article: Nematodes are among the most important causative pathogens of neglected tropical diseases. The increased availability of genomic and transcriptomic data for many understudied nematode species provides a great opportunity to investigate different aspects of their biology. Increasingly, metabolic potential of pathogens is recognized as a critical determinant governing their development, growth and pathogenicity. Comparing metabolic potential among species with distinct trophic ecologies can provide insights on overall biology or molecular adaptations. Furthermore, ascertaining gene expression at pathway level can help in understanding metabolic dynamics over development. Comparison of biochemical pathways (or subpathways, i. e. pathway modules) among related species can also retrospectively indicate potential mistakes in gene-calling and functional annotation. We show with numerous illustrative case studies that comparisons at the level of pathway modules have the potential to uncover biological insights while remaining computationally tractable. Here, we reconstruct and compare metabolic modules found in the deduced proteomes of 13 nematodes and 10 non-nematode species (including hosts of the parasitic nematode species). We observed that the metabolic potential is, in general, concomitant with phylogenetic and/or ecological similarity. Varied metabolic strategies are required among the nematodes, with only 8 out of 51 pathway modules being completely conserved. Enzyme comparison based on topology of metabolic modules uncovered diversification between parasite and host that can potentially guide therapeutic intervention. Gene expression data from 4 nematode species were used to study metabolic dynamics over their life cycles. We report unexpected differential metabolism between immature and mature microfilariae of the human filarial parasite Brugia malayi. A set of genes potentially important for parasitism is also reported, based on an analysis of gene expression in C. elegans and the human hookworm Necator americanus. We illustrate how analyzing and comparing metabolism at the level of pathway modules can improve existing knowledge of nematode metabolic potential and can provide parasitism related insights. Our reconstruction and comparison of nematode metabolic pathways at a pan-phylum and inter-phylum level enabled determination of phylogenetic restrictions and differential expression of pathways. A visualization of our results is available at http: //nematode. net and the program for identification of module completeness (modDFS) is freely available at SourceForge. The methods reported will help biologists to predict biochemical potential of any organism with available deduced proteome, to direct experiments and test hypotheses.
  The phylum Nematoda is one of the most diverse phyla among animals (with some estimates of the number of existing species being as high as 10 million [1]). The phylum contains a range of species occupying very different niches; including human parasitic species. Parasitic nematodes of humans are among the most important causative agents of neglected tropical diseases, with the morbidity from parasitic nematodes rivaling diabetes and lung cancer in disability-adjusted life years [DALY] measurements [2]). The WHO estimates that 2. 9 billion people are infected with parasitic nematodes [3], making them the most common infectious agents of humans, especially in tropical regions of Africa, Asia and the Americas. The most common infections include 120 million cases for filariasis, more than 700 million each for hookworm infections and trichuriasis, and more than 1. 2 billion for Ascaris [4]. While these numbers are ominous, large scale control and eradication programs have been largely successful for dracunculiasis (caused by Dracunculus medenisis [5]), and they are very promising for lymphatic filariasis (Brugia spp. and Wuchereria bancrofti) [6–8] and onchocerciasis (Onchocerca volvulus) [9]. However, elimination using existing approaches may be challenging for important helminthic infections such as soil-transmitted helminthiases due to the high risk of reinfection [10]. The dependence of control programs on a very limited number of drugs makes mass treatment programs vulnerable to evolution of drug resistance [11,12], as suggested by increased treatment failure rates observed in some areas [13–15]. Due to massive drug administration programs and improved hygienic practices, the 1. 04 infections / person observed in 1930 has decreased to 0. 606 infections per person in 2012 [4]. Moreover, the loss due to nematode parasites of domesticated animals and crops is estimated to be tens of billions of dollars per year [16,17]. Increasingly, development of resistance to anthelmintic drugs in veterinary medicine is very pronounced [18] especially since the employment of mass drug administration. While plant parasitic nematodes have devastating effects on crops costing $78 billion per year globally [17], using currently available nematicides to alleviate this burden is not possible because they are not environmentally safe. Hence, there is a pressing need to develop new anthelmintic treatments and pesticides [19] that are environmentally safe and efficient. Efforts for improving control have focused on identifying targets for drugs, vaccines and diagnostics. Speed and efficiency of such drug target identification will benefit from new insights into parasitic biological mechanisms. Metabolic potential is one of the crucial factors that govern a pathogen’s development and pathogenicity [20–23]. In order to determine contrasts in the metabolic potential of parasites and non-parasitic organisms, we reconstructed the metabolic network in parasitic species and studied how they differ from non-parasitic sister species. Parasites may have significant evolutionary constraints for certain metabolic processes compared to their non-parasitic cousins (e. g. xenobiotic and toxic compound catabolism and transport [24,25]). On the other hand, they could allow for relaxed metabolic constraints in other processes (e. g. biosynthesis of important metabolites available from their host). In addition, many parasitic nematodes spend part of their life cycle outside the host (or have multiple hosts). This results in the evolutionary need to maintain or expand biochemical functions [26] in order to meet the requirements of several diverse developmental stages. Such comparisons of metabolic potential have been reported previously for individual parasitic nematodes [27–30], but a phylum-wide comparison has only recently become possible with many genomes available to cover the clades of the phylum as well as the many different niches and habits of parasitic worms. Emerging high-quality genomic databases for parasitic nematodes [31–33] now parallel the advances in publicly available data of other phyla. This should allow for the recognition of broader and more fundamental biological insights, through a transition from inter-species to pan-phylum analyses (e. g. [34,35] in other phyla). The first global analysis of metabolic pathways in Nematoda used partial transcriptomes of 28 species and compared the extent of metabolic pathway representation [36–38]. A general congruence between enzymes associated with the major clades was observed, indicating that many pathways are conserved (with some relatively minor differences) within the nematodes, despite their diversity. These initial findings suggested that there are taxonomically restricted biochemical pathways and that they may serve to direct drug target definition. However, such conclusions were only suggestive and couldn’t be strongly supported from the available data because for most pathways the number of enzymes associated with each major clade correlated with the number of sequences generated. This suggested that the enzyme annotations were likely to be significantly incomplete. Since then, several studies compared metabolic pathways at a single-species level [39–41]. Also, the genome of the food-borne zoonotic parasitic nematode Trichinella spiralis (an extant member of a clade that diverged early in the evolution of the phylum) was recently sequenced [28], allowing pan-phylum comparison based on 4 species spanning the phylum Nematoda. However the inter-specific and pan-phylum studies undertaken thus far have not taken into consideration the topology of the pathways but only the individual enzymes. Here, we perform a comparative metabolic analysis of 13 nematode species (including 5 non-parasitic and 8 parasitic species spanning the phylum [42]) and 10 non-nematodes, including hosts (S1 Table). The genomes for these species are in various stages of completion, with some still awaiting high-quality annotation. The comparison was performed at the metabolic module level rather than full metabolic pathways. As compared to full pathways, modules are smaller, more compact reaction cascades whose presence/absence can provide metabolic insight with higher resolution of detail (S1A Fig). This technique recognizes distinct mechanisms for the same overall metabolic pathway in different species. We analyzed over 500,000 proteins originating from the 23 species and compared the 127,000 predicted enzyme-encoding genes that were associated with over 10,000 KEGG Orthologous (KO) groups. Our modDFS program (publicly available at SourceForge and Nematode. net) helps interested users to use information from the KEGG database [38] and annotated proteomes to find potentially interesting differences between organisms that can then be studied in detail. To illustrate this, we performed in silico comparative metabolomics and report patterns of phylogenetic restriction of metabolic modules and examples of module diversification that can be used in obtaining better approaches to develop novel therapeutics. Finally, while genome annotations can be used to study the potential availability of various pathways to an organism, it doesn’t uncover any information about specific metabolic differences in the same organism under different conditions e. g. developmental stages. We therefore analyzed gene expression at different life cycle stages for 4 nematode species, including non-parasites and parasites.
  KEGG database provides gene-enzyme-pathway associations for many species, where such associations have validating experimental data available. However, for several parasitic nematodes the only feasible way to infer such information at present is through reliable computational prediction of enzyme encoding genes and their associations with pathway reactions and pathways. This is because many parasitic nematode genomes have only recently been published [28,43–46] and systematic enzyme survey studies for them are not feasible yet. Moreover, the graphical visualization of enzyme presence in pathways and modules available from KEGG is only based on single species data, unlike other KEGG-based data visualization methods [47]. We used KAAS for inferring enzymatic activities for the proteomes and in-house scripting for comparative visualization of that data overlaid on pathways (see Methods). Out of the 202 pathway modules present in the KEGG database, 92 were considered to be relevant (see Methods) and analyzed for the 23 species studied, including 14 forks from 6 forked modules (Fig 1B), 11 cyclic modules (Fig 1C), and 75 linear modules without cycles and forks (S2 Table; a visual representation of all reconstructed modules can be seen online http: //www. nematode. net/Pathway_Modules. html). The reconstructed modules were classified into 2 tiers. Tier 1 represents ‘strictly complete’ modules (with at least 1 path from module’s beginning to end having all enzymes annotated in the deduced proteome of the species). While this requirement is needed for a reliable comparison of pathways among species, there are 2 main reasons that it may lead to false negatives: i) the draft nature of the genomes (especially non-Caenorhabditis nematode genomes) and ii) sequence diversification, which could lead to undetectable similarity at a primary sequence level. Hence, tier 2 modules were identified to capture false negatives as a result of missing genes. These represent modules that were ‘leniently complete’ (with at most 1 missing KO, such that, had it been detected it would have resulted in the module being tier 1). This resulted in reducing false negatives and increasing the number of complete modules (S2A Fig). Analysis of tier 1 modules showed that the worms (i. e. the roundworms phyla Nematoda and the flatworms phyla Platyhelminthes) had fewer metabolic modules available to them (average of 32) than the plants and animals analyzed (average of 49), which corresponded with the number of KOs associated with the respective proteomes (Fig 2A). This lower functional diversity relates to the less complex biology of the worm species included in our analysis, but in part may also arise from the draft nature of the genome and imperfect reaction annotation. The latter was confirmed by comparing the increase in completion rates resulting from allowing lenient completion. The % strict completion rates (i. e. the fraction of tier 1 modules) for non-nematode organisms tended to be higher (more than ~70, between 69 and 88, S2A Fig) than the worms (less than ~70, between 54 and 73). This is in part due to the selected host organisms having relatively well-studied and complete genomes. The reconstructed pathways provide an indication of the metabolic potential. However by themselves, they do not establish whether a complete pathway is operative in an organism under a particular developmental stage or condition. Such context-dependent metabolic potential can be studied by using large-scale proteomic and enzyme kinetics data. However, such data does not exist for parasitic nematodes. Therefore, we used RNA-seq based expression to examine the metabolic dynamics during development and investigate context-dependent metabolic features in the phylum Nematoda. The nearly complete transcriptional profiles obtained using RNA-seq data for 3 parasitic nematode species [43,71,72] and C. elegans [73] (S6 Table) were used to assign an abundance value to every complete metabolic module using an enzyme cascade bottleneck calculation (see Methods). As expected, the module abundances are found to be relatively dynamic over the course of organism development. Moreover, clustering of modules according to their abundance profiles across developmental stages uncovers interesting contrasts in development profiles of the sets of modules. We discuss the results for 1 non-parasitic (C. elegans) and 1 parasitic (B. malayi) species here (see S1 Text for A. suum and N. americanus). Of the 34 potentially complete modules in C. elegans, 30 have non-zero abundance in all developmental stages and 1 (M00004) has non-zero abundance in all except the L1 stage. Furthermore, 3 other modules (M00056a, M00056c and M00126) had 0 abundance in all stages due to no detectable expression of some critical enzyme in the module. For each of the 31 modules with detectable abundance levels, we determined the differentially expressed modules using the Z-score statistic (based on the mean and standard deviation of that module’s abundance across all the stages). The Z-scores help identify the modules that are relatively over or under abundant in any given stage as compared to that module’s mean abundance level across all the stages. These Z-scores were then used to obtain clusters of modules that have similar abundance profiles across the developmental stages (Fig 4). The adult and the dauer stage module abundances were generally low as compared to other stages with only a few modules being overabundant (7 modules with >0 Z-scores), and hence they cluster together (S12A Fig). In contrast, the young adult stage has consistently high abundance Z-scores (with 21 modules having >0 Z-scores). Four clusters of co-expressed modules were defined (plotting the abundance Z-scores against the ordered developmental stages, Fig 4). Two clusters included 22 of the 31 modules (clusters 1 and 3) that differ primarily in overabundance in later stages (L4 and Young Adult, overabundant in cluster 1 modules) vs. embryonic stages (overabundant in cluster 3). Cluster 3 includes biosynthesis modules for fatty acids, beta alanine, guanine nucleotides, cysteine and PE. Cluster 1 includes biosynthesis of serine, adenine nucleotides, UMP, glycosaminoglycans and N-glycans, PRPP and CoA along with modules for pentose phosphate pathway, sphingosine degradation and PI metabolism. Cluster 2 (overabundant in L1) includes modules for glycolysis, gluconeogenesis, acyl-CoA synthesis, glutathione biosynthesis and C1-unit interconversion. L1 longevity has been previously linked with metabolic rate and starvation [74]. Cluster 4 (considerably overabundant during the dauer stage, and underabundant during the L2 stage) includes modules for GABA shunt and biosynthesis of IMP, pyrimidine ribonucleotides and triacylglycerol, which is consistent with the known enhancement of cellular maintenance and detoxification processes in dauer stage [41]. Performing analyses of module abundances in different available developmental stages of the filarial parasitic nematode B. malayi defined 4 clusters, with uniformly low abundance of almost every metabolic module in the mature microfilariae as compared to other stages (S12B Fig). On the contrary, most modules (19 out of 25, Clusters 1 and 2) with only a few exceptions that partly define clusters 3 and 4, are overabundant in the L3 stage. The adult stages do not cluster with each other. The male have similar abundance pattern to immature microfilariae and the female have low abundances in a pattern similar to mature microfilariae (with 3 modules being notable exceptions). Fig 4 shows that a majority of modules (14 out of 25) share their module abundance profiles closely (cluster 2), with significant overabundance in the L3 stage. Cluster 1 (comprising modules for fatty acid biosynthesis and methionine and sphingosine degradation) is differentiated from these by a sharp fall in abundance from the eggs and embryonic stage to immature microfilariae stage. They also show relative underabundance in the adult male, as compared to all other clusters. Clusters 3 and 4 show a characteristic overabundance in adult stages (male for cluster 3, and female for cluster 4). Cluster 3 includes glycolysis, glutathione biosynthesis and CoA biosynthesis. Cluster 4 has low relative abundance before adulthood (L4 stage) and comprises modules for PI metabolism and biosynthesis of PRPP and C10-C20 isoprenoid. Previously it has been suggested that transcriptional differences between immature and mature microfilariae may be relatively small despite the change in infectivity [72]. However, we observed that a majority of complete metabolic modules have differential abundance between these 2 stages (14 out of 25 having a Z-score difference higher than 0. 75, including 10 with difference higher than 1). These modules are primarily in clusters 2 and 3. Notably, none of the modules shows increased abundance with microfilariae maturation. Metabolism modules for sulfur-containing amino acids are complete in B. malayi (methionine degradation and cysteine biosynthesis) and have relatively high variation in abundance (root-mean-squared Z-score across all stages ranking 3rd and 8th out of 25). In contrast, most processes associated with protein metabolism (e. g. translation, protein transport, proteasome complex) are known to not be among the most variable [72]. Interestingly, glycolysis-related modules (M00001, M00003 and M00307) were found to be relatively abundant in the late developmental stages of parasites (L3/L4 and adult male in B. malayi, L4 and adult in A. suum—see S1 Text). This is consistent with the L4-enrichment of glycolysis reported previously in B. malayi [75], but the free-living C. elegans showed overabundance of these modules relatively early in the life cycle (L1 to L3 and dauer entry). A higher energy requirement of parasites during L4 and adult stages may overshadow energy consumption of early development. Differentially abundant clusters of metabolic modules were also observed in A. suum and N. americanus (S13 Fig). Previous studies based on partial transcriptomes of various nematodes have indicated differences between nematodes [37]. More specific evidence has emerged in the analyses of transcriptomes generated from comparisons of i) life-cycle stages: detection of S. stercoralis enzymes of nucleotide metabolism present in L1 but absent in the arrested L3i, is consistent with the lack of cell division and DNA replication in the L3i stage [76]; ii) different tissues: statistical enrichment of functions was reported for adult male and female A. suum when different reproductive and non-reproductive tissues were compared [77]. Similar differential expression has been confirmed in this study based on complete RNA-seq based transcriptomes. Analyzing metabolic pathways and enzyme abundance provides important insight into the biology of an organism, especially when used in a comparative study of related species. Reconstructing entire metabolic networks of organisms has previously been done, but the sheer size and complexity of these networks means that doing a comprehensive comparison on a large set of organisms is prohibitively time and resource consuming. Furthermore, previous comparisons in parasitic worms have been based on just the enzyme content of the pathways that ignore topological information, which is too simplistic and likely to only highlight a few major differences and miss potentially useful fine details. Here, we have primarily focused on compact reaction cascades—metabolic modules—that provide some detail about an organism’s metabolic potential and illustrate how a comparison at this scale can relatively easily uncover important biological information. We provide the results for the metabolic potential of 23 organisms including 13 nematodes that include representatives from various taxonomic clades and different trophic ecologies (non-parasitic, necromenic and plant or animal parasitic). This information provides a platform for experimental follow up that could confirm in silico predictions we have obtained. We also indicate that certain patterns of absence of metabolic modules might be suggestive of missed genecalls, which can be used to improve genome annotations. We note i) the presence of modules that are absent from hosts but present in parasites; ii) metabolic enzyme variation between the human hookworm N. americanus and the human genome, even within metabolic modules that are complete in both the species; and iii) comparison of closely related clade V worms of genus Caenorhabditis and the necromenic worm Pristionchus pacificus showed examples of gain and loss of metabolic potentials, which is likely to provide insight into the evolution from free-living stage to parasitic dependence when the specific modular differences are explored in detail. However, we have to note that our analysis of metabolic potential is sensitive to our ability to correctly detect enzymes in proteomes of the organism under study and to the accuracy and completeness of the reference pathways we use for the reconstruction. One could advance the metabolic pathway reconstruction by improving both enzyme predictions and pathway mapping. First, one could reduce false negatives in enzyme predictions (resulting from reliance only on sequence similarity for annotation [87]) by undertaking alternate and independent methods for functional annotation, including i) utilizing the difference in sequence diversity in enzyme classes, to refine the likelihood estimation of a query protein belonging to any enzyme class [88], ii) performing Functionally Discriminating Residue recognition [89], and iii) discriminating between the functional module characteristics of different enzyme activities [90]. Using this approach on 4 nematode species, we obtained a 28% expansion of the ECs (S14 Fig). Second, in addition to KEGG, one could use some of the 133 other metabolic pathway databases available on Pathguide [91]. A majority of these are species-specific (e. g. [92]) or function-specific (e. g. [93]), but many are extensive enough to improve many organisms’ pathway reconstruction [94–96], and can produce complementary results to improve annotations [97]. Comparison of the pathways across diverse organisms could be performed to detect similar topologies [98–100]. Specifically for parasitic nematodes, the newly generated RNAseq data from 35 species and 57 stages [31,33] will provide wealth of information and pathway analysis at a level not possible before. Finally, we report dynamic metabolic module expression between developmental stages with clusters of coexpressed modules that share overall expression profiles during development. In B. malayi, we especially note the differential metabolic dynamics of the immature and mature microfilariae. An enzyme abundance level comparison of parasitic and non-parasitic alike stages provided a set of enzymes that are likely to be implicated in parasitism-specific functions. This is the first comprehensive analysis of metabolic modules of nematodes, at a pan-Phylum level and in context of non-nematode species. The obtained results have wide applications starting from evolutionary events such as adaptations to different niches, to taxonomic restrictions that can drive pathogen control and prevention. Also, importantly, we show here that comparative study of metabolism at the level of small modules leads can lead to important biological insights without being as time and resource intensive as a study of full scale pathway reconstructions.
  The proteome to KO mapping was done using a standalone implementation of KAAS [87] version 2, available from the KAAS website. We used the recommended bidirectional best hit (BBH) method for annotations with a threshold BLAST [101] mapping score of 35 bits. The eukaryotic representative set was used. C. elegans was included for annotating C. elegans also. S1 Table shows the results for KAAS annotation of the 23 proteomes. It is also worth noting that the enzyme annotation at a primary sequence level is suboptimal for some of the species included in our analysis, for example while Trichinella spiralis is represented in the KEGG database, because of greater evolutionary distance with the other species in the KAAS dataset the enzyme annotation could be suboptimal. A module consists of 1 or more reaction steps (S1B Fig), each step consisting of 1 or more reactions that change a set of substrates into a set of products. A step is considered to be present if at least 1 set of reactions can occur in the organism i. e. there are proteins that map to at least 1 KO of each reaction that is needed to complete the reaction step. The module definitions were downloaded from KEGG database on May 8,2012. This has a total of 542 module definitions, 202 of which are “Pathway modules” (S1C Fig). Pathway modules are the enzymatic cascade modules that we analyzed. These modules represent 76 metabolic pathways. We obtained a reduced set of modules based on their relevance to nematodes under study. This was done by excluding any module from the analysis set which had less than 66% of its associated KOs in all of the nematode genomes whose genome were deemed to be close to completion (all except T. spiralis, M. incognita and P. pacificus). Also, any modules with a fork leading to more than 1 branch of products (e. g. Fig 1B) were redefined as separate modules with 1 product branch per module. There were 6 such forked modules. The reduced set of modules thus obtained had 92 members. A script was written to ascertain whether a given module should be considered to be ‘complete’ within a species given the module definition, reaction data and KAAS based KO mappings of the organism’s proteome. Some assumptions were made in order to clearly define the given data and the problem. The statistical package R[103] was used to calculate the Pearson correlation coefficient between species based on presence/absence of the modules that were present in at least 1 of the species under consideration. The distance metric on which the dendrogram was based, is defined as: d (S1, S2) =1−corr (S1, S2) where corr (S1, S2) is the Pearson correlation coefficient. The N. americanus genome assembly [43] was scanned using the genes from 11 nematode species that mapped to K00942 as query. A locally installed translated BLAST (tblastn) [104] was used and the best hit with E-value threshold of 1e-5 was reported as a match. As an alternative, we also used HMMER [105] to use a profile-HMM using the known K00942 mapped nematode genes as the query to scan the N. americanus genome assembly. Both the methods returned the same region as a match. The module abundance was calculated using a “bottleneck analysis”, i. e. assuming that the overall module output will be determined by the low abundance of certain key enzyme (s), while the abundance of other higher abundance enzymes will have no impact on the output. Determining module abundance does not merely find the lowest-abundance enzymes, but also analyzes how the module topology may impact substrate concentrations throughout the module (S1D Fig). In order to obtain an insight into variation among stages for the modules, normalizing for the overall abundance level of the module across all stages, a Z-score was calculated based on the mean and standard deviation of the module abundances across all stages. S7 Table contains the module abundance Z-scores for all the modules potentially complete in the 4 species whose expression was analyzed. Given these Z-scores in all the available stages, the data is clustered (S8 Table) and corresponding dendrograms obtained by using heatmap. 2 function of gplots R library. The default dist function is used to calculate the distance metric for clustering. In each species, a cluster threshold distance was selected such that 4 module clusters were obtained. All the KOs that were mapped to both C. elegans and N. americanus proteomes, and had an abundance value of at least 0. 1 in all stages, were ranked according to their abundance in each stage (Dauer and Adult in C. elegans and the corresponding developmental stages, L3 and Adult, in N. americanus). The rank differences were calculated by subtracting Adult stage rank from the L3/Dauer stage rank. A threshold was chosen to obtain the top and bottom quartile (i. e. 25%) KOs in the lists, providing a list of KOs that are significantly higher ranked in L3/Dauer stage (the top quartile, with positive rank difference) or significantly higher ranked in Adult stage (the bottom quartile, with negative rank difference). These thresholds were 0. 12 and -0. 12 for N. americanus, and 0. 08 and -0. 1 for C. elegans. To enrich KOs for parasitism-related functions, we examined the KOs overabundant in the parasitic Adult stage of the hookworm but underabundant in the free-living adult stage of C. elegans (i. e. overabundant in Dauer). There were 133 such KOs, which were mapped to 167 genes. KEGG categories that were significantly enriched in these KOs as compared to the whole proteome were found using Fisher’s exact test with a threshold one-tailed p-value of 0. 05 (Table 2). The KOs that were mapped to N. americanus proteome but not mapped to the C. elegans proteome were examined for KOs that are overabundant in the parasitic adult stage of N. americanus but are not encoded in the C. elegans genome. A similar analysis to the one mentioned above was done which resulted in 5 KEGG categories, only 2 of which were not part of the list obtained earlier. These are ‘Lysosome’ and ‘Glutathione Metabolism’ (italicized in Table 2). All module reconstructions generated by this study are publicly available at http: //www. nematode. net/Pathway_Modules. html. The open source modDFS software for identifying complete modules can be obtained at www. nematode. net/Pathway_Modules. html and at http: //sourceforge. net/projects/moddfs/.
summary: We reconstructed metabolic pathways of 23 organisms including 13 nematode species, using their complete deduced protein coding sequences and compared them to 10 non-nematodes. We observed that metabolic potential availability is concomitant with phylogenetic and/or ecological similarity, with the exceptions providing interesting case studies. We also studied changes in metabolic profiles under different developmental stages of 4 nematode species using stage-specific transcriptomic data. A comparison of the variation patterns in these profiles led to recognition of modules that share metabolic profiles at various life-cycle stages or during development. The undertaken analysis improved genome annotation and the obtained results provided insight into parasitism, resulting in identification of taxonomically-restricted pathways and enzymes that may provide new mechanisms for control of nematode infections.
title: Pan-phylum Comparison of Nematode Metabolic Potential