Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines

Primary Cell Culture as a Model System for Evolutionary Molecular Physiology

Primary cell culture is a powerful model system to address fundamental questions about organismal physiology at the cellular level, especially for species that are difficult, or impossible, to study under natural or semi-natural conditions. Due to their ease of use, primary fibroblast cultures are the dominant model system, but studies using both somatic and germ cells are also common. Using these models, genome evolution and phylogenetic relationships, the molecular and biochemical basis of differential longevities among species, and the physiological consequences of life history evolution have been studied in depth. With the advent of new technologies such as gene editing and the generation of induced pluripotent stem cells (iPSC), the field of molecular evolutionary physiology will continue to expand using both descriptive and experimental approaches.

Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast

BACKGROUND

Many computational methods have been developed to detect non-reference transposable element (TE) insertions using short-read whole genome sequencing data. The diversity and complexity of such methods often present challenges to new users seeking to reproducibly install, execute, or evaluate multiple TE insertion detectors.

RESULTS

We previously developed the McClintock meta-pipeline to facilitate the installation, execution, and evaluation of six first-generation short-read TE detectors. Here, we report a completely re-implemented version of McClintock written in Python using Snakemake and Conda that improves its installation, error handling, speed, stability, and extensibility. McClintock 2 now includes 12 short-read TE detectors, auxiliary pre-processing and analysis modules, interactive HTML reports, and a simulation framework to reproducibly evaluate the accuracy of component TE detectors. When applied to the model microbial eukaryote Saccharomyces cerevisiae, we find substantial variation in the ability of McClintock 2 components to identify the precise locations of non-reference TE insertions, with RelocaTE2 showing the highest recall and precision in simulated data. We find that RelocaTE2, TEMP, TEMP2 and TEBreak provide consistent estimates of [Formula: see text]50 non-reference TE insertions per strain and that Ty2 has the highest number of non-reference TE insertions in a species-wide panel of [Formula: see text]1000 yeast genomes. Finally, we show that best-in-class predictors for yeast applied to resequencing data have sufficient resolution to reveal a dyad pattern of integration in nucleosome-bound regions upstream of yeast tRNA genes for Ty1, Ty2, and Ty4, allowing us to extend knowledge about fine-scale target preferences revealed previously for experimentally-induced Ty1 insertions to spontaneous insertions for other copia-superfamily retrotransposons in yeast.

CONCLUSION

McClintock ( https://github.com/bergmanlab/mcclintock/ ) provides a user-friendly pipeline for the identification of TEs in short-read WGS data using multiple TE detectors, which should benefit researchers studying TE insertion variation in a wide range of different organisms. Application of the improved McClintock system to simulated and empirical yeast genome data reveals best-in-class methods and novel biological insights for one of the most widely-studied model eukaryotes and provides a paradigm for evaluating and selecting non-reference TE detectors in other species.

Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast

BACKGROUND

Many computational methods have been developed to detect non-reference transposable element (TE) insertions using short-read whole genome sequencing data. The diversity and complexity of such methods often present challenges to new users seeking to reproducibly install, execute, or evaluate multiple TE insertion detectors.

RESULTS

We previously developed the McClintock meta-pipeline to facilitate the installation, execution, and evaluation of six first-generation short-read TE detectors. Here, we report a completely re-implemented version of McClintock written in Python using Snakemake and Conda that improves its installation, error handling, speed, stability, and extensibility. McClintock 2 now includes 12 short-read TE detectors, auxiliary pre-processing and analysis modules, interactive HTML reports, and a simulation framework to reproducibly evaluate the accuracy of component TE detectors. When applied to the model microbial eukaryote Saccharomyces cerevisiae, we find substantial variation in the ability of McClintock 2 components to identify the precise locations of non-reference TE insertions, with RelocaTE2 showing the highest recall and precision in simulated data. We find that RelocaTE2, TEMP, TEMP2 and TEBreak provide a consistent and biologically meaningful view of non-reference TE insertions in a species-wide panel of ∼1000 yeast genomes, as evaluated by coverage-based abundance estimates and expected patterns of tRNA promoter targeting. Finally, we show that best-in-class predictors for yeast have sufficient resolution to reveal a dyad pattern of integration in nucleosome-bound regions upstream of yeast tRNA genes for Ty1, Ty2, and Ty4, allowing us to extend knowledge about fine-scale target preferences first revealed experimentally for Ty1 to natural insertions and related copia-superfamily retrotransposons in yeast.

CONCLUSION

McClintock (https://github.com/bergmanlab/mcclintock/) provides a user-friendly pipeline for the identification of TEs in short-read WGS data using multiple TE detectors, which should benefit researchers studying TE insertion variation in a wide range of different organisms. Application of the improved McClintock system to simulated and empirical yeast genome data reveals best-in-class methods and novel biological insights for one of the most widely-studied model eukaryotes and provides a paradigm for evaluating and selecting non-reference TE detectors for other species.

The path to immortalization of cells starts by managing stress through gene duplications

The genomes of immortalized cell lines (and cancer cells) are characterized by multiple types of aberrations, ranging from single nucleotide polymorphisms (SNPs) to structural rearrangements that have accumulated over time. Consequently, it is difficult to estimate the relative impact of different aberrations, the order of events, and which gene functions were under selective pressure at the early stage towards cellular immortalization. Here, we have established novel cell cultures derived from Drosophila melanogaster embryos that were sampled at multiple time points over a one-year period. Using short-read DNA sequencing, we show that copy-number gain in preferentially stress-related genes were acquired in a dominant fraction of cells in 300-days old cultures. Furthermore, transposable elements were active in cells of all cultures. Only a few (<1%) SNPs could be followed over time, and these showed no trend to increase or decrease. We conclude that the early cellular responses of a novel culture comprise sequence duplication and transposable element activity. During immortalization, positive selection first occurs on genes that are related to stress response before shifting to genes that are related to growth.

Local assembly of long reads enables phylogenomics of transposable elements in a polyploid cell line

Animal cell lines often undergo extreme genome restructuring events, including polyploidy and segmental aneuploidy that can impede de novo whole-genome assembly (WGA). In some species like Drosophila, cell lines also exhibit massive proliferation of transposable elements (TEs). To better understand the role of transposition during animal cell culture, we sequenced the genome of the tetraploid Drosophila S2R+ cell line using long-read and linked-read technologies. WGAs for S2R+ were highly fragmented and generated variable estimates of TE content across sequencing and assembly technologies. We therefore developed a novel WGA-independent bioinformatics method called TELR that identifies, locally assembles, and estimates allele frequency of TEs from long-read sequence data (https://github.com/bergmanlab/telr). Application of TELR to a ∼130x PacBio dataset for S2R+ revealed many haplotype-specific TE insertions that arose by transposition after initial cell line establishment and subsequent tetraploidization. Local assemblies from TELR also allowed phylogenetic analysis of paralogous TEs, which revealed that proliferation of TE families in vitro can be driven by single or multiple source lineages. Our work provides a model for the analysis of TEs in complex heterozygous or polyploid genomes that are recalcitrant to WGA and yields new insights into the mechanisms of genome evolution in animal cell culture.