Costa, M. R. | Search bioRxiv

bioRxiv2018

Evidence of Muller glia conversion into retina ganglion cells using Neurogenin2

Macular Degeneration, Glaucoma, and Retinitis Pigmentosa are all leading causes of irreversible visual impairment in the elderly, affecting hundreds of millions of patients. Muller glia cells (MGC), the main type of glia found in the vertebrate retina, can resume proliferation in the adult injured retina and contribute to tissue repair. Also, MGC can be genetically reprogrammed through the expression of the transcription factor (TF) Achaete-scute homolog 1 (ASCL1) into induced neurons (iNs), displaying key hallmarks of photoreceptors, bipolar and amacrine cells, which may contribute to regenerate the damaged retina. Here, we show that the TF neurogenin 2 (NEUROG2) is also sufficient to lineage-reprogram MGC into iNs. The efficiency of MGC lineage conversion by NEUROG2 is similar to that observed after expression of ASCL1. However, reprogramming efficiency is affected by previous exposure to EGF and FGF2 during the expansion of MGC population. Transduction of either Neurog2 or Ascl1 led to the upregulation of key retina neuronal genes in MGC-derived iNs, but only NEUROG2 induced a consistent increase in the expression of putative retinal ganglion cell (RGC) genes. In vivo electroporation of Neurog2 in the neonatal retina also induced a shift in the generation of retinal cell subtypes, favoring the differentiation RGCs at the expense of MGCs. Altogether, our data indicate that Neurog2 induces lineage conversion of MGCs into RGC-like iNs.

neuroscience↗

bioRxiv2018

Multiple-gene targeting and mismatch tolerance can confound analysis of genome-wide pooled CRISPR screens

Genome-wide loss-of-function screens using the CRISPR/Cas9 system allow the efficient discovery of cancer cell vulnerabilities. While several studies have focused on correcting for DNA cleavage toxicity biases associated with copy number alterations, the effects of sgRNAs co-targeting multiple genomic loci in CRISPR screens have not been discussed yet. In this work, we analyze CRISPR essentiality screen data from 391 cancer cell lines to characterize biases induced by multi-target sgRNAs. We investigate two types of multi-targets: on-targets predicted through perfect sequence complementarity, and off-targets predicted through sequence complementarity with up to two nucleotide mismatches. We found that the number of on-targets and off-targets both increase sgRNA activity in a cell line-specific manner, and that existing additive models of gene knockout effects fail at capturing genetic interactions that may occur between co-targeted genes. We use synthetic lethality between paralog genes to show that genetic interactions can introduce biases in essentiality scores estimated from multi-target sgRNAs. We further show that single-mismatch tolerant sgRNAs can confound the analysis of gene essentiality and lead to incorrect co-essentiality functional networks.

bioinformatics↗