ubiquinone oxidoreductase |
| VY93_RS02220 | Acyl-CoA oxidase; DNA methylase |
| Others | |
| VY93_RS03020 | Recombinase Flp protein |
| VY93_RS02085 | Permease (ABC-type glycerol-3-phosphate transport system; carbohydrate ABC transporter) |
Table 4.
M. synoviae ATCC 25204 PPFs predicted as hemagglutinin.
| NCBI Gene ID | Putative Function |
|---|---|
| VY93_RS00850 | Phosphotransferase; hemagglutinin |
| VY913_RS01400 | Phase-variable hemagglutinin |
| VY93_RS03820 | Phase-variable hemagglutinin |
| VY93_RS01245 | Phase-variable hemagglutinin |
| VY93_RS01250 | Phase-variable hemagglutinin |
| VY93_RS01255 | Phase-variable hemagglutinin |
| VY93_RS01260 | Phase-variable hemagglutinin |
| VY93_RS01280 | Phase-variable hemagglutinin |
| VY93_RS01285 | Phase-variable hemagglutinin |
| VY93_RS04265 | Phase-variable hemagglutinin |
| VY93_RS04120 | Phase-variable hemagglutinin |
| VY93_RS01315 | Phase-variable hemagglutinin |
| VY93_RS01350 | Phase-variable hemagglutinin |
| VY93_RS01420 | Phase-variable hemagglutinin |
| VY93_RS01425 | Phase-variable hemagglutinin |
| VY93_RS01450 | Phase-variable hemagglutinin |
| VY93_RS01455 | Phase-variable hemagglutinin |
| VY93_RS01380 | Phase-variable hemagglutinin |
| VY93_RS04325 | Phase-variable hemagglutinin |
| VY93_RS01460 | Phase-variable hemagglutinin |
| VY93_RS01270 | Phase-variable hemagglutinin |
| VY93_RS01330 | Phase-variable hemagglutinin |
| VY93_RS01410 | Phase-variable hemagglutinin |
| VY93_RS00340 | Phase-variable hemagglutinin |
| VY93_RS03730 | Phase-variable hemagglutinin |
Table 5.
M. synoviae ATCC 25204 PPFs predicted as lipoprotein.
| NCBI Gene ID | Putative Function |
|---|---|
| VY93_RS00960 | P60-like lipoprotein |
| VY93_RS04200 | Membrane lipoprotein |
| VY93_RS01720 | Membrane lipoprotein |
| VY93_RS00485 | P37-like ABC transporter substrate-binding lipoprotein |
| VY93_RS03535 | Lipoprotein |
| VY93_RS01895 | Lipoprotein |
| VY93_RS01900 | Lipoprotein |
| VY93_RS02090 | Lipoprotein |
| VY93_RS00965 | Membrane protein P80 |
| VY93_RS01825 | Murein lipoproteins |
| VY93_RS04030 | Mycoplasma lipoprotein (MG045) |
| VY93_RS00405 | Mycoplasma lipoprotein; fimbrial protein |
| VY93_RS00700 | Membrane protein |
Table 6.
M. synoviae ATCC 25204 PPFs predicted as putative DNA-binding proteins.
| NCBI Gene ID | Putative Function |
|---|---|
| VY93_RS00780 | Putative helix-turn-helix protein (YlxM/p13-like) |
| VY93_RS00990 | Ribbon-helix-helix protein |
| VY93_RS01500 | Transcription factors |
| VY93_RS00780 | Putative helix-turn-helix protein (YlxM/p13-like) |
| VY93_RS01550 | Nucleic acid binding |
| VY93_RS03080 | Transcriptional regulatory protein |
| VY93_RS00845 | Guanine nucleotide exchange factor (GEF) domain of SopE; Pleckstrin homology-related domain protein |
Table 7.
M. synoviae ATCC 25204 PPFs predicted as putative other functions.
| NCBI Gene ID | Putative Function |
|---|---|
| VY93_RS01990 | Nematode chemoreceptor |
| VY93_RS04250 | Elongation factor EF-Ts |
| VY93_RS00240 | Seadorna_VP6 |
| VY93_RS03025 | Pore-forming protein, afimbrial adhesin AFA-I |
| VY93_RS02525 | Cadherins; anticodon_Ia_like protein |
| VY93_RS02420 | Smr protein |
| VY93_RS01110 | Super-infection exclusion protein B; |
| VY93_RS00790 | Transmembrane protein 51 |
| VY93_RS01765 | RDD-like protein domain protein |
| VY93_RS01760 | Transcription antitermination factor NusB |
| VY93_RS02865 | Replication initiation and membrane attachment protein |
| VY93_RS02705 | LMP repeated-region protein |
| VY93_RS00715 | Aromatic cluster surface protein |
| VY93_RS00710 | Aromatic cluster surface protein |
| VY93_RS00705 | Aromatic cluster surface protein |
| VY93_RS00680 | Periplasmic binding protein, Laci family transcriptional regulator, sucrose operon repressor |
| VY93_RS03280 | Chaperonin Cpn60/TCP-1 |
| VY93_RS02250 | SMC-like protein; zinc finger protein; SH2 motif-like domain protein |
| VY93_RS02310 | SH2 domain-containing protein |
| VY93_RS03725 | Myosin head, motor region-containing protein |
| VY93_RS00215 | Intracellular septation protein A |
| VY93_RS02950 | Herpesvirus BTRF1 protein |
| VY93_RS03520 | Signal-induced proliferation-associated 1-like protein 2 isoform X4; MARVEL domain-containing protein; cell division factor |
| VY93_RS03805 | RNA-binding S4 domain-containing protein |
| VY93_RS00640 | MtN3 and saliva-related transmembrane protein |
| VY93_RS03665 | Mitochondrial carrier |
| VY93_RS01690 | RPS2 ribosomal protein S2 |
| VY93_RS02790 | PotD/PotF family extracellular solute-binding protein; excinuclease ABC; aromatic cluster surface protein |
| VY93_RS03145 | Secretin-receptor family |
| VY93_RS01565 | ThrRS/AlaRS common domain superfamily |
| VY93_RS01215 | Lipocalins |
| VY93_RS00545 | Lipocalins |
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Si, D.; Sun, J.; Guo, L.; Yang, F.; Tian, X.; He, S.; Li, J. Hypothetical Proteins of Mycoplasma synoviae Reannotation and Expression Changes Identified via RNA-Sequencing. Microorganisms 2023, 11, 2716. https://doi.org/10.3390/microorganisms11112716
AMA Style
Si D, Sun J, Guo L, Yang F, Tian X, He S, Li J. Hypothetical Proteins of Mycoplasma synoviae Reannotation and Expression Changes Identified via RNA-Sequencing. Microorganisms. 2023; 11(11):2716. https://doi.org/10.3390/microorganisms11112716
Chicago/Turabian StyleSi, Duoduo, Jialin Sun, Lei Guo, Fei Yang, Xingmiao Tian, Shenghu He, and Jidong Li. 2023. "Hypothetical Proteins of Mycoplasma synoviae Reannotation and Expression Changes Identified via RNA-Sequencing" Microorganisms 11, no. 11: 2716. https://doi.org/10.3390/microorganisms11112716
APA StyleSi, D., Sun, J., Guo, L., Yang, F., Tian, X., He, S., & Li, J. (2023). Hypothetical Proteins of Mycoplasma synoviae Reannotation and Expression Changes Identified via RNA-Sequencing. Microorganisms, 11(11), 2716. https://doi.org/10.3390/microorganisms11112716
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