Haynes, D. | Search bioRxiv

bioRxiv2023

Loss of Grem1-articular cartilage progenitor cells causes osteoarthritis.

Osteoarthritis (OA), which carries an enormous disease burden across the world, is characterised by irreversible degeneration of articular cartilage (AC), and subsequently bone. The cellular cause of OA is unknown. Here, using lineage tracing in mice, we show that the BMP-antagonist Gremlin 1 (Grem1) marks a novel chondrogenic progenitor (CP) cell population in the articular surface that generates joint cartilage and subchondral bone during development and adulthood. Notably, this CP population is depleted in injury-induced OA, and with age. OA is also induced by toxin-mediated ablation of Grem1 CP cells in young mice. Transcriptomic analysis and functional modelling in mice revealed articular surface Grem1-lineage cells are dependent on Foxo1; ablation of Foxo1 in Grem1-lineage cells led to early OA. This analysis identified FGFR3 signalling as a therapeutic target, and injection of its activator, FGF18, caused proliferation of Grem1-lineage CP cells, increased cartilage thickness, and reduced OA pathology. We propose that OA arises from the loss of CP cells at the articular surface secondary to an imbalance in progenitor cell homeostasis and present a new progenitor population as a locus for OA therapy.

cell biology↗

bioRxiv2022

Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn's disease

Microbial strains have closely related genomes but may have different phenotypes in the same environment. Shotgun metagenomic sequencing can capture the genomes of all strains present in a community but strain-resolved analysis from shotgun sequencing alone remains difficult. We developed an approach to identify and interrogate strain-level differences in groups of metagenomes. We use this approach to perform a meta-analysis of stool microbiomes from individuals with and without inflammatory bowel disease (IBD; Crohns disease, ulcerative colitis; n = 605), a disease for which there are not specific microbial biomarkers but some evidence that microbial strain variation may stratify by disease state. We first developed a machine learning classifier based on compressed representations of complete metagenomes (FracMinHash sketches) and identified genomes that correlate with IBD subtype. To rescue variation that may not have been present in the genomes, we then used assembly graph genome queries to recover strain variation for correlated genomes. Lastly, we developed a novel differential abundance framework that works directly on the assembly graph to uncover all sequence variants correlated with IBD. We refer to this approach as dominating set differential abundance analysis and have implemented it in the spacegraphcats software package. Using this approach, we identified five bacterial strains that are associated with Crohns disease. Our method captures variation within the entire sequencing data set, allowing for discovery of previously hidden disease associations.

bioinformatics↗