Genomic and biochemical approaches in the discovery of mechanisms for selective neuronal vulnerability to oxidative stress

<p>Abstract</p> <p>Background</p> <p>Oxidative stress (OS) is an important factor in brain aging and neurodegenerative diseases. Certain neurons in different brain regions exhibit selective vulnerability to OS. Currently little is known about the underlying mechanisms of this selective neuronal vulnerability. The purpose of this study was to identify endogenous factors that predispose vulnerable neurons to OS by employing genomic and biochemical approaches.</p> <p>Results</p> <p>In this report, using <it>in vitro </it>neuronal cultures, <it>ex vivo </it>organotypic brain slice cultures and acute brain slice preparations, we established that cerebellar granule (CbG) and hippocampal CA1 neurons were significantly more sensitive to OS (induced by paraquat) than cerebral cortical and hippocampal CA3 neurons. To probe for intrinsic differences between <it>in vivo </it>vulnerable (CA1 and CbG) and resistant (CA3 and cerebral cortex) neurons under basal conditions, these neurons were collected by laser capture microdissection from freshly excised brain sections (no OS treatment), and then subjected to oligonucleotide microarray analysis. GeneChip-based transcriptomic analyses revealed that vulnerable neurons had higher expression of genes related to stress and immune response, and lower expression of energy generation and signal transduction genes in comparison with resistant neurons. Subsequent targeted biochemical analyses confirmed the lower energy levels (in the form of ATP) in primary CbG neurons compared with cortical neurons.</p> <p>Conclusion</p> <p>Low energy reserves and high intrinsic stress levels are two underlying factors for neuronal selective vulnerability to OS. These mechanisms can be targeted in the future for the protection of vulnerable neurons.</p

The mutational constraint spectrum quantified from variation in 141,456 humans

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.publishedVersionPeer reviewe

Author Correction: The mutational constraint spectrum quantified from variation in 141,456 humans (Nature, (2020), 581, 7809, (434-443), 10.1038/s41586-020-2308-7)

10.1038/s41586-020-03174-8Nature590784

Author Correction: The mutational constraint spectrum quantified from variation in 141,456 humanS

In this Article, author Marquis P. Vawter was missing from the Genome Aggregation Database Consortium list. They are associated with the affiliation: ‘Department of Psychiatry &amp; Human Behavior, University of California Irvine, Irvine, CA, USA’, and contributed to the generation of the primary data incorporated into the gnomAD resource. In addition, in the legend to Fig. 1, ‘ten’ should have been ‘seven’ in the sentence: “a, Uniform manifold approximation and projection (UMAP)46,47 plot depicting the ancestral diversity of all individuals in gnomAD, using seven principal components.” The original Article has been corrected online

Author Correction: Transcript expression-aware annotation improves rare variant interpretation

In this Article, author Marquis P. Vawter was missing from the Genome Aggregation Database Consortium list. They are associated with the affiliation: ‘Department of Psychiatry &amp; Human Behavior, University of California Irvine, Irvine, CA, USA’, and contributed to the generation of the primary data incorporated into the gnomAD resource. The original Article has been corrected online