Investigators at the Beijing Institute of Radiation Medicine and elsewhere search for structural variants that may contribute to high-altitude adaptations in Tibet. Using long-read genome sequences for 15 Tibetan individuals and 10 Han Chinese individuals, the team identified more than 38,000 distinct structural variants, including thousands not documented in the past. When they used these insights to genotype hundreds of additional genomes, the authors saw 69 structural variants with distinct patterns in genomes from Han Chinese and Tibetan populations, along with 80 related genes suspected of contributing to altitude adaptation and half a dozen potentially adaptive structural variants that appear to stem from historical introgressions with archaic hominins. "Overall, our results highlight the important role of [structural variants] in the evolutionary processes of Tibetans' adaptation to the Qinghai-Tibet Plateau," they write, "and provide a valuable resource for future high-altitude adaptation studies."
A Chinese Academy of Sciences- and Fudan University-led team describes distinct epigenetic subtypes of lung adenocarcinoma (LUAD) that appear to have ties to disease progression and patient outcomes. Using high-resolution ChIP-seq, the researchers tracked the distribution of the histone H3K27ac mark — which tends to coincide with transcriptionally active genes, super-enhancers, enhancers, and other active regulatory elements — in tumor and matched normal samples from 42 individuals with LUAD, bringing in additional RNA sequence data to distinguish between a prognostically poor group of tumors marked by proliferation-prone, de-differentiated cells and a set of tumors found in patients with better outcomes that had altered core regulator activity affecting a proposed tumor suppressor gene called CLU. "Taken together, our study expands the understanding of LUAD complexity by a systematic analysis of epigenetic and transcriptomic signatures," they write, "providing [an] important supplement to current histologic and molecular classifications."
Finally, researchers at the Ontario Institute for Cancer Research and University of Toronto uncover regulatory mutations in thousands of cancer genomes with the help of a statistical method known as the "regression models for localized mutations," or RM2. "[W]e developed a new statistical framework that quantifies the activity of mutational processes and signatures on specific classes of non-coding elements of the cancer genome," the team says. With whole-genome sequences, ChIP-seq profiles, and other data for some 2,419 samples spanning several cancer types, for example, the authors assessed mutational processes at transcription start sites, tissue-specific open chromatin regions, and CTCF transcription factor regulator binding sites. "Our method and catalogue of localized mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair, and driver gene discovery," they report, adding that "functional and genetic correlates of mutational processes suggest mechanistic hypotheses for future studies."