No support for the adaptive hypothesis of lagging-strand encoding in bacterial genomes
Liu, H.; Zhang, J. G.
Show abstract
Genes are preferentially encoded on the leading instead of the lagging strand of DNA replication in most bacterial genomes1. This bias likely results from selection against lagging-strand encoding, which can cause head-on collisions between DNA polymerases and RNA polymerases that induce transcriptional abortion, replication delay, and possibly mutagenesis1. But there are still genes encoded on the lagging strand, an observation that has been explained by a balance between deleterious mutations bringing genes from the leading to the lagging strand and purifying selection purging such mutations2. This mutation-selection balance hypothesis predicts that the probability that a gene is encoded on the lagging strand decreases with the detriment of its lagging-strand encoding relative to leading-strand encoding, explaining why highly expressed genes and essential genes are underrepresented on the lagging strand3,4. In a recent study, Merrikh and Merrikh proposed that the observed lagging-strand encoding is adaptive instead of detrimental, due to beneficial mutations brought by the potentially increased mutagenesis resulting from head-on collisions5. They reported empirical observations from comparative genomics that were purported to support their hypothesis5. Here we point out methodological flaws and errors in their analyses and logical problems of their interpretation. Our reanalysis of their data finds no evidence for the adaptive hypothesis.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- The genomic landscape of recombination rate variation in Chlamydomonas reinhardtii reveals effects of linked selection 91%
- Convergent evolution and structural adaptation to the deep ocean in the protein folding chaperonin CCTα 91%
- Ancient and modern genomes reveal microsatellites maintain a dynamic equilibrium through deep time 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.