A unified genealogy of modern and ancient genomes is presented in Science this week, setting a foundation for building a comprehensive understanding of human genomic diversity. The ability to sequence the genomes of modern-day and ancient individuals has transformed the study of human history and evolution but characterizing ancestral relationships from the totality of human genomic variation remains an unsolved challenge. Starting from the notion that the ancestral relationships of all humans who have ever lived can be described by a single genealogy, investigators from Harvard University apply statistical and computational methods to infer a unified tree sequence of 3,601 modern and eight high-coverage ancient human genome sequences compiled from eight datasets. The structure is a lossless and compact representation of 27 million ancestral haplotype fragments and 231 million ancestral lineages linking genomes from these datasets back in time, they write, and benefits from the use of thousands of ancient samples compiled from more than 100 publications to constrain and date relationships. The scientists use the tree to recover relationships between individuals and populations, as well as to identify descendants of ancient samples. They also describe a simple nonparametric estimator of the geographical location of ancestors that recapitulates key events in human history. GenomeWeb has more on this, here.
An analysis of DNA methylation patterns in patients with amyotrophic lateral sclerosis (ALS) is reported in Science Translational Medicine this week, implicating metabolic, inflammatory, and cholesterol pathways in the disease. Previous research has linked epigenetic modifications to neurodegenerative conditions such as ALS, suggesting that specific epigenetic patterns could provide insights into disease pathophysiology and aid in the identification of potential drug targets. In the newly published study, an international team led by scientists from University Medical Center Utrecht examined DNA methylation in blood samples from nearly 10,000 ALS patients and controls, identifying 45 differentially methylated positions (DMPs) annotated to 42 genes that are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity, with several of the DMPs associated with disease progression and survival rates, suggesting that "they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions," the authors write. GenomeWeb also covers this, here.
Despite the efficacy of platinum-based chemotherapy for metastatic cancer treatment, chemoresistance frequently occurs and remains an unmet challenge in oncology. To characterize chemotherapy resistance processes in one type of cancer that often becomes resistant to chemotherapy — high-grade serous ovarian cancer (HGSOC) — a group from the University of Helsinki prospectively collected patient tissue samples before and after chemotherapy and analyzed their transcriptomic profiles at a single-cell resolution. As reported in this week's Science Advances, they discovery a consistent increase in stress-associated cell state during chemotherapy, a finding they validate by RNA in situ hybridization and bulk RNA sequencing. "The stress-associated state exists before chemotherapy, is subclonally enriched during the treatment, and associates with poor progression-free survival," they write. "Co-occurrence with an inflammatory cancer-associated fibroblast subtype in tumors implies that chemotherapy is associated with stress response in both cancer cells and stroma, driving a paracrine feed-forward loop." The findings define a cell state that allows biomarker-based prediction and targeting of chemoresistance, they add.