Mangiameli, S. | Search bioRxiv

bioRxiv2020

Replication-transcription conflicts promote adaptive evolution

Cells may be able to promote adaptive evolution in a gene-specific and temporally-controlled manner. Genes encoded on the lagging strand have a higher mutation rate and evolve faster than genes on the leading strand. This effect is likely driven by head-on replication-transcription conflicts, which occur when lagging strand genes are transcribed during DNA replication. We previously suggested that the ability to selectively increase mutagenesis in a subset of genes may provide an adaptive advantage for cells. However, it is also possible that this effect could be neutral or even highly deleterious. Distinguishing between these models is important because, if the adaptive model is correct, it would indicate that 1) head-on conflicts, which are generally deleterious, can also provide a benefit to cells, and 2) cells possess the remarkable ability to fine-tune adaptive evolution. Furthermore, investigating these models may address the long-standing debate regarding whether accelerated evolution through conflicts can be adaptive. To distinguish between the adaptive and neutral models, we conducted single nucleotide polymorphism (SNP) analyses on wild strains of bacteria, from divergent phyla. To test the adaptive hypothesis, we analyzed convergent mutation patterns. As a simple test of the neutral hypothesis, we performed in silico modeling. Our results show that convergent mutations are enriched in lagging strand genes and that these mutations are unlikely to have arisen by chance. Additionally, we observe that convergent mutation frequency has a stronger positive correlation with gene-length in lagging strand genes. This effect strongly suggests that head-on conflicts between the DNA replication and transcription machineries are a key mechanism driving the formation of convergent mutations. Together, our data indicate that head-on replication-transcription conflicts can promote adaptive evolution in a variety of bacterial species, and potentially other organisms.

evolutionary biology↗

bioRxiv2020

HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes

Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of up to 100 chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome scRNA-seq, making HyPR-seq a powerful method for targeted RNA profiling in single cells.

genomics↗