Biology subjects

Duhagon, M. A.

Publications and source records attributed to Duhagon, M. A..

4 recordsLinked to original sources

bioRxiv2026

Expression-based annotation identifies and enables quantification of small vault RNAs (svtRNAs) in human cells

BackgroundSmall non-coding RNAs (sncRNAs) play central roles in post-transcriptional gene regulation. In addition to canonical microRNAs (miRNAs), fragments derived from vault RNAs (vtRNAs), called small vault RNAs (svtRNAs), have been reported in human cells. However, the absence of a standardized annotation framework has hindered their systematic detection, quantification, and comparison across small RNA sequencing (small RNA-seq) studies. MethodsWe developed an expression-based annotation strategy to identify svtRNAs from human small RNA-seq datasets. Using FlaiMapper followed by structure and expression-based filtering, we generated two annotation sets: a stringent "miRNA-like" set enriched in Argonaute-associated datasets, and (ii) a broader "Total" set derived from total small RNA-seq libraries under relaxed structural constraints. We explored the expression of the annotated svtRNAs across the different datasets analyzed: multiple normal and tumor-derived human cell lines, including Argonaute immunoprecipitation datasets. ResultsWe identified a repertoire of svtRNAs that are detected across independent datasets and, in several cases, reach abundance levels comparable to canonical miRNAs. Several highly abundant svtRNAs correspond to molecules with experimental validation from prior studies, supporting the robustness of our annotation strategy. Importantly, the same "dominant" (in terms of gene expression) svtRNAs emerged independently from Argonaute-associated and total small RNA datasets, supporting the idea of enzymatically consistent, reproducible svtRNA processing. We further identified svtRNAs derived from distinct vtRNA precursors that could share identical seed sequences, suggesting the possibility of svtRNA families with potential miRNA-like regulatory properties. We provide a standardized annotation that enables reproducible svtRNA quantification. ConclusionsOur study establishes a comprehensive expression-based annotation resource for human svtRNAs. By enabling their systematic detection and reproducible quantification, we show that svtRNAs appear to represent an abundant component of the human small RNA landscape.

bioinformatics↗

bioRxiv2025

Single-cell RNA-seq reveals trans-sialidase-like superfamily gene expression heterogeneity in Trypanosoma cruzi populations

Trypanosoma cruzi, the causative agent of Chagas disease, presents a major public health challenge in Central and South America, affecting approximately 8 million people and placing millions more at risk. The T. cruzi life cycle includes transitions between epimastigote, metacyclic trypomastigote, amastigote, and blood trypomastigote stages, each marked by distinct morphological and molecular adaptations to different hosts and environments. Unlike other trypanosomatids such as Trypanosoma brucei, T. cruzi does not employ a monoallelic model of antigenic variation; instead, it relies on a diverse repertoire of cell-surface associated proteins encoded by large multigene families, which are essential for infectivity and immune evasion. This study analyzes cell-specific transcriptomes using single-cell RNA sequencing of amastigote and trypomastigote cells to characterize stage-specific surface protein expression during mammalian infection. Through clustering and identification of cell-specific markers, we assigned cells to distinct parasite developmental forms. Analysis of individual cells revealed that surface protein-coding genes, especially members of the trans-sialidase like superfamily (TcS), are expressed with greater heterogeneity than single-copy genes. Moreover, no recurrent combinations of TcS genes were observed between individual cells in the population. Remarkably, a small subset of TcS mRNAs, encoded by genes preferentially located in the core genomic compartment, are frequently detected across the cell population, whereas the vast majority of TcS mRNAs show low detection frequencies and are mainly encoded in the disruptive compartment. Our findings thus reveal transcriptomic heterogeneity within trypomastigote populations where each cell displays unique TcS expression profiles. Focusing on the diversity of surface protein expression, this research aims to deepen our understanding of T. cruzi cellular biology and infection strategies.

genomics↗

bioRxiv2021

Transcriptome-wide analysis of microRNA-mRNA correlations in unperturbed tissue transcriptomes identifies microRNA targeting determinants.

MicroRNAs are small RNAs that regulate gene expression through complementary base pairing with their target mRNAs. Given the small size of the pairing region and the large number of mRNAs that each microRNA can control, identifying biologically relevant targets is difficult. Since current knowledge of target recognition and repression has mainly relied on in vitro studies, we sought to determine if the interrogation of gene expression data of unperturbed tissues could yield new insight into these processes. The transcriptome-wide repression of all the microRNA-mRNA canonical interaction sites (seed and 3-supplementary regions, identified by sole base complementarity) was calculated as a normalized Spearman correlation (Z-score) between the abundance of the transcripts in the PRAD-TCGA tissues (RNA-seq and small RNA-seq data of 546 samples). Using the repression values obtained, we confirmed established properties or microRNA targeting efficacy, such as the preference for gene regions (3UTR > CDS > 5UTR), the correspondence between repression and seed length (6mer < 7mer < 8mer), and the contribution to the repression exerted by the 3-supplementary pairing at nucleotides 13-16 of the microRNA. Our results suggest that the 7mer-A1 seed could be more repressive than the 7mer-m8, while they have similar efficacy when they interact using the 3-supplementary pairing. The 6mer+suppl sites yielded a normalized Z-score of repression similar to the sole 7mer-A1 seeds, alerting its potential biological relevance. We then used the approach to further characterize the 3-supplementary pairing using 39 microRNAs that hold repressive 3-supplementary interactions. The analysis of the bridge between seed and 3-supplementary pairing sites confirmed the optimum +1 offset previously evidenced, but higher offsets appear to have similar repressive strength. The selected microRNAs show a low GC content at positions 13-16 and base preferences that allow a sequence motif identification. Our study demonstrates that transcriptome-wide analysis of microRNA-mRNA correlations in large, matched RNA-seq and small-RNA-seq data can uncover hints of microRNA targeting determinants operating in the in vivo unperturbed set. Finally, we provide a bioinformatic tool to identify microRNA-mRNA candidate interactions based on sequence complementarity of the seed and 3-supplementary regions.

genomics↗

bioRxiv2020

Pan-Cancer chromatin analysis of the human vtRNA genes uncovers their association with cancer biology.

The vault RNAs (vtRNAs) are a class of 84-141 nt eukaryotic non-coding RNAs transcribed by RNA polymerase III, named for their association with the conserved vault particle, a riboprotein complex whose function remains poorly understood. Of the 4 human vtRNA genes, the three clustered at locus 1, i.e. vtRNA1-1, vtRNA1-2 and vtRNA1-3, are integral components of the vault particle, while vtRNA2-1 is a more divergent homologue located in a second locus. Gene expression studies of vtRNAs in large cancer cohorts have been hindered by the failure of vtRNA sequencing using conventional transcriptomic approaches. However, since the vtRNAs transcription is regulated by DNA methylation, the analysis of the chromatin status of their promoters is a suitable surrogate approach to study their expression. Here we infer the landscape of vtRNA expression in cancer from the genome-wide DNA methylation (Illumina Infinium Human Methylation 450 K BeadChip) and chromatin accessibility (ATAC-seq) data of The Cancer Genome Atlas (TCGA). On average, vtRNA1-1 has the most accessible chromatin, followed by vtRNA1-2, vtRNA2-1 and vtRNA1-3. The correlation of the chromatin status of the vtRNA promoters and the binding sites of a common core of transcription factors stands for their transcriptional co-regulation by factors related to viral infection. Yet, vtRNA2-1 is the most independently regulated vtRNA homologue across tissue types. VtRNA1-1 and vtRNA1-3 chromatin status does not significantly change in cancer, though vtRNA1-3 promoter has repressive chromatin marks in a few cancer types. However, vtRNA2-1 and vtRNA1-2 expression are widely deregulated in neoplastic tissues and is compatible with a broad oncogenic role of vtRNA1-2, and both tumor suppressor and oncogenic functions of vtRNA2-1 depending of tissue contexts. Yet, vtRNA1-1, vtRNA1-2 and vtRNA2-1 promoter DNA methylation predicts a shorter patient overall survival cancer-wide. In addition, gene ontology analyses of co-regulated genes identifies a chromosome 5 regulatory domain controlling vtRNA1-1 and neighboring genes, and epithelial differentiation, immune and thyroid cancer gene sets for vtRNA1-2, vtRNA2-1 and vtRNA1-3 respectively. Furthermore, vtRNA expression patterns are associated with cancer immune subtypes. Finally, vtRNA1-2 expression is positively associated with cell proliferation and wound healing, in agreement with its oncogenic expression profile. Overall, our study presents the landscape of vtRNA expression cancer-wide, identifying co-regulated gene networks and ontological pathways associated with the different vtRNA genes that may account for their diverse roles in cancer.

cancer biology↗