Skip to main content
NCBI home page
Journal of Genetic Engineering & Biotechnology logoLink to Journal of Genetic Engineering & Biotechnology
. 2024 Jan 23;22(1):100353. doi: 10.1016/j.jgeb.2024.100353

Subtractive genomics study of Xanthomonas oryzae pv. Oryzae reveals repurposable drug candidate for the treatment of bacterial leaf blight in rice

Ishtiaque Ahammad a,1, Tabassum Binte Jamal a,1, Anika Bushra Lamisa a,1, Arittra Bhattacharjee a, Nayeematul Zinan b, Md Zahid Hasan Chowdhury b, Shah Mohammad Naimul Islam b, Kazi Md Omar Faruque c, Zeshan Mahmud Chowdhury a, Mohammad Uzzal Hossain a, Keshob Chandra Das d, Chaman Ara Keya e, Md Salimullah d,⁎
PMCID: PMC10980872  PMID: 38494267

Highlights

  • •

    Selection of non-homologous and essential proteins as potential drug targets to minimize cross-reactivity.

  • •

    Pathogen-specific proteins involved in unique metabolic pathways were prioritized.

  • •

    dnaN-encoded Beta sliding clamp protein was identified as a promising drug target due to its high connectivity.

  • •

    Molecular docking and MD simulation demonstrated stable complexes formation between the drug and the target protein.

Keywords: Bacterial leaf blight, Xanthomonas oryzae pv. oryzae, Rice, Subtractive genomics, PPI network

Abstract

Background

Xanthomonas oryzae pv. oryzae is a plant pathogen responsible for causing one of the most severe bacterial diseases in rice, known as bacterial leaf blight that poses a major threat to global rice production. Even though several experimental compounds and chemical agents have been tested against X. oryzae pv. oryzae, still no approved drug is available. In this study, a subtractive genomic approach was used to identify potential therapeutic targets and repurposible drug candidates that could control of bacterial leaf blight in rice plants.

Results

The entire proteome of the pathogen underwent an extensive filtering process which involved removal of the paralogous proteins, rice homologs, non-essential proteins. Out of the 4382 proteins present in Xoo proteome, five hub proteins such as dnaA, dnaN, recJ, ruvA, and recR were identified for the druggability analysis. This analysis led to the identification of dnaN-encoded Beta sliding clamp protein as a potential therapeutic target and one experimental drug named [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid that can be repurposed against it. Molecular docking and 100 ns long molecular dynamics simulation suggested that the drug can form stable complexes with the target protein over time.

Conclusion

Findings from our study indicated that the proposed drug showed potential effectiveness against bacterial leaf blight in rice caused by X. oryzae pv. oryzae. It is essential to keep in consideration that the procedure for developing novel drugs can be challenging and complicated. Even the most promising results from in silico studies should be validated through further in vitro and in vivo investigation before approval.

1. Background

Rice constitutes a significant agricultural product and serves as a fundamental component of the diet for approximately 50 % of the global population. Various pathogens are responsible for global reduction in rice yield1. Xanthomonas oryzae pv. oryzae (Xoo) belonging to the Xanthomonadaceae family is one of them. It is a gram-negative, rod-shaped bacterium and produces extracellular polysaccharides, iron-chelating siderophores, and type III-secretion dependent effectors which are essential for virulence2. This pathogen causes a vascular disease called bacterial leaf blight (BLB) in rice crops which develops from the invasion of Xoo in rice leaves via lesions, openings, and hydathodes at the leaf tip and multiplies in the intercellular spaces of the underlying epitheme3. Besides, necrotic lesions are produced by Xoo due to the active multiplication when it passes through the xylem of the rice plant and thus develop symptoms of the disease2. Thus lesion length and bacterial growth rate could be served as markers of the progression of the blight diseases4.

BLB disease has emerged as one of the most detrimental rice diseases in recent decades, particularly in South East Asia5. It has been found to cause yield losses of approximately 30–50 % in rice crops globally6. The concern of this pathogenic disease grew heavier since its transmission and dissemination was reported in Africa and America as well. It has also become a major problem in hybrid rice cultivation areas like Bangladesh, China, Vietnam, Myanmar and some other countries as well7. In the early decades, Xoo was treated with a bordeaux mixture, antibiotics, various copper and mercurial compounds which were administered at very low concentrations8. The current situation has gotten worse due to the emergence of resistance against various bactericides that used to control Xoo. This resistance has developed as a result of the frequent and prolonged use of these bactericides9, 10. On the other hand, the persistent usage of heavy metals and the rise of multidrug resistance strains have resulted in the emergence of novel strategies. It is reliable as well as environmentally friendly to treat bacterial blight with resistant cultivars8. However, the discovery of new antibacterial agents that function through novel targets or different mechanisms of action is still an effective approach to limit the growth of Xoo.

The conventional approach to drug development requires investing a large amount of both time and resources11. In recent years, notable advancements in computational biology and the application of bioinformatics approaches have contributed to the reduction of dependency on traditional laboratory-based experimental investigations. These approaches enabled the identification of potential therapeutic candidates for repurposing, the design of structure-based analogs, and the development of drugs, investigation of host-pathogen interactions, and among other things12, 13. One of these bioinformatics strategies is subtractive genomics, which compares the pathogen and host proteomes to identify non-homologous proteins that are unique to the pathogen and have distinct metabolic pathways and therapeutic properties necessary for its survival14, 15. Considering their lack of interaction with the host, it is plausible to suggest that these essential proteins could serve as potential therapeutic targets16.

In this study, the whole Xoo proteome was analyzed using a subtractive genomics approach. We utilized a range of computational tools, software applications, and biological databases in order to find the proteins that are essential for the pathogen's viability. Subsequently, drug candidates targeting the most promising proteins were subjected to screening and evaluated using molecular docking and dynamics simulation experiments.

2. Methods

A subtractive genomic approach was utilized to identify novel therapeutic targets against the pathogenic bacterium Xoo. Fig. 1 provides a brief overview of the workflow.

Fig. 1.

Fig. 1

Open in a new tab

Overall work flow of subtractive genomic study against Xoo. The study was conducted using 10 sequential steps to ensure that it was carried out in a systematic and organized manner.

2.1. Retrieval of whole proteome

The complete proteome of Xoo strain KACC10331 was retrieved from the Uniprot Proteomes database in the FASTA file format17.

2.2. Identification of non-paralogous sequences

The complete proteome of Xoo was submitted to the CD-HIT suite to remove the paralogous sequences. The threshold value was set at 0.6 meaning that sequences with 60 % or greater similarity were considered as redundant sequences. The non-paralogous proteins were then selected for subsequent analysis18.

2.3. Identification of non-homologous proteins

Further analysis was directed towards identifying the sequences which are not homologous to the host (rice) proteome. The host proteome was retrieved from Uniprot Proteomes database (Proteome ID- UP000059680). The selected proteins were eventually analyzed using BLASTp search (E-value threshold of 10-5) against the Oryza sativa proteome. The resulting hits were divided into “Hits Found,” which are homologous sequences between the host and the pathogen, and “No-Hits Found,” which are non-homologous sequences. The non-homologous sequences that have no association with the Oryza sativa were selected for analysis afterwards19.

2.4. Identification of essential non-homologous proteins

To identify essential non-homologous proteins of Xoo, Database of Essential Genes (DEG) was utilized. As a result, proteins with E ≤ 10−100, bit score > 100, and identity 25 % were considered as essential proteins20.

2.5. Metabolic pathway analysis

Using KEGG Automatic Annotation Server (KAAS), metabolic pathways of Xoo essential proteins were analyzed. The functional characteristics of genes are annotated by KAAS through the utilization of BLAST searches against the KEGG GENES database, which has been manually curated. For each protein, KO numbers were assigned, and KEGG pathways were generated automatically. Proteins with KOs that corresponded to both the pathogen and the host were eliminated21.

2.6. Subcellular localization analysis

The target proteins of Xoo were submitted in the PSORTb 3.0 web server to identify subcellular localization of the proteins22. The fundamental concept of Subcellular Localization (SCL) is to use BLAST to search for all required non-homologous proteins against proteins with known subcellular locations. Using this approach, the tools such as PSORTb 3.0 classify proteins into various categories based on their location within the cell.

2.7. Protein-protein interaction analysis

The STRING Database was used for the establishment of the protein–protein interactions (PPI) network23. The PPI network was visualized using the Cytoscape software24. The PPI network comprises both direct (physical) and indirect (functional) interactions between proteins. For the centrality analysis of the proteins, CytoNCA app was used25. Top 5 proteins in the PPI network based on the highest number of interactions (degree parameter) were regarded as the 'hub' proteins.

2.8. Druggability of essential protein

In order to evaluate their potential as drug targets, the cytoplasmic hub proteins were searched in the DrugBank database using the BLASTp algorithm. Proteins with a bit score higher than 100 and an E-value less than 10-100 were considered potential druggable targets. Novel drug targets of Xoo were identified that did not exhibit a significant degree of matching with the drug DrugBank database26.

2.9. Molecular docking

The chemical structure of the selected drug repurposable candidate was retrieved from DrugBank. It was docked against the target cytoplasmic protein using Webina 1.0.3 web server (https://durrantlab.pitt.edu/webina/). Pymol (https://pymol.org/2/) and Poseview (https://proteins.plus/) were utilized to visualize the protein–ligand interactions after successful molecular docking.

2.10. Molecular dynamic simulation

Following the process of molecular docking, a Molecular Dynamics (MD) simulation of 100 ns (ns) period was carried out for both the apo-receptor and the drug-receptor complex. The GROningen MAchine for Chemical Simulations (GROMACS) software, specifically version 2020.6, was utilized for this purpose27. The proteins were surrounded by the Transferable Intermolecular Potential 3P (TIP3) water model28. The protein-water system was energetically minimized with CHARMM36m force-field29. K+ and Cl- ions were added to make the system neutral. After executing energy minimization, the system underwent isothermal-isochoric (NVT) and isobaric (NPT) equilibration processes. Then, a 100 ns production MD simulation was done. In order to assess the dynamic characteristics of the proteins, various parameters including Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), and Solvent Accessible Surface Area (SASA) were assessed.

3. Result

This study utilized a subtractive genomics approach to identify potential drug targets for Xoo. For this purpose, the complete set of proteins of this bacterial pathogen were screened using several bioinformatics tools. Fig. 2 summarizes the step-wise filtering process that was employed to narrow down the list of candidate proteins.

Fig. 2.

Fig. 2

Open in a new tab

Summary of protein counts for identifying novel drug targets in Xoo. The data represented in this chart pertains to the number of proteins of various types including selected non-paralogous proteins, essential proteins, non-homologous proteins, hub proteins, and drug target proteins.

3.1. Identifying non-paralogous proteins through paralogous protein exclusion

Out of the 4382 proteins present in Xoo proteome, 745 were identified as paralogous with 60 % identity using the CD-HIT suite. Since these proteins were redundant as potential drug targets, they were excluded from further analysis due to the potential risk of cross-reactivity and undesired toxicity in rice plants. This resulted in a remaining set of 3637 non-paralogous proteins.

3.2. Uncovering non-homologous proteins excluding homologs

Proteins extracted from the upstream analysis were analyzed using the BLASTp to compare them against the proteome of Oryza sativa, with the aim of identifying any proteins that exhibit homology. The BLASTp analysis identified a total of 1399 proteins that showed homology. Consequently, these proteins were excluded from further downstream analysis due to concerns regarding the potential for cross-reactivity and undesired toxicity in rice. As a result, a selection of 2238 non-homologous proteins was chosen for the following analysis.

3.3. Non-homologous protein analysis identifies proteins essential to xoo's survival

In addition, a BLASTp analysis was conducted using the DEG database in order to identify essential proteins that might have potential as viable candidates for drug discovery. As a result, 404 essential proteins were chosen highlighting their significance as promising drug targets.

3.4. Metabolic pathway analysis unveils essential proteins unique to Xoo

The essential proteins identified from the DEG analysis were evaluated for their involvement in metabolic pathways using the KEGG-KASS server. The results identified 321 unique proteins that were involved in metabolic pathways specific to Xoo and were not present in rice. A detailed list of the essential genes, and their KO is depicted in Supplementary Table 1.

3.5. Subcellular localization predicts cytoplasmic proteins as potential drug targets

Protein localization plays a crucial role in identifying therapeutic targets as many proteins can be present in multiple locations. It is one of the major factors that influences the design of new drugs, and therefore, serves as an important parameter for drug development. For instance, cell membrane proteins are often targeted for vaccine development, while cytoplasmic proteins are commonly targeted for therapeutic purposes. Out of the 321 essential proteins identified, 153 of them were located in the cytoplasm, 97 were present in the cytoplasmic membrane, 21 were located in the outer membrane, 10 proteins were classified as periplasmic proteins, while 40 proteins remained unclassified as depicted in Fig. 3.

Fig. 3.

Fig. 3

Open in a new tab

Prediction of the subcellular localization of the proteins involved in the pathogen specific pathways.

3.6. Revealing hub proteins through PPI network analysis

The PPI network analysis revealed the presence of a total of 141 proteins (also referred to as nodes) and 454 interactions (also referred to as edges) within the network. The network was further analyzed using the CytoNCA plugin in Cytoscape, which identified five hub proteins based on their degree parameter representing the number of connections each protein has with others within the network. The proteins identified as hubs were dnaA, dnaN, recJ, ruvA, and recR (Fig. 4).

Fig. 4.

Fig. 4

Open in a new tab

A network of protein–protein interactions (PPI) of pathogen-specific cytoplasmic membrane proteins. The five hub proteins with the highest connectivity have been highlighted in yellow. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.7. Drug target identification leading to potential therapeutic candidates

The DrugBank web server was used to analyze the 5 hub proteins. It uncovered that dnaN and recR had corresponding drugs [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid and Imidazole respectively. After analyzing the protein–protein interactions, it was found that dnaN had more connectivity and was more regulatory than the other hub protein. Therefore, dnaN was chosen for further analysis as a potential drug target. The remaining hub proteins (dnaA, recJ, and ruvA) did not match to any drugs in the DrugBank database, suggesting that they can be considered as potential novel therapeutic targets.

3.8. Docking analysis highlights significant molecular interactions between the drug and target protein

Following drug target identification, a molecular docking analysis was conducted to study the interaction between the selected drug ([(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid) and predicted structures of the dnaN-encoded Beta sliding clamp protein. The results exhibited a substantial binding energy value of −5.7 kcal/mol, indicating significant molecular interactions between the drug and protein. The docking analysis also revealed that Asp238A and certain Bromide molecules were involved in the interaction between the drug and protein as shown in Fig. 5.

Fig. 5.

Fig. 5

Open in a new tab

2D and 3D interaction diagram between the target protein and the candidate drug molecule. The diagram illustrates the key interactions between the protein and the drug, which occur through Asp238A and specific Bromide molecules.

3.9. MD simulation assesses conformational changes and stability of drug-receptor complex

To assess conformational changes, the RMSD was calculated. A significant alteration in the RMSD value indicates structural changes due to ligand binding. In Fig. 6, the RMSD profile of the apo-receptor is represented by the green line, while the drug-receptor complex is depicted by the blue line. The RMSD value for the drug-receptor complex measured approximately 0.3 nm, whereas the apo-receptor showed an approximate value of 0.4 nm. Notably, a significant conformational difference was observed after 60 ns (Fig. 6). The mobility of proteins in the presence or absence of the selected drug is illustrated by the RMSF. Greater flexibility at specific amino acid positions is indicated by higher RMSF values. In Fig. 7, the RMSF profile of the proteins is displayed. RMSF peaks were observed at approximately the 100th residue, 150th residue, and 300th residue for the protein, while significantly lower mobility was noted in the drug-receptor complex. To predict the stability of the hydrophobic core in proteins, SASA was implemented. A positive correlation between the SASA value and the probability of protein destabilization due to solvent accessibility exists. The SASA values for the apo-receptor and drug-receptor were approximately 180 nm2 and 185 nm2, respectively (Fig. 8). The drug-protein complex consistently exhibited a higher SASA value throughout the simulation. The degree of protein compactness was assessed using the Rg. In Fig. 9, the Rg value for the drug-receptor complex was found to be less than 2.6 nm, while the apo-receptor exhibited a value exceeding 2.6 nm at the end of the simulation.

Fig. 6.

Fig. 6

Open in a new tab

The RMSD profile of the apo-receptor (Green) and receptor-drug complex (Blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7.

Fig. 7

Open in a new tab

The RMSF profile of the apo-receptor (Green) and receptor-drug complex (Blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 8.

Fig. 8

Open in a new tab

The SASA profile of the apo-receptor (Green) and receptor-drug complex (Blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 9.

Fig. 9

Open in a new tab

The Rg profile of the apo-receptor (Green) and receptor-drug complex (Blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

Rice is a crucial dietary component for large parts of the world and contributes to 20 % of their daily caloric intake30. However, various biotic and abiotic stresses have emerged as significant challenges in its cultivation31, 32. Among these, bacterial leaf blight (BLB) caused by Xoo is one of the most damaging and severe diseases of rice in Asia33. So far several compounds have been studied to manage BLB but the detrimental effects of conventional pesticides on both humans and the environment have highlighted the need for the development of novel or repurposable drugs to treat this disease34, 35. In this regard, subtractive genomics is a promising approach to identify specific therapeutic targets for the pathogen as it potentially reduces the risk of off-target effects in the development of repurposable drugs36.

In this study, a comprehensive analysis of 4382 proteins in Xoo was conducted utilizing various bioinformatics tools and databases. In order to remove duplicate proteins, a clustering approach was used, resulting in a final set of 3637 distinct proteins. Since some of these proteins may be similar to those found in humans, targeting them could be life-threatening37. In addition, targeting non-homologous proteins has been suggested to be a viable approach for the development of novel drugs39. Thus, non-homologous proteins were selected from the distinct set to minimize cross-reactivity and adverse effects38. Moreover, in bacteria, essential genes are crucial for survival, and as such, they are the preferred targets for vaccine and antibacterial drugs development39, 40. From the non-redundant protein set, essential proteins were identified through screening. To find effective drug targets, it is important to focus on unique pathways that are specific to pathogens. Proteins involved in these pathways are pathogen-specific. It is already known that non-homologous proteins involved in more than one pathway are significant targets41. However, being unique and involved in metabolic pathways is not enough as it may not have therapeutic benefits. Therefore, the most attractive drug targets are pathogen-specific proteins that are both unique and essential42. Additionally, different tools were employed to determine localization of these proteins. This serves as an important factor during the identification of suitable and effective drug targets43. Membrane-bound proteins are often challenging to purify and assess, making cytoplasmic proteins more desirable drug targets44. Out of the unique pathways specific to the pathogen, 153 cytoplasmic proteins were identified for downstream analysis. PPIs play a fundamental role in diverse biological processes, and hub proteins with a greater number of interactions in the network compared to other proteins can aid in selecting or prioritizing targets during drug development45, 46. From the PPI network, dnaN was identified as a promising drug target due to its high connectivity and regulatory role.

Previous research has underlined the dnaN-encoded Beta sliding clamp protein as a promising candidate for drug development against various pathogens, including Mycobacterium tuberculosis and Helicobacter pylori47, 48. However, this specific study is unique in its focus on exploring the potential of dnaN in Xoo as a target for drug development, an aspect not previously explored. The beta clamp sliding protein plays a pivotal role in DNA replication by forming a ring around the DNA, securely attaching the DNA polymerase to the template, facilitating rapid and continuous DNA synthesis49. The specific functions of the DNA polymerase III beta sliding clamp in Xoo include enhancing the processivity of DNA polymerase III, coordinating with various replication proteins to ensure smooth replication, and potentially regulating the timing and frequency of replication events based on internal cellular cues and environmental signals50.

Molecular docking was performed to evaluate the effectiveness of the drug candidate, [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid, in inhibiting the activity of dnaN-encoded Beta sliding clamp protein. Given that the beta clamp protein plays a crucial role in DNA replication by tethering the DNA polymerase to the template and ensuring swift and accurate DNA synthesis, the inhibition of this protein by the identified drug has significant implications for the replication process.The strong interaction between the drug and the dnaN-encoded Beta sliding clamp protein suggests that the drug could effectively disrupt the binding of the clamp protein to the DNA template, thereby impeding the progression of the DNA polymerase during replication. Molecular dynamics simulation was conducted to assess the possible impact of the potential drug on the structural dynamic properties of the target protein. In presence of the drug, the target protein showed altered conformational change in the protein structure. This was evident from the reduced RMSD value of Fig. 6. RMSF analysis revealed that the binding of the selected drug decreased the mobility of a number of amino acid residues, particularly at positions ∼ 100 th, ∼150 th, and ∼ 300 th of dnaN. The reduced mobility of specific amino acid residues could further hinder the ability of the beta clamp protein to properly encircle the DNA and support the DNA polymerase's processivity. The SASA analysis indicated that drug binding led to an increase in the solvent accessible surface of the protein. This suggested that the selected drug increased the solubility of the hydrophobic center of the protein which might damage the internal structure and abolish its function. Moreover, the increased solvent accessibility of the hydrophobic core, coupled with the altered protein structure, could potentially interfere with the beta clamp protein's interaction with other replication proteins, leading to the overall impairment of the DNA replication process. Lastly, the Rg analysis revealed that the binding of the drug to the protein led to a more condensed protein structure resulting in a more stable form than the apo form. All of these outcomes indicated that the binding of the drug induced significant changes in the conformation of the protein structure, affected the movement of specific amino acid residues, and enhanced the accessibility of the hydrophobic core to the surrounding solvent.

5. Conclusion

In this study, the dnaN-encoded Beta sliding clamp protein was identified as a potential drug target. From druggability analysis, a repurposable drug against the target protein was found ([(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid). Subsequent investigations should involve conducting in vitro and in vivo tests to assess the safety and efficacy of the drug. The results obtained from this study could contribute to the fight against bacterial leaf blight disease associated with Xoo.

Funding

This study was funded under the “Research & Development Project 2022-23”, Ministry of Science and Technology, Government of the People’s Republic of Bangladesh (Grant number 39.06.2672.001.14.002.22/328).

CRediT authorship contribution statement

Ishtiaque Ahammad: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Tabassum Binte Jamal: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Anika Bushra Lamisa: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Arittra Bhattacharjee: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Nayeematul Zinan: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. Md. Zahid Hasan Chowdhury: Data curation, Formal analysis, Investigation, Methodology, Software, validation, Visualization, Writing – original draft. Shah Mohammad Naimul Islam: Writing – reveiw & editing, Supervision. Kazi Md. Omar Faruque: Conceptualization, Investigation, Data curation, Validation, Resources, Supervision. Zeshan Mahmud Chowdhury: Writing – review & editing. Mohammad Uzzal Hossain: Data curation, Formal analysis, Investigation, Methodology, Software, validation, Visualization, Writing - original draft.. Keshob Chandra Das: . Chaman Ara Keya: . Md Salimullah: Resources, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jgeb.2024.100353.

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Supplementary data 1
mmc1.xlsx (14.6KB, xlsx)

References

  • 1.Fukagawa N.K., Ziska L.H. Rice: Importance for Global Nutrition. J Nutr Sci Vitaminol (tokyo). 2019;65(Supplement):S2–S3. doi: 10.3177/jnsv.65.S2. [DOI] [PubMed] [Google Scholar]
  • 2.He Y.W., Wu J., Cha J.S., Zhang L.H. Rice bacterial blight pathogen Xanthomonas oryzae pv. oryzae produces multiple DSF-family signals in regulation of virulence factor production. BMC Microbiol. 2010;10(1):187. doi: 10.1186/1471-2180-10-187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Niño-Liu D.O., Ronald P.C., Bogdanove A.J. Xanthomonas oryzae pathovars: model pathogens of a model crop. Mol Plant Pathol. 2006;7(5):303–324. doi: 10.1111/j.1364-3703.2006.00344.x. [DOI] [PubMed] [Google Scholar]
  • 4.Ke Y., Hui S., Yuan M. Xanthomonas oryzae pv. oryzae Inoculation and Growth Rate on Rice by Leaf Clipping Method. BIO-Protoc [internet]. 2017 doi: 10.21769/BioProtoc.2568. https://bio-protocol.org/e2568 [cited 2023 May 14];7(19). Available from: [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Amin T., Gupta V., Sharma A., et al. Distribution of Xanthomonas oryzae pv. oryzae Pathotypes in Basmati-Rice-Growing Areas of Jammu and Kashmir, India. Agronomy. 2023;13(3):713. [Google Scholar]
  • 6.Noh T.H., Lee D.K., Park J.C., et al. Effects of Bacterial Leaf Blight Occurrence on Rice Yield and Grain Quality in Different Rice Growth Stage. Res Plant Dis. 2007;13(1):20–23. [Google Scholar]
  • 7.Verdier V., Vera Cruz C., Leach J.E. Controlling rice bacterial blight in Africa: Needs and prospects. J Biotechnol. 2012;159(4):320–328. doi: 10.1016/j.jbiotec.2011.09.020. [DOI] [PubMed] [Google Scholar]
  • 8.Min J., Lin D., Zhang Q., Zhang J., Yu Z. Structure-based virtual screening of novel inhibitors of the uridyltransferase activity of Xanthomonas oryzae pv. oryzae GlmU. Eur J Med Chem. 2012;53:150–158. doi: 10.1016/j.ejmech.2012.03.051. [DOI] [PubMed] [Google Scholar]
  • 9.Cao M.H., Tang B.H., Ruan Y., et al. Development of specific and selective bactericide by introducing exogenous metabolite of pathogenic bacteria. Eur J Med Chem. 2021;225 doi: 10.1016/j.ejmech.2021.113808. [DOI] [PubMed] [Google Scholar]
  • 10.Sahu S.K., Zheng P., Yao N. Niclosamide Blocks Rice Leaf Blight by Inhibiting Biofilm Formation of Xanthomonas oryzae. Front Plant Sci. 2018;29(9):408. doi: 10.3389/fpls.2018.00408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Lin X., Li X., Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Mol Basel Switz. 2020;25(6):1375. doi: 10.3390/molecules25061375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Khan K., Jalal K., Uddin R. An integrated in silico based subtractive genomics and reverse vaccinology approach for the identification of novel vaccine candidate and chimeric vaccine against XDR Salmonella typhi H58. Genomics. 2022;114(2) doi: 10.1016/j.ygeno.2022.110301. [DOI] [PubMed] [Google Scholar]
  • 13.Taylor-Robinson D. Diagnosis and antimicrobial treatment of Mycoplasma genitalium infection: sobering thoughts. Expert Rev Anti Infect Ther. 2014;12(6):715–722. doi: 10.1586/14787210.2014.919220. [DOI] [PubMed] [Google Scholar]
  • 14.Chakrabarty R.P., Alam A.S.M.R.U., Shill D.K., Rahman A. Identification and qualitative characterization of new therapeutic targets in Stenotrophomonas maltophilia through in silico proteome exploration. Microb Pathog. 2020;149 doi: 10.1016/j.micpath.2020.104293. [DOI] [PubMed] [Google Scholar]
  • 15.Sachdev K., Gupta M.K. A comprehensive review of computational techniques for the prediction of drug side effects. Drug Dev Res. 2020;81(6):650–670. doi: 10.1002/ddr.21669. [DOI] [PubMed] [Google Scholar]
  • 16.Uddin R., Jamil F. Prioritization of potential drug targets against P. aeruginosa by core proteomic analysis using computational subtractive genomics and Protein-Protein interaction network. Comput Biol Chem. 2018;74:115–122. doi: 10.1016/j.compbiolchem.2018.02.017. [DOI] [PubMed] [Google Scholar]
  • 17.Boutet E., Lieberherr D., Tognolli M., Schneider M., Bairoch A. UniProtKB/Swiss-Prot. Methods Mol Biol Clifton NJ. 2007;406:89–112. doi: 10.1007/978-1-59745-535-0_4. [DOI] [PubMed] [Google Scholar]
  • 18.Huang Y., Niu B., Gao Y., Fu L., Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinforma Oxf Engl. 2010;26(5):680–682. doi: 10.1093/bioinformatics/btq003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Johnson M, Zaretskaya I, Raytselis Y, et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W5-9. [DOI] [PMC free article] [PubMed]
  • 20.Zhang R, Ou HY, Zhang CT. DEG: a database of essential genes. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D271-272. [DOI] [PMC free article] [PubMed]
  • 21.Moriya Y., Itoh M., Okuda S., Yoshizawa A.C., Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35(Web Server issue):W182–W185. doi: 10.1093/nar/gkm321. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Yu N.Y., Wagner J.R., Laird M.R., et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinforma Oxf Engl. 2010;26(13):1608–1615. doi: 10.1093/bioinformatics/btq249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Szklarczyk D., Gable A.L., Nastou K.C., et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–D612. doi: 10.1093/nar/gkaa1074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Shannon P., Markiel A., Ozier O., et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504. doi: 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Tang Y., Li M., Wang J., Pan Y., Wu F.X. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 2015;127:67–72. doi: 10.1016/j.biosystems.2014.11.005. [DOI] [PubMed] [Google Scholar]
  • 26.Wishart D.S., Feunang Y.D., Guo A.C., et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–D1082. doi: 10.1093/nar/gkx1037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Van Der Spoel D., Lindahl E., Hess B., Groenhof G., Mark A.E., Berendsen H.J.C. GROMACS: Fast, flexible, and free. J Comput Chem. 2005;26(16):1701–1718. doi: 10.1002/jcc.20291. [DOI] [PubMed] [Google Scholar]
  • 28.Casares D., Escribá P.V., Rosselló C.A. Membrane Lipid Composition: Effect on Membrane and Organelle Structure, Function and Compartmentalization and Therapeutic Avenues. Int J Mol Sci. 2019;20(9):2167. doi: 10.3390/ijms20092167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Huang J., Rauscher S., Nawrocki G., et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods. 2017;14(1):71–73. doi: 10.1038/nmeth.4067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Mohanty S, Wassmann R, Nelson A, Moya P, Jagadish SVK. significance for food security and vulnerability.
  • 31.Jain L., Kumar V., Jain S.K., Kaushal P., Ghosh P.K. Isolation of bacteriophages infecting Xanthomonas oryzae pv. oryzae and genomic characterization of novel phage vB_XooS_NR08 for biocontrol of bacterial leaf blight of rice. Front Microbiol. 2023;16(14) doi: 10.3389/fmicb.2023.1084025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Shekhar S., Sinha D., Kumari A. An Overview of Bacterial Leaf Blight Disease of Rice and Different Strategies for its Management. Int J Curr Microbiol Appl Sci. 2020;9(4):2250–2265. [Google Scholar]
  • 33.Banerjee A., Roy S., Bag M.K., et al. A survey of bacterial blight (Xanthomonas oryzae pv. oryzae) resistance in rice germplasm from eastern and northeastern India using molecular markers. Crop Prot. 2018;112:168–176. [Google Scholar]
  • 34.Pathak V.M., Verma V.K., Rawat B.S., et al. Current status of pesticide effects on environment, human health and it’s eco-friendly management as bioremediation: A comprehensive review. Front Microbiol. 2022;17(13) doi: 10.3389/fmicb.2022.962619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Stockwell V.O., Duffy B. Use of antibiotics in plant agriculture: -EN- -FR- Utilisation des antibiotiques en agriculture (productions végétales) -ES- Uso de antibióticos en la agricultura. Rev Sci Tech OIE. 2012;31(1):199–210. doi: 10.20506/rst.31.1.2104. [DOI] [PubMed] [Google Scholar]
  • 36.Ashraf B., Atiq N., Khan K., Wadood A., Uddin R. Subtractive genomics profiling for potential drug targets identification against Moraxella catarrhalis. PLoS One. 2022;17(8) doi: 10.1371/journal.pone.0273252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Rodriguez-R L.M., Tsementzi D., Luo C., Konstantinidis K.T. Iterative subtractive binning of freshwater chronoseries metagenomes identifies over 400 novel species and their ecologic preferences. Environ Microbiol. 2020;22(8):3394–3412. doi: 10.1111/1462-2920.15112. [DOI] [PubMed] [Google Scholar]
  • 38.Saleem H, Ashfaq UA, Nadeem H, et al. Subtractive genomics and molecular docking approach to identify drug targets against Stenotrophomonas maltophilia. Shahzad AN, editor. PLOS ONE. 2021 Dec 15;16(12):e0261111. [DOI] [PMC free article] [PubMed]
  • 39.Ahmad S., Navid A., Akhtar A.S., Azam S.S., Wadood A., Pérez-Sánchez H. Subtractive Genomics, Molecular Docking and Molecular Dynamics Simulation Revealed LpxC as a Potential Drug Target Against Multi-Drug Resistant Klebsiella pneumoniae. Interdiscip Sci Comput Life Sci. 2019;11(3):508–526. doi: 10.1007/s12539-018-0299-y. [DOI] [PubMed] [Google Scholar]
  • 40.Kaur H., Kalia M., Taneja N. Identification of novel non-homologous drug targets against Acinetobacter baumannii using subtractive genomics and comparative metabolic pathway analysis. Microb Pathog. 2021;152 doi: 10.1016/j.micpath.2020.104608. [DOI] [PubMed] [Google Scholar]
  • 41.P.G. Department of Zoology, ANS College, Magadh University, Patna (Barh)-803213, India, Sudha R, Katiyar A, Katiyar P, Singh H, Prasad P. Identification of potential drug targets and vaccine candidates in Clostridium botulinum using subtractive genomics approach. Bioinformation. 2019 Jan 31;15(1):18–25. [DOI] [PMC free article] [PubMed]
  • 42.Khan K., Jalal K., Khan A., Al-Harrasi A., Uddin R. Comparative Metabolic Pathways Analysis and Subtractive Genomics Profiling to Prioritize Potential Drug Targets Against Streptococcus pneumoniae. Front Microbiol. 2022;10(12) doi: 10.3389/fmicb.2021.796363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Pan X., Li H., Zeng T., et al. Identification of Protein Subcellular Localization With Network and Functional Embeddings. Front Genet. 2021;20(11) doi: 10.3389/fgene.2020.626500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Masomian M., Ahmad Z., Ti Gew L., Poh C.L. Development of Next Generation Streptococcus pneumoniae Vaccines Conferring Broad Protection. Vaccines. 2020;8(1):132. doi: 10.3390/vaccines8010132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Fu Y., Guo Y., Wang Y., et al. Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder. Comput Biol Chem. 2015;56:41–48. doi: 10.1016/j.compbiolchem.2015.03.003. [DOI] [PubMed] [Google Scholar]
  • 46.Feng Y., Wang Q., Wang T. Drug Target Protein-Protein Interaction Networks: A Systematic Perspective. BioMed Res Int. 2017;2017 doi: 10.1155/2017/1289259. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Messiah G., Tan P., Billones B.J. Pharmacophore-Based Virtual Screening and Molecular Docking of Approved and Experimental Drugs Against Helicobacter pylori DNA Polymerase III β-Clamp. Acta Sci Pharm Sci. 2019;3(12):26–32. [Google Scholar]
  • 48.Machaba K.E., Cele F.N., Mhlongo N.N., Soliman M.E.S. Sliding Clamp of DNA Polymerase III as a Drug Target for TB Therapy: Comprehensive Conformational and Binding Analysis from Molecular Dynamic Simulations. Cell Biochem Biophys. 2016;74(4):473–481. doi: 10.1007/s12013-016-0764-3. [DOI] [PubMed] [Google Scholar]
  • 49.Mulye M., Singh M.I., Jain V. From Processivity to Genome Maintenance: The Many Roles of Sliding Clamps. Genes. 2022;13(11):2058. doi: 10.3390/genes13112058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Neuwald A.F. Evolutionary clues to DNA polymerase III beta clamp structural mechanisms. Nucleic Acids Res. 2003;31(15):4503–4516. doi: 10.1093/nar/gkg486. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data 1
mmc1.xlsx (14.6KB, xlsx)

Articles from Journal of Genetic Engineering & Biotechnology are provided here courtesy of Academy of Scientific Research and Technology, Egypt

RESOURCES