Dataset Information

HEPN RNases - an emerging class of functionally distinct RNA processing and degradation enzymes.

ABSTRACT: HEPN (Higher Eukaryotes and Prokaryotes Nucleotide-binding) RNases are an emerging class of functionally diverse RNA processing and degradation enzymes. Members are defined by a small α-helical bundle encompassing a short consensus RNase motif. HEPN dimerization is a universal requirement for RNase activation as the conserved RNase motifs are precisely positioned at the dimer interface to form a composite catalytic center. While the core HEPN fold is conserved, the organization surrounding the HEPN dimer can support large structural deviations that contribute to their specialized functions. HEPN RNases are conserved throughout evolution and include bacterial HEPN RNases such as CRISPR-Cas and toxin-antitoxin associated nucleases, as well as eukaryotic HEPN RNases that adopt large multi-component machines. Here we summarize the canonical elements of the growing HEPN RNase family and identify molecular features that influence RNase function and regulation. We explore similarities and differences between members of the HEPN RNase family and describe the current mechanisms for HEPN RNase activation and inhibition.

SUBMITTER: Pillon MC

PROVIDER: S-EPMC7856873 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

HEPN RNases - an emerging class of functionally distinct RNA processing and degradation enzymes.

Pillon Monica C MC Gordon Jacob J Frazier Meredith N MN Stanley Robin E RE

Critical reviews in biochemistry and molecular biology 20201222 1

HEPN (Higher Eukaryotes and Prokaryotes Nucleotide-binding) RNases are an emerging class of functionally diverse RNA processing and degradation enzymes. Members are defined by a small α-helical bundle encompassing a short consensus RNase motif. HEPN dimerization is a universal requirement for RNase activation as the conserved RNase motifs are precisely positioned at the dimer interface to form a composite catalytic center. While the core HEPN fold is conserved, the organization surrounding the H ...[more]

PMID: 33349060

Similar Datasets

Project description:BackgroundThe major role of enzymatic toxins that target nucleic acids in biological conflicts at all levels has become increasingly apparent thanks in large part to the advances of comparative genomics. Typically, toxins evolve rapidly hampering the identification of these proteins by sequence analysis. Here we analyze an unexpectedly widespread superfamily of toxin domains most of which possess RNase activity.ResultsThe HEPN superfamily is comprised of all α-helical domains that were first identified as being associated with DNA polymerase β-type nucleotidyltransferases in prokaryotes and animal Sacsin proteins. Using sensitive sequence and structure comparison methods, we vastly extend the HEPN superfamily by identifying numerous novel families and by detecting diverged HEPN domains in several known protein families. The new HEPN families include the RNase LS and LsoA catalytic domains, KEN domains (e.g. RNaseL and Ire1) and the RNase domains of RloC and PrrC. The majority of HEPN domains contain conserved motifs that constitute a metal-independent endoRNase active site. Some HEPN domains lacking this motif probably function as non-catalytic RNA-binding domains, such as in the case of the mannitol repressor MtlR. Our analysis shows that HEPN domains function as toxins that are shared by numerous systems implicated in intra-genomic, inter-genomic and intra-organismal conflicts across the three domains of cellular life. In prokaryotes HEPN domains are essential components of numerous toxin-antitoxin (TA) and abortive infection (Abi) systems and in addition are tightly associated with many restriction-modification (R-M) and CRISPR-Cas systems, and occasionally with other defense systems such as Pgl and Ter. We present evidence of multiple modes of action of HEPN domains in these systems, which include direct attack on viral RNAs (e.g. LsoA and RNase LS) in conjunction with other RNase domains (e.g. a novel RNase H fold domain, NamA), suicidal or dormancy-inducing attack on self RNAs (RM systems and possibly CRISPR-Cas systems), and suicidal attack coupled with direct interaction with phage components (Abi systems). These findings are compatible with the hypothesis on coupling of pathogen-targeting (immunity) and self-directed (programmed cell death and dormancy induction) responses in the evolution of robust antiviral strategies. We propose that altruistic cell suicide mediated by HEPN domains and other functionally similar RNases was essential for the evolution of kin and group selection and cell cooperation. HEPN domains were repeatedly acquired by eukaryotes and incorporated into several core functions such as endonucleolytic processing of the 5.8S-25S/28S rRNA precursor (Las1), a novel ER membrane-associated RNA degradation system (C6orf70), sensing of unprocessed transcripts at the nuclear periphery (Swt1). Multiple lines of evidence suggest that, similar to prokaryotes, HEPN proteins were recruited to antiviral, antitransposon, apoptotic systems or RNA-level response to unfolded proteins (Sacsin and KEN domains) in several groups of eukaryotes.ConclusionsExtensive sequence and structure comparisons reveal unexpectedly broad presence of the HEPN domain in an enormous variety of defense and stress response systems across the tree of life. In addition, HEPN domains have been recruited to perform essential functions, in particular in eukaryotic rRNA processing. These findings are expected to stimulate experiments that could shed light on diverse cellular processes across the three domains of life.ReviewersThis article was reviewed by Martijn Huynen, Igor Zhulin and Nick Grishin.

Dataset Information

HEPN RNases - an emerging class of functionally distinct RNA processing and degradation enzymes.

Publications

HEPN RNases - an emerging class of functionally distinct RNA processing and degradation enzymes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets