New Twists in Detecting mRNA Modification Dynamics

Abstract

Modified nucleotides in mRNA are an essential addition to the standard genetic code of four nucleotides in animals, plants, and their viruses. The emerging field of epitranscriptomics examines nucleotide modifications in mRNA and their impact on gene expression. The low abundance of nucleotide modifications and technical limitations, however, have hampered systematic analysis of their occurrence and functions. Selective chemical and immunological identification of modified nucleotides has revealed global candidate topology maps for many modifications in mRNA, but further technical advances to increase confidence will be necessary. Single-molecule sequencing introduced by Oxford Nanopore now promises to overcome such limitations, and we summarize current progress with a particular focus on the bioinformatic challenges of this novel sequencing technology.

Keywords: 2′-O-ribose methylation; Nanopore; m(5)C; m(6)A; m(6)Am; mRNA modifications.

Figure 1

Catalog of Currently Known mRNA Modifications. (A) RNA modifications at the mRNA cap. The first transcribed base (base 1) is linked first to an inverted guanosine by a 5′–5′-triphosphate linkage and subsequently becomes methylated to 7-methylguanosine (m⁷G) by RNMT (RNA guanine 7-methyltransferase). The first and second nucleotides of the mRNA can be ribose 2′-O-methylated (cOMe) by the cap methyltransferases CMTr1 and CMTr2. If base 1 is a ribose 2′-O-methylated adenosine it can be further methylated to N⁶,2′-O-dimethyladenosine (m⁶Am) by PCIF1 (phosphorylated CTD-interacting factor 1). In the splice leader sequence added to all mRNAs in trypanosomes (shaded in grey) the third and fourth nucleotides are also 2′-O-ribose methylated, the first adenosine is trimethylated to N⁶N⁶,2′-O-trimethyladenosine (m⁶₂Am) instead of m⁶Am, and the fourth base is methylated to 3,2′-O-dimethyluridine. (B) Internal mRNA nucleotides carry different modifications depending on the nucleotide (Box 2 for enzymes). Adenosine can be converted to inosine by adenosine deaminases (ADARs), or methylated to N¹-methyladenosine (m¹A), N⁶-methyladenosine (m⁶A), or N⁶,N⁶-dimethyladenosine. Cytidine can be converted to uridine (U), acetylated N⁴-acetylcytidine (ac⁴C), or methylated to 3-methylcytidine (m³C) or 5-methylcytidine (m⁵C). m⁵C can be converted to 5-hydroxymethylcytidine (hm⁵C). Guanosine can be methylated to 7-methylguanosine (m⁷G) or oxidized to 7,8-dihydro-8-oxoguanosine (8-oxoG). Uridine can be converted to pseudouridine (Ψ). Finally, the ribose sugars of all nucleotides can be 2′-O-methylated (Nm).

Figure 2

Nanopore Sequencing. (A) Schematic of the library preparation procedure for Nanopore direct RNA sequencing. PolyA RNA is enriched using oligo-dT primers and a reverse transcription (RT) adaptor is ligated. After second-strand synthesis, the sequencing adapter RMX, which is preloaded with motor protein and tether protein, is then ligated. (B) Schematic of Nanopore direct RNA sequencing. The motor protein feeds the RNA molecule through the nanopore in the 3′–5′ direction. The five bases passing through a nanopore cause a characteristic disruption in the current which is stored as raw signal. (C) A current trace (squiggle plot) showing the raw signal generated by nanopore sequencing of a single mRNA molecule. Leader and adapter sequences are shaded yellow and pink, the polyA tail is shaded green, and the mRNA body is shaded orange. The inset (top right) illustrates how the nucleotide sequence is inferred from the raw current trace originating from a sliding window of five nucleotides (k-mer). Machine-learning algorithms are then used to calculate the probability that a signal corresponds to a given k-mer, thus inferring the nucleotide sequence from the calculated probabilities. (D) The two features recorded by Oxford Nanopore Technologies (ONT) sequencers are the current signal (in arbitrary units, AU) and the time that a given k-mer takes to transverse the pore (signal length, retention time or 'dwell'). The scatter plot depicts the distribution of mean current and signal length for 100 reads each in a different sequence context of the unmodified k-mer CACCC (blue) and the modified k-mer CAm⁵CCC (orange, identified by parallel bisulfite sequencing, where m⁵C is 5-methylcytosine). Note that, despite an identical k-mer, the signal varies as a result of different measurements and intrinsic noise in different reads, and possibly also by the different surrounding sequence of a given k-mer. This variability can be represented as a signal density plot for each k-mer, depicted in the top-right inset (density distribution for raw current signal). RNA modifications can affect raw current reads as well as signal length, resulting in a shift in signal distributions (e.g., divergence between blue and orange). However, these signal shifts can be modest, as shown by the largely overlapping density plots for CACCC and CAm⁵CCC, making accurate prediction of modified bases a computational challenge. Plots were generated with Sequoia, an interactive visual analytics platform for interpretation and feature extraction from ONT sequencing datasets [138].