Journal of Computer Science & Systems Biology

Modularity and Distribution of Sulfur Metabolism Genes in Bacterial Populations: Search and Design

Abstract

Andrew Kuznetsov

Biological Engineering involves global DNA sampling and modular design from genetic parts. A new approach reflected by natural history is based on the recognition of interchangeable DNA fragments that move around the world due to horizontal gene transfer. According to the large scale, metagenomics provide opportunities to sequence whole genomes within environmental populations. Annotated gene sequences, protein structures, and metabolic data can be used to design small biosystems from interchangeable genetic parts, the same as from functional modules. To illustrate this, the 21 genes for sulfur metabolism were inferred from the genome of bacterium Vesicomyosocius okutanii HA, and the distribution of two gene clusters (dissimilatory sulfite reductase - dsr and sulfur-oxidation - sox) within environmental samples was investigated. The correlation between the dsr and sox clusters for the experimental set of 41 stations was R = 0.86 which demonstrates the complementarity of dsr and sox metabolic pathways in environmental populations. Hypothetical functions were assigned using comparisons with known proteins. The 18 reads from symbionts of gutless worm Olavius algarvensis showed a high identity to large AprA protein from V.okutanii. In addition, comparative 3D modeling of hypothetical DsrB protein revealed sulfite reductase ferredoxin-like half domain, sulfite reductase 4Fe-4S domain, and a repressor of phase-1 flagellin. The simplistic reconstruction of sulfur metabolism from parts and examples of hierarchical modularity in nature are given. The origin of modularity is considered in the context of minimal cell and horizontal gene transfer. The role of ancient sulphur metabolism in modularization is discussed under the umbrella of iron-sulfur world theory (Wächtershäuser, 1988), deep-hot biosphere model (Gold, 1992), and radiolysis hypothesis (Garzón and Garzón, 2001). The reverse engineering approach based on natural genetic modules is proposed for understanding early life.