Because of many recent improvements made in the last decades in automated synthesis of biomolecules such as oligonucleotides (DNA/RNA), polypeptides, carbohydrates, and modified derivatives of these, and in high-resolution and sensitive analytical instruments, our understanding of cells, cell structures and their dynamics at the molecular level has dramatically increased. A collection of human genomes is now available, scientists studied the 3D organization of the genome, and a data set of genomes is available at the ATCC genome portal: Human Genome Collection.Initial sequencing and analysis of the human genome | Nature, Genomes | ATCC Genome Portal.
Molecular medicine aims to link “omic” approaches to cancer research. Understanding and characterizing metabolism in cancer and healthy cells is the key to developing new specific and efficient therapeutics targeting cancer cells. The knowledge of possible marks and their functions on biomolecules involved in the metabolism of human cells is expected to create a comprehensive picture of human and tumor biology at the systems and molecular level.
Multi-omics approaches enable the characterization and quantification of regulatory marks in the genome, the epigenome, the transcriptome, the epitranscriptome, the proteome, and the epiproteome. This knowledge is expected to increase our understanding of eukaryotic metabolism and physiology, helping to create improved personalized therapeutics.
Research conducted during the last decades revealed that cancer is a disease driven by genetic mutations, epigenetic modifications, a misregulated transcriptome, now known as the epitranscriptome, and a misregulated proteome, known as the epiproteome. The so-called epitranscriptome is now understood as the chemical modifications of RNA that regulates and alters the activity of RNA molecules. An example is the hypermethylation of DNA associated with silencing tumor suppressor genes and aberrant histone modifications. A signaling pathway project is underway to integrate an “omics” knowledgebase for mammalian cellular signaling pathways. This database contains curated data sets validated using alignment with the canonical literature knowledge and gene target-level integration of transcriptomic and cistromic data points. [Signaling pathways Project]
Research in recent decades has made it clear that the addition or removal of RNA modifications in various RNA species regulates a broad spectrum of RNA regulatory processes. These RNA-regulating processes regulate specific sets of genes. Therefore, the context of the RNA molecule and the RNA effector enzymes involved determine the molecular destiny of any given RNA transcript. Interestingly, subcellar localization of both RNAs and RNA-modifying proteins, the number of transcripts of specific cellular RNAs, the various RNA types, the folding of RNAs, RNA-protein interactions, and responses to stimuli such as DNA or RNA damage determine the metabolisms of RNA modifications. Defects in any of these processes may lead to cancer progression. Table 1 illustrates the interplay of “omics” systems.
Table 1: "Omics" Systems
Genome DNA | Epigenome DNA modified: 5hmC, 5mC, 5fC, 3mC, 4mC, 6mA. | Transcriptome RNA: rRNA, tRNA, snRNA, mRNA, lncRNA, miRNA, etc. | Epitranscriptome RNA modified: Ψ, m5C, m1A, m6A, m5A, etc. | Proteome Proteins | Epiproteome Proteins modified: PTMs. |
SNP CNV LOH Rearrangement | DNA modifications: | Alternative splicing RNA editing | RNA modifications: | Protein isoforms Peptides and micropeptides. | Protein post-translational modifications (PTMs): |
NGS, WES WGS, FISH, CGH ChiP-seq DNA microarray Targeted DNA seq | DNA methylation array Pyrosequencing Bisulfite sequencing (BS) | RNA seq, SLAM-seq, RNA microarray Targeted RNA seq, RNA Exome Capture Seq Ribosome profiling qRT-PCR | Methylated RNA IP-seq, miCLIP, RNA BS-seq, m1A/m6A-seq, DART-seq Quantification of RNA mods by LC-MS | Mass Spectrometry Protein Array Immuno-precipitation Immuno-fluorescence Western Blot Analysis | Mapping PTMs by mass spectrometry (LC-MS/MS) SILAC, HPLC Phospho-Kinase array Western Blot Analysis |
Legend: CGH = comparative genomic hybridization, ChiP-seq = chromatin immunoprecipitation DNA sequencing, CNV = copy number variation, DART-seq = Diversity Arrays Technology, FISH = fluorescence in situ hybridization, LOH = loss of heterozygosity, NGS = next generation sequencing, SILAC = Stable isotope labeling by amino acids in cell culture, SNP = single-nucleotide polymorphism, WES = whole exome sequencing.
Table 2: RNA Modifications and Enzymes
Important RNA modifications | Enzymes that modify RNA |
m1A: 1-methyladenosine, ms2i6A: 2-methylthio-N6-isopentenyl-adenosine, i6A: N6-isopentenyladenosine, m6A: N6-methyladenosine, m3C: 3-methylcytosine, m5C: 5-methylcytosine, ac4C: N4-acetylcytosine, m7Gpp(pN): 7-methylguanosine cap, m7G: 7-methylguanosine internal, m2,2G: N2,N2,-dimethylguanosine, m2G: N2-methylguanosine, Q: queuosine, yW et al.: Wybutosine and derivatives, m5U: 5-methyluridine, ncm5U: 5-carbamoyl-methyluridine, mcm5U: 5-methoxycarbonyl-methyluridine, mcm5s2U: 5-methoxycarbonylmethyl-2-thiouridine, D: dihydrouridine, Ψ: pseudouridine, Nm: 2′-O-Methylnucleotide, m(pN): 5′ phosphate monomethylation, A-to-I: Deamination of Adenosine, C-to-U: Deamination of Cytosine.
| ADAR1-3: Adenosine Deaminase RNA Specific 1–3, ALKBH1/3/5/8: AlkB Homolog 1/3/5/8, APOBEC1/3G: Apolipoprotein B mRNA Editing BCDIN3D: BCDIN3 Domain Containing BUD23: RRNA Methyltransferase And Ribosome CDK5RAP1: CDK5 Regulatory Subunit Associated Protein 1, CMTR1/2: Cap Methyltransferase 1/2, CTU1/2: Cytosolic Thiouridylase Subunit 1/2, DKC1: Dyskerin Pseudouridine Synthase 1, DNMT2: tRNA Aspartic Acid Methyltransferase 1, DUS2: Dihydrouridine Synthases 2, ELP3: Elongator Acetyltransferase Complex Subunit 3, FTO: FTO Alpha-Ketoglutarate Dependent Dioxygenase, HENMT1: HEN Methyltransferase 1, METTL1/2/3/6/8/14/16: Methyltransferase Like-1/2/3/6/8/16, NAT10: N-Acetyltransferase 10, NSUN1-5: NOP2/Sun RNA Methyltransferase 1–5, NUDT16: Nudix Hydrolase 16, RNMT: RNA Guanine-7 Methyltransferase, TGT: Queuine TRNA-Ribosyltransferase Catalytic Subunit 1, TRIT1: tRNA Isopentenyltransferase 1, TRMT1/2A/2B1/5/6/10C/11/61A/61B/112: TYW2: tRNA-YW Synthesizing Protein 2 Homolog.
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RNA modifications known as epitranscriptomic marks
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Table 3: Epitrancriptome of small non-coding RNAs
SncRNAs Species | Described Chemical Modifiction | Writers | Readers |
miRNA | m6A | METTL3/ METTL14 | HNRNPA2B1/ HNRNPC |
m7G | METTL1 | / | |
2′-O-Me | HEN1 | / | |
5′Pme2 | BCDIN3D | / | |
Uridylation | TUT7/4/2 | / | |
A to I | ADARs | / | |
o8G | / | / | |
8-OHG | / | / | |
piRNA | 2′-O-Me | HEN1 | |
snRNA | Ψ | Box H/ACA RNP/ Pus1 and Pus7 | / |
2′-O-Me | Box C/D RNP | / | |
m6A | METTL16 | / | |
m6Am | METTL4 | / | |
TMG | TGS1 | / | |
m5C | / | YPS | |
snoRNA | Ψ | Box H/ACA RNP | / |
m6A | / | / | |
tsRNA | m5C | DNMT2/ NSUN2 | / |
m2G | DNMT2 | / | |
Q | QTRT1/QTRT2 | / | |
2′-O-Me | TRM7/FTSJ1 | / | |
m1A | TRMT6/61A | / | |
m3C | METTL2/ METTL6 | / | |
m1G | TRMT10A | / | |
hm5C | TET2 | / | |
Ψ | PUS7 | / | |
mcm5S2 | / | / |
(Source: Li et al. 2021; Wang et al. 2022)
Esteve-Puig R, Bueno-Costa A, Esteller M. Writers, readers and erasers of RNA modifications in cancer. Cancer Lett. 2020 Apr 1;474:127-137. [PubMed]
Li X, Peng J, Yi C. The epitranscriptome of small non-coding RNAs. Noncoding RNA Res. 2021;6(4):167-173. [PMC]
Wang S, Li H, Lian Z, Deng S. The Role of RNA Modification in HIV-1 Infection. Int J Mol Sci. 2022;23(14):7571. [PMC]
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