# Impact of mRNA modifications induced by glycemic stress on the efficiency of protein translation

Alice Cleynen, Shafi Mahmud, Orlane Rossini, Nikolay E Shirokikh

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Alice Cleynen, Shafi Mahmud, Orlane Rossini, Nikolay E Shirokikh. Impact of mRNA modifications induced by glycemic stress on the efficiency of protein translation. 2026. hal-05532470

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
IMPACT OF mRNA MODIFICATIONS INDUCED BY GLYCEMIC STRESS ON THE EFFICIENCY OF PROTEIN TRANSLATION

Alice Cleynen¹,² & Shafi Mahmud³& Orlane Rossini² & Nikolay Shirokikh⁴

¹ IRL FAMSI, Australian National University, Canberra, ACT, Australia

² IMAG, Univ Montpellier, CNRS, Montpellier, France

³ John Curtin Medical School, Australian National University, Canberra, ACT, Australia

⁴ School of Human Sciences, The University of Western Australia, Perth, Australia

**Résumé.** *Stochastic Translation Efficiency* (STE) est un indicateur important pour l'étude des mécanismes d'adaptation cellulaire, mais sa mesure expérimentale demeure complexe. L'objectif de ce travail est de construire un modèle prédictif de STE à l'aide de méthodes de machine learning intégrant des données multi-omiques chez la levure *Saccharomyces cerevisiae*. Les données analysées portent sur environ 5 000 gènes et combinent plusieurs types de variables explicatives: caractéristiques structurelles, modifications de l'ARN messager et mesures de dégradation. Les observations sont disponibles sous deux conditions expérimentales : une condition contrôle et une condition soumise à un stress glycémique de 10 minutes. Le modèle est entraîné à partir des données issues de la condition contrôle. Il repose sur une architecture en trois étapes : (i) une réduction de dimension destinée à traiter la forte corrélation et la redondance des variables explicatives, (ii) une méthode de clustering visant à identifier des groupes de gènes, et (iii) un modèle de régression permettant de prédire la STE. Cette approche intégrée permet de traiter la complexité et la grande dimensionnalité des données multi-omiques, tout en améliorant la stabilité et la robustesse des prédictions de l'efficacité de traduction.

**Mots-clés.** multi-omiques, réduction de dimension, clustering, machine learning

**Abstract.** Stochastic Translation Efficiency (STE) is a key indicator for studying cellular adaptation mechanisms, but measuring it experimentally remains complex. The objective of this work is to construct a predictive model of STE using machine learning methods that integrate multi-omic data in yeast *Saccharomyces cerevisiae*. The data analysed covers approximately 5,000 genes and combines several types of explanatory variables: structural characteristics, messenger RNA modifications and degradation measurements. The observations are available under two experimental conditions: a control condition and a condition subjected to 10 minutes of glycaemic stress. The model is trained using data from the control condition. It is based on a three-step architecture: (i) dimension reduction to address the high correlation and redundancy of explanatory variables, (ii) a clustering method to identify gene groups, and (iii) a regression model to predict STE. This integrated approach addresses the complexity and high dimensionality of multi-omic data, while improving the stability and robustness of translation efficiency predictions.

**Keywords.** multi-omics, dimension reduction, clustering, machine learning
# 1 Introduction

Genetic information is encoded in DNA. Genes, the coding regions of DNA, are transcribed into single-stranded RNA, which carries the instructions needed to make proteins. Proteins are the main functional units that allow cells to operate. Although all cells in an organism share the same DNA, their differences arise from which genes are expressed and how the resulting RNAs are translated.

While many diseases, are linked to DNA mutations [Sin23; Kel+25], understanding normal cellular diversity requires examining RNA. Regulation of RNA levels and translation allows cells to rapidly adapt to environmental stress. In yeast, for example, cells adjust mRNA production, degradation, and translation in response to heat shock, oxidative stress, or osmotic changes [Cas+11; CP18; Nad+25; Ho+18]. However, the role of RNA modifications in this adaptive network, especially under nutrient deprivation, is still poorly understood.

Stochastic Translation Efficiency (STE) measures how quickly and abundantly proteins are produced from mRNAs. STE is a key indicator of cellular adaptation to changing environments. Directly measuring STE experimentally is complex and costly, which limits our understanding of these adaptive mechanisms.

With the increasing availability of high-throughput multi-omics data, integrating diverse biological information across large samples has become more feasible than ever. This has led to the development of numerous computational tools designed specifically for multi-omics data integration and interpretation, enabling comprehensive modeling of complex biological processes [Sub+20]. Importantly, these different omics layers are generated using distinct experimental technologies, each characterized by its own sources of technical variability, measurement scale, and bias. Consequently, appropriate and modality-specific normalization and preprocessing steps are required prior to integration to ensure comparability across datasets. However, these datasets are often large and highly correlated, making classical statistical analysis challenging.

The goal of this study is to build a predictive model of STE from multi-omics data. Importantly, the omics layers considered here are generated using an integrated experimental technology that enables the simultaneous measurement of multiple molecular modalities from the same biological molecules, thereby reducing cross-sample variability and improving data coherence. The model must handle a large number of variables and identify the most relevant factors for protein translation. To achieve this, we combine three steps: dimension reduction to remove redundancy and simplify the data, clustering to group genes with similar behavior, and regression modeling to predict STE. This integrated approach allows us to capture the complexity of the data while producing stable and robust predictions.

# 2 Data

We analyzed a dataset comprising 5,033 genes characterized by 86 features encompassing multiple layers of post-transcriptional regulation. These features can be grouped into three
main categories: structural, mRNA degradation and mRNA modification features. The analysis focuses on evaluating how well the structural, degradation, and modification features collectively explain and predict STE, computed with two different models, across genes. Additionally, we incorporate a binary indicator denoting whether at least one RNA modification is present (1) or absent (0) within the first or last 21 reads of a given gene.

To assess the generalizability and robustness of our approach, we constructed two separate datasets corresponding to the non-starved (NS) and starved (S10) conditions. The starved condition (S10) corresponds to glucose deprivation for 10 minutes prior to measurement. All statistical analyses and machine learning models were trained on the NS dataset and subsequently evaluated on the independent S10 dataset to test their performance across conditions.

# 3 STE Feature Association

## 3.1 Statistical Analyses

To identify features associated with STE, we employed multiple complementary statistical approaches to assess associations between molecular features and STE. Pearson correlation was used to assess linear relationships, while MIC and Spearman correlation captured more general and potentially non-linear dependencies between continuous variables. All statistical tests were corrected for multiple comparisons using the Benjamini-Hochberg procedure.

Features with an adjusted Pearson p-value < 0.01 were considered linearly associated with STE. Among features with a MIC above the first quartile, those with an adjusted Spearman p-value < 0.01 were subsequently identified as being associated with STE. All these features were retained for subsequent analyses.

We hypothesized that the remaining STE-associated features might be correlated and potentially redundant. To evaluate this, we first standardized the data and then performed a principal component analysis (PCA). We used a circle correlation plot to examine how much variance is captured by the first two principal components and to identify potential groups of features that tend to vary together.

## 3.2 Preliminary Results

Among the 85 features analyzed, 57 were found to be significantly associated with STE from both models, based on the Pearson correlation test (adjusted p-value < 0.01). Using the MIC as an exploratory screening criterion, 64 features exhibited a MIC value above the first quartile threshold (0.0799). Although this threshold is relatively low, it ensures the selection of features showing at least minimal dependency with STE. Among these 64 preselected features, 52 were confirmed to be significantly associated with STE using the Spearman correlation test (adjusted p-value < 0.01). To retain all potentially relevant associations, we considered the union of features identified by both approaches, resulting in 68 STE-associated
<figure><img src="image_3.png" /><figcaption>Figure 1: PCA Circle of Correlations for STE-Associated Features</figcaption></figure>

features that were carried forward for subsequent analyses.

On Figure 1, the first two dimensions of PCA explain 24.5% of the total variance, and the circle of correlations shows that many features cluster together along similar directions, illustrating substantial redundancy among the variables.

# 4 STE Prediction

## 4.1 The machine learning pipeline

We implement machine learning workflows, illustrated in Figure 2, based on previous statistical analyses to reduce data redundancy.

*Autoencoder.* For the set of binary features encoding the presence (1) or absence (0) of transcript modifications, we employed an autoencoder to obtain a low-dimensional representation summarizing their joint structure. Hyperparameters were optimized using a randomized search strategy over the following components: latent dimension *d*, number of hidden layers, number of neurons per layer, activation functions, learning rate, batch size, and $\ell_2$ regularization strength. For each hyperparameter configuration, the model was trained using the Adam optimizer with early stopping based on validation loss to prevent overfitting. The optimal configuration was chosen as the one minimizing the validation reconstruction error.
## 4.2 Preliminary results

<figure><img src="image_4.png" /><figcaption>Figure 2: Multi-omic pipeline for identifying determinants of translational efficiency.</figcaption></figure>

*Clustering.* As a first step, we clustered genes under the hypothesis that distinct groups of genes may exhibit different relationships between molecular features and STE. We employed mixture regression-based clustering allowing each cluster to be characterized by its own regression model while performing variable selection among the 67 retained features. For a given number of clusters *K*, the conditional distribution of STE for gene *j* is modeled as a finite mixture of linear regressions:

$$f(STE_j|X_j) = \sum_{k=1}^{K} \pi_k \mathcal{N}(\mu_k + \sum_{i=1}^{67} \beta_{ik} X_{ij}, \sigma_k^2),$$

where $\pi_k$ are the mixing proportion, $\mu_k$ is the cluster specific intercept, $\beta_{ik}$ are the regression coefficients for feature *i* in cluster *k*, $X_{ij}$ denotes feature *i* for gene *j*, $\sigma_k^2$ is the cluster specific variance. The number of clusters *K* was selected by fitting models with $K = 1, \dots, 6$ and choosing the optimal model according to the Bayesian Information Criterion (BIC).

*Random Forests.* For each gene cluster, a separate Random Forest regression model was trained on the NS dataset to account for cluster-specific heterogeneity in the relationship between molecular features and STE. Predictors included the 67 selected continuous features and the latent representation of binary modification features derived from the autoencoder. Model performance was evaluated using nested 5-fold cross-validation, with inner-loop hyper-parameter tuning and outer-loop estimation of generalization error based on MSE. Variable importance was assessed using mean decrease in impurity. Final models were retrained on the full dataset within each cluster using the optimal hyperparameters.

## 4.2 Preliminary results

*Clustering.* As shown on figure 3a for both STE values, the optimal number of cluster is 3 according to BIC criterion. Figure 3b illustrate STE value distribution within each cluster.

*Random Forest.* The results indicate that STE from Model 2 consistently outperforms STE from Model 1 when the Random Forest is trained on the full dataset, as reflected by a
<figure><img src="image_5.png" /><figcaption>Figure 3: STE values within each cluster.</figcaption></figure>

<table><caption>Table 1: Mean Squared Error (MSE) ± standard deviation of the Random Forest models.</caption><thead><tr><th></th><th>STE Model 1</th><th>STE Model 2</th></tr></thead><tbody><tr><td>Cluster 1</td><td>0.06 ± 0.071</td><td>0.05 ± 0.038</td></tr><tr><td>Cluster 2</td><td>0.28 ± 0.013</td><td>0.08 ± 0.005</td></tr><tr><td>Cluster 3</td><td>0.31 ± 0.067</td><td>0.44 ± 0.061</td></tr><tr><td>Full dataset</td><td>0.72 ± 0.019</td><td>0.59 ± 0.011</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Figure 4: Feature importance per cluster.</figcaption></figure>

lower MSE (0.59 vs 0.72). When examining cluster-specific performance, predictive accuracy varies substantially across clusters. In particular, Clusters 1 and 2 show markedly lower MSE values compared to the global model, suggesting that modeling within more homogeneous gene groups improves prediction. Conversely, Cluster 3 exhibits higher variability and reduced performance, especially for Model 2. Overall, these findings support the relevance of the supervised clustering step for enhancing predictive performance, although its benefit appears cluster-dependent. Results are sum up in Table 1. Structural variables appear to be major predictors of STE in all clusters (Figure 4).

# 5 Conclusion

In conclusion, our preliminary findings reveal substantial heterogeneity across gene clusters, supporting the existence of distinct translational regulatory regimes. The proposed integrative framework effectively captures this complexity by modeling redundancy and cluster-specific patterns in multi-omic data. Ongoing integration of glycemic stress-specific features will further clarify whether RNA modification and degradation mechanisms contribute to condition-dependent variations in translational efficiency.
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[Ho+18] Yi-Hsuan Ho et al. "Decoupling yeast cell division and stress defense implicates mRNA repression in translational reallocation during stress". In: *Current biology* : CB 28.16 (Aug. 2018), p. 2673. DOI: 10.1016/j.cub.2018.06.044.

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