Selected Grantee Publications
Genetic Diversity of 1,845 Rhesus Macaques Improves Genetic Variation Interpretation and Identifies Disease Models
Wang et al., Nature Communications. 2024.
https://www.nature.com/articles/s41467-024-49922-6
Nonhuman primates are ideal models for certain human diseases, including retinal and neurodevelopmental disorders. Using a reverse genetics approach, researchers profiled the genetic diversity of rhesus macaque populations across eight primate research centers in the United States and uncovered rhesus macaques carrying naturally occurring pathogenic mutations. They identified more than 47,000 single-nucleotide variants in 374 genes that had been previously linked with retinal and neurodevelopmental disorders in humans. These newly identified variants can be used to study human disease pathology and to test novel treatments. Supported by ORIP (P51OD011107, P51OD011106, P40OD012217, S10OD032189), NEI, NIAID, and NIMH.
Natural Disaster and Immunological Aging in a Nonhuman Primate
Watowich et al., PNAS. 2022.
https://www.pnas.org/content/119/8/e2121663119
Weather-related disasters can exacerbate existing morbidities and increase mortality risk. Researchers examined Hurricane Maria’s impact on immune cell gene expression in large, age-matched, cross-sectional samples from free-ranging rhesus macaques (Macaca mulatta) living on an isolated island. Hurricane Maria was significantly associated with differential expression of 4% of immune-cell-expressed genes and was correlated with age-associated alterations in gene expression, in addition to expression of key immune genes, dysregulated proteostasis networks, and greater expression of inflammatory immune cell-specific marker genes. These findings illuminate that natural disasters might become biologically embedded and contribute to earlier onset of disease and death. Supported by ORIP (P40OD012217), NIA, NIMH.
Deep Learning Is Widely Applicable to Phenotyping Embryonic Development and Disease
Naert et al., Development. 2021.
https://pubmed.ncbi.nlm.nih.gov/34739029/
Genome editing simplifies the generation of new animal models for congenital disorders. The authors illustrate how deep learning (U-Net) automates segmentation tasks in various imaging modalities. They demonstrate this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). They provide a library of pre-trained networks and detailed instructions for applying deep learning to datasets and demonstrate the versatility, precision, and scalability of deep neural network phenotyping on embryonic disease models. Supported by ORIP (P40OD010997, R24OD030008), NICHD, NIDDK, and NIMH.
Multiplexed Drug-Based Selection and Counterselection Genetic Manipulations in Drosophila
Matinyan et al., Cell Reports. 2021.
https://www.cell.com/cell-reports/pdf/S2211-1247(21)01147-5.pdf
Many highly efficient methods exist which enable transgenic flies to contribute to diagnostics and therapeutics for human diseases. In this study, researchers describe a drug-based genetic platform with four selection and two counterselection markers, increasing transgenic efficiency by more than 10-fold compared to established methods in flies. Researchers also developed a plasmid library to adapt this technology to other model organisms. This highly efficient transgenic approach significantly increases the power of not only Drosophila melanogaster but many other model organisms for biomedical research. Supported by ORIP (P40OD018537, P40OD010949, R21OD022981), NCI, NHGRI, NIGMS, and NIMH.