Metabolic bacterial genes and the construction of high-level composite lineages of life

Hundreds of novel composite genes and chimeric genes with bacterial origins contributed to haloarchaeal evolution

BACKGROUND

Haloarchaea, a major group of archaea, are able to metabolize sugars and to live in oxygenated salty environments. Their physiology and lifestyle strongly contrast with that of their archaeal ancestors. Amino acid optimizations, which lowered the isoelectric point of haloarchaeal proteins, and abundant lateral gene transfers from bacteria have been invoked to explain this deep evolutionary transition. We use network analyses to show that the evolution of novel genes exclusive to Haloarchaea also contributed to the evolution of this group.

RESULTS

We report the creation of 320 novel composite genes, both early in the evolution of Haloarchaea during haloarchaeal genesis and later in diverged haloarchaeal groups. One hundred and twenty-six of these novel composite genes derived from genetic material from bacterial genomes. These latter genes, largely involved in metabolic functions but also in oxygenic lifestyle, constitute a different gene pool from the laterally acquired bacterial genes formerly identified. These novel composite genes were likely advantageous for their hosts, since they show significant residence times in haloarchaeal genomes-consistent with a long phylogenetic history involving vertical descent and lateral gene transfer-and encode proteins with optimized isoelectric points.

CONCLUSIONS

Overall, our work encourages a systematic search for composite genes across all archaeal major groups, in order to better understand the origins of novel prokaryotic genes, and in order to test to what extent archaea might have adjusted their lifestyles by incorporating and recycling laterally acquired bacterial genetic fragments into new archaeal genes.

Biological action in Read-Write genome evolution

Many of the most important evolutionary variations that generated phenotypic adaptations and originated novel taxa resulted from complex cellular activities affecting genome content and expression. These activities included (i) the symbiogenetic cell merger that produced the mitochondrion-bearing ancestor of all extant eukaryotes, (ii) symbiogenetic cell mergers that produced chloroplast-bearing ancestors of photosynthetic eukaryotes, and (iii) interspecific hybridizations and genome doublings that generated new species and adaptive radiations of higher plants and animals. Adaptive variations also involved horizontal DNA transfers and natural genetic engineering by mobile DNA elements to rewire regulatory networks, such as those essential to viviparous reproduction in mammals. In the most highly evolved multicellular organisms, biological complexity scales with 'non-coding' DNA content rather than with protein-coding capacity in the genome. Coincidentally, 'non-coding' RNAs rich in repetitive mobile DNA sequences function as key regulators of complex adaptive phenotypes, such as stem cell pluripotency. The intersections of cell fusion activities, horizontal DNA transfers and natural genetic engineering of Read-Write genomes provide a rich molecular and biological foundation for understanding how ecological disruptions can stimulate productive, often abrupt, evolutionary transformations.

Towards Consensus Gene Ages

Correctly estimating the age of a gene or gene family is important for a variety of fields, including molecular evolution, comparative genomics, and phylogenetics, and increasingly for systems biology and disease genetics. However, most studies use only a point estimate of a gene’s age, neglecting the substantial uncertainty involved in this estimation. Here, we characterize this uncertainty by investigating the effect of algorithm choice on gene-age inference and calculate consensus gene ages with attendant error distributions for a variety of model eukaryotes. We use 13 orthology inference algorithms to create gene-age datasets and then characterize the error around each age-call on a per-gene and per-algorithm basis. Systematic error was found to be a large factor in estimating gene age, suggesting that simple consensus algorithms are not enough to give a reliable point estimate. We also found that different sources of error can affect downstream analyses, such as gene ontology enrichment. Our consensus gene-age datasets, with associated error terms, are made fully available at so that researchers can propagate this uncertainty through their analyses (geneages.org).