Novel therapeutic targets identification of Delftia tsuruhatensis strain TR1180 employing subtractive genomics approach

doi:10.21203/rs.3.rs-2480920/v1

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Novel therapeutic targets identification of Delftia tsuruhatensis strain TR1180 employing subtractive genomics approach

https://doi.org/10.21203/rs.3.rs-2480920/v1

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Delftia tsuruhatensis is an opportunistic pathogen, that causes human infection in immunocompromised individuals. The organism was found to be infectious in the respiratory tract and urinary tract infections. Isolation of D. tshuruhatensis showed resistance to common antibiotics, resulting in an alarming signal from the pathogen. Importantly, the D. tshuruhatensis strain was found to have the property of having IMP-1 Metallo-β-Lactamase, which could hydrolyze β-lactam antibiotics. To combat the challenge of antibiotic resistance, novel drug targets can be effective. For suggesting the novel drug targets, the entire proteome of D. tshuruhatensis strain TR1180 was subjected to subtractive genomic analysis using a variety of bioinformatic tools and servers. To identify human homologue proteins of the pathogen and proteins involved in common metabolic pathways between the pathogen and host, various bioinformatics tools and web servers were used. Only 62 proteins were found to be linked to pathogen-specific pathways; these proteins were then further screened to single out membrane-antigenic proteins that could be targeted by medications or vaccines. The novel therapeutic targets with the highest level of antigenicity were discovered to be ‘Nitrate transporter’ and ‘Phospho-N-acetylmuramoyl-pentapeptide-transferase’.

Delftia tsuruhatensis

Antibiotic resistance

Novel therapeutic target

Subtractive genomics

vaccine targets

Metabolic pathway

In 2003, a single species named Delftia tsuruhatensis belonging to the genus Delftia was first isolated in Japan, mainly from sludge (Shigematsu et al., 2003). Delftia is a genus of opportunistic, aerobic, motile, non-spore-forming, nonfermenting, and gram-negative bacteria that belongs to the Comamonadaceae family (Wen et al., 1999). D. tsuruhatensis has several interesting yet crucial functions, making them a primary target for researchers. Studies on these species are focused on environmental applications. For instance, this bacteria can biodegrade organic contaminants, including phenolic compounds and chlorobenzene (Jimenez et al., 2012, Ye et al., 2019). Delftibactin, a non-ribosomal peptide produced by D. tsuruhatensis showed effective antimicrobial activity against methicillin-resistant Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii and vancomycin-resistant Enterococcus(Tejman-Yarden et al., 2019).

These gram-negative, non-fermentative rods have lately emerged as pathogens in patients with permanent indwelling devices and immunocompromised persons (Van de Louw et al., 2021, Albu et al., 2018). In recent times, D. tsuruhatensis has also been identified as an opportunistic pathogen causing human infections. In 2011, the first incidence of human catheter-related infection caused by D. tsuruhatensis was identified when a 53-year-old woman with severe idiopathic pulmonary hypertension was infected with the bacteria and manifested flu-like symptoms, chills, and critical immunological condition (Preiswerk et al., 2011). Subsequently, a few research proved the positive association of D. tsuruhatensis with respiratory tract diseases like bronchiectasis (Ekanayake et al., 2020). One study reported that D. tsuruhatensis also causes common female-predominant infections like urinary tract infections (Majumder et al., 2018). As a result, D. tsuruhatensis is emerging as an opportunistic human pathogen that has the versatility to cause a variety of human diseases. But the development of antibiotic resistance in D. tsuruhatensis has been a matter of concern in providing appropriate medication to patients infected by the bacteria. First isolated D. tsuruhatensis from human infection showed resistance to common antibiotics such as ampicillin, cefuroxime, cephalothin, gentamicin, tobramycin, amikacin, and colistin. However, it showed susceptibility to amoxicillin-clavulanate, piperacillin-tazobactam, fluoroquinolones, and carbapenems (Preiswerk et al., 2011). A clinical D. tsuruhatensis strain isolated from a breast cancer patient had similar resistance characteristics(Tabak et al., 2013). Lately, IMP-1 Metallo-β-Lactamase producing D. tsuruhatensis strain has been isolated(Cho et al., 2021). IMP-1 Metallo-β-Lactamase effectively hydrolyzes β-lactam antibiotics, and its presence indicates antibiotic resistance development in D. tsuruhatensis (Yamaguchi et al., 2005). One genomic analysis of a D. tsuruhatensis strain TR1180 isolates verified the existence of 17 antibiotic resistance-related genes in the bacterial whole genome (Cheng et al., 2021). These shreds of evidence directly indicate the potential evolution of drug resistance for D. tsuruhatensis strains and urge the novel therapeutics development for the bacteria to treat the patients more effectively.

In some recent previous researches, subtractive genomics has been applied to find new therapeutic targets for species-specific infections (Naorem et al., 2022, Khan et al., 2022, Fatoba et al., 2021). In this study, we have used a subtractive proteomics approach on the whole proteome of D. tsuruhatensis strain TR1180 to identify novel therapeutic targets for future therapeutic development. We identified essential pathogenic proteins from the bacterium using several bioinformatics methods and databases. Proteins, crucial for pathogen survival, non-homologous to humans, and located in the bacterial membrane were shortlisted for their suitability to function as a putative therapeutic target. All the human homologous proteins found in the proteome of the bacteria were consciously excluded from our list because developing therapeutic compounds targeting these proteins can lead to unintentional blockage of host proteins. Finally, nominated proteins were filtered to identify only antigenic proteins that were not yet identified as possible treatment targets.

The whole proteome of Delftia tsuruhatensis strain TR1180 was analysed to identify novel drug targets and vaccine candidates by using a subtractive genomics approach. The overall workflow is illustrated in Fig. 1.

2.1 Whole proteome retrieval of pathogen:

The whole proteome of D. tsuruhatensis strain TR1180 (Assembly GCA_009362995.1) was retrieved from the UniProt database (https://www.uniprot.org/)(research, 2019).

2.2 Removal of paralogous proteins using CD-Hit:

The protein sequences were screened through the CD-Hit suite to exclude the paralogous sequences from the D. tsuruhatensis proteome(Huang et al., 2010). To remove duplicate sequences using this tool, a threshold score of 0.8 (80% sequence identity) was selected. Protein paralogs that shared more than 80% of their identity were not considered for further screening. Proteins having < 100 amino acids were also excluded manually in this step. The remaining proteins were identified as non-paralogous and were considered for further screening.

2.3 selection of non-homologous proteins to Human proteome:

To avoid therapeutic drugs interacting with the active site of the host homolog proteins, it is important to exclude proteins that have similar functions as human proteins. Non-paralogous proteins selected from the previous step were screened through BLASTp against Homo sapiens ref sequence in the Ensemble genome database 107 (Zerbino et al., 2018). The similarity threshold value was set at 10^− 4. Any protein from the pathogen having BLASTp value above the threshold was considered homologous to the Human proteome. Homologous human proteins were excluded and proteins being non-homologous to human proteome were selected for the next steps.

2.4 Selection of essential non-homologous proteins:

To identify proteins containing essential genes for the pathogen’s survival, Database of Essential Genes (DEG) was used(Luo et al., 2014). BLASTp was carried out for all non-homologous proteins using threshold cut-off expectation value ≤ 10^− 100, minimum bit score of 100 and identity ≥ 40%. Proteins fulfilling all three conditions were selected for the next steps and proteins failing to fulfill set parameters were excluded. Translated products from essential genes plays important role in bacterial cell growth and maintaining cell viability(Peters et al., 2016).

2.5 Metabolic pathway analysis of non-homologous proteins:

The KEGG (Kyoto encyclopedia of gene and genome) database was used to analyze pathways of previously selected non-homologous proteins(Kanehisa et al., 2019). The main aim of this step is to identify proteins involved in only pathogen-specific metabolic pathways without crosslinking with any human-specific pathways. Identified essential non-homologous proteins were subjected to BLASTp through the KAAS (version 2.1) server of the KEGG database and three letters of KEGG organism code “del” which is specific for D. tsuruhatensis were selected(Moriya et al., 2007). All proteins crosslinking with both pathogenic pathways and human pathways were excluded. Proteins involved in only pathogen-specific pathways were enlisted as unique proteins along with their KO number. KO (KEGG Orthology) assignments identify proteins associated with metabolism. Proteins consisting of KO number but not associated with any human pathways were also enlisted separately. However, proteins with no KO assignment and metabolic pathway involvement were also excluded from the data set.

2.6 Determination of Subcellular localization:

Since D. tsuruhatensis is characterized as a Gram-negative bacterium, there are five probable subcellular regions where its proteins can be located. The cytoplasm, inner membrane, periplasm, outer membrane, and extracellular space are among them. Cytoplasmic proteins are putative targets for drugs. On the other hand, proteins located at bacterial membranes can be considered potential targets for both drug and vaccine development(Gong et al., 2019, Solanki et al., 2021). To determine subcellular localizations of unique metabolic proteins shortlisted in the previous step, CELLO v.2.5(Yu et al., 2006), PSORTb v.3.0.3(Peabody et al., 2016), and Pslpred(Bhasin et al., 2005) servers were used. The final locations of unique proteins were determined by the same location predicted by all three servers or at least any two servers. Proteins located at the bacterial membrane were selected for further analysis and other proteins located in different regions (cytoplasm, periplasm & extracellular space) of the bacterium were excluded.

2.7 Antigenicity prediction:

Proteins located at bacterial membranes were subjected to screening through Vaxijen v.2.0 for determining their ability to induce an immunogenic response in the host system as an antigen(Doytchinova and Flower, 2007). The threshold value for identifying potential antigens was set at 0.4. Proteins scoring ≥ 0.4 Vaxijen scores were selected as potential antigens for further analysis. Proteins scoring < 0.4 scores were excluded from our dataset for their inability to act as antigens.

2.8 Novel drug target identification and druggability screening:

All the potential antigenic proteins selected in the previous step were evaluated through DrugBank v.5.1.9 database to check their novelty(Wishart et al., 2018). DrugBank v.5.1.9 contains a total of 15,152 drug entries that include 2,730 approved small molecules, 1,568 approved biologics (proteins, peptides, vaccines, and allergenics), 134 nutraceuticals and over 6,714 experimental (still in discovery-phase) drugs. Proteins that were absent indicated their potential as novel therapeutic targets, whereas proteins that were present and listed in the database entries suggested they couldn't be chosen as such. Proteins that were already identified as drug targets in the database were omitted because our objective is to find new therapeutic targets for D. tsuruhatensis. Finally, Proteins representing no resemblance while evaluating through the DrugBank database v.5.1.9 were listed as ultimate novel drug targets.

2.9 Conservancy analysis prediction:

Understanding the conservation pattern of the predicted sequences with other widely used strains is necessary to determine the therapeutic spectrum's range throughout the entire homologous bacterial community. The predicted drug target sequences underwent a conservancy analysis using the BLASTp tool on the NCBI server(Johnson et al., 2008). Except for D. tsuruhatensis in the organism option, all parameters were left at their default values for doing protein-protein BLAST in this case.

Identification of novel therapeutic targets against D. tsuruhatensis to develop future drugs for humans was the main aim of conducting this study. We have used several computational tools and databases to perform subtractive proteomic analysis to obtain our findings. A brief overview of subtractive proteomic approaches used in this study is given in Table 1

Table 1

Subtractive genomics analysis brief overview
SI. No	Steps performed	Number of proteins
1	Whole proteome retrieval of Delftia tsuruhatensis	5875
2	CD-HIT suite paralous protein removal	75
3	Proteins containing < 100 amino acids	407
4	Human homologous proteins removal through Ensemble genome browser 107	4043
5	Essential protein selection	267
6	The pathogen-specific pathway involving protein selection	62
7	Membrane protein selection	19
8	Antigenic protein selection	14
9	Novel therapeutic target selection	5

3.1 Detecting paralogous proteins:

The entire proteome of D. tsuruhatensis contains 5875 proteins. CD-Hit server detected 75 paralogous proteins from the proteome with a threshold value above 80%. These proteins showed more than 80% similarity with proteins present in the dataset. Therefore, these 75 paralogous proteins were removed from consideration. Furthermore, 407 proteins containing < 100 amino acids were also removed from the dataset assuming these smaller proteins don’t represent any essentiality to the pathogen. A total of 5393 non-duplicated proteins that contained ≥ 100 amino acids were selected for further analysis.

3.2 selection of non-homologous proteins to Humans:

Over time, homologous proteins were discovered to be engaged in similar biochemical pathways between bacteria and humans. Therefore, unexpected blockage of host proteins should be avoided while developing novel drugs targeting any pathogen. All 5393 non-paralogous proteins of the pathogen were evaluated through BLASTp against the Refseq proteome of Homo sapiens in Ensemble genome browser 107 to prevent cross-reactivity with host proteins. While using BLASTp in the Ensemble genome browser, the maximum E-value was set at 10^− 4 and all the remaining parameters were left default. The result revealed that 1350 proteins were homologous with host proteins as they showed significant similarity hits in BLASTp. These 1350 proteins were excluded from the dataset because drug development targeting these proteins can lead to cross-reactivity along with unintentional blockage of human proteins. The remaining 4043 non-homologous proteins were selected for evaluating their essentiality for the pathogen.

3.3 Selection of essential non-homologous proteins:

Proteins containing essential genes are considered essential proteins. Essential proteins are the most prominent targets for developing novel drugs because anti-bacterial drugs are designed to bind with these types of proteins. Previously selected 4043 proteins were subjected to filter via the DEG database by mentioned parameters. According to our set parameters total of 267 proteins were listed as essential proteins as they fulfilled all the criteria. These 267 essential proteins were subjected to their metabolic pathway analysis and the remaining 3776 non-essential proteins were excluded from our dataset.

3.4 Metabolic pathway analysis of non-homologous proteins:

Identifying essential proteins involved in only pathogen-specific pathways is the most critical step while developing novel therapeutic compounds. Proteins involved in only pathogen-specific pathways without crosslinking with human pathways are called unique proteins. Developing novel drugs targeting these unique proteins is a much safer option as they don’t interrupt any host-specific pathways.

267 essential proteins selected in the previous step were subjected to BLASTp in the KAAS server at the KEGG database. The result revealed that 62 out of 267 proteins were involved in pathogen-specific pathways. These 62 unique proteins are listed in Table 2

Table 2

Unique proteins with their pathogen-specific pathways
SI. No	KO assignment	Protein ID	Protein Name	Pathways
1	K00800	A0A5P8XJ65	Multifunctional fusion protein	Phenylalanine, tyrosine and tryptophan biosynthesis
2	K03149	A0A072T3U6	Thiazole synthase	Thiamine metabolism
3	K02535	A0A072T577	UDP-3-O-acyl-N-acetylglucosamine deacetylase	Lipopolysaccharide biosynthesis
4	K03587	A0A072TH56	Peptidoglycan D,D-transpeptidase FtsI	Peptidoglycan biosynthesis, beta-Lactam resistance
5	K01012	A0A1D8IW93	Biotin synthase	Biotin metabolism
6	K06153	A0A1D8J0B2	Undecaprenyl-diphosphatase	Peptidoglycan biosynthesis, Teichoic acid biosynthesis
7	K11753	A0A4R5GUQ5	Riboflavin biosynthesis protein	Riboflavin metabolism
8	K05515	A0A5P8XDK8	Peptidoglycan D,D-transpeptidase MrdA	Peptidoglycan biosynthesis, beta-Lactam resistance
9	K01736	A0A1D8J9T0	Chorismate synthase	Phenylalanine, tyrosine and tryptophan biosynthesis
10	K03412	A0A5P8XEE2	Protein-glutamate methylesterase/protein-glutamine glutaminase	Two-component system, Bacterial chemotaxis
11	K01735	A0A5P8XFN5	3-dehydroquinate synthase	Phenylalanine, tyrosine and tryptophan biosynthesis
12	K03147	A0A5P8XQV1	Phosphomethylpyrimidine synthase	Thiamine metabolism
13	K01921	A0A5P8XRD5	D-alanine–D-alanine ligase	D-Amino acid metabolism, Peptidoglycan biosynthesis, Vancomycin resistance
14	K01778	A0A5P8XRH1	Diaminopimelate epimerase	Lysine biosynthesis, D-Amino acid metabolism
15	K01924	A0A5P8XRL8	UDP-N-acetylmuramate–L-alanine ligase	Peptidoglycan biosynthesis
16	K01929	A0A5P8XRM7	UDP-N-acetylmuramoyl-tripeptide–D-alanyl-D-alanine ligase	Lysine biosynthesis, Peptidoglycan biosynthesis, Vancomycin resistance
17	K03588	A0A5P8XRN5	Probable peptidoglycan glycosyltransferase FtsW	Cell cycle - Caulobacter
18	K02563	A0A5P8XSF4	UDP-N-acetylglucosamine–N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase	Peptidoglycan biosynthesis, Cell cycle – Caulobacter, Vancomycin resistance
19	K01925	A0A5P8XSK9	UDP-N-acetylmuramoylalanine–D-glutamate ligase	D-Amino acid metabolism, Peptidoglycan biosynthesis
20	K01000	A0A5P8XS32	Phospho-N-acetylmuramoyl-pentapeptide-transferase	Peptidoglycan biosynthesis, Vancomycin resistance
21	K01626	A0A072T5K6	Phospho-2-dehydro-3-deoxyheptonate aldolase	Phenylalanine, tyrosine and tryptophan biosynthesis, Quorum sensing
22	K03590	A0A072T608	Cell division protein FtsA	Cell cycle - Caulobacter
23	K03092	A0A072T769	RNA polymerase sigma-54 factor	Two-component system, Flagellar assembly, Biofilm formation - Vibrio cholerae
24	K00215	A0A5P8XTW1	4-hydroxy-tetrahydrodipicolinate reductase	Monobactam biosynthesis, Lysine biosynthesis
25	K00946	A0A5P8XTQ4	Thiamine-monophosphate kinase	Thiamine metabolism
26	K00674	A0A072T357	2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferase	Lysine biosynthesis
27	K01546	A0A072T7N7	Potassium-transporting ATPase potassium-binding subunit	Two-component system
28	K02454	A0A072TAG4	Type II secretion system protein E	Bacterial secretion system, Biofilm formation - Vibrio cholerae
29	K06041	A0A072TDQ6	Arabinose-5-phosphate isomerase	Lipopolysaccharide biosynthesis
30	K01609	A0A072TG72	Indole-3-glycerol phosphate synthase	Phenylalanine, tyrosine and tryptophan biosynthesis
31	K02313	A0A1D8J4X9	Chromosomal replication initiator protein DnaA	Two-component system, Cell cycle - Caulobacter
32	K18138	A0A1D8J658	Efflux pump membrane transporter	beta-Lactam resistance, Cationic antimicrobial peptide (CAMP) resistance
33	K00748	A0A1I0WFS5	Lipid-A-disaccharide synthase	Lipopolysaccharide biosynthesis
34	K01627	A0A1I0W3N0	2-dehydro-3-deoxyphosphooctonate aldolase	Lipopolysaccharide biosynthesis
35	K02412	A0A5P8XFC9	Flagellar protein export ATPase FliI	Flagellar assembly
36	K18146	A0A5P8XFP8	Efflux pump membrane transporter	beta-Lactam resistance
37	K00677	A0A5P8XIJ5	Acyl-[acyl-carrier-protein]--UDP-N-acetylglucosamine O-acyltransferase	Lipopolysaccharide biosynthesis, Cationic antimicrobial peptide (CAMP) resistance
38	K01657	A0A1I1CPG9	Anthranilate synthase component 1	Phenylalanine, tyrosine and tryptophan biosynthesis, Phenazine biosynthesis, Quorum sensing, Biofilm formation - Pseudomonas aeruginosa
39	K01626	A0A4R5GQ50	Phospho-2-dehydro-3-deoxyheptonate aldolase	Phenylalanine, tyrosine and tryptophan biosynthesis, Quorum sensing
40	K23993	A0A5P8XNE9	Phosphoenolpyruvate–protein phosphotransferase	Phosphotransferase system (PTS)
41	K02454	A0A072TAJ7	General secretion pathway protein E	Bacterial secretion system, Biofilm formation - Vibrio cholerae
42	K11900	A0A072T6R9	EvpB family type VI secretion protein	Biofilm formation - Pseudomonas aeruginosa
43	K11749	A0A5P8XUD1	Zinc metalloprotease	Cell cycle - Caulobacter
44	K03531	A0A5P8XRF2	Cell division protein FtsZ	Cell cycle - Caulobacter
45	K00370	A0A5P8XTL0	Nitrate reductase subunit alpha	Nitrogen metabolism, Two-component system
46	K02556	A0A072THZ6	Chemotaxis protein MotA	Two-component system, Bacterial chemotaxis, Flagellar assembly
47	K02033	A0A072TAZ0	ABC transporter permease	Quorum sensing
48	K18139	A0A1D8J4I4	Efflux transporter outer membrane subunit	Quorum sensing, beta-Lactam resistance
49	K02454	A0A1D8J7G2	Type II secretion system protein E (GspE)	Bacterial secretion system, Biofilm formation - Vibrio cholerae
50	K18323	A0A4R5GK41	Efflux transporter outer membrane subunit	Two-component system
51	K18139	A0A5P8XEZ9	Efflux transporter outer membrane subunit	Quorum sensing, beta-Lactam resistance
52	K00285	A0A5P8XF24	FAD-dependent oxidoreductase	Phenylalanine metabolism, D-Amino acid metabolism,
53	K02035	A0A5P8XGB3	ABC transporter substrate-binding protein	Quorum sensing
54	K00381	A0A5P8XIR0	Nitrite reductase	Sulfur metabolism
55	K07666	A0A5P8XKA6	Response regulator	Two-component system, Quorum sensing
56	K02055	A0A5P8XM54	Extracellular solute-binding protein	Quorum sensing
57	K03406	A0A5P8XM84	HAMP domain-containing protein	Two-component system, Bacterial chemotaxis
58	K07794	A0A5P8XN13	Tripartite tricarboxylate transporter TctB family protein	Two-component system
59	K02770	A0A5P8XPJ1	PTS fructose transporter subunit EIIBC	Fructose and mannose metabolism, Phosphotransferase system (PTS)
60	K02055	A0A5P8XQ11	Extracellular solute-binding protein	Quorum sensing
61	K00371	A0A5P8XSQ9	Nitrate reductase subunit beta	Nitrogen metabolism, Two-component system
62	K02575	A0A5P8XT23	Nitrate/nitrite transporter	Nitrogen metabolism

Among 267 proteins, a total of 69 proteins were not involved in any metabolic pathways but were assigned KO (KEGG Orthology). These proteins are listed in Additional Table 1 as they were found to be non-homologous to human proteins and essential for bacterial survival, making them suitable targets for future drug development. The remaining 205 proteins were eliminated from our dataset because they were either crosslinking with host-specific pathways or not involved in any metabolic pathway with no KO assignment.

3.5 Determination of Subcellular localization:

Determination of subcellular localization of proteins is an important step while developing novel therapeutic drugs as it is observed that proteins can be located at multiple locations according to their role in the system. All 62 unique proteins involved in pathogen-specific pathways were screened through CELLO v.2.5, PSORTb v.3.0.3, and Pslpred servers for detecting proteins located at the bacterial membrane. Out of 62 unique proteins, only 19 proteins were located at the bacterial membrane which makes them the optimum candidate for further analysis. These 19 membrane proteins listed in Table 3 are selected to calculate their ability to function as probable antigens. The remaining 43 proteins located in different locations (cytoplasm, periplasm & extracellular space) were removed from our dataset.

Table 3

Subcellular location of unique proteins
SI. No	Protein ID	Protein Name	Subcellular Location
1	A0A072TH56	Peptidoglycan D,D-transpeptidase FtsI	Membrane
2	A0A1D8J0B2	Undecaprenyl-diphosphatase	Membrane
3	A0A5P8XDK8	Peptidoglycan D,D-transpeptidase MrdA	Membrane
4	A0A5P8XRN5	Probable peptidoglycan glycosyltransferase FtsW	Membrane
5	A0A5P8XSF4	UDP-N-acetylglucosamine–N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase	Membrane
6	A0A5P8XS32	Phospho-N-acetylmuramoyl-pentapeptide-transferase	Membrane
7	A0A072T7N7	Potassium-transporting ATPase potassium-binding subunit	Membrane
8	A0A1D8J658	Efflux pump membrane transporter	Membrane
9	A0A5P8XFP8	Efflux pump membrane transporter	Membrane
10	A0A5P8XUD1	Zinc metalloprotease	Membrane
11	A0A072THZ6	Chemotaxis protein MotA	Membrane
12	A0A072TAZ0	ABC transporter permease	Membrane
13	A0A1D8J4I4	Efflux transporter outer membrane subunit	Membrane
14	A0A4R5GK41	Efflux transporter outer membrane subunit	Membrane
15	A0A5P8XEZ9	Efflux transporter outer membrane subunit	Membrane
16	A0A5P8XM84	HAMP domain-containing protein	Membrane
17	A0A5P8XN13	Tripartite tricarboxylate transporter TctB family protein	Membrane
18	A0A5P8XPJ1	PTS fructose transporter subunit EIIBC	Membrane
19	A0A5P8XT23	Nitrate/nitrite transporter	Membrane

3.6 Antigenicity prediction of membrane proteins:

Therapeutic targets should be able to function as possible antigens by initiating a host immune response. Vaxijen v2.0 server with a threshold minimum antigenic value of 0.4 was utilized to separate antigenic proteins from 19 membrane proteins. Out of 19 membrane proteins, 14 proteins were declared as antigenic by the Vaxijen v2.0 server because of their antigenic value over 0.4. These antigenic proteins are listed in Table 4. The remaining 5 proteins had a Vaxijen score less than 0.4, hence declared non-antigenic. Only antigenic proteins were chosen for establishing their novelty as drug targets.

Table 4

Antigenicity prediction of membrane-located proteins
SI No	Protein ID	Protein Name	Vaxijen Score
1	A0A1D8J658	Efflux pump membrane transporter	0.5801
2	A0A5P8XT23	Nitrate/nitrite transporter	0.5691
3	A0A4R5GK41	Efflux transporter outer membrane subunit	0.5571
4	A0A072TH56	Peptidoglycan D,D-transpeptidase FtsI	0.5495
5	A0A5P8XEZ9	Efflux transporter outer membrane subunit	0.549
6	A0A5P8XFP8	Efflux pump membrane transporter	0.5481
7	A0A5P8XDK8	Peptidoglycan D,D-transpeptidase MrdA	0.5272
8	A0A5P8XM84	HAMP domain-containing protein	0.5261
9	A0A5P8XS32	Phospho-N-acetylmuramoyl-pentapeptide-transferase	0.5061
10	A0A1D8J4I4	Efflux transporter outer membrane subunit	0.4772
11	A0A5P8XPJ1	PTS fructose transporter subunit EIIBC	0.4693
12	A0A5P8XSF4	UDP-N-acetylglucosamine–N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase	0.4332
13	A0A5P8XN13	Tripartite tricarboxylate transporter TctB family protein	0.4151
14	A0A5P8XUD1	Zinc metalloprotease	0.4066

3.7 Novel drug target identification and druggability screening:

DrugBank v.5.1.9 Database was utilized to distinguish novel proteins that could be exploited as drug targets. 9 out of 14 antigenic proteins were found to be similar to available drug targets in DrugBank Database v.5.1.9. Since the main aim of conducting this study is to identify novel targets for drug development, these 9 proteins were eliminated for lack of novelty as drug targets. On the other hand, the remaining 5 proteins can be considered novel therapeutic targets because they are not related to any proteins that are affected by any drugs listed in the DrugBank v.5.1.9 database. No DrugBank ID and drugs are assigned targeting these 5 proteins which advocate their novelty as drug targets. These 5 novel proteins along with the other 9 proteins are listed in Table 5

Table 5

List of druggable proteins with available drugs targets
SI. No	Protein ID	Protein Name	DrugBank ID	Drug Name
1	A0A1D8J658	Efflux pump membrane transporter	DB14879	Cefiderocol
2	A0A4R5GK41	Efflux transporter outer membrane subunit	DB14879	Cefiderocol
3	A0A072TH56	Peptidoglycan D,D-transpeptidase FtsI	DB00535	Cefdinir
4	A0A5P8XEZ9	Efflux transporter outer membrane subunit	DB14879	Cefiderocol
5	A0A5P8XFP8	Efflux pump membrane transporter	DB03825, DB03619, DB04209	Rhodamine 6G,Deoxycholic acid, Dequalinium
6	A0A5P8XDK8	Peptidoglycan D,D-transpeptidase MrdA	DB01413, DB14879	Cefepime, Cefiderocol
7	A0A5P8XM84	HAMP domain-containing protein	DB02365	1,10-Phenanthroline
8	A0A1D8J4I4	Efflux transporter outer membrane subunit	DB14879	Cefiderocol
9	A0A5P8XSF4	UDP-N-acetylglucosamine–N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase	DB02196	Uridine-Diphosphate-N-Acetylgalactosamine
10	A0A5P8XT23	Nitrate/nitrite transporter	N/A	N/A
11	A0A5P8XS32	Phospho-N-acetylmuramoyl-pentapeptide-transferase	N/A	N/A
12	A0A5P8XPJ1	PTS fructose transporter subunit EIIBC	N/A	N/A
13	A0A5P8XN13	Tripartite tricarboxylate transporter TctB family protein	N/A	N/A
14	A0A5P8XUD1	Zinc metalloprotease	N/A	N/A

Representative membrane proteins make up more than 60% of pharmacological targets for therapeutic purposes. Targeting membrane proteins for treatment has several benefits. For instance, computer-based tools and methods can quickly study their function without the need for wet-lab procedures. Several computational tools can be used to predict the two-dimensional and three-dimensional structures of these 5 proteins. AlphaFold which uses artificial intelligence to predict the three-dimensional structure of proteins can be used for predicting the three-dimensional structures of these proteins(Jumper et al., 2021). These three-dimensional data can be further used to design novel drugs targeting these 5 novel proteins (Nitrate transporter, Phospho-N-acetylmuramoyl-pentapeptide-transferase, PTS fructose transporter subunit EIIBC, Tripartite tricarboxylate transporter TctB family protein, and Zinc metalloprotease) enlisted in our study.

3.8 Conservancy analysis of highest antigenic proteins with other strains:

Considering the highest antigenicity prediction, ‘Nitrate transporter (WP_154835941.1)’ and ‘Phospho-N-acetylmuramoyl-pentapeptide-transferase (WP_154835674.1)’ proteins from D. tsuruhatensis considered for conservancy analysis. The protein-protein BLASTp through NCBI represented the conservancy pattern ‘Nitrate transporter’ and ‘Phospho-N-acetylmuramoyl-pentapeptide-transferase’ proteins from D. tsuruhatensis, strain TR1180 with other strains of D. tsuruhatensis. ‘Phospho-N-acetylmuramoyl-pentapeptide-transferase’ showed minimal conservancy while ‘Nitrate transporter’ showed above 99% conservancy with homologous proteins of other classically used D. tsuruhatensis strains. The result of NCBI BLASTp against other strains is represented in Table 6.

Table 6

Conservancy analysis of Nitrate (WP_154835941.1) transporter protein
S. No	Sequence ID	Organism	Identity
1	WP_012203039.1	Delftia	99%
2	WP_013804389.1	Delftia	99%
3	WP_063328024.1	Delftia	99%
4	WP_199139928.1	Delftia sp. ASV31	99%
5	WP_203400046.1	Delftia lacustris	99%
6	WP_202758002.1	Delftia acidovorans	99%
7	WP_097203924.1	Delftia acidovorans	99%
8	WP_043820416.1	Delftia	99%
9	WP_197944857.1	Delftia acidovorans	99%
10	WP_047218530.1	Delftia lacustris	99%
11	WP_046239788.1	Delftia	99%
12	WP_016446636.1	Delftia	99%
13	WP_216293794.1	Delftia acidovorans	99%

The anticipated protein should be conserved in several bacterial strains to assure an effective drug and vaccination target against a specific bacterial infection (Gupta et al., 2017). The "Nitrate transporter" in this instance showed a higher degree of conservation with similar proteins from other D. tsuruhatensis strains from various geographical areas. Consequently, "Nitrate transporter" might be a strong, versatile therapeutic target for D. tsuruhatensis.

Only five membrane proteins (Nitrate transporter, Phospho-N-acetylmuramoyl-pentapeptide-transferase, PTS fructose transporter subunit EIIBC, Tripartite tricarboxylate transporter TctB family protein, and Zinc metalloprotease) were identified as potential novel therapeutic candidates against D. tsuruhatensis by following subtractive genomic approach. Of them, the Nitrate transporter has been predicted as the most suitable drug target against D. tsuruhatensis as well as its role as a potential therapeutic target. As a result, our findings could aid in the development of an appropriate therapeutic and encourage the construction of vaccination against D. tsuruhatensis with fewer trial-and-error iterations of assays, which would eventually help to reduce the time and expense required to carry out additional in vitro research.

Funding

This study did not receive any funding from any funding agency or research institution.

Declaration of competing interest

The authors confirm that they have no known financial or interpersonal conflicts that would have appeared to have an impact on the research presented in this study.

Ethics approval and consent to participate

Not applicable. No impact on ethical standards in this study, and there is no human or animal involvement.

Consent for publication

Authors have no conflict of interest.

Competing interests

All authors declare that they have no competing interests.

Author contribution

MMRS and SM conceptualized the study. SM designed and evaluated the data. SM, ABR, MHK, NN, IJI, and MMRS collected the data. MMRS and SM wrote the manuscript. All authors evaluated the manuscript and revised the manuscript to make any required format adjustments. MMRS supervised the entire study and also acted for all correspondences. All authors approved the final version of the manuscript.

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Novel therapeutic targets identification of Delftia tsuruhatensis strain TR1180 employing subtractive genomics approach

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Abstract

Figures

1. Introduction

2. Methodology

2.1 Whole proteome retrieval of pathogen:

2.2 Removal of paralogous proteins using CD-Hit:

2.3 selection of non-homologous proteins to Human proteome:

2.4 Selection of essential non-homologous proteins:

2.5 Metabolic pathway analysis of non-homologous proteins:

2.6 Determination of Subcellular localization:

2.7 Antigenicity prediction:

2.8 Novel drug target identification and druggability screening:

2.9 Conservancy analysis prediction:

3. Results And Discussion

3.1 Detecting paralogous proteins:

3.2 selection of non-homologous proteins to Humans:

3.3 Selection of essential non-homologous proteins:

3.4 Metabolic pathway analysis of non-homologous proteins:

3.5 Determination of Subcellular localization:

3.6 Antigenicity prediction of membrane proteins:

3.7 Novel drug target identification and druggability screening:

3.8 Conservancy analysis of highest antigenic proteins with other strains:

4. Conclusion

Declarations

References

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