Biology subjects

Fort, R. S.

Publications and source records attributed to Fort, R. S..

5 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

Conserved Axonal Transcriptome Dynamics Underlie PGE2-Induced Sensitisation and Identify Tnfrsf12a/Fn14 as a Regulator of Neuronal Excitability in DRG Neurons

Chronic pain arises when dorsal root ganglion (DRG) neurons become sensitised to noxious inputs, a process driven by inflammatory mediators such as prostaglandin E2 (PGE2). Local translation of axonal mRNAs is a key regulator of nociceptor plasticity, yet how axonal transcriptome dynamics contribute to inflammatory sensitisation remains unclear. Using compartmentalised culture systems and RNA-sequencing, we defined axonal and somatic transcriptomes in embryonic (E16.5) and adult (W8) DRG neurons and assessed their remodelling after PGE2 exposure. We identify a conserved core axonal transcriptome spanning embryonic to adult stages, prominently enriched for ribosomal and mitochondrial functions, consistent with sustained translational and metabolic demands. PGE2 elicited compartment-specific reprogramming: pathways related to sensory processing and pain were upregulated in axons but downregulated in somata. Functionally, prolonged axonal PGE2 exposure enhanced capsaicin-evoked Ca{superscript 2} responses and drove retrograde sensitisation of neuronal somata. Integrating transcriptomics with functional assays, we pinpointed Tnfrsf12a (Fn14), a cytokine receptor linked to regeneration and neuropathic pain, as a PGE2-induced axonal mRNA. Crucially, local axonal knockdown of Tnfrsf12a significantly reduced neuronal excitability, providing proof-of-concept that axonally enriched transcripts can be targeted to modulate sensitisation. These findings position conserved axonal transcriptome programmes as drivers of peripheral sensitisation and establish Tnfrsf12a as a therapeutic candidate for inflammatory pain.

neuroscience↗

bioRxiv2024

Adipocyte-specific deletion of Dbc1 does not recapitulate healthy obesity phenotype but suggests regulation of inflammation signaling

The protein Deleted in Breast Cancer 1 (DBC1), a regulator of several transcription factors and epigenetic modulators, plays determinant roles in metabolism regulation, obesity and aging-related processes. Knockout mice for Dbc1, develop morbid obesity but are protected against liver steatosis, insulin resistance and atherosclerosis. We have proposed that this healthy obesity phenotype was mainly due to the expansion of adipose tissue, avoiding free-fatty acid spillover and metabolic damage in peripheral tissues. To gain more insight about the role of Dbc1 in adipose cells during obesity and its impact on metabolic dysregulation, we generated a conditional Dbc1 KO mouse and backcrossed it with CRE-AdipoQ transgenic mice, aiming to abrogate Dbc1 expression in all mature adipocytes (cAT-Dbc1). cAT-Dbc1 mice showed deletion of Dbc1 specifically in mature adipocytes in different fat depots. We tested the effect of Dbc1 deletion in adipocytes on different aspects of metabolic regulation in male and female mice fed in normal chow and high-fat diets. We found that deletion of Dbc1 in mature adipocytes had no effect on weight gain, glucose tolerance and other markers of metabolic dysregulation, regardless sex. However, Dbc1 KO adipocytes displayed an mRNA expression profile consistent with increased inflammation during obesity. Our results suggest that the healthy phenotype displayed in the whole body Dbc1 KO obese mice is not due to the protein function in mature adipocytes and might involve other cell types present in adipose tissue. Instead, the specific deletion of Dbc1 in mature adipocytes highlights a novel role of Dbc1 in inflammation signaling during obesity.

physiology↗

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↗