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Beta carbonic anhydrases: novel targets for pesticides and anti-parasitic agents in agriculture and livestock husbandry

Parasites & Vectors volume 7, Article number: 403 (2014) Cite this article

Abstract

Background

The genomes of many insect and parasite species contain beta carbonic anhydrase (β-CA) protein coding sequences. The lack of β-CA proteins in mammals makes them interesting target proteins for inhibition in treatment of some infectious diseases and pests. Many insects and parasites represent important pests for agriculture and cause enormous economic damage worldwide. Meanwhile, pollution of the environment by old pesticides, emergence of strains resistant to them, and their off-target effects are major challenges for agriculture and society.

Methods

In this study, we analyzed a multiple sequence alignment of 31 β-CAs from insects, some parasites, and selected plant species relevant to agriculture and livestock husbandry. Using bioinformatics tools a phylogenetic tree was generated and the subcellular localizations and antigenic sites of each protein were predicted. Structural models for β-CAs of Ancylostoma caninum, Ascaris suum, Trichinella spiralis, and Entamoeba histolytica, were built using Pisum sativum and Mycobacterium tuberculosis β-CAs as templates.

Results

Six β-CAs of insects and parasites and six β-CAs of plants are predicted to be mitochondrial and chloroplastic, respectively, and thus may be involved in important metabolic functions. All 31 sequences showed the presence of the highly conserved β-CA active site sequence motifs, CXDXR and HXXC (C: cysteine, D: aspartic acid, R: arginine, H: histidine, X: any residue). We discovered that these two motifs are more antigenic than others. Homology models suggested that these motifs are mostly buried and thus not well accessible for recognition by antibodies.

Conclusions

The predicted mitochondrial localization of several β-CAs and hidden antigenic epitopes within the protein molecule, suggest that they may not be considered major targets for vaccines. Instead, they are promising candidate enzymes for small-molecule inhibitors which can easily penetrate the cell membrane. Based on current knowledge, we conclude that β-CAs are potential targets for development of small molecule pesticides or anti-parasitic agents with minimal side effects on vertebrates.

Background

Various pests, including weeds, insects, and plant viruses, often reduce crop production by 25-50% [1, 2]. On the other hand, the widespread use of synthetic insecticides for controlling pests produces many negative consequences (e.g. insecticide resistance, toxicity to mammals and other non-target animals, residue problems, and environmental pollution). High risk groups exposed to pesticides include: production workers, formulators, sprayers, mixers, loaders, and agricultural farm workers. Residual pesticides represent a real threat for human health. When 9700 samples of fruits and vegetables were analyzed for seven pesticides (Acephate, Chlopyriphos, Chlopyriphos-methyl, Methamidophos, Iprodione, Procymidone, and Chlorothalonil), 5.2% of the samples were found to contain residues. Pesticides can contaminate soil, water, and turf. In addition to killing insects or weeds, pesticides can be toxic to other organisms including birds, fish, beneficial insects, and non-target plants [3]. The extensive use of pesticides, such as Dichlorodiphenyltrichloroethane (DDT), in recent decades has led to their recurrent detection in many surface and ground waters [4]. As a result of these negative consequences, natural products have become popular among consumers [5].

As of the 1960s pesticide resistance had already evolved in some key greenhouse pests, prompting the development of alternative methods of management. The pressure to reduce insecticide usage was reinforced by the adoption of bumble-bees for pollination within greenhouses [6]. Biological control plays a central role in the production of many greenhouse crops. The term “Biopesticide” encompasses a broad array of microbial pesticides, including biochemicals derived from micro-organisms and other natural sources, and those resulting from the incorporation of DNA into various agricultural commodities [7]. Bacteria, fungi, viruses, entomopathogenic nematodes (ENPs), and herbal essential oils are often used as bio-pesticides [8]. Novel approaches to control pests involve targeting of specific insect and parasite enzymes. This can be done using either chemical or biological compounds. Acetylcholinesterase (AChE) of the malaria mosquito (Anopheles gambiae) has been reported as a target site for pesticides [9]. Three pesticides, Atrazine, DDT, and Chlorpyrifos, have been determined to affect the esterase (GE), glutathione S-transferase (GST), cytochrome P450 monooxygenase (P450), and acetylcholinesterase (AChE) activities of Chironomus tentans (an aquatic midge) [4]. Proteinases serving as insect digestive enzymes are defined targets in pest control [10]. Enzyme inhibitors, such as: piperonyl butoxide (PB), a mixed-function oxidase (MFO) inhibitor; triphenyl phosphate (TPP), a carboxyesterase (CarE) inhibitor; and diethyl maleate (DEM), a glutathione S-transferase (GST) inhibitor, have been used to inhibit insect enzymes [11]. Inhibition of Plasmodium falciparum carbonic anhydrase (CA) with aromatic heterocyclic sulfonamides was investigated in 2011 [12]. In another study, a thiabendazole sulfonamide showed a potent inhibitory activity against both mammalian and nematode α-CAs [13].

Five independently evolved classes of CAs (α, β, γ, δ, and ζ) have been identified, of which one or more are found in nearly every cell type, underscoring the general importance of this ubiquitous enzyme in nature [14]. The CAs are involved in several important biological processes, such as respiration and transportation of CO2 and bicarbonate between metabolizing tissues, pH and CO2 homeostasis, electrolyte secretion in different organs, bone resorption, calcification, tumorigenicity, and some biosynthetic reactions including gluconeogenesis, lipogenesis, and ureagenesis [15]. Since 1990, many demonstrated and putative β-CAs have been discovered not only in photosynthetic organisms, but also in eubacteria, yeast, archaeal species [16] and 18 metazoan species [17]. Recently, we reported 52 β-CAs in metazoan and protozoan species [18]. At least one study has shown the effects of β-CA inhibitors as anti-infective agents on different bacterial and fungal pathogens [19], yet this approach has not been tested in vivo in metazoans or protozoans. In this article, we introduce β-CAs as novel potential target enzymes to control agricultural and veterinary insects and parasites which cause enormous economic losses worldwide.

Methods

Identification of putative β-CA enzymes and multiple sequence alignment (MSA)

In total, 23 parasite and 8 plant β-CA sequences relevant to agriculture and livestock husbandry, or as model organisms, and one bacterial sequence (Desulfosporosinus meridiei) were retrieved from UniProt (http://www.uniprot.org/) and NCBI (http://www.ncbi.nlm.nih.gov/). The full list of agriculture and livestock husbandry pests and plants containing β-CA addressed in this research are shown in Table 1. We focused on 98 amino acid residues around the catalytic active site of all tested β-CAs, starting 7 amino acid residues prior to the first highly conserved sequence (CXDXR). The Clustal Omega algorithm [20] within the Jalview program (version 2.8.ob1) (http://www.jalview.org/) was used to create a multiple sequence alignment (MSA) [21].

Table 1 Agriculture and livestock husbandry pests, and plants containing β-CA which applied in this research
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Phylogenetic analysis

All sequences were individually analyzed for completeness and quality. The β-CA sequence for Solenopsis invicta (UniProt ID: E9IP13) was determined to have a spurious exon when the genomic sequence was analyzed by the Exonerate program using the other β-CA proteins as query sequences, and subsequently 17 amino acids were removed [49]. Similarly, the full genome of Acyrthosiphon pisum was analyzed. Of the three Acyrthosiphon pisum β-CA sequences identified in UniProt, two were incomplete (UniProt IDs: C4WVD8 and J9JZY3) and found to be fragments of the same complete protein predicted in our analysis (Acyrthosiphon pisum BCA-2). Finally, the full genome of Ichthyophthirius multifiliis was scanned for β-CA proteins using the same method, and two new putative β-CA proteins were identified (Ichthyophthirius multifiliis BCA-3 and BCA-4).

A protein sequence alignment was created using Clustal Omega [20] based on which the corresponding nucleotide sequences were then codon-aligned by the Pal2Nal program [50]. Using the Desulfosporosinus meridiei bacterial sequence as an outgroup, a phylogenetic analysis was computed using Mr. Bayes v3.2 [51] with the GTR model of codon substitution and all other parameters set to default. In total, 200,000 generations were computed with a final standard deviation of split frequencies of 3.33 × 10−4. The final phylogenetic tree was visualized in FigTree (http://tree.bio.ed.ac.uk/software/figtree/).

Prediction of subcellular localization

Subcellular localization of each identified invertebrate β-CA was predicted using the TargetP webserver (http://www.cbs.dtu.dk/services/TargetP/). TargetP is built from two layers of neural networks, where the first layer contains one dedicated network for each type of targeting sequences, such as cytoplasmic, mitochondrial, or secretory peptides, and the second layer is an integrating network that outputs the actual prediction (cTP = cytoplasmic, mTP = mitochondrial, SP = secretory, or other). It is able to discriminate between cTPs, mTPs, and SPs with sensitivities and specificities higher than what has been obtained with other available subcellular localization predictors [52].

Prediction of antigenic sites in β-CA

The protein sequences of 23 parasite and 8 plant β-CAs were analyzed with the European Molecular Biology Open Software Suite (EMBOSS) program Antigenic (http://emboss.bioinformatics.nl/cgi-bin/emboss/antigenic). EMBOSS Antigenic predicts potentially antigenic regions of a protein sequence, using the method of Kolaskar and Tongaonkar [53]. Application of this method to a large number of proteins has shown that their accuracy is better than most of the known methods [54–56].

Homology modelling

Homology models of four selected β-CAs, including FC551456 (Ancylostoma caninum), F1LE18 (Ascaris suum), E5SH53 (Trichinella spiralis), and C4LXK3 (Entamoeba histolytica) were prepared by first selecting the most suitable template structure. For this purpose, a BLAST search of the PDB database (http://www.rcsb.org/pdb/home/home.do) was performed using each of the four sequences. Results for three out of these four searches revealed that PDB structure 1EKJ (β-CA from Pisum sativum) possessed the most similar sequence, while PDB id 2A5V (β-CA from Mycobacterium tuberculosis) was found to be the most similar to C4LXK3 (Entamoeba histolytica). Clustal Omega was used to prepare a multiple sequence alignment for those six sequences.

The multiple sequence alignment showed nine completely conserved residues within the sequences; the known highly conserved CXDXR and HXXC motifs were among them (data not shown). Homology modelling was performed according to multiple sequence alignment containing FC551456 (Ancylostoma caninum), F1LE18 (Ascaris suum), E5SH53 (Trichinella spiralis), and PDB 1EKJ by using the Modeller program (version 9.13) [57] with PDB model 1EKJ (β-CA from Pisum sativum) as a template. A homology model for C4LXK3 (Entamoeba histolytica) was prepared using PDB 2A5V for pairwise alignment and as a template structure. The resulting models were structurally aligned using the BODIL program [58]. A figure illustrating the homology models was prepared by using the VMD program (version 1.9.1) [59], and edited within Adobe Photoshop (version 13.0.1).

The structural availability of the epitope in the PDB model 1EKJ (β-CA from Pisum sativum) and the homology model based on the β-CA sequence from Ancylostoma caninum was studied by preparing the molecular surface with VMD, using a probe radius of 1.4 Å. The potential epitope residues were excluded from the surface presentation and were shown as Van der Waals (VdW) spheres.

Results

Multiple sequence alignment (MSA)

The MSA of 23 parasite and 8 plant β-CA sequences revealed the presence of the highly characteristic conserved sequence motifs CXDXR and HXXC (C: cysteine, D: aspartic acid, R: arginine, H: histidine, X: any residue) in all sequences. These results verify the presence of the β-CA enzyme in several insects and parasites which are pathogenic to various species of plants and animals and are thus considered relevant to agriculture and livestock husbandry (Figure 1).

Figure 1
figure 1

Multiple sequence alignment of 23 parasite, 8 plant, and one bacterial ( Desulfosporosinus meridiei ) β-CA sequences showing the most conserved region of the active site. The first (CXDXR) and second (HXXC) highly conserved sequences which are involved in zinc atom binding in catalytic active sites of the enzyme are marked with arrows at the top of the figure.

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Phylogenetic analysis

The results of the phylogenetic analysis of DNA sequences encoding 23 parasite and 8 plant β-CAs are shown in Figure 2. From the resulting tree we see four distinct clades, three of which represent distinct potential β-CA targets. From the top, the first clade represents β-CAs of invertebrate pests, the second clade are plant model organisms, the third clade is entirely represented by the four β-CAs of Ichthyophthirius multifiliis, and the final clade represents three species of amoeba. The Entamoeba spp. sequences occupy a midpoint between our outgroup bacteria species and the others.

Figure 2
figure 2

Phylogenetic analysis of β-CAs from 23 parasite and 8 plant species. β-CA from Desulfosporosinus meridiei was used as a bacterial outgroup.

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Prediction of subcellular localization

The results of subcellular localization prediction of β-CAs in selected parasite and plant species are shown in Table 2. The predictions were based on the analysis of full-length β-CA protein sequences. In the Name column, there are both the UniProt ID and species scientific name. The results reveal that 6 of 23 β-CAs from parasites (XP_004537221.1, B0WKV7, U6PDI1, E5SH53, B3S5Y1, and predicted BCA2 in A. pisum) were predicted to have a mitochondrial localization signal; 6 of 8 β-CAs of plants (P17067, Q8LSC8, P27141, D7TWP2, I2FJZ8, and B9GHR1) were predicted to have a chloroplastic localization.

Table 2 Prediction of subcellular localization of 23 pest and 8 plant β-CAs
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Prediction of antigenic sites in β-CA

According to the acceptable 3–85 residue variation in epitope length of an antigen [60] and default parameters of EMBOSS Antigenic database, the minimum length of an antigenic region in this set of β-CAs is 6 amino acid residues. The predictions of antigenic sites in the 31 β-CA proteins are shown in Table 3; the highest score belongs to the most antigenic site.

Table 3 Predicted antigenic sites of 23 pest and 8 plant β-CA primary sequences
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Homology modelling

Homology models of four selected β-CAs verified the predicted localization of conserved residues in the active site. Two loop regions showed high variability in the sequence length which is apparent in the Figure 3C, D and indicated by “*” and “**”. In addition, homology modelling suggested insertion located within the longest α-helix in case of homology models based on 1EKJ (Figure 3C, indicated by “***”).

Figure 3
figure 3

Homology modelling. (A) Cartoon presentation of β-CA from Pisum sativum (PDB 1EKJ). The Zn2+ion is shown as green sphere and the residues in direct contact with the ion are shown as liquorice models and labeled with residue numbers. (B) β-CA from Mycobacterium tuberculosis (PDB 2A5V, light gray). (C) Aligned homology models of β-CAs from Ancylostoma caninum (green), Ascaris suum (blue), and Trichinella spiralis (orange) are shown with PDB 1EKJ (gray). (D) Homology model of and Entamoeba histolytica (red) structurally aligned with PDB 2A5V (light gray). Highly variable loop regions are indicated by stars (“*” and “**”) (C, D). Insertion suggested by homology models of Ancylostoma caninum, Ascaris suum and Trichinella spiralis is indicated by three stars (C). The figure was prepared by using VMD (version 1.9.1).

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To study the molecular availability of the predicted main antigenic epitope, surface exposure of the homology model created from PDB model 1EKJ (β-CA from Pisum sativum) and the homology model based on the β-CA sequence from Ancylostoma caninum were studied by visualizing the molecular surface (Figure 4). The analysis revealed that the majority of the epitope was buried within the structure. The residues considered to be mainly buried in the structure are shown in green, while solvent-exposed residues are shown with red colour. Two residues in PDB model 1EKJ (β-CA from Pisum sativum) appear considerably smaller than their complements in the Ancylostoma caninum-based homology model, and those residues can be considered to be only partially exposed (Figure 4, indicated by yellow colour in the alignment). Taken together, these results indicate that the predicted epitope sequence is mainly buried in β-CA sequences.

Figure 4
figure 4

Determination of the availability of the predicted epitope. The molecular surface of the homology model of β-CA from Ancylostoma caninum is shown as solid grey and the target epitope sequence was excluded from the surface presentation. The epitope residues exposed to solvent are shown as red VdW spheres and numbered, while buried residues are shown with green spheres. An alignment containing PDB 1EKJ and the corresponding sequence from Ancylostoma caninum predicted β-CA is shown. The numbering of the residues in the alignment is according to the Ancylostoma caninum sequence. The yellow residues in the alignment indicate partially buried structure.

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Discussion

Several insect, parasite, and plant genomes contain genes which encode β-CA enzymes. Some of these parasites and insects are either causative agents or vectors of important veterinary, fish farming, and zoonotic diseases (Table 1). For this analysis we selected 31 β-CAs, 23 from parasites and 8 from plants. These sequences were retrieved from protein databases, or predicted from their genomes, and all selected host or vector species are considered important for agriculture or livestock husbandry, or represent model organisms. The most significant species included Ancylostoma caninum, Ascaris suum (Ascaris lumbricoides), Caligus clemensi, Culex quinquefasciatus, Entamoeba spp, Haemonchus contortus, Ichthyophthirius multifiliis, Lepeophtheirus salmonis, Necator americanus, Trichinella spiralis, and Trichoplax adhaerens. One was an important pest in food industries (Tribolium castaneum). There was also an orchard invasive dipteran fruit fly (Ceratitis capitata) and three pests of wood industries, such as Camponotus floridanus, Dendroctonus ponderosae, and Solenopsis invicta.

Our MSA of β-CAs in plants, parasites, and insects showed that they all contain the first (CXDXR) and second (HXXC) highly conserved sequences of β-CA. The presence of β-CA proteins in various insects and parasites and their absence in mammals suggests that these enzymes could be potential targets for the development of novel pesticides or anti-parasitic drugs with minimal side effects on vertebrates. A key requirement for such novel β-CA inhibitors is the high isoform specificity. The distinction among β-CA proteins elucidated in the phylogenetic tree indicates that inhibitors can be created which would target β-CAs specific to different groups of species, leaving those in other species, such as plants, unaffected. Unfortunately, design of highly specific inhibitors will require proper structural data based on protein crystallography. Thus far, β-CA crystal structures from only a few different species are available in PDB database (http://www.rcsb.org/pdb/home/home.do), including some algae, bacteria, archaea, yeast, and a plant Pisum sativum[61].

Our prediction results on the subcellular localization of β-CAs showed that 6 of 23 β-CAs from parasites (XP_004537221.1, B0WKV7, U6PDI1, E5SH53, B3S5Y1, and predicted BCA2 in A. pisum) are probably mitochondrial enzymes. It is well known that several pesticides have unwanted side effects because of their off-target impacts on mitochondria [62]. Blocking of β-CAs in insect and parasitic cells can affect mitochondrial metabolic cycles and possibly eradicate the pathogens. Figure 5 presents 14 categories of known α- and/or β-CA inhibitors, which are able to inhibit catalytic activity of these enzyme families [63, 64]. As the result, inhibition of CA activity would slow down some cellular biochemical pathways in parasites and insects, such as gluconeogenesis, nucleotide biosynthesis, fatty acid synthesis, gastrointestinal function, neuronal signaling, respiration, and reproduction. In plants and algae, it is known that β-CAs are required for CO2 sequestration within chloroplast, and therefore CA inhibition would affect the rate of photosynthesis [65]. Importantly, β-CA inhibition in fungi and Drosophila melanogaster revealed completely different inhibition profiles [17], suggesting that β-CAs of parasites and insects can be inhibited with higher affinity than plant CAs by applying the right inhibitors and concentrations.

Figure 5
figure 5

Effects of 14 CA inhibitors on α- and β-CAs of parasites and insects. Some compounds inhibit members of both α- and β- CA enzyme families. The brown box shows physiological processes where bicarbonate plays a role as a biochemical substrate. The ultimate goal of future research should be the creation of inhibitors specific to both enzyme families and to each isozyme. Ideally, the specific inhibitors would cause tissue- and organ-specific effects in parasites and vectors with minimal off-target effects on other species. Number 1 shows the catalytic pathway of α- and β-CA and number 2 shows the inhibitory effects of α- and β-CA inhibitors.

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Another important goal is to find inhibitors that are specific for β-CAs and do not affect α-CAs at all. This would first require detailed structural data on selected parasite and insect CAs. The resolved structures would then allow high throughput screening of chemical compounds, identification of the most promising inhibitor molecules, and testing of potential compounds in vitro and in vivo.

Vaccination would offer another option to develop anti-parasitic treatments based on β-CAs. In our study we used computational antigen prediction tools, which have been developed to reduce the laboratory work required to identify important antigenic epitopes in pathogenic proteins [66]. The Protegen database (http://www.violinet.org/protegen/) has been used to identify a number of predicted antigens from bacteria, viruses, parasites and fungi, which are involved in immune responses against various infectious and non-infectious diseases [67]. Antigenic site prediction of β-CA of parasites and plants revealed that the first and second highly conserved sequences (CXDXR and HXXC) represent the most plausible antigenic sites of β-CAs. Because these epitopes are located in the region of the active site and are mainly buried (Figure 4), they show very limited promise as vaccine targets. Furthermore, most β-CAs are intracellular proteins which are not readily accessible for immunological recognition. Taking all of these results together, small molecule inhibitors should still be considered the first option when β-CAs are investigated as therapeutic target proteins.

Conclusions

Our present work is the first study that discusses the potential role of β-CAs as target proteins for pesticides and anti-parasitic agents in agriculture and livestock husbandry. Our results could potentially have significant impacts on development of novel pesticides, which would directly benefit both food and forest industries. This is important as pests cause significant costs for agricultural, horticultural, and livestock husbandry products due to production losses [68]. Since β-CA sequences are not present in the genomes of vertebrates, the possible off-target effects in human and vertebrate animals should be minimal if high isozyme specificity is achieved. Discovery and validation of a new generation of β-CA inhibitors as pesticides and anti-parasitic agents would be a novel research field for chemical and pharmaceutical industries to improve safe nutrition and general health in societies.

References

  1. Zalucki MP, Shabbir A, Silva R, Adamson D, Shu-Sheng L, Furlong MJ: Estimating the economic cost of one of the world’s major insect pests, Plutella xylostella (Lepidoptera: Plutellidae): just how long is a piece of string?. J Econ Entomol. 2012, 105 (4): 1115-1129. 10.1603/EC12107.

    Article  PubMed  Google Scholar 

  2. Way MJ: Crop losses by insects and the problem of control. Proc Nutr Soc. 1961, 20: 5-11. 10.1079/PNS19610004.

    Article  CAS  PubMed  Google Scholar 

  3. Aktar MW, Sengupta D, Chowdhury A: Impact of pesticides use in agriculture: their benefits and hazards. Interdiscip Toxicol. 2009, 2 (1): 1-12.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Rakotondravelo ML, Anderson TD, Charlton RE, Zhu KY: Sublethal effects of three pesticides on activities of selected target and detoxification enzymes in the aquatic midge, Chironomus tentans (diptera: chironomidae). Archiv Environ Contam Toxicol. 2006, 51 (3): 360-366. 10.1007/s00244-005-0227-0.

    Article  CAS  Google Scholar 

  5. Ayvaz A, Sagdic O, Karaborklu S, Ozturk I: Insecticidal activity of the essential oils from different plants against three stored-product insects. J Insect Sci. 2010, 10: 21-

    Article  PubMed Central  PubMed  Google Scholar 

  6. Chandler D, Bailey AS, Tatchell GM, Davidson G, Greaves J, Grant WP: The development, regulation and use of biopesticides for integrated pest management. Philos Trans R Soc Lond B Biol Sci. 2011, 366 (1573): 1987-1998. 10.1098/rstb.2010.0390.

    Article  PubMed Central  PubMed  Google Scholar 

  7. Sudakin DL: Biopesticides. Toxicol Rev. 2003, 22 (2): 83-90. 10.2165/00139709-200322020-00003.

    Article  PubMed  Google Scholar 

  8. Glare T, Caradus J, Gelernter W, Jackson T, Keyhani N, Kohl J, Marrone P, Morin L, Stewart A: Have biopesticides come of age?. Trends Biotechnol. 2012, 30 (5): 250-258. 10.1016/j.tibtech.2012.01.003.

    Article  CAS  PubMed  Google Scholar 

  9. Pang YP: Novel acetylcholinesterase target site for malaria mosquito control. PLoS One. 2006, 1: e58-10.1371/journal.pone.0000058.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Haq SK, Atif SM, Khan RH: Protein proteinase inhibitor genes in combat against insects, pests, and pathogens: natural and engineered phytoprotection. Arch Biochem Biophys. 2004, 431 (1): 145-159. 10.1016/j.abb.2004.07.022.

    Article  CAS  PubMed  Google Scholar 

  11. Wu G, Miyata T, Kang CY, Xie LH: Insecticide toxicity and synergism by enzyme inhibitors in 18 species of pest insect and natural enemies in crucifer vegetable crops. Pest Manag Sci. 2007, 63 (5): 500-510. 10.1002/ps.1361.

    Article  CAS  PubMed  Google Scholar 

  12. Krungkrai SR, Krungkrai J: Malaria parasite carbonic anhydrase: inhibition of aromatic/heterocyclic sulfonamides and its therapeutic potential. Asian Pac J Trop Biomed. 2011, 1 (3): 233-242. 10.1016/S2221-1691(11)60034-8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Crocetti L, Maresca A, Temperini C, Hall RA, Scozzafava A, Muhlschlegel FA, Supuran CT: A thiabendazole sulfonamide shows potent inhibitory activity against mammalian and nematode alpha-carbonic anhydrases. Bioorg Med Chem Lett. 2009, 19 (5): 1371-1375. 10.1016/j.bmcl.2009.01.038.

    Article  CAS  PubMed  Google Scholar 

  14. Kumar RS, Ferry JG: Prokaryotic carbonic anhydrases of Earth’s environment. Subcell Biochem. 2014, 75: 77-87. 10.1007/978-94-007-7359-2_5.

    Article  CAS  PubMed  Google Scholar 

  15. Alterio V, Vitale RM, Monti SM, Pedone C, Scozzafava A, Cecchi A, De Simone G, Supuran CT: Carbonic anhydrase inhibitors: X-ray and molecular modeling study for the interaction of a fluorescent antitumor sulfonamide with isozyme II and IX. J Am Chem Soc. 2006, 128 (25): 8329-8335. 10.1021/ja061574s.

    Article  CAS  PubMed  Google Scholar 

  16. Rowlett RS: Structure and catalytic mechanism of the beta-carbonic anhydrases. Biochim Biophys Acta. 2010, 1804 (2): 362-373. 10.1016/j.bbapap.2009.08.002.

    Article  CAS  PubMed  Google Scholar 

  17. Syrjanen L, Tolvanen M, Hilvo M, Olatubosun A, Innocenti A, Scozzafava A, Leppiniemi J, Niederhauser B, Hytonen VP, Gorr TA, Parkkila S, Supuran CT: Characterization of the first beta-class carbonic anhydrase from an arthropod (Drosophila melanogaster) and phylogenetic analysis of beta-class carbonic anhydrases in invertebrates. BMC Biochem. 2010, 11: 28-10.1186/1471-2091-11-28.

    Article  PubMed Central  PubMed  Google Scholar 

  18. Zolfaghari Emameh R, Barker H, Tolvanen ME, Ortutay C, Parkkila S: Bioinformatic analysis of beta carbonic anhydrase sequences from protozoans and metazoans. Parasit Vectors. 2014, 7 (1): 38-10.1186/1756-3305-7-38.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Tobal JM, Balieiro ME: Role of carbonic anhydrases in pathogenic micro-organisms: a focus on Aspergillus fumigatus. J Med Microbiol. 2014, 63 (Pt 1): 15-27.

    Article  CAS  PubMed  Google Scholar 

  20. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Soding J, Thompson JD, Higgins DG: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011, 7: 539-

    Article  PubMed Central  PubMed  Google Scholar 

  21. Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ: Jalview Version 2–a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009, 25 (9): 1189-1191. 10.1093/bioinformatics/btp033.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Stavrinides J, McCloskey JK, Ochman H: Pea aphid as both host and vector for the phytopathogenic bacterium Pseudomonas syringae. Appl Environ Microbiol. 2009, 75 (7): 2230-2235. 10.1128/AEM.02860-08.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Prociv P, Croese J: Human enteric infection with Ancylostoma caninum: hookworms reappraised in the light of a “new” zoonosis. Acta Trop. 1996, 62 (1): 23-44. 10.1016/S0001-706X(96)00016-2.

    Article  CAS  PubMed  Google Scholar 

  24. Leles D, Gardner SL, Reinhard K, Iniguez A, Araujo A: Are Ascaris lumbricoides and Ascaris suum a single species?. Parasit Vectors. 2012, 5: 42-10.1186/1756-3305-5-42.

    Article  PubMed Central  PubMed  Google Scholar 

  25. Jones SR, Prosperi-Porta G, Kim E, Callow P, Hargreaves NB: The occurrence of Lepeophtheirus salmonis and Caligus clemensi (Copepoda: Caligidae) on three-spine stickleback Gasterosteus aculeatus in coastal British Columbia. J Parasitol. 2006, 92 (3): 473-480. 10.1645/GE-685R1.1.

    Article  PubMed  Google Scholar 

  26. Kafil M, Bandani AR, Kaltenpoth M, Goldansaz SH, Alavi SM: Role of Symbiotic Bacteria in the Growth and Development of the Sunn Pest, Eurygaster integriceps. J Insect Sci. 2013, 13: 99-

    Article  PubMed Central  PubMed  Google Scholar 

  27. Tabilio MR, Fiorini D, Marcantoni E, Materazzi S, Delfini M, De Salvador FR, Musmeci S: Impact of the Mediterranean fruit fly (medfly) Ceratitis capitata on different peach cultivars: the possible role of peach volatile compounds. Food Chem. 2013, 140 (1–2): 375-381.

    Article  CAS  PubMed  Google Scholar 

  28. Linthicum KJ: Introduction to the symposium Global Perspective on the Culex pipiens Complex in the 21st century: The Interrelationship of Culex pipiens, quinquefasciatus, molestus and others. J Am Mosq Control Assoc. 2012, 28 (4 Suppl): 4-9.

    Article  PubMed  Google Scholar 

  29. Knapp PA, Soule PT, Maxwell JT: Mountain pine beetle selectivity in old-growth ponderosa pine forests, Montana, USA. Ecol Evol. 2013, 3 (5): 1141-1148. 10.1002/ece3.522.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Feng M, Yang B, Yang L, Fu Y, Zhuang Y, Liang L, Xu Q, Cheng X, Tachibana H: High prevalence of Entamoeba infections in captive long-tailed macaques in China. Parasitol Res. 2011, 109 (4): 1093-1097. 10.1007/s00436-011-2351-2.

    Article  PubMed  Google Scholar 

  31. Peter JW, Chandrawathani P: Haemonchus contortus: parasite problem No. 1 from tropics - Polar Circle. Problems and prospects for control based on epidemiology. Trop Biomed. 2005, 22 (2): 131-137.

    PubMed  Google Scholar 

  32. Buchmann K, Sigh J, Nielsen CV, Dalgaard M: Host responses against the fish parasitizing ciliate Ichthyophthirius multifiliis. Vet Parasitol. 2001, 100 (1–2): 105-116.

    Article  CAS  PubMed  Google Scholar 

  33. Schneider B, Jariwala AR, Periago MV, Gazzinelli MF, Bose SN, Hotez PJ, Diemert DJ, Bethony JM: A history of hookworm vaccine development. Hum Vaccin. 2011, 7 (11): 1234-1244. 10.4161/hv.7.11.18443.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Sun Q, Zhou X: Corpse management in social insects. Int J Biol Sci. 2013, 9 (3): 313-321. 10.7150/ijbs.5781.

    Article  PubMed Central  PubMed  Google Scholar 

  35. Toews MD, Campbell JF, Arthur FH: The presence of flour affects the efficacy of aerosolized insecticides used to treat the red flour beetle, Tribolium castaneum. J Insect Sci. 2010, 10: 196-

    Article  PubMed Central  PubMed  Google Scholar 

  36. Lis LB, Bakula T, Baranowski M, Czarnewicz A: The carcinogenic effects of benzoquinones produced by the flour beetle. Pol J Vet Sci. 2011, 14 (1): 159-164.

    CAS  PubMed  Google Scholar 

  37. El-Mofty MM, Osman SI, Sakr SA, Toulan BA: Carcinogenicity of flour infested with Tribolium castaneum using the Egyptian toad Bufo regularis as a biological test animal. Oncology. 1988, 45 (1): 65-67. 10.1159/000226533.

    Article  CAS  PubMed  Google Scholar 

  38. Alanko K, Tuomi T, Vanhanen M, Pajari-Backas M, Kanerva L, Havu K, Saarinen K, Bruynzeel DP: Occupational IgE-mediated allergy to Tribolium confusum (confused flour beetle). Allergy. 2000, 55 (9): 879-882. 10.1034/j.1398-9995.2000.00572.x.

    Article  CAS  PubMed  Google Scholar 

  39. Gottstein B, Pozio E, Nockler K: Epidemiology, diagnosis, treatment, and control of trichinellosis. Clin Microbiol Rev. 2009, 22 (1): 127-145. 10.1128/CMR.00026-08. Table of Contents

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Schierwater B: My favorite animal, Trichoplax adhaerens. Bioessays. 2005, 27 (12): 1294-1302. 10.1002/bies.20320.

    Article  CAS  PubMed  Google Scholar 

  41. Theologis A, Ecker JR, Palm CJ, Federspiel NA, Kaul S, White O, Alonso J, Altafi H, Araujo R, Bowman CL, Brooks SY, Buehler E, Chan A, Chao Q, Chen H, Cheuk RF, Chin CW, Chung MK, Conn L, Conway AB, Conway AR, Creasy TH, Dewar K, Dunn P, Etgu P, Feldblyum TV, Feng J, Fong B, Fujii CY, Gill JE: Sequence and analysis of chromosome 1 of the plant Arabidopsis thaliana. Nature. 2000, 408 (6814): 816-820. 10.1038/35048500.

    Article  PubMed  Google Scholar 

  42. Majeau N, Coleman JR: Isolation and characterization of a cDNA coding for pea chloroplastic carbonic anhydrase. Plant Physiol. 1991, 95 (1): 264-268. 10.1104/pp.95.1.264.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Hoang CV, Wessler HG, Local A, Turley RB, Benjamin RC, Chapman KD: Identification and expression of cotton (Gossypium hirsutum L.) plastidial carbonic anhydrase. Plant Cell Physiol. 1999, 40 (12): 1262-1270. 10.1093/oxfordjournals.pcp.a029514.

    Article  CAS  PubMed  Google Scholar 

  44. Majeau N, Arnoldo MA, Coleman JR: Modification of carbonic anhydrase activity by antisense and over-expression constructs in transgenic tobacco. Plant Mol Biol. 1994, 25 (3): 377-385. 10.1007/BF00043867.

    Article  CAS  PubMed  Google Scholar 

  45. Jaillon O, Aury JM, Noel B, Policriti A, Clepet C, Casagrande A, Choisne N, Aubourg S, Vitulo N, Jubin C, Vezzi A, Legeai F, Hugueney P, Dasilva C, Horner D, Mica E, Jublot D, Poulain J, Bruyère C, Billault A, Segurens B, Gouyvenoux M, Ugarte E, Cattonaro F, Anthouard V, Vico V, Del Fabbro C, Alaux M, Di Gaspero G, Dumas V: The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature. 2007, 449 (7161): 463-467. 10.1038/nature06148.

    Article  CAS  PubMed  Google Scholar 

  46. Rumeau D, Cuine S, Fina L, Gault N, Nicole M, Peltier G: Subcellular distribution of carbonic anhydrase in Solanum tuberosum L. leaves: characterization of two compartment-specific isoforms. Planta. 1996, 199 (1): 79-88.

    Article  CAS  PubMed  Google Scholar 

  47. Tuskan GA, Difazio S, Jansson S, Bohlmann J, Grigoriev I, Hellsten U, Putnam N, Ralph S, Rombauts S, Salamov A, Schein J, Sterck L, Aerts A, Bhalerao RR, Bhalerao RP, Blaudez D, Boerjan W, Brun A, Brunner A, Busov V, Campbell M, Carlson J, Chalot M, Chapman J, Chen GL, Cooper D, Coutinho PM, Couturier J, Covert S, Cronk Q: The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science. 2006, 313 (5793): 1596-1604. 10.1126/science.1128691.

    Article  CAS  PubMed  Google Scholar 

  48. Moyroud E, Minguet EG, Ott F, Yant L, Pose D, Monniaux M, Blanchet S, Bastien O, Thevenon E, Weigel D, Schmid M, Parcy F: Prediction of regulatory interactions from genome sequences using a biophysical model for the Arabidopsis LEAFY transcription factor. Plant Cell. 2011, 23 (4): 1293-1306. 10.1105/tpc.111.083329.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Slater GS, Birney E: Automated generation of heuristics for biological sequence comparison. BMC Bioinform. 2005, 6: 31-10.1186/1471-2105-6-31.

    Article  Google Scholar 

  50. Suyama M, Torrents D, Bork P: PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 2006, 34: W609-W612. 10.1093/nar/gkl315. Web Server issue

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  51. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Hohna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP: MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012, 61 (3): 539-542. 10.1093/sysbio/sys029.

    Article  PubMed Central  PubMed  Google Scholar 

  52. Emanuelsson O, Nielsen H, Brunak S, von Heijne G: Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol. 2000, 300 (4): 1005-1016. 10.1006/jmbi.2000.3903.

    Article  CAS  PubMed  Google Scholar 

  53. Kolaskar AS, Tongaonkar PC: A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990, 276 (1–2): 172-174.

    Article  CAS  PubMed  Google Scholar 

  54. Rice P, Longden I, Bleasby A: EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 2000, 16 (6): 276-277. 10.1016/S0168-9525(00)02024-2.

    Article  CAS  PubMed  Google Scholar 

  55. Olson SA: EMBOSS opens up sequence analysis. European Molecular Biology Open Software Suite. Brief Bioinform. 2002, 3 (1): 87-91. 10.1093/bib/3.1.87.

    Article  PubMed  Google Scholar 

  56. Mullan LJ, Bleasby AJ: Short EMBOSS User Guide. European Molecular Biology Open Software Suite. Brief Bioinform. 2002, 3 (1): 92-94. 10.1093/bib/3.1.92.

    Article  PubMed  Google Scholar 

  57. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A: Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics. Edited by: Baxevanis AD. 2006, Hoboken, NJ: John Wiley and Sons, Inc, Chapter 5:Unit 5 6

    Google Scholar 

  58. Lehtonen JV, Still DJ, Rantanen VV, Ekholm J, Bjorklund D, Iftikhar Z, Huhtala M, Repo S, Jussila A, Jaakkola J, Pentikäinen O, Nyrönen T, Salminen T, Gyllenberg M, Johnson MS: BODIL: a molecular modeling environment for structure-function analysis and drug design. J Comput Aided Mol Des. 2004, 18 (6): 401-419. 10.1007/s10822-004-3752-4.

    Article  CAS  PubMed  Google Scholar 

  59. Humphrey W, Dalke A, Schulten K: VMD: visual molecular dynamics. J Mol Graph. 1996, 14 (1): 33-38. 10.1016/0263-7855(96)00018-5. 27–38

    Article  CAS  PubMed  Google Scholar 

  60. Singh H, Ansari HR, Raghava GP: Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PLoS One. 2013, 8 (5): e62216-10.1371/journal.pone.0062216.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  61. Kimber MS, Coleman JR, Pai EF: Beta-carbonic anhydrase from Pisum sativum: crystallization and preliminary X-ray analysis. Acta Crystallogr D Biol Crystallogr. 2000, 56 (Pt 7): 927-929.

    Article  CAS  PubMed  Google Scholar 

  62. Yamano T, Morita S: Effects of pesticides on isolated rat hepatocytes, mitochondria, and microsomes II. Arch Environ Contam Toxicol. 1995, 28 (1): 1-7. 10.1007/BF00213961.

    Article  CAS  PubMed  Google Scholar 

  63. Supuran CT: Carbonic anhydrase inhibition/activation: trip of a scientist around the world in the search of novel chemotypes and drug targets. Curr Pharm Des. 2010, 16 (29): 3233-3245. 10.2174/138161210793429797.

    Article  CAS  PubMed  Google Scholar 

  64. McKenna R, Supuran CT: Carbonic anhydrase inhibitors drug design. Subcell Biochem. 2014, 75: 291-323. 10.1007/978-94-007-7359-2_15.

    Article  CAS  PubMed  Google Scholar 

  65. Badger M: The roles of carbonic anhydrases in photosynthetic CO2 concentrating mechanisms. Photosynth Res. 2003, 77 (2–3): 83-94.

    Article  CAS  PubMed  Google Scholar 

  66. Flower DR, Macdonald IK, Ramakrishnan K, Davies MN, Doytchinova IA: Computer aided selection of candidate vaccine antigens. Immunome Res. 2010, 6 (Suppl 2): S1-10.1186/1745-7580-6-S2-S1.

    Article  PubMed Central  PubMed  Google Scholar 

  67. Yang B, Sayers S, Xiang Z, He Y: Protegen: a web-based protective antigen database and analysis system. Nucleic Acids Res. 2011, 39 (Database issue): D1073-D1078.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Gustafson DJ: Rising food costs & global food security: key issues & relevance for India. Indian J Med Res. 2013, 138 (3): 398-410.

    PubMed Central  PubMed  Google Scholar 

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Acknowledgment

To perform these studies RZE received a scholarship support from the Ministry of Science, Research and Technology, and National Institute of Genetic Engineering and Biotechnology of Islamic Republic of Iran. This study was also funded by Finnish Cultural Foundation (HB), Sigrid Juselius Foundation (SP), Jane and Aatos Erkko Foundation (SP), Academy of Finland (VH) and Competitive Research Funding of the Tampere University Hospital (SP, VH).

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Authors and Affiliations

  1. School of Medicine, University of Tampere, 33520, Tampere, Finland

    Reza Zolfaghari Emameh, Harlan Barker & Seppo Parkkila

  2. BioMediTech, University of Tampere, 33520, Tampere, Finland

    Reza Zolfaghari Emameh, Harlan Barker, Vesa P Hytönen, Martti E E Tolvanen & Seppo Parkkila

  3. Fimlab Laboratories Ltd and Tampere University Hospital, Biokatu 4, 33520, Tampere, Finland

    Reza Zolfaghari Emameh, Vesa P Hytönen & Seppo Parkkila

  4. Department of Information Technology, University of Turku, 20014, Turku, Finland

    Martti E E Tolvanen

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  1. Reza Zolfaghari Emameh
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Correspondence to Reza Zolfaghari Emameh.

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The authors declare that they have no competing interests.

Authors’ contributions

All authors participated in the design of the study. RZE carried out the bioinformatics searches on parasites and plant species. RZE and HB participated in the sequence alignment. HB made protein sequence corrections and predictions and performed the phylogenetic analysis. RZE performed the mitochondrial targeting peptide and antigenic site prediction. RZE and VH participated in the homology modelling. RZE, HB and VH drafted the first version of the manuscript. All authors participated in writing further versions and read and approved the final manuscript.

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Zolfaghari Emameh, R., Barker, H., Hytönen, V.P. et al. Beta carbonic anhydrases: novel targets for pesticides and anti-parasitic agents in agriculture and livestock husbandry. Parasites Vectors 7, 403 (2014). https://doi.org/10.1186/1756-3305-7-403

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Keywords

Parasites & Vectors

ISSN: 1756-3305

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