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Scown, C. D.

Publications and source records attributed to Scown, C. D..

4 recordsLinked to original sources

Distinct Strategies for Engineering Bioenergy Traits in Sorghum Drive Divergent Functional Shifts in the Rhizosphere Microbiome

Engineering bioenergy crops for enhanced biomass digestibility and high-value co-products is a promising approach to sustainable biofuel production. However, the impact of these engineered crops on the belowground microbiome remains poorly understood. In this study, we characterized the composition and functional responses of the belowground microbiome associated with transgenic sorghum lines: one with reduced cell wall crosslinking (AT10) and another with elevated protocatechuic acid (PCA) production (Qsub). The lines were grown under field conditions with four irrigation levels, and samples were collected twice during the growing season. We found that the two transgenic types drove a subtle but significant divergence in bacterial communities, both compositionally and functionally. Community-wide functional profiling showed that AT10 rhizospheres were consistently depleted in genes associated with mobile genetic elements, while Qsub rhizospheres were enriched in TonB-dependent transport and vanillate monooxygenase, an enzyme that funnels lignin-derived monomers toward PCA degradation, alongside reduced pectin- and cellulose-degradation genes. Genome-resolved analysis linked these community-level shifts to specific taxa: in Qsub, the enriched MAGs were taxonomically diverse, ranging from genome-reduced Patescibacteria with an episymbiotic lifestyle to taxa enriched in lignin-associated auxiliary activity enzymes. In AT10, the depleted Azospirillaceae MAGs carried unusually high mobile genetic element content, consistent with the community-wide pattern. Collectively, these findings offer new insights into how genetic modifications aimed at improving bioenergy traits shapes the composition and function of the rhizosphere microbiome, with potential downstream consequences for soil carbon cycling.

plant biology↗

Biosensor-Driven Strain Engineering Reveals Key Cellular Processes for Maximizing Isoprenol Production in Pseudomonas putida

Synthetic biology tools have accelerated the generation of simple mutants, but combinatorial testing remains challenging. High-throughput methods struggle translating from proof-of-principle molecules to advanced bioproducts. We address this challenge with a biosensor-driven strategy for enhanced isoprenol production in Pseudomonas putida, a key precursor for sustainable aviation fuel and platform chemicals. This biosensor leverages P. putidas native response to short-chain alcohols via a previously uncharacterized hybrid histidine kinase signaling cascade. Refactoring the biosensor for a conditional growth-based selection enabled identification of competing cellular processes with a [~]16,500-member CRISPRi-library. An iterative combinatorial strain engineering approach yielded an integrated P. putida strain producing [~]900 mg/L isoprenol in glucose minimal medium, a 36-fold increase. Ensemble -omics analysis revealed metabolic rewiring, including amino acid accumulation as key drivers of enhanced production. Techno-economic analysis elucidated the path to economic viability and confirmed the benefits of adding amino acids outweigh the additional costs. This study establishes a robust biosensor driven approach for optimizing other heterologous pathways, accelerating microbial cell factory development. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/643695v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@533605org.highwire.dtl.DTLVardef@ff98fforg.highwire.dtl.DTLVardef@9592fdorg.highwire.dtl.DTLVardef@1e52993_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

Genomics Characterization of an Engineered Corynebacterium glutamicum in Bioreactor Cultivation under Ionic Liquid Stress

Corynebacterium glutamicum is an ideal microbial chassis for the production of valuable bioproducts including amino acids and next-generation biofuels. Here we resequence engineered isopentenol (IP) producing C. glutamicum BRC-JBEI 1.1.2 strain and assess differential transcriptional profiles using RNA sequencing under industrially relevant conditions including scale transition and compare the presence vs. absence of an ionic liquid, cholinium lysinate ([Ch][Lys]). Analysis of the scale transition from shake flask to bioreactor with transcriptomics identified a distinct pattern of metabolic and regulatory responses needed for growth in this industrial format. These differential changes in gene expression corroborate altered accumulation of organic acids and bioproducts, including succinate, acetate, and acetoin that occur when cells are grown in the presence of 50mM [Ch][Lys] in the stirred-tank reactor. This new genome assembly and differential expression analysis of cells grown in a stirred tank bioreactor clarify the cell response of a C. glutamicum strain engineered to produce IP.

systems biology↗

Tree-based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste

The dynamics of microbial communities involved in anaerobic digestion of mixed organic waste are notoriously complex and difficult to model, yet successful operation of anaerobic digestion is critical to the goals of diverting high-moisture organic waste from landfills. Machine learning (ML) is ideally suited to capturing complex and nonlinear behavior that cannot be modeled mechanistically. This study uses 8 years of data collected from an industrial-scale anaerobic co-digestion (AcoD) operation at a municipal wastewater treatment plant in Oakland, California, combined with a powerful automated ML method, Tree-based Pipeline Optimization Tool, to develop an improved understanding of how different waste inputs and operating conditions impact biogas yield. The model inputs included daily input volumes of 31 waste streams and 5 operating parameters. Because different wastes are broken down at varying rates, the model explored a range of time lags ascribed to each waste input ranging from 0 to 30 days. The results suggest that the waste types (including rendering waste, lactose, poultry waste, and fats, oils, and greases) differ considerably in their impact on biogas yield on both a per-gallon basis and a mass of volatile solids basis, while operating parameters are not useful predictors in a carefully operated facility.

bioinformatics↗