Functional Micropeptides Encoded by Long Non-Coding RNAs: A Comprehensive Review

Abstract

Long non-coding RNAs (lncRNAs) were originally defined as non-coding RNAs (ncRNAs) which lack protein-coding ability. However, with the emergence of technologies such as ribosome profiling sequencing and ribosome-nascent chain complex sequencing, it has been demonstrated that most lncRNAs have short open reading frames hence the potential to encode functional micropeptides. Such micropeptides have been described to be widely involved in life-sustaining activities in several organisms, such as homeostasis regulation, disease, and tumor occurrence, and development, and morphological development of animals, and plants. In this review, we focus on the latest developments in the field of lncRNA-encoded micropeptides, and describe the relevant computational tools and techniques for micropeptide prediction and identification. This review aims to serve as a reference for future research studies on lncRNA-encoded micropeptides.

Keywords: Ribo-seq; coding potential prediction; lncRNA; micropeptide; sORF.

FIGURE 1

Schematic illustration of the workflow for bioinformatics prediction and experimental analysis of lncRNA-encoded micropeptides. (A) Bioinformatics prediction: firstly, construct a database of putative lncRNA-encoding micropeptides by applying the results of omics sequencing, and search the putative lncRNA sequences with coding potential through NCBI or NONCODE database; secondly, use calculation tools, and databases such as CPC2, CNIT, ORF Finder, PyhloCSF, etc. to evaluate the coding potential of the putative lncRNA, and deduce the corresponding sORF, and amino acid sequence; thirdly, the deduced amino acid sequences were put into the Pfam and CDD databases to look for them, and if they matched, the search for the putative micropeptide information was continued through the UniProt database; finally, the characteristics and structure of the putative micropeptide were predicted and modeled through calculation tools and databases such as SignalP-5.0, TMHMM, ProtScale and SWISS-MODEL; (B) Laboratory identification: design a series of special vectors to be transfected into specific cells, and apply western blot and immunofluorescence experiments to identify micropeptides; meanwhile, polyclonal antibodies to this micropeptide were designed, and detected by western blot and LC-MS/MS experiments on sample cells and tissues. Based on the results of both experimental procedures, the putative micropeptide was identified as a novel micropeptide, and then the function and mechanism of the micropeptide were investigated.

FIGURE 2

Schematic illustration of the regulatory role of lncRNA-encoded micropeptides in muscle physiological processes as well as disease and tumorigenesis and development. (A) Mechanism of action diagram of micropeptide MLN encoded by lncRNA LINC00948 in skeletal muscle physiological process; (B) Mechanism of action diagram of conserved peptide SPAR encoded by lncRNA LINC00961 in muscle regeneration process; (C) Mechanism of action diagram of micropeptide miPEP155 (P155) encoded by lncRNA MIR155HG in immunity and inflammation; (D) Mechanism of action diagram of the 53-aa conserved peptide encoded by lncRNA HOXB-AS3 in CRC; (E) Mechanism of action diagram of the micropeptide SRSP encoded by lncRNA LOC90024 in CRC; (F) Mechanism of action diagram of the micropeptide CASIMO1 encoded by lncRNA NR_029453 in BC; (G) Mechanism of action diagram of the conserved peptide SMIM30 encoded by LINC00998 in HCC; (H) Mechanism of action diagram of the 99-aa conserved peptide KRASIM encoded by lncRNA NCBP2-AS2 interacting with KRAS in HCC; (I) Mechanism of action diagram of the micropeptide PINT87aa encoded by LINC-PINT interacting with FOXM1 in HCC cell senescence; (J) Mechanism of action diagram of the micropeptide RPS4XL encoded by lnc-Rps41 interacting with RPS6 in PASMC.