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Categorization of metabolome in bacterial systems.

ABSTRACT: Analyses of biological databases such as those of genome, proteome, metabolome etc., have given insights in organization of biological systems. However, current efforts do not utilize the complete potential of available metabolome data. In this study, metabolome of bacterial systems with reliable annotations are analyzed and a simple method is developed to categorize pathways hierarchically, using rational approach. Ninety-four bacterial systems having for each ≥ 250 annotated metabolic pathways were used to identify a set of common pathways. 42 pathways were present in all bacteria which are termed as Core/Stage I pathways. This set of pathways was used along with interacting compounds to categorize pathways in the metabolome hierarchically. In each metabolome non-interacting pathways were identified including at each stage. The case study of Escherichia coli O157, having 433 annotated pathways, shows that 378 pathways interact directly or indirectly with 41 core pathways while 14 pathways are noninteracting. These 378 pathways are distributed in Stage II (289), Stage III (75), Stage IV (13) and Stage V (1) category. The approach discussed here allows understanding of the complexity of metabolic networks. It has pointed out that core pathways could be most ancient pathways and compounds that interact with maximum pathways may be compounds with high biosynthetic potential, which can be easily identified. Further, it was shown that interactions of pathways at various stages could be one to one, one to many, many to one or many to many mappings through interacting compounds. The granularity of the method discussed being high; the impact of perturbation in a pathway on the metabolome and particularly sub networks can be studied precisely. The categorizations of metabolic pathways help in identifying choke point enzymes that are useful to identify probable drug targets. The Metabolic categorizations for 94 bacteria are available at http://115.111.37.202/mpe/.

SUBMITTER: Kolhi S

PROVIDER: S-EPMC3338974 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Interrupted adenylation (A) domains are key to the immense structural diversity seen in the nonribosomal peptide (NRP) class of natural products (NPs). Interrupted A domains are A domains that contain within them the catalytic portion of another domain, most commonly a methylation (M) domain. It has been well documented that methylation events occur with extreme specificity on either the backbone (N-) or side chain (O- or S-) of the amino acid (or amino acid-like) building blocks of NRPs. Here, through taxonomic and phylogenetic analyses as well as multiple sequence alignments, we evaluated the similarities and differences between interrupted A domains. We probed their taxonomic distribution amongst bacterial organisms, their evolutionary relatedness, and described conserved motifs of each type of M domain found to be embedded in interrupted A domains. Additionally, we categorized interrupted A domains and the M domains within them into a total of seven distinct families and six different types, respectively. The families of interrupted A domains include two new families, 6 and 7, that possess new architectures. Rather than being interrupted between the previously described a2-a3 or a8-a9 of the ten conserved A domain sequence motifs (a1-a10), family 6 contains an M domain between a6-a7, a previously unknown interruption site. Family 7 demonstrates that di-interrupted A domains exist in Nature, containing an M domain between a2-a3 as well as one between a6-a7, displaying a novel arrangement. These in-depth investigations of amino acid sequences deposited in the NCBI database highlighted the prevalence of interrupted A domains in bacterial organisms, with each family of interrupted A domains having a different taxonomic distribution. They also emphasized the importance of utilizing a broad range of bacteria for NP discovery. Categorization of the families of interrupted A domains and types of M domains allowed for a better understanding of the trends of naturally occurring interrupted A domains, which illuminated patterns and insights on how to harness them for future engineering studies.

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Categorization of metabolome in bacterial systems.

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