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The Effect of Local Sequence Context on Mutational Bias of Genes Encoded on the Leading and Lagging Strands.

ABSTRACT: All organisms must replicate their genetic information accurately to ensure its faithful transmission. DNA polymerase errors provide an important source of genetic variation that can drive evolution. Understanding the origins of genetic variation will inform our understanding of evolution and the development of genetic diseases. A number of factors have been proposed to influence mutagenesis [1-10]. Here, we used mutation accumulation lines, whole-genome sequencing, and whole-transcriptome analysis to study the locations and rate at which mutations arise in bacteria with as little selection bias as possible [11, 12]. Our analysis of greater than 7,000 replication errors in over 180 sequenced lines that underwent a total of more than 370,000 generations has provided new insights into how DNA polymerase errors sculpt genetic variation and drive evolution. Homopolymer run enrichment outside of genes causes insertions and deletions in these regions. Genes encoded in the lagging strand are transcribed such that RNA polymerase and DNA polymerase collide head-on. Head-on genes have been proposed to mutate at a higher rate than genes transcribed codirectionally with DNA polymerase progression due to conflicts between transcription and DNA replication [6, 10]. We did not detect associations between the number of base pair substitutions in genes and their orientation or expression. Strikingly, any higher mutation rate for head-on genes can be explained by differing sequence composition between the leading and lagging strands and the error bias for DNA polymerase in specific sequence contexts. Therefore, we find local sequence context is the major determinant of mutagenesis in bacteria.

SUBMITTER: Schroeder JW

PROVIDER: S-EPMC4783269 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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The Effect of Local Sequence Context on Mutational Bias of Genes Encoded on the Leading and Lagging Strands.

Schroeder Jeremy W JW Hirst William G WG Szewczyk Gabriella A GA Simmons Lyle A LA

Current biology : CB 20160225 5

All organisms must replicate their genetic information accurately to ensure its faithful transmission. DNA polymerase errors provide an important source of genetic variation that can drive evolution. Understanding the origins of genetic variation will inform our understanding of evolution and the development of genetic diseases. A number of factors have been proposed to influence mutagenesis [1-10]. Here, we used mutation accumulation lines, whole-genome sequencing, and whole-transcriptome analy ...[more]

PMID: 26923786

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Project description:BACKGROUND:The evolutionary history of genes serves as a cornerstone of contemporary biology. Most conserved sequences in mammalian genomes don't code for proteins, yielding a need to infer evolutionary history of sequences irrespective of what kind of functional element they may encode. Thus, sequence-, as opposed to gene-, centric modes of inferring paths of sequence evolution are increasingly relevant. Customarily, homologous sequences derived from the same direct ancestor, whose ancestral position in two genomes is usually conserved, are termed "primary" (or "positional") orthologs. Methods based solely on similarity don't reliably distinguish primary orthologs from other homologs; for this, genomic context is often essential. Context-dependent identification of orthologs traditionally relies on genomic context over length scales characteristic of conserved gene order or whole-genome sequence alignment, and can be computationally intensive. RESULTS:We demonstrate that short-range sequence context-as short as a single "maximal" match- distinguishes primary orthologs from other homologs across whole genomes. On mammalian whole genomes not preprocessed by repeat-masker, potential orthologs are extracted by genome intersection as "non-nested maximal matches:" maximal matches that are not nested into other maximal matches. It emerges that on both nucleotide and gene scales, non-nested maximal matches recapitulate primary or positional orthologs with high precision and high recall, while the corresponding computation consumes less than one thirtieth of the computation time required by commonly applied whole-genome alignment methods. In regions of genomes that would be masked by repeat-masker, non-nested maximal matches recover orthologs that are inaccessible to Lastz net alignment, for which repeat-masking is a prerequisite. mmRBHs, reciprocal best hits of genes containing non-nested maximal matches, yield novel putative orthologs, e.g. around 1000 pairs of genes for human-chimpanzee. CONCLUSIONS:We describe an intersection-based method that requires neither repeat-masking nor alignment to infer evolutionary history of sequences based on short-range genomic sequence context. Ortholog identification based on non-nested maximal matches is parameter-free, and less computationally intensive than many alignment-based methods. It is especially suitable for genome-wide identification of orthologs, and may be applicable to unassembled genomes. We are agnostic as to the reasons for its effectiveness, which may reflect local variation of mean mutation rate.

Project description:Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.Two predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.Predicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.

Dataset Information

The Effect of Local Sequence Context on Mutational Bias of Genes Encoded on the Leading and Lagging Strands.

Publications

The Effect of Local Sequence Context on Mutational Bias of Genes Encoded on the Leading and Lagging Strands.

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