Evolutionary conservation and divergence of the human brain transcriptome.

Evolutionary conservation and divergence of the human brain transcriptome.
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DOI:
10.1186/s13059-020-02257-z
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发表时间:
2021-01-29
期刊:
影响因子:
12.3
通讯作者:
Geschwind DH
Geschwind DH
中科院分区:
生物学1区
文献类型:
--
作者:
Pembroke WG;Hartl CL;Geschwind DH

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Mouse models have allowed for the direct interrogation of genetic effects on molecular, physiological, and behavioral brain phenotypes. However, it is unknown to what extent neurological or psychiatric traits may be human- or primate-specific and therefore which components can be faithfully recapitulated in mouse models. We compare conservation of co-expression in 116 independent data sets derived from human, mouse, and non-human primate representing more than 15,000 total samples. We observe greater changes occurring on the human lineage than mouse, and substantial regional variation that highlights cerebral cortex as the most diverged region. Glia, notably microglia, astrocytes, and oligodendrocytes are the most divergent cell type, three times more on average than neurons. We show that cis-regulatory sequence divergence explains a significant fraction of co-expression divergence. Moreover, protein coding sequence constraint parallels co-expression conservation, such that genes with loss of function intolerance are enriched in neuronal, rather than glial modules. We identify dozens of human neuropsychiatric and neurodegenerative disease risk genes, such as COMT, PSEN-1, LRRK2, SHANK3, and SNCA, with highly divergent co-expression between mouse and human and show that 3D human brain organoids recapitulate in vivo co-expression modules representing several human cell types. We identify robust co-expression modules reflecting whole-brain and regional patterns of gene expression. Compared with those that represent basic metabolic processes, cell-type-specific modules, most prominently glial modules, are the most divergent between species. These data and analyses serve as a foundational resource to guide human disease modeling and its interpretation. The online version contains supplementary material available at 10.1186/s13059-020-02257-z.
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