Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.04.20.050088

Identification and enrichment of SECReTE cis-acting RNA elements in the Coronaviridae and other (+) single-strand RNA viruses

Haimovich, G.·Olender, T.·Baez, C.·Gerst, J. E.
View original source ↗Export metadata ↓

Abstract

cis-acting RNA motifs play a major role in regulating many aspects of RNA biology including posttranscriptional processing, nuclear export, RNA localization, translation and degradation. Here we analyzed the genomes of SARS-CoV-2 and other single-strand RNA (ssRNA) viruses for the presence of a unique cis RNA element called SECReTE. This motif consists of 10 or more consecutive triplet nucleotide repeats where a pyrimidine nucleotide (C or U) in present every third base, and which we identified in mRNAs encoding secreted proteins in bacteria, yeast, and humans. This motif facilitates mRNA localization to the endoplasmic reticulum (ER), along with the enhanced translation and secretion of translated protein. We now examined for SECReTE presence in Group IV and V RNA viruses, the former including the Coronaviridae, like SARS-CoV-2 and other positive (+)ssRNA viruses, and the latter consisting of negative (-) ssRNA viruses. Interestingly, the SARS-CoV-2 genome contains 40 SECReTE motifs at an abundance of ~1.3 SECReTEs/kilobase (kb). Moreover, all ssRNA viruses we examined contain multiple copies of this motif and appears in (+)ssRNA viruses as non-random in occurrence and independent of genome length. Importantly, (+)ssRNA viruses (e.g. Coronaviruses and Hepaciviruses), which utilize ER membranes to create double membrane vesicles to serve as viral replication centers (VRCs), contain more SECReTE motifs per kb as compared to (-)ssRNA viruses (e.g. Rabies, Mumps, and Influenza), that replicate in the nucleus or the cytoplasm, or other (+)ssRNA viruses (e.g. Enteroviruses and Flaviviruses) which employ different organellar membranes. As predicted by our earlier work, SECReTE sequences are mostly found in membranal or ER-associated/secreted proteins. Thus, we propose that SECReTE motifs could be important for the efficient translation and secretion of secreted viral proteins, as well as for VRC formation. Future studies of SECReTE function and identification of SECReTE-binding proteins could provide new drug targets to treat COVID-19 and other (+)ssRNA related diseases.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Haimovich, G., Olender, T., Baez, C., Gerst, J. E.. 2020-04-20. Identification and enrichment of SECReTE cis-acting RNA elements in the Coronaviridae and other (+) single-strand RNA viruses. https://doi.org/10.1101/2020.04.20.050088

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗