Research Article: Pan-phylum Comparison of Nematode Metabolic Potential

Date Published: May 22, 2015

Publisher: Public Library of Science

Author(s): Rahul Tyagi, Bruce A. Rosa, Warren G. Lewis, Makedonka Mitreva, Timothy G. Geary.

Abstract: 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 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.

Partial Text: 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.

Fig 1A presents the overall analysis approach, including the 5 major steps: i) annotation of metabolic enzymes (KO groups), ii) development of an approach to ascertain organism-specific pathway module completion, iii) pathway reconstruction for all nematode species and several non-nematode representatives with available genomic data, iv) in silico intra- and inter-specific comparative metabolomics and v) identification of developmental (i.e. condition-specific) pathway modules using transcription data of genes annotated with KOs. We developed modDFS (“completion of KEGG modules using Depth First Search”), an algorithm that determines whether a module is completely present within an organism. The requisite information to run modDFS is, a) enzyme content encoded in the deduced proteome (as KO groups; seeMethods); and b) the KEGG database or metabolic module definitions. Canonical KEGG pathway maps provided the initial blueprint on which KO based enzyme assignments were overlaid to reconstruct each species metabolic pathways. The modDFS algorithm (outlined in Fig 1A, for details seeMethods) is available for public use at and



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