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In silico Identification of Putative Novel Therapeutic Targets in Xanthomonas campestris Pv. Campestris to Combat Black Rot Disease in Brassica oleracea

Pritam Dey Sarkar1,*, Rimpa Lahkar2, Akshay Kumar Haloi3
1Department of Zoology, Nabajyoti College, Kalgachia-781 319, Assam, India.
2Department of Zoology, Kamrup College, Chamata-781 306, Assam, India.
3Department of Zoology, Bhattadev University, Bajali, Barpeta-781 325, Assam, India.

ABSTRACT

Background: Xanthomonas campestris pv. campestris, the plant pathogenic bacteria causing black rot is a threat in the agriculture of Brassica oleracea crops. It is one of the most economically concerning diseases in the agriculture of cabbages. Effective compete solutions to combat the disease are still lacking.

Methods: In the quest to find an effective solution to combat the disease, an in silico, subtractive genomic and proteomic approach was adopted. Various bioinformatics tools, online databases and servers were used in this study to find the best therapeutic targets in the proteome of the bacteria.

Result: Seven novel targets were filtered out of six hundred and three essential proteins by multi-step subtractive proteomic approaches. Homology and uniqueness were duly checked to prevent any adverse effects in the host plant while using any inhibitory molecule against these seven targets.

INTRODUCTION

Xanthomonas campestris pv. campestris (Xcc) is an agro-economically important plant pathogen which is popular worldwide as the causative agent of black rot (a systemic vascular disease) in brassica crops. It is a Gram-negative bacterium (Vicente and Holub, 2012). Brassica oleracea, the prime host of Xcc (Hayward, 1993), is an agriculturally important plant species which includes widely cultivated vegetables like cabbage, cauliflower, broccoli, brussels, sprouts and kale (Rakow, 2004). Infected seeds are the primary carriers of the disease, even a single infected seed among thousands can lead to a severe outbreak of black rot (Roberts et al., 1999; Cook et al., 1952). The disease is promoted under warm and humid climatic conditions (Walker, 1953). Other potential sources of spreading the infection are transplants, soil, crop debris and carry-over in related weed species (Schaad and Alvarez, 1993). Studies have shown that the bacteria can thrive independently in the soil, for about forty days in winter and twenty days in summer (Dane and Shaw, 1996). Rain and irrigation waters may rapidly spread the disease. Plants infected in black rot exhibit V-shaped yellow or brown lesions or patches spreading from the leaf margins and darkening of the veins (due to bacterial movement in the vascular system). It can cause premature shedding of infected leaves, stunted growth and even cause young plants to die. Secondary bacterial infections in the rotting tissue may aggravate the situation. Under low temperatures, the bacteria may asymptomatically persist in the vascular system and become symptomatic with rising temperature (Cook et al., 1952; Walker, 1953; Schaad and Alvarez, 1993; Nunez  et al., 2018).
 
It is quite difficult to control black rot and any potential source of infection is to be avoided, like pathogen-containing planting material, infected crop detritus, cruciferous weeds, etc. Significant gene resistance has rarely been seen in B. oleracea against the pathogen (Taylor et al., 2002). Garman (1894) first described this as a disease of cabbage in Kentucky, USA. Black rot has since been studied in all countries growing brassica vegetables and has been noted to be the most economically concerning disease of vegetable brassica crops all over the world (Williams, 1980). Various physical (like hot water seed treatment) and chemical treatments are used to combat the disease, but are not effective enough (Krauthausen et al., 2011). Even the development of disease resistant varieties has not been a significant success (Taylor et al., 2002). Currently the management of black rot in cabbage crops is done by the use of resistant varieties, disease free seeds, removing crop detritus and use of antibiotics like Validamycin-A, Kasugamycin, Agrimycin, etc. and bioagents like Trichoderma viride and Pseudomonas fluroscens. But efficacy is partial (Sangwan et al., 2023). Studies have found the development of antibiotic resistance in pathovars of Xanthomonas (Rahman et al., 2014). The black rot disease is therefore a global agricultural threat and complete, holistic effective solutions are still lacking.

In this modern era of computational biology, bioinformatics has proved to be a time as well as cost effective way to effectively identify potential drug targets specific to the host and design or identify effective drug molecules or natural compounds against those targets. Subtractive genomics has gained a wide usage among in silico approaches for the identification of drug targets within a pathogen (Hossain et al., 2017; Hosen et al., 2014; Shalini et al., 2020; Khan et al., 2020; Eniya et al., 2024). The advances in computation biology has put forward genome, gene, protein and metabolic pathway data to the scientific community, free of cost. These advances have opened up a whole new way of identifying therapeutic targets against various pathogens (Joshi and Gautam, 2017; Veni et al., 2022). Analysing the genome and proteome of the pathogen and host helps to identify potential targets to combat pathogens, specially without harming the host. In this study, a subtractive proteomic approach has been adopted to identify potential novel therapeutic targets in Xanthomonas campestris pv. campestris specific to its prime host, Brassica oleracea.

MATERIALS AND METHODS

Subtractive genomic approach
 
Subtractive genomic and proteomic approaches were used to identify novel drug targets in Xanthomonas campestris pv. campestris using various bioinformatics tools and online databases. The overall workflow of the study is shown in Fig 1. This approach filters out sequences from the pathogen and host genome or proteome, compares metabolic pathways and gives out proteins (potential drug targets) that are crucial to the survival of the pathogenic microorganism but are not present in the host. Hence targeting such proteins could provide fruitful results in combatting the pathogen while no harm comes to the host.

Fig 1: Diagrammatic representation of overall workflow used in the study.


 
Retrieval of essential proteins
 
The whole set of predicted essential genes was derived from the NetGenes server (https://rbc-dsai-iitm.github.io/NetGenes) for the organism Xanthomonas campestris pv. campestris. The server hosts a database of essential genes of 2711 bacterial organisms, predicted by machine learning approaches from network data available in the STRING database (https://string-db.org). The corresponding proteins of the essential genes were derived from the NCBI Protein database (https://www.ncbi.nlm.nih.gov/protein). These proteins play a crucial role in the survival of the microorganism (Azhagesan et al., 2018).
 
Identification of membrane proteins
 
The essential proteins may be present in five possible subcellular locations: cytoplasm, inner membrane, outer membrane, periplasm and extracellular space. The PSORTb v 3.0.3 server (Yu et al., 2010) was used to find out the subcellular localization of the proteins and select out the membrane proteins only. Membrane proteins are crucially involved in the physiology of the organism. They have been studied to perform various crucial functions of the cell. They are involved in many transportation activities and also in various cell signalling pathways. Hence membrane proteins serve as ideal drug targets (Gong et al., 2019).
 
Identification of metabolic proteins unique to the pathogen
 
The essential membrane proteins were submitted to the KEGG Automatic Annotation Server (KAAS) in FASTA format for KEGG ortholog (KO) assignment and pathway mapping to screen out the metabolic proteins (Kanehisa and Goto 2000). The server functionally annotates the proteins by carrying out BLAST comparison against its database and automatically assigns a KEGG Ortholog (KO) and designates pathways and BRITE hierarchies associates with the protein. This step was carried out to identify common metabolic pathways between the pathogen and the host. Targeting proteins involved in common metabolic pathways could possibly result in harm to the host species, which is undesirable. Hence, proteins involved in common metabolic pathways of Brassica oleracea were excluded.

Identification of novel drug targets
 
The DrugBank 5.1.9 database (Wishart et al., 2018) was used (with default parameters) to further screen the essential, unique membrane bound metabolic proteins to find the novel ones. The database stores comprehensive details about drugs and drug targets. Proteins with no drug entries were selected as novel.
 
Checking of non-homology with host proteome
 
Functional similarity with the host proteome can lead to unwanted binding of the drug to the homologous host proteins which is undesirable. Hence the screened novel targets were subjected to BLASTp (NCBI) against the Brassica oleracea non-redundant protein sequences (nr) database of NCBI. The threshold for query coverage and percentage identity were taken as 80% and 60% respectively.
 
Essential functions of the target proteins
 
To know the essential functions of the screened novel targets, the fasta sequences of the proteins were subjected to BLAST in the UniProt Knowledge Base server (Bateman et al., 2022).

RESULTS AND DISCUSSION

Using the several above-mentioned computational tools and online databases our study was carried out by step by step filtering out of proteins to derive a final set of novel therapeutic target proteins from the Xcc proteome (graphically shown in Fig 2).

Fig 2: Graphical representation of results of step by step (bottom to top sequentially) subtractive proteomic approach.


 
Subtractive genomic approach
 
This in silico study has effectively deployed the subtractive genomic and proteomic approach as shown in Fig 1. The Fig 1 shows the step by step workflow and the various bioinformatics tools and databases used for the purpose.
 
Selection of essential proteins
 
The number of essential genes of Xanthomonas cam-pestris pv. campestris retrieved from NetGenes server was 603. The corresponding 603 essential proteins were retrieved in FASTA format from the NCBI Protein database. These 603 proteins are crucial to the survival of the pathogenic microorganism.
 
Selection of membrane proteins
 
The 603 essential proteins were uploaded in the psortb server to analyse their subcelluar locations. The server sorted out the membrane proteins from the non-membrane ones. 159 essential proteins were found to be membrane proteins.
 
Selection of unique metabolic proteins
 
The 159 membrane proteins were screened through the KEGG Automatic Annotation Server. Of them, 124 proteins were not involved in any metabolic pathway. 33 proteins were found to be involved in specific metabolic pathways, along with their assigned KO (Table 1). The metabolic pathways of the 33 proteins were further cross-checked to prevent involvement of any common metabolic pathway in the host. Eleven (11) out of 33 metabolic proteins were found to be unique compared to the host, not sharing any common metabolic pathway (Table 1).

Table 1: Membrane proteins involved in specific metabolic pathways.


 
Selection of novel targets
 
Out of the 11 unique metabolic proteins, 4 proteins were found to be similar above default threshold with deposited drug targets in the DrugBank database (Table 2) and hence were not considered novel. The remaining 7 protein (Table 3) did not show any significant similarity with the current druggable targets present in the database and were hence considered as novel targets.

Table 2: Proteins listed as druggable targets in DrugBank.



Table 3: Query coverage, E-value and sequence identity of query sequences against Brassica oleracea proteome.


 
Exclude homologous proteins
 
To prevent any cross reactivity of any drug against the novel targets with any homologous protein if present in the host, we performed BLASTp and filter out the homologous ones, considering a threshold of 80% query coverage and 60% sequence identity. None of the 7 novel proteins were found to exhibit any significant homology with the host proteome (Table 3). The essential functions of these seven potential protein targets selected by subtractive proteomics are discussed in Table 4.

Table 4: Essential functions of the seven potential protein targets selected by subtractive proteomics.

CONCLUSION

This in silico study conducted using subtractive genomic approach revealed seven potential novel therapeutic targets in Xanthomonas campestris pv. campestris. They are involved in essential molecular functions in the bacterium which ensure their survival. Targeting these seven membrane proteins could potentially help combat black rot, due to their involvement in essential metabolic pathways and lack of homology with the host proteome. Molecular docking studies with natural compounds against these seven targets can unravel potential natural ways to combat this disease. Any inhibitory molecule against these 7 targets is less likely to harm the host species Brassica oleracea due to the non-homology. Hence development and study of therapeutic compounds against these seven novel targets could help to combat the back-rot disease in Brassica oleracea and reduce agricultural economic losses caused by the pathogen Xanthomonas campestris pv. campestris in cabbage crops.

DISCLAIMERS

The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.

INFORMED CONSENT

The present study does not involve any human participant or animals.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

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