Purifying Selection in Deeply Conserved Human Enhancers Is More Consistent than in Coding Sequences

(c) 2014 De Silva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Principles of Glomerular Organization in the Human Olfactory Bulb – Implications for Odor Processing

Olfactory sensory neurons (OSN) in mice express only 1 of a possible 1,100 odor receptors (OR) and axons from OSNs expressing the same odor receptor converge into ∼2 of the 1,800 glomeruli in each olfactory bulb (OB) in mice; this yields a convergence ratio that approximates 2∶1, 2 glomeruli/OR. Because humans express only 350 intact ORs, we examined human OBs to determine if the glomerular convergence ratio of 2∶1 established in mice was applicable to humans. Unexpectedly, the average number of human OB glomeruli is >5,500 yielding a convergence ratio of ∼16∶1. The data suggest that the initial coding of odor information in the human OB may differ from the models developed for rodents and that recruitment of additional glomeruli for subpopulations of ORs may contribute to more robust odor representation

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare