Biology subjects

Sands, C.

Publications and source records attributed to Sands, C..

3 recordsLinked to original sources

bioRxiv2023

Programming Probiotics: Diet-responsive gene expression and colonization control in engineered S. boulardii

Saccharomyces boulardii (Sb) is an emerging probiotic chassis for delivering biomolecules to the mammalian gut, offering unique advantages as the only eukaryotic probiotic. However, precise control over gene expression and gut residence time in Sb have remained challenging. To address this, we developed five ligand-responsive gene expression systems and repaired galactose metabolism in Sb, enabling inducible gene expression in this strain. Engineering these systems allowed us to construct AND logic gates, control the surface display of proteins, and turn on protein production in the mouse gut in response to a dietary sugar. Additionally, repairing galactose metabolism expanded Sbs habitat within the intestines and resulted in galactose-responsive control over gut residence time. This work opens new avenues for precise dosing of therapeutics by Sb via control over its in vivo gene expression levels and localization within the gastrointestinal tract.

bioengineering↗

bioRxiv2022

Genome-wide prediction of dominant and recessive neurodevelopmental disorder risk genes

Despite great progress in the identification of neurodevelopmental disorder (NDD) risk genes, there are thousands that remain to be discovered. Computational tools that provide accurate gene-level predictions of NDD risk can significantly reduce the costs and time needed to prioritize and discover novel NDD risk genes. Here, we first demonstrate that machine learning models trained solely on single-cell RNA-sequencing data from the developing human cortex can robustly predict genes implicated in autism spectrum disorder (ASD), developmental and epileptic encephalopathy (DEE), and developmental delay (DD). Strikingly, we find differences in gene expression patterns of genes with monoallelic and biallelic inheritance patterns. We then integrate these expression data with 300 orthogonal features in a semi-supervised machine learning framework (mantis-ml) to train inheritance-specific models for ASD, DEE, and DD. The models have high predictive power (AUCs: 0.84 to 0.95) and top-ranked genes were up to two-fold (monoallelic models) and six-fold (biallelic models) more enriched for high-confidence NDD risk genes than genic intolerance metrics. Across all models, genes in the top decile of predicted risk genes were 60 to 130 times more likely to have publications strongly linking them to the phenotype of interest in PubMed compared to the bottom decile. Collectively, this work provides highly robust novel NDD risk gene predictions that can complement large-scale gene discovery efforts and underscores the importance of incorporating inheritance into gene risk prediction tools (https://nddgenes.com).

genetics↗

bioRxiv2019

Viral and bacterial infection elicit distinct changes in plasma lipids in febrile children

Fever is the most common reason that children present to Emergency Departments in the UK. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. As a result, many children are prescribed antibiotics often unnecessarily, while others with life-threatening bacterial infections can remain untreated. The omics approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n=20) and confirmed viral infection (n=20). We show for the first time that bacterial and viral infection elicit distinct changes in the host lipidome. Glycerophosphoinositol, sphingomyelin, lysophosphotidylcholine and cholesterol sulfate were increased in the confirmed virus infected group, while fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were increased in cases with confirmed bacterial infection. A combination of three lipids achieved the area under the receiver operating characteristic (ROC) curve of 0.918 (95% CI 0.835 to 1). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics.

systems biology↗