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Berzoti-Coelho, M. G.

Publications and source records attributed to Berzoti-Coelho, M. G..

2 recordsLinked to original sources

The CUT&RUN Greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on regions of constant nonspecific signal. This constancy was determined by applying Shannons information entropy, and the set of normalizer regions, which we named the "greenlist," was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental set-ups, cell lines, and antibodies; the approach can even be applied to other organisms or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.

bioinformatics↗

The human developing cerebral cortex is characterized by an increased de novo expression of lncRNAs in excitatory neurons

BackgroundOutstanding human cognitive abilities are computed in the cerebral cortex, a mammalian-specific brain region and the place of massive biological innovation. Long noncoding RNAs (lncRNAs) have emerged as gene regulatory elements with higher evolutionary turnover than mRNAs. The many lncRNAs identified in neural tissues make them candidates for molecular sources of cerebral cortex evolution and disease. Here, we characterized the genomic and cellular shifts that occurred during the evolution of the lncRNA repertoire expressed in the developing cerebral cortex of humans and explored their role in the evolution of this brain region. ResultsUsing systems biology approaches and comparative transcriptomics, we comprehensively annotated the cortical transcriptomes of humans, macaques, mice, and chickens and classified human cortical lncRNAs into evolutionary groups as a function of their predicted minimal ages. LncRNA evolutionary groups showed differences in expression levels, splicing efficiencies, transposable element contents, genomic distributions, and transcription factor binding to their promoters. Furthermore, older lncRNAs showed preferential expression in germinative zones, outer radial glial cells, and cortical inhibitory neurons. In comparison, younger lncRNAs showed preferential expression in cortical excitatory neurons, belonged to human-specific gene coexpression modules, and were dysregulated in autism spectrum disorder. ConclusionsThese results suggest a shift in the roles of cortical lncRNAs over evolution, highlighting the antique lncRNAs as a source of molecular evolution of conserved developmental programs; conversely, the de novo expression of primate and human-specific lncRNAs are sources of molecular evolution and dysfunction of cortical excitatory neurons.

evolutionary biology↗