Past exam of the biochemistry course of the University of Cambridge / 2025 / Paper 2 / Section I

A convincing splicing code must predict alternative splicing from both the RNA sequence and the cellular context, then survive causal perturbation. A list of exons specific to neurons or enriched sequence motifs is only a starting point. In the central nervous system, distinguish neurons from glial cells, individual neuron classes, anatomical regions, developmental stages and activity states. Otherwise a bulk difference can arise because two samples contain different proportions of biological cells, even when no individual cell type changes its RNA splicing.

Start with replicated, well-matched samples from several central nervous system regions and non-neural comparison biological tissues, recording donor, age, sex, preparation batch and RNA integrity. Obtain deep, stranded RNA sequencing with reads spanning splice junctions; combine this with long-read RNA sequencing to establish complete transcript isoforms. Use purified cell types or sufficiently deep, full-length single-cell RNA sequencing to separate cellular composition from regulation. Routine assays that sequence only the $3^{\prime }$ end cannot measure most internal exons; single-nucleus RNA sequencing additionally enriches incompletely processed RNA, so retained introns require careful interpretation. A matched bulk or purified-cell dataset supplies coverage that sparse individual-cell observations lack.

Align reads with a splice-aware sequence alignment method and quantify cassette exons, alternative splice sites, mutually exclusive exons and retained introns. Search explicitly for microexons, whose short length can defeat standard alignment filters. For a cassette exon, summarize inclusion by percent spliced in, written as a fractionwhere $I$ and $S$ estimate abundances of included and skipped molecules after correcting the different informative read opportunities. Adding the two inclusion-junction counts and comparing their sum directly with a single skipping-junction count would bias this estimate. Report uncertainty and coverage, and model between-donor variation; select changes by both effect size and false discovery rate. Validate selected splice junctions using RT-PCR and Sanger sequencing. Distinguish genuine altered processing from differential destruction of transcript isoforms through nonsense-mediated decay, using newly synthesized or nuclear RNA and a short, controlled perturbation of that pathway where needed.

Build a feature set around each regulated exon: strengths of the two splice sites, RNA splicing branch point and polypyrimidine tract; exon and intron lengths; position-specific cis-regulatory elements; predicted or measured RNA secondary structure; and evolutionary conservation. Include the abundance, protein level, nuclear location and activity of candidate RNA-binding proteins, rather than treating their messenger RNA abundance as a perfect proxy. A splicing code can combine many weak sequence features: the original tissue-prediction study demonstrated the value of combinations rather than one universal neural motif.^[ref]

Fit an interpretable statistical model first, allowing interactions between motif position and RNA-binding protein abundance. Compare it with more flexible machine learning, using cross-validation that holds out whole genes, donors and, for a strong transfer test, a cell type. Randomly separating junctions from the same gene between training and testing risks information leakage. Compare against sequence-only, context-only and expression-matched baseline predictors. Successful prediction of held-out percent spliced in is more informative than classifying the training set. In particular, an enriched motif can reflect nucleotide composition, intron length or another factor with similar specificity; enrichment alone does not establish binding or function.

Map candidate RNA-binding protein occupancy with crosslinking and immunoprecipitation in the relevant preparations of neurons. Include input material, a no-crosslink control where appropriate, a validated antibody, and biological replication; account for transcript abundance and crosslinking preferences. Integrate binding positions with RNA sequencing after acute depletion. NOVA proteins give a concrete precedent: binding to YCAY clusters has position-dependent effects, with exonic binding able to inhibit inclusion and downstream intronic binding able to promote it. Here Y denotes a pyrimidine nucleobase. Thus the same motif can support opposite predictions at different positions.^[ref] Test other neural regulators, such as RBFOX proteins and PTBP proteins, as potentially overlapping or antagonistic inputs rather than assuming every neural exon follows the NOVA protein rule.

Finally, make the predictions experimentally vulnerable. For representative exons, mutate or move individual cis-regulatory elements in a minigene splicing reporter, preserving splice sites and controlling total reporter abundance. Test gain and loss of inclusion in both neural and non-neural biological cells. Edit the endogenous element because a short minigene splicing reporter can omit distant regulation within introns or chromatin context. Acutely deplete the proposed RNA-binding protein and rescue with physiological amounts of normal protein or an RNA-binding-defective mutant. Combined depletion tests redundancy; motif mutation plus factor depletion tests whether both act through the same element. A particularly strong result is the predicted reversal after moving a binding site from a repressive to an activating position. Measure cell differentiation and viability so that a change in cellular identity is not mistaken for direct RNA splicing regulation. The resulting code is a tested, context-dependent set of regulatory rules, with an explicit domain of validity.

Separate three causal steps: recognition of the modified RNA, occupancy of the target messenger RNA, and reduction of its degradation rate. Call the newly identified RNA-binding protein R. Its recovery in a screen need not mean it contacts the modified ribonucleotide directly; it could recognize a modification-dependent structure or arrive through another protein. Likewise, more target messenger RNA could reflect increased transcription rather than greater stability.

First reproduce the interaction using endogenous R and the endogenous target. Determine R's protein domains, expression and subcellular distribution, then use reciprocal RNA immunoprecipitation and crosslinking and immunoprecipitation to locate the occupied region. Normalize enrichment to input target abundance and R recovery, and include unrelated RNAs, a non-specific antibody and cells lacking R. Crosslink-based occupancy supports close contact, whereas recovery without crosslinking can preserve indirect complexes. Determine whether the relevant modification actually occurs near the binding site: mass spectrometry of purified target fragments can establish chemical identity, supplemented by a modification-specific mapping assay with known modified and unmodified standards. Bulk mass spectrometry of all cellular RNA cannot assign a modification to this particular transcript or position. Do not assume the modification is N6-methyladenosine merely because it is familiar.

Prepare short synthetic RNAs identical in nucleotide sequence, length, ends and labelling except for the single modified ribonucleotide. Measure binding of purified R over a concentration range by electrophoretic mobility shift assay or fluorescence anisotropy, with competition by unlabelled cognate and unrelated RNA. The dissociation constant distinguishes a quantitative preference from a binary pull-down impression. Verify that R is folded and sufficiently pure; include an RNA-binding-defective R mutant, several surrounding sequences and different modification positions. Modification-specific recovery from a cellular extract can disappear with purified R, revealing a missing partner rather than disproving the original screen.

Distinguish chemical recognition from structural recognition. Compare RNA secondary structure with and without the modification by RNA structure probing. Then disrupt the stem without changing the binding motif, or restore pairing through compensatory mutations. If an unmodified but constitutively exposed motif binds R, and restoring occlusion suppresses binding even when the modification remains, an RNA modification switch is plausible. Direct recognition is supported when modification discrimination persists after accessibility is matched and is lost upon a specific R binding-pocket mutation. Structural work can identify the contacts, but binding and structure controls remain essential. Modification-dependent exposure of a binding motif has an experimental precedent in the N6-methyladenosine-dependent RNA modification switch for HNRNPC.^[ref]

Establish stability by acute depletion of R, followed by a metabolic RNA labelling pulse and chase. Measure loss of labelled target molecules using appropriate spike-ins and a time course spanning the expected half-life, while independently estimating newly synthesized target RNA. SLAM-seq is one possible implementation: chemical treatment makes incorporated 4-thiouridine detectable through characteristic sequencing changes.^[ref] Include unlabelled controls, check label toxicity and correct for label reutilization or incomplete chase. When reporting molecules per biological cell, separate dilution through increasing cell number from chemical degradation. A transcription inhibitor time course provides an orthogonal check, but broad inhibition itself can change decay. Under a constant first-order degradation model, labelled molecules obeyA shorter half-life after R depletion, reversed by normal R but not its RNA-binding-defective mutant, establishes the proposed effect. Allow a more complex fit if the exponential decay model is inconsistent with the measured time course. At steady state, abundance is synthesis rate divided by degradation rate, so abundance alone cannot identify which rate changed.

Use a controlled target reporter gene carrying the occupied region, then remove or mutate that region and measure reporter messenger RNA decay. Preserve polyadenylation, RNA splicing and coding potential where possible, and distinguish site loss from altered RNA secondary structure. Manipulate the relevant modification-producing enzyme acutely, rescuing with normal or catalytically inactive enzyme; verify target-site modification after the manipulation. Cross the two perturbations: if R stabilizes the target specifically through the modification, removing the modification should reduce R occupancy and eliminate or substantially weaken the extra effect of removing R. This epistasis supports a shared pathway, but floor effects or redundant sites can obscure it. A site-specific chemically modified reporter helps separate local recognition from the many indirect consequences of removing a global modification enzyme.

Identify the affected decay step using poly(A) tail measurements, cap-state assays and mapping of decay intermediates. Test whether R excludes a microRNA-loaded Argonaute complex or a decay-promoting RNA-binding protein, blocks an endonuclease, inhibits deadenylation or decapping, or recruits a protective partner such as poly(A)-binding protein. Use protein interaction analysis with and without ribonuclease to distinguish RNA-bridged association from a more direct interaction. R mutants that retain target binding but lose a partner interaction separate recruitment from recognition. Depleting the implicated decay factor should suppress the destabilization caused by R loss; purified-system decay assays can test whether R alone physically protects the substrate.

Measure biological translation and subcellular location in parallel, using ribosome profiling or a controlled biological translation reporter and single-molecule fluorescence in situ hybridization. R might stabilize the target indirectly by altering ribosome loading or compartment access. Inhibiting biological translation can itself alter decay, and colocalization in a granule does not establish protection. The reported stabilizing action of IGF2BP proteins on modified targets provides a candidate mechanism to compare with, rather than an identity to assign to R.^[ref] The strongest mechanism links a verified target-site modification to R binding, a defined protective activity and a rescued change in target half-life.