Modelling of SHMT1 riboregulation predicts dynamic changes of serine and glycine levels across cellular compartments

Monti, Michele; Guiducci, Giulia; Paone, Alessio; Rinaldo, Serena; Giardina, G.; Liberati, Francesca Romana; Cutruzzolà, Francesca; Tartaglia, Gian Gaetano

doi:10.1016/j.csbj.2021.05.019

Cited by 11 publications

(13 citation statements)

References 45 publications

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“…other post-transcriptional modifications, such as SUMOylation or the ability of these enzymes to bind RNA [27,[44][45][46]. dTMP-SC formation may indeed represent a strategy to compartmentalize the enzymes in the cytosol and the effects of stabilization/destabilization of the complex should be investigated because this may represent an innovative and powerful tool for undermining the rewiring of the one-carbon metabolism that tumour cells use to sustain proliferation.…”

Section: Discussionmentioning

confidence: 99%

“…First, in the cytoplasm, SHMT1 and DHFR also participate in the folate cycle, whereas TYMS was shown to take part in mitochondrial de novo dTMP synthesis [ 6 ]; therefore, it is likely that formation of the dTMP‐SC complex may affect not only the dTMP pool, but also the whole one‐carbon metabolism. In addition, given that the complex certainly has a very different surface accessibility with respect to the single enzymes, it is probable that its assembly directly affects/controls other post‐transcriptional modifications, such as SUMOylation or the ability of these enzymes to bind RNA [ 27 , 44 , 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

“…Protein complexes and PPI are commonly formed by cells to increase the efficiency, tunability and control over crucial metabolic pathways [ 26 ]. Many of these protein assemblies undergo complex dynamics, often controlled by other cellular components such as nucleic acids and lipids and even segregate in membrane‐less intracellular compartments [ 27 , 28 ]. Interfering with PPI to highjack protein metabolism is a challenging goal that is feasible only when biochemical and structural data of the target PPI become available [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Cytosolic localization and in vitro assembly of human de novo thymidylate synthesis complex

et al. 2021

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De novo thymidylate synthesis is a crucial pathway for normal and cancer cells. Deoxythymidine monophosphate (dTMP) is synthesized by the combined action of three enzymes: serine hydroxymethyltransferase (SHMT1), dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), with the latter two being targets of widely used chemotherapeutics such as antifolates and 5‐fluorouracil. These proteins translocate to the nucleus after SUMOylation and are suggested to assemble in this compartment into the thymidylate synthesis complex. We report the intracellular dynamics of the complex in cancer cells by an in situ proximity ligation assay, showing that it is also detected in the cytoplasm. This result indicates that the role of the thymidylate synthesis complex assembly may go beyond dTMP synthesis. We have successfully assembled the dTMP synthesis complex in vitro, employing tetrameric SHMT1 and a bifunctional chimeric enzyme comprising human thymidylate synthase and dihydrofolate reductase. We show that the SHMT1 tetrameric state is required for efficient complex assembly, indicating that this aggregation state is evolutionarily selected in eukaryotes to optimize protein–protein interactions. Lastly, our results regarding the activity of the complete thymidylate cycle in vitro may provide a useful tool with respect to developing drugs targeting the entire complex instead of the individual components.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cytosolic localization and in vitro assembly of human de novo thymidylate synthesis complex

et al. 2021

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“…TFs activate and inhibit the transcription of target genes. In turn, target genes produce other TFs or proteins that regulate cell metabolism [ 4 ]. The recurrent architectures constituting the building blocks of GRNs, known as network motifs, have been widely studied and classified in the last decade through systems biology approaches [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Monti

Fiorentino

Milanetti

et al. 2022

Entropy

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Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow one to hierarchically distinguish different architectures of the GRN. We show that the GRN responded differently to the addition of noise in the prediction by the RNN and we related the noise response to the analysis of the attention mechanism. In conclusion, this work provides a way to understand and exploit the attention mechanism of RNNs and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.

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“…TFs activate and inhibit the transcription of target genes. In turn, target genes produce other TFs or proteins that regulate cell metabolism [4]. In this context, it is fundamental to obtain an accurate picture of the interactions among TFs and their target genes, which is known as the GRN inference problem.…”

Section: Introductionmentioning

confidence: 99%

Prediction of gene expression time series and structural analysis of gene regulatory networks using recurrent neural networks

Monti¹,

Fiorentino²,

Milanetti³

et al. 2021

Preprint

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Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural networks (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow to hierarchically distinguish different architectures of the GRN. We show that the GRNs respond differently to the addition of noise in the prediction by the RNN and we relate the noise response to the analysis of the attention mechanism. In conclusion, this work provides a a way to understand and exploit the attention mechanism of RNN and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.

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Modelling of SHMT1 riboregulation predicts dynamic changes of serine and glycine levels across cellular compartments

Abstract: Graphical abstract

Cited by 11 publications

References 45 publications

Cytosolic localization and in vitro assembly of human de novo thymidylate synthesis complex

Cytosolic localization and in vitro assembly of human de novo thymidylate synthesis complex

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Prediction of gene expression time series and structural analysis of gene regulatory networks using recurrent neural networks

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