Oakes, M. L. | bioRxiv Science

bioRxiv2026

Chromosome-level genome assembly and annotation of the degu (Octodon degus)

High-quality reference genomes are essential for comparative and functional genomics yet remain unavailable for many emerging model organisms. Here, we report OctDeg2.0, a chromosome-level genome assembly and annotation of the degu (Octodon degus), generated using PacBio HiFi long-read sequencing and Hi-C scaffolding from a single male individual. Octodon degus is of growing interest as a natural animal model for aging and Alzheimers disease research. The 3.4 billion base-pair assembly comprises 28 autosomes and both sex chromosomes, with markedly improved contiguity and completeness over the prior short-read assembly while maintaining high base accuracy. The new assembly provides substantially enhanced gene annotation, clearer resolution of chromatin architecture and cis-regulatory element landscapes, with much improved characterization of repetitive and structurally complex regions, including centromeres and segmental duplications. We present a streamlined and reproducible pipeline for high-resolution de novo genome assembly and gene annotation applicable to other emerging model organisms. This work establishes a robust genomic resource for studying molecular and cellular mechanisms in health and disease in Octodon degus.

genomics↗

bioRxiv2023

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy of myeloid progenitor cells characterized by successive acquisition of genetic alterations. This inherent heterogeneity poses challenges in the development of precise and effective therapies. To gain insights into the genetic influence on drug response and optimize treatment selection, we performed targeted sequencing, ex vivo drug screening, and single-cell genomic profiling on leukemia cell samples derived from AML patients. We detected genetic signatures associated with sensitivity or resistance to specific agents. By integrating large public datasets, we discovered statistical patterns of co-occurring and mutually exclusive mutations in AML. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct sub- clones within AML patients. Machine learning models were built to predict ex vivo drug sensitivity using the genetic variants. Notably, these models demonstrated high accuracy in predicting sensitivity to some drugs, such as MEK inhibitors. Our study provides valuable resources for characterizing AML patients and predicting drug sensitivity, emphasizing the significance of considering subclonal distribution in drug response prediction. These findings provide a foundation for advancing precision medicine in AML. By tailoring treatment based on individual genetic profiles and functional testing, as well as accounting for the presence of subclones, we envision a future of improved therapeutic strategies for AML patients. One Sentence SummaryIntegrative computational and experimental analysis of mutation patterns and drug responses provide biologic insight and therapeutic guidance for patients with adult AML.

cell biology↗