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Development of innovative multi-epitope mRNA vaccine against central nervous system tuberculosis using in silico approaches
Innovative multi-epitope mRNA vaccine against central nervous system tuberculosis
Shi Huidong Data curation Software Validation Writing – original draft 1
Zhu Yuejie Software Visualization 2
Shang Kaiyu Software Visualization 1
Tian Tingting Software Visualization 1
Yin Zhengwei Software Visualization 1
Shi Juan Software Visualization 1
He Yueyue Software Visualization 3
Ding Jianbing Software Visualization 1
Wang Quan Writing – review & editing 4 *
https://orcid.org/0000-0001-5795-8672
Zhang Fengbo Writing – review & editing 1 *
1 State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
2 Reproductive Medicine Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
3 Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
4 Department of Clinical Laboratory, The Eighth Affiliated Hospital of Xinjiang Medical University, Urumqi, China
Rojekar Satish Editor
Icahn School of Medicine at Mount Sinai Department of Pharmacological Sciences, UNITED STATES OF AMERICA
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: 3197606@qq.com (QW); fengbozhang@xjmu.edu.cn (FZ)
6 9 2024
2024
19 9 e030787718 4 2024
14 7 2024
© 2024 Shi et al
2024
Shi et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Tuberculosis(TB) of the Central nervous system (CNS) is a rare and highly destructive disease. The emergence of drug resistance has increased treatment difficulty, leaving the Bacillus Calmette-Guérin (BCG) vaccine as the only licensed preventative immunization available. This study focused on identifying the epitopes of PknD (Rv0931c) and Rv0986 from Mycobacterium tuberculosis(Mtb) strain H37Rv using an in silico method. The goal was to develop a therapeutic mRNA vaccine for preventing CNS TB. The vaccine was designed to be non-allergenic, non-toxic, and highly antigenic. Codon optimization was performed to ensure effective translation in the human host. Additionally, the secondary and tertiary structures of the vaccine were predicted, and molecular docking with TLR-4 was carried out. A molecular dynamics simulation confirmed the stability of the complex. The results indicate that the vaccine structure shows effectiveness. Overall, the constructed vaccine exhibits ideal physicochemical properties, immune response, and stability, laying a theoretical foundation for future laboratory experiments.

State Key Laboratory of Pathogenesis,Prevention and Treatment of High Incidence Diseases in Central Asia SKL-HIDCA2021-JH11 https://orcid.org/0000-0001-5795-8672
Zhang Fengbo National Natural Science Foundation of China Regional Science Foundation Project 82360394 https://orcid.org/0000-0001-5795-8672
Zhang Fengbo Youth Science and technology top talent Program 2022TSYCCX0112 https://orcid.org/0000-0001-5795-8672
Zhang Fengbo Outstanding Youth Science Foundation of Xinjiang Uygur Autonomous Region 2023D01E12 https://orcid.org/0000-0001-5795-8672
Zhang Fengbo Xinjiang Uygur Autonomous Region science and technology support project 2022E02061 https://orcid.org/0000-0001-5795-8672
Zhang Fengbo This study was supported by State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (SKL-HIDCA2021-JH11, https://caskl.xjmu.edu.cn/); National Natural Science Foundation of China Regional Science Foundation Project, 82360394,https://www.nsfc.gov.cn/publish/portal0/tab442/info92109.htm; Youth Science and technology top talent Program(2022TSYCCX0112,http://kjt.xinjiang.gov.cn/kjt/c100870/201111/acd5ead987f945cba2498ce413a36388.shtml); Outstanding Youth Science Foundation of Xinjiang Uygur Autonomous Region(2023D01E12,http://kjt.xinjiang.gov.cn/kjt/index.shtml) and Xinjiang Uygur Autonomous Region science and technology support project (2022E02061,http://kjt.xinjiang.gov.cn/kjt/c100870/201111/acd5ead987f945cba2498ce413a36388.shtml) Author: F.B Zhang. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityAll relevant data are within the paper and its Supporting information files.
Data Availability

All relevant data are within the paper and its Supporting information files.
==== Body
pmc1. Introduction

Primarily affecting humans, Mtb is an acid-fast aerobic, non-motile, spore-forming bacterium [1]. According to the World Health Organization (WHO) Global TB Report 2022,10.6 million people were diagnosed and 1.6 million people died from TB in 2021. This marks a 3.6% increase from 2020 [2]. It is worth noting that CNS TB is a form of TB, predominantly presenting as tuberculous meningitis (TBM) with a notably high mortality rate [3]. In patients with HIV, the mortality rate for TBM is close to 50% [4]. At the population level, the incidence is highest in children aged 2–4 years [5]. Previous studies have shown that Mtb deposits develop during hematogenous dissemination in the brain parenchyma and meninges, leading to the gradual formation of tuberculoma. The physical rupture of the tuberculoma allows the bacterium to spread directly into the cerebrospinal fluid (CSF), ultimately resulting in tuberculous meningitis. Clinical symptoms primarily manifest as infarction due to vasculitis [6]. Children and HIV co-infected individuals are considered high-risk groups for CNS TB [7]. Despite the existence of antibiotics and effective treatment options, there are several chemotherapy-related complications that persist, such as prolonged treatment durations, severe adverse reactions, poor adherence, and the emergence of multidrug resistance. Consequently, the global management of anti-TB treatment encounters significant obstacles [8–10].

Therapeutic vaccination has emerged as a potential new approach for treating TB [11]. While prophylactic vaccines like BCG can help prevent Mtb infection or active TB development, therapeutic vaccines aim to prevent recurrence post-cure or serve as adjuvant therapy [12]. Current TB vaccine candidates include inactivated, live attenuated, subunit, viral vector, DNA, and mRNA vaccines [13]. The large-scale production of mRNA vaccines has shown a trend towards industrialization during the COVID-19 epidemic [14]. However, no mRNA vaccine has yet been developed for CNS TB. mRNA vaccines work by transferring exogenous mRNA encoding antigen into cells through the expression system, leading to an immune response upon antigen synthesis [15, 16]. These vaccines offer several advantages. Firstly, mRNA can encode and express all genetic information of various proteins, allowing for optimized vaccine development through mRNA sequence modification [17]. Secondly, most mRNA vaccines can be produced and purified using similar procedures, which paves the way for the development of other similar mRNA vaccines [15]. Lastly, in vitro transcription simplifies the production of mRNA vaccines [17].

In this study, two proteins, PknD and Rv0986, from Mtb strain H37Rv were examined. PknD (Rv0931c) encodes a eukaryotic serine-threonine protein kinase with extracellular (sensor) and intracellular kinase domains that are uniquely found in CNS TB [18]. On the other hand, Rv0986 is a component of the ABC transporter complex and plays a role in host cell binding through secretion of adhesion factors or maintenance of mycobacterium cell envelope integrity, also specific to CNS TB [19]. The objective of this research was to develop a new multi-epitope mRNA therapeutic vaccine targeting a protein associated with CNS TB. Various in silico methods which had been used in previous studies such as Immune Epitope Database (IEDB), NetCTLpan1.1, NetMHCIIpan-4.0, and SVMtrip were employed to analyze the antigen epitopes of PknD and Rv0986. Additionally, Cytotoxic T lymphocyte(CTL), Helper T lymphocyte(HTL), and B cell epitopes were connected using AAY, GPGPG, and KK linkers [20]. To investigate the relationship between T cells and alleles, molecular docking was conducted [21]. Furthermore, an analysis of the physicochemical properties, antigenicity, allergenicity, and toxicity of the mRNA vaccine was performed, followed by immune simulations to validate the hypothesis. Codon optimization of the mRNA vaccine was carried out to ensure accurate translation in the host. Subsequently, predictions on the secondary and tertiary structures of the vaccine were made. The resulting tertiary structure was then molecularly docked with TLR-4. Finally, a molecular dynamics simulation was utilized to confirm the stability of the compound [22].

2. Materials and methods

2.1 Sequence source

The amino acid sequences of the target proteins PknD and Rv0986 were retrieved from the UniProt database(https://www.uniprot.org/). Homology analysis of these selected proteins was conducted using MAFFT(version 7) and showed in Jalview(2.11.3.3) software [23]. The research process of this paper is shown in (Fig 1).

10.1371/journal.pone.0307877.g001 Fig 1 The experimental process of this study.

2.2 Selection of target proteins

The software Prot Param(https://web.expasy.org/protparam/) was used to analyze the physicochemical properties of the selected proteins and MEVs. The antigenicity of the protein was analyzed using Vaxi Jen2.0(http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) [24]. AllergenFPv.1.0(http://www.ddg-pharmfac.net/AllerTOP)was used to analyze the sensitization and ToxinPred(https://webs.iiitd.edu.in/raghava/toxinpred/multi_submit.php) was used to analyze the toxicity of the protein.

2.3 Prediction of signal peptides

Before predicting protein epitope, the signal peptide sequence should be removed first. We used SignalP6.0(https://services.healthtech.dtu.dk/service.php?SignalP-6.0) [25] and Signal BLAST(http://sigpep.services.came.sbg.ac.at/signalblast.html) [26] to predict the signal peptide of the protein.

2.4 Prediction of protein T-cell epitopes

Major histocompatibility complex (MHC) molecules play a crucial role in binding and presenting antigenic peptides for recognition by T lymphocytes. MHC class I molecules interact with the CD8T cell subset, while MHC class II molecules interact with the CD4T cell subset. Human leukocyte antigen (HLA) genes are synonymous with human MHC genes [22, 27]. HLA alleles exhibit specificity based on geographical and population factors [28]. In this study, we focused on alleles commonly found in Xinjiang (HLA-A*1101, HLA-A*0201, HLA-A*0301, HLA-DRB1*0701, HLA-DRB1*1501, HLA-DRB1*0301) to predict CTL epitopes and HTL epitopes [29]. CTL epitopes were predicted using IEDB(http://tools.immuneepitope.org/) [30] and NetCTLpan-1.1(https://services.healthtech.dtu.dk/service.php?NetCTLpan-1.1) with an epitope length of 9, and overlapping sequences were screened [31]. It is important to note that NetCTLpan-1.1 indexing starts from 0, so adding 1 is necessary for alignment with IEDB sequences. For HTL epitope prediction, NetMHCIIpan-4.0 (https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-4.0) [32] and IEDB tools were employed. NetMHC-IIpan-4.0 was configured with the original threshold and an epitope length of 15, with dominant epitopes selected based on overlapping sequences.

2.5 Prediction of protein B-cell epitopes

B cell epitope includes linear epitope and conformational epitope.SVMtrip (sysbio.unl.edu/SVMTriP/prediction.php) was used to predict linear B cell epitopes, and the epitope length was set to 20 [33]. The conformational epitope was predicted by Ellipro of IEDB.

2.6 T-cell epitopes molecular docking to HLA alleles

To examine the relationship between T cell epitopes and alleles, we used the HDOCK(http://hdock.phys.hust.edu.cn/) server to select class HLA-I (HLA-A * 02:01) and class HLA-II (HLA-DRB1 * 07:01) alleles for molecular docking with T cell epitopes. Finally, the LIGPLOT(v.2.2.8) was used to evaluate the interactions between epitopes and various residues of MHC alleles.

2.7 mRNA vaccine structure design

To produce mRNA vaccines, highly antigenic, non-allergic, and non-toxic epitopes are connected using linkers such as AAY, GPGPG, and KK for CTL, HTL, and B-cell epitopes, respectively [20]. In order to facilitate the detection and purification of the recombinant protein later, we added a set of HHHHHH sequences to the C-terminal of the amino acid sequence and connected them with GGGS linkers [34]. The 5’-cap structure, essential for mRNA translation, initiates mRNA translation and boosts mRNA stability and efficiency [35]. Translation and degradation efficiency of mRNA can be regulated by 5ʹ- and 3ʹ-UTRs [36]. The Kozak sequence includes a start codon [37]. The tPA Signal (UniProt ID: P00750) aids in secreting the signal sequence of epitopes, enhancing vaccine immunogenicity [38]. It is important to note that we used only the precursor sequence of the tPA signal peptide, which can be transcribed but not translated. Adjuvant resuscitation promoting factor (RpfE) (Rv2450c) can improve adaptive immune response. CTL epitopes are guided to the MHC-I region of the endoplasmic reticulum by the 3’ MITD (Uniprot ID: Q8WV92) [39]. The addition of a Poly(A) tail can facilitate protein translation and enhance mRNA stability [35]. These elements contribute to the successful construction of mRNA vaccines.

2.8 Prediction of homology between vaccine and humans

Non-homologous proteins can be reduced to stimulate the body to produce an adverse immune response [40]. By using NCBI BlastP (https://blast.ncbi.nlm.nih.gov/Blast.cgi) database, comparing vaccine and Homo sapiens (TaxID: 9606) to reduce the risk of autoimmune. An E value greater than 0.5 is considered non-homologous and suitable for vaccine construction [41].

2.9 Assessment of vaccine structure

An effective vaccine must possess appropriate physical and chemical properties. Parameters such as molecular weight, theoretical isoelectric point, amino acid composition, instability index, aliphatic index, and total average hydrophilicity (GRAVY) were evaluated using ProtParam. The isoelectric point (pI) indicates the pH at which a molecule or surface is neutral and impacts solubility at specific pH levels. The aliphatic index is a measure of a protein’s thermal stability, with proteins considered unstable if their instability index exceeds 40. A positive GRAVY value signifies hydrophobicity, while a negative value indicates hydrophilicity [42]. Antigenicity, which refers to an antigen’s ability to selectively bind to antibodies or lymphocytes, was assessed using VaxiJen2.0 during vaccine design. Furthermore, allergic reactions were assessed using AllerTOP2.0 to ensure the vaccine does not induce sensitization [43]. ToxinPred was utilized to evaluate the vaccine’s toxicity, confirming that the constructed vaccine does not produce any toxic effects [44].

2.10 Immune response simulation

C-Immsim was utilized to model the three injection intervals of 1, 84, and 168 to replicate the immunological response induced by the vaccine in the body [45]. The analysis focused on high frequency alleles—HLA-A*1101, HLA-A*0201, HLA-B*5101, HLA-B*3501, HLA-DRB1*0701, and HLA-DRB1*1501—in the Xinjiang population. The simulation parameters included a random seed of 12345, a simulation volume of 50, a simulation step of 1050, and a dose of 1000 units [46].

2.11 Optimization of mRNA codons and in silico cloning

The online codon optimization server JCat tool was utilized for codon optimization and analysis [47]. Escherichia coli was chosen as the expression host [48] and BamHI and XHOI restriction sites were deliberately excluded to obtain the desired DNA sequences. PVAX1 was employed as the vector for electronic cloning, with primers being added and amplified using Snap Gene. The GC content ranged between 30% and 70%, while the primer length was set between 15–30 bp. It was ensured that the upper and lower annealing temperatures were similar. Finally, the amplification process was completed using BamHI and XHOI restriction enzymes.

2.12 Agarose gel electrophoresis

Agarose gel electrophoresis of target genes (post-PCR), vectors, and recombinant plasmids in Snap Gene was done in a TBE buffer at 1% agarose concentration.

2.13 Prediction of secondary structure of mRNA vaccine

To ensure the effectiveness of mRNA transcription, we used the RNAfold(http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi)server to predict the secondary structure of single-stranded RNA sequences. Currently, it can handle sequences up to 10,000 nt for minimum free energy predictions and up to 7,500 nt for partition function computations. The server also predicts the center of mass secondary structure and minimal free energy (MFE) of mRNA.

2.14 Prediction of secondary and tertiary structure of vaccines

The specific conformation formed by the polypeptide backbone atoms spiraling or folding along a defined axis is known as a protein’s secondary structure. To ensure that the vaccine proteins are translated correctly, we analyze the distribution of α helix, β sheet, random coil, and extended chain components of the vaccine, we utilized the SOPMA(http://npsa-pbil.ibcp.fr/cgibin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) server. The SWISS-MODEL(https://swissmodel.expasy.org/assess) tool was employed to predict the stoichiometry and overall structure of the complex through homology modeling, involving steps such as input data submission, template search, template selection, modeling, and model quality assessment. Subsequently, the predicted tertiary structure was refined using the GalaxyWEB(http://galaxy.seoklab.org/) server, which enhances the core structure based on multiple templates and optimizes unreliable loops or termini. This refinement method is recognized for its high performance in template-based modeling [49]. The final 3D model was rendered using Discovery Studio(2019) software.

2.15 Quality testing of models

The PROCHECK(https://saves.mbi.ucla.edu/) server was utilized to assess the quality of the tertiary structure, which indicates the level of agreement between the model structure and the experimental data, along with the geometric features. The server mainly includes PROCHECK, WHATCHECK,ERRAT,Verify-3D and PROVE five commonly used tests. PROCHECK analysis takes high-resolution crystal structure parameters in PDB as reference, and gives a series of stereochemical parameters of the submitted model. After the analysis was completed, the percentage of amino acids in the "most favoured regions"," additional allowed regions", "generously allowed regions ", and" disallowed regions" were listed in the Ramachandran Plot line. It is generally required that the amino acid residues in the optimal region account for more than 90% of the whole protein, and the number of amino acids in the disallowed region should be less than 5% of the total amino acids. It is considered that the conformation of the model conforms to the rules of stereochemistry [50]. Additionally, ProSA(https://prosa.services.came.sbg.ac.at/prosa.php) was employed to validate the protein structure, ProSA is a tool for examining potential errors in 3D structural models of proteins, which uses X-ray analysis, NMR spectroscopy, and theoretical calculations for structural verification of proteins. From this we can get the z-score and its residue energy graph. The z-score represents the overall mass of the model and measures the energy distribution deviation of the total energy of the structure relative to the random conformation. A z-score outside the characteristic range of a natural protein indicates a faulty structure. Energy diagram show local model mass by plotting energy as a function of amino acid sequence position. In general, a positive value corresponds to a problem or error in the model [51].

2.16 Molecular docking

MEV activates the immune system as an antigen, triggering an immune response. In our study, we utilized the HDOCK server to perform molecular docking of MEV with TLR4 (PDB ID: 3FXI). HDOCK employs intrinsic scoring functions for protein-protein and protein-DNA/RNA docking, streamlining the docking process. The procedure involves inputting the FASTA format of MEV and TLR-4, conducting a sequence similarity search to identify homologous sequences in the PDB database, generating homologous templates for the receptor and ligand, comparing these templates to select the best ones, and performing modeling and comparison using Modler and ClustalW, respectively. Global docking is then carried out using the FFT docking program to predict the binding orientation, and the most favorable model is selected from the generated models [52].

2.17 Molecular dynamics simulation

In this study, Gromacs2022.3 was used to simulate the molecular dynamics (MD) of MEV-TLR4 complex [53]. The water molecule (Tip3p water model) served as the solvent in the simulation, which was run at a static temperature of 300K and atmospheric pressure of 1 Bar. The overall charge of the simulated system was neutralized by adding the proper number of Na+ ions. The force field used in the simulation was Amber99sb-ildn. Using a coupling constant of 0.1 ps and a duration of 100 ps, the molecular dynamics simulation system first uses the steepest descent method to minimize the energy. It then performs the isothermal isobaric ensemble (NPT) equilibrium and isothermal isocorph system (NVT) equilibrium for 100,000 steps, respectively. Lastly, a simulation using free molecular dynamics was run. The entire operation took 100ns and involved 5000000 steps, each with a step length of 2fs. Following the completion of the simulation, the trajectory was analyzed using the software’s built-in tool to calculate metrics such as root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonds, protein Radius of Gyration(Gyrate) and Solvent Accessible Surface Area(SASA) for each amino acid trajectory. These calculations were then combined with data on free energy (MMGBSA), free energy topography, and other relevant information.

3. Results

3.1 Selection of target proteins

The UniProt search numbers for PknD and Rv0986 proteins are P9WI79 and P9WQK1, respectively. The amino acids of PknD and Rv0986 are 664aa and 248aa, respectively. In silico methods were used to analyze the antigenicity and sensitization of these proteins, resulting in antigenicity values of 0.5633 for PknD and 0.4736 for Rv0986, both exceeding 0.4. It was determined that both PknD and Rv0986 are non-allergens. Furthermore, the physicochemical properties (Table 1) of PknD and Rv0986 were examined, indicating that they are hydrophilic stable proteins.

10.1371/journal.pone.0307877.t001 Table 1 Physicochemical properties of amino acids.

Amino acids	Serial number	Antigenicity	Allergenicity	Instability index (II)	Grand average of hydropathicity (GRAVY)	
PknD	P9WI79	0.563	non-allergen	26.84	-0.055	
Rv0986	P9WQK1	0.473	non-allergen	32.44	-0.625	

3.2 Sequence retrieval

The high accuracy (Fig 2) of MAFFT in jalview indicates that the two proteins share several homologies, suggesting that they may derive from the same gene and have similar roles in the immune response.

10.1371/journal.pone.0307877.g002 Fig 2 Homologous sequence alignment of proteins.

The blue portions represent similar amino acid sequence (The darker the blue, the more conservative it is).

3.3 Prediction of signal peptides

While we used SignalP6.0 and Signal BLAST to predicte signal peptide of the PknD and Rv0986, there was no signal peptide in the end. Therefore, PknD and Rv0986 proteins are not directed to other suborganelles of the cell or secreted to play roles outside the cell after synthesis (Fig 3) (S1 and S2 Figs).

10.1371/journal.pone.0307877.g003 Fig 3 (A-B) Protein signal peptide was analyzed by SignalP6.0.

Other: the probability that the sequence does not have any signal peptides.

3.4 Prediction of T-cell epitopes

Select two of the top 10 epitopes of the software and pick the overlapping sequence. The antigenicity of epitopes was analyzed by VaxiJen. Allergen v1.0 was used to predict epitope sensitization; Use ToxinPred to predict epitope toxicity. In the end, we obtained 3 CTL dominant epitopes and 11 HTL dominant epitopes (Tables 2 and 3). These epitopes are highly antigenic, non-allergenic and non-toxic (S1–S8 Tables).

10.1371/journal.pone.0307877.t002 Table 2 Physical properties, antigenicity and scores of CTL dominant epitopes.

	Serials	CTL epitopes	Molecular weight	Instability index	Grand average of hydropathicity (GRAVY)	Theoretical PI	Antigenicity	Score	
Rv0986	114–122	ELAGVSQRK	987.12	30.29	-0.756	8.85	1.0888	0.492	
	14–22	WTFREGETR	1181.27	-2.48	-1.767	6.14	1.515	0.423	
PknD	593–601	ITAPWGIAV	927.11	6.13	1.467	5.52	0.939	0.731	

10.1371/journal.pone.0307877.t003 Table 3 Physical properties, antigenicity and scores of HTL dominant epitopes.

	Serials	HTL epitopes	Molecular weight	Instability index	Grand average of hydropathicity (GRAVY)	Theoretical PI	Antigenicity	Score	
Rv0986	199–213	TLIMATHSPSMTQHA	1625.88	52.75	0.033	6.61	0.586	0.388	
	94–108	FQFFNLIPTLTVLEN	1796.10	2.39	0.767	4.00	0.650	0.409	
	93–107	VFQFFNLIPTLTVLE	1781.12	2.39	1.280	4.00	0.665	0.317	
	150–164	GEQQRVAISRALAHN	1649.83	52.95	-0.633	9.61	0.442	0.295	
	70–84	NGFAITQKTERDRTL	1749.94	2.10	-1.100	8.75	0.493	0.323	
	174–188	TGNLDSDTGDKVLDV	1548.62	-16.15	-0.56	3.77	0.890	0.378	
PknD	421–435	GIDFRLSPSGVAVDS	1519.67	43.60	0.333	4.21	2.029	0.774	
	123–137	AAALDAAHANGVTHR	1474.60	-12.01	-0.013	6.96	1.208	0.685	
	420–434	TGIDFRLSPSGVAVD	1533.70	25.10	0.340	4.21	2.000	0.753	
	590–610	PWGIAVDEAGTVYVT	1577.75	-31.53	0.513	3.67	0.716	0.871	
	510–524	NYPEGLAVDTQGAVY	1596.71	-6.61	-0.260	3.67	0.487	0.755	

3.5 Prediction of B-cell epitopes

Three linear epitopes of 20 amino acids were obtained by analysis (S9 Table). For conformational epitopes, we finally selected two discontinuous epitopes with a length greater than 5 amino acids. The predicted score of Rv0986 is 0.812, and the predicted score of PknD is 0.681 (Tables 4 and 5) These epitopes are highly antigenic, non-allergenic and non-toxic (Fig 4).

10.1371/journal.pone.0307877.g004 Fig 4 B cell conformational epitope predicted by Ellipro.

The yellow spheres represent B cell conformational epitopes, the gray area represents the majority of the polyprotein. (A)Rv0986 conformational epitope residues:V228,N229,R230, E231,N232,Q233,T234,D235,Q236,P237,A238,S239,T240,I241,L242,L243,P244,T245,S246,Y247,E248. (B)PknD conformational epitope residues:D242,S243,D244,R245,T246,T261,S262,L263, E264,H265,H266,H267.

10.1371/journal.pone.0307877.t004 Table 4 Physical properties, antigenicity and scores of LBEs dominant epitopes.

	Serials	LBEs epitopes	Molecular weight	Instability index	Grand average of hydropathicity (GRAVY)	Theoretical PI	Antigenicity	Score	
Rv0986	61–80	KPTTGDVTINGFAITQKTER	2177.44	-4.42	-0.72	8.59	0.942	0.562	
PknD	50–69	YSDNAVFRARMQREADTAGR	2314.52	-6.03	-1.13	8.74	1.043	1.000	
	87–106	QFFVEMRMIDGTSLRALLKQ	2383.85	14.94	0.125	8.75	0.5899	0.914	

10.1371/journal.pone.0307877.t005 Table 5 Physical properties, antigenicity and scores of CBEs dominant epitopes.

	Serials	Residues	CCBEs	Molecular weight	Instability index	Grand average of hydropathicity (GRAVY)	Theoretical PI	Antigenicity	Score	
Rv0986	228–248	A:V228,A:N229,A:R230,A:E231,A:N232,A:Q233,A:T234,A:D235,A:Q236,A:P237,A:A238,A:S239,A:T240,A:I241,A:L242,A:L243,A:P244,A:T245,A:S246, A:Y247,A:E248	VNRENQTDQPASTILLPTSYE	2376.56	39.03	-0.910	4.14	0.3140	0.812	
PknD	242–267	A:D242,A:S243,A:D244,A:R245,A:T246,A:T261,A:S262,A:L263,A:E264,A:H265,A:H266,A:H267	DS DSDRTTSLEHHH	1434.44	12.65	-1.983	5.73	0.6945	0.681	

3.6 Molecular docking of T-cell epitopes to HLA alleles

We performed molecular docking simulations to demonstrate the interaction of HLA alleles with selected T cell epitopes. Results for ITAPWGIAV(CTLs) interacting with HLA-A*02:01, Docking Score:-171.21 Confidence Score:0.6045 ligand RMSD (Å):109.11. FQFFNLIPTLTVLEN(HTLs) interacting with HLA-DRB1*07:01 Results, Docking Score-123.45 Confidence Score:0.3703 ligand RMSD (Å):55.09. These results indicate a high affinity for the docking complex (Figs 5 and 6).

10.1371/journal.pone.0307877.g005 Fig 5 Docking complex display (A)Molecular docking between ITAPWGIAV(CTLs) and HLA-A*02:01(B)Molecular docking between FQFFNLIPTLTVLEN(HTLs) and HLA-DRB1*07:01.

10.1371/journal.pone.0307877.g006 Fig 6 (A-B)Epitopes and their corresponding MHC allele interaction using the LIGPLOT.

Hydrogen bonds are represented by dotted green lines, and red semicircular circles indicate residues involved in hydrophobic interactions.

3.7 Construction of novel mRNA vaccines

The mRNA vaccine is constructed from the N to C terminus as follows:5’m7GCap-5’UTR-Kozak sequence-tPA(Signal peptide)-EAAAK Linker-RpfE(Adjuvant)-AAYELAGVSQRKAAYWTFREGETRAAYITAPWGIAVGPGPGTLIMATHSPSMTQHAGPGPGFQFFNLIPTLTVLENGPGPGVFQFFNLIPTLTVLEGPGPGGEQQRVAISRALAHNGPGPGNGFAITQKTERDRTLGPGPGTGNLDSDTGDKVLDVGPGPGGIDFRLSPSGVAVDSGPGPGAAALDAAHANGVTHRGPGPGTGIDFRLSPSGVAVDGPGPGPWGIAVDEAGTVYVTGPGPGNYPEGLAVDTQGAVYKKKPTTGDVTINGFAITQKTERKKYSDNAVFRARMQREADTAGRKKQFFVEMRMIDGTSLRALLKQKKVNRENQTDQPASTILLPTSYEKKDSDRTVYVADRGNDRVVKLTSLEHHHGGGSHHHHHH-MITD sequence-Stop codon-3’UTR-Poly(A)tail. The selected epitopes are connected using three linkers, AAY,GPGPG and KK, which function independently. The linker AAY is breakable, GPGPG is rigid, and KK is flexible (Fig 7). Finally, after homology comparison, we found that no significant similarity was found between the vaccine and the host, and the E value was less than 0.05, indicating that the vaccine we constructed was reasonable (S3 Fig).

10.1371/journal.pone.0307877.g007 Fig 7 Vaccine construct from N-terminal to C-terminal.

3.8 Evaluate the physicochemical properties, antigenicity, allergenicity and toxicity of the MEV

The MEV protein consists of 383 amino acids, with a molecular weight of 40.196 kDa and a theoretical isoelectric point of 8.65. It contains 36 acidic amino acids (Asp+Glu) and 38 basic amino acids (Arg+Lys). The formula for MEV is C1773H2765N521O541S5, with a total of 5,605 atoms. The instability index (II) is 7.49, indicating stability. The aliphatic index is 66.50 and the Grand average of hydropathicity (GRAVY) is -0.472, suggesting hydrophilicity. Furthermore, the vaccine exhibits antigenicity, non-Allergen, and non-Toxicity. In conclusion, the vaccine design appears to be feasible. Finally, (Table 6) presents all the final results.

10.1371/journal.pone.0307877.t006 Table 6 The physiochemical profiling of the mRNA vaccine.

Physiochemical profiling	Measurement	Indication	
Number of amino acids	383	Appropriate	
Molecular weight	40195.92	Appropriate	
Theoretical pI	8.65	Basic	
Total number of negatively charged residues (Asp + Glu)	36	-	
Total number of positively charged residues (Arg + Lys)	38	-	
Formula	C1773H2765N521O541S5	-	
Total number of atoms	5605	-	
Instability index (II)	7.49	Stable	
Aliphatic index	66.50	Thermostable	
Grand average of hydropathicity (GRAVY)	-0.472	Hydrophilic	
Allergenicity	NON-ALLERGEN	Non-Allergen	
Antigenicity	0.9510	Antigenic	
Toxicity	Non-Toxic	Non-Toxic	

3.9 Immune response simulation

Utilizing C-ImmSim online software, we simulated the immune response to three vaccine injections. The results demonstrated a notable increase in the immune response to the second and third injections as compared to the first. As the antigen content decreased, there was a gradual rise in IgM and IgG levels, with IgM consistently surpassing IgG (Fig 8A). This trend can be attributed to the development of immune memory post-antigen exposure, leading to an increase in CTLS and HTL numbers (Fig 8B–8D). Similarly, the presence of memory cells resulted in an augmentation of B cells, crucial for humoral immunity (Fig 8E and 8F). Dendritic cell numbers remained constant (Fig 8G), while macrophage numbers showed a gradual increase (Fig 8H). Moreover, levels of IFN-γ, TGF-β, and IL-2 increased, with a lower Simpson index (D) indicating immune response variations (Fig 8I). These results show that the dose and time interval of injection are reasonable.

10.1371/journal.pone.0307877.g008 Fig 8 (A)Immunoglobulins in various states (B)The Helper T Cell Population in various states (C)The Helper T Cell Population in various states (D)The Cytotoxic T Cell Population in various states (E)The B cell population in various states (F)The B Cell Population in various states (G)Dendritic Cell Population in various states (H)Macrophage Population in various states (I)Cytokines and Interleukins Production with Simpson Index of the immune response.

3.10 mRNA codon optimization and in silico cloning

Codon optimization tools play a crucial role in enhancing the translation of mRNA vaccines within host cells. The quality of codon optimization is typically evaluated based on the codon adaptation index (CAI) and GC content. A CAI value of 1.0 signifies optimal optimization, with values above 0.8 considered favorable. Moreover, a GC content between 30–70% is known to facilitate effective gene expression in human hosts. Post-optimization, the average GC content was determined to be 56.74%. Following primer design principles, we developed an upstream primer (5’-GGATCCGCTGCTTACGAACTGGCTGGT-3’) with a length of 27, an aTm value of 68, and a GC content of 59%, incorporating the BamHI enzyme restriction site at the 5’ end. Similarly, the downstream primer (5’-CTCGAGGTGGTGGTGGTGGTGGTGAGAA-3’) was designed with a length of 28, an aTm value of 66, and a GC content of 61%, featuring the XhoI enzyme restriction site at the 3’ end. Subsequently, the target gene was amplified using Snap Gene, and for cloning purposes, the eukaryotic expression vector PVAX1 was selected. The amplified target gene successfully inserted into the multiple cloning site (MCS) region of the plasmid after removing the previously designed primer restriction site (Fig 9).

10.1371/journal.pone.0307877.g009 Fig 9 Codon optimization and plasmid vector construction (A)Sequence after adaptation (B)The cloned MEV was inserted into the PVAX1 vector (C)After amplified(1161bp).

3.11 Agarose gel electrophoresis

The amount of DNA was consistent with what had been predicted. The amplified sequence of MEV was 1161bp, the vector size of PVAX1 was 2999bp and the recombinant plasmid was 4098bp (Fig 10).

10.1371/journal.pone.0307877.g010 Fig 10 (A)In silico cloning simulation. Codon-optimized multiepitope sequences (red) inserted between the restriction sites BamHI and XhoI in the PVAX1 expression vector (black). (B)Simulated agarose gel electrophoresis results. “1” stands for MEV-PCR,“2”stand for PVAX1,“3” stand for recombinant plasmid.

3.12 Prediction of secondary structure of mRNA vaccine

The input was optimized and submitted to the RNAfold server to predict the mRNA structure and free energy. It was observed that the mRNA exhibited the most favorable secondary structure with a minimum free energy (MFE) of -428.80 kcal/mol, while the centroid secondary structure had a minimum free energy of -288.01 kcal/mol (Fig 11).

10.1371/journal.pone.0307877.g011 Fig 11 (A)Optimal secondary structure (B)Centroid secondary structure.

3.13 Prediction of secondary and tertiary structure of vaccines

The predicted secondary structure of the vaccine revealed that 8.36% consisted of α-helix, 8.62% of β-angle, 48.83% of irregular curling, and 34.20% of extended chain, aligning well with the tertiary structure. The tertiary structure model of the vaccine was constructed using SWISS-MODEL software, further refined on GalaxyWEB, and visualized with Discover Studio. In the visualization, gray indicates random curling, cyan indicates beta-folding, green indicates beta-turning, and red indicates alpha-spiraling (Fig 12).

10.1371/journal.pone.0307877.g012 Fig 12 (A-B)Prediction of vaccine secondary structure(C)Optimize the pre-tertiary structure(D)Optimize the post-tertiary structure.

3.14 Quality testing of models

We used PROCHECK to verify the validity of the tertiary structure, a server that can analyze its geometry and overall structure, and the Ramachandran diagram (Fig 13A) showed that 92.1% of the residues were in the most favoured regions and 6.5% were in the additional allowed regions and 0% were in the generously allowed regions and 1.4% were in the disallowed regions. The results were consistent with stereochemical rules, indicating that the vaccine structure was reasonable. Using ProSA-web, the Z-score (Fig 13B) was predicted to be -2.62, the energy diagram (Fig 13C) shows that most of the sequence positions are negative so the 3D structure we built was appropriate.

10.1371/journal.pone.0307877.g013 Fig 13 (A)The Ramachsndran diagram was analyzed using PROCHECK (B)Analyze the Z-score using the Pro-SA server. (C) Energy diagram using the Pro-SA server.

3.15 Molecular docking

Molecular docking is an essential technique in structure-based molecular design and screening as it forecasts the binding patterns and affinities between ligand and receptor molecules through interaction analysis. The HDOCK server was utilized for docking, with model 1 chosen for further analysis. The findings indicated a docking score of -321.20, ligand RMSD of 38.90A, and a confidence score of 0.9684 (Fig 14). The resulting structure was viewed using Discover Studio, and the intermolecular forces were visualized using PyMOL.

10.1371/journal.pone.0307877.g014 Fig 14 Molecular docking result (A)Docking complex, blue is the vaccine structure, green is the TLR4 receptor. (B)The interaction of MEV-TLR4 docking complex was demonstrated using PyMol. (C)The interaction of MEV-TLR4 complex and its 2D image were analyzed by Ligplot.

3.16 Molecular dynamics simulation

In this experiment, MEV-TLR4 interaction was simulated by Gromacs software (Fig 15), and the simulation results were analyzed. RMSD represents the distance between different structures and the same atom. A lower RMSD value indicates that the protein has high stability, while a higher RMSD indicates that the skeleton has undergone a conformational change during the simulation time.(Generally a range of less than 1 is normal) The blue Complex line in (Fig 15A) represents the RMSD after MEV-TLR4 docking. It can be seen that the docking complex has been fluctuating in the range of 0.2–0.6, with the change range less than 1, and the overall change is stable. The results showed that the MEV-TLR4 interaction showed good characteristics and stability on the overall structure stability. RMSF indicates how flexible and vigorous the protein is throughout the simulation. This parameter determines the applicability of the ligand-receptor interaction over simulated time.(Fig 15B–15E)represents the receptor chain with a variation range of less than 0.3nm from the beginning to the end of the simulation, and (Fig 15F) represents the ligand chain with a variation range of 0.18nm from the beginning of the simulation to 0.32nm. The fluctuation is less than 0.3nm, which indicates that the protein-protein interaction has little effect on the stability of the internal structure of the protein molecule. Hydrogen bonding plays an important role in protein conformation preservation. The number of hydrogen bonds between (Fig 15G) remained between 2 and 14 during the simulation. This indicates that MEV-TLR4 interaction has good characteristics and stability. Gyrate was used to evaluate protein folding state. The Gyrate value between MEV-TLR4 in (Fig 15H) was about 4.12nm from the beginning to the end of the simulation. This suggests that the MEV-TLR4 interaction has little effect on the compactness of the overall structure of the protein molecule. SASA is used to assess the surface area of the protein molecules exposed to the solution and to assess the interactions between them and the stability of the protein structure. The SASA value in (Fig 15I) increased from the initial 715nm2 to 725nm2 during the simulation, and the overall trend remained unchanged, indicating that MEV-TLR4 interaction had little effect on the surface characteristics and stability of protein molecules.

10.1371/journal.pone.0307877.g015 Fig 15 Molecular dynamics simulation results.

(A) The RMSD locus of the receptor, ligand, and complex. The abscissa is the running time of MD simulation, and the ordinate is RMSD-value. (B-F) The RMSF locus of acceptor-ligand, the horizontal coordinate is the amino acid residue base in the docking complex, the ordinate is the rmsf value. (G-I) The trajectory of complex hydrogen bond, Gyrate and SASA respectively.

4. Discussion

Infants and young children are particularly susceptible to CNS TB, a debilitating and poorly understood illness. The antibiotic treatment regimen for CNS TB is based on the experience gained from treating TB. However, the increasing resistance to isoniazid, rifampicin, pyrazinamide, and ethambutol has made treatment more challenging [54–56]. The BCG vaccine is not 100% effective against TBM [57]. However, the development of therapeutic vaccines has addressed this limitation. These vaccines are administered to individuals who are already showing symptoms of the disease [58]. In this study, a multi-epitope mRNA therapeutic vaccine was designed using two proteins, PknD (Rv0931c) and Rv0986, from Mtb strain H37Rv. Previous research has indicated that PknD plays a crucial role in invading the brain endothelium and is a significant microbial factor in CNS diseases [18]. Rv0986 is part of the three-gene operon RV0986-88, which shows strong expression in human blood-brain barrier models during infection and is associated with the virulence and adherence of Mtb [19]. Our study reveals that both PknD and Rv0986 exhibit high antigenicity, low sensitization, and non-toxicity, making them promising candidates for developing multi-epitope vaccines. Furthermore, sequence alignment analysis indicates homology between the two proteins, fulfilling the criteria for vaccine design.

SignalP6.0 was utilized to predict the signal peptides of PknD and Rv0986. Signal peptides, short peptides located at the N-terminal of proteins, are prevalent in both prokaryotes and eukaryotes, influencing protein translation [59, 60]. The unique structural characteristics of signal peptides play a crucial role in the folding and transportation of proteins. Substituting the signal peptide can alter the protein’s expression level [61]. Interestingly, our analysis revealed the absence of signal peptides in either egg white, prompting further investigation.

mRNA vaccines induce responses from both CD4+ and CD8+ T cells, activating both the innate and adaptive immune systems in a balanced manner with a specific response to antigens [62]. Helper T lymphocytes (HTL) initiate both humoral and cell-mediated immune responses, while cytotoxic T lymphocytes (CTL) work to halt the spread of viruses by eliminating virus-infected cells and releasing antiviral cytokines. B lymphocytes play a role in humoral immunity, as they are triggered by antigens to produce memory cells and plasma cells that produce various specific antibodies in reaction to antigens [63, 64]. In order to identify appropriate vaccine candidates, we utilized various online tools to predict CTL, HTL, and B cell epitopes(BES) epitopes [65].

Multi-epitope mRNA vaccines have the ability to stimulate an immune response, leading to the production of cellular and humoral immunity [66–68]. In this study, we utilized IEDB, NetCTLpan1.1, and NetMHCIIpan-4.0 to predict CTL and HTL epitopes, while B-cell epitopes were predicted using IEDB and SVMtrip. Specific connectors were used to link antigen epitopes [69]. The mRNA vaccine structure was optimized for translation and stabilization by incorporating various elements such as 5′ m7G cap sequences, a Poly (A) tail, Globin 5’and 3′UTRs flanking the ORF of the mRNA, an adjuvant, the Kozak sequence, a tPA secretion signal sequence, and the MITD sequence. Molecular docking was performed using Xinjiang high frequency alleles to assess the binding of ligands and receptors in vaccine design. Immunological simulations involved three vaccination doses to evaluate the vaccine’s capacity to elicit humoral and cellular immune responses. The results demonstrated the effectiveness of the vaccination in pathogen elimination, as indicated by a significant increase in IFN-γ over time post-injection, providing further validation for the accuracy of our vaccine design [70–72].

Escherichia coli was selected as the host organism for expressing the recombinant protein [73]. The online codon optimization tool Jact was employed to optimize the amino acid sequence of the vaccine. Subsequently, upstream and downstream primers were added, with BamHI and XhoI cleavage sites inserted at the 5’ and 3’ ends, respectively, to facilitate polymerase chain reaction amplification of the target gene. The vaccine sequence was then cloned into the PVAX1 vector, resulting in a recombinant plasmid of 4098 base pairs. Finally, electron agarose gel electrophoresis was conducted to analyze the target gene, vector, and recombinant plasmid.

TLR-4, a receptor known to be recognized by Mtb, has been shown to activate macrophages and dendritic cells, leading to both innate and adaptive immunity [74]. Upon docking the vaccine with TLR-4, we observed a high binding affinity. The stability of the complex was confirmed through the RMSD diagram and further analyzed using molecular dynamics (MD) simulation. In summary, we employed various in silico methods to design an mRNA therapeutic vaccine targeting CNS TB, laying the groundwork for potential future experimental investigations.

Supporting information

S1 Table MHC-I binding prediction results of Rv0986(IEDB).

(DOCX)

S2 Table MHC-I binding prediction results of Rv0986(NetCTLpan-1.1).

(DOCX)

S3 Table MHC-I binding prediction results of PknD(IEDB).

(DOCX)

S4 Table MHC-I binding prediction results of PknD(NetCTLpan-1.1).

(DOCX)

S5 Table MHC-II binding prediction results of Rv0986(IEDB).

(DOCX)

S6 Table MHC-II binding prediction results of Rv0986(NetMHCIIpan version 4.0.

(DOCX)

S7 Table MHC-II binding prediction results of PknD(IEDB).

(DOCX)

S8 Table MHC-II binding prediction results of PknD(NetMHCIIpan version 4.0).

(DOCX)

S9 Table LBEs results of Rv0986 and PknD(SVMtrip).

(DOCX)

S1 Fig Prediction of signal peptides by signal BLAST(PknD).

(TIF)

S2 Fig Prediction of signal peptides by signal BLAST(Rv0986).

(TIF)

S3 Fig Homology comparison by NCBI BlastP.

(TIF)

Abbreviations

BCG Bacillus Calmette-Guérin

CAI Codon adaptation index

CBE Conformational B-cell epitopes

CNS Central nervous system

CTL Cytotoxic T Lymphocyte

GRAVY Grand average of hydropathicity

HLA Human leukocyte antigen

HTL Helper T Lymphocyte

IEDB Immune Epitope Database

LBE Linear B-cell epitopes

MEV Multiple epitope vaccine

MFE Minimal free energy

MHC Major histocompatibility complex

MITD MHC I-targeting domain

Mtb Mycobacterium tuberculosis

PCR Polymerase chain reaction

RMSD Root mean square deviations

RMSF Root-mean-square fluctuation

TB Tuberculosis

TBM tuberculous meningitis

TLR4 Toll-like receptor 4

UTR Untranslated Region

10.1371/journal.pone.0307877.r001
Decision Letter 0
Rojekar Satish Academic Editor
© 2024 Satish Rojekar
2024
Satish Rojekar
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
15 May 2024

PONE-D-24-15467Development of innovative multi-epitope mRNA vaccine against central nervous system tuberculosis using in silico approachesPLOS ONE

Dear Dr. Zhang,

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

Reviewer #5: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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Reviewer #3: Yes

Reviewer #4: N/A

Reviewer #5: Yes

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Reviewer #1: This insilico approach of developing a multiepitope vaccine for CNS TB is an innovative and thoroughly carried out study. The comprehensive bioinformatics analyses, including sequence homology, antigenicity prediction, and epitope mapping, contribute to a comprehensive understanding of the vaccine candidates' immunogenic potential. Additionally, the incorporation of immunoinformatic tools facilitates the prediction of T-cell and B-cell epitopes, further enhancing the vaccine's efficacy and specificity. Despite the remarkable accomplishments of this study, I have few questions and minor concerns that needs to be addressed.

Please provide clarifications on the following questions:

2. Line 229: Claim “the two proteins are highly homologous, suggesting that they may be derived from the same gene and have similar roles in the immune response”. Did you find any link between the two genes in the previous literatures or perform any additional study to investigate the same other than sequence homology?

3. Did you check similarity between analyzed bacterial proteins and host proteome?

4. Did you by any chance used another software such as Signal BLAST to predict signal peptide?

5. Would you have any other already tested CTL and HTL epitope from previous CNS TB studies / published literature which can be used as comparison with your selected epitope candidates to validate how selected epitope candidates stand out or performing better.

Major concern:

1: Most of the figures are hard to read and see. Image quality is poor and written text is unclear. Please replace with good quality images.

Minor concern:

1. There are various formatting errors like invalid spacing between words in headings and paragraphs. Please check thoroughly.

2. Line 40: Rephrase. Why would TB report 2022 will predict cases in 2021. Prediction happens for future.

3. Line 260: “Docking result between CTLs and HLA-A*02:01 seems confusing. Please rephrase this. Does this mean CTL epitope and HLA-A*02:01?

4. Line 261: Similarly, “Docking result between HTLs and HLA-DRB1*07:01” seems confusing.

5. Section 3.8: Please make a table for MEV properties instead of writing the properties in sentences.

6. Section 9.3: Would it be possible to extrapolate dose and time intervals of injection during simulation of immune response?

7. 3.12 and 3.13 heading both has prediction of secondary structure. Why?

8. Table 4 does not have heading for characteristics measured. No row to define which column represents which characteristics. Include antigenicity values in table 5

Reviewer #2: Following are the comments for the authors to improve on the manuscript -

1. Please use a correct font, font size and text settings for the manuscript

2. Define all the abbreviations in the manuscript for the words where they are used for the first time.

3. Please use the correct style for the citations in the manuscript.

4. The quality and resolution of the images needs to be improved, the aspect ratio for some of the images is incorrect.

5. Please remove the characters from the other languages from the manuscript.

6. please try to reduce the number of images from manuscript, possibly by merging them together.

Reviewer #3: Decision: Minor revision.

General comments:

The authors of this study aimed to develop an innovative mRNA therapeutic vaccine targeting a protein associated with central nervous system tuberculosis, addressing the pressing need for effective treatments for this debilitating condition. Employing in silico techniques such as IEDB, NetCTLpan1.1, NetMHCIIpan-4.0, and SVMtrip, they analyzed the antigen epitopes of PknD and Rv0986. Additionally, CTL, HTL, and B cell epitopes were linked together using AAY, GPGPG, and KK linkers. This research was spurred by the lack of therapeutic options for central nervous system tuberculosis, underscoring its urgency.

The utilization of in silico methods paved the way for designing an mRNA therapeutic vaccine targeting central nervous system tuberculosis, offering a foundation for potential future experimental investigations. The outcomes of these assessments yielded promising observations, indicating the potential efficacy of the developed vaccine.

The authors have presented promising results in the manuscript. However, to optimize its effectiveness for our readers, it would be beneficial to address the following comments for streamlining and enhancement. While the manuscript exhibits good writing and organization, improvements in overall English language usage, particularly in grammar, punctuation, and clarity, would enhance engagement for general readers. Please consider revising the language to maximize effectiveness.

Comments:

1. Line 26: Define ‘BCG’ or provide more descriptive information as it appears first time here in this manuscript. Applicable for all abbreviations throughout the manuscript.

2. Line 33-34: please revise this sentence for effectiveness: ‘The results indicate that the vaccine structure shows promise’.

3. Line 40-41: The tense of the following sentence needs to be revised. If updated and most recent data is available, please provide that. ‘The World Health Organization (WHO) Global TB Report 2022 predicts that 41 10.6 million people will be diagnosed with TB in 2021, with 1.6 million fatalities.’

4. Line 42: Consider defining as this term is used many times in the manuscript ‘Central nervous system tuberculosis’.

5. Line 42: ‘TB’ is not defined, please define it.

6. Line 44: If authors could provide specifics in terms of specific mortality rate from the cited article, that would be helpful for readers.

7. Line 77 to 83: This paragraph has three references cited. This is confusing. Have these previous studies been conducted already? if they are, how they are related to the current study is not clear? please revise the language for clarity. If the intention is to cite for the part of the study, please make it clear.

8. Please revise the whole manuscript for formatting, such as: provide space between the last word in the sentence and the reference parenthesis. Applicable throughout the manuscript.

9. Line 87: link for the database should be before full stop. Please check the formatting throughout the manuscript.

10. 1All figures: Presently, all figures appear distorted and oddly stretched. Please recreate them with appropriate resolution and dimensions to ensure clarity. Additionally, consider increasing the font size on the figures for improved legibility. Some content on certain figures is currently illegible due to distortion. This feedback applies to all figures.

11. All section titles: provide a space between the number and section title, applicable for all section titles.

12. Validation of Models: It is advisable to elaborate on the validation process of the models utilized in this study to assess their reliability. Please address any limitations associated with validating these models to provide insight for readers or researchers interested in utilizing them in the future.

Reviewer #4: The study investigates the innovative epitopes mRNA vaccine by in silico approach. They have utilized bioinformatics tools to justify the epitopes could be the potential candidates for mRNA vaccine. Overall paper makes contribution in finding the novel vaccines for CNS TB. Recommendations for improvements are included in the attachment.

Reviewer #5: In this review, Shi et al, explained a in silico approach for the treatment of CNS Tuberculosis. The authors have explained a detail approach from the selection of the target sequences to codon optimization probabilities to ensure it’s translation in Human host. The authors needs to address some of the queries

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: Yes: Pallapati Anusha Rani

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Attachment Submitted filename: Reviewer comments.docx

Attachment Submitted filename: Comments_PONE-D-24-15467.docx

Attachment Submitted filename: Reviewer Comments _05-14-2024.docx

10.1371/journal.pone.0307877.r002
Author response to Decision Letter 0
Submission Version1
21 Jun 2024

Dear editor & reviewers,

Thank you for your kind patience. We have revised our manuscript (Submission ID: PONE-D-24-15467) carefully by following the guidance provided by the editor and reviewers. Here are the point-by-point responses for the editor and reviewers’ comments.

Response to Academic Editor

1.Please ensure that your manuscript meets PLOS ONE's style requirements.

Response:Dear academic editor,thank you for reviewing our research article and providing valuable suggestions for revisions.We have changed the style of the literature according to the requirements of the PLOS ONE journal reference.

2.Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work.

Response:Dear academic editor,thank you for reviewing our research article and providing valuable suggestions for revisions. We are very willing to share the code, We have stored laboratory program in a separate protocols.io,generate DOI: dx.doi.org/10.17504/protocols.io.81wgbzr31gpk/v1.; The generated link is: https://www.protocols.io/private/C80855412E6C11EFA80E0A58A9FEAC02, is available for editors and referees to check. We added this section to the Data availability statement. If you need any information from us, please feel free to communicate with us.

3. Please upload a new copy of Figures 1, 2, 3A, 3B, 6, 7, 8, 9A, 9B, 9C, 12A and 14 as the detail is not clear.

Response:Dear academic editor,thank you for reviewing our research article and providing valuable suggestions for revisions.For ease of understanding, we have reuploaded all images as per the magazine's image requirements.

Response to Reviewer1

This insilico approach of developing a multiepitope vaccine for CNS TB is an innovative and thoroughly carried out study. The comprehensive bioinformatics analyses, including sequence homology, antigenicity prediction, and epitope mapping, contribute to a comprehensive understanding of the vaccine candidates' immunogenic potential. Additionally, the incorporation of immunoinformatic tools facilitates the prediction of T-cell and B-cell epitopes, further enhancing the vaccine's efficacy and specificity. Despite the remarkable accomplishments of this study, I have few questions and minor concerns that needs to be addressed.

Response:Dear reviewer,thank you for reviewing our research article and providing valuable suggestions for revisions. We take your feedback seriously and have made corresponding revisions based on your suggestions. Here is our response to your proposed modification suggestions:

1.Line 229: Claim “the two proteins are highly homologous, suggesting that they may be derived from the same gene and have similar roles in the immune response”. Did you find any link between the two genes in the previous literatures or perform any additional study to investigate the same other than sequence homology?

Response:Dear reviewer, thank you for your valuable suggestions.In previous studies, pknD and Rv0986 genes have been shown to be involved in invasion of the central nervous system and are specifically expressed in the central nervous system, missing in lung tissue. In the Uniprot database, both genes were found to be from the H37Rv strain. The references are as follows：

(1).Be NA, Bishai WR, Jain SK. Role of Mycobacterium tuberculosis pknD in the pathogenesis of central nervous system tuberculosis. BMC Microbiol. 2012;12:7. Published 2012 Jan 13. doi:10.1186/1471-2180-12-7；

(2).Be NA, Lamichhane G, Grosset J, et al. Murine model to study the invasion and survival of Mycobacterium tuberculosis in the central nervous system [published correction appears in J Infect Dis. 2009 Jan 15;199(2):290]. J Infect Dis. 2008;198(10):1520-1528. doi:10.1086/592447；

(3).Jain SK, Paul-Satyaseela M, Lamichhane G, Kim KS, Bishai WR. Mycobacterium tuberculosis invasion and traversal across an in vitro human blood-brain barrier as a pathogenic mechanism for central nervous system tuberculosis. J Infect Dis. 2006;193(9):1287-1295. doi:10.1086/502631.

These articles are cited in manuscripts.

2.Did you check similarity between analyzed bacterial proteins and host proteome?

Response:Dear reviewer, thank you for your valuable suggestions.We build a multi-epitope vaccine, and the sequence used to build the vaccine is just a part of the protein. In order to avoid homology between the vaccine and the host, we used the BlastP database to compare homology between the vaccine and Homo sapiens (TaxID: 9606). An E value greater than 0.5 is considered non-homologous and suitable for vaccine construction. The results showed that no significant correlation was found between the two. We have added this section in Line145-149 and Line 324-326.The screenshot of the result is in the supporting information and is called S3(TIF).

3.Did you by any chance used another software such as Signal BLAST to predict signal peptide?

Response:Dear reviewer, thank you for your valuable suggestions.According to your suggestion, we used Signal BLAST to predict the signal peptides of the proteins PknD and Rv0986 again, and the results showed that there were no signal peptides here.The screenshot of the result is in the supporting information and is called S1(TIF), S2 (TIF).

4.Would you have any other already tested CTL and HTL epitope from previous CNS TB studies / published literature which can be used as comparison with your selected epitope candidates to validate how selected epitope candidates stand out or performing better.

Response:Dear reviewer, thank you for your valuable suggestions.The proteins we selected appear specifically in the central nervous system, and there is currently no literature analyzing multiepitope vaccines for CNS TB, which is also the innovation of our paper. However, there are many literature reports on tuberculosis multi-epitope vaccines. Compared with their selected CTL/HTL epitopes, the epitopes we selected all have higher antigenicity, non-allergenic, non-toxic, and good physicochemical properties. The references are as follows:

(1).Andongma BT, Huang Y, Chen F, et al. In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis. Comput Struct Biotechnol J. 2023;21:991-1004. Published 2023 Jan 16. doi:10.1016/j.csbj.2023.01.019

(2).Al Tbeishat H. Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications. Sci Rep. 2022;12(1):4645. Published 2022 Mar 17. doi:10.1038/s41598-022-08506-4

(3).Jiang F, Han Y, Liu Y, et al. A comprehensive approach to developing a multi-epitope vaccine against Mycobacterium tuberculosis: from in silico design to in vitro immunization evaluation. Front Immunol. 2023;14:1280299. Published 2023 Nov 2. doi:10.3389/fimmu.2023.1280299

Major concern

1: Most of the figures are hard to read and see. Image quality is poor and written text is unclear. Please replace with good quality images.

Response:Dear reviewer, thank you for your valuable suggestions.We have made changes to the quality and resolution of all images in accordance with the magazine's image requirements.

Minor concern:

1. There are various formatting errors like invalid spacing between words in headings and paragraphs. Please check thoroughly.

Response:Dear reviewer, thank you for your valuable suggestions.We have thoroughly checked the word spacing between the words in headings and paragraph in the manuscript to ensure compliance with the journal requirements.

2.Line 40: Rephrase. Why would TB report 2022 will predict cases in 2021. Prediction happens for future.

Response:Dear reviewer,we are sorry for the tense error due to our negligence. We have rephrase the sentence in line 39-40.

3.Line 260: “Docking result between CTLs and HLA-A*02:01 seems confusing. Please rephrase this. Does this mean CTL epitope and HLA-A*02:01?

Response:Dear reviewer,We are very sorry for the error of expression due to our carelessness. In line 260, we want to express the choice of an ITAPWGIAV epitope in the CTLs to interface with HLA-A*02:01. We have corrected it in the line 302.

4. Line 261: Similarly, “Docking result between HTLs and HLA-DRB1*07:01” seems confusing.

Response:We are very sorry for the error of expression due to our carelessness. In line 261, we want to express that one of the FQFFNLIPTLTVLEN opes in HTLs is selected to interface with HLA-DRB1*07:01. We have corrected it in the line 303-304.

5.Section 3.8: Please make a table for MEV properties instead of writing the properties in sentences.

Response:Dear reviewer, thank you for your valuable suggestions.We have made a table for MEV properties and named it Table 6.

6.Section 3.9: Would it be possible to extrapolate dose and time intervals of injection during simulation of immune response?

Response:Dear reviewer,we have described in section 2.10 three doses of the vaccine for 1 day, 84 days, 168 days, each dose of 1000 units.Based on the simulations, the doses and intervals we injected were reasonable. We have added this sentence to line 349.

7.3.12 and 3.13 heading both has prediction of secondary structure. Why?

Response:Dear reviewer, 3.12 predicts the secondary structure of mRNA in order to ensure the effectiveness of mRNA transcription. 3.13 predicts the secondary structure of the vaccine protein in order to ensure the validity of the translation. For ease of understanding, we have been explained in the 2.12 and 2.13.

8.Table 4 does not have heading for characteristics measured. No row to define which column represents which characteristics. Include antigenicity values in table 5.

Response:Dear reviewer, we are sorry for the problem caused by our oversight. We have added a title to Table4. The antigenicity value is added in Table5. Finally, the antigenicity of MEV we constructed is 0.9510, which has strong antigenicity.

Response to Reviewer2

1.Please use a correct font, font size and text settings for the manuscript

Response:Dear reviewer, thank you for your valuable suggestions.We have used the correct font and font size for the text in the manuscript as required by the journal. Strains and restriction enzyme names are used in italics.

2.Define all the abbreviations in the manuscript for the words where they are used for the first time.

Response:Dear reviewer, thank you for your valuable suggestions.We have defined all the abbreviations used for the first time in the manuscript.

3.Please use the correct style for the citations in the manuscript.

Response:Dear reviewer, thank you for your valuable suggestions.We have changed the style of the literature according to the requirements of the PLOS ONE journal reference.

4. The quality and resolution of the images needs to be improved, the aspect ratio for some of the images is incorrect.

Response:Dear reviewer, thank you for your valuable suggestions.We have made changes to the quality and resolution of all images in accordance with the magazine's image requirements.

5. Please remove the characters from the other languages from the manuscript.

Response:Dear reviewer, thank you for your valuable suggestions.We have checked the language and font in the manuscript to make sure it meets the requirements of the journal.

6.please try to reduce the number of images from manuscript, possibly by merging them together.

Response:Dear reviewer, thank you for your valuable suggestions.We have merged Fig3A,Fig3B into Fig3, Fig9A,B,C into Fig9, Fig11A,B into Fig11, Fig12A,B,C,D into Fig12, Fig13A,B,C into Fig13, Fig14A,B,C into Fig14.

Response to Reviewer3

General comments:

The authors of this study aimed to develop an innovative mRNA therapeutic vaccine targeting a protein associated with central nervous system tuberculosis, addressing the pressing need for effective treatments for this debilitating condition. Employing in silico techniques such as IEDB, NetCTLpan1.1, NetMHCIIpan-4.0, and SVMtrip, they analyzed the antigen epitopes of PknD and Rv0986. Additionally, CTL, HTL, and B cell epitopes were linked together using AAY, GPGPG, and KK linkers. This research was spurred by the lack of therapeutic options for central nervous system tuberculosis, underscoring its urgency.

The utilization of in silico methods paved the way for designing an mRNA therapeutic vaccine targeting central nervous system tuberculosis, offering a foundation for potential future experimental investigations. The outcomes of these assessments yielded promising observations, indicating the potential efficacy of the developed vaccine.

The authors have presented promising results in the manuscript. However, to optimize its effectiveness for our readers, it would be beneficial to address the following comments for streamlining and enhancement. While the manuscript exhibits good writing and organization, improvements in overall English language usage, particularly in grammar, punctuation, and clarity, would enhance engagement for general readers. Please consider revising the language to maximize effectiveness

Response:Dear reviewer,thank you for your full recognition of our manuscript, we have fully considered your comments, made grammatical changes to the full text, checked the use of punctuation, and re-uploaded all images in accordance with the magazine's requirements for image clarity. Here is our response to your comments.

1.Line 26: Define ‘BCG’ or provide more descriptive information as it appears first time here in this manuscript. Applicable for all abbreviations throughout the manuscript.

Response:Dear reviewer, thank you for your valuable suggestions.We have defined BCG and added it to the abbreviations at the end of the manuscript .

2. Line 33-34: please revise this sentence for effectiveness: ‘The results indicate that the vaccine structure shows promise’.

Response:Dear reviewer, thank you for your valuable suggestions.We have rephrased the sentence in lines 32.

3.Line 40-41: The tense of the following sentence needs to be revised. If updated and most recent data is available, please provide that. ‘The World Health Organization (WHO) Global TB Report 2022 predicts that 41 10.6 million people will be diagnosed with TB in 2021, with 1.6 million fatalities.

Response:Dear reviewer, we are sorry for the tense error due to our negligence. We have rephrase the sentence in line 39-40.

4.Line 42: Consider defining as this term is used many times in the manuscript ‘Central nervous system tuberculosis’.

Response:Dear reviewer, thank you for your valuable suggestions.We have defined central nervous system tuberculosis in line 23.

5.Line 42: ‘TB’ is not defined, please define it.

Response:Dear reviewer, thank you for your valuable suggestions.We have defined TB in line 23.

6.Line 44: If authors could provide specifics in terms of specific mortality rate from the cited article, that would be helpful for readers.

Response:Dear reviewer, thank you for your valuable suggestions.We have provided specific details of mortality in line 42-44 and have cited relevant literature.The references are as follows:

(1).Wilkinson RJ, Rohlwink U, Misra UK, et al. Tuberculous meningitis. Nat Rev Neurol. 2017;13(10):581-598. doi:10.1038/nrneurol.2017.120

(2).Mezochow A, Thakur K, Vinnard C. Tuberculous Meningitis in Children and Adults: New Insights for an Ancient Foe. Curr Neurol Neurosci Rep. 2017;17(11):85. Published 2017 Sep 20. doi:10.1007/s11910-017-0796-0

7.Line 77 to 83: This paragraph has three references cited. This is confusing. Have these previous studies been conducted already? if they are, how they are related to the current study is not clear? please revise the language for clarity. If the intention is to cite for the part of the study, please make it clear.

Response:Dear reviewer, thank you for your valuable suggestions.In lines 77-83, we only want to refer to the method part of the literature. The literature is relevant to the design of multi-epitope vaccines, so these methods are also applicable in this study.We have explained this in lines 73-74

8.Please revise the whole manuscript for formatting, such as: pr

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0307877.r003
Decision Letter 1
Rojekar Satish Academic Editor
© 2024 Satish Rojekar
2024
Satish Rojekar
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
15 Jul 2024

Development of innovative multi-epitope mRNA vaccine against central nervous system tuberculosis using in silico approaches

PONE-D-24-15467R1

Dear Dr. Zhang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Satish Rojekar, Ph.D.

Academic Editor

PLOS ONE

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Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

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Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

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Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

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Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

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Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The review still need minor revision for formatting errors to enhance the readability of the article. Some of the images are still not clear and needs replacement.

Reviewer #3: Thank you for your prompt and comprehensive responses to my critical questions regarding your manuscript. I appreciate the rigor and thoroughness with which you addressed each point. Your detailed explanations have satisfactorily clarified my concerns, and I am now confident in the validity and robustness of your work.

Based on your responses and the thorough explanations provided, I am pleased to accept your manuscript for publication.

Reviewer #4: I think that the authors have adequately addressed the comments made in the

revised version of the manuscript. Therefore, I have no further comments.

Reviewer #5: In this review, Shi et al, explained a in silico approach for the treatment of CNS Tuberculosis. The authors have explained a detail approach from the selection of the target sequences to codon optimization probabilities to ensure it’s translation in Human host.

Authors addressed all of the major concerns mentioned in the previous report.

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: Yes: PALLAPATI ANUSHA RANI

**********

10.1371/journal.pone.0307877.r004
Acceptance letter
Rojekar Satish Academic Editor
© 2024 Satish Rojekar
2024
Satish Rojekar
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
26 Aug 2024

PONE-D-24-15467R1

PLOS ONE

Dear Dr. Zhang,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

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on behalf of

Dr. Satish Rojekar

Academic Editor

PLOS ONE
==== Refs
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