bioRxivBiochemistry
RNA-binding proteins with mixed charge domains self-assemble and aggregate in Alzheimer’s Disease
I. Bishof, E. B. Dammer, D. M. Duong, M. Gearing, J. J. Lah, A. I. Levey, N. T. Seyfried
Abstract
U1 small nuclear ribonucleoprotein 70 kDa (U1-70K) and other RNA binding proteins (RBPs) are mislocalized to cytoplasmic neurofibrillary Tau aggregates in Alzheimers disease (AD), yet understanding of the mechanisms that cause their aggregation is limited. Many RBPs that aggregate in neurodegenerative diseases self-assemble into RNA granules through intrinsically disordered low complexity (LC) domains. We report here that a LC domain within U1-70K of mixed charge, containing highly repetitive complementary repeats of basic (R/K) and acidic (D/E) residues, shares many of the same properties of the Q/N-rich LC domains found in the RBPs TDP-43 and FUS. These properties include the ability to self-assemble into oligomers, and to form nuclear granules. To analyze the functional roles of the U1-70K LC domains, we performed co-immunoprecipitation and quantitative mass spectrometry analysis of recombinant U1-70K and deletions lacking the C-terminal LC domain(s). A network-driven approach resolved functional classes of U1-70K interacting proteins that showed dependency on the U1-70K LC domain(s) for their interaction. This included structurally similar RBPs, such as LUC7L3 and RBM25, which require their respective mixed charge domains for reciprocal interactions with U1-70K and for participation in nuclear RNA granules. Strikingly, a significant proportion of RBPs with mixed charge domains have elevated insolubility in AD brain proteome compared to controls. Furthermore, we show that the mixed charge LC domain of U1-70K can interact with Tau from AD brain. These findings highlight mechanisms for mixed charge domains in stabilizing RBP interactions and in potentially mediating co-aggregation with pathological Tau isoforms in AD.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: protein folding & dynamics
- approach: statistical model
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