Birth, Death, and Diversification of Mobile Promoters in Prokaryotes

Abstract

A previous study of prokaryotic genomes identified large reservoirs of putative mobile promoters (PMPs), that is, homologous promoter sequences associated with nonhomologous coding sequences. Here we extend this data set to identify the full complement of mobile promoters in sequenced prokaryotic genomes. The expanded search identifies nearly 40,000 PMP sequences, 90% of which occur in noncoding regions of the genome. To gain further insight from this data set, we develop a birth-deathdiversification model for mobile genetic elements subject to sequence diversification; applying the model to PMPs we are able to quantify the relative importance of duplication, loss, horizontal gene transfer (HGT), and diversification to the maintenance of the PMP reservoir. The model predicts low rates of HGT relative to the duplication and loss of PMP copies, rapid dynamics of PMP families, and a pool of PMPs that exist as a single copy in a genome at any given time, despite their mobility. We report evidence of these "singletons" at high frequencies in prokaryotic genomes. We also demonstrate that including selection, either for or against PMPs, was not necessary to describe the observed data.

Disentangling the effects of selection and loss bias on gene dynamics

“…Likewise, gene loss at rate l per copy leads to a decrease in the copy number. Duplication, HGT, and gene loss define a classical birth-death-transfer model at the genome level (24)(25)(26)(27)29). Selection is introduced through a contribution s to the fitness of a genome (s is positive for beneficial genes and negative for costly genes), which is multiplied by the gene copy number k. Specifically, we assume that fitness is additive, there is no epistasis, and the fitness contributions of all genes from the same family are the same.…”

“…Multiple variants of the duplication-transfer-loss model and related multitype branching processes have been widely used to study the evolution of gene copy numbers (24,25,28,49), especially in the context of transposons and other genetic parasites (22,23,26,27,50). To make the models tractable, most studies make simplifying assumptions, such as stationary state, absence of duplication, or lack of selection, and obtain the model parameters from the copy number distributions observed in large genomic datasets, relying on the assumption that model parameters are homogeneous across taxa.…”

“…To disentangle selection and loss bias, we first obtained an exact, time-dependent solution of the linear duplication-transfer-loss model with selection that governs the dynamics of gene copy numbers in a population of genomes (24)(25)(26)(27)(28). When applied to a large genomic data set, the model provides maximum likelihood estimates of the neutral equivalent (effective) loss bias, a composite parameter that amalgamates the effects of intrinsic loss bias (the loss bias before the action of selection) and selection.…”

How sequence populations persist inside bacterial genomes

“…Nevertheless, studies of TEs in asexually reproducing organisms followed shortly after the first studies on eukaryotes ( Sawyer and Hartl 1986 ). The authors assume, similar to sexually reproducing organisms, “ that the TE performs no function for the host and, that the reduction in fitness with increased copy number is due to effects such as impairment of beneficial genes by transposition or homologous recombination.” These models can explain the distribution of simple TEs such as insertion sequences (ISs), and even short repetitive sequences assumed to act as promoters (mobile promoters, MPs) as long as there is replicative horizontal gene transfer ( hgt ) ( Sawyer and Hartl 1986 ; Dolgin and Charlesworth 2006 ; Matus-Garcia et al 2012 ; Bichsel et al 2013 ; van Passel et al 2014 ).…”

DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood

“…Many evolutionary processes involve transitions among different discrete characteristic states, including changes in morphological characteristics [ 1 ], sequence gain and loss [ 2 , 3 ], gene family expansion and contraction [ 4 ], gain and loss of mobile promoters [ 5 ] and epigenetic characteristics such as methylation [ 6 ]. Evolutionary rates of discrete characters have been estimated using programs primarily developed for constructing ancestral character states such as the ACE function of the APE package [ 7 ] in R, standalone programs BayesTraits [ 8 ] and Mesquite [ 9 ].…”