Biology subjects

Lowery, R.

Publications and source records attributed to Lowery, R..

3 recordsLinked to original sources

bioRxiv2026

A synthetic PGC1α co-activator reprograms microglial immunometabolism to preserve cone function in retinal degeneration

Retinitis pigmentosa (RP) is genetically heterogeneous, yet initiating mutations converge on primary rod degeneration followed by cone dysfunction and loss. Activated microglia are implicated in this secondary phase, but whether selective immunometabolic reprogramming of microglia preserves retinal function remains unknown. In human iPSC-derived microglia and retinal CD11b cells, PGC1 induction suppressed LPS-driven interferon signaling and cytokine release, yet Seahorse analysis and isotopologue tracing showed that energy metabolism remained altered. We therefore fused PGC1 to a multivalent p65-HSF1 module, yielding ~2.5-fold higher nuclear receptor output than native PGC1 and coordinated engagement of oxidative and anti-inflammatory programs in mouse retinal CD11b cells. Microglia-restricted expression in humanized RHOP347L mice preserved photopic responses through six months, increased retinal thickness and promoted ramified subretinal IBA1 morphology. Microglial immunometabolism thus represents a potentially genotype-independent neuroprotective target in RP, and engineered PGC1 co-activators provide a means to sustain oxidative metabolism under inflammatory pressure.

immunology↗

bioRxiv2025

Developing future resilience from signatures of adaptation across the sorghum pangenome

While the green revolution adapted a handful of crops to homogenous and high-input industrialized agriculture, much of the global population still relies on local food production from low-input smallholder farms that grow highly variable crop cultivars. The high diversity of the grain and bioenergy crop sorghum 1-4, and many other crops that were not homogenized during the green revolution 5, not only provides the raw materials for breeders to make substantial gains in cultivar improvement, but also constrains breeding efforts due to highly specialized locally adapted plant phenotypes 6. Here, we construct a 33-member pangenome and identify trait-associated variants in 1,988 cultivars and landraces. We then apply these resources to explore the complex interplay between historical contingency, ongoing adaptation, and the potential for future gains through climate-aware genome-enabled breeding. Specifically, our analyses conclusively demonstrate that multiple nested, deeply diverged, and previously uncharacterized structural variants in the domestication gene SHATTERING1 distinguish the previously established multicentric origin of sorghum. We then apply landscape genomics tests to reveal how gene flow, adaptation, and secondary contact created the complex genetic mosaic in current global breeding networks. Further analysis of climate-gene associations highlights candidate loci underlying adaptation, including the biosynthetic gene cluster for the cyanogenic glucoside dhurrin. Combined, the pangenome-informed variants developed here will enable both trait discovery and subsequent marker assays to accelerate breeding and provide a framework for similar applications in other diverse and non-model crops.

genomics↗

bioRxiv2023

A Model-Based Hierarchical Bayesian Approach to Sholl Analysis

SO_SCPLOWUMMARYC_SCPLOWDue to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still the gold standard for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a fully parametric model-based approach for analyzing Sholl data. We generalize our model to a hierarchical Bayesian framework so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to three real data examples and perform simulation studies comparing the proposed method with a popular alternative.

neuroscience↗