O-linked glycosylation is an important post-translational modification of mucin-type protein, changes to which are important biomarkers of cancer. For this study of the enzymes of O-glycosylation, we developed a shorthand notation for representing GalNAc-linked oligosaccharides, a method for their graphical interpretation, and a pattern-matching algorithm that generates networks of enzyme-catalysed reactions. Software for generating glycans from the enzyme activities is presented, and is also available online. The degree distributions of the resulting enzyme-reaction networks were found to be Poisson in nature. Simple graph-theoretic measures were used to characterise the resulting reaction networks. From a study of in-silico single-enzyme knockouts of each of 25 enzymes known to be involved in mucin O-glycan biosynthesis, six of them, β-1,4-galactosyltransferase (β4Gal-T4), four glycosyltransferases and one sulfotransferase, play the dominant role in determining O-glycan heterogeneity. In the absence of β4Gal-T4, all Lewis X, sialyl-Lewis X, Lewis Y and Sda/Cad glycoforms were eliminated, in contrast to knockouts of the N-acetylglucosaminyltransferases, which did not affect the relative abundances of O-glycans expressing these epitopes. A set of 244 experimentally determined mucin-type O-glycans obtained from the literature was used to validate the method, which was able to predict up to 98% of the most common structures obtained from human and engineered CHO cell glycoforms.
Glycosylation is a major post-translational modification of proteins, in which a carbohydrate moiety, called a glycan, is covalently attached to an amino acid of the polypeptide, to form a glycoprotein [1]. N-linked glycans are attached to an asparagine (N) residue appearing in the consensus sequence Asn-X-Ser/Thr, where X is not Pro, while O-linked glycans are attached to the hydroxyl group of a serine or threonine residue. A study of potential glycosylation sites indicated that three quarters of proteins may be glycosylated, with about 10% of these O-glycosylated [2]. Glycans are formed by the sequential addition of monosaccharides from nucleotide-sugar donors to the glycoprotein acceptor, a process that is catalysed by glycosyltransferase enzymes, which are located in the endoplasmic reticulum and Golgi apparatus. Mucins are a class of large glycoproteins that contain a large number of Ser/Thr in close proximity, which can be heavily O-glycosylated. The initial step of mucin-type glycosylation is the attachment of a GalNAc (N-acetyl-d-galactosamine) to an unoccupied Ser/Thr on the protein acceptor. Modification of mucin O-glycosylation is an important biomarker in cancer detection [3–8]. In the innate immune response, cell-cell recognition is dependent on the expression of a number of different carbohydrate epitopes on carrier proteins, which include both sulfated and non-sulfated versions of Lewis X (Lex), Lewis A (Lea), Lewis B (Leb) [9] and, more rarely, Lewis Y (Ley) antigens [10]. Of the several theoretical treatments of glycosylation which have now appeared, most have considered N-glycosylation rather than O-glycosylation [11]. The method of Kawano et al. [12] for predicting glycan structures from gene expression data was able to predict the appearance of a variety of glycosylated structures, including O-linked. The model by Gerken and co-workers focused on the initiation of O-glycosylation [13]. Liu et al. [14] described an object-oriented method of construction of networks of O-glycan biosynthesis that was used to predict levels of sialyl-Lewis X (SLex), an important antigenic determinant, and more recently a computational approach based on MATLAB has been used to predict pathways of N- and O-linked glycosylation [15,16]. In the present work, we have taken an alternative, bottom-up, approach to modelling the de novo biosynthesis of mucin O-glycans. In order to facilitate computational analysis, we introduce a formal language (see [17]) for identifying individual glycan structures, a method for representing glycans graphically, based on these identifiers, and describe a method for generating networks of reactions based on the activities of enzymes involved in mucin protein O-glycosylation. A mathematical model of N-linked glycosylation has been developed, [18] whose structure identifiers are based on Linear Code; Spahn et al. have developed a Markov-chain model based on this system. [19]. As it seeks to uncover the nature of the reaction networks of O-glycosylation, this work both validates and extends the approach used by these earlier studies. With a rapidly increasing number of studies employing nuclease-based genome-editing technologies, such as zinc-finger nuclease (ZFN) [20] and CRISPR/Cas9 [21], for biotechnological applications, it is important to consider the possible phenotypic effects that may result from knock-ins or knockouts of the glycosyltransferase genes, and the corresponding changes to the glycome. We anticipate that the methods we describe here will be of use in predicting such changes within the context of O-glycosylation networks.
We introduce a formal grammar [24], Γ = (ΣN, ΣT, P, S), where ΣN is a set of nonterminal symbols and ΣT is a set of terminal symbols. ΣN and ΣT are disjoint sets, meaning that they share no members in common. S defines a starting symbol and P is a set of production rules, each element of which maps a single non-terminal symbol to a string of one or more symbols drawn from ΣT∪ΣN, or to the null (empty) string, ϵ. Σ N = { Z, A, B, C, m, d, l } Σ T = { 2,3, 4,6, 8, a, b, f, s, K, L, N, S, T, V, Y, [, ] } P = Z → A T A → ϵ | B B V B → ϵ | [ C m l d ] C → ϵ | C m l d | C [ C m l d ] m → f | s | K | L | N | S | V | Y d → 2 | 3 | 4 | 6 | 8 l → ϵ | a | b S = Z The grammar generates a language L by the successive substitution of nonterminal symbols with the right-hand sides of production rules in P. The set ΣT ∪ ΣN is the alphabet of L, and strings of symbols generated by Γ are the words of the language. We define a structure identifier as a word of L that contains only symbols drawn from ΣT. The following sequence of strings serves as an example of a derivation within the grammar. For brevity, some steps are the result of several simultaneous applications of production rules. Z A T { Z → A T } B B VT { A → B B V } [ C m l d ][ C m l d ] VT { B → [ C m l d ] } [ C m l d ][ m l d m l d ] VT { C → C m l d, C → ϵ } [ m l d ][ m l d m l d ] VT { C → ϵ } [S l d ][ S l d L l d ] VT { m → S, m → L } [S6][ S 3 L 3 ] VT { d → 6, d → 3, l → ϵ } The final string in the list is a word in Γ denoting disialylated T antigen, commonly known as “diST”, a core-1 O-glycan. The linear string identifiers described in this work can be used to draw glycan structures in the manner of turtle graphics [26]. Reading the identifier from right to left, the drawing method acts according to the current symbol: if the symbol is an element of the set {f, K, L, N, S, V, Y, s}, it draws the symbol corresponding to the monosaccharide at the current drawing position; if the string character is a right bracket, ], the current position and orientation information are pushed onto a stack, and are popped from the stack on meeting a left bracket. A two-pass approach is employed, with the bond framework being drawn on the first pass, and the sugar symbols drawn on the second. A suite of Perl scripts was written for the generation of structure identifiers by enzyme simulation, for parsing, and rendering as Scalable Vector Graphics (SVG) image files. A library of functions was written as a Perl module, which enabled (i) the translation of structure identifiers to and from the IUPAC condensed-form one-line notation; (ii) identification of common epitopes, such as Lex, based on regular-expression patterns; (iii) parsing of O-glycan strings by an LL (1) parser based on a simplified version of Γ; (iv) rendering of string identifiers as SVG, in either UOXF or CFG styles.
Not all of the structures encoded by the formal grammar are feasible, in that structures such as [S3][L3]VT are syntactically correct, but chemically impossible, since it describes a sialic acid (S) and galactose (L) both 3-linked to the same N-acetylgalactosamine (V). In order to generate a set of biologically relevant O-glycans, therefore, a set of regular-expression based substitution rules was developed to mimic the actions of each of the enzymes shown in Table 2; throughout this work, numbers in bold face refer to the corresponding activities in this table. The rules were incorporated into a Perl script, which took a single O-glycan identifier as the initial substrate, and applied each of the substitutions in turn to output a set of products. The initial structure defaulted to the non-glycosylated site, ‘T’, but any valid glycan structure could be supplied by the user as a starting point. The process was applied iteratively, such that each new product formed was presented as a substrate to every enzyme upon the next iteration. Where an enzyme rule could match at more than one position, as in the case of diantennary O-glycans, the identifier was split, using the current regular expression, and then each part substituted according to the same rule, before reassembling the parts, with the new string being added to the pool of possible products. Branching level and extension by poly-N-acetyllactosamine repeating units could be controlled by placing an optional limit on the total number of GlcNAc residues incorporated. Restrictions could be placed on individual enzyme activities by conditionals employing Boolean logic. The program could also be limited to use a subset of the enzymes. Simulations terminated after a prescribed number of iterations, or after any iteration in which no new products had been generated. The output of the program for three iterations of the method is shown in Fig 2. A web-application front end to the enzyme simulator (see Methods) is available online at http: //www. boxer. tcd. ie/glycologue. The enzymes of Table 2 can be divided into five main classes of activity: initiation (2), core formation (5,6, 8,9), branching and extension (1,7, 10,12,19), sugar modification (20–22) and termination (3,4, 11,13–18,23–25). The terminal residue of an oligosaccharide is the monosaccharide appearing at its non-reducing end. In the current model, the two methods of termination were fucosylation or sialylation of a terminal galactose. Sulfation was the only type of non-glycosyltransferase modification that was considered. Oligosaccharide chains can be of type 1 (ending in Galβ1-3GlcNAc-) or type 2 (ending in Galβ1-4GlcNAc-). The enzyme rules were reversed, so that a single monosaccharide was removed at each step of the simulation. Any O-glycan structure supplied as an initial substrate to the reversed enzyme simulator was considered to be predictable, or deducible, within the system if its final step was the removal of the terminal GalNAc from the protein by the enzyme ppGalNAc-T, according to VT -- ppGalNAc-Ts --> T. If the simulation ended with no new products formed, and without reaching the non-glycosylated site, the glycan was considered non-predictable within the system. The reaction data provided by the method described earlier, and depicted in Fig 2, were used to generate network graphs in GraphViz (www. graphviz. org), with O-glycan identifiers as nodes and with edges representing enzyme-catalysed reactions, colour-coded according to the monosaccharide being transferred. The enzyme simulator also allowed enzymes to be knocked out in silico, either individually or in groups, with each knockout resulting in a different reaction network. A web application, O-Glycologue (see Methods) was developed in order to view the structures obtained for a particular set of knockouts, and compare them with the structures obtained for the “wild-type” network, defined as the network obtained with all 25 of the enzymes active. The method is illustrated with an example taken from a study on salivary MUC7 glycans [45], a triantennary core-2 structure with the structure identifier [S3L4[f3][s6]Y6][[S3L4[f3][s6]Y6][S3L4[f3][s6]Y3]L3]VT (Fig 4A). The reversed reaction network is shown in Fig 4B, which successfully removed all monosaccharides in 17 iterations using the nine enzyme activities 1,2, 5–7,11,16,19 and 20. The network of reactions produced when the enzyme simulator was run in the forward direction with only these enzymes active is shown in Fig 4C. With all 25 enzyme activities enabled, 18 iterations of the method generated 13,127,561 unique O-glycans, in 34,215,049 reactions. All structure identifiers generated by the enzyme simulations were shown to be valid according to the parser. Different epitopes could be determined from the terminal sequences of the identifier string, and were counted as percentages of the total number of glycans formed: Lewis A ([L3[f4]Y, 13. 2%), Lewis X ([L4[f3]Y, 25. 0%), sialyl-Lewis A ([S3L3[f4]Y, 4. 2%), sialyl-Lewis X ([S3L4[f3]Y, 8. 4%), Lewis B ([[f2]L3[f4]Y, 4. 3%), Lewis Y ([[f2]L4[f3]Y, 8. 2%), H antigen ([[f2]L3Y, 9. 4%), A ([V3[f2]L3[f4]Y, 1. 9%), B ([La3[f2]L, 17. 5%), Sda/Cad ([S3[Vb4]L, 12. 7%) and other (24. 7%). Depending on the degree of branching, several different epitopes could appear together on the same O-glycan. Overall, 227 different pattern combinations of recognised epitopes could be distinguished, such as Lewis A with the H antigen. As a consequence of the method used to produce the network, in which the products at iteration n + 1 are dependent only upon those arising from iteration n, the growth function can be approximated by a discrete logistic map, ν (n + 1) = bν (n), b > 1, with solution ν (n) = abn. Although the total population is therefore expected to grow exponentially, by setting a limit on the maximum number of GlcNAc residues incorporated in each O-glycan, it was possible to close the networks, so that eventually no further structures were added to the glycan pool (Fig 5B). Under the assumption of irreversibility of each reaction, the network can be viewed as a rooted, directed acyclic graph G = (V, E), where V and E are sets of nodes and edges, respectively, with each node representing a distinct O-glycan and edges representing enzyme-catalysed reactions in which O-glycans appear as substrates or products. The degree of a node is defined as the number of its immediate neighbours to which it is connected by an edge. For a directed graph, the number of incoming nodes is called the in-degree, and the number of outgoing nodes is defined as the out-degree. An important network property is the degree distribution, which is frequently expressed in terms of the probability, P (k), that a randomly selected node will be of degree k. Many real networks possess the property of hierachical clustering of nodes [46] with a degree distribution that displays a power-law tail, P (k) ∼k − λ. In contrast, our reaction network displayed a Poisson-like distribution that is characteristic of random networks [47]. After 14 iterations, the average degree of the network, 〈k〉, was calculated to be 4. 36, with the in-degree and out-degree averages each equal, at half of this value. A bilog plot of the degree-distribution of the network (node degree frequency vs degree) is non-linear, as shown in Fig 5C, indicating that the network is not self-similar [48], or scale-invariant. That the degree distribution of a reaction network arising from a fully deterministic system has the characteristics of a random network may be a natural outcome of the method that was used to generate the glycan structure libraries. Since this method is essentially combinatoric, in that every possible acceptor-product is discovered from every substrate, we conjecture that its degree distribution can be described by a binomial function. Newman et al. [49] have shown that networks with a binomial degree distribution become Poisson when the number of nodes is large. Quantitative measures of the connectedness of the reaction network are provided by the α, β and γ indices [50]. The β index is the ratio of the number of edges, e, to the number of nodes, v: β = e v (1) The definitions of the non-planar versions of the α and γ indices, which allow for edges to cross at non-nodal positions in the plane, are α = (e - v) v (v - 1) / 2 - (v - 1) (2) and γ = 2 e v (v - 1). (3) The α index represents the number of cycles in a graph to the maximum number of possible cycles, and will take a value between 0 and 1. The γ index is the ratio of the number of edges to the total number of edges in the fully connected network, v (v − 1). Local clustering coefficients were also computed, and averaged across the complete reaction network [51]. The clustering coefficient, Ci, provides a measure of the fractional degree to which nearest neighbours of node i are connected to each other. Let ki be the number of immediate neighbours of node i. Since there can be at most ki (ki − 1) edges between ki nodes, for a directed graph, the value of Ci is defined as C i = E i k i (k i - 1) (4) where Ei is the number of existing edges between the neighbours of node i. An average network clustering coefficient, 〈C〉, was defined over the whole reaction network. The values of β and 〈C〉, which were calculated at each iteration of the enzyme simulation, showed an increase overall, monotonically above the iteration 7, while the non-planar γ index decayed uniformly from unity (Fig 5D). The increase in β index approximated to linearity above iteration 8. We simulated the effects of knocking out individual enzymes, observing the changes incurred in the topology of this reaction network. O-Glycan heterogeneity was most strongly influenced by the activities of Gcnt2, C2/4Gn-T, β3Gn-T2/3/4/5/7, β3Gn-T6 and β4Gal-T4, as quantified by the changes in the indices in Fig 6A–6C. Changes to local clustering coefficients were also noticeable, although they were not as marked. In the absence of enzyme β3Gn-T2/3/4/5/7 (10), the network closed after 20 iterations, and in the absence of β4Gal-T4 (1), the network was closed after 14 iterations, since no further extension of antennae was possible in the absence of either of these activities. Changes to the α and γ indices were notable only for these two enzymes (Fig 6B). Changes to the distributions of common epitopes are given in Table 3. The occurrences of each epitope, expressed as a percentage of the total number of unique O-glycans, were obtained for 12-iteration networks with the enzyme knocked out as indicated, and from which the sulfotransferases (20–22) had been omitted. Excluded from the results are ppGalNAc-Ts and the knockouts of the sialyltransferases 17 and 18, which showed no alteration from “wild type” (wt). Since more than one epitope can be expressed on a single O-glycan, the numbers on each line in the table need not sum to 100. The β4Gal-T4 knockout was found to eliminate all glycans expressing Lex, SLex, Ley and Sda antigens, indicating that it is an essential component of their biosynthesis; an increase in the percentage of O-glycans bearing the B antigen was also observed. The greatest decrease in the total number of glycans formed was observed with this knockout (not shown). Single-enzyme knockouts of the N-acetylglucosaminyltransferases did not affect the distributions of these epitopes so markedly, as might be expected from their functions in core formation, elongation and branching, rather than termination. Knocking out the β-1,3-galactosyltransferase activity eliminated only O-glycans expressing the B antigen. The predictive power of the enzyme simulator was tested by comparing the in-silico generated O-glycans against fifteen published collections of such structures that had been identified experimentally: mucin O-glycans from human colon [52,53]; structures of MUC1 mucin glycoforms obtained from normal and cancerous breast epithelial cell lines [54]; poly-N-acetyllactosamine extended structures of leukosialin glycoprotein obtained from promyelocytic and myelogenous leukaemia cell lines [55]; leukosialin O-glycans expressed in T-lymphocytic leukemia [56] and erythroid, myeloid, and T-lymphoid cell lines [57]; O-glycans from salivary MUC7, a major component of mucin glycoprotein 2 (MG2) [45]; O-glycans of Tamm-Horsfall glycoprotein [58]; sulfated core-2 and core-4 oligosaccharides obtained from mucins associated with chronic bronchitis [59]; bovine serum fetuin, human serum IgA1 and secretory IgA, human neutrophil gelatinase B and glycophorin A O-glycans [60]; extended core-1 and core-2 O-glycans from Chinese hamster ovary (CHO) cells transfected with β3Gn-T3 [61]; MUC1 and MUC4 O-glycans from bovine and human milk [62], normal human serum [63] and a human gastric adenocarcinoma cell line (MKN45) [64]; mucin from normal descending colon [65]; recombinant mucins from engineered CHO cells [66]. In all, 244 unique O-glycan structures were collected from these studies and assigned structure identifiers. Multiple identifiers were assigned where a number of different configurations was possible. For example, the monosialylated forms of Galβ1-3 (Galβ1-4GlcNAcβ1-6) GalNAc-R [64] were represented by the separate identifiers [L4Y6][S3L3]VT and [S3L4Y6][L3]VT. Each member of the set of experimentally determined O-glycans was supplied to the reversed enzyme simulator as the starting substrate, and tested for predictability within the system. Overall, 87% of the unique O-glycan structures were predicted by the method, which was able to reproduce any of the extended branched core 1–4 structures, with sialyl-Lewis X, Lewis Y, Lewis A or -B terminals and their 3′- and 6-sulfated variants. Table 4 lists the O-glycans determined experimentally that appeared in more than one of the studies, and thus independently verified, in descending order of frequency. Shown are the structure identifier, the supporting literature and a check next to those structures that were predicted in silico. Of the 45 oligosaccharides most commonly occurring, 44 were predicted by the model, giving a coverage of 98%.
From analysis of the grammar, and the results of the enzyme simulations, we predict that a highly heterogeneous population of mucin O-glycans is likely to result if even a limited subset of the enzyme activities of Table 2 is expressed. In-silico enzyme knockouts have identified β4Gal-T4 as a key regulator of the complexity of O-glycosylation networks, in keeping with our earlier observations on the influence of this enzyme on N-linked glycosylation in engineered Chinese hamster ovary cells [67]. The number of iterations was chosen according to the type of in-silico experiment: trends in the changes to the indices were discernable by iteration 15, hence this value was chosen for the enzyme-knockout studies; 18 is the maximum number of iterations of the basic model that were possible within the available memory (32 GB), with all 25 enzymes active and no limitations placed on the number of GlcNAcs. Not all of the enzymes in the current model will be present in all species, or active at all times. The full network is therefore a chimeric construct, but one which could be tailored for specific cases as needed, by considering only the enzymes known to be expressed in a particular organism or tissue. The O-Glycologue web application, described in Methods, provides an easy way to experiment with the effects of knockouts or knock-ins of the enzymes of O-glycosylation. The transferase activities leading to cores 5 through 8 are as yet uncharacterized [1], but could be added in future to account for such structures as are occasionally found in colonic tissues. The O-glycan structure [L4Y3L4[f3]Y6][L3]VT was also not predicted by the current model (Table 4). Although its appearance could be the result of a wider acceptor specificity of β3Gn-T2/3/4/5/7 (10) that would allow this enzyme to act according to the pattern *[Lb4[fa3]Y*T, it could also be the result of fucosylation of an inner GlcNAc by one of the several known α1,3-fucosyltransferase variants, such as FUT4 [68]. The pattern corresponding to the substrate acceptor in such a case would be *Lb4Y*T. An additional α1,3-fucosylation pattern that was evident from this data set is the sequence *L4[f3]Y6*, evident in ten of the non-predicted glycans from two studies [60,62], and in the sole non-predicted structure of Table 4. It is likely that a fucosyltransferase activity exists that is yet to be characterized, and which acts on type-2 chains with a preference for the 6-linked GlcNAc of core-2 or core-4 O-glycans. In the future, these reactions, as well as those of other fucosyltranserases that are distinguished by different substrate specificities, could be incorporated into the simulator either as additional rules or as refinements of the existing rule (11). Some structures that were not predicted may also have been mischaracterised. For example, the non-predicted glycan structure described by Podolsky [52], to which we assigned the identifier [S6][[S6L3Y6][S6L3Y3]L4Y3]VT, is in the same paper identified as a type-2 structure, which could be predicted. Our validation study therefore provides a lower bound on the number of structures that can be predicted. Certain poly-6-sialylated structures, including [S6][S6L3Y3[S6]L4Y3]VT, were not predicted. It is possible that a sialyltransferase activity exists in colon that recognises galactose at a distance from the non-reducing end of an oligosaccharide; for instance, an alternative reaction of ST6GlcNAc-I (18) might be CMP-S + *Y3Lb4Y*T = CMP + *Y3[Sa6]Lb4Y*T. Our analysis of the monosaccharide content of O-glycans extracted from the CFG database revealed that the frequency of occurrence of Neu5Ac was between two and three times the total of the remaining monosaccharides of lesser occurrence: Glc, GlcA, Kdn, and Neu5Gc. Of these, Neu5Gc, or N-glycolylneuraminic acid, is of particular interest because it is immunogenic in humans as a result of the silencing of CMP-N-acetylneuraminate monooxygenase (EC 1. 14. 18. 2). This enzyme, which is active in other mammalian species, adds a single oxygen to CMP-N-acetylneuraminate to form CMP-N-glycolylneuraminate. Neu5Gc obtained in the diet can become incorporated into the cell surface glycome, especially that of cancerous tissue, making it a potential target for immunotherapy [69]. Sialic acids entering the cell via endocytic pathways become activated by the nuclear enzyme CMP-sialate synthase (EC 2. 7. 7. 43, N-acylneuraminate cytidylyltransferase) [70]. Together with the observation that CMP-Neu5Gc can readily substitute for the native donor in reactions catalysed by the sialyltransferases from other species [71], a reasonable assumption is that Neu5Gc is incorporated into human glycoforms by this means. Thus, while Neu5Ac may be the dominant component of the sialylated epitopes expressed in O-linked and N-linked glycoproteins, a portion of such glycans generated by the enzyme simulator could be considered as terminating in Neu5Gc. If the sialyltransferase activities of Table 2 were allowed to act with CMP-Kdn as donor, an additional six structures from the validation study could be predicted by the model, increasing coverage of the data set to 89%. The notation we have described provides a succinct way to encode structural information for both graphical representation and modelling. Other linear string representations of carbohydrates exist, such as LINUCS [72] and Linear Code [31], which are broader in scope than O-GalNAc glycosylation, and are supported by established glycoinformatic software tools, such as GlycoWorkbench [73]. An advantage of the modelling language described in this work is that it is able to encode the sialic acid Neu5Gc, which cannot be expressed in Linear Code. A more general, and widely supported carbohydrate encoding format is GlycoCT [32]. More recently, the Web3 Unique Representation of Carbohydrate Structures (WURCS) formalism was introduced with an even wider scope [74]. The GlycoForm web application, described in the methods, is able to output any O-glycan structure identifier as both IUPAC, Linear Code and GlycoCT condensed formats, making it interoperable with other software and databases. For the purposes of modelling and display, however, the advantages of the structure identifiers presented in this work are twofold; first, adherence to a strictly one-letter system for the monosaccharides reduces the memory requirements, which can be large when all enzymes of the model are allowed to act; second, the lexical analysis is simplified, since in the drawing algorithm each character can act as a single instruction. The method could be adapted to other systems, depending on the intended application. For instance, other enzyme activities could be included to account for branch termination by α-GlcNAc, as has been observed in porcine gastric mucins [10], but not commonly on human glycoproteins [42]. The formal grammar could be modified to describe N-glycans, such as those expressed on immunoglobulins [75], the hypermannosylated glycans produced by yeasts [76], or glycans initiated through O-linked fucose [77] or mannose [78]. Additional reaction rules could be supplied, as needed, to support the enzyme activities of galactose 6-O-sulfotransferase and α-2,8-sialyltransferase. A limitation of the current implementation is that not all routes to a product may be included: for example, the simulated activity of Core-2 forming enzyme (5) does not recognise a 3-linked sialic acid on the lower arm of Core 1. The alternative route to [Y6][S3L3]VT could be accommodated by including sialic acid as an option to the reaction pattern, similar to the case for reactions that allow sulfation of Gal or GlcNAc. Although we have restricted our subject to the enzymes of O-glycan biosynthesis, the actions of glycosidases, which are involved in O-glycan degradation, may have an important regulatory role. For example, it is known that α-l-fucosidase (EC 3. 2. 1. 51) is downregulated in certain types of colorectal cancer [79], from which we infer that an increase in Lewis-type epitopes might be the result of both increased fucosyltransferase activity in Golgi and decreased fucosidase activity in either tissue or plasma. In the future, therefore, this model could be extended to include enzymes involved in the catabolism of O-linked glycoproteins. A quantitative analysis of O-linked glycosylation, incorporating the kinetic parameters of the enzymes involved, would be a natural extension, and development along these lines is proceeding. The web application, O-Glycologue, provides a convenient way to draw O-glycan structures from the identifiers used in this work, and to explore the wide variety of possible oligosaccharide structures formed by the activities of several known enzymes of O-glycosylation. While a MATLAB-based system for modelling N- and O-linked glycosylation has recently appeared [15], the system described in this article requires neither installation by the user nor a commercial software license. To our knowledge, O-Glycologue is the first tool capable of testing the effects of knockouts of the enzymes of O-linked glycosylation on glycoform heterogeneity. As a knowledge-based system, it should be useful to glycobiologists interested in predicting the biosynthetic pathways forming particular O-glycans. Given that the glycoslation of mucins is known to change during cancer progression [7,69], the software may be an aid to discovering the enzyme activities most responsible for the formation of particular cancer biomarkers. In conclusion, we have presented a method for encoding and displaying mucin-type O-glycans, and a method for generating reaction networks from enzymes known to act in O-glycosylation. The formal grammar and the enzyme reaction rules of Table 2, together with an initial glycan identifier as an axiom, comprise the deductive apparatus of a formal system for the modelling and display of these O-glycans. Through an analysis of the reaction networks, we predict that β4Gal-T4 is a key regulator of mucin-type O-glycan heterogeneity, along with β3Gn-T2/3/4/5/7, Gcnt2, C1Gal-T, C2Gn-T and CHST4/6. A comparison of the output of the model with experimentally derived glycans suggests the existence of several novel activities. This approach, which has been validated by structure predictions and the effects of enzyme removal, is intended to form a basis for future kinetic evaluations, and extensions to accommodate other types of glycan structure.
Our objective being to model the enzymes of mucin-type O-linked glycosylation, we first developed a model language to represent O-glycan structures succinctly in linear string form, to which a set of pattern-matching rules was then applied to simulate the activities of a set of 25 glycosyltransferase and sulfotransferase enzymes. The modelling language (a formal language), together with the set of transformation rules representing the enzymes of the model. comprise the deductive apparatus of a formal system. The system, implemented in software, was able to predict a highly heterogeneous set of structures when all enzymes were allowed to act, including many clinically important epitopes such as sialyl-Lewis X. We studied the effects of single-enzyme knockouts on the properties of the resulting enzyme-catalysed reaction networks and determined the enzymes most likely to be responsible for heterogeneity.
linguistics
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carbohydrates
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cognitive psychology
glycosylation
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language
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grammar
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mucin
psychology
organic chemistry
post-translational modification
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A Knowledge-Based System for Display and Prediction of O-Glycosylation Network Behaviour in Response to Enzyme Knockouts