Biology subjects

McCulloch, J. A.

Publications and source records attributed to McCulloch, J. A..

3 recordsLinked to original sources

bioRxiv2025

Gut microbial interaction networks control autoimmunity to neuroretina

The gut microbiome influences the development of immune-mediated inflammatory diseases, including autoimmune uveitis, a sight-threatening ocular inflammation driven by retina-specific T cells1. Using a model of spontaneous autoimmune uveitis (sEAU) we showed that gut commensals provide immune stimuli that trigger disease2. Here we report that uveitis-promoting microbes are present in human gut flora and that colonization of germ-free (GF) mice with commensals from healthy human donors was sufficient to provoke disease. Severity of sEAU correlated with expansion of Akkermansia and contraction of short-chain fatty acid (SCFA)-producing Firmicutes, followed by decreased SCFA levels and a dominant gut Th1 effector response. Mechanistic gain-of-function experiments, enriching GF sEAU mice with Akkermansia, reproduced these microbiome, metabolite and immune phenotype shifts, and exacerbated disease, suggesting that Akkermansia promotes autoimmunity by outcompeting SCFA-producers and enhancing Th1-type responses. An inverse correlation between Akkermansia (Verrucomicrobia) and Firmicutes was also present in patients with uveitis, multiple sclerosis and Crohns disease. These findings reveal a stereotypic gut microbial interaction network that regulates systemic immune balance, and may represent an ecologically conserved mechanism through which the gut microbiome modulates autoimmune and inflammatory diseases.

immunology↗

bioRxiv2024

Automated model discovery for textile structures:The unique mechanical signature of warp knitted fabrics

Textile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 214 =16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics-woven or knitted, weft knit or warp knit, polymeric or metallic-and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery as a novel strategy for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2023

JAMS - A framework for the taxonomic and functional exploration of microbiological genomic data

Shotgun microbiome sequencing analysis presents several challenges to accurately and consistently depict sample composition and functional potential. Here we present a two-part framework - JAMS (Just a Microbiology System) - whereby with raw fastq files and metadata as input, meaningful analysis within a sample and between a sample can be performed with ease for either shotgun or 16S sequences. JAMS is the first package to provide seamless deconvolution of functions into their taxonomic contributors. We validated our JAMS framework on two human gut shotgun metagenome test datasets against the popular tool MetaPhlAn 4. We further demonstrate the application of the JAMS package, particularly the plotting functions, on a mouse shotgun dataset.

bioinformatics↗