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Huggins, J.

Publications and source records attributed to Huggins, J..

2 recordsLinked to original sources

Defining mutational signatures of lung cancer-associated carcinogens through in vitro exposure of human airway epithelial cells

While distinct environmental exposures imprint unique mutational signatures on cancer genomes, the specific causal patterns for many known carcinogens remain uncharacterized in relevant human tissues. To address this gap, we developed a novel, physiologically relevant system that uses a combination of airway epithelial cells and whole genome sequencing to characterize mutational patterns induced by genotoxic carcinogens associated with lung cancer. After validating the platforms accuracy by successfully recapturing the known signature for Benzo(a)pyrene (BaP), we used this system to gain detailed insights into the types of mutations that occur with exposure to N-nitrosotris-(2-chloroethyl) urea (NTCU) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), genotoxic compounds that induce lung squamous cell carcinoma and lung adenocarcinoma in mouse models, respectively. Cells exposed to NTCU had significantly more somatic SNVs compared to control samples. An average of 82.3% of mutations in NTCU samples were attributed to a novel mutational signature distinct from those in the COSMIC database but highly correlated with recent in vivo mouse models. In contrast, NNK exposure did not demonstrate a distinct mutational pattern above background at both high and low concentrations. Ultimately, this in vitro system provides a robust platform to define causal links between environmental exposures and mutational patterns in lung cancer mutagenesis. Statement of SignificanceIn vitro exposure of N-nitrosotris-(2-chloroethyl) urea to airway epithelial cells revealed a distinct mutational signature.

bioinformatics↗

Mapping the North American Terrestrial Carbon Cycle: A Process-based Reanalysis Using State Data Assimilation (SDA)

AbstractThe ability to accurately assess ecosystem C budgets across scales from individual sites to continents is essential for C accounting, management, and ultimately mitigating climate change. State data assimilation (SDA) provides a framework for harmonizing observations with models, while robustly accounting for and reducing multiple sources of uncertainty. In this study, we employed a hybrid SDA framework that combines process-based terrestrial biosphere modeling, hierarchical Bayesian inference, and machine learning to harmonize bottom-up and remotely-sensed data streams for 8,000 pre-selected 1km2 locations across North America within a hybrid structure. Combining bottom-up soils data (SoilGrids) with spectral (MODIS and Landsat) and microwave (SMAP) remote sensing helps constrain the major C and water stocks through space and time. Machine learning is used both to identify and correct systematic errors in the process model (SIPNET) and to interpolate the pre-selected locations onto a 1km grid, making it computationally feasible to generate annual ensemble maps of the North American carbon budget. Furthermore, the uncertainties for each variable were reduced compared to those from observations or models alone. Spatiotemporal analysis showed a slight decrease in aboveground biomass (AGB) across the western US, a loss of leaf area across the boreal, and a slight greening of the Alaskan tundra. The uncertainty trends suggest a significant reduction in the uncertainty about soil organic carbon (SOC), the largest C reservoir. Validation results show that we accurately estimate C pools, compared to the assimilated data streams and held-out observations of AGB from GEDI, ICESat-2, and the US FIA, and SOC from the ISCN network. Our ML-debiasing algorithm further improved the accuracy of major C pools (AGB, SOC). In general, our continental SDA framework will facilitate global C MRV (monitoring, reporting, and verification) by providing accurate and precise C-cycle estimates, along with their corresponding spatiotemporal uncertainties.

ecology↗