Human Tissues Exhibit Diverse Composition of Translation Machinery

Triple coding in human SRD5A1 mRNA

Background

Nucleotide sequence can be translated in three reading frames from 5' to 3' producing distinct protein products. Many examples of RNA translation in two reading frames (dual coding) have been identified so far.

Results

We report simultaneous translation of mRNA transcripts derived from SRD5A1 locus in all three reading frames that result in the synthesis of long proteins. This occurs due to initiation at three nearby AUG codons occurring in all three-reading frame. Only one of the three proteoforms contains the conserved catalytical domain of SDRD5A1 produced either from the second or the third AUG codon depending on the transcript. Paradoxically, ribosome profiling data and expression reporters indicate that the most efficient translation produces catalytically inactive proteoforms. While phylogenetic analysis suggests that the long triple decoding region is specific to primates, occurrence of nearby AUGs in all three reading frames is ancestral to placental mammals. This suggests that their evolutionary significance belongs to regulation of translation rather than biological role of their products. By analysing multiple publicly available ribosome profiling data and with gene expression assays carried out in different cellular environments, we show that relative expression of these proteoforms is mutually dependent and vary across environments supporting this conjecture. A remarkable feature of triple decoding is its resistance to indel mutations with apparent implications to clinical interpretation of genomic variants.

Conclusion

We argue for the importance of identification, characterisation and annotation of productive RNA translation irrespective of the presumed biological roles of the products of this translation.

Long-read subcellular fractionation and sequencing reveals the translational fate of full-length mRNA isoforms during neuronal differentiation

Alternative splicing (AS) alters the cis-regulatory landscape of mRNA isoforms, leading to transcripts with distinct localization, stability, and translational efficiency. To rigorously investigate mRNA isoform-specific ribosome association, we generated subcellular fractionation and sequencing (Frac-seq) libraries using both conventional short reads and long reads from human embryonic stem cells (ESCs) and neural progenitor cells (NPCs) derived from the same ESCs. We performed de novo transcriptome assembly from high-confidence long reads from cytosolic, monosomal, light, and heavy polyribosomal fractions and quantified their abundance using short reads from their respective subcellular fractions. Thousands of transcripts in each cell type exhibited association with particular subcellular fractions relative to the cytosol. Of the multi-isoform genes, 27% and 19% exhibited significant differential isoform sedimentation in ESCs and NPCs, respectively. Alternative promoter usage and internal exon skipping accounted for the majority of differences between isoforms from the same gene. Random forest classifiers implicated coding sequence (CDS) and untranslated region (UTR) lengths as important determinants of isoform-specific sedimentation profiles, and motif analyses reveal potential cell type-specific and subcellular fraction-associated RNA-binding protein signatures. Taken together, our data demonstrate that alternative mRNA processing within the CDS and UTRs impacts the translational control of mRNA isoforms during stem cell differentiation, and highlight the utility of using a novel long-read sequencing-based method to study translational control.

Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) stands as a significant global health challenge, distinguished by its aggressive progression from the esophageal epithelium. Central to this malignancy is sphingolipid metabolism, a critical pathway that governs key cellular processes, including apoptosis and immune regulation, thereby influencing tumor behavior. The advent of single-cell and transcriptome sequencing technologies has catalyzed significant advancements in oncology research, offering unprecedented insights into the molecular underpinnings of cancer.

METHODS

We explored sphingolipid metabolism-related genes in ESCC using scRNA-seq data from GEO and transcriptome data from TCGA. We assessed 97 genes in epithelial cells with AUCell, UCell, and singscore algorithms, followed by bulk RNA-seq and differential analysis to identify prognosis-related genes. Immune infiltration and potential immunotherapeutic strategies were also investigated, and tumor gene mutations and drug treatment strategies were analyzed.

RESULT

Our study identified distinct gene expression patterns, highlighting ARSD, CTSA, DEGS1, and PPTQ's roles in later cellular stages. We identified seven independent prognostic genes and created a precise nomogram for prognosis.

CONCLUSION

This study integrates single-cell and transcriptomic data to provide a reliable prognostic model associated with sphingolipid metabolism and to inform immunotherapy and pharmacotherapy for ESCC at the genetic level. The findings have significant implications for precision therapy in esophageal cancer.

Ribosome decision graphs for the representation of eukaryotic RNA translation complexity

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, within both annotated protein-coding and noncoding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term ribosome decision graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the latter "translons." Nondeterministic events, such as initiation, reinitiation, selenocysteine insertion, or ribosomal frameshifting, are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions and for analyzing genetic variation and quantitative genome-wide data on translation for characterization of regulatory modulators of translation.

Signaling plasticity in the integrated stress response

The Integrated Stress Response (ISR) is an essential homeostatic signaling network that controls the cell's biosynthetic capacity. Four ISR sensor kinases detect multiple stressors and relay this information to downstream effectors by phosphorylating a common node: the alpha subunit of the eukaryotic initiation factor eIF2. As a result, general protein synthesis is repressed while select transcripts are preferentially translated, thus remodeling the proteome and transcriptome. Mounting evidence supports a view of the ISR as a dynamic signaling network with multiple modulators and feedback regulatory features that vary across cell and tissue types. Here, we discuss updated views on ISR sensor kinase mechanisms, how the subcellular localization of ISR components impacts signaling, and highlight ISR signaling differences across cells and tissues. Finally, we consider crosstalk between the ISR and other signaling pathways as a determinant of cell health.

eIF4E as a molecular wildcard in metazoans RNA metabolism

The evolutionary origin of eukaryotes spurred the transition from prokaryotic-like translation to a more sophisticated, eukaryotic translation. During this process, successive gene duplication of a single, primordial eIF4E gene encoding the mRNA cap-binding protein eukaryotic translation initiation factor 4E (eIF4E) gave rise to a plethora of paralog genes across eukaryotes that underwent further functional diversification in RNA metabolism. The ability to take different roles is due to eIF4E promiscuity in binding many partner proteins, rendering eIF4E a highly versatile and multifunctional player that functions as a molecular wildcard. Thus, in metazoans, eIF4E paralogs are involved in various processes, including messenger RNA (mRNA) processing, export, translation, storage, and decay. Moreover, some paralogs display differential expression in tissues and developmental stages and show variable biochemical properties. In this review, we discuss recent advances shedding light on the functional diversification of eIF4E in metazoans. We emphasise humans and two phylogenetically distant species which have become paradigms for studies on development, namely the fruit fly Drosophila melanogaster and the roundworm Caenorhabditis elegans.

Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, both within annotated protein-coding and non-coding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term Ribosome Decision Graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the later 'translons'. Non-deterministic events, such as initiation, re-initiation, selenocysteine insertion or ribosomal frameshifting are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions, analysis of genetic variation and quantitative genome-wide data on translation for characterisation of regulatory modulators of translation.

Monitoring integrated stress response in live Drosophila

Cells exhibit stress responses to various environmental changes. Among these responses, the integrated stress response (ISR) plays a pivotal role as a crucial stress signaling pathway. While extensive ISR research has been conducted on cultured cells, our understanding of its implications in multicellular organisms remains limited, largely due to the constraints of current techniques that hinder our ability to track and manipulate the ISR in vivo. To overcome these limitations, we have successfully developed an internal ribosome entry site (IRES)-based fluorescent reporter system. This innovative reporter enables us to label Drosophila cells, within the context of a living organism, that exhibit eIF2 phosphorylation-dependent translational shutoff - a characteristic feature of the ISR and viral infections. Through this methodology, we have unveiled tissue- and cell-specific regulation of stress response in Drosophila flies and have even been able to detect stressed tissues in vivo during virus and bacterial infections. To further validate the specificity of our reporter, we have engineered ISR-null eIF2αS50A mutant flies for stress response analysis. Our results shed light on the tremendous potential of this technique for investigating a broad range of developmental, stress, and infection-related experimental conditions. Combining the reporter tool with ISR-null mutants establishes Drosophila as an exceptionally powerful model for studying the ISR in the context of multicellular organisms.