Biology subjects

Little, A.

Publications and source records attributed to Little, A..

8 recordsLinked to original sources

bioRxiv2026

Microbiome histidine competition mediates dietary control of systemic imidazole propionate

The gut microbiome produces numerous metabolites that influence mammalian health. While microbiome composition and diet influence metabolite concentrations, how these factors interact remains incompletely defined. Here we find production of imidazole propionate (ImP), a microbial metabolite associated with cardiometabolic and neurodegenerative diseases, is determined by the balance of competing metabolic pathways that catabolize histidine to ImP or short-chain fatty acids (SCFAs). We show glutamate serves as a preferred substrate that selectively inhibits histidine conversion to SCFAs, redirecting flux to increased ImP production across mouse- and human-derived microbial communities. We find dietary monosodium glutamate (MSG) acting via this mechanism boosts ImP production in the mouse gut, transiently impairing glucose tolerance and increasing systemic ImP. These findings show that predictable interactions between dietary substrate and microbial competition control systemic ImP levels, providing a mechanistic framework for understanding microbiome metabolite production more broadly.

microbiology↗

bioRxiv2025

Cutaneous lupus features specialized stromal niches and altered retroelement expression

Cutaneous Lupus is an inflammatory skin disease causing highly morbid inflamed skin and hair loss. In order to investigate the pathophysiology of cutaneous lupus, we performed single-cell RNA and spatial sequencing of lesional and non-lesional cutaneous lupus skin compared to healthy controls. Pathway enrichment analyses of lesional keratinocytes revealed elevated responses to type I interferon, type II interferon, tumor necrosis factor, and apoptotic signaling. Detailed clustering demonstrated unique fibroblasts specific to lupus skin with likely roles in inflammatory cell recruitment and fibrosis. We also evaluated the association of retroelement expression with type I interferons in the skin. We observed increased retroelement expression which correlated with interferon-stimulated genes across multiple cell types. Moreover, we saw elevated expression of genes involved in RIG-I and cGAS-STING pathways, which transduce elevated nucleic acid signals. Treatment of active cutaneous lupus with Anifrolumab reduced RIG-I and cGAS-STING pathways in addition to the most abundant retroelement family, L2b. Our studies better define type I interferon-mediated immunopathology in cutaneous lupus and identify an association between retroelement expression and interferon signatures in cutaneous lupus.

immunology↗

bioRxiv2024

Detecting Clinically Relevant Topological Structures in Multiplexed Spatial Proteomics Imaging Using TopKAT

Novel multiplexed spatial proteomics imaging platforms expose the spatial architecture of cells in the tumor microenvironment (TME). The diverse cell population in the TME, including its spatial context, has been shown to have important clinical implications, correlating with disease prognosis and treatment response. The accelerating implementation of spatial proteomic technologies motivates new statistical models to test if cell-level images associate with patient-level endpoints. Few existing methods can robustly characterize the geometry of the spatial arrangement of cells and also yield both a valid and powerful test for association with patient-level outcomes. We propose a topology-based approach that combines persistent homology with kernel testing to determine if topological structures created by cells predict continuous, binary, or survival clinical endpoints. We term our method TopKAT (Topological Kernel Association Test) and show that it can be more powerful than statistical tests grounded in the spatial point process model, particularly when cells arise along the boundary of a ring. We demonstrate the properties of TopKAT through simulation studies and apply it to two studies of triple negative breast cancer where we show that TopKAT recovers clinically relevant topological structures in the spatial distribution of immune and tumor cells.

bioinformatics↗

bioRxiv2024

Weighted variance component test for the integrative multi-omics analysis of microbiome data

Metabolic dysregulation and alterations have been linked to various diseases and conditions. Innovations in high-throughput technology now allow rapid profiling of the metabolome and metagenome -- often the gene content of bacterial populations -- for characterizing metabolism. Due to the small sample sizes and high dimensionality of the data, pathway analysis (wherein the effect of multiple genes or metabolites on an outcome is cumulatively assessed) of metabolomic data is commonly conducted and also represents a standard for metagenomic analysis. However, how to integrate both data types remains unclear. Recognizing that a metabolic pathway can be complementarily characterized by both metagenomics and metabolomics, we propose a weighted variance components framework to test if the joint effect of genes and metabolites in a biological pathway is associated with outcomes. The approach allows analytic p-value calculation, correlation between data types, and optimal weighting. Power simulations show that our approach often outperforms other strategies while maintaining type I error. The approach is illustrated on real data.

bioinformatics↗

bioRxiv2021

Clustering and visualization of single-cell RNA-seq data using path metrics

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.

genomics↗

bioRxiv2021

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular phenotypes emerge from a hierarchy of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be extracted from the statistical pattern of proteome variation as measured across thousands of bacteria and that these hierarchies reflect the emergence of complex bacterial phenotypes. We describe the mathematics underlying our statistical approach and validate our results through gene-set enrichment analysis and comparison to existing experimentally-derived hierarchical databases. We demonstrate the biological utility of our unbiased hierarchical models by creating a model of motility in Pseudomonas aeruginosa and using it to discover a previously unappreciated genetic effector of twitch-based motility. Overall, our approach, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), predicts hierarchies of protein interaction networks describing emergent biological function using only the statistical pattern of bacterial proteome variation.

systems biology↗

bioRxiv2020

The p-coumaroyl arabinoxylan transferase HvAT10 underlies natural variation in whole-grain cell wall phenolic acids in cultivated barley

Phenolic acids in cereal grains have important health-promoting properties and influence digestibility for industrial or agricultural uses. Here we identify alleles of a single BAHD p-coumaroyl arabinoxylan transferase gene, HvAT10, as responsible for the natural variation in cell wall-esterified p-coumaric and ferulic acid in whole grain of a collection of cultivated two-row spring barley genotypes. We show that HvAT10 is rendered non-functional by a premature stop codon mutation in approximately half of the genotypes in our mapping panel. The causal mutation is virtually absent in wild and landrace germplasm suggesting an important function for grain arabinoxylan p-coumaroylation pre-domestication that is dispensable in modern agriculture. Intriguingly, we detected detrimental impacts of the mutated locus on barley grain quality traits. We propose that HvAT10 could be a focus for future grain quality improvement or for manipulating phenolic acid content of wholegrain food products.

plant biology↗

bioRxiv2019

Feature Design for Protein Interface hotspots using KFC2 and Rosetta

Protein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally charac-terizing protein residues that contribute the most to protein-protein interaction affin-ity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein-protein interfaces provides important information about how to inhibit therapeutically relevant protein-protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein-protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A 2-way and 3-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.

bioinformatics↗