Human L1 Transposition Dynamics Unraveled with Functional Data Analysis

Jul 28, 2020· D Chen, MA Cremona, Z Qi, RD Mitra, F Chiaromonte, KD Makova · 0 min read
Abstract
Long INterspersed Elements-1 (L1s) constitute >17% of the human genome and still actively transpose in it. Characterizing L1 transposition across the genome is critical for understanding genome evolution and somatic mutations. However, to date, L1 insertion and fixation patterns have not been studied comprehensively. To fill this gap, we investigated three genome-wide data sets of L1s that integrated at different evolutionary times: 17,037 de novo L1s (from an L1 insertion cell-line experiment conducted in-house), and 1,212 polymorphic and 1,205 human-specific L1s (from public databases). We characterized 49 genomic features—proxying chromatin accessibility, transcriptional activity, replication, recombination, etc.—in the ±50 kb flanks of these elements. These features were contrasted between the three L1 data sets and L1-free regions using state-of-the-art Functional Data Analysis statistical methods, which treat high-resolution data as mathematical functions. Our results indicate that de novo, polymorphic, and human-specific L1s are surrounded by different genomic features acting at specific locations and scales. This led to an integrative model of L1 transposition, according to which L1s preferentially integrate into open-chromatin regions enriched in non-B DNA motifs, whereas they are fixed in regions largely free of purifying selection—depleted of genes and noncoding most conserved elements. Intriguingly, our results suggest that L1 insertions modify local genomic landscape by extending CpG methylation and increasing mononucleotide microsatellite density. Altogether, our findings substantially facilitate understanding of L1 integration and fixation preferences, pave the way for uncovering their role in aging and cancer, and inform their use as mutagenesis tools in genetic studies.
Type
Publication
Molecular Biology and Evolution
publication
Di (Bruce) Chen
Authors
Ph.D. student
Born and raised up in Nanjing, China. Trained as a computational biologist and amateur violinist. Big fan of backpacking, sci-fi, and research. Current work leverages bioinformatics tools and statistics in comparative genomics, with special interest in the regional variation of mutation rate in human genome.
Marzia Cremona
Authors
Visiting Research Professor
Marzia develops statistical techniques for the analysis of large, high-dimensional and complex data – in particular functional data. An important aspect of her research is its collaborative and multidisciplinary nature. Indeed, much of her work is at the interface between statistics and computational biology, and her main application area is in the “Omics” sciences.
Francesca Chiaromonte
Authors
Francesca is a statistician developing methods for the analysis of large, high-dimensional and complex data, and applying such methods in contemporary “Omics” sciences and other scientific fields – including Meteorology and Economics.
Kateryna Makova, Ph.D.
Authors
Professor
Kateryna is interested in genomics, evolution, and human genetics. Her lab studies mutations using both computational and experimental approaches. Additional topics of interest include sex chromosome evolution and genomics of childhood obesity.