Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2018 May 25;13(5):e0197511. doi: 10.1371/journal.pone.0197511

Mining of potential drug targets through the identification of essential and analogous enzymes in the genomes of pathogens of Glycine max, Zea mays and Solanum lycopersicum

Rangeline Azevedo da Silva 1,*, Leandro de Mattos Pereira 2, Melise Chaves Silveira 1, Rodrigo Jardim 1, Antonio Basilio de Miranda 1
Editor: David A Lightfoot3
PMCID: PMC5969768  PMID: 29799863

Abstract

Pesticides are one of the most widely used pest and disease control measures in plant crops and their indiscriminate use poses a direct risk to the health of populations and environment around the world. As a result, there is a great need for the development of new, less toxic molecules to be employed against plant pathogens. In this work, we employed an in silico approach to study the genes coding for enzymes of the genomes of three commercially important plants, soybean (Glycine max), tomato (Solanum lycopersicum) and corn (Zea mays), as well as 15 plant pathogens (4 bacteria and 11 fungi), focusing on revealing a set of essential and non-homologous isofunctional enzymes (NISEs) that could be prioritized as drug targets. By combining sequence and structural data, we obtained an initial set of 568 cases of analogy, of which 97 were validated and further refined, revealing a subset of 29 essential enzymatic activities with a total of 119 different structural forms, most belonging to central metabolic routes, including the carbohydrate metabolism, the metabolism of amino acids, among others. Further, another subset of 26 enzymatic activities possess a tertiary structure specific for the pathogen, not present in plants, men and Apis mellifera, which may be of importance for the development of specific enzymatic inhibitors against plant diseases that are less harmful to humans and the environment.

Introduction

One of the major challenges for plant breeders is to maintain high levels of quality and production of cultures. Diseases caused by plant pathogens are one of the main factors limiting the productivity of large commodities, such as soybean (Glycine max), corn (Zea mays) and tomato (Solanum lycopersicum) [1,2]. Use of pesticides is one of the most commonly used alternatives to plant pathogens control, being used in a wide variety of crops [3].

Pesticides affect various population groups, including farm workers, residents in neighboring areas, consumers and wild animals [4,5]. Handling and consumption of these products are responsible for a series of conditions including acute intoxications [6], Parkinson’s disease [7], skin diseases [8], congenital malformations [9] and the onset of cancer after long periods of exposure [10]. An increase of 93% in the world’s consumption of pesticides was observed in the last two decades, while in Brazil, the largest consumer of pesticides in the world [11, 12], this increase was of 190%. New control alternatives are desired, where the new measures do not affect the development and production of the plant and present a lower risk of contamination for man and the environment [13,14].

Enzymes catalyze hundreds of successive reactions, consisting of highly coordinated processes indispensable for the maintenance of the life of an organism [15, 16]. Essential enzymes, which tend to be conserved between closely related organisms [17, 18] have been the subject of study as targets for diseases caused by a variety of organisms [19–25], including plant pathogens like Pseudomonas syringae [26] and Xanthomonas spp. [27]. Comparative genomic approaches, taking advantage of the huge amount of sequence data generated in the last decade, may contribute in several ways to the identification of key enzymes in the phytopathogens’ genomes [28, 29].

Enzyme classification follows rules defined by the International Union of Biochemistry and Molecular Biology Nomenclature Committee (NC-IUBMB), in association with the International Union of Pure and Applied Chemistry (IUPAC). A four-digit classification scheme known as the Enzyme Commission Number (EC) was proposed by this committee [30]. The first three digits are those that define the catalyzed reaction, the second and third comprise the subclasses of the reactions, and the fourth digit is a unique identifier that corresponds to the catalytic activity itself. Enzymes can also be grouped into families based on sequence similarity, and families are organized into superfamilies according to the catalytic activity [31]. Sequence motifs and domain architecture are the main criteria employed, but other characteristics can be used [32]. This diversity may result in functional overlap: these cases are known as non-homologous isofunctional enzymes (NISEs), also known as functional analogous enzymes [33, 34]. Analogous enzymes perform the same biochemical function, but have different evolutionary origins, with distinct primary structures whose differences are reflected in their tertiary structures [35]. Convergent evolution, initially thought to be a rare phenomenon in enzyme evolution, has been demonstrated for several enzymes including superoxide dismutase [36–38] and proteases [39]. Later, cases of functional analogy were found in most biochemical pathways [40–42]. Most importantly, the structural differences found between analogous enzymes from the plant and the phytopathogen, a consequence of their different evolutionary origins, may be exploited for the design of specific molecules that will interact only with the form found in the phytopathogen, leaving the plant and other important species, particularly men itself and Apis mellifera, one of the most important pollinators [43,44], unharmed.

Thus, the objective of this study was to develop and implement a computational approach to i) identify and validate a set of NISEs, ii) reveal a subset of essential analogous enzymes and iii) disclose a subset of specific enzymatic structures, possessed only by the pathogens. To test our approach, we studied the genomes of three plants of great economic importance and worldwide distribution, Glycine max, Zea mays and Solanum lycopersicum, 15 bacterial and fungal plant pathogens, the genomes of Homo sapiens, Apis mellifera and two beneficial microorganisms, Bacillus subtilis and Trichoderma harzianum.

Material and methods

The analyzes were performed in four main stages: data preparation, clustering, functional inference, structural validation, and essentiality. A flowchart of the methodology is shown in Fig 1.

Fig 1. Identification of essential, non-homologous isofunctional enzymes.

Fig 1

Open in a new tab

Datasets and clustering

The datasets of predicted proteins for each genome studied in this work were obtained from UniprotKB (version 2015_10 http://www.uniprot.org/) and RefSeq (Version 70, http://www.ncbi.nlm.nih.gov/). These datasets contained several proteins annotated as "uncharacterized", "hypothetical" and / or "putative". Three plant genomes were analyzed: G. max, Z. mays and S. lycopersicum. Pathogens were chosen according to the geographic distribution of the disease, most of them with a cosmopolitan occurrence. The pathogens analyzed comprise eleven fungal and four bacterial genomes, all pathogenic to one or more species of the plants studied. Also included were the genomes of Homo sapiens, Apis mellifera (pollinator), Trichoderma harzianum (soil fungus) and Bacillus subtilis (plant growth promoting bacteria) (Table 1).

Table 1. Description of the predicted proteins datasets of the organisms included in this study.

Organisms Database Accession NCBI Reference #Ptn Unch. Hyp. Put. Annot. (%)
Glycine max RefSeq NC_016088 [45] 59374 23618 __ 1566 61
Aspergillus flavus 1* RefSeq GCA_000006275.2 [46] 13287 5380 __ __ 59
Fusarium oxysporum 2* Uniprot GCA_000222805.1 [47] 17385 16,684 __ 1 8
Phytophthora sojae * RefSeq AAQY00000000 [48] 26106 __ 25279 125 2,8
Sclerotinia sclerotiorum * RefSeq AAGT00000000.1 [49] 12902 12,042 __ 3 6,6
Xanthomonas axonopodis ** RefSeq CP004399 [50] 4496 1413 __ 35 67
Solanum lycopersicum Uniprot AEKE00000000 [51] 31683 28785 __ __ 9,1
Botrytis cinerea * RefSeq NZ_AAID00000000.1 [52] 14687 __ 8,696 __ 40
Fusarium oxysporum 3* Uniprot GCA_000149955.2 [53] 15811 15,148 __ __ 4,3
Moniliophthora perniciosa * Uniprot ABRE00000000 [54] 12915 12,741 __ __ 1,3
Pseudomonas syringae ** RefSeq NC_004578.1 [55] 5449 __ 1446 __ 73
Ralstonia solanacearum ** RefSeq NC_003295.1 [56] 4400 696 135 1292 56
Zea mays RefSeq LPUQ00000000 [57] 59384 __ 2363 2300 92
Aspergillus flavus 4* RefSeq GCA_000952835.1 [58] 13561 __ 5423 5884 16
Colletotrichum graminicola * RefSeq ACOD00000000 [59] 11910 __ 5,381 __ 54
Gibberella moniliformis* Uniprot AAIM00000000.2 [60] 17384 13,71 __ __ 21
Exserohilum turcicum * RefSeq AIHT00000000 [61] 4248 __ 11159 1 3,6
Pantoea ananatis ** RefSeq CP001875 [62] 4302 707 __ 14 83
Apis mellifera Uniprot AADG00000000 [63] 13514 12511 __ 5 7,3
Trichoderma harzianum * Uniprot MRYK00000000 [64] 11480 7704 __ 3 32
Bacillus subtilis** Uniprot NC_000964 [65] 26433 1299 __ 301 93
Homo sapiens Uniprot CM000663 [66] 63487 1338 __ 1071 96
Open in a new tab

__No proteins in this category

* Fungi

** Bacteria

1 A. flavus NRRL3357

2 F. oxysporum Fo5176

3 F. oxysporum 4287

4 A. flavus AF70.

#Ptn., total number of proteins; Unch., uncharacterized proteins; Hyp., hypothetical proteins; Put.,putative proteins; Annot.%, annotation percentage

The complete, annotated set of enzymes was extracted from KEGG (release 73.0, January 2015) and contained 1,524,871 protein sequences, from 298 Eukaryotes, 3014 Eubacteria and 175 Archaea genomes. Sequences with less than 60 amino acids were removed. To clusterize the sequences into groups based on sequence similarity, we used the AnEnPi pipeline [67]. A similarity score with a cut-off value of 120 was used for all BLASTp pairwise comparisons since this score separates enzymes with different tertiary structures [34]. Results were parsed to obtain, for each enzymatic activity as defined by their Enzyme Commission (EC) number, files containing one or more groups of primary structures. If for a given enzymatic activity, only one group was produced at the end of the clusterization step, then all sequences would be considered homologous, and that enzymatic activity was removed from the analysis. On the other hand, if more than one group was produced, then sequences in the same group were considered homologous, with a score above 120, while sequences allocated in different groups were considered analogous (potential NISEs), with a score smaller than 120. In other words, sequences allocated in the same group have similar tertiary structures, while sequences allocated in different groups have different folding patterns, which reflects their different evolutionary origins [34, 35, 68].

Protein function inference

The groups of homologous sequences generated after the clustering step using the KEGG dataset were used for reannotation (with the pipeline AnEnPi) of the predicted proteins from the organisms in this study, which were compared, in a pairwise manner, to each primary protein structure within each protein functional group from KEGG. For the biochemical function inference, a cutoff value of 10−20 was used, a highly restrictive value that gives greater reliability to the results [67, 69–71]. Sequences with scores below this threshold were removed from the analysis.

NISEs: Identification, structural validation and essentiality

The search for cases of analogy (NISEs) between enzymes from plants and pathogens was performed through the analysis of the groups produced after the clustering step and functional inference. For this, one of the modules of AnEnPi was used together with in-house scripts to parse and filter the results. To validate the identified NISEs, that is, to verify if the enzymes found are cases of evolutionary convergence, we classified the sequences in accordance with their folds using the SUPERFAMILY database. The information in this database is based on a collection of Hidden Markov Models [72], which represent the structural domains of proteins classified by SCOP [73].

Heteromultimeric enzymes, enzymes annotated with the term "subunit" and sequences without an associated fold were excluded from the final list. Fused domains were maintained in our analysis, as in the case of the family "Dimeric alpha + beta barrel", which is an evolutionarily conserved group of protein families [73, 74]. Enzymes with the same EC number, but displaying different folds and, consequently, belonging to different superfamilies, were considered potential NISEs.

The Database of Essential Genes (DEG, 14.7, October/2016, http://www.essentialgene.org/) was used as a reference for the search for essential activities in the pathogens studied. A BLASTp search was performed between all enzymatic sequences identified as analogous against the DEG database. An e-value of 10−5 was used as threshold. Later, another BLASTp search was performed between all enzymatic sequences identified as analogues against the predicted proteins of organisms that should not be affected by an eventual inhibitor for the target identified in phytopathogen (H. sapiens, A. mellifera, T. harzianum and B. subtilis). An e-value of 10−5 was used as threshold.

Results

Data preparation, clustering and functional activity inference

After cleaning and preparation, the initial dataset obtained from KEGG was reduced to 1,225,682 protein sequences distributed over 3,893 enzymatic activities. After clusterization, this dataset was used for the reannotation of the predicted proteins of the plants and phytopathogens, comprising 444198 individual sequences in 2096 enzymatic activities from the three plants and their 15 pathogens. Predicted proteins from H. sapiens, A. mellifera, T. harzianum and B. subtilis were also reannotated, comprising 114914 individual sequences in 2008 enzymatic activities. Annotation quality of the downloaded sets of predicted proteins varied greatly. Before the reannotation procedure, the best annotated organism among the plants was Z. mays, with approximately 90% of their proteins characterized, while S. lycopersicum presented only 9% of its proteins annotated. Among the pathogens, P. ananatis presented 83% of its entire conceptual proteome annotated and M. perniciosa had only 1.3% of its proteins characterized. After the functional inference step, where only enzymes were reannotated, on average 15% of the proteins of each organism were associated with an enzymatic activity (data not shown).

Potential NISEs: Identification and validation

Initially, a total of 568 cases of potential NISEs was identified, and from this set 97 cases were validated (Table 2, see S1 Table for more details). Sequences labeled with "subunit" or "chain" (324 cases), enzymes displaying the same fold (55 cases), and sequences without an associated fold in the SUPERFAMILY database (92 cases) were excluded. Cases of analogy were validated for all the pathogens studied: only one case was found for P. sojae and S. sclerotiorum, while 14 cases were found for A. flavus AF70. In total, 13 cases of analogy were found in the comparisons between G. max and its pathogens, 23 cases between S. lycopersicum and its pathogens, and 61 cases between Z. mays and its pathogens (Table 2).

Table 2. Number of potential, validated, specific and essential NISEs.

Numbers in parenthesis indicate the number of enzymatic activities identified.

Host Pathogens Potential NISEs Validated Specific* Essential
G. max A. flavus1 25 4 3 2
F. oxysporum2 21 4 4 1
P. sojae 25 1 1 1
S. sclerotiorum 21 1 1 0
  X. axonopodis 12 3 2 2
S. lycopersicum B. cinerea 18 3 2 1
F. oysporum3 30 6 5 2
M. perniciosa 23 4 2 2
P. syringae 38 5 4 5
  R. solanacearum 32 5 5 5
Z. mays A. flavus4 64 14 8 7
C. graminicola 69 13 7 9
E. turcicum 62 12 7 9
G. moniliformis 65 10 6 5
  P. ananatis 63 12 11 7
Total   568 97 (39) 68 (26) 58 (29)
Open in a new tab

* Number of pathogen’s specific tertiary structures

1 A. flavus NRRL3357

2 F. oxysporum Fo5176

3 F. oxysporum 4287

4 A. flavus AF70.

The validated NISEs (97 cases), comprising 39 different enzymatic activities, participate in central metabolic pathways including the carbohydrate metabolism (13 enzymatic activities), amino acid metabolism (8), energy metabolism (6), biosynthesis of secondary metabolites (4) and lipid metabolism (4). Eight enzymatic activities belong to other pathways such as xenobiotics degradation, metabolism of cofactors and vitamins, nucleotide metabolism and metabolism of other amino acids (Fig 2). It is important to remember that one enzymatic activity may participate in more than one pathway.

Fig 2. Functional classification of the validated NISEs.

Fig 2

Open in a new tab

Numbers in parenthesis indicate the amount of essential enzymatic activities.

Essential NISEs

After the validation step a screening for essential enzymes was performed, revealing 58 cases of analogy (Table 3), involving 29 different essential enzymatic activities, corresponding to 119 different structures, for all organisms analyzed in this study. In the carbohydrate metabolism, the most frequent case was catalase, classified as essential for three pathogens of G. max (A. flavus, F. oxysporum and P. sojae), three pathogens of S. lycopersicum (F. oxysporum, P. seryngae and R. solanacearum) and three pathogens of Z. mays (A. flavus, E. turcicum and C. graminicola). Members of the pentoses pathway, like ribose 5-phosphate isomerase, ribulose-phosphate 3-epimerase and glyoxalase I, were identified in three Z. mays’ pathogens (A. flavus, G. moniliformis and C. graminicola). Another frequent case, the enzyme cyclin-dependent kinase, was found for four of the five pathogens of Z. mays (A. flavus, E. turcicum, C. graminicola and G. moniliformis).

Table 3. Essential and analogous enzymes.

NISEs Essentiality data
Hosts ID Sequence Host Pathogens ID sequence pathogens EC number Enzyme ID DEG** E-value
G. max NP_001235974.1 A. flavus XP_002384918.1 1.11.1.6* Catalase DEG10110209 2,00E-068
G. max XP_003557098.2 A. flavus XP_002377297.1 1.11.1.7* Peroxidase __ __
G. max XP_006600684.1 A. flavus XP_002376298.1 1.2.1.3 Aldehyde dehydrogenase (NAD+) DEG20180006 1,00E-065
G. max XP_006600243.1 A. flavus XP_002382374.1 2.6.1.1 Aspartate transaminase __ __
G. max NP_001235974.1 F. oxysporum 9FP11|F9FP11_FUSOF 1.11.1.6* Catalase DEG10110209 0
G. max XP_003555725.2 F. oxysporum F9FYF1_FUSOF 1.15.1.1* Superoxide dismutase __ __
G. max XP_006600243.1 F. oxysporum F9G466_FUSOF 2.6.1.1 Aspartate transaminase __ __
G. max XP_006598804.1 F. oxysporum F9G2J4_FUSOF 4.4.1.5 Lactoylglutathione lyase __ __
G. max NP_001235974.1 P. sojae XP_009521283.1 1.11.1.6* Catalase DEG10110209 8,00E-115
G. max XP_003557098.2 S. sclerotiorum XP_001585507.1 1.11.1.7* Peroxidase __ __
G. max NP_001235974.1 X. axonopodis WP_042823856.1 1.11.1.6* Catalase __ __
G. max XP_006605648.1 X. axonopodis WP_054320474.1 1.15.1.1* Superoxide dismutase DEG20241649 6,00E-018
G. max XP_006601861.1 X. axonopodis WP_033483073.1 6.4.1.2 Acetyl-CoA carboxylase DEG10030125 4,00E-057
S. lycopersicum K4CN29_SOLLC B. cinerea XP_001560519.1 3.1.3.2 Acid phosphatase __ __
S. lycopersicum LGUL_SOLLC B. cinerea XP_001550649.1 4.4.1.5 Lactoylglutathione lyase __ __
S. lycopersicum P21568|CYPH_SOLLC B. cinerea XP_001545186.1 5.2.1.8 Peptidylprolyl isomerase DEG20241291 1,00E-046
S. lycopersicum K4BVX3_SOLLC F. oxysporum A0A0D2YKD1_FUSO4 1.11.1.6* Catalase DEG10110209 0
S. lycopersicum Q7XAV2_SOLLC F. oxysporum A0A0D2YE80_FUSO4 1.15.1.1* Superoxide dismutase __ __
S. lycopersicum K4CN29_SOLLC F. oxysporum A0A0D2YGA3_FUSO4 3.1.3.2 Acid phosphatase __ __
S. lycopersicum Q42875_SOLLC F. oxysporum A0A0D2XJE6_FUSO4 3.2.1.4 Cellulase __ __
S. lycopersicum Q8GZD8_SOLLC F. oxysporum A0A0D2XCV3_FUSO4 3.4.11.5 Prolyl aminopeptidase DEG20210010 7,00E-014
S. lycopersicum LGUL_SOLLC F. oxysporum A0A0D2XLV4_FUSO4 4.4.1.5 Lactoylglutathione lyase __ __
S. lycopersicum P15003|PER1_SOLLC M. perniciosa E2LX62_MONPE 1.11.1.7* Peroxidase __ __
S. lycopersicum Q9FVN0|AMT13_SOLLC M. perniciosa E2M162_MONPE 2.7.13.3 Histidine-kinase DEG20070330 4,00E-036
S. lycopersicum Q8GZD8_SOLLC M. perniciosa E2LYM3_MONPE 3.4.11.1 Leucyl aminopeptidase __ __
S. lycopersicum K4CJ01_SOLLC M. perniciosa E2LAS1_MONPE 5.4.2.8 Phosphomannomutase DEG20020210 5,00E-030
S. lycopersicum K4BVX3_SOLLC P. seryngae NP_794283.1 1.11.1.6* Catalase DEG10270348 0
S. lycopersicum P15003|PER1_SOLLC P. seryngae NP_794565.1 1.11.1.7* Peroxidase DEG10180459 4,00E-010
S. lycopersicum K4CN29_SOLLC P. seryngae NP_791387.1 3.1.3.2 Acid phosphatase DEG10290292 1,00E-084
S. lycopersicum Q05539|CHIA_SOLLC P. seryngae NP_794777.1 3.2.1.14 Chitinase DEG10250423 5,00E-019
S. lycopersicum P21568|CYPH_SOLLC P. seryngae NP_791005.1 5.2.1.8 Peptidylprolyl isomerase DEG10470303 2,00E-059
S. lycopersicum K4BVX3_SOLLC R. solanacearum AGH83314.1 1.11.1.6* Catalase DEG10270348 0
S. lycopersicum P15003|PER1_SOLLC R. solanacearum AGH86619.1 1.11.1.7* Peroxidase DEG10350205 2,00E-008
S. lycopersicum Q9FVN0|AMT13_SOLLC R. solanacearum AGH84344.1 2.7.13.3 Histidine kinase DEG10330275 1,00E-065
S. lycopersicum Q05539|CHIA_SOLLC R. solanacearum AGH83721.1 3.2.1.14 Chitinase DEG10260021 1,00E-017
S. lycopersicum K4C2F1_SOLLC R. solanacearum AGH86735.1 4.2.1.1 Carbonic anhydrase DEG10050308 4,00E-038
Z. mays NP_001304298.1 A. flavus B8NGN0_ASPFN 1.10.2.2 Quinol-cytochrome-c reductase DEG20091193 1,00E-054
Z. mays XP_008660914.1 A. flavus B8NX24_ASPFN 1.11.1.6* Catalase DEG10110209 2,00E-068
Z. mays XP_008664058.1 A. flavus B8NC39_ASPFN 1.11.1.7* Peroxidase __ __
Z. mays NP_001145525.1 A. flavus B8N164_ASPFN 1.11.1.15* Peroxiredoxin __ __
Z. mays XP_008664254.1 A. flavus B8NB79_ASPFN 2.1.1.43 Histone-lysine N-methyltransferase DEG20051547 7,00E-012
Z. mays XP_008665261.1 A. flavus B8N9N8_ASPFN 2.5.1.18 Glutathione transferase __ __
Z. mays XP_008660232.1 A. flavus B8NQM9_ASPFN 2.6.1.1 Aspartate transaminase __ __
Z. mays XP_008663534.1 A. flavus B8N9A7_ASPFN 2.7.11.22 Cyclin-dependent kinase DEG20010254 6,00E-067
Z. mays XP_008664470.1 A. flavus B8NB93_ASPFN 3.1.3.2 Acid phosphatase __ __
Z. mays XP_008656307.1 A. flavus B8NQT3_ASPFN 3.2.2.22 rRNA N-glycosylase __ __
Z. mays XP_008655471.1 A. flavus B8NWM8_ASPFN 4.2.1.1 Carbonic anhydrase DEG20101870 2,00E-011
Z. mays NP_001148888.1 A. flavus B8NT23_ASPFN 4.4.1.5 Lactoylglutathione lyase __ __
Z. mays NP_001149850.1 A. flavus B8N7U5_ASPFN 5.1.3.1 Ribulose-phosphate 3-epimerase DEG20210336 6,00E-110
Z. mays X|P_008644870.1 A. flavus B8NFW5_ASPFN 5.3.1.6 Ribose-5-phosphate isomerase DEG10140248 3,00E-012
Z. mays XP_008657765.1 E. turcicum XP_008026270.1 1.1.1.27 L-lactate dehydrogenase DEG20010346 1,00E-086
Z. mays NP_001105310.2 E. turcicum XP_008029291.1 1.11.1.6* Catalase DEG10110209 0
Z. mays XP_008664058.1 E. turcicum XP_008030871.1 1.11.1.7* Peroxidase DEG10400636 4,00E-080
Z. mays NP_001145525.1 E. turcicum XP_008025877.1 1.11.1.15* Peroxiredoxin __ __
Z. mays XP_008664254.1 E. turcicum XP_008025860.1 2.1.1.43 Histone-lysine N-methyltransferase DEG20240496 3,00E-018
Z. mays XP_008663534.1 E. turcicum XP_008024068.1 2.7.11.22 Cyclin-dependent kinase DEG20090883 2,00E-041
Z. mays XP_008651541.1 E. turcicum XP_008029497.1 3.1.1.31 6-phosphogluconolactonase __ __
Z. mays XP_008664470.1 E. turcicum XP_008024834.1 3.1.3.2 Acid phosphatase DEG10390008 1,00E-063
Z. mays NP_001148888.1 E. turcicum XP_008026072.1 4.4.1.5 Lactoylglutathione lyase __ __
Z. mays NP_001136955.1 E. turcicum XP_008024266.1 4.6.1.1 Adenylate cyclase DEG10030767 2,00E-010
Z. mays NP_001149850.1 E. turcicum XP_008028934.1 5.1.3.1 Ribulose-phosphate 3-epimerase DEG20210336 1,00E-108
Z. mays XP_008644870.1 E. turcicum XP_008028444.1 5.3.1.6 Ribose-5-phosphate isomerase DEG10080091 8,00E-015
Z. mays XP_008657765.1 C. graminicola XP_008097388.1 1.1.1.27 L-lactate dehydrogenase DEG20010346 1,00E-091
Z. mays XP_008660914.1 C. graminicola XP_008098502.1 1.11.1.6* Catalase DEG10110209 0
Z. mays XP_008664058.1 C. graminicola XP_008095952.1 1.11.1.7* Peroxidase DEG10400636 3,00E-079
Z. mays NP_001145525.1 C. graminicola XP_008093145.1 1.11.1.15* Peroxiredoxin __ __
Z. mays XP_008663534.1 C. graminicola XP_008094831.1 2.7.11.22 Cyclin-dependent kinase DEG20010254 6,00E-050
Z. mays XP_008651541.1 C. graminicola XP_008100128.1 3.1.1.31 6-phosphogluconolactonase __ __
Z. mays XP_008675577.1 C. graminicola XP_008100081.1 3.1.1.4 Phospholipase A2 DEG20240063 2,00E-026
Z. mays XP_008664470.1 C. graminicola XP_008094949.1 3.1.3.2 Acid phosphatase __ __
Z. mays XP_008658269.1 C. graminicola XP_008092609.1 3.1.13.4 Poly(A)-specific ribonuclease DEG20240339 7,00E-092
Z. mays XP_008677367.1 C. graminicola XP_008097450.1 3.1.3.3 Phosphoserine phosphatase DEG20211963 6,00E-052
Z. mays NP_001148888.1 C. graminicola XP_008096879.1 4.4.1.5 Lactoylglutathione lyase __ __
Z. mays NP_001149850.1 C. graminicola XP_008091175.1 5.1.3.1 Ribulose-phosphate 3-epimerase DEG20210336 8,00E-113
Z. mays XP_008644870.1 C. graminicola XP_008098210.1 5.3.1.6 Ribose-5-phosphate isomerase DEG10080091 1,00E-015
Z. mays NP_001145525.1 G. moniliformis W7LPB7_GIBM7 1.11.1.15* Peroxiredoxin __ __
Z. mays XP_008660232.1 G. moniliformis W7MC41_GIBM7 2.6.1.1 Asparate transaminase __ __
Z. mays XP_008663534.1 G. moniliformis W7MSL6_GIBM7 2.7.11.22 Cyclin-dependent kinase DEG20011066 2,00E-036
Z. mays XP_008651541.1 G. moniliformis W7M0K8_GIBM7 3.1.1.31 6-phosphogluconolactonase __ __
Z. mays XP_008658269.1 G. moniliformis W7M4G2_GIBM7 3.1.13.4 Poly(A)-specific ribonuclease DEG20240339 1,00E-088
Z. mays XP_008664470.1 G. moniliformis W7NDR6_GIBM7 3.1.3.2 Acid phosphatase DEG10390008 2,00E-013
Z. mays XP_008655784.1 G. moniliformis W7M5R3_GIBM7 3.2.1.4 Cellulase __ __
Z. mays NP_001148888.1 G. moniliformis W7LNQ2_GIBM7 4.4.1.5 Lactoylglutathione lyase __ __
Z. mays NP_001136955.1 G. moniliformis W7MFF7_GIBM7 4.6.1.1 Adenylate cyclase DEG20090256 1,00E-090
Z. mays NP_001149850.1 G. moniliformis W7M917_GIBM7 5.1.3.1 Ribulose-phosphate 3-epimerase DEG20210336 1,00E-107
Z. mays NP_001105310.2 P. ananatis D4GMF4_PANAM 1.11.1.6* Catalase __ __
Z. mays XP_008667406.1 P. ananatis D4GL47_PANAM 1.11.1.15* Peroxiredoxin DEG10030767 1,00E-006
Z. mays XP_008672910.1 P. ananatis D4GCI2_PANAM 1.16.3.1* Ferroxidase __ __
Z. mays XP_008660532.1 P. ananatis D4GJ68_PANAM 2.1.3.3 Ornithine carbamoyltransferase DEG10350142 9,00E-055
Z. mays XP_008657589.1 P. ananatis D4GHC5_PANAM 2.3.1.51 1-acylglycerol-3-phosphate O-acyltransferase DEG10480294 2,00E-093
Z. mays XP_008656415.1 P. ananatis D4GHA1_PANAM 2.7.2.3 Phosphoglycerate kinase __ __
Z. mays XP_008662013.1 P. ananatis D4GMM0_PANAM 2.7.4.8 Guanylate kinase DEG10030351 9,00E-064
Z. mays XP_008672924.1 P. ananatis D4GGT2_PANAM 3.1.1.5 Lysophospholipase __ __
Z. mays XP_008651541.1 P. ananatis D4GFB8_PANAM 3.1.1.31 6-phosphogluconolactonase __ __
Z. mays XP_008650400.1 P. ananatis D4GCE1_PANAM 3.1.3.11 Fructose-bisphosphatase DEG10480226 2,00E-090
Z. mays XP_008672875.1 P. ananatis D4GMQ4_PANAM 4.2.1.96 4a-hydroxytetrahydrobiopterin dehydratase DEG10470424 3,00E-034
Z. mays NP_001105425.1 P. ananatis D4GK89_PANAM 4.3.3.7 4-hydroxy-tetrahydrodipicolinate synthase DEG10180422 1,00E-020
Open in a new tab

*Enzymes of the antioxidant system.

** Accession number in DEG.

In the amino acid metabolism, several enzymes were identified as essential and analogous, like carbonic anhydrase for R. solanacearum and. A. flavus AF70; prolyl aminopeptidase, for F. oxysporum 4287; transaminase, for A. flavus AF70, G. moniliformis, A. flavus NRRL3357 and F. oxysporum Fo5176. Chitinases were found as essential and analogous for P. seryngae and R. solanacearum (Table 3).

Analogous and essential enzymes were also found in the metabolism of lipids and biosynthesis of secondary metabolites pathways. Acetyl-CoA carboxylase was identified in. X. axonopodis and phospholipase A2 in C. graminicola. Ornithine carbamoyltransferase, identified in P. ananatis, participates in the amino acid metabolism (S2 Table). Some enzymatic activities found to be essential for some pathogens have not been identified as essential in others: these cases are represented by enzymes encoded by different genes. In this group we can cite enzymes belonging to the antioxidant system (AS), composed of enzymes involved with the detoxification of reactive oxygen species (ROS) such as catalase, peroxidase, superoxide dismutase, peroxiredoxin, among others.

Analogous enzymes in the antioxidant system

One group of enzymes that stood out among the validated NISEs, including non-essential activities, were the enzymes that comprise the antioxidant system (AS). In all comparisons made between plants and their pathogens, except in the case of B. cinerea, for at least one of the functional activities of the antioxidant system, the host enzyme and its counterpart in the pathogen are structurally different (Table 4). In total, 27 cases of analogy were found for the antioxidant system, including catalase (CAT), peroxidase (POX), superoxide dismutase (SOD), ferroxidase (HEPH) and peroxiredoxin (PRDX). In our results, CAT was identified as an essential enzyme for 9 of the 14 pathogens studied, and POX was identified as essential in E. turcicum, C. graminicola, P. seryngae and R. solanacearum. SOD was identified as an essential enzyme for X. axonopodis. Among the pathogens analyzed, there are two species with distinct strains, A. flavus (NRRL3357, AF70) and F. oxysporum (Fo5176, 4287). No differences were observed between different lineages as in the case of A. flavus and F. oxysporum. It is important to emphasize that the AS enzymatic activities are present in all the genomes included in the present work; however, only the cases of validated NISEs have been shown, which explain gaps in the absence/presence pattern observed for HEPH, PRDX and SOD (Table 4).

Table 4. Alternative enzymatic forms found among the enzymes of the antioxidant system.

Organisms Structural forms
CAT POX SOD HEPH PRDX
G. max ①* ⑤   ③ ⑥ ⑳ ① ④ ⑥ ⑦    
A. flavus1 ❷   ⓬      
F. oxysporum2 ❷     ⓮  
P. sojae ❷        
S. sclerotiorum   ⓬      
X. axonopodis     ❸                                        
S. lycopersicum ① ⑤   ③ ⑥   ① ④ ⑥ ⑦    
B. cinerea        
F. oysporum3 ❷     ⓮  
M. perniciosa   ⓬      
P. syringae ❷   ⓰      
R. solanacearum         ❻       ⓲                          
Z. mays ① ⑤   ③     ② ⑥   ① ⑩
A. flavus4 ❷   ⓬       ❾
C. graminicola ❷   ❻       ❾
E. turcicum ❷   ❻       ❾
G. moniliformis         ❾
P. ananatis     ❸                         ❼ ❷    
Open in a new tab

1 A. flavus NRRL3357

2 F. oxysporum Fo5176

3 F. oxysporum 4287

4 A. flavus AF70.

*Numbers represent the groups where a sequence was located. Only validated cases of analogy are shown. Black circles indicate structural forms validated found only on the pathogen.

Specific structural forms

After obtaining the final list of validated, essential NISEs between the plant hosts and their pathogens, a search for these enzymatic activities was performed on the predicted proteins of H. sapiens, A. mellifera, B. subtilis and T. harzianum. The objective of this comparison was to find specific structural enzymatic forms of the pathogen in the genomes of species that should not be affected by an eventual inhibitor targeting that particular structural form, mainly H. sapiens and A. mellifera. Of the 97 NISEs validated, 68 specific structural forms of the pathogen (in relation to the plant host, men and bee) were found (Table 5). They are distributed over 26 enzymatic activities (16 of them being essential). From these 68 structural forms, 39 were present in T. harzianum and 17 in B. subtilis, which is expected since these organisms belong to the same kingdoms of the phytopathogens studied in this work (Fungi and Bacteria).

Table 5. Phytopathogen specific enzymatic structural forms.

Comparison     Structural forms
Plant** Pathogen** EC Number ID Sequence Pathogens Pathogens Plant H. sapiens A. mellifera T. harzianum B. subtilis
Gm Af 1.11.1.6‡ XP_002384918.1 1Δ, 2 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Gm Af 1.11.1.7 XP_002377297.1 3, 6, 12 3, 6, 20 1, 3 1, 3, 6 3, 6, 12* 7
Gm Af 2.6.1.1 XP_002382374.1 1, 5 1 1 1 1, 5* 1
Gm Fo 1.11.1.6‡ F9FP11_FUSOF 1, 2 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Gm Fo 1.15.1.1‡ F9FYF1_FUSOF 1, 4, 7, 14 1, 4, 6, 7 1, 4, 7 1, 4, 7 1, 4, 7, 14* 1, 4
Gm Fo 2.6.1.1 F9G466_FUSOF 1, 5 1 1 1 1, 5* 1
Gm Fo 4.4.1.5 F9G2J4_FUSOF 1, 3 1, 8 1 1 1, 3* 1, 3, 6, 7, 11
Gm Ps 1.11.1.6‡ XP_009521283.1 1, 2 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Gm Ss 1.11.1.7 XP_001585507.1 3, 6, 12 3, 6, 20 1, 3 1, 3, 6 3, 6, 12* 7
Gm Xa 1.11.1.6 WP_042823856.1 3 1, 5 1, 5 1, 5 1, 2 1, 3*, 8
Gm Xa 6.4.1.2‡ WP_033483073.1 1, 6 1 1 2 1 1, 6*
Sl Bc 3.1.3.2 XP_001560519.1 2, 3, 7, 13 2, 6, 9, 11 2, 4, 7 2, 4, 5, 7, 20 2, 3, 4, 5, 7, 13* __
Sl Bc 4.4.1.5 XP_001550649.1 1, 3 1, 8 1 1 1, 3* 1, 3*
Sl Fo 1.11.1.6‡ A0A0D2YKD1_FUSO4 1, 2, 5 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Sl Fo 1.15.1.1 A0A0D2YE80_FUSO4 1, 4, 7, 14 1, 4, 6, 7 1, 4, 7 1, 4, 7 1, 4, 7, 14* 1, 4
Sl Fo 3.1.3.2 A0A0D2YGA3_FUSO4 1, 2, 3, 4, 7, 13 2, 4, 6, 9 2, 4, 7 2, 4, 5, 7, 20 2, 3, 4, 5, 7, 13 __
Sl Fo 3.2.1.4 A0A0D2XJE6_FUSO4 1, 6 1 __ 1 1 1, 3
Sl Fo 4.4.1.5 A0A0D2XLV4_FUSO4 1, 3 1, 8 1 1 1, 3* 1, 3*, 6, 7, 11
Sl Mp 1.11.1.7 E2LX62_MONPE 6, 12 3, 6 1, 3 1, 3, 6 3, 6, 12* 7
Sl Mp 3.4.11.1 E2LYM3_MONPE 1, 11 1 1 1 __ 1
Sl Psy 1.11.1.6‡ NP_794283.1 1, 2 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Sl Psy 1.11.1.7‡ NP_794565.1 6, 16, 18, 19 3, 6 1, 3 1, 3, 6 3, 6, 12 7
Sl Psy 3.1.3.2‡ NP_791387.1 1, 3 2, 6, 9, 11 2, 4, 7 2, 4, 5, 7, 20 1*, 3, 4, 5, 7, 13 __
Sl Psy 3.2.1.14‡ NP_794777.1 1, 3 1 1, 10 1, 4 1 __
Sl Rs 1.11.1.6‡ AGH83314.1 6 1, 5 1, 5 1, 5 1, 2 1, 3, 8
Sl Rs 1.11.1.7‡ AGH86619.1 6, 18 3, 6 1, 3 1, 3, 6 3, 6, 12 7
Sl Rs 2.7.13.3‡ AGH84344.1 1, 21, 23, 24, 33, 36 1, 20 2, 12, 13, 20 12, 20 1 1
Sl Rs 3.2.1.14‡ AGH83721.1 3 1 1, 10 1, 4 1 __
Sl Rs 4.2.1.1‡ AGH86735.1 1, 3, 13 1, 2, 5 1, 2 1, 2, 3 1, 2 1, 3, 5, 12
Zm Af 1.11.1.15 |B8N164_ASPFN 1, 9 1, 10 1 1 1, 9* 1
Zm Af 1.11.1.6‡ B8NX24_ASPFN 1, 2 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Zm Af 1.11.1.7 B8NC39_ASPFN 3, 6, 12 3 1, 3 1, 3, 6 3, 6, 12* 7
Zm Af 2.6.1.1 B8NQM9_ASPFN 1, 5 1 1 1 1, 5* 1
Zm Af 3.1.3.2 B8NB93_ASPFN 2, 13 2, 4, 6, 9 2, 4, 7 2, 4, 5, 7, 20 2, 3, 4, 5, 7, 13* __
Zm Af 3.2.2.22 B8NQT3_ASPFN 5 1, 7 __ __ __ __
Zm Af 4.4.1.5 B8NT23_ASPFN 1, 3 1, 8 1 1 1, 3* 1, 3*, 6, 7, 11
Zm Af 5.3.1.6‡ B8NFW5_ASPFN 1, 2 1 1 1 1, 2* __
Zm Cg 1.1.1.27‡ XP_008100733.1 2, 12 1, 12 1, 12 1, 12 1, 2*, 12 1, 11
Zm Cg 1.11.1.15 XP_008093145.1 1, 9 1, 10 1 1 1, 9* 1, 9*
Zm Cg 1.11.1.6‡ XP_008098502.1 1, 2, 5 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Zm Cg 3.1.1.31 XP_008100128.1 1, 2 1 1, 4 1 1, 2* 2*
Zm Cg 3.1.3.2 XP_008094949.1 2, 3, 5, 13 2, 4, 6, 9 2, 4, 7 2, 4, 5, 7, 20 2, 3, 4, 5, 7, 13* __
Zm Cg 4.4.1.5 XP_008096879.1 1, 3 1, 8 1 1 1, 3* 1, 3*, 6, 7, 11
Zm Cg 5.3.1.6‡ XP_008098210.1 1, 2 1 1 1 1, 2* 2*
Zm Et 1.11.1.15 XP_008025877.1 1, 9 1, 10 1 1 1, 9* 1
Zm Et 1.11.1.6‡ XP_008029291.1 1, 2, 5 1, 5 1, 5 1, 5 1, 2* 1, 3, 8
Zm Et 3.1.1.31 XP_008029497.1 1, 2 1 1, 4 2 1, 2* 2*
Zm Et 3.1.3.2‡ XP_008024834.1 1, 2, 3, 13 2, 4, 6, 9 2, 4, 7 2, 4, 5, 7, 20 1*, 3, 4, 5, 7, 13 __
Zm Et 4.4.1.5 XP_008026072.1 1, 3 1, 8 1 1 1, 3* 1, 3*, 6, 7, 11
Zm Et 4.6.1.1‡ XP_008024266.1 2, 8, 10 2, 17, 18 2, 8 2, 6, 8, 13 2, 8 4
Zm Et 5.3.1.6‡ XP_008028444.1 1, 2 1 1 1 1, 2* __
Zm Gm 1.11.1.15 W7LPB7_GIBM7 1, 2, 9 1, 10 1 1 1, 9* 1
Zm Gm 2.6.1.1 W7MC41_GIBM7 1, 5 1 1 1 1, 5* 1
Zm Gm 3.1.1.31 W7M0K8_GIBM7 1, 2 1 1, 4 1 1 2*
Zm Gm 3.1.3.2‡ W7NDR6_GIBM7 1, 2, 3, 7, 13 2, 4, 6, 9 2, 4, 7 2, 4, 5, 7, 20 2 __
Zm Gm 3.2.1.4 W7M5R3_GIBM7 1, 6 1 __ 1 1 1, 3
Zm Gm 4.4.1.5 W7LNQ2_GIBM7 1, 3 1, 8 1 1 1 1, 3*, 6, 7, 11
Zm Pa 1.11.1.15‡ D4GL47_PANAM 1, 2 1, 10 1 1 1 1
Zm Pa 1.11.1.6 D4GMF4_PANAM 3, 5, 6 1, 5 1, 5 1, 5 1 1, 3*, 8
Zm Pa 1.16.3.1 D4GCI2_PANAM 2, 7 2, 6 2, 4, 6 2, 6 6 1
Zm Pa 2.1.3.3‡ D4GJ68_PANAM 2 1, 10 1, 10 10 1 1, 10
Zm Pa 2.7.2.3 D4GHA1_PANAM 3 1 1 1 1 1, 3*, 9
Zm Pa 2.7.4.8 D4GMM0_PANAM 1, 4, 7 1 1 1 1 1, 7
Zm Pa 3.1.1.31 D4GFB8_PANAM 2, 6 1 1, 4 1 1 2*
Zm Pa 3.1.1.5 D4GGT2_PANAM 2, 5 6, 7, 18 1, 4, 6, 7, 9, 10, 17, 18 1, 6, 7, 9, 17, 18 1 __
Zm Pa 3.1.3.11‡ D4GCE1_PANAM 10, 12 1 1, 8 1 1 3, 11
Zm Pa 4.2.1.96‡ D4GMQ4_PANAM 2 1 1 1 1 2*
Zm Pa 4.3.3.7‡ D4GK89_PANAM 1, 2 1 __ __ __ 1, 4
Open in a new tab

**Gm: G. Max, Af: A. flavus, Fo: F. oxsyporum, Ps: P. sojae, Ss: S. sclerotiorum, Xa: X. axonopodis, Sl: S. lycopersicum, Rs: R. solanacearum, Psy: P. syringae, Mp: M. perniciosa. Bc: B. cinerea, Zm: Z. mays, Pa: P. ananatis, Gm: G. moniliformis, Et: E. turcicum, Gg: C. graminicola.

Δ Numbers represent the different structures. Numbers in bold are the specific phytopathogen enzymatic structural forms.

‡ Essential enzymes.

__ Enzymatic activity not found.

*Structural form homologous to the pathogen.

Discussion

The correct description of the analogous enzymes is important for the practical tasks of metabolic reconstruction and enzymatic nomenclature. In addition to this practical importance, these enzymes represent important evolutionary phenomenon, existence shows that for various biochemical problems, evolutionarily independent solutions may appear [35]. The main works on the practical application of analogous enzymes describes studies of metabolic pathways and inhibitory targets for human pathogens [42, 69–70]. In the case of our study, we sought a practical application, focused on the solution of an agronomic problem.

Essential enzymes are one of the primary targets for the development of inhibitors of any kind; however, species that share essential enzymatic functions may inadvertently be affected by products developed with other applications in mind [75]. Pesticides are commonly targeted at these functions, and their damaging effects on several species including man himself and several vital species such as pollinators and beneficial microorganisms are reason for great concern [76–78]. In fact, it is estimated that approximately 35% of the crops are dependent on pollinators for sexual reproduction, and pesticides are the main factor contributing to the current decrease of the pollinator population [44, 79].

Through the joint use of primary structure data, tertiary structure data and essentiality data, beginning with 444198 individual sequences, comprising 2096 enzymatic activities in 3 plants and 15 phytopathogens, we have disclosed a subset of analogous sequences in 29 essential enzymatic activities present both in the plant and the pathogen. These belong to several components of the central metabolism of plant and pathogens, being involved in the carbohydrate metabolism, the metabolism of amino acids, the detoxification of reactive oxygen species and others, thus offering several opportunities as targets.

Interestingly, the subset of non-essential NISEs contains several enzymes important in the context of host-pathogen interactions, such as cellulases, chitinases, glutathione transferase and lysophospholipase. Blocking or inhibiting these enzymes would, in principle, decrease virulence and / or delay the defense mechanisms of the pathogen [80, 81]. Inhibition of cellulases and chitinases has also been proposed as a strategy for the development of new antifungal drugs for aspergillosis in humans [22]. Glutathione transferase play an essential role in the protection of necrotrophic fungi against toxic metabolites derived from plants and reactive oxygen species [82], while lysophospholipase has been implicated with virulence in Cryptococcus neoformans [83].

Some of the diversity found for the enzymes of the antioxidant system, both in terms of enzymatic activities and in structural forms, may be explained by evolutionary pressures: during the co-evolution between plants and their pathogens, it is likely that different antioxidant enzymes of plants have adapted to overcome the pathogen virulence mechanisms [84, 85]. The role of these enzymes in mechanisms of virulence, susceptibility to infections, development of drug targets and evaluation of pesticide effects has been studied for SOD [86–90], CAT [91–94] and POX [95].

Essential enzymes from the central metabolism have also been studied as potential drug targets in several organisms. Glucose-6-phosphate isomerase has been studied as a target for infections caused by Plasmodium falciparum [96], Trypanossoma spp [97], Toxoplasma gondii [98], and Leishamania ssp [99], acetyl-CoA carboxylase for L. major [100, 101], and ribose 5-phosphate isomerase in other organisms [102]. Deletion of these genes usually results in a severe reduction in growth rates and virulence [103–105], and they have been studied as drug targets in other organisms [106–109].

Eighteen of the 29 enzymatic activities identified in this study as analogous and essential were identified in databases of drug targets such as TDR Drug Targets (http://tdrtargets.org/), DrugBank (https://www.drugbank.ca/) and Potential Drug Target Database (http://www.dddc.ac.cn/pdtd/), meaning they are being studied or employed as a drug target for at least one pathogen. Among them we can mention enzymes from the carbohydrate and amino acids metabolism such as lactoylglutathione lyase, acetyl-CoA carboxylase, carbonic anhydrase, and enzymes of the AS like catalase, peroxidase, peroxiredoxin and superoxide dismutase. Since these enzymatic activities present multiple tertiary structures, we are not able to tell, from this data, which one is under study; nonetheless, these findings give indirect support to our analyzes, corroborating the idea that essential enzymes with specific structural forms have great potential as drug targets as described in our study. Improvements in the annotation of genes and their products, and a better experimental characterization of enzymatic activities, would allow the use of less-stringent criteria in our procedures, mainly in data cleaning and filtering, but also in clustering and structural validation, increasing the number of essential and analogous enzymes that could be further studied as potential drug targets.

Conclusions

The approach employed in this study enabled the elaboration of lists of essential and analogous enzymes, most belonging to the central metabolism and/or involved in host-pathogen interactions, with potential to be a drug target. These enzymes provide an opportunity for the discovery of targets with considerable structural differences over their counterpart in beneficial organisms such as pollinators. Inclusion of structural data allows the disclosure of specific structural forms, facilitating the development of environment-friendly enzyme inhibitors, which may be of great importance for agricultural use.

Supporting information

S1 Table. Non-homologous isofunctional enzymes found in this study.

(XLS)

Click here for additional data file. (57.5KB, xls)
S2 Table. Distribution of metabolic pathways in essential analogous enzymes.

(XLS)

Click here for additional data file. (27KB, xls)

Acknowledgments

RAS recognizes CAPES (Coordination for the Improvement of Higher Education Personnel, Brazil) for supporting her with a scholarship during her DSc program. The authors also thank the staff of the Laboratory of Systems and Computational Biology in conducting this study and to Dr. Fábio Motta, Dr. Marcos Catanho and Dr. Monete Rajão for their help in the discussions.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by Coordination for the Improvement of Higher Education Personnel, Brazil (http://www.capes.gov.br/): (RAS, MCS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Oerke E. C. Crop losses to pests. J Agric Sci. 2006;144: 31–43. [Google Scholar]
  • 2.Sadras VO, Villalobos FJ, Fereres E. Limitations to Crop Productivity In: Villalobos F, Fereres E. Principles of Agronomy for Sustainable Agriculture. Springer International Publishing; 2016. [Google Scholar]
  • 3.FAO International Code of Conduct on Pesticide Management–Guidance on Pest and Pesticide Management Policy Development. 2013. Available from: http://www.fao.org.
  • 4.Berny P. Pesticides and the intoxication of wild animals. Journal of Veterinary Pharmacol Ther. 2007;30: 93–100. [DOI] [PubMed] [Google Scholar]
  • 5.Asogwa EU, Dongo LN. Problems associated with pesticide usage and application in Nigerian cocoa production: A review. Afr J Agric Res. 2009;4: 675–683. [Google Scholar]
  • 6.Nigatu AW, Bråtveit M, Moen BE. Self-reported acute pesticide intoxications in Ethiopia. BMC public health. 2016;16 (1): 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Hancock DB, Martin ER, Mayhew GM, Stajich JM, Jewett R, Stacy MA, et al. Pesticide exposure and risk of Parkinson's disease: A family-based case-control study. BMC Neurology. 2008;8: 6 doi: 10.1186/1471-2377-8-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Spiewak R. Pesticides as a cause of occupational skin diseases in farmers. Ann Agric Environ Med. 2001;8(1): 1–5. [PubMed] [Google Scholar]
  • 9.Ueker ME, Silva VM, Moi GP, Pignati WA, Mattos IE, Silva AGC. Parenteral exposure to pesticides and occurence of congenital malformations: hospital-based case–control study. BMC Pediatrics. 2016;16: 125 doi: 10.1186/s12887-016-0667-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Parrón T, Requena M, Hernández AF, Alarcón R. Environmental exposure to pesticides and cancer risk in multiple human organ systems. Toxicol Lett. 2014;230(2): 157–65. doi: 10.1016/j.toxlet.2013.11.009 [DOI] [PubMed] [Google Scholar]
  • 11.Carneiro FF, Pignati W, Rigotto RM, Augusto LGS, Rizollo A, Muller NM, et al. Dossiê ABRASCO–Um alerta sobre os impactos dos agrotóxicos na saúde Rio de Janeiro: ABRASCO; 2012. Available from: www.abrasco.org.br. [Google Scholar]
  • 12.ANVISA. Agência Nacional de Vigilância Sanitária. Programa de Análise de Resíduos de Agrotóxicos em Alimentos (PARA). Relatório de Atividades de 2011 e 2012. Brasília: Agência Nacional de Vigilância Sanitária. 2013. Available from: http://portal.anvisa.gov.br/documents.
  • 13.ABRASCO–Um alerta sobre os impactos dos agrotóxicos na saúde. Parte 2—Agrotóxicos, Saúde, Ambiente e Sustentabilidade. 2015. Available from: www.abrasco.org.br.
  • 14.ABRASCO–Um alerta sobre os impactos dos agrotóxicos na saúde. EPSJV- Expressão Popular. 2015. Available from: www.abrasco.org.br.
  • 15.Papin JA, Price ND, Wibakc S J, Fell DA, Palsson BO. Metabolic pathways in the post-genome era. Trends Pharmacol Sci. 2003;28: 250–58. [DOI] [PubMed] [Google Scholar]
  • 16.Lehninger AL, Nelson DL, Cox MM. Principles of Biochemistry In: Enzymes. New York, 2008. pp. 191–225.
  • 17.Jordan IK, Rogozin IB, Wolf YI, Koonin EV. Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res. 2002;12: 962–968. doi: 10.1101/gr.87702 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Silander OK, Ackermann M. The constancy of gene conservation across divergent bacterial orders. BMC Res Notes. 2009;2: 2 doi: 10.1186/1756-0500-2-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Pancholi V, Chhatwal GS. Housekeeping enzymes as virulence factors for pathogens. J Med Microbiol. 2003;293: 391–401. [DOI] [PubMed] [Google Scholar]
  • 20.Ouaissi M, Ouaissi A. Histone Deacetylase Enzymes as Potential Drug Targets in Cancer and Parasitic Diseases. J Biomed Biotechnol. 2006; 2006: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Fu ZQ, Guo M, Jeong B-J, Tian F, Elthon TE, Cerny RL, et al. A type III effector ADP-ribosylates RNA-binding proteins and quells plant immunity. Nature. 2007;447: 284–289. doi: 10.1038/nature05737 [DOI] [PubMed] [Google Scholar]
  • 22.Schüttelkopf AW, Gros L, Blair DE, Frearson JA, van Aalten DM, Gilbert IH. Acetazolamide-based fungal chitinase inhibitors. Bioorg Med Chem. 2010;18(23): 8334–40. doi: 10.1016/j.bmc.2010.09.062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Wyatt PG, Gilbert IH, Read KD, Fairlamb AH. Target Validation: Linking Target and Chemical Properties to Desired Product Profile. Curr Top Med Chem. 2011;11: 1275–83. doi: 10.2174/156802611795429185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kappes B, Tews I, Binter A, Macheroux P. PLP-dependent enzymes as potential drug targets for protozoan diseases. Biochim Biophys Acta. 2011;1814(11): 567–76. [DOI] [PubMed] [Google Scholar]
  • 25.Vassar R. BACE1 inhibitor drugs in clinical trials for Alzheimer’s disease. Alzheimers Res Ther. 2014;6: 89 doi: 10.1186/s13195-014-0089-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Katara P, Grover A, Sharma V. In silico prediction of drug targets in phytopathogenic Pseudomonas syringae pv. phaseolicola: charting a course for agrigenomics translation research. OMICS. 2012;16(12): 700–6. doi: 10.1089/omi.2011.0141 [DOI] [PubMed] [Google Scholar]
  • 27.Hotson A, Chosed R, Shu H, Orth K, Mudgett MB. Xanthomonas type III effector XopD targets SUMO-conjugated proteins in planta. Mol Microbiol. 2003;50(2): 377–89. [DOI] [PubMed] [Google Scholar]
  • 28.Sintchenko V, Roper MP. Pathogen genome bioinformatics. Methods Mol Biol. 2014;1168: 173–193. doi: 10.1007/978-1-4939-0847-9_10 [DOI] [PubMed] [Google Scholar]
  • 29.Fields FR, Lee SW, McConnell MJ. Using bacterial genomes and essential genes for the development of new antibiotics. Biochem Pharmacol. 2017;134: 74–86. doi: 10.1016/j.bcp.2016.12.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.IUBMB Nomenclature Commission. Enzyme nomenclature Academic Press, San Diego, CA: 1992. [Google Scholar]
  • 31.Gough J, Chothia C. SUPERFAMILY: HMMs representing all proteins of known structure. SCOP sequence searches, alignments and genome assignments. Nucleic Acids Res. 2002;1:30(1): 268–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wilson D, Pethica R, Zhou Y, Talbot C, Vogel C, Madera M, et al. SUPERFAMILY—sophisticated comparative genomics, data mining, visualization and phylogeny. Nucleic Acids Res. 2009;37(1): 380–386. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Fitch WM. Distinguishing Homologous from Analogous Proteins. Syst Biol. 1970;19(2): 99–113. [PubMed] [Google Scholar]
  • 34.Galperin MY, Walker DR, Koonin EV. Analogous enzymes: independent inventions in enzyme evolution. Genome Res. 1998; 8(8): 779–90. [DOI] [PubMed] [Google Scholar]
  • 35.Omelchenko MV, Galperin MY, Wolf YI, Koonin EV. Non-homologous isofunctional enzymes: a systematic analysis of alternative solutions in enzyme evolution. Biol Direct. 2010;5: 31 doi: 10.1186/1745-6150-5-31 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Stallings WC, Powers TB, Pattridge KA, Fee JA, Ludwig ML. Iron superoxide dismutase from Escherichia coli at 3.1-Å resolution: A structure unlike that of copper/zinc protein at both monomer and dimer levels. Proc Natl Acad Sci USA. 1983;80: 3884–3888. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Lobkovsky E, Moews PC, Liu H, Zhao H, Frere JM, Knox JR. Evolution of an enzyme activity: Crystallographic structure at 2-A resolution of cephalosporinase from the ampC gene of Enterobacter cloacae P99 and comparison with a class A penicillinase. Proc Natl Acad Sci USA. 1993;193(90): 11257–11261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Carfi A, Pares S, Duee E, Galleni M, Duez C, Frere JM, et al. The 3-D structure of a zinc metallo-beta-lactamase from Bacillus cereus reveals a new type of protein fold. EMBO Journal. 1995;14: 4914–4921. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Buller AR; Townsend CA. Intrinsic evolutionary constraints on protease structure, enzyme acylation, and the identity of the catalytic triad. Proc Natl Acad Sci USA 2013;110 (8): 653–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Koonin EV, Mushegian AR, Bork P. Non-orthologous gene displacement. Trends Genet. 1996;12: 334–336. [PubMed] [Google Scholar]
  • 41.Gherardini PF, Wass MN, Helmer-Citterich M, Sternberg MJE. 2007. Convergent evolution of enzyme active sites is not a rare phenomenon. J Mol Biol. 372:817–845. doi: 10.1016/j.jmb.2007.06.017 [DOI] [PubMed] [Google Scholar]
  • 42.Piergiorge RF, Miranda AB, Guimarães ACG, Catanho M. Functional Analogy in Human Metabolism: Enzymes with Different Biological Roles or Functional Redundancy? Genome Biol Evol. 2017;9(6): 1624–1636. doi: 10.1093/gbe/evx119 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Goulson D. Conserving wild bees for crop pollination. J Food Agr Environ. 2003;1: 142–144. [Google Scholar]
  • 44.Klein AM, Vaissiere JH, Cane JH, Steffan-Dewenter I, Cunningham SA, Kremen C, et al. Importance of pollinators in changing landscapes for world crops. Proceedings Proc R Soc Lond [Biol]. 2007;274: 303–313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, et al. Genome sequence of the palaeopolyploid soybean. Nature. 2010; 463: 178–183. doi: 10.1038/nature08670 [DOI] [PubMed] [Google Scholar]
  • 46.Nierman WC, Yu J, Fedorova-Abrams ND, Losada L, Cleveland TE, Bhatnagar D, et al. Genome Sequence of Aspergillus flavus NRRL 3357, a Strain That Causes Aflatoxin Contamination of Food and Feed. Genome Announc. 2015;6(3): e00168–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Thatcher LF, Gardiner DM, Kazan K, Manners JM. A highly conserved effector in Fusarium oxysporum is required for full virulence on Arabidopsis. Mol Plant Microbe Interact. 2012; 25: 180–190. doi: 10.1094/MPMI-08-11-0212 [DOI] [PubMed] [Google Scholar]
  • 48.Tyler BM, Tripathy S, Zhang X, Dehal P, Jiang RH, Aerts A, et al. Phytophthora genome sequences uncover evolutionary origins and mechanisms of pathogenesis. Science. 2006;313(5791):1261–1266. doi: 10.1126/science.1128796 [DOI] [PubMed] [Google Scholar]
  • 49.Amselem J, Cuomo CA, van Kan JAL, Viaud M, Benito EP, Couloux A, et al. Genomic Analysis of the Necrotrophic Fungal Pathogens Sclerotinia sclerotiorumand Botrytis cinerea. PLoS Genetics. 2011;7(8): e1002230 doi: 10.1371/journal.pgen.1002230 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Kim JG1, Choi S, Oh J, Moon JS, Hwang I. Comparative analysis of three indigenous plasmids from Xanthomonas axonopodis pv. glycines. Plasmid. 2006;56(2): 79–87. doi: 10.1016/j.plasmid.2006.03.001 [DOI] [PubMed] [Google Scholar]
  • 51.Kahlau S, Aspinall S, Gray JC, Bock R. Sequence of the tomato chloroplast DNA and evolutionary comparison of solanaceous plastid genomes. J Mol Evol. 2006;63: 194–207. doi: 10.1007/s00239-005-0254-5 [DOI] [PubMed] [Google Scholar]
  • 52.Staats M, van Kan JA. Genome update of Botrytis cinerea strains B05.10 and T4. Eukaryot Cell. 2012;11: 1413–141 doi: 10.1128/EC.00164-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Ma LJ, Does HCV, Borkovich KA, Coleman JJ, Daboussi MJ, Pietro A, et al. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium. Nature. 2010;18: 464(7287): 367–373. doi: 10.1038/nature08850 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Mondego JM, Carazzolle MF, Costa GG, Formighieri EF, Parizzi LP, Rincones J. A genome survey of Moniliophthora perniciosa gives new insights into Witches' Broom Disease of cacao. BMC Genomics. 2008;18(9): 548. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Feil H, Feil WS, Chain P, Larimer F, DiBartolo G, Copeland A, et al. Comparison of the complete genome sequences of Pseudomonas syringae pv. syringae B728a and pv. tomato DC3000. Proc Natl Acad Sci USA. 2005; 2:(31):11064–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Salanoubat M, Genin S, Artiguenave F, Gouzy J, Mangenot S, Arlat M, et al. Genome sequence of the plant pathogen Ralstonia solanacearum. Nature. 2002;415(6871): 497–502. doi: 10.1038/415497a [DOI] [PubMed] [Google Scholar]
  • 57.Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, et al. The B73 maize genome: complexity, diversity, and dynamics. Science. 2009;326(5956): 1112–1115. doi: 10.1126/science.1178534 [DOI] [PubMed] [Google Scholar]
  • 58.Faustinelli PC, Wang XM, Palencia ER, Arias RS. Genome Sequences of Eight Aspergillus flavus spp. and One A. parasiticus sp., Isolated from Peanut Seeds in Georgia. Genome Announc. 2016; 4(2): e00278–16. doi: 10.1128/genomeA.00278-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.O'Connell RJ, Thon MR, Hacquard S, Amyotte SG, Kleemann J, Torres MF, et al. Lifestyle transitions in plant pathogenic Colletotrichum fungi deciphered by genome and transcriptome analyses. Nat Genet. 2012;44: 1060–1065. doi: 10.1038/ng.2372 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Cuomo CA, Güldener U, Xu JR, Trail F, Turgeon BG, Di Pietro A, et al. The Fusarium graminearum genome reveals a link between localized polymorphism and pathogen specialization. Science. 2007;317(5843):1400–2. doi: 10.1126/science.1143708 [DOI] [PubMed] [Google Scholar]
  • 61.Condon BJ, Leng Y, Wu D, Bushley KE, Ohm RA, Otillar R, et al. Comparative Genome Structure, Secondary Metabolite, and Effector Coding Capacity across Cochliobolus Pathogens. PLoS Genetics. 2013;9(1): e1003233 doi: 10.1371/journal.pgen.1003233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.De Maayer P, Chan W, Martin DAJ, Blom J, Venter SN, Duffy B, et al. Integrative conjugative elements of the ICEPan family play a potential role in Pantoea ananatis ecological diversification and antibiosis. Front Microbiol. 2015; 6: 576 doi: 10.3389/fmicb.2015.00576 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Honeybee Genome Sequencing Consortium. Insights into social insects from the genome of the honeybee Apis mellifera. Nature. 2006;443(7114): 931–949. doi: 10.1038/nature05260 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Baroncelli R, Piaggeschi G, Fiorini L, Bertolini E, Zapparata A, PèDraft ME. Whole-Genome Sequence of the Biocontrol Agent Trichoderma harzianum T6776. Genome Announc. 2015;3(3): e00647–15. doi: 10.1128/genomeA.00647-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Kunst F, Ogasawara N, Moszer I, Albertini AM, Alloni G, Azevedo V. et al. The complete genome sequence of the gram-positive bacterium Bacillus subtilis. Nature. 1997;390(6657): 249–56. doi: 10.1038/36786 [DOI] [PubMed] [Google Scholar]
  • 66.International Human Genome Sequencing Consortium. Human Genome. Nature. 2001;409: 860–921. doi: 10.1038/35057062 [DOI] [PubMed] [Google Scholar]
  • 67.Otto TD, Guimarães AC, Degrave WM, Miranda AB. AnEnPi: identification and annotation of analogous enzymes. BMC Bioinformatics. 2008;9: 544 doi: 10.1186/1471-2105-9-544 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Galperin MY, Koonin EV. Divergence and Convergence in Enzyme Evolution. J Biol Chem. 2012;287(1): 21–28. doi: 10.1074/jbc.R111.241976 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Gomes MR, Guimarães A C R, Miranda AB. Specific and Nonhomologous Isofunctional Enzymes of the Genetic Information Processing Pathways as Potential Therapeutical Targets for Tritryps. Enzyme Res. 2011;2011: 8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Alves-Ferreira M, Guimarães AC, Capriles PV, Dardenne LE, Degrave WM. A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information. Mem Inst Oswaldo Cruz. 2009;104(8): 1100–10. [DOI] [PubMed] [Google Scholar]
  • 71.Capriles PV, Guimarães AC, Otto TD, Miranda AB, Dardenne LE, Degrave WM. Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas' disease treatment. BMC Genomics. 2010;11: 610 doi: 10.1186/1471-2164-11-610 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Gough J, Karplus K, Hughey R, Chothia C. Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol. 2001;313(4): 903–19. doi: 10.1006/jmbi.2001.5080 [DOI] [PubMed] [Google Scholar]
  • 73.Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol. 1995;247(4): 536–4. doi: 10.1006/jmbi.1995.0159 [DOI] [PubMed] [Google Scholar]
  • 74.Celis AI, DuBois JL. Substrate, product, and cofactor: The extraordinarily flexible relationship between the CDE superfamily and heme. Arch Biochem Biophys. 2015;574: 3–17. doi: 10.1016/j.abb.2015.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Guengerich FP. Cytochrome P450s and other enzymes in drug metabolism and toxicity. AAPS J. 2008;8(1): 101–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Aktar MDW, Sengupta D, Chowdhury A. Impact of pesticides use in agriculture: their benefits and hazards. Interdiscip Toxicol. 2009;2(1): 1–12. doi: 10.2478/v10102-009-0001-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Fishel FM. Pesticides effects on nontarget organisms PI-85. Pesticide information of‐ fice, Florida Cooperative Extension Service, IFAS, University of Florida, Gainesville, FL, USA; 2011. Available from: http://edis.ifas.ufl.edu/pi122. [Google Scholar]
  • 78.Pan-Germany. Pesticide and health hazards. Facts and figures. 2012;1–16. Available from: www.pangermany.org.
  • 79.Nakasu EYT, Williamson SM, Edwards MG, Fitches EC, Gatehouse JA, Wright GA, et al. Novel biopesticide based on a spider venom peptide shows no adverse effects on honeybees. Proc. Biol Sci. 2014; 281(1787): 20140619 doi: 10.1098/rspb.2014.0619 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Kamoun S and Kado CI. A plant-inducible gene of Xanthomonas campestris pv. campestris encodes an exocellular component required for growth in the host and hypersensitivity on nonhosts. J Bacteriol. 1990;172(9): 5165–5172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Ray SK, Rajeshwari R, Sonti RV. Mutants of Xanthomonas oryzae pv. oryzae deficient in general secretory pathway are virulence deficient and unable to secrete xylanase. Mol Plant Microbe Interact. 2000;13: 394–401. doi: 10.1094/MPMI.2000.13.4.394 [DOI] [PubMed] [Google Scholar]
  • 82.Calmes B, Morel-Rouhier M, Bataillé-Simoneau N, Gelhaye E, Guillemette T, Simoneau P. Characterization of glutathione transferases involved in the pathogenicity of Alternaria brassicicola. BMC Microbiology. 2015;15: 123 doi: 10.1186/s12866-015-0462-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Chen SC, Muller M, Zhou J Z, Wright LC, Sorrel TC. Phospholipase activity in Cryptococcus neoformans: a new virulence factor? J Infect Dis. 1997;175: 414–420. [DOI] [PubMed] [Google Scholar]
  • 84.Torres MA, Jones JDJ, Dangl JL. Reactive oxygen species signaling in response to pathogens. Plant Physiol. 2006;141: 37378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Eaton CJ, Cox MP, Scott B. What triggers grass endophytes to switch from mutualism to pathogenesis? Plant Sci. 2011;180: 190–5. doi: 10.1016/j.plantsci.2010.10.002 [DOI] [PubMed] [Google Scholar]
  • 86.Warshawsky A, Rogachev I, Patil Y, Baszkin A, Weiner L, Jonathan G. Copper-Specific Chelators as Synergists to Herbicides:  1. Amphiphilic Dithiocarbamates, Synthesis, Transport through Lipid Bilayers, and Inhibition of Cu/Zn Superoxide Dismutase Activity. Langmuir. 2001;17: 5621–35. [Google Scholar]
  • 87.Cox GM, Harrison TS, McDade HC, Taborda CP, Heinrich G, Casadevall A, et al. Superoxide Dismutase Influences the Virulence of Cryptococcus neoformans by Affecting Growth within Macrophages. Infect Immun. 2003;71(1): 173–180. doi: 10.1128/IAI.71.1.173-180.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Karadag H, Ozhan F. Effect of cyprodinil and fludioxonil pesticides on bovine liver catalase activity. Biotechnol Biotechnol Equip. 2015; 29(1): 40–4. doi: 10.1080/13102818.2014.992740 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Pratt AJ, DiDonato M, Shin DS, Cabelli DE, Bruns CK, Belzer CA, et al. Structural, Functional, and Immunogenic Insights on Cu,Zn Superoxide Dismutase Pathogenic Virulence Factors from Neisseria meningitidis and Brucella abortus. J Bacteriol. 2015;197(24): 3834–3847. doi: 10.1128/JB.00343-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Yao SH, Guoa Y, Wanga YZ, Zhanga D, Xub L, Tang WH. A cytoplasmic Cu-Zn superoxide dismutase SOD1 contributes to hyphal growth and virulence of Fusarium graminearum. Fungal Genet Biol. 2016;91: 32–42. doi: 10.1016/j.fgb.2016.03.006 [DOI] [PubMed] [Google Scholar]
  • 91.Heym B, Alzari PM, Honore N, Cole ST. Missense mutations in the catalase–peroxidase gene, katG, are associated with isoniazid resistance in Mycobacterium tuberculosis. Mol Microbiol. 1995;15: 235–245. [DOI] [PubMed] [Google Scholar]
  • 92.Pym AS, Domenech P, Honoré N, Song J, Deretic V, Cole ST. Regulation of catalase-peroxidase (KatG) expression, isoniazid sensitivity and virulence by furA of Mycobacterium tuberculosis. Mol Microbiol. 2000;40(4): 879–889. [DOI] [PubMed] [Google Scholar]
  • 93.Ishiga Y, Ichinose Y. Pseudomonas syringae pv. tomato OxyR Is Required for Virulence in Tomato and Arabidopsis. Mol Plant Microbe Interact. 2016;29(2): 119–31. doi: 10.1094/MPMI-09-15-0204-R [DOI] [PubMed] [Google Scholar]
  • 94.Yu C, Wang N, Wu M, Tian F, Chen H, Yang F. OxyR-regulated catalase CatB promotes the virulence in rice via detoxifying hydrogen peroxide in Xanthomonas oryzae pv. oryzae. BMC Microbiology. 2016;16: 269 doi: 10.1186/s12866-016-0887-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Mir AA, Park SY, Sadat MA, Kim S, Choi J, Jeon J, et al. Systematic characterization of the peroxidase gene family provides new insights into fungal pathogenicity in Magnaporthe oryzae. Sci Rep. 2015;5: 11831 doi: 10.1038/srep11831 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Barret MP. The Pentose Phosphate Pathway and Parasitic Protozoa. Parasitol Today. 1997;13: 11–16. [DOI] [PubMed] [Google Scholar]
  • 97.Verlinde CLMJ, Hannaert V, Blonski C, Willson M, Périé JJ, Fothergill-Gilmore LA, et al. Glycolysis as a target for the design of new anti-trypanosome drugs. Drug Resist Update. 2001;4: 1–14. [DOI] [PubMed] [Google Scholar]
  • 98.Tomavo S. The differential expression of multiple isoenzyme forms during stage conversion of Toxoplasma gondii: an adaptive developmental strategy. Int J Parasitol. 2001;31: 1023–31. [DOI] [PubMed] [Google Scholar]
  • 99.Barrett MP, Gilbert IH. Perspectives for new drugs against trypanosomiasis and leishmaniasis. Curr Top Med Chem. 2002; 2: 471–482. [DOI] [PubMed] [Google Scholar]
  • 100.Tong L, Harwood HJJ. Acetyl-coenzyme A carboxylases: versatile targets for drug discovery. J Cell Biochem. 2006;99(6): 1476–88. doi: 10.1002/jcb.21077 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 101.Chawla B, Madhubala R. Drug targets in Leishmania. J Parasit Dis. 2010;34(1): 1–13. doi: 10.1007/s12639-010-0006-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Zhang RG, Andersson CE, Savchenko A, Skarina T, Evdokimova E, Beasley S. Structure of Escherichia coli Ribose-5-Phosphate Isomerase: A Ubiquitous Enzyme of the Pentose Phosphate Pathway and the Calvin Cycle. Structure. 2003;11(1): 31–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Sørensen KI, Hove-Jensen B. Ribose catabolism of Escherichia coli: characterization of the rpiB gene encoding ribose phosphate isomerase B and of the rpiR gene, which is involved in regulation of rpiB expression. J Bacteriol. 1996;178(4):1003–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Loureiro I, Faria J, Clayton C, Macedo-Ribeiro S, Santarem N, Roy N, et al. Ribose 5-phosphate isomerase B knockdown compromises Trypanosoma brucei blood stream form infectivity. PLoS Negl Trop Dis. 2015;9(1): e3430 doi: 10.1371/journal.pntd.0003430 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Kaur PK, Tripathi N, Desale J, Neelagiri S, Yadav S, Bharatam PS, et al. Mutational and Structural Analysis of Conserved Residues in Ribose-5-Phosphate Isomerase B from Leishmania donovani: Role in Substrate Recognition and Conformational Stability. PloS ONE. 2016;11(3):e0150764 doi: 10.1371/journal.pone.0150764 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Juhnke H, Krems B, Kotter P, Entian KD. Mutants that show increased sensitivity to hydrogen peroxide reveal an important role for the pentose-phosphate pathway in protection of yeast against oxidative stress. Mol Gen Genet. 1996;252: 456–64. [DOI] [PubMed] [Google Scholar]
  • 107.Li D, Zhu Y, Tang Q, Lu H, Li H, Yang Y, et al. A new G6PD knockdown tumor-cell line with reduced proliferation and increased susceptibility to oxidative stress. Cancer Biother Radiopharm. 2009;24: 81–90. doi: 10.1089/cbr.2008.0494 [DOI] [PubMed] [Google Scholar]
  • 108.Chauhan SC, Padmanabhan PK, Madhubala R. Glyoxalase Pathway of Trypanosomatid Parasites: A Promising Chemotherapeutic Target. Curr Drug Targets. 2008;9(11): 957–65. [DOI] [PubMed] [Google Scholar]
  • 109.Silva MS, Ferreira AEN, Gomes R, Tomás AM, Freire AP, Cordeiro C, et al. The glyoxalase pathway in protozoan parasites. Int J Med Microbiol. 2012;302: 225–29. doi: 10.1016/j.ijmm.2012.07.005 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

S1 Table. Non-homologous isofunctional enzymes found in this study.

(XLS)

Click here for additional data file. (57.5KB, xls)
S2 Table. Distribution of metabolic pathways in essential analogous enzymes.

(XLS)

Click here for additional data file. (27KB, xls)

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES