Biology subjects

Willett, R.

Publications and source records attributed to Willett, R..

3 recordsLinked to original sources

bioRxiv2025

Collective Microbial Effects Drive Toxin Bioremediation and Enable Rational Design

The metabolic activity of microbial communities is essential for host and environmental health, influencing processes from immune regulation to bioremediation. Given this importance, the rational design of microbiomes with targeted functional properties is an important objective. Designing microbial consortia with targeted functions is challenging due to complex community interactions and environmental heterogeneity. Community-function landscapes address this challenge by statistically inferring impacts of species presence or absence on function. Similar to fitness landscapes, community-function landscapes are shaped by both additive effects and interactions (epistasis) among species that influence function. Here, we apply the community-function landscape approach to design synthetic microbial consortia to degrade the toxic environmental contaminant bisphenol-A (BPA). Using synthetic communities of BPA-degrading isolates, we map community-function landscapes across increasing BPA concentrations, where higher BPA means greater toxicity. As toxicity increases, so does epistasis, indicating that collective effects become more important in degradation. Further, we leverage landscapes to rationally design communities with predictable BPA degradation dynamics in vitro. Remarkably, designed synthetic communities are able to remediate BPA in contaminated soils. Our results demonstrate that toxicity can drive epistatic interactions in community-function landscapes and that these landscapes can guide microbial consortia design for bioremediation.

microbiology↗

bioRxiv2024

Chromatin Structures from Integrated AI and Polymer Physics Model

The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure from indirect measures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.

biophysics↗

bioRxiv2024

RosetteArray Platform for Quantitative High-Throughput Screening of Human Neurodevelopmental Risk

Neural organoids have revolutionized how human neurodevelopmental disorders (NDDs) are studied. Yet, their utility for screening chemical hazards and prospective therapeutics for NDDs is limited by a lack of morphological reproducibility and cost-effective scalability. Here, we describe the RosetteArray platform, which can be used as an off-the-shelf, 96-well plate assay that standardizes incipient forebrain and spinal cord organoid morphogenesis as adherent, micropatterned, 3-D, singularly polarized neural rosette tissues ([~]200 and [~]800 per well, respectively). Seeded directly from cryopreserved human pluripotent stem cells, RosetteArrays are cultured over 6-8 days and fixed, immunostained and imaged in situ to enable artificial intelligence-based quantitative analysis. By screening the inception of [~]75,000 neural organoids throughout this manuscript, we provide proof-of-concept demonstrations of the platforms utility for detecting developmental neurotoxicity hazard and screening genetic and environmental factors known to cause clinical Neural Tube Defect risk. Given the documented perturbation of rosette morphogenesis in neural organoid models of several NDDs, the RosetteArray platform could enable quantitative high-throughput screening (qHTS) of human neurodevelopmental risk across regulatory and precision medicine applications.

neuroscience↗