Regenerating zebrafish scales express a subset of evolutionary conserved genes involved in human skeletal disease

BACKGROUND: Scales are mineralised exoskeletal structures that are part of the dermal skeleton. Scales have been mostly lost during evolution of terrestrial vertebrates whilst bony fish have retained a mineralised dermal skeleton in the form of fin rays and scales. Each scale is a mineralised collagen plate that is decorated with both matrix-building and resorbing cells. When removed, an ontogenetic scale is quickly replaced following differentiation of the scale pocket-lining cells that regenerate a scale. Processes promoting de novo matrix formation and mineralisation initiated during scale regeneration are poorly understood. Therefore, we performed transcriptomic analysis to determine gene networks and their pathways involved in dermal scale regeneration. RESULTS: We defined the transcriptomic profiles of ontogenetic and regenerating scales of zebrafish and identified 604 differentially expressed genes (DEGs). These were enriched for extracellular matrix, ossification, and cell adhesion pathways, but not in enamel or dentin formation processes indicating that scales are reminiscent to bone. Hypergeometric tests involving monogenetic skeletal disorders showed that DEGs were strongly enriched for human orthologues that are mutated in low bone mass and abnormal bone mineralisation diseases (P< 2× 10(−3)). The DEGs were also enriched for human orthologues associated with polygenetic skeletal traits, including height (P< 6× 10(−4)), and estimated bone mineral density (eBMD, P< 2× 10(−5)). Zebrafish mutants of two human orthologues that were robustly associated with height (COL11A2, P=6× 10(−24)) or eBMD (SPP1, P=6× 10(−20)) showed both exo- and endo- skeletal abnormalities as predicted by our genetic association analyses; col11a2(Y228X/Y228X) mutants showed exoskeletal and endoskeletal features consistent with abnormal growth, whereas spp1(P160X/P160X) mutants predominantly showed mineralisation defects. CONCLUSION: We show that scales have a strong osteogenic expression profile comparable to other elements of the dermal skeleton, enriched in genes that favour collagen matrix growth. Despite the many differences between scale and endoskeletal developmental processes, we also show that zebrafish scales express an evolutionarily conserved sub-population of genes that are relevant to human skeletal disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-021-01209-8

LINE-1 Evasion of Epigenetic Repression in Humans

Epigenetic silencing defends against LINE-1 (L1) retrotransposition in mammalian cells. However, the mechanisms that repress young L1 families and how L1 escapes to cause somatic genome mosaicism in the brain remain unclear. Here we report that a conserved Yin Yang 1 (YY1) transcription factor binding site mediates L1 promoter DNA methylation in pluripotent and differentiated cells. By analyzing 24 hippocampal neurons with three distinct single-cell genomic approaches, we characterized and validated a somatic L1 insertion bearing a 3' transduction. The source (donor) L1 for this insertion was slightly 5' truncated, lacked the YY1 binding site, and was highly mobile when tested in\ua0vitro. Locus-specific bisulfite sequencing revealed that the donor L1 and other young L1s with mutated YY1 binding sites were hypomethylated in embryonic stem cells, during neurodifferentiation, and in liver and brain tissue. These results explain how L1 can evade repression and retrotranspose in the human body

Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach

Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 106 datapoints, establishing its usability for large-scale datasets, like genomics. Furthermore, RFlocalfdr is compatible with any RF implementation that returns a VIM and counts, making it a versatile feature selection tool that reduces false discoveries