- Open Access
“Phylogenetic and evolutionary analysis of functional divergence among Gamma glutamyl transpeptidase (GGT) subfamilies”
© Verma et al. 2015
Received: 30 April 2015
Accepted: 2 September 2015
Published: 14 September 2015
γ-glutamyltranspeptidase (GGT) is a bi-substrate enzyme conserved in all three domains of life. It catalyzes the cleavage and transfer of γ-glutamyl moiety of glutathione to either water (hydrolysis) or substrates like peptides (transpeptidation). GGTs exhibit great variability in their enzyme kinetics although the mechanism of catalysis is conserved. Recently, GGT has been shown to be a virulence factor in microbes like Helicobacter pylori and Bacillus anthracis. In mammalian cells also, GGT inhibition prior to chemotherapy has been shown to sensitize tumors to the therapy. Therefore, lately both bacterial and eukaryotic GGTs have emerged as potential drug targets, but the efforts directed towards finding suitable inhibitors have not yielded any significant results yet. We propose that delineating the residues responsible for the functional diversity associated with these proteins could help in design of species/clade specific inhibitors.
In the present study, we have carried out phylogenetic analysis on a set of 47 GGT-like proteins to address the functional diversity. These proteins segregate into various subfamilies, forming separate clades on the tree. Sequence conservation and motif prediction studies show that even though most of the highly conserved residues have been characterized biochemically in previous studies, a significant number of novel putative sites and motifs are discovered that vary in a clade specific manner. Many of the putative sites predicted during the functional divergence type I and type II analysis, lie close to the known catalytic residues and line the walls of the substrate binding cavity, reinforcing their role in modulating the substrate specificity, catalytic rates and stability of this protein.
The study offers interesting insights into the evolution of GGT-like proteins in pathogenic vs. non-pathogenic bacteria, archaea and eukaryotes. Our analysis delineates residues that are highly specific to each GGT subfamily. We propose that these sites not only explain the differences in stability and catalytic variability of various GGTs but can also aid in design of specific inhibitors against particular GGTs. Thus, apart from the commonly used in-silico inhibitor screening approaches, evolutionary analysis identifying the functional divergence hotspots in GGT proteins could augment the structure based drug design approaches.
This article was reviewed by Andrei Osterman, Christine Orengo, and Srikrishna Subramanian. For complete reports, see the Reviewers’ reports section
Gamma glutamyl transpeptidase (GGT; EC 126.96.36.199) is a bi-substrate enzyme which catalyzes the cleavage of γ-glutamyl linkage of glutathione (GSH) and the transfer of its γ-glutamyl group to acceptor substrates like water (hydrolysis reaction) or other amino acids or peptides (transpeptidation reaction) [1–3]. Evolutionarily, GGT is ubiquitous and conserved in all three domains of life, prokaryotes, eukaryotes and archaea . GGT plays a key role in glutathione metabolism and balances the levels of intracellular cysteine in cells, thus helping in maintenance of the redox equilibrium inside the cells [5, 6]. Other than glutathione metabolism, GGT is also a key enzyme for other metabolic processes like arachidonic acid metabolism, cyanoamino acid metabolism, gamma-glutamyl cycle, hypoglycin biosynthesis, and taurine and hypotaurine metabolism [7–12].
GGT proteins are member of N-terminal nucleophile hydrolase (NTN-hydrolase) superfamily of enzymes. These are heterodimeric proteins, composed of a large and a small subunit, which result from cleavage of a single protein chain through an auto-catalytic processing reaction after synthesis [13–15]. Members of the GGT protein family share a conserved “sandwich like” 3D structural domain which is made up of four layered αββα fold, except in case of CapD protein from B. anthracis, which has been reported to have six layered ααββαα like fold. The catalytic site of GGT appears to be formed of two consecutive pockets, the donor and the acceptor sites. The donor site, where the substrate donating γ-glutamyl group binds, has been more extensively characterized so far whereas much less is known about the residues participating in the acceptor site . Some members of the GGT protein family possess an additional flexible loop covering the substrate binding cleft, known as lid-loop region. This lid-loop has been proposed to influence the transpeptidation reaction of GGT proteins .
In all GGT proteins characterized so far, a conserved threonine (Thr) acts as nucleophile during it’s auto-processing into small and large subunits as well as during it’s catalysis reaction [1–3]. In the first step of catalysis, the hydroxyl group of Thr attacks the carbonyl group of the glutathione substrate (Additional file 1). The second step is the formation of a transition state. The third step involves the release of ‘Cys-Gly’ from glutathione substrate, leading to the formation of a γ-glutamyl-GGT intermediate complex (Additional file 1). This intermediate complex is stabilized through hydrogen bonds of the substrate with two conserved glycines of GGT, commonly known as “oxyanione hole residues”. The fourth and the final step of this mechanism involves the transfer of the γ-glutamyl moiety to water or short peptide.
A vast variety of work characterizing various GGT homologs from many species has been done in past because of its importance in clinical as well as biotechnological sectors. Clinically, GGT activity in human serum is a common diagnostic indicator of several diseases including liver cancer, alcoholic hepatitis, disrupted bile formation, pancreatic cancer and other hepatic or biliary tract-associated diseases. While GGT deficiency leads to diseases like glutathionemia and glutathionuria associated with mental retardation, its overexpression has been implicated in asthama, parkinson, arthritis and cardiovascular diseases in humans [18–22]. In mammalian cells, GGT inhibition prior to chemotherapy treatments has been shown to sensitize tumours to the therapy . Thus, there are instances where inhibiting GGT activity offers physiological benefits, thus necessitating the need to design inhibitors against GGTs. In microbes, GGT is known to be a virulence factor associated with anchoring the capsule to the bacterial cell wall as well as participating in capsule remodelling in Bacillus anthracis. GGT has been also associated with the colonization of the gastric mucosa by Helicobacter pylori, the pathogenic bacteria known for ulcer and gastric cancer [24–26]. The CD4-positive T cells of the host are crucial for bacterial elimination from the host, which are known to be inhibited by Helicobacter pylori GGT, thus promoting the survival of the pathogen . Inhibitors targeting these microbial GGTs may thus complement or augment the effect of currently available antibiotics. Given the above observations, there have been continuous efforts to design inhibitors against both the human as well as the microbial GGTs. The most obvious inhibitors for this enzyme are the donor substrate (glutamate) analogs but these appear to be toxic for human use, leaving the scope open for design of novel GGT inhibitors. Recently, some progress has been reported in this area, with the design of a novel class of species-specific inhibitor (OU749) against GGT, which seems to inhibit human GGT specifically but have no effect on GGTs from closely related species like rat and mice . However, the details of its mode of binding and inhibition are not known in detail yet. Other than its medical significance, GGT also happens to be a biotechnologically useful enzyme [20, 29–34]. The three dimensional structures of GGTs from varied organisms, including human GGT1, E. coli, H. pylori, B. subtilis, B. licheniformis, B. halodurans and T. acidophilum, with and without substrate analogues, small ions or inhibitors bound to the protein, are now available (Additional file 2) [17, 29–31, 35–37]. Crystal structures are also available for GGT-like protein “CapD” from B. anthracis . Comparison of these structures is expected to assist the design of inhibitors against specific GGTs and also help in delineating features responsible for substrate specificity and protein stability, thus providing leads to engineering GGT proteins with desirable biotechnological properties.
In the present study we undertook a comprehensive comparative analysis of 47 GGT-like proteins from all three domains of life. We have looked for conserved motifs and compared the distribution of residues known to be functionally important, which may thereby affect the conservation or diversification of functions in GGT-family proteins. The phylogenetic tree clusters the GGT protein sequences into various clades. Well established statistical methods were used to determine whether GGT genes of specific clades are under positive selection with respect to the background tree. Further, type I and type II divergence analyses were carried out for the GGT clades showing positive selection for the identification of specific divergent sites which might be contributing to the functional divergence. Here, we discuss the potential role of these divergent sites in protein structure and function. Although, experimental characterization may be required for validating the role of these sites, current study shows promise in being able to identify sites that may be responsible for differences in substrate specificities or imparting other valuable functional or physiochemical differences observed in GGT proteins.
Results and discussion
Phylogenetic analysis of the GGT proteins
Such a distribution of GGT proteins on the phylogenetic tree offers several interesting insights into the evolutionary history of GGT proteins. The “Bact1” clade, exclusively composed of bacterial organisms known to be pathogenic for human, appears to be more closely related to the eukaryotic clade “Euk” than to the “Bact2” clade on the same branch (B1). This implies that the host and pathogenic organisms share more similarities in their GGT protein sequences than these pathogenic bacteria do with other bacterial GGTs. Also, in case of some organisms like B. licheniformis and B. subtilis which code for two GGT-like proteins, the two proteins are distantly placed on two separate branches B1 and B2, unlike what is expected from paralogs (Fig. 1). While one GGT protein from each organism is part of the “Bact2” clade on branch B1, and the other GGT-like protein clusters with the extremophiles “Bact4ext” clade on branch B2. This suggests that either the two GGT proteins in these organisms have diverged quite a long time back and have acquired vastly different functions or one of the proteins is a canonical GGT, while the other has been introduced into the ancestors of B. subtilis/B. licheniformis through horizontal gene transfer from some other archaeal or extremophilic organisms. Since most archaeal organisms naturally inhabit in extreme environmental conditions like low pH or/and high temperature conditions, it is not surprising that the “Arch” clade holding GGT sequences from such archaeal organisms are most closely related to the “Bact4ext” clade.
Interestingly, the phylogenetic tree also resolved the GGT proteins into two distinct groups: the first group comprises of GGT sequences having the lid-loop fragment (ggtLid+) and the other group includes GGT sequences missing the lid-loop fragment (ggtLid-) (Fig. 1). This lid-loop fragment is a small stretch of about 12 amino acids in some GGT proteins, which makes a lid like structure covering the opening of the substrate binding cavity of respective proteins. This lid-loop is present in GGTs from E. coli (Pro438-Gly449), H. pylori (Pro427-Gly438) and “Euk” (human; Ser428-Ser438) but absent from GGTs comprising the “Bact4ext” (B. haloduran), “Arch” (T. acidophilum, S. solfataricus), “Bact3” (CapD of B. anthracis) and “Bact2” (B. subtilis) clusters (Additional file 3). The presence of this lid has earlier been suggested to enhance the transpeptidase activity in GGT proteins .
Conserved motifs and functionally important residues in GGT proteins
Other than the three motifs picked up by MEME, we were able to identify another small stretch of amino acid (GXXGG), which shows high conservation in alignments (Additional file 3). The last two ‘glycine’ residues of this motif are part of the oxyanion hole and known to play significant role in stabilization of substrate intermediate complex formed during GGT enzyme reaction [17, 29–31]. It is evident from the multiple sequence alignment that the three glycine residues of this small motif are conserved in all GGT proteins but “XX” part of the motif varies in a clade specific manner. In prokaryotic GGTs (“Bact1”, “Bact2” and “Bact3”), XX is represented by SP (serine-proline), in “Bact4ext” clade this XX motif is occupied by VM (valine-methionine), whereas in archaeal GGTs (“Arch” clade) the same was replaced by CA (cysteine-alanine) and the corresponding position is occupied by AS (Alanine-Serine) in “Euk” clade. In the second GGT-like protein from both B. subtilis and B. licheniformis, which clusters between “Arch” and “Bact4ext” clades, this XX position is occupied by TQ (Threonine-Glutamine) (Additional file 3).
Overall, results of motif and sequence conservation analysis show that the residues in the neighborhood of amino acids already known to affect GGT function vary in a clade specific manner. Conservation of such amino acids within the clade but their variation in clade specific manner suggests that these residues play a significant role in imparting clade specific differences in the enzyme activity and stability of GGTs. Further experimental validation of these sites in a systematic manner should be able to delineate their role in GGT function. The identification of novel motif, M1, suggests that the large subunit of GGT may have a role in structural integrity and function of GGT proteins, which has not been addressed in any previous studies so far.
Detection of positive selection in GGT-family
Results of LRT for positive selection analysis in GGT-family members of selected clades
Parameter estimated (frequency ‘f’, omega ‘ω’)
2∆LnL for LRT Pairs M2/M2a
(ω0 = ω1)
(ω0 = ω1)
(ω0 = ω1)
(ω0 = ω1)
Functional divergence in GGT-family
Divergence analysis for GGT subfamilies: Divergence type I and type II analyses of two clusters comparisons are shown here in details
Bacteria 1/Bacteria 4-Extremophile
θ I = 0.49 ± 0.08
θ II =0.31 ± 0.09
θ I = 0.58 ± 0.12
θ II = 0.31 ± 0.08
P = 0.75
R = 9.50
P = 0.70
R = 10.35
a) Bacteria 1 (B1) (E. coli) vs. Bacteria 4-Extremophile (B2) (B. halodurans)
b) Bacteria 1 (E. coli) vs. eukaryotic (human) GGTs
c) Functional divergence in other GGT subfamilies
Similar FDI and FDII analysis was also carried out for four other pairs of clades, e.g. “Bact1” vs. “Arch”, “Bact1” vs. “Bact3”, “Bact4ext” vs. “Arch” and “Bact3” vs. “Arch”. For all the above mentioned subsets, the values of θI and θII were greater than 0, thus suggesting that strong functional divergence signals could be picked up between these clusters (Additional file 4). The sites predicted from such analysis of these subclusters of GGT-family are shown in Additional file 4. Organisms in bacteria 1 clade are mostly pathogenic and their GGT proteins contain the lid-loop structure which is missing from the GGTs of the Bacteria 3 clade. The detailed analysis of these sites would help in delineating the amino acids that are responsible for differences in biochemical features and structural stability in GGTs of these clades. Also, comparison of the archaeal sequences with various bacterial clades could possibly highlight the amino acid residues which may impart extra stability to such enzymes, as GGTs characterized till date from various archaeal organisms show high thermostability.
Our phylogenetic analysis of GGT proteins from different organisms divides the GGTs into various clades and offers several interesting insights into the evolution and relatedness of these GGTs. Sequence conservation studies and motif detection show that most of the highly conserved residues have been characterized biochemically in various previous studies and are already known to be functionally important for the enzyme activity of this protein. However, the present study focuses on the residues that are highly specific to each GGT subfamily and underlines their importance in imparting unique functional properties to the GGT proteins of each clade.
The present study highlights the clade specific variation in the GXXGG motif, where SP (XX) of bacterial GGTs is substituted by VM, CA, AS in extremophilic bacteria, archaea, and eukaryotes respectively, which could explain the differences in rates of enzyme reaction in GGTs of these clades as this motif is known to be involved in GGT-substrate complex intermediate formation and the rate of final product release [17, 30]. We have been able to identify another motif on the large subunit of the GGT protein, which has never been reported before and none of the amino acids that are part of this motif have been biochemically characterized earlier. Detailed studies targeting the role of these amino acids could finally reveal the possible role of large subunit in the overall function and stability of the GGT protein. The fact that large subunit has an important role to play in GGT function and structure is also underlined by the large number of type 2 divergence sites picked up large subunit of GGT protein while comparing the eukaryotic and bacteria 1 GGT clades.
Our analysis also shows that many sites predicted to be contributing to type 2 functional divergence are quite often found lining the substrate binding cavity and are close to the highly conserved known functional residues. This implies that they may be affecting the biochemical environment of the binding cavity and influencing the catalytic residues, thereby contributing to the functional differences among GGT-like proteins of various clades. Similarly, the putative divergent sites identified on the backbone of these proteins may be contributing to differences in structural stability. We propose that studying functional divergence between various clades could possibly highlight residues that are part of the GGT acceptor site, the complete identification of which has eluded researchers so far. Further biochemical characterization of the putative divergent sites identified by our study could help in delineating the role of these amino acids in imparting thermal/pH stability and those responsible for differences in enzyme kinetics and substrate specificities. Because many of the putative divergent sites are found to line the substrate binding cavity of the GGT protein, we expect that this knowledge could also be useful in design of inhibitors that are clade/species specific. Therefore, identifying functional divergence hotspots in the proteins could possibly augment the structure based drug-design approaches in general.
GGT protein sequences and phylogenetic tree analysis
The complete cDNA and protein sequences of GGT-family were retrieved from NCBI database. Aminoacid sequences of GGTs that have been biochemically characterized earlier (E.coli GGT; AAA23869, H.pylori GGT; AAG34111, B.subtilis GGT; BAM52495, B.anthracis GGT; BAA03126, B.halodurans GGT; NP_241733, T.acidophilum GGT; CAC12123, and Human GGT; NP_038347, S.cerevisiae GGT; AAB67344) were used to find similar sequences using BLASTp. Since no GGT has been biochemically characterized in detail from plants and archaea, the GGT from model organisms Arabidopsis thaliana (AtGGT; CAA89206) and Sulfolobus solfataricus (SsGGT; NP_344523) were used as seed sequences respectively for plant and archaeal kingdoms, and the top five related sequences obtained by BLASTp were included in this comparative study. All sequences for which structural information is available have also been included. Thus, in the present study, GGT representative sequences from three domains: prokaryotes, eukaryotes and archaea are included. Two bacterial organisms (Bacillus subtilis and Bacillus licheniformis) have two GGT like genes, which have both been also included with the intention of comparison.
A PSI-BLAST search against the NR database, to find sequences distantly related to the seed sequences, results in more than 20,000 sequences. The Pfam database also has 21767 GGT-like proteins (as on 10/07/2015), but this dataset is highly redundant. When CD-HIT was used to reduce redundancy at a cutoff of 100 %, the number of sequences reduces from 21767 to 8117 . At 50 % identity cut-off, 760 sequences are left, many of which are partial and short, removing which leads to a dataset of 487 sequences. This subset achieved is however too diverse and the resulting tree does not give any stable separation of sequences into clades. Such a subset defeats the purpose of the proposed study since it collects only the most diverse members and fails to highlight any significant similarities between the members of any clade. The main objective of the sequence selection had to be inclusion of sequences that have already been characterized biochemically, and to select sequences close to these seed sequences so that we can expect them to be functionally similar to the seed sequences. This then helps to amplify the signal of residues conserved in one clade versus the other clade.
The final dataset comprises a total of 47 GGT protein sequences, which were aligned using different algorithms like T-coffee, ClustalW, and MUSCLE [44–46]. An alignment score of 96 (by T-coffee), showing that the quality of alignment is good, was achieved after some manual editing in Bioedit . Additionally, multiple sequence alignment was also performed by sequence alignment packages inbuilt in MEGA 5.10 . The conserved motifs among these protein sequences were identified by MEME server using default parameters . Based on the manual observations and highest alignment score, the multiple sequence alignment done by MUSCLE was taken as the final alignment. The average length of GGT sequences included in this analysis was 565 amino acids.
The phylogenetic trees of these 47 GGT proteins sequences were constructed by maximum likelihood (ML) and neighbor joining (NJ) methods based on gamma corrected Jones-Taylor-Thornton (JTT) with boot strapping of 1000 steps. The resulting trees resolved the GGT proteins into two main branches, B1 and B2, with each branch being further divided into three distinct clades. All the six main clades achieved 99 % boot strap values in the ML phylogenetic tree. Internal nodes of these clades have boot-strap values > 55 % in the ML phylogenetic-tree suggesting that the overall quality and strength of the constructed tree is good. ProtTest was used to choose the best-fitting model to build the phylogenetic tree .
Detection of positive selection in gamma glutamyltranspeptidase subfamily clusters
The conservation of few amino acids in a clade wise manner indicates that the proteins in various clades may have adapted to different functions. Whether these clades are under selective pressure for functional divergence can be predicted by observing changes in the evolutionary rates and to test this hypothesis we used ‘Codeml’ program . In this method, a certain clade (prokaryotes, eukaryotes, extremophiles, and archaea) was selected as foreground set for which a positive selection model was applied while the null model was applied for the other clades of tree (considered as background). The data used for detection of positive selection in gamma-glutamyl transpeptidase family was generated by selecting a subset of 23 DNA sequences from the earlier data set of 47 sequences. The selected subset was based on the priority of higher diversifications roots observed in phylogenetic tree. Aligned DNA sequences were back-translated to MEGA 5.10 to achieve a codon alignment. Phylogenetic tree for this subset was constructed by Phyml . The codon alignment and phylogenetic tree were used as input data to run Codeml analysis implemented in PALM 4.6 program package . The Codeml program predict the measurement of the relative rate of nonsynonymous (dN) to synonymous substitution (dS), or ω = dN/dS, using Nei–Gojobori method . The branches likely undergoing positive selection were predicted by using branch models implemented in Codeml program of PALM package. In Codeml analysis, we assumed that the branches of our interest have different dN/dS ratio as compared to rest of the three branches. In this study we compared three models namely M0, can be called as one ratio model where all branches have equal rate (dN/dS = constant value), M2 (null model) was used to detect null hypothesis and M2a (alternate model) model used for detecting positive selection or acceleration in the post duplication branch forming the novel subfamilies of GGT and third model [53–56]. Both M2 and M2a were compared on the basis of their LRT values.
Divergence analysis to detect sites contributing to functional changes in GGT subfamilies
The sites contributing to the type 1 functional divergence (FDI) are the ones that are highly conserved in one cluster but are variable in the other cluster, thus reflecting changes in evolutionary rates between the two clusters at these sites. The type 2 functional divergence (FDII) analysis predicts sites which although conserved in both clusters but are quite distinct in their biochemical features, thereby accounting for functional shifts between the two clusters. DIVERGE 3.0 was used to predict the functional divergence between the different clades of the GGT-family . In this study we analyzed two types of functional divergence, known as type 1 and type 2 [58–62]. Multiple sequence alignment of GGT sequences are subjected to DIVERGE 3.0 and a rooted NJ tree was generated using Poisson correction distance measure. Cluster size of each subfamily was fixed to > 4 sequences. The effective numbers of functionally related divergent sites are defined as minimum number of sites, such that when they are completely removed, the coefficient of functional divergence (θ) for the rest of sites approaches to zero. The site specific posterior probability cut off values of type 1 and type 2 functional divergence sites were obtained by conventional methods. Here we ranked amino acid sites according to their posterior probability and the corresponding coefficient of functional divergence (θ) is calculated. Then the amino acid site exhibiting highest posterior probability was completely removed from MSA file using Bioedit and then the coefficient of functional divergence (θ*) is recalculated. The same steps are repeated until the condition of θ* approaching to zero is satisfied or θ* satisfy the condition of θ* < se* where se* is the standard error of the θ* used to control the long tail problem. When the θ* < se* condition is achieved, the calculation coefficient of functional divergence (θ*) step was terminated. The removed specific amino acids sites were counted as effective functional divergence sites between the two subfamilies of GGT family. In this study, the value of divergence coefficient (θ) achieved is significantly larger than zero, indicating good functional divergence signal between the clusters. Further, we analysed the divergence sites by positioning these sites on the 3D structures of GGT proteins of E. coli (2DBX) and human (4GDX). Pymol molecular viewer was used for 3D visualization of proteins .
Reviewer 1, first report (Dr Andrei Osterman, Burnham Institute, United States of America)
The article provides a rather detailed analysis of evolutionary divergence and conservation of certain structure-functional features within a very interesting and uniquely ubiquitous gamma glutamyl transpeptidase enzyme family. Despite the depth of the analysis, eloquent use of available tools and well-organized presentation of the main findings, the actual impact of the manuscript appears questionable. Even the selection of 47 representative species (given thousands available genomes) is not clearly justified, and, to make things worse, may introduce some biases at the level of conclusions. E.g., the most striking example of a potential bias is the statement (page 5) that “The“Bact1” clade, exclusively composed of bacterial organisms known to be pathogenic for human…” A closer look at the list shows that the entire branch is simply composed of Proteobacteria, and all selected representatives just happen to be related to known pathogens (while myriads of nonpathogenic Proteobacteria were simply not included in the analysis!). Moreover, a number of potentially interesting and important aspects are just mentioned in passing while many other rather technical and mundane observations are described in detail. For example “valuable functional or physiochemical differences” or “biotechnological and biomedical applications” of GGT enzymes are mentioned several times, but no details and no clear functional associations with the observed structural variations (emerging from this analysis) are conveyed. As a result, the overall impression from this paper is that the authors laid out a nice framework for the actual predictive analysis (to be followed by experimental testing) but stopped short of any real breakthroughs in structure-functional or mechanistic understanding of the family or its particular representatives. Thus, while they duly emphasize the utility of comparative analysis for supporting the development of specific inhibitors, the paper hardly provides any tangible guidelines for such development (beyond pointing to clade-specific variations in the vicinity of universally conserved residues). To make it simple, the current version of a paper comes thru as an introduction or a setup for a story, but THE STORY itself remains untold. This overall impression holds despite a few interesting observations on evolution (e.g. on large similarity between Bact1 and Euk branches as compared to many other Bacteria) or patterns of sequence conservation/ variation as they do not seem to lead to a new level of understanding. Moreover, all methodology used in the paper is rather established, and it is unlikely that yet another illustration of its utility on the example of a particular family (no matter how important) would be of sufficiently broad interest.
Quality of written English: Acceptable.
Author’s response: We thank the reviewer for his appreciation of the use of tools and in-depth analysis.
As far as the sequence selection is concerned, it seems that we have not been able to clearly communicate our methodology and intent of sequence selection since all the three reviewers have similar queries. Please let us clarify again that we have indeed grappled with the issue of including “diversity” vs. “selecting closely related sequences” for quite some time during the initial part of the studies. This is reflected in the mistake in the “Methods section” where we mention that sequences were selected through PSI-BLAST. Indeed, we had tried PSI-BLAST to see what variety of sequences we would get from the databases and as mentioned by another reviewer, we do get more than 20,000 sequences. The Pfam database also has 21767 GGT-like proteins (as on 10/07/2015) but this dataset is highly redundant. When we try to narrow them down to a smaller set through CD-hit like approaches, a cutoff of 100 % reduces the numbers from 21767 to 8117 sequences. At 50 % identity cut-off, 760 sequences are left, many of which are partial and short, removing which leads to a dataset of 487 sequences. This subset achieved is however too diverse and the resulting tree does not give any stable separation of sequences into clades. Such a subset defeats the purpose of the study since it collects only the most diverse members and fails to highlight any significant similarities between the members of any clade. This kind of alignment gives us information about residues that are conserved across all GGTs, like the catalytic center threonine and two glycines. This information is available for last many years and does not add anything now to our understanding of functional variability among different GGTs. The main objective of the sequence selection thus had to be inclusion of sequences that have already been characterized biochemically, and to select sequences close to these seed sequences so that we can expect them to be functionally similar to the seed sequences. This then helps to amplify the signal of residues conserved in one clade versus the other clade. All sequences for which structural information is available have also been included. Since no GGT-like protein has been structurally or biochemically characterized yet from archaea and plants, so in these cases we took the GGT protein from the model organism of each domain (A. thaliana (plant) and S. solfataricus (Archaea)) as the seed sequence. The closest homologs of these GGT sequences were used to generate representation from the three domains of life. We have now edited the Methods section to remove the comment about PSI-BLAST usage and we hope that this clarifies our methodology of selection of GGT sequences.
To address reviewer’s comment about “entire branch is simply composed of Proteobacteria, and all selected representatives just happen to be related to known pathogens (while myriads of nonpathogenic Proteobacteria were simply not included in the analysis!)”, we would like to draw attention to the fact that at least two members of this clade; “Eubacteriaceae bacterium” and “Peptoniphilus indolicus”, belong to Firmicutes division, so the “Bact 1” clade is not simply “Proteobacteria”. Also on reviewer’s suggestion, we chose sequences from five nonpathogenic proteobacteria and recreated the tree by using Maximum likelihood (ML) method of MEGA. In the new tree, the sequences from these “nonpathogenic proteobacteria” form a separate clade on the tree close to Bacteria 2 clade but far from the “Bact 1” clade whereas all other clades of the tree remain unchanged (Additional file 5 : Figure S3). This concurs with our earlier observation.
We can understand the reviewer’s point of view when he mentions that the “actual story remains untold”. However, the present analysis was only meant to investigate if the approach of comparing GGT proteins (which exhibit functional variability) through “functional divergence analysis” could provide us any leads into what residues would be responsible for this diversity of enzyme activity. In the study, we have enough circumstantial evidence to suggest that this approach may indeed serve its purpose. This has been corroborated by reviewer himself when he suggests that the paper does “point to clade-specific variations in the vicinity of universally conserved residues”. In our opinion, “the actual predictive analysis (to be followed by experimental testing)”, is too detailed and vast for it to be included in this one paper. It will require analysis of the possible influence of every single putative residue predicted through functional divergence between any two given clades with respect to the hundreds of mutational studies reported on the GGT proteins of these clades. It will also entail exploring the possible role of these amino acids in designing novel specific inhibitors against members of the either clade. We are already halfway through such analysis for the Bact1 vs Euk clades and hope to report some interesting findings soon. We also plan to test these mutations in the wet-lab to understand the influence of each amino acid on substrate specificity ad enzyme kinetics. Until then, we had only wanted to share our excitement over the fact that such an approach could possibly pick up residues responsible for functional differences and also lead to development of more specific inhibitors.
Reviewer 1, second report (Dr Andrei Osterman, Burnham Institute, United States of America)
The article provides a detailed analysis of evolutionary divergence and conservation of structure-functional features within a very interesting and uniquely ubiquitous gamma glutamyl transpeptidase (GGT) enzyme family. For this analysis, authors have selected a rather limited subset of GGT family to enable the analysis of variations within relatively compact phylogenetic groups with functionally and/or structurally characterized representatives. As a result, they built a framework for elucidation of evolutionary relationships and potentially important structure-functional variations within this family, a subject of further computational and experimental analysis. Thus, mapping of clade-specific variations in the vicinity of universally conserved residues suggests the principal possibility and provides a support for development of specific inhibitors. Among interesting evolutionary observations is a larger sequence similarity of a particular Bacterial branch of GGT family with the Eukaryotic branch as compared to its similarity with other Bacterial branches.
Quality of written English: Acceptable
Author’s response: We thank the reviewer for his positive reception.
Reviewer 2, first report (Christine Orengo, University College London, United Kingdom)
This manuscript presents the results of a phylogenetic analysis of the glutamyl transpeptidases (GGTs), together with other studies to detect conserved sequence motifs which were interpreted in a structural context. These analyses allowed the authors to detect several motifs comprising some residues with known biochemical characterization. Studies of sites under positive selection are also performed. Some residues identified in the studies locate to the large subunit suggesting a functional role for this subunit which has not been previously appreciated and with important considerations for drug design. The work is comprehensive, the manuscript very clearly written and the figures are all useful and well presented. The authors present a good case for the importance of gamma glutamyl transpeptidases (GGTs) and the potential value of inhibitors specific to subfamilies of these enzymes. Their choice of methods such as Codeml for maximum likelihood analysis is good and the efforts that are made to interpret their results in the context of protein structure and mechanism of action are commendable. This is an important family of proteins and the studies provide important insights into active site differences that could have potential impacts on function.
I have one major criticism of this work and several minor criticisms.
Author’s response: We thank the reviewer for her appreciation of the methodology and analysis.
The major criticism of this work is that although the authors say in Methods that they use PSI-BLAST to identify GGTs, which in my hands finds many thousands of significant matches in GenBank to their seed sequences, all their analyses are restricted to just the 47 proteins that have structures in the PDB. I'm concerned that these 47 proteins are not representative of the known phylogenetic diversity of these enzymes and that their results may differ from the results of a broader analysis. I appreciate that this work largely focuses on a structure-based interpretation of the results but this does not really justify restricting the whole study, including phylogenetic analysis, to only those proteins with an experimentally determined structure when so many more sequences are available.
Author’s response: We are sorry about the inclusion of the comment about usage of PSI-BLAST in collecting sequences. It was indeed used in initial stages to gather all the distant variants of GGT-like proteins but it is definitely not required for collecting the subset that is used in the present study. It had been inadvertently left in the methods section, which is now edited to correct this. The selection of present subset of GGT-sequences is discussed in detail in response to the query by reviewer 1. We do hope that it addresses the query of reviewer 2 also.
Minor criticisms are:
1. On page six, for example, horizontal gene transfer is suggested as being important but surely this would tend to compromise the validity of phylogenetic analysis. What evidence is there for the presence of GGT genes on plasmids etc. and how common is this?
Author’s response: We only meant to say that the distribution of sequences on the tree shows some interesting evolutionary paths taken by GGT-like proteins. In the case of two GGT-like proteins found in B.licheniformis and B.subtilis, horizontal gene transfer has been suggested as one of the possibilities. Truly, we havn’t really investigated the evidence for or against this hypothesis as it was not the main focus of the manuscript, although we are definitely interested in examining the role of HGT in GGT evolution in future works. At this point, we think even though this hypothesis only lends a possibility and has not been proved, it does not compromise anything on the main aim of the paper, so this comment could possibly stay. If the reviewers disagree, we are willing to edit the manuscript to remove the comment on HGT.
2. On page 8 the authors show a higher ratio of non-polar to polar residues in extremophilic and archaeal GGTs which is interesting but I'm not sure why they don't calculate the statistical significance of this result. I would suggest that Fisher's exact test is appropriate for this small set of categorical data and in my hands their results are significant at P < 0.01.
Author’s response: On page 8 of the manuscript we have reported higher ratios of non polar to polar amino acids in the binding cavity of extremophilic and archaeal GGTs as compared to GGTs from eukaryotes and bacteria. In our opinion, although this observation gives us a hint that differences in the cavities of different GGTs may be related to hydrophobicity, it did not appear to do so in any statistically significant manner, thus no such test was reported earlier. After reviewer’s suggestion, we tried the Fisher’s test, where our results are significant at P < 0.01 in some cases but they are not significant at the same level in other cases. Therefore no point is being made in the manuscript for the same.
Furthermore, the authors refer to studies in the literature reporting differences in substrate specificities and binding affinities but make no comment on the different substrates involved and whether changes in the affinities correlate with the changes in the nature of the binding site. It would be helpful if they could comment on this.
Author’s response: The vast literature on biochemical characterization of different GGTs generally focuses on affinity of different substrates (like glutamate, glutathione and inhibitors like acivicin and azaserine) for the given enzyme. However, to the best of our knowledge, there is no report so far correlating these differences among various GGTs, to it’s cavity structure and environment, although an excellent review was recently put across in form of a book chapter by the veterans of GGT field: “Gamma-Glutamyl transpeptidase: Structure and function” of “Springer Briefs in Biochemistry and Molecular Biology” (DOI: 10.1007/978-3-0348-0682-4_1 ) which is included as reference no. 34 in the manuscript. This chapter enumerates in detail the variety of differences seen in the enzymatic activity of different GGTs and has served as the starting point for most of our work on GGT, where we are attempting to correlate the structural and functional differences seen among various GGTs. Correlating the enormous number of mutations reported earlier with the structure of binding cavity has not been quick and easy, especially because GTT is a bi-substrate enzyme (donor substrate and acceptor substrate) plus the fact that the acceptor site is not well defined till now. We are in the process of comparing the structures of binding cavities of various GGT proteins reported so far and hope to be able to report some correlations soon, but have refrained from reporting any makeshift observations in this manuscript.
3. On page 13 in Methods the authors list several sequence alignment programs that are tried and also admit to "some manual editing in Bioedit". Since their analysis is restricted to enzymes with solved structures then a sequence alignment based on a structural alignment would really be the gold standard and, indeed, the sequences are quite diverse so it is surprising that they have not tried to do this.
Author’s response: Yes, we sure could have tried that. We now tried “TCoffee Expresso” for all forty seven GGT proteins sequences using information of the structures available in PDB (Additional file 5 : Figure S4). The results of this alignment when compared with the sequence alignment used in the manuscript earlier show no major difference, specifically none with respect to the sites discussed in the paper. Probably the sequences are similar enough to generate a respectable alignment even though the proteins exhibit functional variability. This again suggests to us that small differences in these sequences may be responsible for this variability.
4. In the caption for Table 2 the authors state two levels of significance (P < 0.05 and P < 0.01). I'm not sure why they don't just state significance at P < 0.01.
Author’s response: Yes, we totally agree that reporting significance at P < 0.01 should have been enough. It has been an oversight, where the output of the program (which reports at two level of significances P < 0.05 and P < 0.01) has been included ditto into the manuscript. The corrections have been made and incorporated in manuscript.
5. 22 of the 47 NCBI gi codes given in supp1.doc are obsolete.
Author’s response: The NCBI gi numbers of all 47 GGT sequences have been carefully checked and replaced with the new gi numbers from the NCBI database.
6. Only four Figures are referred to in the text and there are only captions for four but there are eight Figures in the version of the manuscript that I have and the extra figures look potentially interesting.
Author’s response: Actually the last two figures are sets of three figures each, Figs. 3 (a, b and c) and Figs. 4 (a, b and c) but it appears that during uploading of these figures, they were automatically segregated as figure number 3, 4, 5, 6, 7, and 8, even though they were marked as 3a, 3b, 3c, 4a, 4b and 4c. In the revised manuscript the changes have been edited again.
Quality of written English: Acceptable
Reviewer 2, second report (Christine Orengo, University College London, United Kingdom)
The authors have provided satisfactory responses to address all my concerns and I would be happy for the paper to be published now. However, I do think they should make the rationale for their selection of sequences for the analysis much more explicit in their methods section especially since all the reviewers were confused and concerned by this. There is still not much detail in the methods section. I think they could just add some of the text that they wrote in their response to reviewer 1 ie they should explain why they don’t use all the sequences available via PSIBLAST or Pfam because the set is then too diverse so it doesn’t give a stable enough tree and the alignments are problematic and make it difficult to identify the conserved residues in the different branches of the tree being analyzed. They should also specify the cut-off that they use with BLAST when identifying close homologues. I could not find this.
Quality of written English: Acceptable
Author’s response: We thank the reviewer for her positive reception. As per the reviewer’s suggestion, more details of sequence selection criteria are now incorporated in text (methodology section, first paragraph).
Reviewer 3, first report (Dr. Srikrishna Subramanian, Institute of Microbial Technology, India)
In the paper entitled, “Phylogenetic and evolutionary analysis of functional divergence among Gamma glutamyl transpeptidase (GGT) sub-families”, Verma et al, based on the analysis of 47 GGT sequences argue for the evolutionary divergence of this family into two major branches, with a total of four clades specific to eukaryotes, archaea, pathogenic and non-pathogenic bacteria. They further rationalize clade-specific differences in GGT based on sequence conservation, selection, and functional divergence. Apart from reiterating the importance of the catalytic and binding-site residues for function the study identifies clade-specific changes that may be involved in conferring functional divergence. In addition, they highlight the plausible importance of the large/heavy subunit of GTT in function. The following concerns, if addressed, could likely help improve the manuscript.
Author’s response: We thank the reviewer for his valuable suggestions to improve the manuscript.
1. The GGT family of the Pfam database (PF01019) contains more than 20,000 sequences. The authors however use only 47 GTT sequences for all their analysis. It is not clear from reading the manuscript as to how the authors arrived at this set of sequences and if it is an unbiased dataset that represents the diversity of the GTT family. A larger subset, selected by clustering all sequences of the GTT family (PF01019) could possibly provide more robust results.
Author’s response: We think part of this question has been answered before (as part of response to query by reviewer 1). We fully agree that the 47 sequences do not take into account the complete diversity of the GGT sequences found in all organism, but we would only like to reiterate that the objective of the present work is not just to explore complete diversity among GGT sequences but to correlate some of the observed sequence diversity to functional variability. This makes it necessary for us to use sequences which have been biochemically characterized earlier as seed sequences. We then find homologs that are close to these seed sequences for amplifying the signal of similarity. The homologs that are used to make this alignment have to be pretty close to the seed sequence because we have to be confident of them having similar biochemical characteristics as the seed sequence. Only then can we possibly correlate the similarities and differences among two clades to be responsible for functional differences. Adding sequences that are too different does increase diversity but dilutes the signal of amino acid conservation and since no structural or biochemical information is available for most of these GGT protein sequences, the correlation cannot be established.
2. It is not clear after reading the introduction as to which residues are involved in the enzymatic reaction and what the mechanism is? A schematic diagram of the enzymatic reaction together with a short description of the structure, catalytic mechanism and functionally-important residues from each subunit should be provided.
Author’s response: Following reviewer’s advice, a short paragraph describing the enzymatic mechanism of GGT has now been included in the “Background” as 3 rd para. In GGT enzymes, three conserved residues are directly involved in the reaction mechanism. The conserved N-terminal threonine (Thr) residue of the small subunit acts as nucleophile and it is involved in both the autoprocessing and the glutathione catalysis. The other two conserved glycine residues (Gly-Gly) are known as oxyanion hole residues, play a key role in stabilization of gamma-glutamyl-GGT enzyme intermediate complex. A schematic diagram (Additional file 1 ) of GGT enzyme mechanism has been incorporated in the background section of the manuscript. Also Table 1 in the manuscript enumerates and compares residues that have been shown to be part of the binding cavity and are known to interact with the substrate and influence the enzyme activity in GGTs from various organisms.
3. The sequence motif-conservation logos shown in the manuscript are misleading as they are based on the conservation-patterns of only 47 sequences and do not take into account the diversity of the GTT family. For example, in Motif1, E. coli Asp75 is shown to be absolutely conserved, but if we look at the HMM profile of GTT family in Pfam, this Asp is highly but not absolutely conserved.
Author’s response: In continuation of our response to Major Concern 1 by the same reviewer, we believe that small differences in the conservation of individual amino acids in 47 sequences versus 20000 sequences of Pfam should not affect the analysis in any significant way since the objective here is to compare two well separated clades which are known to exhibit functional variability.
4. The numbers reported in the Tables and Additional files need to be rounded off in a consistent manner. Currently the statistical-values for attributes in the results for selection and divergence analysis are shown from one to six decimal places? How is this meaningful?
Author’s response: In Tables (2, 3) and Additional file 3 , all values are original, which were achieved in statistical calculations of positive selection and functional divergence analysis using various softwares and were used as such. At the suggestion of the reviewer, these values in tables and additional files have now been rounded off to the second place of decimal to maintain consistency.
1. The manuscript is difficult to read and needs to be edited carefully. Many places have inappropriate use of CAPS in words. For e.g. ‘Codeml’ and ‘Codeml’ seem to be used interchangeably. Please look for similar errors.
Author’s response: In light of the reviewer’s comment, the manuscript has been carefully revised and all similar errors are now fixed.
2. Scientific names should be written as per standard convention. for e.g. please correct has E. Coli, H. Pylori, etc. in the 3rd paragraph of the Background section. There are several such errors that need to be fixed.
Author’s response: All similar errors have been corrected and incorporated in the manuscript.
3. The references for the programs used for data analysis are provided only in the Methods section. They need to be provided at the first instance they are mentioned. For example in the first line of the Functional divergence in GGT-family section, Codeml is mentioned without a citation and is cited only later in the manuscript.
Author’s response: This has been corrected and all programs are now cited with suitable references at the first instance.
4. Consistent use of either one letter or three letter codes of the amino acids could improve readability.
Author’s response: All changes have been incorporated and amino acids are now denoted by single letter code.
5. The use of the terms larger and smaller subunits is awkward at times. For example in the Results section "The larger subunit has largely been neglected…". Why not call this the large and small subunit or better still the heavy and light subunits as mentioned previously in literature.
Author’s response: All changes have been made and the terms “small” and “large” subunit are now used consistently throughout the manuscript.
6. Please provide PDB identifiers for the structurally characterized GGTs when those are referred. For example, in the background section when the authors refer to “the three dimensional structures of GGTs from varied organisms, including human GGT1, E. coli, H. pylori, B. subtilis, B. halodurans and T. acidophilum, with and without substrate analogues”.
Author’s response: All information regarding the structures of GGT deposited in PDB databases is now summarized in tabular form and incorporated as Additional file 2 of the supplementary material.
Author’s response: Actually the last two figures are sets of three figures each, Fig. 3 ( a , b and c ) and Fig. 4 ( a , b and c ) but it appears that during uploading of these figures, they were automatically segregated as figure number 3, 4, 5, 6, 7, and 8, even though they were marked as 3a, 3b, 3c, 4a, 4b and 4c. In the revised manuscript the changes have been edited again.
8. Further the structural figures are too cluttered to follow. I would suggest using a program like Ligplot to show these residues.
Author’s response: Following reviewer suggestion, we tried LIGPLOT but it seems to be unavailable at the moment. In our experience, LIGPLOT is especially helpful in highlighting residues interacting with the substrate, but in the current setting, we needed to represent the overall spread of the residues that have been predicted through DIVERGE program to be involved in functional divergence (ex Figs. 3b and 4b ). This figure is meant to convey the higher concentration of such putative sites near the binding cavity of GGT. This is again emphasized through Figs. 3c and 4c which marks only residues that are part of the binding site, and among these, the ones that are predicted to be involved in functional divergence are marked in red, suggesting that quite a few residues that are forming the cavity can be statistically predicted to be contributing to functional divergence.
Quality of written English: Needs some language corrections before being published.
Author’s response: The manuscript has been revised and corrections have been made.
Reviewer 3, second report (Dr. Srikrishna Subramanian, Institute of Microbial Technology, India)
I don't have any major comments. There is a typo in the spelling of transpeptidation in the last line of the Additional file 1.
Quality of written English: Acceptable
Author’s response: We thank the reviewer for his positive response. Suggested changes have been incorporated in Additional file 1 .
This work was supported by CSIR (Council for scientific and industrial research, India) project grant (38(1312)/11/EMR-II) to MG. VVV is a recipient of senior research fellowship from Bioinformatics (BIC) Division of ICMR (Indian Council of Medical Research) (BIC/11(11)/2013).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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