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Genome level analysis of bacteriocins of lactic acid bacteria
Accepted Manuscript Title: Genome Level Analysis of Bacteriocins of Lactic Acid Bacteria Author: Neetigyata Pratap Singh Abhay Tiwari Ankiti Bansal Shruti Thakur Garima Sharma Reema Gabrani PII: DOI: Reference:
S1476-9271(15)00030-4 http://dx.doi.org/doi:10.1016/j.compbiolchem.2015.02.013 CBAC 6405
To appear in:
Computational Biology and Chemistry
Received date: Revised date: Accepted date:
29-8-2014 9-2-2015 21-2-2015
Please cite this article as: Singh, Neetigyata Pratap, Tiwari, Abhay, Bansal, Ankiti, Thakur, Shruti, Sharma, Garima, Gabrani, Reema, Genome Level Analysis of Bacteriocins of Lactic Acid Bacteria.Computational Biology and Chemistry http://dx.doi.org/10.1016/j.compbiolchem.2015.02.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Genome Level Analysis of Bacteriocins of Lactic Acid Bacteria Neetigyata Pratap Singh#, Abhay Tiwari#, Ankiti Bansal, Shruti Thakur, Garima Sharma, Reema Gabrani* Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, NOIDA, India #
Both authors have contributed equally
*
Corresponding author. Tel: +91-120-2594210; Email: [email protected]
Graphical abstract
Highlights
Identification of bacteriocin secretors lactic acid bacteria and biosynthetic gene with bioinformatic tools. Phylogenetic tree of LAB with rpoB and gyrB (housekeeping genes) by MEGA6 tool. Physiochemical and secondary structure analysis of putative strains by computational methodology. ABSTRACT
Bacteriocins are antimicrobial peptides which are ribosomally synthesized by mainly all bacterial species. LABs (Lactic Acid Bacteria) are a diverse group of bacteria that include around 20 genera of various species. Though LABs have a tremendous potential for production of anti-microbial peptides, this group of bacteria is still underexplored for bacteriocins. To study the diversity among bacteriocin encoding clusters and the putative bacteriocin precursors, genome mining was performed on 20 different species of LAB not reported to be bacteriocin producers. The phylogenetic tree of gyrB, rpoB, and 16S rRNA were constructed using MEGA6 software to analyse the diversity among strains. Putative bacteriocins operons identified were found to be diverse and were further characterized on the basis of physiochemical properties and the secondary structure. The presence of at least two cysteine residues in most of the observed putative bacteriocins leads to disulphide bond formation and provide stability. Our data suggests that LABs are prolific source of low molecular weight non modified peptides.
Key words: - Bacteriocins, BAGEL3, MEGA6, ProtParam, SOPMA
1. INTRODUCTION
Bacteriocins are ribosomally synthesized antimicrobial produced by both Gram positive and Gram negative bacteria [1]. They have been shown to be active against pathogens including bacteria, either of the same species (narrow spectrum), or across genera (broad spectrum) [2]. Thus the bacteriocins help the producer bacteria to gain a competitive advantage in its niche environment to reduce the competitors to gain resources and form an important component of chemical defense system. Bacteriocins are mainly small, cationic and are composed of more than 30% hydrophobic residues [3]. The cationic charge of these peptides ensures electrostatic affinity with the negatively charged bacterial outer membrane, while the hydrophobic part of the peptide interacts with the cell membrane and enters the double lipid membrane. Bacteriocins of Gram-positive bacteria are more diverse than the bacteriocins described for the Gram-negative bacteria. Among the Gram positive bacteria, bacteriocins produced by the lactic acid bacteria (LAB) have gained particular attention nowadays [4]. LABs are generally regarded as safe (GRAS) and so are their metabolic products, bacteriocins. These substances find application in the food industry as natural preservatives and can also prove to be an effective alternative to antibiotics. Bacteriocins produced by the LAB have been categorized into three different classes according to their biochemical and genetic properties. Class I (lantibiotics) contains small peptides and unusual post-translational modified residues. Class II bacteriocins are non lantibiotics, small in size (<10kDa) and heat stable. These are further classified into class IIa containing pediocin like bacteriocin, class IIb comprises of two component bacteriocin which means that two different peptides act synergistically to form active pore complex. Class IIc bacteriocins have circular structure and class IId consists of the remaining linear, nonpediocin and single peptides. Class III large thermo sensitive peptides have been recently reclassified as bacteriolysins by Perez et al. [5]. The differences in the properties result in large divergence between bacteriocins. Bacteriocins are produced from ribosomally synthesized precursor proteins that generally consist of a C terminal core peptide and a conserved N-terminal leader sequence. This leader sequence is recognized and cleaved by a processing peptidase giving rise to a mature bacteriocin [6]. The core peptide may undergo further post-translational modifications such
as lanthionine formation, dehydration, or cyclization [7]. The proteins involved in the modification, export, and bacteriocins regulation are often encoded by the genes present in the vicinity of the gene encoding the precursor protein [8]. These genes could be located on either chromosome, plasmid or on transposon [9]. Bacteriocin production is a species specific phenomenon and difference in the location of biosynthetic gene, for example, immunity and transporter gene, can result in the variant form of bacteriocin and impart novel properties to it [10,11]. Identification of a bacteriocin is a time consuming and tedious process where bacteriocin producing bacteria are isolated, screened and identified at molecular level. Further the potential bacteriocin needs to be purified, identified, characterized and extensively tested for inhibition of growth of other bacteria [12]. Dirix et al. (2004) [13], Wang et al. (2011) [14] and Singh and Sareen (2014) [15] have described the identification of putative bacteriocins by exploring genomic DNA sequence for the presence of bacteriocin genes and also for the biosynthetic genes, essential for the bacteriocin secretion and immunity, in the vicinity.
The current study was undertaken to identify the putative bacteriocin cluster from 34 different species of Lactic Acid Bacteria (LAB), for which no bacteriocin production has been reported till now. The web-based freely accessible software tool, BAGEL, was used for genome mining of bacteriocins and their biosynthetic clusters. Molecular evolutionary genetic analysis (MEGA6) tool was used to analyse the phylogenetic relationship between the selected strains. The physiochemical properties and the secondary structure of the identified putative bacteriocins were determined by using the bioinformatics tools ProtParam and SOPMA, respectively. Combining the bioinformatics approach with extensive wet lab techniques can provide an efficient method to identify and characterize the novel bacteriocins.
2. MATERIALS AND METHODS 2.1. Putative Bacteriocin gene cluster BAGEL is a hidden Markov model (HMM)-based software tool which enables genome mining for bacteriocin and their biosynthetic clusters through a knowledge based data base [16]. Genome sequences of the 34 identified species were retrieved from BACTIBASE and NCBI and used as a query for BAGEL3 to identify the putative bacteriocin operons.
BAGEL3 uses DNA nucleotide sequences in FASTA format as input and these sequences are analyzed in parallel using two different approaches, one is based on finding the genes that commonly surround the bacteriocin genes and the other is based on finding the gene itself [17, A]. 2.2 Phylogenetic study
The phylogenetic analysis was conducted by MEGA6 tool using 16s rRNA and housekeeping (rpoB and gyrB) genes. Gene sequences of gyrB, rpoB and 16s rRNA of 34 identified species were retrieved from NCBI in FASTA format and were aligned by clustalW method using MEGA6 tool [18]. The phylogenetic tree was constructed from aligned sequences using maximum likelihood method. 2.3 Physiochemical analysis ProtParam, a computational tool, was used to calculate various physical and chemical parameters of bacteriocins. The computed parameters included the molecular weight, pI, number of amino acids and cysetine residues, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (GRAVY) [19, B]. 2.4. Secondary Structure Prediction SOPMA (Self-Optimized Prediction Method with Alignment) has been used for secondary structure prediction. The protein sequence of the predicted bacteriocin was used as a query to run SOPMA. Predictions were available by Email to [email protected] and also on a Web page [20, C]. 3. RESULTS AND DISCUSSION 3.1. Identification of putative bacteriocin gene clusters Different databases like BACTIBASE and NCBI were used to identify 34 different species of LAB for which no bacteriocin has been reported till date. Genome mining of these 34 species of LAB using BAGEL 3 resulted in the detection of putative bacteriocin operons in 20 different species belonging to 3 different genera Streptococcus, Leuconostoc and Lactobacillus [Supplementary Table 1]. Detection of small peptide sequences in newly sequenced genomes is problematic [21]. Short sequences are poorly detected by Basic Local Alignment Search Tool (BLAST) and similar
sequence analysis methods and detection of uncurated small ORF may result in the annotation of many spurious small ORFs. This is particularly the case for bacteriocins which are a very varied group of antimicrobial peptides produced by bacteria and are usually encoded by small, poorly conserved ORFs [17]. BAGEL takes advantage of the fact that structural bacteriocin gene is surrounded by other biosynthetic genes responsible for its modification, export to the extracellular milieu and regulation of bacteriocin production. BAGEL allows ORF which makes it independent of GenBank annotations and thus prevents the omission of small non-conserved ORFs which are the most probable candidates for bacteriocin genes. BAGEL has been successfully used to identify putative bacteriocin genes in different species of LAB such as Lactococcus lactis IL1403, Lactobacillus plantarum, Streptococcus pneumoniae R6, Streptococcus pneumoniae TIGR4, Streptococcus pyogenes NC002737 and Streptococcus pyogenes NC004070. LAB’s among other gram positive bacteria are particularly prolific in bacteriocin production. Seven putative bacteriocin genes reported in Streptococcus pneumoniae TIGR4 have been identified by BAGEL [22]. LAB bacteriocins have been classified into Class I, Class II and Class III based on the observed common characteristics [23]. Class I (lantibiotics) and class II (small heat-stable non-lanthionine-containing peptides) bacteriocins are the most abundant and thoroughly studied bacteriocins [24, 25]. Analysis carried out on putative bacteriocin by BAGEL3 indicates that Class II was the most predominant type, present in 16 bacterial genome followed by Class III which was present in 6 genomes whereas Class I bacteriocin was not found in any of the analyzed genomes. Along with the putative structural bacteriocin gene, BAGEL3 also identified other biosynthetic genes present in the same operon. It was observed that Class II operon was more detailed in terms of the number of genes found in the vicinity of the bacteriocin gene as compared to the Class III operon. Representative operons from class II and III have been shown in Fig. 1. The data also indicates that certain biosynthetic genes could not be identified in the class II and class III operons (Fig. 1). It implies that either the functionally active bacteriocin is not synthesized by the respective bacteria or the biosynthetic genes located on plasmid could support the transport of bacteriocin. The research data supports that the immunity genes and the other genes required for the bacteriocin production could be supported by the gene sequence present on plasmid. Research conducted by Birri et.al on bacteriocin avicinA of subclass IIa, produced by Enterococcus avium, showed that both the structure gene and
immunity gene are present on the same operon on the chromosome, whereas the structural gene for the closely related bacteriocin Mundticin KS produced by E. mundtii NFRI 7393 strain are located on the plasmid [26]. CLASS III Operon1
Operon 2
Operon 3
CLASS II Operon 4
Operon 5
Operon 6
Key:
Fig.1: Gene clusters of different LAB species belonging to class II and class III bacteriocins have been shown. Operon 1: Lactobacillus crispatus ST1; Operon 2: Streptococcus lutetiensis 033; Operon 3: Lactobacillus kefiranofaciens ZW3; Operon 4: Lactobacillus casei BD II; Operon 5: Lactobacillus ruminis ATCC 27782; Operon 6: Streptococcus oralis Uo5. 3.2 Phylogenetic analysis The phylogenetic tree constructed using gyrB, rpoB and 16S rRNA gene sequences of 34 LAB species is shown in Fig. 2, supplementary Fig.1 and supplementary Fig.2, respectively. Phylogenetic tree constructed from gyrB gene, encoding the subunit B protein of DNA gyrase, sequences showed that the taxa which did not show any putative bacteriocin operon as predicted by BAGEL3 were found to be distributed across various clads. The phylogenetic analysis done for gyrB and rpoB, which encodes the β subunit of RNA polymerase, was more discriminative and gave a higher resolution as compared to the 16S rRNA gene sequences analysis. The phylogenetic tree based on 16S rRNA did not clearly differentiate the Streptococcus oligofermentans AS1.3089 and Lactobacillus casei BDII whereas the tree created using gyrB and rpoB gene sequences created a separate cluster for Streptococcus sp. and Lactobacillus sp. The housekeeping genes gyrB and rpoB have been shown to be more accurate phylogenetic markers for Streptococcus genus to trace the evolutionary relationship between various species [27].
Fig.2: Maximum Likelihood tree based on gyrB gene from thirty four LAB genomes constructed in MEGA6. Phylogenetic tree also represents the 14 strains which are marked by arrows indicates the absence of bacteriocin clusters.
3. 3. Physiochemical analysis of putative bacteriocin
A number of physicochemical properties are generally studied to provide information about the composition and structure of bacteriocins. Bacteriocins are extremely heterogeneous in nature. They differ widely in many characteristics including molecular weight, pI, presence of particular groups of amino acids, numbers of amino acid residues and net positive charge. In our study the molecular weight of all class III bacteriocins was in between 32000 Da – 38000 Da except for one found in L.Karinofaciens, whose molecular weight was 16474.9 Da. The molecular weight for the Class II bacteriocins was found to be between 2000 Da and 12000 Da except for L. crispatus bacteriocin whose weight was 23850 Da [Supplementary table 2]. Important characteristics of bacteriocins are related to their net charge.
Many of the low-
molecular-weight bacteriocins are cationic at pH 7.0 which is applicable for both the lantibiotic and non-lanthionine-containing bacteriocins. It has been observed that the LAB bacteriocins have greater antibacterial activity at lower pH values (< 5) as their adsorption to the cell surface of bacteria is pH dependent [28]. Physiochemical analysis of the predicted bacteriocins showed that 55% of them were positively charged. The
putative
bacteriocins
from
Lactobacillus_casei_BD_II,
Streptococcus_pseudopneumoniae, S. pneumonia ATCC 700669, S._dysgalactiae ATCC 12394 have shown high value of aliphatic index which points towards the relative stability at the diverse range of temperature. The instability index provides an estimate of the stability of the tested protein. A protein showing instability index <40 is predicted to be stable, whereas a value >40 predicts the protein to be unstable. In this study, only five of the 52 bacteriocins identified from LAB species were found to be unstable [Supplementary table 2 (appendix 2)]. The instability of a protein is determined based on the occurrence of certain dipeptides and relates to the in vivo half life of a protein [29]. Grand average of hydropathicity index indicates the solubility of a protein, a positive GRAVY index is shown by hydrophobic compound while a negative GRAVY index indicates the hydrophilicity of a given compound. The GRAVY index showed that there were almost equal number of hydrophobic (45 %) and hydrophilic (55%) bacteriocins. The low GRAVY of S. oralis Uo5 (AOI_1;orf011); S. parasanguinis FW213 (AOI_1;orf015) and S. intermedius JTH08 (AOI_1;orf018) indicates that these bacteriocins can possibly have better interaction with water.
Cysteine content is another important feature of bacteriocins. Some non lanthionine containing bacteriocins are called thiobiotics as they contain only a single cysteine residue which is present in the reduced thiol form for bactericidal [30], and some have no cysteine residue at all e g. lactococcins A, M, G. Class IIa bacteriocins have at least two cysteine residues with disulfide bridge and are called cystibiotics [31]. In the study, 36 bacteriocins with at least two cysteine residues, four with exactly one cysteine residue and twelve with no cysteine residue were identified [Supplementary table 2]. Hence most of the putative bacteriocins having at least two cysteine residues can form disulphide bonds and play an important role in their stabilization [32].
3.4. Secondary structure prediction Bacteriocin structure and function based analysis helps in knowing the molecular specificity of a given bacteriocin. The secondary structure study of protein allows us to recognize the folds and to classify the structural motifs present in them. Secondary structure predictions, helix, beta bridge and random coils were studied in putative bacteriocins using SOPMA tool. Our study showed that class II bacteriocins contain α helix spanned through 14%- 79% residues while class III bacteriocins displayed α helix spanning through 12%- 30% residues [Supplymentary table 3 (appendix 3)].The data has shown that the existence of alpha helices in the sequence is very important in determination of its function. The peptide sequence and secondary structure analysis of lactococcin Q and lactococcin G have reported the existence of α-helical structure at the same position and their anti-bacterial mode of action was also found to be similar. Moreover, the secondary structure analyses of both the bacteriocins showed the existence of four identical alpha helices, having amphiphillic characteristics which are thought to play a major role in bactericidal mode of action [33]. The secondary structure prediction can assist in knowing the specificity and mode of action of a particular bacteriocin and to find the similarity among two different bacteriocins. 4. CONCLUSION Bacteriocins have continued to gain importance due to their potentially large number of applications in healthcare and food industry. The classical way of identifying bacteriocins involve determining the biological activity of the putative strain for effective inhibition of other bacteria. Although advancements in research methodologies has led to increase in discovery of the novel bacteriocins, but finding a potential strain still remains a tedious and time consuming process. On the other hand, computational approaches can help in identifying
the bacteriocin producing strains and checking their functionality through knowledge based database approach. This approach helps in identifying the prospective bacteriocins; further reducing the complexity for their validation. LABs hold huge potential for acting as an important source of novel bacteriocins with interesting therapeutic properties. This study was undertaken to identify the potential bacteriocins secretors in LAB hitherto not reported for its production using bioinformatics tools. Fifty two putative bacteriocins were identified across 20 LAB species. The phylogenetic tree of rpoB, gyrB(housekeeping) genes was constructed by using MEGA6 tool. These bacteriocins were mainly low molecular weight, heat stable and belonged to class II type. The putative structural as well as the biosynthetic genes were identified by BAGEL3. Certain biosynthetic genes were not identified in the class II and class III operons wherein the production of the bacteriocins might be supported by the corresponding gene on the plasmid. The information gained through this study would be useful in gaining further information on biosynthetic machinery of bacteriocins. This will not help in identifying prospective bacteriocins and improve the bacteriocin gene cluster annotation in lactic acid bacteria but will also facilitate wet lab techniques in discovering novel bacteriocins. Acknowledgments The authors thank the Department of Biotechnology, Jaypee Institute of Information Technology, Noida, UP, India, for providing the infrastructural facility to carry out the work.
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DOI:
S1476-9271(15)00030-4 http://dx.doi.org/doi:10.1016/j.compbiolchem.2015.02.013 CBAC 6405
To appear in:
Computational Biology and Chemistry
Received date: Revised date: Accepted date:
29-8-2014 9-2-2015 21-2-2015
Please cite this article as: Singh, Neetigyata Pratap, Tiwari, Abhay, Bansal, Ankiti, Thakur, Shruti, Sharma, Garima, Gabrani, Reema, Genome Level Analysis of Bacteriocins of Lactic Acid Bacteria.Computational Biology and Chemistry http://dx.doi.org/10.1016/j.compbiolchem.2015.02.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Genome Level Analysis of Bacteriocins of Lactic Acid Bacteria Neetigyata Pratap Singh#, Abhay Tiwari#, Ankiti Bansal, Shruti Thakur, Garima Sharma, Reema Gabrani* Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, NOIDA, India #
Both authors have contributed equally
*
Corresponding author. Tel: +91-120-2594210; Email: [email protected]
Graphical abstract
Highlights
Identification of bacteriocin secretors lactic acid bacteria and biosynthetic gene with bioinformatic tools. Phylogenetic tree of LAB with rpoB and gyrB (housekeeping genes) by MEGA6 tool. Physiochemical and secondary structure analysis of putative strains by computational methodology. ABSTRACT
Bacteriocins are antimicrobial peptides which are ribosomally synthesized by mainly all bacterial species. LABs (Lactic Acid Bacteria) are a diverse group of bacteria that include around 20 genera of various species. Though LABs have a tremendous potential for production of anti-microbial peptides, this group of bacteria is still underexplored for bacteriocins. To study the diversity among bacteriocin encoding clusters and the putative bacteriocin precursors, genome mining was performed on 20 different species of LAB not reported to be bacteriocin producers. The phylogenetic tree of gyrB, rpoB, and 16S rRNA were constructed using MEGA6 software to analyse the diversity among strains. Putative bacteriocins operons identified were found to be diverse and were further characterized on the basis of physiochemical properties and the secondary structure. The presence of at least two cysteine residues in most of the observed putative bacteriocins leads to disulphide bond formation and provide stability. Our data suggests that LABs are prolific source of low molecular weight non modified peptides.
Key words: - Bacteriocins, BAGEL3, MEGA6, ProtParam, SOPMA
1. INTRODUCTION
Bacteriocins are ribosomally synthesized antimicrobial produced by both Gram positive and Gram negative bacteria [1]. They have been shown to be active against pathogens including bacteria, either of the same species (narrow spectrum), or across genera (broad spectrum) [2]. Thus the bacteriocins help the producer bacteria to gain a competitive advantage in its niche environment to reduce the competitors to gain resources and form an important component of chemical defense system. Bacteriocins are mainly small, cationic and are composed of more than 30% hydrophobic residues [3]. The cationic charge of these peptides ensures electrostatic affinity with the negatively charged bacterial outer membrane, while the hydrophobic part of the peptide interacts with the cell membrane and enters the double lipid membrane. Bacteriocins of Gram-positive bacteria are more diverse than the bacteriocins described for the Gram-negative bacteria. Among the Gram positive bacteria, bacteriocins produced by the lactic acid bacteria (LAB) have gained particular attention nowadays [4]. LABs are generally regarded as safe (GRAS) and so are their metabolic products, bacteriocins. These substances find application in the food industry as natural preservatives and can also prove to be an effective alternative to antibiotics. Bacteriocins produced by the LAB have been categorized into three different classes according to their biochemical and genetic properties. Class I (lantibiotics) contains small peptides and unusual post-translational modified residues. Class II bacteriocins are non lantibiotics, small in size (<10kDa) and heat stable. These are further classified into class IIa containing pediocin like bacteriocin, class IIb comprises of two component bacteriocin which means that two different peptides act synergistically to form active pore complex. Class IIc bacteriocins have circular structure and class IId consists of the remaining linear, nonpediocin and single peptides. Class III large thermo sensitive peptides have been recently reclassified as bacteriolysins by Perez et al. [5]. The differences in the properties result in large divergence between bacteriocins. Bacteriocins are produced from ribosomally synthesized precursor proteins that generally consist of a C terminal core peptide and a conserved N-terminal leader sequence. This leader sequence is recognized and cleaved by a processing peptidase giving rise to a mature bacteriocin [6]. The core peptide may undergo further post-translational modifications such
as lanthionine formation, dehydration, or cyclization [7]. The proteins involved in the modification, export, and bacteriocins regulation are often encoded by the genes present in the vicinity of the gene encoding the precursor protein [8]. These genes could be located on either chromosome, plasmid or on transposon [9]. Bacteriocin production is a species specific phenomenon and difference in the location of biosynthetic gene, for example, immunity and transporter gene, can result in the variant form of bacteriocin and impart novel properties to it [10,11]. Identification of a bacteriocin is a time consuming and tedious process where bacteriocin producing bacteria are isolated, screened and identified at molecular level. Further the potential bacteriocin needs to be purified, identified, characterized and extensively tested for inhibition of growth of other bacteria [12]. Dirix et al. (2004) [13], Wang et al. (2011) [14] and Singh and Sareen (2014) [15] have described the identification of putative bacteriocins by exploring genomic DNA sequence for the presence of bacteriocin genes and also for the biosynthetic genes, essential for the bacteriocin secretion and immunity, in the vicinity.
The current study was undertaken to identify the putative bacteriocin cluster from 34 different species of Lactic Acid Bacteria (LAB), for which no bacteriocin production has been reported till now. The web-based freely accessible software tool, BAGEL, was used for genome mining of bacteriocins and their biosynthetic clusters. Molecular evolutionary genetic analysis (MEGA6) tool was used to analyse the phylogenetic relationship between the selected strains. The physiochemical properties and the secondary structure of the identified putative bacteriocins were determined by using the bioinformatics tools ProtParam and SOPMA, respectively. Combining the bioinformatics approach with extensive wet lab techniques can provide an efficient method to identify and characterize the novel bacteriocins.
2. MATERIALS AND METHODS 2.1. Putative Bacteriocin gene cluster BAGEL is a hidden Markov model (HMM)-based software tool which enables genome mining for bacteriocin and their biosynthetic clusters through a knowledge based data base [16]. Genome sequences of the 34 identified species were retrieved from BACTIBASE and NCBI and used as a query for BAGEL3 to identify the putative bacteriocin operons.
BAGEL3 uses DNA nucleotide sequences in FASTA format as input and these sequences are analyzed in parallel using two different approaches, one is based on finding the genes that commonly surround the bacteriocin genes and the other is based on finding the gene itself [17, A]. 2.2 Phylogenetic study
The phylogenetic analysis was conducted by MEGA6 tool using 16s rRNA and housekeeping (rpoB and gyrB) genes. Gene sequences of gyrB, rpoB and 16s rRNA of 34 identified species were retrieved from NCBI in FASTA format and were aligned by clustalW method using MEGA6 tool [18]. The phylogenetic tree was constructed from aligned sequences using maximum likelihood method. 2.3 Physiochemical analysis ProtParam, a computational tool, was used to calculate various physical and chemical parameters of bacteriocins. The computed parameters included the molecular weight, pI, number of amino acids and cysetine residues, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (GRAVY) [19, B]. 2.4. Secondary Structure Prediction SOPMA (Self-Optimized Prediction Method with Alignment) has been used for secondary structure prediction. The protein sequence of the predicted bacteriocin was used as a query to run SOPMA. Predictions were available by Email to [email protected] and also on a Web page [20, C]. 3. RESULTS AND DISCUSSION 3.1. Identification of putative bacteriocin gene clusters Different databases like BACTIBASE and NCBI were used to identify 34 different species of LAB for which no bacteriocin has been reported till date. Genome mining of these 34 species of LAB using BAGEL 3 resulted in the detection of putative bacteriocin operons in 20 different species belonging to 3 different genera Streptococcus, Leuconostoc and Lactobacillus [Supplementary Table 1]. Detection of small peptide sequences in newly sequenced genomes is problematic [21]. Short sequences are poorly detected by Basic Local Alignment Search Tool (BLAST) and similar
sequence analysis methods and detection of uncurated small ORF may result in the annotation of many spurious small ORFs. This is particularly the case for bacteriocins which are a very varied group of antimicrobial peptides produced by bacteria and are usually encoded by small, poorly conserved ORFs [17]. BAGEL takes advantage of the fact that structural bacteriocin gene is surrounded by other biosynthetic genes responsible for its modification, export to the extracellular milieu and regulation of bacteriocin production. BAGEL allows ORF which makes it independent of GenBank annotations and thus prevents the omission of small non-conserved ORFs which are the most probable candidates for bacteriocin genes. BAGEL has been successfully used to identify putative bacteriocin genes in different species of LAB such as Lactococcus lactis IL1403, Lactobacillus plantarum, Streptococcus pneumoniae R6, Streptococcus pneumoniae TIGR4, Streptococcus pyogenes NC002737 and Streptococcus pyogenes NC004070. LAB’s among other gram positive bacteria are particularly prolific in bacteriocin production. Seven putative bacteriocin genes reported in Streptococcus pneumoniae TIGR4 have been identified by BAGEL [22]. LAB bacteriocins have been classified into Class I, Class II and Class III based on the observed common characteristics [23]. Class I (lantibiotics) and class II (small heat-stable non-lanthionine-containing peptides) bacteriocins are the most abundant and thoroughly studied bacteriocins [24, 25]. Analysis carried out on putative bacteriocin by BAGEL3 indicates that Class II was the most predominant type, present in 16 bacterial genome followed by Class III which was present in 6 genomes whereas Class I bacteriocin was not found in any of the analyzed genomes. Along with the putative structural bacteriocin gene, BAGEL3 also identified other biosynthetic genes present in the same operon. It was observed that Class II operon was more detailed in terms of the number of genes found in the vicinity of the bacteriocin gene as compared to the Class III operon. Representative operons from class II and III have been shown in Fig. 1. The data also indicates that certain biosynthetic genes could not be identified in the class II and class III operons (Fig. 1). It implies that either the functionally active bacteriocin is not synthesized by the respective bacteria or the biosynthetic genes located on plasmid could support the transport of bacteriocin. The research data supports that the immunity genes and the other genes required for the bacteriocin production could be supported by the gene sequence present on plasmid. Research conducted by Birri et.al on bacteriocin avicinA of subclass IIa, produced by Enterococcus avium, showed that both the structure gene and
immunity gene are present on the same operon on the chromosome, whereas the structural gene for the closely related bacteriocin Mundticin KS produced by E. mundtii NFRI 7393 strain are located on the plasmid [26]. CLASS III Operon1
Operon 2
Operon 3
CLASS II Operon 4
Operon 5
Operon 6
Key:
Fig.1: Gene clusters of different LAB species belonging to class II and class III bacteriocins have been shown. Operon 1: Lactobacillus crispatus ST1; Operon 2: Streptococcus lutetiensis 033; Operon 3: Lactobacillus kefiranofaciens ZW3; Operon 4: Lactobacillus casei BD II; Operon 5: Lactobacillus ruminis ATCC 27782; Operon 6: Streptococcus oralis Uo5. 3.2 Phylogenetic analysis The phylogenetic tree constructed using gyrB, rpoB and 16S rRNA gene sequences of 34 LAB species is shown in Fig. 2, supplementary Fig.1 and supplementary Fig.2, respectively. Phylogenetic tree constructed from gyrB gene, encoding the subunit B protein of DNA gyrase, sequences showed that the taxa which did not show any putative bacteriocin operon as predicted by BAGEL3 were found to be distributed across various clads. The phylogenetic analysis done for gyrB and rpoB, which encodes the β subunit of RNA polymerase, was more discriminative and gave a higher resolution as compared to the 16S rRNA gene sequences analysis. The phylogenetic tree based on 16S rRNA did not clearly differentiate the Streptococcus oligofermentans AS1.3089 and Lactobacillus casei BDII whereas the tree created using gyrB and rpoB gene sequences created a separate cluster for Streptococcus sp. and Lactobacillus sp. The housekeeping genes gyrB and rpoB have been shown to be more accurate phylogenetic markers for Streptococcus genus to trace the evolutionary relationship between various species [27].
Fig.2: Maximum Likelihood tree based on gyrB gene from thirty four LAB genomes constructed in MEGA6. Phylogenetic tree also represents the 14 strains which are marked by arrows indicates the absence of bacteriocin clusters.
3. 3. Physiochemical analysis of putative bacteriocin
A number of physicochemical properties are generally studied to provide information about the composition and structure of bacteriocins. Bacteriocins are extremely heterogeneous in nature. They differ widely in many characteristics including molecular weight, pI, presence of particular groups of amino acids, numbers of amino acid residues and net positive charge. In our study the molecular weight of all class III bacteriocins was in between 32000 Da – 38000 Da except for one found in L.Karinofaciens, whose molecular weight was 16474.9 Da. The molecular weight for the Class II bacteriocins was found to be between 2000 Da and 12000 Da except for L. crispatus bacteriocin whose weight was 23850 Da [Supplementary table 2]. Important characteristics of bacteriocins are related to their net charge.
Many of the low-
molecular-weight bacteriocins are cationic at pH 7.0 which is applicable for both the lantibiotic and non-lanthionine-containing bacteriocins. It has been observed that the LAB bacteriocins have greater antibacterial activity at lower pH values (< 5) as their adsorption to the cell surface of bacteria is pH dependent [28]. Physiochemical analysis of the predicted bacteriocins showed that 55% of them were positively charged. The
putative
bacteriocins
from
Lactobacillus_casei_BD_II,
Streptococcus_pseudopneumoniae, S. pneumonia ATCC 700669, S._dysgalactiae ATCC 12394 have shown high value of aliphatic index which points towards the relative stability at the diverse range of temperature. The instability index provides an estimate of the stability of the tested protein. A protein showing instability index <40 is predicted to be stable, whereas a value >40 predicts the protein to be unstable. In this study, only five of the 52 bacteriocins identified from LAB species were found to be unstable [Supplementary table 2 (appendix 2)]. The instability of a protein is determined based on the occurrence of certain dipeptides and relates to the in vivo half life of a protein [29]. Grand average of hydropathicity index indicates the solubility of a protein, a positive GRAVY index is shown by hydrophobic compound while a negative GRAVY index indicates the hydrophilicity of a given compound. The GRAVY index showed that there were almost equal number of hydrophobic (45 %) and hydrophilic (55%) bacteriocins. The low GRAVY of S. oralis Uo5 (AOI_1;orf011); S. parasanguinis FW213 (AOI_1;orf015) and S. intermedius JTH08 (AOI_1;orf018) indicates that these bacteriocins can possibly have better interaction with water.
Cysteine content is another important feature of bacteriocins. Some non lanthionine containing bacteriocins are called thiobiotics as they contain only a single cysteine residue which is present in the reduced thiol form for bactericidal [30], and some have no cysteine residue at all e g. lactococcins A, M, G. Class IIa bacteriocins have at least two cysteine residues with disulfide bridge and are called cystibiotics [31]. In the study, 36 bacteriocins with at least two cysteine residues, four with exactly one cysteine residue and twelve with no cysteine residue were identified [Supplementary table 2]. Hence most of the putative bacteriocins having at least two cysteine residues can form disulphide bonds and play an important role in their stabilization [32].
3.4. Secondary structure prediction Bacteriocin structure and function based analysis helps in knowing the molecular specificity of a given bacteriocin. The secondary structure study of protein allows us to recognize the folds and to classify the structural motifs present in them. Secondary structure predictions, helix, beta bridge and random coils were studied in putative bacteriocins using SOPMA tool. Our study showed that class II bacteriocins contain α helix spanned through 14%- 79% residues while class III bacteriocins displayed α helix spanning through 12%- 30% residues [Supplymentary table 3 (appendix 3)].The data has shown that the existence of alpha helices in the sequence is very important in determination of its function. The peptide sequence and secondary structure analysis of lactococcin Q and lactococcin G have reported the existence of α-helical structure at the same position and their anti-bacterial mode of action was also found to be similar. Moreover, the secondary structure analyses of both the bacteriocins showed the existence of four identical alpha helices, having amphiphillic characteristics which are thought to play a major role in bactericidal mode of action [33]. The secondary structure prediction can assist in knowing the specificity and mode of action of a particular bacteriocin and to find the similarity among two different bacteriocins. 4. CONCLUSION Bacteriocins have continued to gain importance due to their potentially large number of applications in healthcare and food industry. The classical way of identifying bacteriocins involve determining the biological activity of the putative strain for effective inhibition of other bacteria. Although advancements in research methodologies has led to increase in discovery of the novel bacteriocins, but finding a potential strain still remains a tedious and time consuming process. On the other hand, computational approaches can help in identifying
the bacteriocin producing strains and checking their functionality through knowledge based database approach. This approach helps in identifying the prospective bacteriocins; further reducing the complexity for their validation. LABs hold huge potential for acting as an important source of novel bacteriocins with interesting therapeutic properties. This study was undertaken to identify the potential bacteriocins secretors in LAB hitherto not reported for its production using bioinformatics tools. Fifty two putative bacteriocins were identified across 20 LAB species. The phylogenetic tree of rpoB, gyrB(housekeeping) genes was constructed by using MEGA6 tool. These bacteriocins were mainly low molecular weight, heat stable and belonged to class II type. The putative structural as well as the biosynthetic genes were identified by BAGEL3. Certain biosynthetic genes were not identified in the class II and class III operons wherein the production of the bacteriocins might be supported by the corresponding gene on the plasmid. The information gained through this study would be useful in gaining further information on biosynthetic machinery of bacteriocins. This will not help in identifying prospective bacteriocins and improve the bacteriocin gene cluster annotation in lactic acid bacteria but will also facilitate wet lab techniques in discovering novel bacteriocins. Acknowledgments The authors thank the Department of Biotechnology, Jaypee Institute of Information Technology, Noida, UP, India, for providing the infrastructural facility to carry out the work.
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